System

Abstract

An object of a system according to an embodiment is to make it easy to sell a reuse product and to improve a sales success rate.SOLUTION: A system according to an embodiment includes a product description generation unit, a photograph processing unit, a background image generation unit, a price setting unit, and a correction unit. The product description generation unit automatically generates a product description. The photograph processing unit trims and processes the photograph of the product. The background image generation unit generates and processes a background image matching the commodity. A price setting part investigates the market price of merchandise, and sets a price plan. The correction part corrects and changes the price and the description after the exhibition.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that listing reused products is time-consuming and lacks ingenuity to improve sales success rates.

[0005] The system according to the embodiment aims to make it easier to put up for sale reused products and to improve the success rate of sales. [Means for solving the problem]

[0006] The system according to the embodiment includes a product description generation unit, a photo editing unit, a background image generation unit, a price setting unit, and a correction unit. The product description generation unit automatically generates product descriptions. The photo editing unit crops and edits product photos. The background image generation unit generates and edits background images that match the products. The price setting unit researches the market price of the product and sets a suggested price. The correction unit corrects and changes the price and description after the product is listed. [Effects of the Invention]

[0007] The system according to the embodiment makes it easy to put up reused products for sale and improves the success rate of sales. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The listing support system according to the embodiment of the present invention is a system that allows users to easily list their own items for sale by simply taking a photo of the item, thereby improving the sales success rate. This allows users to easily list their items for sale and improve the sales success rate.

[0029] The listing support system according to the embodiment includes a product description generation unit, a photo editing unit, a background image generation unit, a pricing unit, and a correction unit. The product description generation unit analyzes product photos and automatically creates product description proposals based on the product's features and condition. For example, the AI ​​generation unit generates a description such as, "This product is in like-new condition and has only been used a few times. It is made of high-quality materials and is very durable." The AI ​​generation unit also generates the description based on the product photo and basic information provided by the user (e.g., product name, category). The photo editing unit automatically crops the product photo and adjusts the brightness and contrast as needed. For example, the AI ​​generation unit removes the product's background and crops it to highlight the product itself. The AI ​​also adjusts the brightness and color of the photo to maximize the product's appeal. The background image generation unit generates an appropriate background image based on the product's features and combines it with the product photo. For example, the AI ​​generation unit generates an elegant background for luxury products and a natural landscape for outdoor gear. This enhances the product's appeal. The pricing unit analyzes online data to investigate the prices at which similar products are traded. When a user inputs their preference, such as "I want to sell immediately" or "I want to sell as high as possible," the generation AI sets the optimal price proposal based on that preference. For example, if the user "want to sell immediately," the AI ​​proposes a price slightly lower than the market price, whereas if the user "want to sell as high as possible," the AI ​​proposes a price close to the market price. The correction unit analyzes the number of product views and the responses of potential buyers and automatically corrects or modifies the price and description as necessary. For example, if a product receives many views but no purchases, the generation AI suggests lowering the price slightly. Furthermore, if the description contains insufficient information, the AI ​​suggests additional explanation. This allows the listing support system according to the embodiment to easily perform the listing process and improve the sales success rate. For example, by simply taking a photo of the product, the generation AI automatically creates a description, edits the photo, and sets an appropriate price, allowing the user to complete the listing effortlessly. Furthermore, the generation AI also appropriately modifies the price and description after the product is listed, thereby increasing the sales success rate.

[0030] The product description generation unit can analyze the user's past listing history and generate descriptions that match the user's preferences. For example, the generation AI analyzes the user's past listing history and learns the expressions and phrases that the user prefers. For example, the generation AI extracts keywords and phrases that the user frequently uses and generates descriptions based on them. This generates descriptions that match the user's preferences, improving the sales success rate.

[0031] The product description generator can increase the product's appeal to buyers by including usage scenarios and specific examples of use in the description. For example, the product description generator uses a generation AI to automatically generate product usage scenarios and incorporate them into the description. For example, it can present specific usage examples such as, "This camera is perfect for travel and outdoor activities, and allows you to easily capture beautiful scenery." This increases the product's appeal to buyers by including usage scenarios and specific examples of use.

