system

Abstract

We provide the system. [Solution] An artificial intelligence method that automatically generates listing information based on product information, Robotic means to automate product photography and packaging, An artificial conversational tool for handling customer inquiries, A system that includes this.

Description

Technical Field

[0004] , , ,

[0005] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The process of putting products up for sale online requires a lot of time and effort, and it is a particularly heavy burden for users lacking expertise. This includes multiple complex tasks such as creating product information, taking photos, and handling inquiries, resulting in the problem that smooth sales activities are hindered. To solve this problem, there is a need for a system that automates the process from product listing to sales and shipping smoothly and efficiently while minimizing the user's effort.

Means for Solving the Problems

[0005] This invention provides an artificial intelligence system that automatically generates listing information based on product information, eliminating the need for users to manually input information. Furthermore, it incorporates a robotic system that automatically photographs and packages products, reducing the actual workload. In addition, it can process customer inquiries using an artificial conversational system, enabling quick and appropriate responses. By providing a system that automates the entire process from listing to sales and shipping, it solves problems users face due to lack of time and expertise, allowing for efficient product handling.

[0006] "Product information" refers to detailed data about the products being sold, including information such as product name, category, condition, and pricing.

[0007] "Listing information" refers to the set of information necessary to list a product on an online marketplace, and includes data such as product description, price, and photos.

[0008] "Artificial intelligence means" refers to a technology or method that uses computer software to analyze data and provide optimal solutions based on specified criteria.

[0009] A "robot means" is a mechanical device or group of devices that automatically performs physical actions and can carry out work processes such as photographing and packaging products.

[0010] "Artificial conversational tools" are technologies or systems that use natural language understanding and generation to interact with users and customers and respond to their inquiries.

[0011] "Collection" refers to the process of receiving goods for delivery, and usually means the act of transporting the goods from the shipping source.

[0012] "Shipping" refers to the series of procedures involved in handing over packaged goods to a delivery company for delivery to the buyer. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides a system that completely automates the product listing process, enabling users to sell products without spending a lot of time. This system combines artificial intelligence, robotics, and artificial conversational tools to efficiently manage all stages from listing to sales and shipping.

[0035] The server receives product information, analyzes the product's characteristics using artificial intelligence, and automatically generates optimal listing information. For example, the server analyzes clothing product information received from a user and generates listing information that includes a price setting that takes market value into account, based on factors such as "brand," "size," and "condition," as well as selling points.

[0036] The generated listing information is sent to the user via their device for review. At this stage, users can easily provide feedback, such as making corrections or approvals. The server then automatically lists the product on the online marketplace using the reviewed listing information.

[0037] After an item is listed, the server uses an AI chatbot to respond to customer inquiries 24 hours a day. This chatbot uses natural language processing to analyze customer questions and provides quick and accurate answers.

[0038] Physical tasks such as product photography and packaging are performed by robots. A server sends shooting instructions to the robot, which generates high-quality product images based on specified angles and conditions. Once the shooting is complete, the robot carefully packages the products and prepares them for shipment.

[0039] Once a sale is complete, the server automatically initiates the shipping process. The terminal instructs a robot to perform a final check of the product and prepare it for delivery, ensuring that the product is properly handed over to the shipping company. In this way, users can complete the entire process from listing to shipping seamlessly and efficiently.

[0040] This invention significantly reduces the time and effort users spend on listing products, enabling consistent, fast, and accurate sales.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user sends a request for product pickup via their device. The device receives this request and sends it to the server.

[0044] Step 2:

[0045] The server processes the received pickup request and sends the necessary information to the robot. The robot then begins collecting the goods.

[0046] Step 3:

[0047] The server receives image data of collected items and uses artificial intelligence to analyze the characteristics of the items. This allows it to derive the item's category, condition, and price suggestion.

[0048] Step 4:

[0049] The server generates optimal listing information based on the analysis results and sends it to the user via the terminal. The user reviews this information and requests corrections as needed.

[0050] Step 5:

[0051] The server automatically lists products on the online marketplace using listing information approved by the user.

[0052] Step 6:

[0053] A robot photographs the product under specified conditions and sends the generated image data to a server. The server then uses this data to update the product listing information.

[0054] Step 7:

[0055] When an item is sold, the server detects the completion of the sale and sends a notification to the user via the terminal.

[0056] Step 8:

[0057] The server uses an AI chatbot to receive customer inquiries in real time and generate appropriate responses.

[0058] Step 9:

[0059] After the sale is complete, the server sends a shipping instruction to the robot, which then performs a final check of the product.

[0060] Step 10:

[0061] The robot completes the preparation for shipment and hands the goods over to the delivery company. The terminal sends a shipment completion notification and tracking information to the user.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] The process of listing, selling, and shipping products on online marketplaces involves many tasks that require time and effort from human hands, and there is a need to improve efficiency. Therefore, the challenge is to automate tasks such as product information analysis, customer support, and logistics preparation, so that users can conduct sales activities quickly and with minimal effort.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes information processing means, operation means, and communication means. This enables the entire process from receiving and analyzing product information to generating listing information, responding to customer inquiries, photographing and packaging products, and even collection and shipping procedures to be automated, allowing users to efficiently carry out sales activities.

[0067] "Information processing means" refers to a part of a system that has the function of generating optimal listing information by receiving and analyzing product information.

[0068] "Operating means" refers to an element of a system that has the function of automatically photographing and packaging products using a structure.

[0069] "Communication means" refers to a system element that has the function of receiving and responding to customer inquiries using a dialogue system.

[0070] This system is designed to completely automate the online marketplace sales process. Servers, terminals, and users work together to efficiently handle everything from product information processing to sales and shipping.

[0071] The server receives product information provided by the user. This information includes "product name," "brand," "size," and "condition." Based on this information, a generative AI model analyzes the product's characteristics and generates optimal listing information. The prompt used at this time is "Please generate detailed listing information for this product." The server then refers to a market database and outputs the optimal price and selling points, taking market prices into consideration.

[0072] The generated listing information is sent to the user via their device. The user can review this information and make corrections as needed. Once the user approves the information, it is sent back to the server and listed on the online marketplace.

[0073] The server activates an AI chatbot to respond to customer inquiries. This chatbot uses natural language processing to analyze customer questions and provide quick and appropriate answers. For example, if a customer asks, "What material is this product made of?", the chatbot will respond, "This product is 100% cotton."

[0074] Product photography and packaging are handled by robots, which are the operating mechanisms within the system. Following instructions from the server, the robots photograph the products from specified angles and under designated conditions, generating high-quality images. Afterward, they carefully package the products and prepare them for shipment.

[0075] This system allows users to seamlessly manage all processes, from listing items to customer service and shipping. This significantly reduces the burden on users and enables fast and accurate product sales.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The server receives product information from the user. This includes "product name," "brand," "size," and "condition." The received information is used as input data for subsequent analysis and listing information generation. The server supplies this input to the generation AI model and begins analyzing the product's characteristics. This analysis process uses the prompt "Generate detailed listing information for this product" to perform data calculations that extract the most suitable listing information.

[0079] Step 2:

[0080] The server uses a generative AI model to generate optimal listing information based on the analysis results. The model performs data processing, retrieving market price information from a market database and calculating pricing and selling points. The output listing information includes insights such as expected demand and competitive landscape. This output serves as preparatory data for presentation to users.

[0081] Step 3:

[0082] The terminal receives listing information from the server and displays it to the user. In this step, the user can review the listing information through the terminal and adjust the price or edit the selling points. If the information is modified by the user, the modified listing information is output and sent back to the server.

[0083] Step 4:

[0084] The server lists the product on the online marketplace using the verified listing information. Here, the listing information is automatically entered via the API of each marketplace, completing the product listing process. Confirmation of listing completion is received as output to the marketplace and notified to the user. This notification allows the user to confirm that the product has been listed correctly.

[0085] Step 5:

[0086] The server activates an AI chatbot to handle customer inquiries. The chatbot receives customer questions as input and analyzes the content using natural language processing. Based on the analysis results, it immediately generates and outputs an appropriate answer. For example, if a customer asks, "What is the material of this product?", the chatbot will output an answer such as, "This product is 100% cotton."

[0087] Step 6:

[0088] The server sends instructions to the robot for photographing and packaging the products. The robot receives these instructions as input and photographs the products under the specified conditions. The captured images are output to the server in high resolution and attached to the listing information. The robot then packages the products and prepares them for shipping.

[0089] Step 7:

[0090] The server initiates the shipping process when a sale is completed. The server receives the necessary shipping information as input and prepares the package for handover to the delivery company. Specifically, it generates shipping labels and outputs pickup instructions to the logistics company, completing the process to ensure the product is delivered correctly. After completion, the user is notified that the shipment has been completed.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] In recent years, with the expansion of e-commerce, opportunities for individuals to list and sell products online have increased. However, this process involves a great deal of effort, including data entry, packaging, and customer service, which is burdensome for sellers. Furthermore, improving the accuracy of listing information, such as setting prices based on market rates, is also a challenge. Therefore, there is a need for technology that can efficiently automate the process from listing to sales and shipping, thereby reducing the burden on sellers.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes information processing means for automatically generating listing information based on product information, mechanical device means for automating product photography and packaging, conversation processing means for handling customer inquiries, image recognition means for acquiring and analyzing product images from a smartphone, and user interface means for allowing the user to confirm and approve the generated listing information. This enables the user to efficiently manage the product listing process and make accurate listings based on market prices while reducing manual information input.

[0096] "Information processing means for automatically generating listing information based on product information" refers to technology that analyzes data on products to be sold and automatically constructs optimal sales information.

[0097] "Mechanical devices and means for automating product photography and packaging" refers to a system that mechanically performs product photography and packaging for shipment, thereby replacing human labor.

[0098] A "conversational processing system for handling customer inquiries" is an interactive system that automatically responds to questions and requests from purchasers.

[0099] "Image recognition means for acquiring and analyzing product images from a smartphone" refers to a technology that analyzes product images acquired using a mobile device and extracts necessary information.

[0100] "User interface means for allowing users to review and approve generated listing information" refers to screen display and operation functions that present automatically generated sales information to the user and allow them to review and approve the content.

[0101] "Information generation means for receiving information related to collection and generating instructions for collection" refers to technology that receives data necessary for picking up goods and creates instructions to support appropriate collection.

[0102] "Information generation means for calculating a fair price based on market information of a product" refers to a method for analyzing market trends and rationally setting the selling price of a product.

[0103] "An information update mechanism that automatically generates shipping instructions and prepares products for handover to the delivery organization when a product is sold" refers to a system that, when a product is purchased, sequentially processes the next delivery steps and prepares the product for accurate shipment.

[0104] "A means of updating information that allows users to update product information after listing it" refers to a function that allows users to enter or modify additional information even after a product has been listed.

[0105] A system for carrying out this invention consists of multiple components, including a server, a terminal with a user interface, and a robotic device for handling goods.

[0106] The server analyzes product information and automatically generates listing information based on it. This process utilizes open-source natural language processing libraries and image recognition technologies. For example, Google's TENSORFLOW® is used to process and analyze product images. The generated listing information is then constructed using artificial intelligence technology to consider market prices and include appropriate pricing information.

[0107] The terminal is a device that users can operate from their hand, like a smartphone. Users take product images with their smartphones and upload those images to the server. The listing information provided by the server is notified to the user via the terminal, and the user can review, modify, and approve the information. This allows for easy feedback to be sent back to the server through the user interface.

[0108] Robotic means are used in the photography and packaging processes. The robotic equipment photographs the products according to specified conditions and maintains high image quality. Furthermore, it properly packages the products in preparation for shipment. The robot's operation is instructed from a server and executed in the most optimal procedure.

[0109] Furthermore, customer inquiries are handled through an AI chatbot. By utilizing natural language processing libraries, a system is in place to respond immediately to customer questions. This enables 24-hour customer support.

