system

Abstract

The system according to this embodiment aims to streamline the listing and sale of products and improve the sales success rate. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a pricing unit, a modification unit, and a recommendation unit. The reception unit receives product photos. The generation unit creates product descriptions based on the photos received by the reception unit and performs cropping and processing of the photos. The pricing unit researches market prices based on the information generated by the generation unit and sets the price. The modification unit modifies and changes the price and description set by the pricing unit as appropriate. The recommendation unit automatically finds products desired by the buyer and recommends them periodically.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, in the listing and selling on flea market apps, it is time-consuming to create product descriptions and set prices, and there is room for improvement in terms of improving the sales success rate.

[0005] The system according to the embodiment aims to improve the efficiency of product listing and selling and improve the sales success rate.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a pricing unit, a modification unit, and a recommendation unit. The reception unit receives product photos. The generation unit creates product descriptions based on the photos received by the reception unit and performs cropping and processing of the photos. The pricing unit researches market prices based on the information generated by the generation unit and sets the price. The modification unit modifies and changes the price and description set by the pricing unit as appropriate. The recommendation unit automatically finds products desired by the buyer and recommends them periodically. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the listing and sale of products and improve the sales success rate. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The flea market app listing and sales support system according to an embodiment of the present invention is a system that improves convenience for sellers and buyers by utilizing a generation AI. This system automatically creates product descriptions, crops and edits photos, researches market prices, and sets prices simply by the seller taking photos of their items, thereby improving the success rate of sales. Furthermore, it provides buyers with a function that automatically finds desired products and recommends them periodically. For example, when a seller takes photos of an item and uploads them to the app, the generation AI automatically creates a product description and crops and edits the photos. The generation AI researches market prices and sets prices according to the seller's wishes. For example, it sets a low price if the seller wants to sell quickly, and a high price if they want to sell for as high a price as possible. In addition, the generation AI makes appropriate corrections and changes to the price and description even after listing, further improving the success rate of sales. When a buyer inputs the conditions for the product they want, the generation AI finds products that match those conditions and recommends them periodically. For example, if a buyer wants "white running shoes," the generation AI finds suitable products from the market and presents them to the buyer. This system makes it possible to increase the value of reused goods, reduce the effort required for use, and improve the likelihood of selling them. As a result, the listing and sales support system for flea market apps can improve convenience for both sellers and buyers and increase the success rate of sales.

[0029] The flea market app listing and sales support system according to this embodiment comprises a reception unit, a generation unit, a pricing unit, a modification unit, and a recommendation unit. The reception unit receives product photos. The reception unit can receive product photos taken with, for example, a smartphone or digital camera. The reception unit can also automatically determine the resolution and format of the photos and convert them to an appropriate format. The generation unit uses generation AI to create product descriptions based on the photos received by the reception unit and performs cropping and processing of the photos. The generation unit generates, for example, text that explains the features and advantages of the product in detail. The generation unit can also crop unnecessary parts of the photos and perform processing such as color correction and filter application. The pricing unit researches market prices based on the information generated by the generation unit and sets the price. The pricing unit analyzes, for example, the prices of competing products and past sales data to set the optimal price. The pricing unit can also adjust the price according to the seller's wishes. The modification unit modifies and changes the price and description set by the pricing unit as appropriate. The editing unit updates prices and descriptions according to sales status and market trends, for example. The editing unit can also make revisions based on feedback from sellers. The recommendation unit automatically finds products that buyers want and recommends them periodically. For example, when a buyer enters the conditions for a product they want, the recommendation unit finds products that match those conditions and presents them to the buyer. The recommendation unit can also recommend related products based on the buyer's past purchase history and search history. As a result, the listing and sales support system for the flea market app according to this embodiment can improve convenience for sellers and buyers and increase the success rate of sales.

[0030] The reception desk accepts product photos. For example, it can accept product photos taken with smartphones or digital cameras. Specifically, when a user uploads a photo through the app, the reception desk automatically detects the resolution and format of the photo and converts it to the appropriate format. For example, it accepts photos in different formats such as JPEG, PNG, and HEIC, and converts them to the standard JPEG format as needed. It can also use AI to improve the resolution of low-resolution photos. Furthermore, the reception desk can analyze the photo's metadata (date and time of shooting, location information, etc.) and use it to supplement product information. For example, it can evaluate the freshness of a product based on the date and time of shooting, and predict region-specific demand based on location information. This allows the reception desk to easily enable users to upload high-quality photos, improving the overall accuracy and efficiency of the system.

[0031] The generation unit uses a generation AI to create product descriptions based on photos received by the reception unit, and also performs cropping and editing of the photos. For example, the generation AI generates text that explains the features and benefits of a product in detail. Specifically, the generation AI analyzes product photos and extracts features such as color, shape, and texture. Based on these features, it generates text that explains the use and benefits of the product. For example, the generation AI extracts material and design features from a photo of clothing and generates a description such as, "This jacket is made of high-quality wool and is perfect for the cold season." The generation unit also crops unnecessary parts of the photo and performs processing such as color correction and applying filters. For example, if there are unwanted objects in the background, the generation AI automatically crops those parts to highlight only the product. It also performs color correction so that the product's color is displayed as close to the actual color as possible. Furthermore, it can apply filters to improve the overall appearance of the photo. In this way, the generation unit enables users to create high-quality product descriptions and photos without much effort, maximizing the appeal of the product.

[0032] The pricing unit investigates market prices and sets prices based on information generated by the generation unit. For example, the pricing unit analyzes the prices of competing products and past sales data to set the optimal price. Specifically, the pricing unit uses AI to analyze market trends in real time and understand fluctuations in the prices and demand of competing products. For example, it investigates the prices at which products in the same category are being traded and calculates the optimal price based on that data. It can also predict price fluctuations at specific times or events based on past sales data and reflect this in the pricing. Furthermore, the pricing unit can adjust prices according to the seller's wishes. For example, if a seller wants to sell quickly, the price can be set lower, and conversely, if they want to sell at a high price, the price can be set higher. The pricing unit also has a dynamic pricing function that can automatically adjust prices according to sales conditions and market trends. This allows the pricing unit to enable sellers to sell their products at the optimal price and increase the success rate of sales.

[0033] The editing unit modifies and changes the prices and descriptions set by the pricing unit as needed. For example, the editing unit updates prices and descriptions according to sales status and market trends. Specifically, the editing unit uses AI to monitor market trends in real time and modifies prices and descriptions as necessary. For example, if a particular product is unsold, it may lower the price to boost sales. It can also adjust prices accordingly if the prices of competing products fluctuate. Furthermore, the editing unit can make modifications based on feedback from sellers. For example, if a seller points out an error in a product description, the editing unit will review the content and correct it appropriately. It can also adjust prices according to requests from sellers who wish to change the price. In this way, the editing unit can always appropriately modify prices and descriptions based on the latest information, enabling sellers to sell their products under optimal conditions.