[0032] The product description generation unit automatically generates product descriptions in multiple languages, making it possible to cater to international markets. For example, the product description generation unit uses a generation AI to automatically translate product descriptions and generate them in multiple languages. For example, it supports major languages ​​such as English, French, and Chinese. This allows product descriptions to be automatically generated in multiple languages, making it possible to cater to international markets.

[0033] The product description generation unit can automatically generate video and audio explanations related to the product description, making it possible to appeal to the user through multimedia. For example, the product description generation unit uses a generation AI to automatically generate a video that explains the product's features and incorporates it into the product description. For example, the video can visually explain how to use the product and its features. This makes it possible to appeal to the user through multimedia by automatically generating video and audio explanations related to the product description.

[0034] The photo processing unit can recognize the shape or color of the product and select the optimal cropping method. For example, the generative AI recognizes the shape of the product and selects the optimal cropping method. For example, it automatically detects the outline of the product and removes the background to highlight the product. This recognizes the shape and color of the product and selects the optimal cropping method to maximize the product's appeal.

[0035] The photo editing section can automatically apply specific filters and effects to emphasize the texture and feel of a product. For example, the photo editing section uses generative AI to analyze the texture and feel of a product and automatically apply filters and effects to emphasize it. For example, a filter that emphasizes gloss can be applied to metal products. This emphasizes the texture and feel of a product, maximizing its appeal.

[0036] The photo processing unit can automatically generate a 360-degree view of a product, allowing the purchaser to view the product from all angles. For example, the photo processing unit uses a generation AI to analyze multiple photos of a product and automatically generate a 360-degree view. For example, photos of the product from all angles are synthesized to provide an interactive view. This automatically generates a 360-degree view of the product, allowing the purchaser to view the product from all angles.

[0037] The photo manipulation unit can automatically add a comparison object to visually demonstrate the size and scale of the product. For example, the generative AI can automatically add a comparison object to visually demonstrate the size and scale of the product. For example, the photo manipulation unit can place a common object (e.g., a coin or pen) next to the product. This automatically adds a comparison object to visually demonstrate the size and scale of the product, making it easier for buyers to understand the size of the product.

[0038] The background image generation unit can propose multiple background images based on the product category and characteristics, allowing the user to select from them. For example, the background image generation unit uses a generation AI to analyze the product category and characteristics and automatically generate multiple background images. For example, it proposes an elegant background for luxury products, and a natural landscape for outdoor goods. This maximizes the product's appeal by proposing multiple background images based on the product category and characteristics and allowing the user to select from them.

[0039] The background image generation unit automatically generates background images that match the season and trends, maximizing the appeal of the product. For example, the background image generation unit uses a generation AI to analyze the season and trends and automatically generate background images that match them. For example, it suggests a cherry blossom background in spring and a beach background in summer. This automatically generates background images that match the season and trends, maximizing the appeal of the product.

[0040] The background image generation unit automatically generates background images that match the target demographic of a product, making it possible to appeal to a specific purchasing demographic. For example, the background image generation unit uses generation AI to analyze the target demographic of a product and automatically generate a background image that matches that. For example, it suggests a pop background for products aimed at young people and a subdued background for products aimed at seniors. This allows the automatic generation of background images that match the target demographic of a product, making it possible to appeal to a specific purchasing demographic.

[0041] The background image generation unit generates a background image that simulates a product usage scene, allowing the purchaser to get a concrete image of how the product will be used. For example, the background image generation unit uses a generation AI to simulate a product usage scene and generates a background image based on that. For example, for outdoor goods, a campsite background is used. In this way, by generating a background image that simulates a product usage scene, the purchaser can get a concrete image of how the product will be used.

[0042] The pricing unit can analyze past transaction data and propose optimal prices according to seasons and events. For example, the pricing unit uses a generation AI to analyze past transaction data and propose optimal prices according to seasons and events. For example, the price of gift items can be increased during the Christmas season. This improves the sales success rate by analyzing past transaction data and proposing optimal prices according to seasons and events.

[0043] The pricing unit sets prices that take into account the rarity and exclusivity of the product, creating a premium feel. For example, the pricing unit uses a generation AI to analyze the rarity and exclusivity of the product and sets prices that create a premium feel based on that. For example, a higher price is set for limited edition products. In this way, setting prices that take into account the rarity and exclusivity of the product creates a premium feel and improves sales success rates.