[0110] For example, when a user sells a used shirt, they take multiple photos with their smartphone. The server then automatically recognizes details such as the brand and condition based on these photos and generates listing information according to market value. Once the user reviews it and taps "Approve," the item is immediately listed on the online marketplace.

[0111] This system also supports prompt messages. For example, a possible input to the generation AI model could be something like, "Please provide a concrete example of how to automatically generate sales information from product images taken with a smartphone and streamline the listing process."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The user uses their smartphone to take pictures of the products they want to sell. This generates product image data. The user then uploads these images from their device to the server. During this process, the images are appropriately compressed and prepared as data packets for upload.

[0115] Step 2:

[0116] The server receives the uploaded product images. The received image data is then analyzed using image recognition technology. This analysis extracts characteristic information such as the product's "brand," "size," and "condition." TensorFlow is one example of the technology used.

[0117] Step 3:

[0118] The server compares the analyzed feature information with a market price database. This allows it to calculate a fair price and automatically generate listing information, including selling points. The output is a data object containing the listing information.

[0119] Step 4:

[0120] The generated listing information is sent from the server to the terminal. The terminal presents the listing information to the user via a user interface. The user reviews the information and makes any necessary corrections or approvals. Once the user's input is confirmed, the updated listing information is sent back to the server.

[0121] Step 5:

[0122] The server automatically lists the product on the online marketplace using the finalized listing information. This step utilizes API integration technology with the marketplace.

[0123] Step 6:

[0124] When an item is sold, the server sends instructions to the robotic device to take photos and pack the item. The robot takes photos of the item from the specified angle and saves the images in high resolution. It also automatically completes the packing process, making the item ready for shipment.

[0125] Step 7:

[0126] After the sale, the server responds to customer inquiries through an AI chatbot. The chatbot uses a natural language processing model to analyze questions in real time and provide appropriate answers. The response to the customer is output as an optimized reply from the server.

[0127] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0128] This invention integrates an emotion engine that recognizes user emotions and optimizes the overall system's operation, in addition to conventional systems that automate the product listing process. This improves the user experience and maximizes results.

[0129] The server analyzes the user's facial expressions and tone of voice, and evaluates their emotional state through an emotion engine. Based on this evaluation, the server automatically generates appropriate listing information and promotional suggestions for the user. For example, if a user shows dissatisfaction with a product, the emotion engine will determine this to be "negative," and the server will present alternative product information that the user is likely to be interested in.

[0130] Furthermore, when a user interacts with customer support, the device uses an emotion engine to analyze the user's emotional state in real time. Based on this analysis, the server generates and sends a personalized response to the user. For example, if a user is frustrated during an inquiry, the system adjusts to provide a more polite and considerate response.

[0131] Emotional data is also used in sales management. Servers link users' past emotional history with their purchase history to develop sales promotion strategies. For example, sending specific promotional messages when a user expresses positive emotions can further stimulate their purchasing intent.

[0132] In this way, the integration of the server, terminal, and emotion engine enables flexible responses tailored to users, which was not possible with conventional automated listing systems, and is expected to improve usability and boost sales. This system is particularly aimed at providing advanced services to users with a wide range of different emotional states.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The user enters product information via their device and sends a listing request to the system. The device receives this information and sends it to the server.

[0136] Step 2:

[0137] The server receives product information, and the emotion engine analyzes the user's current emotional state. This includes the user's facial expressions and voice tone as captured by the device's camera.

[0138] Step 3:

[0139] The emotion engine classifies the user's emotions as either "positive," "neutral," or "negative," and communicates the evaluation result to the server.

[0140] Step 4:

[0141] The server takes the user's emotional state into account and uses AI to generate optimized listing information. For example, if a user is in a negative state, the server will create listing information that emphasizes the benefits of the suggested product and positive customer reviews.

[0142] Step 5:

[0143] The terminal presents the generated listing information to the user and prompts for additional feedback and approval. After the user approves, the server lists the product on the online marketplace.

[0144] Step 6:

[0145] When a product is listed, the server uses an AI chatbot and an emotion engine to generate appropriate responses based on the content of the conversation when interacting with customers regarding their inquiries.

[0146] Step 7:

[0147] When a product is sold, the server detects the completion of the sale, re-evaluates the user's emotional state, and sends feedback to the user to enhance their positive experience.

[0148] Step 8:

[0149] Based on the analysis results of the emotion engine, the terminal starts preparing the product for shipment and sends a shipping instruction to the robot. The robot then includes an encouraging message that takes emotions into consideration in the package.

[0150] Step 9:

[0151] The server hands over the goods to the shipping company, and the terminal notifies the user of the shipping completion details. Feedback and service provision at each step optimize the user experience.

[0152] (Example 2)

[0153] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0154] Providing optimal sales strategies and customer service tailored to user emotions during the product listing process is challenging. Traditional systems often fail to consider user emotions, resulting in a lower quality user experience. Furthermore, responding quickly and appropriately to customer inquiries presents a significant challenge.

[0155] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0156] In this invention, the server includes intelligent means for automatically generating listing information based on product data, analysis means for analyzing facial expression data and voice data and evaluating the emotional state, and generation means for generating appropriate information based on the user's emotional state. This enables flexible sales strategies and customer support that respond to the user's emotions, thereby improving the user experience.

[0157] "Product data" refers to a collection of information that describes the product itself, including data such as price, description, photos, and stock status.

[0158] "Listing information" refers to information necessary for selling a product, and is presented on an online platform in a format that users can view.

[0159] An "intelligent tool" is a system that uses computer programs to automatically make judgments and analyses and generate output that aligns with a specific purpose.

[0160] "Facial expression data" refers to digital information used to record and analyze a user's facial movements and expressions.

[0161] "Voice data" refers to digital audio information used to record and analyze a user's voice.

[0162] "Analytical means" refers to a process or system for analyzing data to derive specific conclusions or evaluations.

[0163] "Emotional state" refers to the psychological and emotional state a user is experiencing at a given moment.

[0164] A "generation method" is a process or system for creating new information or results based on specific data or conditions.

[0165] A "dialogue method" is a means of communication for exchanging information between a user and a system.

[0166] This invention provides a product listing system that reflects the user's emotional state. This enables a personalized experience for the user.

[0167] Users browse products through the terminal and perform actions as needed. The terminal is equipped with a high-resolution camera and a high-sensitivity microphone, and uses facial recognition software such as OpenCV and speech analysis software such as Google Cloud Speech-to-Text API to collect user facial expression data and voice data. This data is transferred to the server in real time.

[0168] The server analyzes this data using a generative AI model and evaluates the user's emotional state through an emotion engine. Neural networks and natural language processing technologies are utilized in this process. Based on the evaluation results, the server generates highly relevant product information and promotions for the user.

[0169] For example, if a user tilts their head while browsing a product, the device captures this facial expression and sends it to the server. The server recognizes this emotional state as "doubt" and, taking this state into consideration, generates other product information that the user might be interested in. This allows the user to receive more relevant suggestions.

[0170] An example of a prompt message is, "Evaluate whether the user is satisfied with the product, and based on that evaluation, present the following listing information."

[0171] This system is expected to enable the development of sales strategies tailored to the emotions of individual users, significantly improving the user experience.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] Users browse products through their devices. The devices use their built-in cameras and microphones to capture the user's facial expressions and voice in real time. This facial and voice data is collected as input. Since the collected data is raw, it is processed into a format suitable for analysis.

[0175] Step 2:

[0176] The device sends collected facial expression and voice data to the server. The server receives this data as input and analyzes it using a generative AI model and an emotion engine. Specifically, a facial recognition algorithm extracts facial features, and a voice analysis system analyzes the tone and tempo of the voice. Based on these analyses, the server outputs the user's emotional state (e.g., positive, negative, neutral).

[0177] Step 3:

[0178] The server uses the user's emotional state, obtained as an input from the analysis results, to generate highly relevant product information and promotions. Here, the generating AI model optimizes the product and promotional content. For example, if the user is rated as "negative," data calculations are performed to suggest alternative, related products. The generated information is output as a recommendation list of products the user is likely to be interested in next.

[0179] Step 4:

[0180] The server sends the generated recommendation list to the device. The device displays this list on the user interface. By referring to the recommended product information, the user is prompted to select or purchase additional products. At this point, the UI operates in a way that maximizes the user experience, taking screen design and other factors into consideration.

[0181] (Application Example 2)

[0182] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0183] In modern commerce, there is a demand for improved user experience and streamlined listing processes. However, traditional systems struggle to respond flexibly to user emotions, resulting in a failure to maximize user purchasing intent. Furthermore, the lack of individually tailored responses leads to inconsistent customer service quality.

[0184] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0185] In this invention, the server includes information processing means for automatically generating listing information based on product information, mechanical means for automating product photography and packaging, sentiment analysis means for analyzing the customer's emotional state and generating user-specific responses, and dialogue means for handling customer inquiries. This enables flexible product suggestions and promotions based on user emotions, improving the user experience and optimizing the entire commercial transaction process.

[0186] "Information processing means" refers to a device or software that automatically generates listing information from product information by analyzing, transforming, and generating data.

[0187] "Mechanical means" refers to artificial devices or programs that have the function of automating the photography and packaging of products.

[0188] "Emotional analysis means" refers to a method or system that analyzes a user's facial expressions and voice data to identify their emotional state and generate individual responses based on the results.

[0189] A "dialogue tool" is a technology or application that automatically generates and responds to customer inquiries.

[0190] The server executes the program necessary to realize the present invention. First, using information processing means, it automatically generates listing information based on product information obtained from the product database. This eliminates the need for manual input and enables efficient product management. By automating the product photography and packaging processes using mechanical means, work efficiency is greatly improved.

[0191] Next, a device equipped with emotion analysis capabilities analyzes data collected using the user's camera and microphone. This process utilizes software such as SmileML and Google Cloud Speech-to-Text to identify emotions based on the user's facial expressions and tone of voice. The analysis results are sent to an analysis engine such as IBM Watson® Tone Analyzer, where the user's emotional state is evaluated. This emotion data is then input into recommendation systems such as Amazon Personalize, which automatically suggest the most relevant products and promotional information to the user.

[0192] Furthermore, the server utilizes interactive methods to generate appropriate responses to customer inquiries, enabling more consistent customer service. For example, if a user shows dissatisfaction while browsing new products online, the system improves the user experience by suggesting alternative products in a timely manner based on the user's past data.

[0193] An example of using prompts with a generative AI model is a specific prompt sentence such as, "Suggest alternative products that this user might be interested in. The user's sentiment is negative."

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The server retrieves product information from the product database. At this time, the product information is entered as digital data, including product name, price, and description. Information processing equipment analyzes this information and automatically generates listing information. This listing information includes all necessary fields.

[0197] Step 2:

[0198] The automated system photographs and packages the products. A terminal outputs control signals to the camera used for product photography, acquiring high-resolution images. Then, a robotic arm is used to package the products in the specified manner, and information indicating that packaging is complete is output.

[0199] Step 3:

[0200] The device acquires data in real time from the user's camera and microphone. This data includes the user's facial expressions and voice data, and is analyzed using SmileML or Google Cloud Speech-to-Text. The emotion analysis tool identifies the emotional state and outputs the results.

[0201] Step 4:

[0202] The server uses IBM Watson Tone Analyzer to perform sentiment assessment. This analyzes the identified emotional state and quantitatively evaluates what emotions the user is experiencing. This assessment result is then used in the next step.

[0203] Step 5:

[0204] The server utilizes Amazon Personalize to recommend products and promotions that best match the user's sentiment evaluation results. A generative AI model is used to generate a prompt message, "Suggested products based on the user's negative sentiment," and outputs a list of suggested products. This list is displayed on the device, prompting the user to select a product.