[0034] The recommendation system automatically finds products that customers desire and recommends them regularly. For example, if a customer enters the criteria for a product they want, the recommendation system will find products that match those criteria and present them to the customer. Specifically, the recommendation system uses AI to analyze the criteria entered by the customer and search for the most suitable product based on that analysis. For example, if a customer enters "red dress," the recommendation system will search all red dresses in its database and present the most suitable product. The recommendation system can also recommend related products based on the customer's past purchase and search history. For example, if a customer has previously purchased sports equipment, it will recommend related new products and sale information. Furthermore, the recommendation system learns the customer's behavior patterns and can provide recommendations at the optimal time for each individual customer. For example, if a customer tends to use the app at a specific time of day, it will provide recommendations tailored to that time. In this way, the recommendation system can provide customers with the most suitable products in a timely manner, increasing their purchase intent.

[0035] The generation unit can create product descriptions using a generation AI and perform cropping and editing of photos. For example, the generation unit can generate text that explains the features and benefits of a product in detail. The generation unit can also crop unnecessary parts of photos and perform processing such as color correction and filter application. The generation unit can also crop unnecessary parts of photos and perform processing such as color correction and filter application. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs a photo of a product into the generation AI, which generates a product description and performs cropping and editing of the photo.

[0036] The pricing unit uses a generating AI to research market prices and set prices that meet the seller's needs. For example, the pricing unit analyzes the prices of competing products and past sales data to set the optimal price. The pricing unit can also adjust prices according to the seller's wishes. Some or all of the above processes in the pricing unit are performed using the generating AI. For example, the pricing unit inputs product information into the generating AI, which then researches market prices and sets the optimal price.

[0037] The editing function can modify and change the price and description of an item after it has been listed. For example, the editing function can update the price and description according to sales status and market trends. The editing function can also make revisions based on feedback from sellers. Some or all of the above processes in the editing function may be performed using AI or not. For example, the editing function can input sales data into the AI, which then modifies the price and description.

[0038] The recommendation unit can automatically find products that the buyer desires and recommend them periodically. For example, if the buyer inputs the criteria for the product they want, the recommendation unit will find products that match those criteria and present them to the buyer. The recommendation unit can also recommend related products based on the buyer's past purchase and search history. Some or all of the above processes in the recommendation unit are performed using a generation AI. For example, the recommendation unit inputs the buyer's desired criteria into the generation AI, which then finds the relevant products and generates recommendation information.

[0039] The reception department can analyze a user's past listing history and select the optimal reception method. For example, the reception department prioritizes accepting listings for product categories that the user has frequently listed in the past. The reception department can also suggest the best way to take photos based on the user's past listing history. Furthermore, the reception department can suggest the optimal timing for acceptance based on the user's past listing history. In this way, the optimal reception method can be selected by analyzing the user's past listing history. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department inputs the user's listing history data into the AI, and the AI ​​selects the optimal reception method.

[0040] The reception unit can filter photos upon receipt based on the user's current projects and areas of interest. For example, the reception unit prioritizes receiving photos related to the user's current projects. It can also filter and receive relevant photos based on the user's areas of interest. Furthermore, the reception unit can receive the most relevant photos according to the progress of the user's projects. This allows for the reception of highly relevant photos by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit inputs the user's project data into the AI, which then filters the relevant photos.

[0041] The reception unit can prioritize receiving photos that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit will prioritize receiving photos related to that region. The reception unit can also prioritize receiving photos of relevant product categories based on the user's current location. Furthermore, the reception unit can prioritize receiving the most suitable photos based on the user's geographical location information. This allows for the priority of receiving highly relevant photos by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's geographical location data into AI, and the AI ​​will prioritize receiving relevant photos.

[0042] The reception desk can analyze the user's social media activity when receiving photos and accept relevant photos. For example, the reception desk can accept relevant photos based on photos the user has shared on social media. The reception desk can also prioritize accepting photos of product categories of interest to the user based on their social media activity. Furthermore, the reception desk can analyze the content of the user's social media posts and accept the most suitable photos. In this way, relevant photos can be accepted by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into an AI, and the AI ​​can accept relevant photos.

[0043] The generation unit can adjust the level of detail in the product description based on the product's importance. For example, it can generate a detailed description for expensive products. It can also generate a concise description for everyday products. Furthermore, for products targeted at a specific group, it can generate a description with a level of detail appropriate for that group. This allows for the generation of appropriate product descriptions by adjusting the level of detail based on the product's importance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs product information into the generation AI, and the generation AI adjusts the level of detail in the description based on the product's importance.

[0044] The generation unit can apply different generation algorithms depending on the product category when generating product descriptions. For example, in the case of fashion products, the generation unit generates descriptions that reflect trend information. In the case of electronic devices, the generation unit can also generate descriptions that include technical details. Furthermore, in the case of books, the generation unit can generate descriptions that include a summary of the content. In this way, appropriate product descriptions can be generated by applying different generation algorithms depending on the product category. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs product category information into the generation AI, and the generation AI applies a generation algorithm appropriate to the category to generate the product description.

[0045] The generation unit can determine the priority of product descriptions based on the product submission date when generating them. For example, the generation unit will prioritize the generation of descriptions for new products. The generation unit can also adjust the priority according to the submission date for seasonal or sale items. Furthermore, the generation unit can set priorities based on the submission date according to the user's wishes. This allows for the generation of appropriate product descriptions by determining the priority of descriptions based on the product submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs product submission date data into the generation AI, and the generation AI determines the priority of descriptions based on the submission date.

[0046] The generation unit can adjust the order of product descriptions based on their relevance when generating them. For example, it might describe the main features of a product first, followed by details. It can also prioritize highly relevant information by referencing descriptions of similar products. Furthermore, it can optimize the order of descriptions according to the product category. This allows for the generation of appropriate product descriptions by adjusting the order based on product relevance. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs product relevance data into the generation AI, which then adjusts the order of the descriptions based on their relevance.

[0047] The pricing unit can analyze market trends for a product and set the optimal price. For example, the pricing unit can set the optimal price based on real-time market data. It can also analyze past market trends and predict price fluctuations. Furthermore, the pricing unit can set the optimal price by referencing the prices of competing products. This allows for the setting of the optimal price by analyzing market trends. Some or all of the above processes in the pricing unit may be performed using AI, or not. For example, the pricing unit can input market data into the AI, which then analyzes market trends and sets the optimal price.

[0048] The pricing unit can customize prices by referring to the seller's past sales history when setting prices. For example, the pricing unit can set the optimal price by referring to the prices of items the seller has sold in the past. The pricing unit can also extract and apply successful pricing patterns from the seller's sales history. Furthermore, the pricing unit can provide pricing advice based on the seller's past sales history. This allows the seller to set the optimal price by referring to their past sales history. Some or all of the above processes in the pricing unit may be performed using AI or not. For example, the pricing unit can input the seller's sales history data into the AI, and the AI ​​can customize the optimal price.