[0044] The pricing unit compares prices on different marketplaces and can suggest the optimal listing destination. For example, the generation AI collects price data from different marketplaces and suggests the optimal listing destination. For example, it compares the prices at which the same product is traded on different platforms. This allows it to compare prices on different marketplaces and suggest the optimal listing destination.

[0045] The pricing section automatically generates price proposals for product bundle sales and set sales, enabling the diversification of sales strategies. In the pricing section, for example, a generation AI automatically generates price proposals for product bundle sales and set sales. For example, a discount price is set for multiple products. This allows the diversification of sales strategies by automatically generating price proposals for product bundle sales and set sales.

[0046] The correction department can monitor market trends in real time and correct prices and descriptions at the optimal time. For example, the generation AI monitors market trends in real time and corrects prices at the optimal time. For example, it raises prices when demand increases. This allows the sales success rate to be improved by monitoring market trends in real time and correcting prices and descriptions at the optimal time.

[0047] The correction unit can analyze buyer feedback and improve the description and price based on that. For example, the generation AI analyzes buyer feedback and improves the description based on that. For example, it adds information that buyers want. In this way, by analyzing buyer feedback and improving the description and price based on that, the sales success rate increases.

[0048] The correction department can analyze the number of views and click rates after listing and propose effective revision suggestions. For example, the generation AI can analyze the number of views and click rates after listing and propose effective revision suggestions. For example, if there are many views but no purchases, the correction department can make suggestions to improve the description. In this way, by analyzing the number of views and click rates after listing and proposing effective revision suggestions, the sales success rate can be improved.

[0049] The correction unit can monitor the trends of competing products after they are listed and make corrections accordingly. For example, the correction unit monitors the trends of competing products after the generation AI has listed them and makes corrections accordingly. For example, if the price of a competing product drops, the price is adjusted. In this way, by monitoring the trends of competing products after they are listed and making corrections accordingly, the sales success rate is improved.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The listing support system may further include a suggestion unit that analyzes a user's past purchase history and automatically suggests products similar to those purchased by the user in the past. For example, if a user previously purchased a camera, the suggestion unit may suggest camera-related accessories and lenses. The system may also predict and suggest products that the user will like based on ratings and reviews of products purchased by the user in the past. This allows the system to increase sales opportunities by utilizing the user's purchase history to suggest related products.

[0052] The listing support system can further include a coupon provision unit that analyzes user purchasing behavior and automatically provides coupons and discounts to encourage purchases of products the user is considering purchasing. For example, if a user has viewed a particular product multiple times, a discount coupon for that product can be provided. Also, a coupon can be provided even if a user adds a product to their cart but does not purchase it. This makes it possible to encourage purchases by analyzing user purchasing behavior and providing coupons at the appropriate time.

[0053] The listing support system can also include a recommendation unit that automatically recommends products that a user might be interested in based on the user's purchase history and browsing history. For example, it can suggest products related to products the user has previously purchased. It can also prioritize the suggestion of products in categories that the user frequently browses. This makes it possible to increase sales opportunities by utilizing the user's purchase history and browsing history to suggest related products.

[0054] The listing support system can further include a review display unit that analyzes users' purchasing behavior and automatically displays reviews and ratings for products the user is considering purchasing to encourage them to purchase. For example, when a user is viewing a specific product, highly rated reviews for that product can be displayed preferentially. Also, if the user is unsure about a purchase, positive ratings from other buyers can be displayed. This allows users to be encouraged to purchase by analyzing their purchasing behavior and displaying reviews and ratings at the appropriate time.