[0205] Step 6:

[0206] The server uses dialogue mechanisms to generate responses to user inquiries. Based on the input inquiry content and sentiment evaluation results, natural language processing technology is used to generate the optimal answer, which is then displayed to the user as output.

[0207] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0208] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0209] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0213] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0214] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0215] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0216] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0217] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0218] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0219] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0220] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0221] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0222] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0223] This invention provides a system that completely automates the product listing process, enabling users to sell products without spending a lot of time. This system combines artificial intelligence, robotics, and artificial conversational tools to efficiently manage all stages from listing to sales and shipping.

[0224] The server receives product information, analyzes the product's characteristics using artificial intelligence, and automatically generates optimal listing information. For example, the server analyzes clothing product information received from a user and generates listing information that includes a price setting that takes market value into account, based on factors such as "brand," "size," and "condition," as well as selling points.

[0225] The generated listing information is sent to the user via their device for review. At this stage, users can easily provide feedback, such as making corrections or approvals. The server then automatically lists the product on the online marketplace using the reviewed listing information.

[0226] After an item is listed, the server uses an AI chatbot to respond to customer inquiries 24 hours a day. This chatbot uses natural language processing to analyze customer questions and provides quick and accurate answers.

[0227] Physical tasks such as product photography and packaging are performed by robots. A server sends shooting instructions to the robot, which generates high-quality product images based on specified angles and conditions. Once the shooting is complete, the robot carefully packages the products and prepares them for shipment.

[0228] Once a sale is complete, the server automatically initiates the shipping process. The terminal instructs a robot to perform a final check of the product and prepare it for delivery, ensuring that the product is properly handed over to the shipping company. In this way, users can complete the entire process from listing to shipping seamlessly and efficiently.

[0229] This invention significantly reduces the time and effort users spend on listing products, enabling consistent, fast, and accurate sales.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The user sends a request for product pickup via their device. The device receives this request and sends it to the server.

[0233] Step 2:

[0234] The server processes the received pickup request and sends the necessary information to the robot. The robot then begins collecting the goods.

[0235] Step 3:

[0236] The server receives image data of collected items and uses artificial intelligence to analyze the characteristics of the items. This allows it to derive the item's category, condition, and price suggestion.

[0237] Step 4:

[0238] The server generates optimal listing information based on the analysis results and sends it to the user via the terminal. The user reviews this information and requests corrections as needed.

[0239] Step 5:

[0240] The server automatically lists products on the online marketplace using listing information approved by the user.

[0241] Step 6:

[0242] A robot photographs the product under specified conditions and sends the generated image data to a server. The server then uses this data to update the product listing information.

[0243] Step 7:

[0244] When an item is sold, the server detects the completion of the sale and sends a notification to the user via the terminal.

[0245] Step 8:

[0246] The server uses an AI chatbot to receive customer inquiries in real time and generate appropriate responses.

[0247] Step 9:

[0248] After the sale is complete, the server sends a shipping instruction to the robot, which then performs a final check of the product.

[0249] Step 10:

[0250] The robot completes the preparation for shipment and hands the goods over to the delivery company. The terminal sends a shipment completion notification and tracking information to the user.

[0251] (Example 1)

[0252] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0253] The process of listing, selling, and shipping products on online marketplaces involves many tasks that require time and effort from human hands, and there is a need to improve efficiency. Therefore, the challenge is to automate tasks such as product information analysis, customer support, and logistics preparation, so that users can conduct sales activities quickly and with minimal effort.

[0254] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0255] In this invention, the server includes information processing means, operation means, and communication means. This enables the entire process from receiving and analyzing product information to generating listing information, responding to customer inquiries, photographing and packaging products, and even collection and shipping procedures to be automated, allowing users to efficiently carry out sales activities.

[0256] "Information processing means" refers to a part of a system that has the function of generating optimal listing information by receiving and analyzing product information.

[0257] "Operating means" refers to an element of a system that has the function of automatically photographing and packaging products using a structure.

[0258] "Communication means" refers to a system element that has the function of receiving and responding to customer inquiries using a dialogue system.

[0259] This system is designed to completely automate the online marketplace sales process. Servers, terminals, and users work together to efficiently handle everything from product information processing to sales and shipping.

[0260] The server receives product information provided by the user. This information includes "product name," "brand," "size," and "condition." Based on this information, a generative AI model analyzes the product's characteristics and generates optimal listing information. The prompt used at this time is "Please generate detailed listing information for this product." The server then refers to a market database and outputs the optimal price and selling points, taking market prices into consideration.

[0261] The generated listing information is sent to the user via their device. The user can review this information and make corrections as needed. Once the user approves the information, it is sent back to the server and listed on the online marketplace.

[0262] The server activates an AI chatbot to respond to customer inquiries. This chatbot uses natural language processing to analyze customer questions and provide quick and appropriate answers. For example, if a customer asks, "What material is this product made of?", the chatbot will respond, "This product is 100% cotton."

[0263] Product photography and packaging are handled by robots, which are the operating mechanisms within the system. Following instructions from the server, the robots photograph the products from specified angles and under designated conditions, generating high-quality images. Afterward, they carefully package the products and prepare them for shipment.

[0264] This system allows users to seamlessly manage all processes, from listing items to customer service and shipping. This significantly reduces the burden on users and enables fast and accurate product sales.

[0265] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0266] Step 1:

[0267] The server receives product information from the user. This includes "product name," "brand," "size," and "condition." The received information is used as input data for subsequent analysis and listing information generation. The server supplies this input to the generation AI model and begins analyzing the product's characteristics. This analysis process uses the prompt "Generate detailed listing information for this product" to perform data calculations that extract the most suitable listing information.

[0268] Step 2:

[0269] The server uses a generative AI model to generate optimal listing information based on the analysis results. The model performs data processing, retrieving market price information from a market database and calculating pricing and selling points. The output listing information includes insights such as expected demand and competitive landscape. This output serves as preparatory data for presentation to users.

[0270] Step 3:

[0271] The terminal receives listing information from the server and displays it to the user. In this step, the user can review the listing information through the terminal and adjust the price or edit the selling points. If the information is modified by the user, the modified listing information is output and sent back to the server.

[0272] Step 4:

[0273] The server lists the product on the online marketplace using the verified listing information. Here, the listing information is automatically entered via the API of each marketplace, completing the product listing process. Confirmation of listing completion is received as output to the marketplace and notified to the user. This notification allows the user to confirm that the product has been listed correctly.

[0274] Step 5:

[0275] The server activates an AI chatbot to handle customer inquiries. The chatbot receives customer questions as input and analyzes the content using natural language processing. Based on the analysis results, it immediately generates and outputs an appropriate answer. For example, if a customer asks, "What is the material of this product?", the chatbot will output an answer such as, "This product is 100% cotton."

[0276] Step 6:

[0277] The server sends instructions to the robot for photographing and packaging the products. The robot receives these instructions as input and photographs the products under the specified conditions. The captured images are output to the server in high resolution and attached to the listing information. The robot then packages the products and prepares them for shipping.

[0278] Step 7:

[0279] The server initiates the shipping process when a sale is completed. The server receives the necessary shipping information as input and prepares the package for handover to the delivery company. Specifically, it generates shipping labels and outputs pickup instructions to the logistics company, completing the process to ensure the product is delivered correctly. After completion, the user is notified that the shipment has been completed.

[0280] (Application Example 1)

[0281] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0282] In recent years, with the expansion of e-commerce transactions, the opportunities for individuals to list and sell products online have been increasing. However, this process requires a lot of labor such as information input, packaging, and customer support, which is a burden for the sellers. Also, improving the accuracy of listing information such as price setting based on the market price is a difficult problem. Therefore, there is a demand for a technology that can efficiently automate the process from listing to selling and shipping to reduce the burden on the sellers.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0284] In this invention, the server includes information processing means for automatically generating listing information based on product information, mechanical device means for automating product photography and packaging, conversation processing means for processing inquiries from customers, image recognition means for acquiring and analyzing images of products from a smartphone, and user interface means for allowing the user to confirm the generated listing information and obtain approval. Thereby, the user can efficiently manage the product listing process and perform accurate listings based on the market price while reducing manual information input.

[0285] The "information processing means for automatically generating listing information based on product information" is a technology for analyzing data of products to be sold and automatically constructing optimal sales information.

[0286] The "mechanical device means for automating product photography and packaging" is a system that mechanically performs product photography and shipping packaging and replaces human labor.

[0287] The "conversation processing means for processing inquiries from customers" is an interactive system that automatically responds to questions and requests from purchasers.

[0288] "Image recognition means for acquiring and analyzing product images from a smartphone" refers to a technology that analyzes product images acquired using a mobile device and extracts necessary information.

[0289] "User interface means for allowing users to review and approve generated listing information" refers to screen display and operation functions that present automatically generated sales information to the user and allow them to review and approve the content.

[0290] "Information generation means for receiving information related to collection and generating instructions for collection" refers to technology that receives data necessary for picking up goods and creates instructions to support appropriate collection.

[0291] "Information generation means for calculating a fair price based on market information of a product" refers to a method for analyzing market trends and rationally setting the selling price of a product.

[0292] "An information update mechanism that automatically generates shipping instructions and prepares products for handover to the delivery organization when a product is sold" refers to a system that, when a product is purchased, sequentially processes the next delivery steps and prepares the product for accurate shipment.

[0293] "A means of updating information that allows users to update product information after listing it" refers to a function that allows users to enter or modify additional information even after a product has been listed.

[0294] A system for carrying out this invention consists of multiple components, including a server, a terminal with a user interface, and a robotic device for handling goods.

[0295] The server analyzes product information and automatically generates listing information based on it. This process utilizes open-source natural language processing libraries and image recognition technologies. For example, Google's TensorFlow is used to process and analyze product images. The generated listing information is then constructed using artificial intelligence technology to consider market prices and include appropriate pricing information.

[0296] The terminal is a device that users can operate from their hand, like a smartphone. Users take product images with their smartphones and upload those images to the server. The listing information provided by the server is notified to the user via the terminal, and the user can review, modify, and approve the information. This allows for easy feedback to be sent back to the server through the user interface.

[0297] Robotic means are used in the photography and packaging processes. The robotic equipment photographs the products according to specified conditions and maintains high image quality. Furthermore, it properly packages the products in preparation for shipment. The robot's operation is instructed from a server and executed in the most optimal procedure.

[0298] Furthermore, customer inquiries are handled through an AI chatbot. By utilizing natural language processing libraries, a system is in place to respond immediately to customer questions. This enables 24-hour customer support.

[0299] For example, when a user sells a used shirt, they take multiple photos with their smartphone. The server then automatically recognizes details such as the brand and condition based on these photos and generates listing information according to market value. Once the user reviews it and taps "Approve," the item is immediately listed on the online marketplace.

[0300] This system also supports prompt sentences. For example, as an input example to the generative AI model, content such as "Please show specific examples of how to automatically generate sales information from an image of a product taken with a smartphone and streamline the listing process" can be considered.

[0301] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0302] Step 1:

[0303] The user uses a smartphone to take a picture of the product to be sold, thereby generating product image data. The user uploads the image from the terminal to the server. At that time, the image is appropriately compressed and prepared as a data packet for uploading.

[0304] Step 2:

[0305] The server receives the uploaded product image and performs analysis on the received image data using image recognition technology. Through this analysis, feature information such as the "brand", "size", and "condition" of the product is extracted. An example of the technology used is TensorFlow.

[0306] Step 3:

[0307] Based on the analyzed feature information, the server collates it with the market price database, thereby calculating the appropriate price and automatically generating listing information including sales points. What is output is a data object including listing information.

[0308] Step 4:

[0309] The generated listing information is sent from the server to the terminal. The terminal presents the listing information to the user via the user interface. The user checks the information and makes corrections or approvals. When the user's input is confirmed, the updated listing information is sent back to the server.

[0310] Step 5:

[0311] The server automatically lists the product on the online marketplace using the finalized listing information. This step utilizes API integration technology with the marketplace.