[0049] The pricing unit can set the optimal price by considering the geographical distribution of the product. For example, if a product is popular in a particular region, the pricing unit will set a price appropriate for that region. The pricing unit can also analyze geographical supply and demand to set the optimal price. Furthermore, the pricing unit can adjust prices by considering market trends in each region. In this way, the optimal price can be set by considering the geographical distribution of the product. Some or all of the above processes in the pricing unit may be performed using AI or not. For example, the pricing unit can input geographical distribution data into the AI, and the AI ​​can set the optimal price.

[0050] The pricing unit can improve the accuracy of pricing by referring to relevant literature on the product during the pricing process. For example, the pricing unit can set prices by referring to research papers and market reports related to the product. It can also improve the accuracy of pricing based on literature on the product's characteristics. Furthermore, the pricing unit can set the optimal price by utilizing data obtained from relevant literature. This improves the accuracy of pricing by referring to relevant literature on the product. Some or all of the above processes in the pricing unit may be performed using AI, or not. For example, the pricing unit can input relevant literature data into the AI, which can then improve the accuracy of pricing.

[0051] The editing unit can analyze the product's sales status in real time and make the optimal corrections. For example, the editing unit can revise the price and description based on real-time sales data. It can also revise the product photos according to sales status. Furthermore, the editing unit can make the optimal corrections by considering real-time market trends. This allows for optimal corrections by analyzing the product's sales status in real time. Some or all of the above processes in the editing unit may be performed using AI or not. For example, the editing unit can input sales data into AI, and the AI ​​can make the optimal corrections.

[0052] The editing unit can improve the accuracy of its modifications by referring to relevant market data for the product during the editing process. For example, the editing unit can modify the price and description based on market data related to the product. It can also make optimal modifications by referring to market data for competing products. Furthermore, the editing unit can analyze market trends and optimize the modifications to the product. This improves the accuracy of the modifications by referring to relevant market data for the product. Some or all of the above processes in the editing unit may be performed using AI or not. For example, the editing unit can input relevant market data into the AI, which can then improve the accuracy of the modifications.

[0053] The recommendation system can suggest the most suitable products by referring to past recommendation history when making recommendations. For example, the recommendation system can suggest related products based on products the user has purchased in the past. It can also prioritize suggesting products of interest based on the user's past recommendation history. Furthermore, the recommendation system can analyze the user's purchase history and suggest the most suitable products. This allows the system to suggest the most suitable products by referring to past recommendation history. Some or all of the above processes in the recommendation system may be performed using AI, or not. For example, the recommendation system can input past recommendation history data into an AI, which then suggests the most suitable products.

[0054] The recommendation system can customize recommendations based on the buyer's current areas of interest. For example, it might prioritize suggesting products in categories the buyer is currently interested in. It can also suggest relevant products based on the buyer's recent search history. Furthermore, it can customize and suggest the most suitable products according to the buyer's areas of interest. This allows the system to suggest appropriate products by customizing recommendations based on the buyer's current areas of interest. Some or all of the above processes in the recommendation system may be performed using AI or not. For example, the recommendation system could input the buyer's areas of interest data into an AI, which would then customize and suggest the most suitable products.

[0055] The recommendation system can suggest the most suitable products by considering the buyer's geographical location when making recommendations. For example, if the buyer is in a specific region, the recommendation system can suggest popular products in that region. The recommendation system can also suggest related products based on the buyer's geographical location. Furthermore, the recommendation system can suggest the most suitable products based on the buyer's current location. This allows the recommendation system to suggest the most suitable products by considering the buyer's geographical location. Some or all of the above processing in the recommendation system may be performed using AI, or not. For example, the recommendation system can input the buyer's geographical location data into an AI, which then suggests the most suitable products.

[0056] The recommendation system can analyze the buyer's social media activity and suggest relevant products when making recommendations. For example, the recommendation system can suggest relevant products based on products the buyer has shared on social media. It can also prioritize suggesting products of interest based on the buyer's social media activity. Furthermore, the recommendation system can analyze the content of the buyer's social media posts and suggest the most suitable products. In this way, it can suggest relevant products by analyzing the buyer's social media activity. Some or all of the above processes in the recommendation system may be performed using AI, or not. For example, the recommendation system can input the buyer's social media data into an AI, which then suggests relevant products.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The reception department can analyze a user's past listing history and select the optimal reception method. For example, it can prioritize accepting listings for product categories that the user has frequently listed in the past. It can also suggest the best way to take photos based on the user's past listing history. Furthermore, it can suggest the optimal timing for acceptance based on the user's past listing history. In this way, the optimal reception method can be selected by analyzing the user's past listing history. Some or all of the above processes in the reception department may be performed using AI, or they may not. For example, the reception department inputs the user's listing history data into the AI, and the AI ​​selects the optimal reception method.

[0059] The reception unit can filter photos upon receipt based on the user's current projects and areas of interest. For example, it can prioritize photos related to the user's current projects. It can also filter and receive relevant photos based on the user's areas of interest. Furthermore, it can receive the most relevant photos according to the progress of the user's projects. This allows for the reception of highly relevant photos by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs the user's project data into the AI, and the AI ​​filters the relevant photos.

[0060] The reception unit can prioritize receiving photos that are highly relevant to the user, taking into account the user's geographical location information. For example, if the user is in a specific region, it can prioritize receiving photos related to that region. It can also prioritize receiving photos of relevant product categories based on the user's current location. Furthermore, it can prioritize receiving the most suitable photos based on the user's geographical location information. In this way, by considering the user's geographical location information, it can prioritize receiving photos that are highly relevant. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location data into the AI, and the AI ​​will prioritize receiving relevant photos.

[0061] The reception unit can analyze the user's social media activity when receiving photos and accept relevant photos. For example, it can accept relevant photos based on photos the user has shared on social media. It can also prioritize accepting photos of product categories the user is interested in based on their social media activity. Furthermore, it can analyze the content of the user's social media posts and accept the most suitable photos. In this way, relevant photos can be accepted by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media data into the AI, and the AI ​​will accept relevant photos.