[0055] The listing support system can further include a bundle suggestion unit that automatically suggests bundles of products that a user might be interested in based on the user's purchase history and browsing history. For example, it can suggest sets of products related to products that the user has previously purchased. It can also suggest combinations of products from categories that the user frequently browses. This makes it possible to increase sales opportunities by utilizing the user's purchase history and browsing history to suggest bundles of related products.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The product description generator analyzes product photos and automatically creates product description ideas based on the product's features and condition. For example, the AI ​​might generate a description such as, "This product is in like-new condition and has only been used a few times. It is made of high-quality materials and is very durable." The AI ​​also generates a description based on product photos and basic information provided by the user (e.g., product name, category). Step 2: The photo editor automatically crops the product photo and adjusts the brightness and contrast as needed. For example, the generative AI removes the background of the product and crops it to highlight the product itself. It also adjusts the brightness and color of the photo to maximize the product's appeal. Step 3: The background image generator generates an appropriate background image based on the product's characteristics and combines it with the product photo. For example, the generator AI can generate an elegant background for luxury products, or a natural landscape for outdoor goods, enhancing the product's appeal. Step 4: The pricing unit analyzes data on the internet to find out what prices similar products are being traded at. When the user inputs their preference, such as "I want to sell immediately" or "I want to sell for as much as possible," the AI ​​will set the optimal price based on that. For example, if the user wants to sell immediately, the AI ​​will suggest a price slightly lower than the market price, and if the user wants to sell for as much as possible, the AI ​​will suggest a price close to the market price. Step 5: The correction section analyzes the number of product views and the responses of potential buyers, and automatically corrects and changes the price and description as necessary. For example, if there are many views but no purchases, the generation AI will suggest lowering the price slightly. Also, if there is insufficient information in the description, it will suggest adding additional explanations.

[0058] (Example 2) The listing support system according to the embodiment of the present invention is a system that allows users to easily list their own items for sale by simply taking a photo of the item, thereby improving the sales success rate. This allows users to easily list their items for sale and improve the sales success rate.

[0059] The listing support system according to the embodiment includes a product description generation unit, a photo editing unit, a background image generation unit, a pricing unit, and a correction unit. The product description generation unit analyzes product photos and automatically creates product description proposals based on the product's features and condition. For example, the AI ​​generation unit generates a description such as, "This product is in like-new condition and has only been used a few times. It is made of high-quality materials and is very durable." The AI ​​generation unit also generates the description based on the product photo and basic information provided by the user (e.g., product name, category). The photo editing unit automatically crops the product photo and adjusts the brightness and contrast as needed. For example, the AI ​​generation unit removes the product's background and crops it to highlight the product itself. The AI ​​also adjusts the brightness and color of the photo to maximize the product's appeal. The background image generation unit generates an appropriate background image based on the product's features and combines it with the product photo. For example, the AI ​​generation unit generates an elegant background for luxury products and a natural landscape for outdoor gear. This enhances the product's appeal. The pricing unit analyzes online data to investigate the prices at which similar products are traded. When a user inputs their preference, such as "I want to sell immediately" or "I want to sell as high as possible," the generation AI sets the optimal price proposal based on that preference. For example, if the user "want to sell immediately," the AI ​​proposes a price slightly lower than the market price, whereas if the user "want to sell as high as possible," the AI ​​proposes a price close to the market price. The correction unit analyzes the number of product views and the responses of potential buyers and automatically corrects or modifies the price and description as necessary. For example, if a product receives many views but no purchases, the generation AI suggests lowering the price slightly. Furthermore, if the description contains insufficient information, the AI ​​suggests additional explanation. This allows the listing support system according to the embodiment to easily perform the listing process and improve the sales success rate. For example, by simply taking a photo of the product, the generation AI automatically creates a description, edits the photo, and sets an appropriate price, allowing the user to complete the listing effortlessly. Furthermore, the generation AI also appropriately modifies the price and description after the product is listed, thereby increasing the sales success rate.

[0060] The product description generation unit can analyze the user's past listing history and generate descriptions that match the user's preferences. For example, the generation AI analyzes the user's past listing history and learns the expressions and phrases that the user prefers. For example, the generation AI extracts keywords and phrases that the user frequently uses and generates descriptions based on them. This generates descriptions that match the user's preferences, improving the sales success rate.

[0061] The product description generator can increase the product's appeal to buyers by including usage scenarios and specific examples of use in the description. For example, the product description generator uses a generation AI to automatically generate product usage scenarios and incorporate them into the description. For example, it can present specific usage examples such as, "This camera is perfect for travel and outdoor activities, and allows you to easily capture beautiful scenery." This increases the product's appeal to buyers by including usage scenarios and specific examples of use.