[0312] Step 6:

[0313] When an item is sold, the server sends instructions to the robotic device to take photos and pack the item. The robot takes photos of the item from the specified angle and saves the images in high resolution. It also automatically completes the packing process, making the item ready for shipment.

[0314] Step 7:

[0315] After the sale, the server responds to customer inquiries through an AI chatbot. The chatbot uses a natural language processing model to analyze questions in real time and provide appropriate answers. The response to the customer is output as an optimized reply from the server.

[0316] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0317] This invention integrates an emotion engine that recognizes user emotions and optimizes the overall system's operation, in addition to conventional systems that automate the product listing process. This improves the user experience and maximizes results.

[0318] The server analyzes the user's facial expressions and tone of voice, and evaluates their emotional state through an emotion engine. Based on this evaluation, the server automatically generates appropriate listing information and promotional suggestions for the user. For example, if a user shows dissatisfaction with a product, the emotion engine will determine this to be "negative," and the server will present alternative product information that the user is likely to be interested in.

[0319] Furthermore, when a user interacts with customer support, the device uses an emotion engine to analyze the user's emotional state in real time. Based on this analysis, the server generates and sends a personalized response to the user. For example, if a user is frustrated during an inquiry, the system adjusts to provide a more polite and considerate response.

[0320] Emotional data is also used in sales management. Servers link users' past emotional history with their purchase history to develop sales promotion strategies. For example, sending specific promotional messages when a user expresses positive emotions can further stimulate their purchasing intent.

[0321] In this way, the integration of the server, terminal, and emotion engine enables flexible responses tailored to users, which was not possible with conventional automated listing systems, and is expected to improve usability and boost sales. This system is particularly aimed at providing advanced services to users with a wide range of different emotional states.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] The user enters product information via their device and sends a listing request to the system. The device receives this information and sends it to the server.

[0325] Step 2:

[0326] The server receives product information, and the emotion engine analyzes the user's current emotional state. This includes the user's facial expressions and voice tone as captured by the device's camera.

[0327] Step 3:

[0328] The emotion engine classifies the user's emotions as either "positive," "neutral," or "negative," and communicates the evaluation result to the server.

[0329] Step 4:

[0330] The server takes the user's emotional state into account and uses AI to generate optimized listing information. For example, if a user is in a negative state, the server will create listing information that emphasizes the benefits of the suggested product and positive customer reviews.

[0331] Step 5:

[0332] The terminal presents the generated listing information to the user and prompts for additional feedback and approval. After the user approves, the server lists the product on the online marketplace.

[0333] Step 6:

[0334] When a product is listed, the server uses an AI chatbot and an emotion engine to generate appropriate responses based on the content of the conversation when interacting with customers regarding their inquiries.

[0335] Step 7:

[0336] When a product is sold, the server detects the completion of the sale, re-evaluates the user's emotional state, and sends feedback to the user to enhance their positive experience.

[0337] Step 8:

[0338] Based on the analysis results of the emotion engine, the terminal starts preparing the product for shipment and sends a shipping instruction to the robot. The robot then includes an encouraging message that takes emotions into consideration in the package.

[0339] Step 9:

[0340] The server hands over the goods to the shipping company, and the terminal notifies the user of the shipping completion details. Feedback and service provision at each step optimize the user experience.

[0341] (Example 2)

[0342] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0343] Providing optimal sales strategies and customer service tailored to user emotions during the product listing process is challenging. Traditional systems often fail to consider user emotions, resulting in a lower quality user experience. Furthermore, responding quickly and appropriately to customer inquiries presents a significant challenge.

[0344] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0345] In this invention, the server includes intelligent means for automatically generating listing information based on product data, analysis means for analyzing facial expression data and voice data and evaluating the emotional state, and generation means for generating appropriate information based on the user's emotional state. This enables flexible sales strategies and customer support that respond to the user's emotions, thereby improving the user experience.

[0346] "Product data" refers to a collection of information that describes the product itself, including data such as price, description, photos, and stock status.

[0347] "Listing information" refers to information necessary for selling a product, and is presented on an online platform in a format that users can view.

[0348] An "intelligent tool" is a system that uses computer programs to automatically make judgments and analyses and generate output that aligns with a specific purpose.

[0349] "Facial expression data" refers to digital information used to record and analyze a user's facial movements and expressions.

[0350] "Voice data" refers to digital audio information used to record and analyze a user's voice.

[0351] "Analytical means" refers to a process or system for analyzing data to derive specific conclusions or evaluations.

[0352] "Emotional state" refers to the psychological and emotional state a user is experiencing at a given moment.

[0353] A "generation method" is a process or system for creating new information or results based on specific data or conditions.

[0354] A "dialogue method" is a means of communication for exchanging information between a user and a system.

[0355] This invention provides a product listing system that reflects the user's emotional state. This enables a personalized experience for the user.

[0356] Users browse products through the terminal and perform actions as needed. The terminal is equipped with a high-resolution camera and a high-sensitivity microphone, and uses facial recognition software such as OpenCV and speech analysis software such as Google Cloud Speech-to-Text API to collect user facial expression data and voice data. This data is transferred to the server in real time.

[0357] The server analyzes this data using a generative AI model and evaluates the user's emotional state through an emotion engine. Neural networks and natural language processing technologies are utilized in this process. Based on the evaluation results, the server generates highly relevant product information and promotions for the user.

[0358] For example, if a user tilts their head while browsing a product, the device captures this facial expression and sends it to the server. The server recognizes this emotional state as "doubt" and, taking this state into consideration, generates other product information that the user might be interested in. This allows the user to receive more relevant suggestions.

[0359] An example of a prompt message is, "Evaluate whether the user is satisfied with the product, and based on that evaluation, present the following listing information."

[0360] This system is expected to enable the development of sales strategies tailored to the emotions of individual users, significantly improving the user experience.

[0361] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0362] Step 1:

[0363] Users browse products through their devices. The devices use their built-in cameras and microphones to capture the user's facial expressions and voice in real time. This facial and voice data is collected as input. Since the collected data is raw, it is processed into a format suitable for analysis.

[0364] Step 2:

[0365] The device sends collected facial expression and voice data to the server. The server receives this data as input and analyzes it using a generative AI model and an emotion engine. Specifically, a facial recognition algorithm extracts facial features, and a voice analysis system analyzes the tone and tempo of the voice. Based on these analyses, the server outputs the user's emotional state (e.g., positive, negative, neutral).

[0366] Step 3:

[0367] The server uses the user's emotional state, obtained as an input from the analysis results, to generate highly relevant product information and promotions. Here, the generating AI model optimizes the product and promotional content. For example, if the user is rated as "negative," data calculations are performed to suggest alternative, related products. The generated information is output as a recommendation list of products the user is likely to be interested in next.

[0368] Step 4:

[0369] The server sends the generated recommendation list to the device. The device displays this list on the user interface. By referring to the recommended product information, the user is prompted to select or purchase additional products. At this point, the UI operates in a way that maximizes the user experience, taking screen design and other factors into consideration.

[0370] (Application Example 2)

[0371] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0372] In modern commerce, there is a demand for improved user experience and streamlined listing processes. However, traditional systems struggle to respond flexibly to user emotions, resulting in a failure to maximize user purchasing intent. Furthermore, the lack of individually tailored responses leads to inconsistent customer service quality.

[0373] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0374] In this invention, the server includes information processing means for automatically generating listing information based on product information, mechanical means for automating product photography and packaging, sentiment analysis means for analyzing the customer's emotional state and generating user-specific responses, and dialogue means for handling customer inquiries. This enables flexible product suggestions and promotions based on user emotions, improving the user experience and optimizing the entire commercial transaction process.

[0375] "Information processing means" refers to a device or software that automatically generates listing information from product information by analyzing, transforming, and generating data.

[0376] "Mechanical means" refers to artificial devices or programs that have the function of automating the photography and packaging of products.

[0377] "Emotional analysis means" refers to a method or system that analyzes a user's facial expressions and voice data to identify their emotional state and generate individual responses based on the results.

[0378] A "dialogue tool" is a technology or application that automatically generates and responds to customer inquiries.

[0379] The server executes the program necessary to realize the present invention. First, using information processing means, it automatically generates listing information based on product information obtained from the product database. This eliminates the need for manual input and enables efficient product management. By automating the product photography and packaging processes using mechanical means, work efficiency is greatly improved.

[0380] Next, a device equipped with emotion analysis capabilities analyzes data collected using the user's camera and microphone. This process utilizes software such as SmileML and Google Cloud Speech-to-Text to identify emotions based on the user's facial expressions and tone of voice. The analysis results are sent to an analysis engine such as IBM Watson Tone Analyzer, where the user's emotional state is evaluated. This emotion data is then input into recommendation systems such as Amazon Personalize, which automatically suggest the most relevant products and promotional information to the user.

[0381] Furthermore, the server utilizes interactive methods to generate appropriate responses to customer inquiries, enabling more consistent customer service. For example, if a user shows dissatisfaction while browsing new products online, the system improves the user experience by suggesting alternative products in a timely manner based on the user's past data.

[0382] An example of using prompts with a generative AI model is a specific prompt sentence such as, "Suggest alternative products that this user might be interested in. The user's sentiment is negative."

[0383] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0384] Step 1:

[0385] The server retrieves product information from the product database. At this time, the product information is entered as digital data, including product name, price, and description. Information processing equipment analyzes this information and automatically generates listing information. This listing information includes all necessary fields.

[0386] Step 2:

[0387] The automated system photographs and packages the products. A terminal outputs control signals to the camera used for product photography, acquiring high-resolution images. Then, a robotic arm is used to package the products in the specified manner, and information indicating that packaging is complete is output.

[0388] Step 3:

[0389] The device acquires data in real time from the user's camera and microphone. This data includes the user's facial expressions and voice data, and is analyzed using SmileML or Google Cloud Speech-to-Text. The emotion analysis tool identifies the emotional state and outputs the results.

[0390] Step 4:

[0391] The server uses IBM Watson Tone Analyzer to perform sentiment assessment. This analyzes the identified emotional state and quantitatively evaluates what emotions the user is experiencing. This assessment result is then used in the next step.

[0392] Step 5:

[0393] The server utilizes Amazon Personalize to recommend products and promotions that best match the user's sentiment evaluation results. A generative AI model is used to generate a prompt message, "Suggested products based on the user's negative sentiment," and outputs a list of suggested products. This list is displayed on the device, prompting the user to select a product.

[0394] Step 6:

[0395] The server uses dialogue mechanisms to generate responses to user inquiries. Based on the input inquiry and sentiment evaluation results, natural language processing technology is used to generate the optimal answer, which is then displayed to the user as output.

[0396] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0397] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0398] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0399] [Third Embodiment]

[0400] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0401] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0402] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0403] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0404] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0405] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0406] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0407] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0408] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0409] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0410] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0411] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0412] This invention provides a system that completely automates the product listing process, enabling users to sell products without spending a lot of time. This system combines artificial intelligence, robotics, and artificial conversational tools to efficiently manage all stages from listing to sales and shipping.

[0413] The server receives product information, analyzes the product's characteristics using artificial intelligence, and automatically generates optimal listing information. For example, the server analyzes clothing product information received from a user and generates listing information that includes a price setting that takes market value into account, based on factors such as "brand," "size," and "condition," as well as selling points.

[0414] The generated listing information is sent to the user via their device for review. At this stage, users can easily provide feedback, such as making corrections or approvals. The server then automatically lists the product on the online marketplace using the reviewed listing information.

[0415] After an item is listed, the server uses an AI chatbot to respond to customer inquiries 24 hours a day. This chatbot uses natural language processing to analyze customer questions and provides quick and accurate answers.