[0062] The generation unit can adjust the level of detail in the product description based on the product's importance. For example, it can generate a detailed description for expensive products, and a concise description for everyday products. Furthermore, for products targeted at a specific group, it can generate a description with a level of detail appropriate for that group. By adjusting the level of detail in the description based on the product's importance, it is possible to generate an appropriate product description. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs product information into the generation AI, and the generation AI adjusts the level of detail in the description based on the product's importance.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives product photos. For example, it can accept product photos taken with a smartphone or digital camera. The reception desk can also automatically detect the resolution and format of the photos and convert them to the appropriate format. Step 2: The generation unit uses generation AI to create product descriptions based on the photos received by the reception unit, and performs cropping and editing of the photos. For example, it generates text that explains the product's features and benefits in detail, crops out unnecessary parts of the photos, and performs processing such as color correction and filter application. Step 3: The pricing unit researches market prices based on the information generated by the generation unit and sets the price. For example, it analyzes the prices of competing products and past sales data to set the optimal price. It can also adjust the price according to the seller's wishes. Step 4: The editing section modifies and changes the price and description set by the pricing section as needed. For example, it can update the price and description according to sales status and market trends, and make revisions based on feedback from sellers. Step 5: The recommendation section automatically finds products that the buyer wants and recommends them periodically. For example, if the buyer enters the criteria for the product they want, the system will find products that match those criteria and present them to the buyer. It can also recommend related products based on the buyer's past purchase and search history.

[0065] (Example of form 2) The flea market app listing and sales support system according to an embodiment of the present invention is a system that improves convenience for sellers and buyers by utilizing a generation AI. This system automatically creates product descriptions, crops and edits photos, researches market prices, and sets prices simply by the seller taking photos of their items, thereby improving the success rate of sales. Furthermore, it provides buyers with a function that automatically finds desired products and recommends them periodically. For example, when a seller takes photos of an item and uploads them to the app, the generation AI automatically creates a product description and crops and edits the photos. The generation AI researches market prices and sets prices according to the seller's wishes. For example, it sets a low price if the seller wants to sell quickly, and a high price if they want to sell for as high a price as possible. In addition, the generation AI makes appropriate corrections and changes to the price and description even after listing, further improving the success rate of sales. When a buyer inputs the conditions for the product they want, the generation AI finds products that match those conditions and recommends them periodically. For example, if a buyer wants "white running shoes," the generation AI finds suitable products from the market and presents them to the buyer. This system makes it possible to increase the value of reused goods, reduce the effort required for use, and improve the likelihood of selling them. As a result, the listing and sales support system for flea market apps can improve convenience for both sellers and buyers and increase the success rate of sales.

[0066] The flea market app listing and sales support system according to this embodiment comprises a reception unit, a generation unit, a pricing unit, a modification unit, and a recommendation unit. The reception unit receives product photos. The reception unit can receive product photos taken with, for example, a smartphone or digital camera. The reception unit can also automatically determine the resolution and format of the photos and convert them to an appropriate format. The generation unit uses generation AI to create product descriptions based on the photos received by the reception unit and performs cropping and processing of the photos. The generation unit generates, for example, text that explains the features and advantages of the product in detail. The generation unit can also crop unnecessary parts of the photos and perform processing such as color correction and filter application. The pricing unit researches market prices based on the information generated by the generation unit and sets the price. The pricing unit analyzes, for example, the prices of competing products and past sales data to set the optimal price. The pricing unit can also adjust the price according to the seller's wishes. The modification unit modifies and changes the price and description set by the pricing unit as appropriate. The editing unit updates prices and descriptions according to sales status and market trends, for example. The editing unit can also make revisions based on feedback from sellers. The recommendation unit automatically finds products that buyers want and recommends them periodically. For example, when a buyer enters the conditions for a product they want, the recommendation unit finds products that match those conditions and presents them to the buyer. The recommendation unit can also recommend related products based on the buyer's past purchase history and search history. As a result, the listing and sales support system for the flea market app according to this embodiment can improve convenience for sellers and buyers and increase the success rate of sales.

[0067] The reception desk accepts product photos. For example, it can accept product photos taken with smartphones or digital cameras. Specifically, when a user uploads a photo through the app, the reception desk automatically detects the resolution and format of the photo and converts it to the appropriate format. For example, it accepts photos in different formats such as JPEG, PNG, and HEIC, and converts them to the standard JPEG format as needed. It can also use AI to improve the resolution of low-resolution photos. Furthermore, the reception desk can analyze the photo's metadata (date and time of shooting, location information, etc.) and use it to supplement product information. For example, it can evaluate the freshness of a product based on the date and time of shooting, and predict region-specific demand based on location information. This allows the reception desk to easily enable users to upload high-quality photos, improving the overall accuracy and efficiency of the system.

[0068] The generation unit uses a generation AI to create product descriptions based on photos received by the reception unit, and also performs cropping and editing of the photos. For example, the generation AI generates text that explains the features and benefits of a product in detail. Specifically, the generation AI analyzes product photos and extracts features such as color, shape, and texture. Based on these features, it generates text that explains the use and benefits of the product. For example, the generation AI extracts material and design features from a photo of clothing and generates a description such as, "This jacket is made of high-quality wool and is perfect for the cold season." The generation unit also crops unnecessary parts of the photo and performs processing such as color correction and applying filters. For example, if there are unwanted objects in the background, the generation AI automatically crops those parts to highlight only the product. It also performs color correction so that the product's color is displayed as close to the actual color as possible. Furthermore, it can apply filters to improve the overall appearance of the photo. In this way, the generation unit enables users to create high-quality product descriptions and photos without much effort, maximizing the appeal of the product.

[0069] The pricing unit investigates market prices and sets prices based on information generated by the generation unit. For example, the pricing unit analyzes the prices of competing products and past sales data to set the optimal price. Specifically, the pricing unit uses AI to analyze market trends in real time and understand fluctuations in the prices and demand of competing products. For example, it investigates the prices at which products in the same category are being traded and calculates the optimal price based on that data. It can also predict price fluctuations at specific times or events based on past sales data and reflect this in the pricing. Furthermore, the pricing unit can adjust prices according to the seller's wishes. For example, if a seller wants to sell quickly, the price can be set lower, and conversely, if they want to sell at a high price, the price can be set higher. The pricing unit also has a dynamic pricing function that can automatically adjust prices according to sales conditions and market trends. This allows the pricing unit to enable sellers to sell their products at the optimal price and increase the success rate of sales.

[0070] The editing unit modifies and changes the prices and descriptions set by the pricing unit as needed. For example, the editing unit updates prices and descriptions according to sales status and market trends. Specifically, the editing unit uses AI to monitor market trends in real time and modifies prices and descriptions as necessary. For example, if a particular product is unsold, it may lower the price to boost sales. It can also adjust prices accordingly if the prices of competing products fluctuate. Furthermore, the editing unit can make modifications based on feedback from sellers. For example, if a seller points out an error in a product description, the editing unit will review the content and correct it appropriately. It can also adjust prices according to requests from sellers who wish to change the price. In this way, the editing unit can always appropriately modify prices and descriptions based on the latest information, enabling sellers to sell their products under optimal conditions.

