System

Abstract

An object of a system according to an embodiment is to enable anyone to easily sell an item for sale.SOLUTION: A system according to an embodiment includes an image analysis unit, an explanation generation unit, a price setting unit, an image correction unit, a hashtag generation unit, a chatbot unit, and an advice provision unit. The image analysis unit analyzes an image uploaded by a user. The description generation unit generates a description on the basis of the image analyzed by the image analysis unit. The price setting unit sets a price on the basis of the description generated by the description generation unit. The image correction unit corrects the image analyzed by the image analysis unit. The hashtag generation section generates a hashtag based on the image analyzed by the image analysis section. The chatbot unit supports the exchange of sales. The advice providing unit provides advice on a product that is easy to sell.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] With conventional technology, users of flea market apps found the listing process to be a hassle, and many users only purchased or viewed items.

[0005] The system according to the embodiment aims to enable anyone to easily put up and sell items. [Means for solving the problem]

[0006] The system according to the embodiment includes an image analysis unit, a description generation unit, a price setting unit, an image correction unit, a hashtag generation unit, a chatbot unit, and an advice providing unit. The image analysis unit analyzes images uploaded by users. The description generation unit generates descriptions based on images analyzed by the image analysis unit. The price setting unit sets prices based on descriptions generated by the description generation unit. The image correction unit corrects images analyzed by the image analysis unit. The hashtag generation unit generates hashtags based on images analyzed by the image analysis unit. The chatbot unit supports buying and selling transactions. The advice providing unit provides advice on products that are likely to sell. [Effects of the Invention]

[0007] The system according to the embodiment allows anyone to easily put up and sell items. [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) A flea market app system according to an embodiment of the present invention is a system that utilizes AI to enable users to easily list and sell items. In this system, users simply upload an image, and AI automatically sets the description and price, automatically corrects the image, and generates hashtags. Furthermore, chatbots and AI generation support the buying and selling process, and AI also provides advice on which products are likely to sell. This allows the flea market app system to easily enable users to list and sell items, thereby expanding total merchandise value and global circulation.

[0029] A flea market app system according to an embodiment includes an image analysis unit, a description generation unit, a price setting unit, an image correction unit, a hashtag generation unit, a chatbot unit, and an advice providing unit. The image analysis unit analyzes images uploaded by users. For example, the image analysis unit identifies objects in the images using object recognition technology. The image analysis unit can also identify people in the images using face recognition technology. The image analysis unit can also analyze the texture of the images using texture analysis technology. The description generation unit generates descriptions based on images analyzed by the image analysis unit. For example, the description generation unit generates descriptions using natural language generation technology. The description generation unit can also generate descriptions using template-based generation technology. The description generation unit can also generate descriptions using a generation AI. The price setting unit sets prices based on the descriptions generated by the description generation unit. For example, the price setting unit sets prices by referring to market prices. The price setting unit can also set prices by taking into account the balance between supply and demand. The price setting unit can also set prices using a generation AI. The image correction unit corrects the image analyzed by the image analysis unit. For example, the image correction unit adjusts the color tone of the image using color correction technology. The image correction unit can also remove noise from the image using noise removal technology. The image correction unit can also adjust the clarity of the image using sharpness adjustment technology. The hashtag generation unit generates hashtags based on the image analyzed by the image analysis unit. For example, the hashtag generation unit generates hashtags using keyword extraction technology. The hashtag generation unit can also generate hashtags using trend analysis technology. The hashtag generation unit can also generate hashtags using generation AI. The chatbot unit supports buying and selling transactions. For example, the chatbot unit answers user questions using natural language understanding technology. The chatbot unit can also manage dialogue with users using dialogue management technology. The chatbot unit can also answer user questions using FAQ support technology. The advice provision unit provides advice on products that are likely to sell.For example, the advice providing unit identifies products that are likely to sell using data analysis technology. The advice providing unit can also provide advice based on expert knowledge. The advice providing unit can also provide advice based on user feedback. This allows the flea market app system according to the embodiment to easily sell products, thereby increasing total distribution amount and global circulation.

[0030] The image analysis unit can identify the material or brand of a product from an image and generate a detailed description based on that information. For example, the image analysis unit uses a generation AI to analyze uploaded images and identify the material and brand of a product. For example, it analyzes an image of a leather bag and generates a detailed description such as, "This bag is made of high-quality cowhide leather and is a Gucci brand." The image analysis unit also uses a generation AI to analyze the features of an image and identify the material and brand of a product. For example, it analyzes an image of sports shoes and generates a description such as, "These shoes are made of breathable mesh material and are a Nike brand." The image analysis unit also uses a generation AI to analyze the color and shape of an image and identify the material and brand of a product. For example, it analyzes an image of jewelry and generates a description such as, "This necklace is made of 18K gold and is a Tiffany & Co." This allows the generation AI to identify the material and brand of a product and generate a detailed description, thereby enhancing the product's appeal.