[0062] The product description generation unit can use the emotion estimation function to generate a description that reflects the user's emotions toward the product. For example, the product description generation unit uses the emotion estimation function to generate a description that reflects the user's positive emotions toward the product. For example, the product description generation unit uses emotional expressions such as, "This product will enrich your life and make every day more enjoyable." This generates a description that reflects the user's emotions toward the product, thereby improving the sales success rate.

[0063] The product description generation unit automatically generates product descriptions in multiple languages, making it possible to cater to international markets. For example, the product description generation unit uses a generation AI to automatically translate product descriptions and generate them in multiple languages. For example, it supports major languages ​​such as English, French, and Chinese. This allows product descriptions to be automatically generated in multiple languages, making it possible to cater to international markets.

[0064] The product description generation unit can automatically generate video and audio explanations related to the product description, making it possible to appeal to the user through multimedia. For example, the product description generation unit uses a generation AI to automatically generate a video that explains the product's features and incorporates it into the product description. For example, the video can visually explain how to use the product and its features. This makes it possible to appeal to the user through multimedia by automatically generating video and audio explanations related to the product description.

[0065] The product description generation unit can use the emotion estimation function to predict the emotional response of a buyer when reading the description and select the optimal expression. For example, the product description generation unit uses the emotion estimation function to predict the emotional response of a buyer when reading the description and selects an expression that elicits positive emotions. For example, the unit uses an expression such as, "This product will enrich your life and make every day more enjoyable." This allows the unit to predict the emotional response of a buyer when reading the description and select the optimal expression, thereby improving the sales success rate.

[0066] The photo processing unit can recognize the shape or color of the product and select the optimal cropping method. For example, the generative AI recognizes the shape of the product and selects the optimal cropping method. For example, it automatically detects the outline of the product and removes the background to highlight the product. This recognizes the shape and color of the product and selects the optimal cropping method to maximize the product's appeal.

[0067] The photo editing section can automatically apply specific filters and effects to emphasize the texture and feel of a product. For example, the photo editing section uses generative AI to analyze the texture and feel of a product and automatically apply filters and effects to emphasize it. For example, a filter that emphasizes gloss can be applied to metal products. This emphasizes the texture and feel of a product, maximizing its appeal.

[0068] The photo editing unit can use the emotion estimation function to analyze the emotion a user feels when looking at a photo and apply the most attractive processing. For example, the photo editing unit uses the emotion estimation function to analyze the emotion a user feels when looking at a photo and applies the processing to bring out positive emotions. For example, the brightness and contrast are adjusted to maximize the appeal of a product. In this way, the emotion a user feels when looking at a photo is analyzed and the processing to make the photo look the most attractive is applied, thereby maximizing the appeal of the product.

[0069] The photo processing unit can automatically generate a 360-degree view of a product, allowing the purchaser to view the product from all angles. For example, the photo processing unit uses a generation AI to analyze multiple photos of a product and automatically generate a 360-degree view. For example, photos of the product from all angles are synthesized to provide an interactive view. This automatically generates a 360-degree view of the product, allowing the purchaser to view the product from all angles.

[0070] The photo manipulation unit can automatically add a comparison object to visually demonstrate the size and scale of the product. For example, the generative AI can automatically add a comparison object to visually demonstrate the size and scale of the product. For example, the photo manipulation unit can place a common object (e.g., a coin or pen) next to the product. This automatically adds a comparison object to visually demonstrate the size and scale of the product, making it easier for buyers to understand the size of the product.

[0071] The photo editing unit can use the emotion estimation function to predict the emotional response of a purchaser when viewing a photo and perform optimal trimming and editing. For example, the photo editing unit uses the emotion estimation function to predict the emotional response of a purchaser when viewing a photo and perform trimming and editing to elicit positive emotions. For example, the brightness and contrast are adjusted to maximize the appeal of a product. In this way, the emotional response of a purchaser when viewing a photo can be predicted and optimal trimming and editing can be performed to maximize the appeal of the product.

[0072] The background image generation unit can propose multiple background images based on the product category and characteristics, allowing the user to select from them. For example, the background image generation unit uses a generation AI to analyze the product category and characteristics and automatically generate multiple background images. For example, it proposes an elegant background for luxury products, and a natural landscape for outdoor goods. This maximizes the product's appeal by proposing multiple background images based on the product category and characteristics and allowing the user to select from them.