[0071] The recommendation system automatically finds products that customers desire and recommends them regularly. For example, if a customer enters the criteria for a product they want, the recommendation system will find products that match those criteria and present them to the customer. Specifically, the recommendation system uses AI to analyze the criteria entered by the customer and search for the most suitable product based on that analysis. For example, if a customer enters "red dress," the recommendation system will search all red dresses in its database and present the most suitable product. The recommendation system can also recommend related products based on the customer's past purchase and search history. For example, if a customer has previously purchased sports equipment, it will recommend related new products and sale information. Furthermore, the recommendation system learns the customer's behavior patterns and can provide recommendations at the optimal time for each individual customer. For example, if a customer tends to use the app at a specific time of day, it will provide recommendations tailored to that time. In this way, the recommendation system can provide customers with the most suitable products in a timely manner, increasing their purchase intent.

[0072] The generation unit can create product descriptions using a generation AI and perform cropping and editing of photos. For example, the generation unit can generate text that explains the features and benefits of a product in detail. The generation unit can also crop unnecessary parts of photos and perform processing such as color correction and filter application. The generation unit can also crop unnecessary parts of photos and perform processing such as color correction and filter application. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs a photo of a product into the generation AI, which generates a product description and performs cropping and editing of the photo.

[0073] The pricing unit uses a generating AI to research market prices and set prices that meet the seller's needs. For example, the pricing unit analyzes the prices of competing products and past sales data to set the optimal price. The pricing unit can also adjust prices according to the seller's wishes. Some or all of the above processes in the pricing unit are performed using the generating AI. For example, the pricing unit inputs product information into the generating AI, which then researches market prices and sets the optimal price.

[0074] The editing function can modify and change the price and description of an item after it has been listed. For example, the editing function can update the price and description according to sales status and market trends. The editing function can also make revisions based on feedback from sellers. Some or all of the above processes in the editing function may be performed using AI or not. For example, the editing function can input sales data into the AI, which then modifies the price and description.

[0075] The recommendation unit can automatically find products that the buyer desires and recommend them periodically. For example, if the buyer inputs the criteria for the product they want, the recommendation unit will find products that match those criteria and present them to the buyer. The recommendation unit can also recommend related products based on the buyer's past purchase and search history. Some or all of the above processes in the recommendation unit are performed using a generation AI. For example, the recommendation unit inputs the buyer's desired criteria into the generation AI, which then finds the relevant products and generates recommendation information.

[0076] The reception desk can estimate the user's emotions and adjust the timing of photo submission based on the estimated emotions. For example, if the user is stressed, the reception desk can quickly submit photos to minimize the effort required. If the user is relaxed, the reception desk can also provide detailed guidance to improve the quality of the photos. Furthermore, if the user is in a hurry, the reception desk can provide a simple interface to quickly submit photos. This improves user convenience by adjusting the timing of photo submission according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's facial expression data into the generative AI, which estimates the emotions and adjusts the submission timing.

[0077] The reception department can analyze a user's past listing history and select the optimal reception method. For example, the reception department prioritizes accepting listings for product categories that the user has frequently listed in the past. The reception department can also suggest the best way to take photos based on the user's past listing history. Furthermore, the reception department can suggest the optimal timing for acceptance based on the user's past listing history. In this way, the optimal reception method can be selected by analyzing the user's past listing history. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department inputs the user's listing history data into the AI, and the AI ​​selects the optimal reception method.

[0078] The reception unit can filter photos upon receipt based on the user's current projects and areas of interest. For example, the reception unit prioritizes receiving photos related to the user's current projects. It can also filter and receive relevant photos based on the user's areas of interest. Furthermore, the reception unit can receive the most relevant photos according to the progress of the user's projects. This allows for the reception of highly relevant photos by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit inputs the user's project data into the AI, which then filters the relevant photos.

[0079] The reception unit can estimate the user's emotions and determine the priority of photos to receive based on the estimated emotions. For example, if the user is excited, the reception unit may prioritize important photos. It may also prioritize detailed photos if the user is relaxed. Furthermore, if the user is stressed, it may prioritize simple photos. This allows for prioritizing important photos based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs user facial expression data into a generative AI, which estimates the emotions and determines the priority of photos.

[0080] The reception unit can prioritize receiving photos that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit will prioritize receiving photos related to that region. The reception unit can also prioritize receiving photos of relevant product categories based on the user's current location. Furthermore, the reception unit can prioritize receiving the most suitable photos based on the user's geographical location information. This allows for the priority of receiving highly relevant photos by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's geographical location data into AI, and the AI ​​will prioritize receiving relevant photos.

[0081] The reception desk can analyze the user's social media activity when receiving photos and accept relevant photos. For example, the reception desk can accept relevant photos based on photos the user has shared on social media. The reception desk can also prioritize accepting photos of product categories of interest to the user based on their social media activity. Furthermore, the reception desk can analyze the content of the user's social media posts and accept the most suitable photos. In this way, relevant photos can be accepted by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into an AI, and the AI ​​can accept relevant photos.

[0082] The generation unit can estimate the user's emotions and adjust the expression of the product description based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed and polite product description. If the user is in a hurry, the generation unit can also generate a concise and to-the-point product description. Furthermore, if the user is excited, the generation unit can generate a product description using emotionally evocative language. By adjusting the expression of the product description according to the user's emotions, a more appropriate product description can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs user emotion data into the generation AI, and the generation AI adjusts the expression of the product description based on the emotions.

[0083] The generation unit can adjust the level of detail in the product description based on the product's importance. For example, it can generate a detailed description for expensive products. It can also generate a concise description for everyday products. Furthermore, for products targeted at a specific group, it can generate a description with a level of detail appropriate for that group. This allows for the generation of appropriate product descriptions by adjusting the level of detail based on the product's importance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs product information into the generation AI, and the generation AI adjusts the level of detail in the description based on the product's importance.

[0084] The generation unit can apply different generation algorithms depending on the product category when generating product descriptions. For example, in the case of fashion products, the generation unit generates descriptions that reflect trend information. In the case of electronic devices, the generation unit can also generate descriptions that include technical details. Furthermore, in the case of books, the generation unit can generate descriptions that include a summary of the content. In this way, appropriate product descriptions can be generated by applying different generation algorithms depending on the product category. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs product category information into the generation AI, and the generation AI applies a generation algorithm appropriate to the category to generate the product description.

[0085] The generation unit can estimate the user's emotions and adjust the length of the product description based on those emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise description. If the user is relaxed, the generation unit can also generate a detailed description. Furthermore, if the user is excited, the generation unit can generate a description using emotionally evocative language. This allows for the generation of appropriate product descriptions by adjusting their length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs user emotion data into the generation AI, which then adjusts the length of the product description based on the emotion.

[0086] The generation unit can determine the priority of product descriptions based on the product submission date when generating them. For example, the generation unit will prioritize the generation of descriptions for new products. The generation unit can also adjust the priority according to the submission date for seasonal or sale items. Furthermore, the generation unit can set priorities based on the submission date according to the user's wishes. This allows for the generation of appropriate product descriptions by determining the priority of descriptions based on the product submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs product submission date data into the generation AI, and the generation AI determines the priority of descriptions based on the submission date.