[0416] Physical tasks such as product photography and packaging are performed by robots. A server sends shooting instructions to the robot, which generates high-quality product images based on specified angles and conditions. Once the shooting is complete, the robot carefully packages the products and prepares them for shipment.

[0417] Once a sale is complete, the server automatically initiates the shipping process. The terminal instructs a robot to perform a final check of the product and prepare it for delivery, ensuring that the product is properly handed over to the shipping company. In this way, users can complete the entire process from listing to shipping seamlessly and efficiently.

[0418] This invention significantly reduces the time and effort users spend on listing products, enabling consistent, fast, and accurate sales.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] The user sends a request for product pickup via their device. The device receives this request and sends it to the server.

[0422] Step 2:

[0423] The server processes the received pickup request and sends the necessary information to the robot. The robot then begins collecting the goods.

[0424] Step 3:

[0425] The server receives image data of collected items and uses artificial intelligence to analyze the characteristics of the items. This allows it to derive the item's category, condition, and price suggestion.

[0426] Step 4:

[0427] The server generates optimal listing information based on the analysis results and sends it to the user via the terminal. The user reviews this information and requests corrections as needed.

[0428] Step 5:

[0429] The server automatically lists products on the online marketplace using listing information approved by the user.

[0430] Step 6:

[0431] A robot photographs the product under specified conditions and sends the generated image data to a server. The server then uses this data to update the product listing information.

[0432] Step 7:

[0433] When an item is sold, the server detects the completion of the sale and sends a notification to the user via the terminal.

[0434] Step 8:

[0435] The server uses an AI chatbot to receive customer inquiries in real time and generate appropriate responses.

[0436] Step 9:

[0437] After the sale is complete, the server sends a shipping instruction to the robot, which then performs a final check of the product.

[0438] Step 10:

[0439] The robot completes the preparation for shipment and hands the goods over to the delivery company. The terminal sends a shipment completion notification and tracking information to the user.

[0440] (Example 1)

[0441] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0442] The process of listing, selling, and shipping products on online marketplaces involves many tasks that require time and effort from human hands, and there is a need to improve efficiency. Therefore, the challenge is to automate tasks such as product information analysis, customer support, and logistics preparation, so that users can conduct sales activities quickly and with minimal effort.

[0443] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0444] In this invention, the server includes information processing means, operation means, and communication means. This enables the entire process from receiving and analyzing product information to generating listing information, responding to customer inquiries, photographing and packaging products, and even collection and shipping procedures to be automated, allowing users to efficiently carry out sales activities.

[0445] "Information processing means" refers to a part of a system that has the function of generating optimal listing information by receiving and analyzing product information.

[0446] "Operating means" refers to an element of a system that has the function of automatically photographing and packaging products using a structure.

[0447] "Communication means" refers to a system element that has the function of receiving and responding to customer inquiries using a dialogue system.

[0448] This system is designed to completely automate the online marketplace sales process. Servers, terminals, and users work together to efficiently handle everything from product information processing to sales and shipping.

[0449] The server receives product information provided by the user. This information includes "product name," "brand," "size," and "condition." Based on this information, a generative AI model analyzes the product's characteristics and generates optimal listing information. The prompt used at this time is "Please generate detailed listing information for this product." The server then refers to a market database and outputs the optimal price and selling points, taking market prices into consideration.

[0450] The generated listing information is sent to the user via their device. The user can review this information and make corrections as needed. Once the user approves the information, it is sent back to the server and listed on the online marketplace.

[0451] The server activates an AI chatbot to respond to customer inquiries. This chatbot uses natural language processing to analyze customer questions and provide quick and appropriate answers. For example, if a customer asks, "What material is this product made of?", the chatbot will respond, "This product is 100% cotton."

[0452] Product photography and packaging are handled by robots, which are the operating mechanisms within the system. Following instructions from the server, the robots photograph the products from specified angles and under designated conditions, generating high-quality images. Afterward, they carefully package the products and prepare them for shipment.

[0453] This system allows users to seamlessly manage all processes, from listing items to customer service and shipping. This significantly reduces the burden on users and enables fast and accurate product sales.

[0454] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0455] Step 1:

[0456] The server receives product information from the user. This includes "product name," "brand," "size," and "condition." The received information is used as input data for subsequent analysis and listing information generation. The server supplies this input to the generation AI model and begins analyzing the product's characteristics. This analysis process uses the prompt "Generate detailed listing information for this product" to perform data calculations that extract the most suitable listing information.

[0457] Step 2:

[0458] The server uses a generative AI model to generate optimal listing information based on the analysis results. The model performs data processing, retrieving market price information from a market database and calculating pricing and selling points. The output listing information includes insights such as expected demand and competitive landscape. This output serves as preparatory data for presentation to users.

[0459] Step 3:

[0460] The terminal receives listing information from the server and displays it to the user. In this step, the user can review the listing information through the terminal and adjust the price or edit the selling points. If the information is modified by the user, the modified listing information is output and sent back to the server.

[0461] Step 4:

[0462] The server lists the product on the online marketplace using the verified listing information. Here, the listing information is automatically entered via the API of each marketplace, completing the product listing process. Confirmation of listing completion is received as output to the marketplace and notified to the user. This notification allows the user to confirm that the product has been listed correctly.

[0463] Step 5:

[0464] The server activates an AI chatbot to handle customer inquiries. The chatbot receives customer questions as input and analyzes the content using natural language processing. Based on the analysis results, it immediately generates and outputs an appropriate answer. For example, if a customer asks, "What is the material of this product?", the chatbot will output an answer such as, "This product is 100% cotton."

[0465] Step 6:

[0466] The server sends instructions to the robot for photographing and packaging the products. The robot receives these instructions as input and photographs the products under the specified conditions. The captured images are output to the server in high resolution and attached to the listing information. The robot then packages the products and prepares them for shipping.

[0467] Step 7:

[0468] The server initiates the shipping process when a sale is completed. The server receives the necessary shipping information as input and prepares the package for handover to the delivery company. Specifically, it generates shipping labels and outputs pickup instructions to the logistics company, completing the process to ensure the product is delivered correctly. After completion, the user is notified that the shipment has been completed.

[0469] (Application Example 1)

[0470] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0471] In recent years, with the expansion of e-commerce, opportunities for individuals to list and sell products online have increased. However, this process involves a great deal of effort, including data entry, packaging, and customer service, which is burdensome for sellers. Furthermore, improving the accuracy of listing information, such as setting prices based on market rates, is also a challenge. Therefore, there is a need for technology that can efficiently automate the process from listing to sales and shipping, thereby reducing the burden on sellers.

[0472] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0473] In this invention, the server includes information processing means for automatically generating listing information based on product information, mechanical device means for automating product photography and packaging, conversation processing means for handling customer inquiries, image recognition means for acquiring and analyzing product images from a smartphone, and user interface means for allowing the user to confirm and approve the generated listing information. This enables the user to efficiently manage the product listing process and make accurate listings based on market prices while reducing manual information input.

[0474] "Information processing means for automatically generating listing information based on product information" refers to technology that analyzes data on products to be sold and automatically constructs optimal sales information.

[0475] "Mechanical devices and means for automating product photography and packaging" refers to a system that mechanically performs product photography and packaging for shipment, thereby replacing human labor.

[0476] A "conversational processing system for handling customer inquiries" is an interactive system that automatically responds to questions and requests from purchasers.

[0477] "Image recognition means for acquiring and analyzing product images from a smartphone" refers to a technology that analyzes product images acquired using a mobile device and extracts necessary information.

[0478] "User interface means for allowing users to review and approve generated listing information" refers to screen display and operation functions that present automatically generated sales information to the user and allow them to review and approve the content.

[0479] "Information generation means for receiving information related to collection and generating instructions for collection" refers to technology that receives data necessary for picking up goods and creates instructions to support appropriate collection.

[0480] "Information generation means for calculating a fair price based on market information of a product" refers to a method for analyzing market trends and rationally setting the selling price of a product.

[0481] "An information update mechanism that automatically generates shipping instructions and prepares products for handover to the delivery organization when a product is sold" refers to a system that, when a product is purchased, sequentially processes the next delivery steps and prepares the product for accurate shipment.

[0482] "A means of updating information that allows users to update product information after listing it" refers to a function that allows users to enter or modify additional information even after a product has been listed.

[0483] A system for carrying out this invention consists of multiple components, including a server, a terminal with a user interface, and a robotic device for handling goods.

[0484] The server analyzes product information and automatically generates listing information based on it. This process utilizes open-source natural language processing libraries and image recognition technologies. For example, Google's TensorFlow is used to process and analyze product images. The generated listing information is then constructed using artificial intelligence technology to consider market prices and include appropriate pricing information.

[0485] The terminal is a device that users can operate from their hand, like a smartphone. Users take product images with their smartphones and upload those images to the server. The listing information provided by the server is notified to the user via the terminal, and the user can review, modify, and approve the information. This allows for easy feedback to be sent back to the server through the user interface.

[0486] Robotic means are used in the photography and packaging processes. The robotic equipment photographs the products according to specified conditions and maintains high image quality. Furthermore, it properly packages the products in preparation for shipment. The robot's operation is instructed from a server and executed in the most optimal procedure.

[0487] Furthermore, customer inquiries are handled through an AI chatbot. By utilizing natural language processing libraries, a system is in place to respond immediately to customer questions. This enables 24-hour customer support.

[0488] For example, when a user sells a used shirt, they take multiple photos with their smartphone. The server then automatically recognizes details such as the brand and condition based on these photos and generates listing information according to market value. Once the user reviews it and taps "Approve," the item is immediately listed on the online marketplace.

[0489] This system also supports prompt messages. For example, a possible input to the generation AI model could be something like, "Please provide a concrete example of how to automatically generate sales information from product images taken with a smartphone and streamline the listing process."

[0490] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0491] Step 1:

[0492] The user uses their smartphone to take pictures of the products they want to sell. This generates product image data. The user then uploads these images from their device to the server. During this process, the images are appropriately compressed and prepared as data packets for upload.

[0493] Step 2:

[0494] The server receives the uploaded product images. The received image data is then analyzed using image recognition technology. This analysis extracts characteristic information such as the product's "brand," "size," and "condition." TensorFlow is one example of the technology used.

[0495] Step 3:

[0496] The server compares the analyzed feature information with a market price database. This allows it to calculate a fair price and automatically generate listing information, including selling points. The output is a data object containing the listing information.

[0497] Step 4:

[0498] The generated listing information is sent from the server to the terminal. The terminal presents the listing information to the user via a user interface. The user reviews the information and makes any necessary corrections or approvals. Once the user's input is confirmed, the updated listing information is sent back to the server.

[0499] Step 5:

[0500] The server automatically lists the product on the online marketplace using the finalized listing information. This step utilizes API integration technology with the marketplace.

[0501] Step 6:

[0502] When an item is sold, the server sends instructions to the robotic device to take photos and pack the item. The robot takes photos of the item from the specified angle and saves the images in high resolution. It also automatically completes the packing process, making the item ready for shipment.

[0503] Step 7:

[0504] After the sale, the server responds to customer inquiries through an AI chatbot. The chatbot uses a natural language processing model to analyze questions in real time and provide appropriate answers. The response to the customer is output as an optimized reply from the server.

[0505] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0506] This invention integrates an emotion engine that recognizes user emotions and optimizes the overall system's operation, in addition to conventional systems that automate the product listing process. This improves the user experience and maximizes results.

[0507] The server analyzes the user's facial expressions and tone of voice, and evaluates their emotional state through an emotion engine. Based on this evaluation, the server automatically generates appropriate listing information and promotional suggestions for the user. For example, if a user shows dissatisfaction with a product, the emotion engine will determine this to be "negative," and the server will present alternative product information that the user is likely to be interested in.

[0508] Furthermore, when a user interacts with customer support, the device uses an emotion engine to analyze the user's emotional state in real time. Based on this analysis, the server generates and sends a personalized response to the user. For example, if a user is frustrated during an inquiry, the system adjusts to provide a more polite and considerate response.