[0031] The image correction unit can add an image editing function to automatically remove the background of an image and highlight only the product. For example, the image correction unit allows the generation AI to automatically remove the background of an uploaded image and highlight only the product. For example, it can remove furniture and walls in the background to clearly display only the product. The image correction unit also allows the generation AI to detect the edges of an image and remove the background to highlight the product. For example, it can remove people and scenery in the background to highlight only the product. The image correction unit also allows the generation AI to separate the foreground and background of an image and remove the background to highlight the product. For example, it can remove clutter in the background to cleanly display only the product. This makes it possible to remove the background and highlight only the product to enhance its appeal.

[0032] The description generation unit can automatically generate a story or episode related to a product when an image is uploaded and add it to the description. For example, the generation AI in the description generation unit analyzes the uploaded image and automatically generates a story or episode related to the product. For example, it generates a description such as, "This bag is perfect for travel and has plenty of storage space." The description generation unit also analyzes the features of the image and automatically generates an episode related to the product. For example, it generates a description such as, "These shoes are used in marathons and are popular with many runners." The description generation unit also analyzes the background information of the image and automatically generates a story related to the product. For example, it generates a description such as, "This jewelry is often given on special anniversaries and symbolizes eternal love." This allows the product's appeal to be enhanced by adding stories and episodes related to the product.

[0033] The chatbot unit can analyze the past purchase history of a prospective purchaser and provide individually customized answers. In the chatbot unit, for example, the generation AI analyzes the past purchase history of a prospective purchaser and provides individually customized answers. For example, it generates an answer such as, "It is of the same quality as the product you purchased previously." In addition, the generation AI analyzes the purchase history of a prospective purchaser and provides related product information. For example, it generates an answer such as, "It can be used in combination with a product you previously purchased." In addition, the generation AI makes individually customized suggestions based on the past purchase history of a prospective purchaser. For example, it generates an answer such as, "It is the same brand as the product you purchased previously." In this way, the user experience can be improved by providing individually customized answers to prospective purchasers.

[0034] The chatbot unit can add a multilingual support function to respond according to the user's language and culture. The chatbot unit, for example, adds a multilingual support function that enables the chatbot to respond according to the user's language. For example, it supports multiple languages ​​such as English, French, and Chinese. The chatbot unit also adds a multilingual support function that enables the generation AI to respond according to the user's culture. For example, it generates answers that take cultural background into consideration. The chatbot unit also builds a system that adds a multilingual support function that enables the chatbot to respond according to the user's language and culture. For example, it automatically switches languages ​​based on the user's language settings. This makes it possible to improve the global user experience by responding according to the user's language and culture.

[0035] The chatbot unit also supports voice input and can support interactions using voice recognition technology. The chatbot unit, for example, builds a system in which the chatbot supports voice input and supports voice interactions. For example, a user inputs a question by voice, and the chatbot responds by voice. The chatbot unit also uses a generation AI to analyze the voice input and support voice interactions. For example, when a user inquires about product information by voice, the chatbot responds by voice. The chatbot unit also adds a function to the chatbot to support voice input and support voice interactions. For example, the chatbot uses voice recognition technology to convert the user's voice into text and responds. This makes it possible to improve user convenience by supporting voice input.

[0036] The chatbot unit can analyze buying and selling transactions and provide advice to improve the success rate of transactions. For example, the chatbot unit uses a generation AI to analyze buying and selling transactions and provide advice to improve the success rate of transactions. For example, the chatbot unit provides advice such as, "This product will be easier to sell if you lower the price a little." The chatbot unit also uses a generation AI to analyze past transaction data and make suggestions to improve the success rate. For example, the chatbot unit provides advice such as, "This product will be easier to sell if you add a photo." The chatbot unit also builds a system in which the generation AI analyzes buying and selling transactions in real time and provides advice to improve the success rate of transactions. For example, the chatbot unit provides advice such as, "This product will be easier to sell if you add more details to the description." By providing advice to improve the success rate of transactions, user satisfaction can be increased.

[0037] The advice providing unit can analyze sales data for each region, identify best-selling products specific to the region, and provide advice. For example, the generation AI analyzes sales data for each region, identifies best-selling products specific to the region, and provides advice. For example, the advice providing unit provides advice such as, "Winter coats sell well in this region." The advice providing unit also identifies products that are popular in a specific region based on the sales data for the region, and provides advice to the user. For example, the advice providing unit provides advice such as, "Outdoor goods are popular in this region." The advice providing unit also analyzes sales data for each region, identifies best-selling products according to the season or event, and provides advice. For example, the advice providing unit provides advice such as, "Yukatas sell well in this region during summer festivals." In this way, by analyzing sales data for each region, identifying best-selling products specific to the region, and providing advice, it is possible to increase sales opportunities for users.