[0073] The background image generation unit automatically generates background images that match the season and trends, maximizing the appeal of the product. For example, the background image generation unit uses a generation AI to analyze the season and trends and automatically generate background images that match them. For example, it suggests a cherry blossom background in spring and a beach background in summer. This automatically generates background images that match the season and trends, maximizing the appeal of the product.

[0074] The background image generation unit can use the emotion estimation function to generate a background image that reflects the emotion felt by the user when viewing the background image. For example, the background image generation unit uses the emotion estimation function to analyze the emotion felt by the user when viewing the background image and generate a background image that elicits positive emotions. For example, a bright and warm background is used. This maximizes the appeal of the product by generating a background image that reflects the emotion felt by the user when viewing the background image.

[0075] The background image generation unit automatically generates background images that match the target demographic of a product, making it possible to appeal to a specific purchasing demographic. For example, the background image generation unit uses generation AI to analyze the target demographic of a product and automatically generate a background image that matches that. For example, it suggests a pop background for products aimed at young people and a subdued background for products aimed at seniors. This allows the automatic generation of background images that match the target demographic of a product, making it possible to appeal to a specific purchasing demographic.

[0076] The background image generation unit generates a background image that simulates a product usage scene, allowing the purchaser to get a concrete image of how the product will be used. For example, the background image generation unit uses a generation AI to simulate a product usage scene and generates a background image based on that. For example, for outdoor goods, a campsite background is used. In this way, by generating a background image that simulates a product usage scene, the purchaser can get a concrete image of how the product will be used.

[0077] The background image generation unit can use the emotion estimation function to predict the emotional response of a purchaser when viewing a background image and select an optimal background image. For example, the background image generation unit uses the emotion estimation function to predict the emotional response of a purchaser when viewing a background image and selects a background image that elicits positive emotions. For example, a bright and warm background is used. This allows the emotional response of a purchaser when viewing a background image to be predicted and the optimal background image to be selected, thereby maximizing the appeal of the product.

[0078] The pricing unit can analyze past transaction data and propose optimal prices according to seasons and events. For example, the pricing unit uses a generation AI to analyze past transaction data and propose optimal prices according to seasons and events. For example, the price of gift items can be increased during the Christmas season. This improves the sales success rate by analyzing past transaction data and proposing optimal prices according to seasons and events.

[0079] The pricing unit sets prices that take into account the rarity and exclusivity of the product, creating a premium feel. For example, the pricing unit uses a generation AI to analyze the rarity and exclusivity of the product and sets prices that create a premium feel based on that. For example, a higher price is set for limited edition products. In this way, setting prices that take into account the rarity and exclusivity of the product creates a premium feel and improves sales success rates.

[0080] The price setting unit can use the emotion estimation function to generate a price proposal that reflects the emotion the user feels about the price setting. For example, the price setting unit uses the emotion estimation function to analyze the emotion the user feels about the price setting and generate a price proposal that elicits positive emotions. For example, the price setting unit sets a price that makes the user feel satisfied. In this way, by generating a price proposal that reflects the emotion the user feels about the price setting, user satisfaction is increased.

[0081] The pricing unit compares prices on different marketplaces and can suggest the optimal listing destination. For example, the generation AI collects price data from different marketplaces and suggests the optimal listing destination. For example, it compares the prices at which the same product is traded on different platforms. This allows it to compare prices on different marketplaces and suggest the optimal listing destination.

[0082] The pricing section automatically generates price proposals for product bundle sales and set sales, enabling the diversification of sales strategies. In the pricing section, for example, a generation AI automatically generates price proposals for product bundle sales and set sales. For example, a discount price is set for multiple products. This allows the diversification of sales strategies by automatically generating price proposals for product bundle sales and set sales.

[0083] The price setting unit can use the emotion estimation function to predict the emotional reaction of a buyer when he or she sees the price and set an optimal price. For example, the price setting unit uses the emotion estimation function to predict the emotional reaction of a buyer when he or she sees the price and set a price that elicits positive emotions. For example, it sets a price that makes the buyer feel like they got a good deal. In this way, by predicting the emotional reaction of a buyer when he or she sees the price and setting an optimal price, the sales success rate can be improved.