[0509] Emotional data is also used in sales management. Servers link users' past emotional history with their purchase history to develop sales promotion strategies. For example, sending specific promotional messages when a user expresses positive emotions can further stimulate their purchasing intent.

[0510] In this way, the integration of the server, terminal, and emotion engine enables flexible responses tailored to users, which was not possible with conventional automated listing systems, and is expected to improve usability and boost sales. This system is particularly aimed at providing advanced services to users with a wide range of different emotional states.

[0511] The following describes the processing flow.

[0512] Step 1:

[0513] The user enters product information via their device and sends a listing request to the system. The device receives this information and sends it to the server.

[0514] Step 2:

[0515] The server receives product information, and the emotion engine analyzes the user's current emotional state. This includes the user's facial expressions and voice tone as captured by the device's camera.

[0516] Step 3:

[0517] The emotion engine classifies the user's emotions as either "positive," "neutral," or "negative," and communicates the evaluation result to the server.

[0518] Step 4:

[0519] The server takes the user's emotional state into account and uses AI to generate optimized listing information. For example, if a user is in a negative state, the server will create listing information that emphasizes the benefits of the suggested product and positive customer reviews.

[0520] Step 5:

[0521] The terminal presents the generated listing information to the user and prompts for additional feedback and approval. After the user approves, the server lists the product on the online marketplace.

[0522] Step 6:

[0523] When a product is listed, the server uses an AI chatbot and an emotion engine to generate appropriate responses based on the content of the conversation when interacting with customers regarding their inquiries.

[0524] Step 7:

[0525] When a product is sold, the server detects the completion of the sale, re-evaluates the user's emotional state, and sends feedback to the user to enhance their positive experience.

[0526] Step 8:

[0527] Based on the analysis results of the emotion engine, the terminal starts preparing the product for shipment and sends a shipping instruction to the robot. The robot then includes an encouraging message that takes emotions into consideration in the package.

[0528] Step 9:

[0529] The server hands over the goods to the shipping company, and the terminal notifies the user of the shipping completion details. Feedback and service provision at each step optimize the user experience.

[0530] (Example 2)

[0531] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0532] Providing optimal sales strategies and customer service tailored to user emotions during the product listing process is challenging. Traditional systems often fail to consider user emotions, resulting in a lower quality user experience. Furthermore, responding quickly and appropriately to customer inquiries presents a significant challenge.

[0533] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0534] In this invention, the server includes intelligent means for automatically generating listing information based on product data, analysis means for analyzing facial expression data and voice data and evaluating the emotional state, and generation means for generating appropriate information based on the user's emotional state. This enables flexible sales strategies and customer support that respond to the user's emotions, thereby improving the user experience.

[0535] "Product data" refers to a collection of information that describes the product itself, including data such as price, description, photos, and stock status.

[0536] "Listing information" refers to information necessary for selling a product, and is presented on an online platform in a format that users can view.

[0537] An "intelligent tool" is a system that uses computer programs to automatically make judgments and analyses and generate output that aligns with a specific purpose.

[0538] "Facial expression data" refers to digital information used to record and analyze a user's facial movements and expressions.

[0539] "Voice data" refers to digital audio information used to record and analyze a user's voice.

[0540] "Analytical means" refers to a process or system for analyzing data to derive specific conclusions or evaluations.

[0541] "Emotional state" refers to the psychological and emotional state a user is experiencing at a given moment.

[0542] A "generation method" is a process or system for creating new information or results based on specific data or conditions.

[0543] A "dialogue method" is a means of communication for exchanging information between a user and a system.

[0544] This invention provides a product listing system that reflects the user's emotional state. This enables a personalized experience for the user.

[0545] Users browse products through the terminal and perform actions as needed. The terminal is equipped with a high-resolution camera and a high-sensitivity microphone, and uses facial recognition software such as OpenCV and speech analysis software such as Google Cloud Speech-to-Text API to collect user facial expression data and voice data. This data is transferred to the server in real time.

[0546] The server analyzes this data using a generative AI model and evaluates the user's emotional state through an emotion engine. Neural networks and natural language processing technologies are utilized in this process. Based on the evaluation results, the server generates highly relevant product information and promotions for the user.

[0547] For example, if a user tilts their head while browsing a product, the device captures this facial expression and sends it to the server. The server recognizes this emotional state as "doubt" and, taking this state into consideration, generates other product information that the user might be interested in. This allows the user to receive more relevant suggestions.

[0548] An example of a prompt message is, "Evaluate whether the user is satisfied with the product, and based on that evaluation, present the following listing information."

[0549] This system is expected to enable the development of sales strategies tailored to the emotions of individual users, significantly improving the user experience.

[0550] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0551] Step 1:

[0552] Users browse products through their devices. The devices use their built-in cameras and microphones to capture the user's facial expressions and voice in real time. This facial and voice data is collected as input. Since the collected data is raw, it is processed into a format suitable for analysis.

[0553] Step 2:

[0554] The device sends collected facial expression and voice data to the server. The server receives this data as input and analyzes it using a generative AI model and an emotion engine. Specifically, a facial recognition algorithm extracts facial features, and a voice analysis system analyzes the tone and tempo of the voice. Based on these analyses, the server outputs the user's emotional state (e.g., positive, negative, neutral).

[0555] Step 3:

[0556] The server uses the user's emotional state, obtained as an input from the analysis results, to generate highly relevant product information and promotions. Here, the generating AI model optimizes the product and promotional content. For example, if the user is rated as "negative," data calculations are performed to suggest alternative, related products. The generated information is output as a recommendation list of products the user is likely to be interested in next.

[0557] Step 4:

[0558] The server sends the generated recommendation list to the device. The device displays this list on the user interface. By referring to the recommended product information, the user is prompted to select or purchase additional products. At this point, the UI operates in a way that maximizes the user experience, taking screen design and other factors into consideration.

[0559] (Application Example 2)

[0560] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0561] In modern commerce, there is a demand for improved user experience and streamlined listing processes. However, traditional systems struggle to respond flexibly to user emotions, resulting in a failure to maximize user purchasing intent. Furthermore, the lack of individually tailored responses leads to inconsistent customer service quality.

[0562] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0563] In this invention, the server includes information processing means for automatically generating listing information based on product information, mechanical means for automating product photography and packaging, sentiment analysis means for analyzing the customer's emotional state and generating user-specific responses, and dialogue means for handling customer inquiries. This enables flexible product suggestions and promotions based on user emotions, improving the user experience and optimizing the entire commercial transaction process.

[0564] "Information processing means" refers to a device or software that automatically generates listing information from product information by analyzing, transforming, and generating data.

[0565] "Mechanical means" refers to artificial devices or programs that have the function of automating the photography and packaging of products.

[0566] "Emotional analysis means" refers to a method or system that analyzes a user's facial expressions and voice data to identify their emotional state and generate individual responses based on the results.

[0567] A "dialogue tool" is a technology or application that automatically generates and responds to customer inquiries.

[0568] The server executes the program necessary to realize the present invention. First, using information processing means, it automatically generates listing information based on product information obtained from the product database. This eliminates the need for manual input and enables efficient product management. By automating the product photography and packaging processes using mechanical means, work efficiency is greatly improved.

[0569] Next, a device equipped with emotion analysis capabilities analyzes data collected using the user's camera and microphone. This process utilizes software such as SmileML and Google Cloud Speech-to-Text to identify emotions based on the user's facial expressions and tone of voice. The analysis results are sent to an analysis engine such as IBM Watson Tone Analyzer, where the user's emotional state is evaluated. This emotion data is then input into recommendation systems such as Amazon Personalize, which automatically suggest the most relevant products and promotional information to the user.

[0570] Furthermore, the server utilizes interactive methods to generate appropriate responses to customer inquiries, enabling more consistent customer service. For example, if a user shows dissatisfaction while browsing new products online, the system improves the user experience by suggesting alternative products in a timely manner based on the user's past data.

[0571] An example of using prompts with a generative AI model is a specific prompt sentence such as, "Suggest alternative products that this user might be interested in. The user's sentiment is negative."

[0572] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0573] Step 1:

[0574] The server retrieves product information from the product database. At this time, the product information is entered as digital data, including product name, price, and description. Information processing equipment analyzes this information and automatically generates listing information. This listing information includes all necessary fields.

[0575] Step 2:

[0576] The automated system photographs and packages the products. A terminal outputs control signals to the camera used for product photography, acquiring high-resolution images. Then, a robotic arm is used to package the products in the specified manner, and information indicating that packaging is complete is output.

[0577] Step 3:

[0578] The device acquires data in real time from the user's camera and microphone. This data includes the user's facial expressions and voice data, and is analyzed using SmileML or Google Cloud Speech-to-Text. The emotion analysis tool identifies the emotional state and outputs the results.

[0579] Step 4:

[0580] The server uses IBM Watson Tone Analyzer to perform sentiment assessment. This analyzes the identified emotional state and quantitatively evaluates what emotions the user is experiencing. This assessment result is then used in the next step.

[0581] Step 5:

[0582] The server utilizes Amazon Personalize to recommend products and promotions that best match the user's sentiment evaluation results. A generative AI model is used to generate a prompt message, "Suggested products based on the user's negative sentiment," and outputs a list of suggested products. This list is displayed on the device, prompting the user to select a product.

[0583] Step 6:

[0584] The server uses dialogue mechanisms to generate responses to user inquiries. Based on the input inquiry and sentiment evaluation results, natural language processing technology is used to generate the optimal answer, which is then displayed to the user as output.

[0585] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0586] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0587] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0588] [Fourth Embodiment]

[0589] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0590] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0591] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0592] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0593] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0594] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0595] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0596] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0597] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0598] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0599] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0600] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0601] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0602] This invention provides a system that completely automates the product listing process, enabling users to sell products without spending a lot of time. This system combines artificial intelligence, robotics, and artificial conversational tools to efficiently manage all stages from listing to sales and shipping.

[0603] The server receives product information, analyzes the product's characteristics using artificial intelligence, and automatically generates optimal listing information. For example, the server analyzes clothing product information received from a user and generates listing information that includes a price setting that takes market value into account, based on factors such as "brand," "size," and "condition," as well as selling points.

[0604] The generated listing information is sent to the user via their device for review. At this stage, users can easily provide feedback, such as making corrections or approvals. The server then automatically lists the product on the online marketplace using the reviewed listing information.

[0605] After an item is listed, the server uses an AI chatbot to respond to customer inquiries 24 hours a day. This chatbot uses natural language processing to analyze customer questions and provides quick and accurate answers.

[0606] Physical tasks such as product photography and packaging are performed by robots. A server sends shooting instructions to the robot, which generates high-quality product images based on specified angles and conditions. Once shooting is complete, the robot carefully packages the products, preparing them for shipment.

[0607] Once a sale is complete, the server automatically initiates the shipping process. The terminal instructs a robot to perform a final check of the product and prepare it for delivery, ensuring that the product is properly handed over to the shipping company. In this way, users can complete the entire process from listing to shipping seamlessly and efficiently.

[0608] This invention significantly reduces the time and effort users spend on listing products, enabling consistent, fast, and accurate sales.

[0609] The following describes the processing flow.

[0610] Step 1:

[0611] The user sends a request for product pickup via their device. The device receives this request and sends it to the server.

[0612] Step 2:

[0613] The server processes the received pickup request and sends the necessary information to the robot. The robot then begins collecting the goods.

[0614] Step 3:

[0615] The server receives image data of collected items and uses artificial intelligence to analyze the characteristics of the items. This allows it to derive the item's category, condition, and price suggestion.

[0616] Step 4:

[0617] The server generates optimal listing information based on the analysis results and sends it to the user via the terminal. The user reviews this information and requests corrections as needed.

[0618] Step 5:

[0619] The server automatically lists products on the online marketplace using listing information approved by the user.