[0038] The advice providing unit can generate advice that takes into account not only past sales data but also current market trends and seasonal factors. For example, the generation AI in the advice providing unit analyzes past sales data and current market trends and generates advice that also takes seasonal factors into account. For example, the advice providing unit provides advice such as, "Eco bags are currently trending, and they are especially popular in the summer." The generation AI also provides advice that takes seasonal factors into account based on past sales data and market trends. For example, the advice providing unit provides advice such as, "Boots sell well in the fall." The generation AI also analyzes market trends and seasonal factors in real time and generates advice by combining it with past sales data. For example, the advice providing unit provides advice such as, "Sportswear is currently trending, and it is especially popular in the spring." This allows the user to increase sales opportunities by providing advice that takes market trends and seasonal factors into account.

[0039] The advice providing unit can analyze the user's past listing history and provide individually customized advice on products that are likely to sell. For example, the generation AI analyzes the user's past listing history and provides individually customized advice on products that are likely to sell. For example, the advice providing unit provides advice such as, "Items similar to products that were previously listed will sell well." The advice providing unit also analyzes the user's listing history and provides related product information. For example, the advice providing unit provides advice such as, "It is effective to sell this item in combination with products that were previously listed." The advice providing unit also provides individually customized suggestions based on the user's past listing history. For example, the advice providing unit provides advice such as, "Products in the same category as products that were previously listed will sell well." In this way, by analyzing the user's past listing history and providing individually customized advice, the user's sales opportunities can be increased.

[0040] The advice providing unit can integrate data from other flea market apps and online marketplaces to provide advice from a broader perspective. For example, the generation AI in the advice providing unit integrates data from other flea market apps and online marketplaces to provide advice from a broader perspective. For example, the advice providing unit provides advice such as, "Products that are popular on other platforms are also likely to sell here." The advice providing unit also analyzes data from multiple marketplaces, identifies common trends, and provides advice. For example, the advice providing unit provides advice such as, "Products that are best-selling on multiple platforms are also popular here." The advice providing unit also builds a system in which the generation AI provides advice from a broader perspective based on data from other flea market apps and online marketplaces. For example, the advice providing unit provides advice such as, "Use sales data from other platforms as a reference to identify products that are also likely to sell here." This allows the generation AI to integrate data from other flea market apps and online marketplaces and provide advice from a broader perspective, thereby increasing users' sales opportunities.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The flea market app system may further include a recommendation unit that analyzes a user's purchasing history and makes individually customized product recommendations. For example, the recommendation unit may recommend products similar to products previously purchased. The recommendation unit may also analyze a user's purchasing patterns and recommend products according to seasons or trends. Furthermore, the recommendation unit may recommend related products based on the user's interests. This may improve the user's purchasing experience and increase sales.

[0043] The flea market app system can further include a timing advice unit that analyzes user behavior data and advises the optimal listing timing. For example, the timing advice unit can advise the user that listing at a specific time of day or day of the week is more likely to sell based on past data. It can also suggest the optimal listing timing depending on the season or event. Furthermore, it can analyze the user's past listing history and identify and advise on the timing with the highest success rate. This can improve the user's listing success rate.

[0044] The flea market app system may further include a bundle suggestion unit that suggests bundled sales of related products based on the user's purchasing history. For example, the bundle suggestion unit may suggest selling a set of products related to a previously purchased product. The bundle suggestion unit may also analyze the user's purchasing patterns and suggest bundled sales according to seasons and trends. Furthermore, the system may suggest selling related products in a bundle based on the user's interests. This may improve the user's purchasing experience and increase sales.

[0045] The flea market app system may further include a priority display unit that prioritizes the display of products from a specific brand or category based on the user's purchasing history. For example, the priority display unit may prioritize the display of products from a brand that the user has previously purchased. The priority display unit may also analyze the user's purchasing patterns and prioritize the display of products from a specific category. Furthermore, related products may be prioritized based on the user's interests. This may improve the user's purchasing experience and increase sales.