[0084] The correction department can monitor market trends in real time and correct prices and descriptions at the optimal time. For example, the generation AI monitors market trends in real time and corrects prices at the optimal time. For example, it raises prices when demand increases. This allows the sales success rate to be improved by monitoring market trends in real time and correcting prices and descriptions at the optimal time.

[0085] The correction unit can analyze buyer feedback and improve the description and price based on that. For example, the generation AI analyzes buyer feedback and improves the description based on that. For example, it adds information that buyers want. In this way, by analyzing buyer feedback and improving the description and price based on that, the sales success rate increases.

[0086] The correction unit can use the emotion estimation function to optimize the description and price based on the emotional response of the buyer. The correction unit, for example, uses the emotion estimation function to optimize the description based on the emotional response of the buyer. For example, it uses expressions that elicit positive emotions. This improves the sales success rate by optimizing the description and price based on the emotional response of the buyer.

[0087] The correction department can analyze the number of views and click rates after listing and propose effective revision suggestions. For example, the generation AI can analyze the number of views and click rates after listing and propose effective revision suggestions. For example, if there are many views but no purchases, the correction department can make suggestions to improve the description. In this way, by analyzing the number of views and click rates after listing and proposing effective revision suggestions, the sales success rate can be improved.

[0088] The correction unit can monitor the trends of competing products after they are listed and make corrections accordingly. For example, the correction unit monitors the trends of competing products after the generation AI has listed them and makes corrections accordingly. For example, if the price of a competing product drops, the price is adjusted. In this way, by monitoring the trends of competing products after they are listed and making corrections accordingly, the sales success rate is improved.

[0089] The correction unit can use the emotion estimation function to monitor the emotional reactions of the buyer in real time and make optimal corrections. The correction unit, for example, uses the emotion estimation function to monitor the emotional reactions of the buyer in real time and make optimal corrections. For example, the correction unit improves the description to elicit positive emotions. In this way, by monitoring the emotional reactions of the buyer in real time and making optimal corrections, the sales success rate can be improved.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The listing support system may further include a suggestion unit that analyzes a user's past purchase history and automatically suggests products similar to those purchased by the user in the past. For example, if a user previously purchased a camera, the suggestion unit may suggest camera-related accessories and lenses. The system may also predict and suggest products that the user will like based on ratings and reviews of products purchased by the user in the past. This allows the system to increase sales opportunities by utilizing the user's purchase history to suggest related products.

[0092] The listing support system may further include a suggestion unit that estimates the user's emotions and suggests products that the user may be interested in based on the estimated emotions. For example, product categories for which the user has previously expressed positive emotions may be suggested preferentially. Also, if the user is feeling stressed, products with a relaxing effect may be suggested. In this way, by suggesting products based on the user's emotions, user satisfaction can be increased.

[0093] The listing support system can further include a coupon provision unit that analyzes user purchasing behavior and automatically provides coupons and discounts to encourage purchases of products the user is considering purchasing. For example, if a user has viewed a particular product multiple times, a discount coupon for that product can be provided. Also, a coupon can be provided even if a user adds a product to their cart but does not purchase it. This makes it possible to encourage purchases by analyzing user purchasing behavior and providing coupons at the appropriate time.

[0094] The listing support system can also use its emotion estimation function to analyze the emotions felt when a user views a product and generate customized product descriptions that elicit positive emotions. For example, it can emphasize the product's appeal by using expressions that make the user feel excited or happy. It can also generate descriptions that ease the user's feelings of anxiety or doubt. This allows customizing product descriptions based on the user's emotions to increase the user's desire to purchase.

[0095] The listing support system can also include a recommendation unit that automatically recommends products that a user might be interested in based on the user's purchase history and browsing history. For example, it can suggest products related to products the user has previously purchased. It can also prioritize the suggestion of products in categories that the user frequently browses. This makes it possible to increase sales opportunities by utilizing the user's purchase history and browsing history to suggest related products.

[0096] The listing support system can also use its emotion estimation function to analyze the emotions felt by users when they purchase a product and provide after-sales service that elicits positive emotions. For example, it can send messages that make users feel satisfied after their purchase. It can also provide support to ease dissatisfaction if a user feels dissatisfied after their purchase. This allows for increased customer satisfaction by providing after-sales service based on the user's emotions.