[0620] Step 6:

[0621] A robot photographs the product under specified conditions and sends the generated image data to a server. The server then uses this data to update the product listing information.

[0622] Step 7:

[0623] When an item is sold, the server detects the completion of the sale and sends a notification to the user via the terminal.

[0624] Step 8:

[0625] The server uses an AI chatbot to receive customer inquiries in real time and generate appropriate responses.

[0626] Step 9:

[0627] After the sale is complete, the server sends a shipping instruction to the robot, which then performs a final check of the product.

[0628] Step 10:

[0629] The robot completes the preparation for shipment and hands the goods over to the delivery company. The terminal sends a shipment completion notification and tracking information to the user.

[0630] (Example 1)

[0631] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0632] The process of listing, selling, and shipping products on online marketplaces involves many tasks that require time and effort from human hands, and there is a need to improve efficiency. Therefore, the challenge is to automate tasks such as product information analysis, customer service, and logistics preparation, so that users can conduct sales activities quickly and with minimal effort.

[0633] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0634] In this invention, the server includes information processing means, operation means, and communication means. This enables the entire process, from receiving and analyzing product information to generating listing information, responding to customer inquiries, photographing and packaging products, and even collection and shipping procedures, to be automated, allowing users to efficiently carry out sales activities.

[0635] "Information processing means" refers to a part of a system that has the function of generating optimal listing information by receiving and analyzing product information.

[0636] "Operating means" refers to an element of a system that has the function of automatically photographing and packaging products using a structure.

[0637] "Communication means" refers to a system element that has the function of receiving and responding to customer inquiries using a dialogue system.

[0638] This system is designed to completely automate the online marketplace sales process. Servers, terminals, and users work together to efficiently handle everything from product information processing to sales and shipping.

[0639] The server receives product information provided by the user. This information includes "product name," "brand," "size," and "condition." Based on this information, a generative AI model analyzes the product's characteristics and generates optimal listing information. The prompt used at this time is "Please generate detailed listing information for this product." The server then refers to a market database and outputs the optimal price and selling points, taking market prices into consideration.

[0640] The generated listing information is sent to the user via their device. The user can review this information and make corrections as needed. Once the user approves the information, it is sent back to the server and listed on the online marketplace.

[0641] The server activates an AI chatbot to respond to customer inquiries. This chatbot uses natural language processing to analyze customer questions and provide quick and appropriate answers. For example, if a customer asks, "What material is this product made of?", the chatbot will respond, "This product is 100% cotton."

[0642] Product photography and packaging are handled by robots, which are the operating mechanisms within the system. Following instructions from the server, the robots photograph the products from specified angles and under designated conditions, generating high-quality images. Afterward, they carefully package the products and prepare them for shipment.

[0643] This system allows users to seamlessly manage all processes, from listing products to customer service and shipping. This significantly reduces the burden on users and enables fast and accurate product sales.

[0644] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0645] Step 1:

[0646] The server receives product information from the user. This includes "product name," "brand," "size," and "condition." The received information is used as input data for subsequent analysis and listing information generation. The server supplies this input to the generation AI model and begins analyzing the product's characteristics. This analysis process uses the prompt "Generate detailed listing information for this product" to perform data calculations that extract the most suitable listing information.

[0647] Step 2:

[0648] The server uses a generative AI model to generate optimal listing information based on the analysis results. The model performs data processing, retrieving market price information from a market database and calculating pricing and selling points. The output listing information includes insights such as expected demand and competitive landscape. This output serves as preparatory data for presentation to users.

[0649] Step 3:

[0650] The terminal receives listing information from the server and displays it to the user. In this step, the user can review the listing information through the terminal and adjust the price or edit the selling points. If the information is modified by the user, the modified listing information is output and sent back to the server.

[0651] Step 4:

[0652] The server lists the product on the online marketplace using the verified listing information. Here, the listing information is automatically entered via the API of each marketplace, completing the product listing process. Confirmation of listing completion is received as output to the marketplace and notified to the user. This notification allows the user to confirm that the product has been listed correctly.

[0653] Step 5:

[0654] The server activates an AI chatbot to handle customer inquiries. The chatbot receives customer questions as input and analyzes the content using natural language processing. Based on the analysis results, it immediately generates and outputs an appropriate answer. For example, if a customer asks, "What is the material of this product?", the chatbot will output an answer such as, "This product is 100% cotton."

[0655] Step 6:

[0656] The server sends instructions to the robot for photographing and packaging the products. The robot receives these instructions as input and photographs the products under the specified conditions. The captured images are output to the server in high resolution and attached to the listing information. The robot then packages the products and prepares them for shipping.

[0657] Step 7:

[0658] The server initiates the shipping process when a sale is completed. The server receives the necessary shipping information as input and prepares the package for handover to the delivery company. Specifically, it generates shipping labels and outputs pickup instructions to the logistics company, completing the process to ensure the product is delivered correctly. After completion, the user is notified that the shipment has been completed.

[0659] (Application Example 1)

[0660] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0661] In recent years, with the expansion of e-commerce, opportunities for individuals to list and sell products online have increased. However, this process involves a great deal of effort, including data entry, packaging, and customer service, which is burdensome for sellers. Furthermore, improving the accuracy of listing information, such as setting prices based on market rates, is also a challenge. Therefore, there is a need for technology that can efficiently automate the process from listing to sales and shipping, thereby reducing the burden on sellers.

[0662] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0663] In this invention, the server includes information processing means for automatically generating listing information based on product information, mechanical device means for automating product photography and packaging, conversation processing means for handling customer inquiries, image recognition means for acquiring and analyzing product images from a smartphone, and user interface means for allowing the user to confirm and approve the generated listing information. This enables the user to efficiently manage the product listing process and make accurate listings based on market prices while reducing manual information input.

[0664] "Information processing means for automatically generating listing information based on product information" refers to technology that analyzes data on products to be sold and automatically constructs optimal sales information.

[0665] "Mechanical devices and means for automating product photography and packaging" refer to systems that mechanically perform product photography and packaging for shipment, thereby replacing human labor.

[0666] A "conversational processing system for handling customer inquiries" is an interactive system that automatically responds to questions and requests from purchasers.

[0667] "Image recognition means for acquiring and analyzing product images from a smartphone" refers to a technology that analyzes product images acquired using a mobile device and extracts necessary information.

[0668] "User interface means for allowing users to review and approve generated listing information" refers to screen display and operation functions that present automatically generated sales information to the user and allow them to review and approve the content.

[0669] "Information generation means for receiving information related to collection and generating instructions for collection" refers to technology that receives data necessary for picking up goods and creates instructions to support appropriate collection.

[0670] "Information generation means for calculating a fair price based on market information of a product" refers to a method for analyzing market trends and rationally setting the selling price of a product.

[0671] "An information update mechanism that automatically generates shipping instructions and prepares products for handover to the delivery organization when a product is sold" refers to a system that, when a product is purchased, sequentially processes the next delivery steps and prepares the product for accurate shipment.

[0672] "A means of updating information that allows users to update product information after listing it" refers to a function that allows users to enter or modify additional information even after a product has been listed.

[0673] A system for carrying out this invention consists of multiple components, including a server, a terminal with a user interface, and a robotic device for handling goods.

[0674] The server analyzes product information and automatically generates listing information based on it. This process utilizes open-source natural language processing libraries and image recognition technologies. For example, Google's TensorFlow is used to process and analyze product images. The generated listing information is then constructed using artificial intelligence technology to consider market prices and include appropriate pricing information.

[0675] The terminal is a device that users can operate from their hand, like a smartphone. Users take product images with their smartphones and upload those images to the server. The listing information provided by the server is notified to the user via the terminal, and the user can review, modify, and approve the information. This allows for easy feedback to be sent back to the server through the user interface.

[0676] Robotic means are used in the photography and packaging processes. The robotic equipment photographs the products according to specified conditions and maintains high image quality. Furthermore, it properly packages the products in preparation for shipment. The robot's operation is instructed from a server and executed in the most optimal procedure.

[0677] Furthermore, customer inquiries are handled through an AI chatbot. By utilizing natural language processing libraries, a system is in place to respond immediately to customer questions. This enables 24-hour customer support.

[0678] For example, when a user sells a used shirt, they take multiple photos with their smartphone. The server then automatically recognizes details such as the brand and condition based on these photos and generates listing information according to market value. Once the user reviews it and taps "Approve," the item is immediately listed on the online marketplace.

[0679] This system also supports prompt messages. For example, a possible input to the generation AI model could be something like, "Please provide a concrete example of how to automatically generate sales information from product images taken with a smartphone and streamline the listing process."

[0680] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0681] Step 1:

[0682] The user uses their smartphone to take pictures of the products they want to sell. This generates product image data. The user then uploads these images from their device to the server. During this process, the images are appropriately compressed and prepared as data packets for upload.

[0683] Step 2:

[0684] The server receives the uploaded product images. The received image data is then analyzed using image recognition technology. This analysis extracts characteristic information such as the product's "brand," "size," and "condition." TensorFlow is one example of the technology used.

[0685] Step 3:

[0686] The server compares the analyzed feature information with a market price database. This allows it to calculate a fair price and automatically generate listing information, including selling points. The output is a data object containing the listing information.

[0687] Step 4:

[0688] The generated listing information is sent from the server to the terminal. The terminal presents the listing information to the user via a user interface. The user reviews the information and makes any necessary corrections or approvals. Once the user's input is confirmed, the updated listing information is sent back to the server.

[0689] Step 5:

[0690] The server automatically lists the product on the online marketplace using the finalized listing information. This step utilizes API integration technology with the marketplace.

[0691] Step 6:

[0692] When an item is sold, the server sends instructions to the robotic device to take photos and pack the item. The robot takes photos of the item from the specified angle and saves the images in high resolution. It also automatically completes the packing process, making the item ready for shipment.

[0693] Step 7:

[0694] After the sale, the server responds to customer inquiries through an AI chatbot. The chatbot uses a natural language processing model to analyze questions in real time and provide appropriate answers. The response to the customer is output as an optimized reply from the server.

[0695] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0696] This invention integrates an emotion engine that recognizes user emotions and optimizes the overall system's operation, in addition to conventional systems that automate the product listing process. This improves the user experience and maximizes results.

[0697] The server analyzes the user's facial expressions and tone of voice, and evaluates their emotional state through an emotion engine. Based on this evaluation, the server automatically generates appropriate listing information and promotional suggestions for the user. For example, if a user shows dissatisfaction with a product, the emotion engine will determine this to be "negative," and the server will present alternative product information that the user is likely to be interested in.

[0698] Furthermore, when a user interacts with customer support, the device uses an emotion engine to analyze the user's emotional state in real time. Based on this analysis, the server generates and sends a personalized response to the user. For example, if a user is frustrated during an inquiry, the system adjusts to provide a more polite and considerate response.

[0699] Emotional data is also used in sales management. Servers link users' past emotional history with their purchase history to develop sales promotion strategies. For example, sending specific promotional messages when a user expresses positive emotions can further stimulate their purchasing intent.

[0700] In this way, the integration of the server, terminal, and emotion engine enables flexible responses tailored to users, which was not possible with conventional automated listing systems, and is expected to improve usability and boost sales. This system is particularly aimed at providing advanced services to users with a wide range of different emotional states.

[0701] The following describes the processing flow.

[0702] Step 1:

[0703] The user enters product information via their device and sends a listing request to the system. The device receives this information and sends it to the server.

[0704] Step 2:

[0705] The server receives product information, and the emotion engine analyzes the user's current emotional state. This includes the user's facial expressions and voice tone as captured by the device's camera.

[0706] Step 3:

[0707] The emotion engine classifies the user's emotions as either "positive," "neutral," or "negative," and communicates the evaluation result to the server.