[0087] The generation unit can adjust the order of product descriptions based on their relevance when generating them. For example, it might describe the main features of a product first, followed by details. It can also prioritize highly relevant information by referencing descriptions of similar products. Furthermore, it can optimize the order of descriptions according to the product category. This allows for the generation of appropriate product descriptions by adjusting the order based on product relevance. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs product relevance data into the generation AI, which then adjusts the order of the descriptions based on their relevance.

[0088] The pricing unit can estimate the user's emotions and adjust the pricing method based on the estimated emotions. For example, if the user is relaxed, the pricing unit can provide detailed pricing options. If the user is in a hurry, it can also provide simple pricing options. Furthermore, if the user is excited, the pricing unit can suggest pricing methods that enhance those emotions. This allows for appropriate pricing by adjusting the pricing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the pricing unit is performed using AI. For example, the pricing unit inputs user emotion data into the generative AI, which then adjusts the pricing method based on the emotions.

[0089] The pricing unit can analyze market trends for a product and set the optimal price. For example, the pricing unit can set the optimal price based on real-time market data. It can also analyze past market trends and predict price fluctuations. Furthermore, the pricing unit can set the optimal price by referencing the prices of competing products. This allows for the setting of the optimal price by analyzing market trends. Some or all of the above processes in the pricing unit may be performed using AI, or not. For example, the pricing unit can input market data into the AI, which then analyzes market trends and sets the optimal price.

[0090] The pricing unit can customize prices by referring to the seller's past sales history when setting prices. For example, the pricing unit can set the optimal price by referring to the prices of items the seller has sold in the past. The pricing unit can also extract and apply successful pricing patterns from the seller's sales history. Furthermore, the pricing unit can provide pricing advice based on the seller's past sales history. This allows the seller to set the optimal price by referring to their past sales history. Some or all of the above processes in the pricing unit may be performed using AI or not. For example, the pricing unit can input the seller's sales history data into the AI, and the AI ​​can customize the optimal price.

[0091] The pricing unit can estimate the user's emotions and determine pricing priorities based on those emotions. For example, if the user is in a hurry, the pricing unit will set a price quickly. If the user is relaxed, the pricing unit can also set a detailed price. Furthermore, if the user is excited, the pricing unit can set a price that enhances that emotion. This allows for appropriate pricing by determining pricing priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the pricing unit is performed using AI. For example, the pricing unit inputs user emotion data into the generative AI, which then determines pricing priorities based on the emotions.

[0092] The pricing unit can set the optimal price by considering the geographical distribution of the product. For example, if a product is popular in a particular region, the pricing unit will set a price appropriate for that region. The pricing unit can also analyze geographical supply and demand to set the optimal price. Furthermore, the pricing unit can adjust prices by considering market trends in each region. In this way, the optimal price can be set by considering the geographical distribution of the product. Some or all of the above processes in the pricing unit may be performed using AI or not. For example, the pricing unit can input geographical distribution data into the AI, and the AI ​​can set the optimal price.

[0093] The pricing unit can improve the accuracy of pricing by referring to relevant literature on the product during the pricing process. For example, the pricing unit can set prices by referring to research papers and market reports related to the product. It can also improve the accuracy of pricing based on literature on the product's characteristics. Furthermore, the pricing unit can set the optimal price by utilizing data obtained from relevant literature. This improves the accuracy of pricing by referring to relevant literature on the product. Some or all of the above processes in the pricing unit may be performed using AI, or not. For example, the pricing unit can input relevant literature data into the AI, which can then improve the accuracy of pricing.

[0094] The editing unit can estimate the user's emotions and adjust the timing of corrections based on the estimated emotions. For example, if the user is stressed, the editing unit can make quick corrections. It can also make detailed corrections if the user is relaxed. Furthermore, if the user is in a hurry, it can make simple corrections. This allows for corrections to be made at the appropriate time by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the editing unit are performed using AI. For example, the editing unit inputs user emotion data into the generative AI, which then adjusts the timing of corrections based on the emotions.

[0095] The editing unit can analyze the product's sales status in real time and make the optimal corrections. For example, the editing unit can revise the price and description based on real-time sales data. It can also revise the product photos according to sales status. Furthermore, the editing unit can make the optimal corrections by considering real-time market trends. This allows for optimal corrections by analyzing the product's sales status in real time. Some or all of the above processes in the editing unit may be performed using AI or not. For example, the editing unit can input sales data into AI, and the AI ​​can make the optimal corrections.

[0096] The editing unit can estimate the user's emotions and determine the priority of corrections based on the estimated emotions. For example, if the user is in a hurry, the editing unit will make quick corrections. If the user is relaxed, the editing unit can also make detailed corrections. Furthermore, if the user is excited, the editing unit can make corrections that enhance the emotion. This allows for appropriate corrections by determining the priority of corrections according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editing unit is performed using AI. For example, the editing unit inputs user emotion data into the generative AI, and the generative AI determines the priority of corrections based on the emotions.

[0097] The editing unit can improve the accuracy of its modifications by referring to relevant market data for the product during the editing process. For example, the editing unit can modify the price and description based on market data related to the product. It can also make optimal modifications by referring to market data for competing products. Furthermore, the editing unit can analyze market trends and optimize the modifications to the product. This improves the accuracy of the modifications by referring to relevant market data for the product. Some or all of the above processes in the editing unit may be performed using AI or not. For example, the editing unit can input relevant market data into the AI, which can then improve the accuracy of the modifications.

[0098] The recommendation section can estimate the user's emotions and adjust how recommendations are displayed based on those emotions. For example, if the user is relaxed, the recommendation section may display detailed recommendations. If the user is in a hurry, it may display concise recommendations. Furthermore, if the user is excited, it may display recommendations that enhance their emotions. By adjusting how recommendations are displayed according to the user's emotions, appropriate recommendations can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation section is performed using AI. For example, the recommendation section inputs the user's emotion data into the generative AI, which then adjusts how recommendations are displayed based on the emotion.

[0099] The recommendation system can suggest the most suitable products by referring to past recommendation history when making recommendations. For example, the recommendation system can suggest related products based on products the user has purchased in the past. It can also prioritize suggesting products of interest based on the user's past recommendation history. Furthermore, the recommendation system can analyze the user's purchase history and suggest the most suitable products. This allows the system to suggest the most suitable products by referring to past recommendation history. Some or all of the above processes in the recommendation system may be performed using AI, or not. For example, the recommendation system can input past recommendation history data into an AI, which then suggests the most suitable products.

[0100] The recommendation system can customize recommendations based on the buyer's current areas of interest. For example, it might prioritize suggesting products in categories the buyer is currently interested in. It can also suggest relevant products based on the buyer's recent search history. Furthermore, it can customize and suggest the most suitable products according to the buyer's areas of interest. This allows the system to suggest appropriate products by customizing recommendations based on the buyer's current areas of interest. Some or all of the above processes in the recommendation system may be performed using AI or not. For example, the recommendation system could input the buyer's areas of interest data into an AI, which would then customize and suggest the most suitable products.