[0097] The listing support system can further include a review display unit that analyzes users' purchasing behavior and automatically displays reviews and ratings for products the user is considering purchasing to encourage them to purchase. For example, when a user is viewing a specific product, highly rated reviews for that product can be displayed preferentially. Also, if the user is unsure about a purchase, positive ratings from other buyers can be displayed. This allows users to be encouraged to purchase by analyzing their purchasing behavior and displaying reviews and ratings at the appropriate time.

[0098] The listing support system can also use its emotion estimation function to analyze the emotions a user feels after purchasing a product and send follow-up messages designed to elicit positive emotions. For example, it can send messages that make users feel joy and satisfaction after a purchase. It can also send support messages to ease any anxiety or doubts a user may have after a purchase. This allows for increased customer satisfaction by sending follow-up messages based on the user's emotions.

[0099] The listing support system can further include a bundle suggestion unit that automatically suggests bundles of products that a user might be interested in based on the user's purchase history and browsing history. For example, it can suggest sets of products related to products that the user has previously purchased. It can also suggest combinations of products from categories that the user frequently browses. This makes it possible to increase sales opportunities by utilizing the user's purchase history and browsing history to suggest bundles of related products.

[0100] The listing support system can further use the emotion estimation function to analyze the emotions a user feels after purchasing a product and provide customized after-sales service to elicit positive emotions. For example, it can provide benefits and services that make the user feel satisfied after the purchase. It can also provide support to alleviate dissatisfaction if the user feels dissatisfied after the purchase. This allows for the provision of customized after-sales service based on the user's emotions, thereby increasing customer satisfaction.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The product description generator analyzes product photos and automatically creates product description ideas based on the product's features and condition. For example, the AI ​​might generate a description such as, "This product is in like-new condition and has only been used a few times. It is made of high-quality materials and is very durable." The AI ​​also generates a description based on product photos and basic information provided by the user (e.g., product name, category). Step 2: The photo editor automatically crops the product photo and adjusts the brightness and contrast as needed. For example, the generative AI removes the background of the product and crops it to highlight the product itself. It also adjusts the brightness and color of the photo to maximize the product's appeal. Step 3: The background image generator generates an appropriate background image based on the product's characteristics and combines it with the product photo. For example, the generator AI can generate an elegant background for luxury products, or a natural landscape for outdoor goods, enhancing the product's appeal. Step 4: The pricing unit analyzes data on the internet to find out what prices similar products are being traded at. When the user inputs their preference, such as "I want to sell immediately" or "I want to sell for as much as possible," the AI ​​will set the optimal price based on that. For example, if the user wants to sell immediately, the AI ​​will suggest a price slightly lower than the market price, and if the user wants to sell for as much as possible, the AI ​​will suggest a price close to the market price. Step 5: The correction section analyzes the number of product views and the responses of potential buyers, and automatically corrects and changes the price and description as necessary. For example, if there are many views but no purchases, the generation AI will suggest lowering the price slightly. Also, if there is insufficient information in the description, it will suggest adding additional explanations.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to 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 imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a product description generation unit that automatically generates a product description; a photo editing section for trimming and editing product photos; a background image generating unit that generates and processes a background image that matches the product; A pricing department that investigates market prices of products and sets price proposals; A correction unit that corrects and changes the price and description after listing. A system characterized by:

2. The product description generation unit Automatically generate video and audio descriptions related to the above description to promote the product through multimedia.

2. The system of claim 1.

3. The photo processing unit includes: Recognize the shape or color of the product and select the optimal trimming method 2. The system of claim 1.

4. The background image generation unit Automatically generate background images that match the season and trends, maximizing the appeal of the product.

2. The system of claim 1.

5. The price setting unit Pricing will be set taking into consideration the rarity and limited nature of the product, creating a premium feel.

2. The system of claim 1.

6. The correction unit Optimize the description and price based on the buyer's emotional response 2. The system of claim 1.

7. The product description generation unit Generate descriptions that reflect the user's feelings about the product 2. The system of claim 1.

8. The photo processing unit includes: Analyze the emotions the user feels when viewing the photo and edit it to make it look the most attractive.

2. The system of claim 1.

Images