[0708] Step 4:

[0709] The server takes the user's emotional state into account and uses AI to generate optimized listing information. For example, if a user is in a negative state, the server will create listing information that emphasizes the benefits of the suggested product and positive customer reviews.

[0710] Step 5:

[0711] The terminal presents the generated listing information to the user and prompts for additional feedback and approval. After the user approves, the server lists the product on the online marketplace.

[0712] Step 6:

[0713] When a product is listed, the server uses an AI chatbot and an emotion engine to generate appropriate responses based on the content of the conversation when interacting with customers regarding their inquiries.

[0714] Step 7:

[0715] When a product is sold, the server detects the completion of the sale, re-evaluates the user's emotional state, and sends feedback to the user to enhance their positive experience.

[0716] Step 8:

[0717] Based on the analysis results of the emotion engine, the terminal starts preparing the product for shipment and sends a shipping instruction to the robot. The robot then includes an encouraging message that takes emotions into consideration in the package.

[0718] Step 9:

[0719] The server hands over the goods to the shipping company, and the terminal notifies the user of the shipping completion details. Feedback and service provision at each step optimize the user experience.

[0720] (Example 2)

[0721] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0722] Providing optimal sales strategies and customer service tailored to user emotions during the product listing process is challenging. Traditional systems often fail to consider user emotions, resulting in a lower quality user experience. Furthermore, responding quickly and appropriately to customer inquiries presents a significant challenge.

[0723] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0724] In this invention, the server includes intelligent means for automatically generating listing information based on product data, analysis means for analyzing facial expression data and voice data and evaluating the emotional state, and generation means for generating appropriate information based on the user's emotional state. This enables flexible sales strategies and customer support that respond to the user's emotions, thereby improving the user experience.

[0725] "Product data" refers to a collection of information that describes the product itself, including data such as price, description, photos, and stock status.

[0726] "Listing information" refers to information necessary for selling a product, and is presented on an online platform in a format that users can view.

[0727] An "intelligent tool" is a system that uses computer programs to automatically make judgments and analyses and generate output that aligns with a specific purpose.

[0728] "Facial expression data" refers to digital information used to record and analyze a user's facial movements and expressions.

[0729] "Voice data" refers to digital audio information used to record and analyze a user's voice.

[0730] "Analytical means" refers to a process or system for analyzing data to derive specific conclusions or evaluations.

[0731] "Emotional state" refers to the psychological and emotional state a user is experiencing at a given moment.

[0732] A "generation method" is a process or system for creating new information or results based on specific data or conditions.

[0733] A "dialogue method" is a means of communication for exchanging information between a user and a system.

[0734] This invention provides a product listing system that reflects the user's emotional state. This enables a personalized experience for the user.

[0735] Users browse products through the terminal and perform actions as needed. The terminal is equipped with a high-resolution camera and a high-sensitivity microphone, and uses facial recognition software such as OpenCV and speech analysis software such as Google Cloud Speech-to-Text API to collect user facial expression data and voice data. This data is transferred to the server in real time.

[0736] The server analyzes this data using a generative AI model and evaluates the user's emotional state through an emotion engine. Neural networks and natural language processing technologies are utilized in this process. Based on the evaluation results, the server generates highly relevant product information and promotions for the user.

[0737] For example, if a user tilts their head while browsing a product, the device captures this facial expression and sends it to the server. The server recognizes this emotional state as "doubt" and, taking this state into consideration, generates other product information that the user might be interested in. This allows the user to receive more relevant suggestions.

[0738] An example of a prompt message is, "Evaluate whether the user is satisfied with the product, and based on that evaluation, present the following listing information."

[0739] This system is expected to enable the development of sales strategies tailored to the emotions of individual users, significantly improving the user experience.

[0740] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0741] Step 1:

[0742] Users browse products through their devices. The devices use their built-in cameras and microphones to capture the user's facial expressions and voice in real time. This facial and voice data is collected as input. Since the collected data is raw, it is processed into a format suitable for analysis.

[0743] Step 2:

[0744] The device sends collected facial expression and voice data to the server. The server receives this data as input and analyzes it using a generative AI model and an emotion engine. Specifically, a facial recognition algorithm extracts facial features, and a voice analysis system analyzes the tone and tempo of the voice. Based on these analyses, the server outputs the user's emotional state (e.g., positive, negative, neutral).

[0745] Step 3:

[0746] The server uses the user's emotional state, obtained as an input from the analysis results, to generate highly relevant product information and promotions. Here, the generating AI model optimizes the product and promotional content. For example, if the user is rated as "negative," data calculations are performed to suggest alternative, related products. The generated information is output as a recommendation list of products the user is likely to be interested in next.

[0747] Step 4:

[0748] The server sends the generated recommendation list to the device. The device displays this list on the user interface. By referring to the recommended product information, the user is prompted to select or purchase additional products. At this point, the UI operates in a way that maximizes the user experience, taking screen design and other factors into consideration.

[0749] (Application Example 2)

[0750] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0751] In modern commerce, there is a demand for improved user experience and streamlined listing processes. However, traditional systems struggle to respond flexibly to user emotions, resulting in a failure to maximize user purchasing intent. Furthermore, the lack of individually tailored responses leads to inconsistent customer service quality.

[0752] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0753] In this invention, the server includes information processing means for automatically generating listing information based on product information, mechanical means for automating product photography and packaging, sentiment analysis means for analyzing the customer's emotional state and generating user-specific responses, and dialogue means for handling customer inquiries. This enables flexible product suggestions and promotions based on user emotions, improving the user experience and optimizing the entire commercial transaction process.

[0754] "Information processing means" refers to a device or software that automatically generates listing information from product information by analyzing, transforming, and generating data.

[0755] "Mechanical means" refers to artificial devices or programs that have the function of automating the photography and packaging of products.

[0756] "Emotional analysis means" refers to a method or system that analyzes a user's facial expressions and voice data to identify their emotional state and generate individual responses based on the results.

[0757] A "dialogue tool" is a technology or application that automatically generates and responds to customer inquiries.

[0758] The server executes the program necessary to realize the present invention. First, using information processing means, it automatically generates listing information based on product information obtained from the product database. This eliminates the need for manual input and enables efficient product management. By automating the product photography and packaging processes using mechanical means, work efficiency is greatly improved.

[0759] Next, a device equipped with emotion analysis capabilities analyzes data collected using the user's camera and microphone. This process utilizes software such as SmileML and Google Cloud Speech-to-Text to identify emotions based on the user's facial expressions and tone of voice. The analysis results are sent to an analysis engine such as IBM Watson Tone Analyzer, where the user's emotional state is evaluated. This emotion data is then input into recommendation systems such as Amazon Personalize, which automatically suggest the most relevant products and promotional information to the user.

[0760] Furthermore, the server utilizes interactive methods to generate appropriate responses to customer inquiries, enabling more consistent customer service. For example, if a user shows dissatisfaction while browsing new products online, the system improves the user experience by suggesting alternative products in a timely manner based on the user's past data.

[0761] An example of using prompts with a generative AI model is a specific prompt sentence such as, "Suggest alternative products that this user might be interested in. The user's sentiment is negative."

[0762] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0763] Step 1:

[0764] The server retrieves product information from the product database. At this time, the product information is entered as digital data, including product name, price, and description. Information processing equipment analyzes this information and automatically generates listing information. This listing information includes all necessary fields.

[0765] Step 2:

[0766] The automated system photographs and packages the products. A terminal outputs control signals to the camera used for product photography, acquiring high-resolution images. Then, a robotic arm is used to package the products in the specified manner, and information indicating that packaging is complete is output.

[0767] Step 3:

[0768] The device acquires data in real time from the user's camera and microphone. This data includes the user's facial expressions and voice data, and is analyzed using SmileML or Google Cloud Speech-to-Text. The emotion analysis tool identifies the emotional state and outputs the results.

[0769] Step 4:

[0770] The server uses IBM Watson Tone Analyzer to perform sentiment assessment. This analyzes the identified emotional state and quantitatively evaluates what emotions the user is experiencing. This assessment result is then used in the next step.

[0771] Step 5:

[0772] The server utilizes Amazon Personalize to recommend products and promotions that best match the user's sentiment evaluation results. A generative AI model is used to generate a prompt message, "Suggested products based on the user's negative sentiment," and outputs a list of suggested products. This list is displayed on the device, prompting the user to select a product.

[0773] Step 6:

[0774] The server uses dialogue mechanisms to generate responses to user inquiries. Based on the input inquiry and sentiment evaluation results, natural language processing technology is used to generate the optimal answer, which is then displayed to the user as output.

[0775] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0776] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0777] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0778] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0779] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0780] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0781] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0782] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0783] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0784] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0785] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0786] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0787] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0788] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0789] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0790] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0791] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0792] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0793] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0794] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0795] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0796] The following is further disclosed regarding the embodiments described above.

[0797] (Claim 1)

[0798] An artificial intelligence method that automatically generates listing information based on product information,

[0799] Robotic means to automate product photography and packaging,

[0800] An artificial conversational tool for handling customer inquiries,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, which receives information regarding collection and generates instructions for carrying out collection.

[0804] (Claim 3)

[0805] The system according to claim 1, which generates shipping instructions and automatically prepares to hand over the goods to a delivery company when a product is sold.

[0806] "Example 1"

[0807] (Claim 1)

[0808] Information processing means for analyzing product information and generating optimal listing information,

[0809] An operating means for manipulating a structure to photograph and package products,

[0810] A communication method that uses a dialogue system to respond to customer inquiries,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, which receives information regarding collection and generates instructions.

[0814] (Claim 3)

[0815] The system according to claim 1, which, upon completion of a transaction, executes shipping procedures and prepares to hand over the goods to a logistics company.

[0816] "Application Example 1"

[0817] (Claim 1)

[0818] An information processing means that automatically generates listing information based on product information,

[0819] A mechanical device and means for automating product photography and packaging,

[0820] A conversational processing method for handling customer inquiries,

[0821] An image recognition method for acquiring and analyzing product images from a smartphone,

[0822] A user interface means for allowing users to review and approve the generated listing information,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, comprising information generation means for receiving information related to collection, generating instructions for carrying out collection, and calculating a fair price based on market information for the goods.

[0826] (Claim 3)

[0827] The system according to claim 1, which generates shipping instructions when a product is sold, automatically prepares the product for handover to a shipping organization, and includes an information update means that allows a user to update information after listing a product.

[0828] "Example 2 of combining an emotion engine"

[0829] (Claim 1)

[0830] An intelligent means that automatically generates listing information based on product data,

[0831] An analytical means for analyzing facial expression data and voice data to evaluate emotional state,

[0832] A generation means for generating appropriate information based on the user's emotional state,

[0833] A dialogue mechanism for handling customer inquiries,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, which receives information regarding collection and generates instructions for carrying out collection.

[0837] (Claim 3)

[0838] The system according to claim 1, which generates shipping instructions and automatically prepares to hand over the goods to a delivery company when a product is sold.

[0839] "Application example 2 when combining with an emotional engine"

[0840] (Claim 1)

[0841] An information processing means that automatically generates listing information based on product information,

[0842] Mechanical means to automate product photography and packaging,

[0843] A sentiment analysis means that analyzes the emotional state of a customer and generates a user-specific response,

[0844] A dialogue mechanism for handling customer inquiries,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, which receives information regarding collection and generates instructions for carrying out collection.

[0848] (Claim 3)

[0849] The system according to claim 1, which generates shipping instructions and automatically prepares to hand over the goods to a delivery company when a product is sold. [Explanation of symbols]

[0850] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An artificial intelligence method that automatically generates listing information based on product information, Robotic means to automate product photography and packaging, An artificial conversational tool for handling customer inquiries, A system that includes this.

2. The system according to claim 1, which receives information regarding collection and generates instructions for carrying out collection.

3. The system according to claim 1, which generates shipping instructions and automatically prepares to hand over the product to a delivery company when the product is sold.

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