[0101] The recommendation section can estimate the user's emotions and determine the priority of recommendations based on those emotions. For example, if the user is relaxed, the recommendation section will prioritize displaying detailed recommendations. It can also prioritize displaying concise recommendations if the user is in a hurry. Furthermore, if the user is excited, the recommendation section can prioritize recommendations that evoke those emotions. This allows the recommendation section to provide appropriate recommendations by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation section is performed using AI. For example, the recommendation section inputs user emotion data into the generative AI, which then determines the priority of recommendations based on the emotions.

[0102] The recommendation system can suggest the most suitable products by considering the buyer's geographical location when making recommendations. For example, if the buyer is in a specific region, the recommendation system can suggest popular products in that region. The recommendation system can also suggest related products based on the buyer's geographical location. Furthermore, the recommendation system can suggest the most suitable products based on the buyer's current location. This allows the recommendation system to suggest the most suitable products by considering the buyer's geographical location. Some or all of the above processing in the recommendation system may be performed using AI, or not. For example, the recommendation system can input the buyer's geographical location data into an AI, which then suggests the most suitable products.

[0103] The recommendation system can analyze the buyer's social media activity and suggest relevant products when making recommendations. For example, the recommendation system can suggest relevant products based on products the buyer has shared on social media. It can also prioritize suggesting products of interest based on the buyer's social media activity. Furthermore, the recommendation system can analyze the content of the buyer's social media posts and suggest the most suitable products. In this way, it can suggest relevant products by analyzing the buyer's social media activity. Some or all of the above processes in the recommendation system may be performed using AI, or not. For example, the recommendation system can input the buyer's social media data into an AI, which then suggests relevant products.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The reception unit can estimate the user's emotions and adjust the timing of photo submission based on the estimated emotions. For example, if the user is stressed, the photo submission can be processed quickly to minimize the effort required. If the user is relaxed, detailed guidance can be provided to improve the quality of the photos. Furthermore, if the user is in a hurry, a simple interface can be provided to quickly submit the photos. This improves user convenience by adjusting the timing of photo submission according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs user facial expression data into the generative AI, which estimates the emotions and adjusts the submission timing.

[0106] The generation unit can estimate the user's emotions and adjust the expression of the product description based on the estimated emotions. For example, if the user is relaxed, it can generate a detailed and polite product description. If the user is in a hurry, it can generate a concise and to-the-point product description. Furthermore, if the user is excited, it can generate a product description using expressions that enhance the user's emotions. In this way, by adjusting the expression of the product description according to the user's emotions, a more appropriate product description can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs user emotion data into the generation AI, and the generation AI adjusts the expression of the product description based on the emotions.

[0107] The pricing unit can estimate the user's emotions and adjust the pricing method based on the estimated emotions. For example, if the user is relaxed, it can offer detailed pricing options. If the user is in a hurry, it can offer simple pricing options. Furthermore, if the user is excited, it can suggest pricing methods that enhance those emotions. This allows for appropriate pricing by adjusting the pricing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the pricing unit is performed using AI. For example, the pricing unit inputs user emotion data into the generative AI, which then adjusts the pricing method based on the emotions.

[0108] The editing unit can estimate the user's emotions and adjust the timing of corrections based on the estimated emotions. For example, if the user is stressed, corrections can be made quickly. If the user is relaxed, more detailed corrections can be made. Furthermore, if the user is in a hurry, simple corrections can be made. This allows for corrections to be made at the appropriate time by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editing unit is performed using AI. For example, the editing unit inputs user emotion data into the generative AI, which then adjusts the timing of corrections based on the emotions.

[0109] The recommendation unit can estimate the user's emotions and adjust how recommendations are displayed based on those emotions. For example, if the user is relaxed, it can display detailed recommendations. If the user is in a hurry, it can display concise recommendations. Furthermore, if the user is excited, it can display recommendations that enhance their emotions. By adjusting how recommendations are displayed according to the user's emotions, appropriate recommendations can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit is performed using AI. For example, the recommendation unit inputs the user's emotion data into the generative AI, which then adjusts how recommendations are displayed based on the emotion.

[0110] The reception department can analyze a user's past listing history and select the optimal reception method. For example, it can prioritize accepting listings for product categories that the user has frequently listed in the past. It can also suggest the best way to take photos based on the user's past listing history. Furthermore, it can suggest the optimal timing for acceptance based on the user's past listing history. In this way, the optimal reception method can be selected by analyzing the user's past listing history. Some or all of the above processes in the reception department may be performed using AI, or they may not. For example, the reception department inputs the user's listing history data into the AI, and the AI ​​selects the optimal reception method.

[0111] The reception unit can filter photos upon receipt based on the user's current projects and areas of interest. For example, it can prioritize photos related to the user's current projects. It can also filter and receive relevant photos based on the user's areas of interest. Furthermore, it can receive the most relevant photos according to the progress of the user's projects. This allows for the reception of highly relevant photos by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs the user's project data into the AI, and the AI ​​filters the relevant photos.

[0112] The reception unit can prioritize receiving photos that are highly relevant to the user, taking into account the user's geographical location information. For example, if the user is in a specific region, it can prioritize receiving photos related to that region. It can also prioritize receiving photos of relevant product categories based on the user's current location. Furthermore, it can prioritize receiving the most suitable photos based on the user's geographical location information. In this way, by considering the user's geographical location information, it can prioritize receiving photos that are highly relevant. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location data into the AI, and the AI ​​will prioritize receiving relevant photos.

[0113] The reception unit can analyze the user's social media activity when receiving photos and accept relevant photos. For example, it can accept relevant photos based on photos the user has shared on social media. It can also prioritize accepting photos of product categories the user is interested in based on their social media activity. Furthermore, it can analyze the content of the user's social media posts and accept the most suitable photos. In this way, relevant photos can be accepted by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media data into the AI, and the AI ​​will accept relevant photos.

[0114] The generation unit can adjust the level of detail in the product description based on the product's importance. For example, it can generate a detailed description for expensive products, and a concise description for everyday products. Furthermore, for products targeted at a specific group, it can generate a description with a level of detail appropriate for that group. By adjusting the level of detail in the description based on the product's importance, it is possible to generate an appropriate product description. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs product information into the generation AI, and the generation AI adjusts the level of detail in the description based on the product's importance.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reception desk receives product photos. For example, it can accept product photos taken with a smartphone or digital camera. The reception desk can also automatically detect the resolution and format of the photos and convert them to the appropriate format. Step 2: The generation unit uses generation AI to create product descriptions based on the photos received by the reception unit, and performs cropping and editing of the photos. For example, it generates text that explains the product's features and benefits in detail, crops out unnecessary parts of the photos, and performs processing such as color correction and filter application. Step 3: The pricing unit researches market prices based on the information generated by the generation unit and sets the price. For example, it analyzes the prices of competing products and past sales data to set the optimal price. It can also adjust the price according to the seller's wishes. Step 4: The editing section modifies and changes the price and description set by the pricing section as needed. For example, it can update the price and description according to sales status and market trends, and make revisions based on feedback from sellers. Step 5: The recommendation section automatically finds products that the buyer wants and recommends them periodically. For example, if the buyer enters the criteria for the product they want, the system will find products that match those criteria and present them to the buyer. It can also recommend related products based on the buyer's past purchase and search history.

[0117] 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.

[0118] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the reception unit, generation unit, pricing unit, modification unit, and recommendation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives product photos using the camera 42 or reception device 38 of the smart device 14. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and uses generation AI to create product descriptions and crop / process photos. The pricing unit researches market prices and sets prices by, for example, the specific processing unit 290 of the data processing unit 12. The modification unit modifies and changes prices and descriptions by, for example, the specific processing unit 290 of the data processing unit 12. The recommendation unit recommends products to buyers by, for example, the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0124] 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.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0126] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] 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.

[0128] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the reception unit, generation unit, pricing unit, modification unit, and recommendation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives product photos using the camera 42 and microphone 238 of the smart glasses 214. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and uses generation AI to create product descriptions and crop / process photos. The pricing unit researches market prices and sets prices, for example, by the specific processing unit 290 of the data processing unit 12. The modification unit modifies and changes prices and descriptions, for example, by the specific processing unit 290 of the data processing unit 12. The recommendation unit recommends products to buyers, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0140] 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.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0142] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] 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.

[0144] 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.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the reception unit, generation unit, pricing unit, modification unit, and recommendation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives product photos using the camera 42 and microphone 238 of the headset terminal 314. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and uses generation AI to create product descriptions and crop / process photos. The pricing unit researches market prices and sets prices by, for example, the specific processing unit 290 of the data processing unit 12. The modification unit modifies and changes prices and descriptions by, for example, the specific processing unit 290 of the data processing unit 12. The recommendation unit recommends products to buyers by, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0156] 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.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0158] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] 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.

[0160] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] 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.

[0162] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the reception unit, generation unit, pricing unit, modification unit, and recommendation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives product photos using the camera 42 and microphone 238 of the robot 414. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and uses generation AI to create product descriptions and crop / process photos. The pricing unit researches market prices and sets prices by, for example, the specific processing unit 290 of the data processing unit 12. The modification unit modifies and changes prices and descriptions by, for example, the specific processing unit 290 of the data processing unit 12. The recommendation unit recommends products to buyers by, for example, the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0170] 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.

[0171] Figure 9 shows the 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.

[0172] 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.

[0173] 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.

[0174] 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, and motorcycles, 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 based, for example, 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.

[0175] 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."

[0176] 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.

[0177] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] 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 other things 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.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) A reception desk for receiving product photos, A generation unit creates a product description based on the photographs received by the reception unit and performs cropping and processing of the photographs. A pricing unit that investigates market prices and sets prices based on the information generated by the generation unit, A modification unit that appropriately modifies and changes the price and description set by the aforementioned pricing unit, It includes a recommendation unit that automatically finds products desired by the buyer and recommends them periodically. A system characterized by the following features. (Note 2) The generating unit is The AI ​​generates product descriptions and performs cropping and editing on photos. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned pricing unit is The AI ​​generates data to research market prices and set a price that meets the seller's requirements. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned modification section is, The price and description will be revised and changed as needed after the item is listed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The recommendation unit is, It automatically finds products that the buyer wants and recommends them regularly. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of photo submissions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past listing history and select the most suitable acceptance method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving photos, the system filters them based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the photos to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving photos, the system prioritizes accepting photos that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving photos, the system analyzes the user's social media activity and accepts relevant photos. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is The system estimates the user's emotions and adjusts the wording of the product description based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating product descriptions, adjust the level of detail in the description based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating product descriptions, different generation algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is The system estimates the user's emotions and adjusts the length of the product description based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating product descriptions, the priority of the descriptions is determined based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating product descriptions, the order of the descriptions is adjusted based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned pricing unit is It estimates user sentiment and adjusts pricing based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned pricing unit is When setting prices, we analyze market trends for the product to determine the optimal price. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned pricing unit is When setting prices, customize the price by referring to the seller's past sales history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned pricing unit is The system estimates user sentiment and determines pricing priorities based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned pricing unit is When setting prices, we consider the geographical distribution of the products to determine the optimal price. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned pricing unit is When setting prices, refer to relevant literature for the product to improve the accuracy of pricing. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned modification section is, It estimates the user's emotions and adjusts the timing of corrections based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned modification section is, During the revision process, we analyze product sales data in real time to make the most optimal adjustments. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned modification section is, It estimates user sentiment and determines the priority of modifications based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned modification section is, When making corrections, we refer to relevant market data for the product to improve the accuracy of the corrections. The system described in Appendix 1, characterized by the features described herein. (Note 28) The recommendation unit is, It estimates the user's emotions and adjusts how recommendations are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The recommendation unit is, When making recommendations, the system refers to past recommendation history to suggest the most suitable products. The system described in Appendix 1, characterized by the features described herein. (Note 30) The recommendation unit is, When making recommendations, customize the recommendations based on the buyer's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 31) The recommendation unit is, It estimates the user's emotions and determines the priority of recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The recommendation unit is, When making recommendations, the system takes the buyer's geographical location into consideration to suggest the most suitable products. The system described in Appendix 1, characterized by the features described herein. (Note 33) The recommendation unit is, When making recommendations, the system analyzes the buyer's social media activity to suggest relevant products. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk for receiving product photos, A generation unit creates a product description based on the photographs received by the reception unit and performs cropping and processing of the photographs. A pricing unit that investigates market prices and sets prices based on the information generated by the generation unit, A modification unit that appropriately modifies and changes the price and description set by the aforementioned pricing unit, It includes a recommendation unit that automatically finds products desired by the buyer and recommends them periodically. A system characterized by the following features.

2. The generating unit is The AI ​​generates product descriptions and performs cropping and editing on photos. The system according to feature 1.

3. The aforementioned pricing unit is The AI ​​generates data to research market prices and sets a price that meets the seller's requirements. The system according to feature 1.

4. The aforementioned modification section is, The price and description will be revised and changed as needed after the item is listed. The system according to feature 1.

5. The recommendation unit is, It automatically finds products that the buyer wants and recommends them regularly. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of photo submissions based on those emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past listing history and select the most suitable acceptance method. The system according to feature 1.

8. The aforementioned reception unit is When receiving photos, the system filters them based on the user's current projects and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the photos to be accepted based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When receiving photos, the system prioritizes accepting photos that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

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