[0046] The flea market app system can further include a delivery suggestion unit that suggests the optimal delivery method based on user behavior data. For example, the delivery suggestion unit suggests the optimal delivery method based on past delivery history. It can also suggest the optimal delivery method based on the user's location and delivery destination. Furthermore, it can analyze the user's purchase history and preferentially suggest specific delivery methods. This can improve the user's delivery experience and increase satisfaction.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The image analysis unit analyzes the image uploaded by the user. For example, it can use object recognition technology to identify objects in the image, or face recognition technology to identify people in the image. It can also use texture analysis technology to analyze the texture of the image. Step 2: The description generator generates a description based on the image analyzed by the image analyzer. For example, the description can be generated using natural language generation technology, template-based generation technology, or generation AI. Step 3: The pricing unit sets the price based on the description generated by the description generation unit. For example, the price can be set by referring to the market price, taking into account the balance between supply and demand, or using a generation AI. Step 4: The image correction unit corrects the image analyzed by the image analysis unit. For example, the image may be corrected using a color correction technique to adjust the color tone of the image, a noise reduction technique to remove noise from the image, or a sharpness adjustment technique to adjust the clarity of the image. Step 5: The hashtag generator generates hashtags based on the images analyzed by the image analyzer. For example, hashtags can be generated using keyword extraction technology, trend analysis technology, or generation AI. Step 6: The chatbot supports the buying and selling process. For example, it can answer user questions using natural language understanding technology, manage user interactions using dialogue management technology, and answer user questions using FAQ technology. Step 7: The advice providing unit provides advice on products that are likely to sell. For example, it can identify products that are likely to sell using data analysis technology, provide advice based on expert knowledge, or provide advice based on user feedback.

[0049] (Example 2) A flea market app system according to an embodiment of the present invention is a system that utilizes AI to enable users to easily list and sell items. In this system, users simply upload an image, and AI automatically sets the description and price, automatically corrects the image, and generates hashtags. Furthermore, chatbots and AI generation support the buying and selling process, and AI also provides advice on which products are likely to sell. This allows the flea market app system to easily enable users to list and sell items, thereby expanding total merchandise value and global circulation.

[0050] A flea market app system according to an embodiment includes an image analysis unit, a description generation unit, a price setting unit, an image correction unit, a hashtag generation unit, a chatbot unit, and an advice providing unit. The image analysis unit analyzes images uploaded by users. For example, the image analysis unit identifies objects in the images using object recognition technology. The image analysis unit can also identify people in the images using face recognition technology. The image analysis unit can also analyze the texture of the images using texture analysis technology. The description generation unit generates descriptions based on images analyzed by the image analysis unit. For example, the description generation unit generates descriptions using natural language generation technology. The description generation unit can also generate descriptions using template-based generation technology. The description generation unit can also generate descriptions using a generation AI. The price setting unit sets prices based on the descriptions generated by the description generation unit. For example, the price setting unit sets prices by referring to market prices. The price setting unit can also set prices by taking into account the balance between supply and demand. The price setting unit can also set prices using a generation AI. The image correction unit corrects the image analyzed by the image analysis unit. For example, the image correction unit adjusts the color tone of the image using color correction technology. The image correction unit can also remove noise from the image using noise removal technology. The image correction unit can also adjust the clarity of the image using sharpness adjustment technology. The hashtag generation unit generates hashtags based on the image analyzed by the image analysis unit. For example, the hashtag generation unit generates hashtags using keyword extraction technology. The hashtag generation unit can also generate hashtags using trend analysis technology. The hashtag generation unit can also generate hashtags using generation AI. The chatbot unit supports buying and selling transactions. For example, the chatbot unit answers user questions using natural language understanding technology. The chatbot unit can also manage dialogue with users using dialogue management technology. The chatbot unit can also answer user questions using FAQ support technology. The advice provision unit provides advice on products that are likely to sell.For example, the advice providing unit identifies products that are likely to sell using data analysis technology. The advice providing unit can also provide advice based on expert knowledge. The advice providing unit can also provide advice based on user feedback. This allows the flea market app system according to the embodiment to easily sell products, thereby increasing total distribution amount and global circulation.

[0051] The image analysis unit can identify the material or brand of a product from an image and generate a detailed description based on that information. For example, the image analysis unit uses a generation AI to analyze uploaded images and identify the material and brand of a product. For example, it analyzes an image of a leather bag and generates a detailed description such as, "This bag is made of high-quality cowhide leather and is a Gucci brand." The image analysis unit also uses a generation AI to analyze the features of an image and identify the material and brand of a product. For example, it analyzes an image of sports shoes and generates a description such as, "These shoes are made of breathable mesh material and are a Nike brand." The image analysis unit also uses a generation AI to analyze the color and shape of an image and identify the material and brand of a product. For example, it analyzes an image of jewelry and generates a description such as, "This necklace is made of 18K gold and is a Tiffany & Co." This allows the generation AI to identify the material and brand of a product and generate a detailed description, thereby enhancing the product's appeal.

[0052] The image correction unit can add an image editing function to automatically remove the background of an image and highlight only the product. For example, the image correction unit allows the generation AI to automatically remove the background of an uploaded image and highlight only the product. For example, it can remove furniture and walls in the background to clearly display only the product. The image correction unit also allows the generation AI to detect the edges of an image and remove the background to highlight the product. For example, it can remove people and scenery in the background to highlight only the product. The image correction unit also allows the generation AI to separate the foreground and background of an image and remove the background to highlight the product. For example, it can remove clutter in the background to cleanly display only the product. This makes it possible to remove the background and highlight only the product to enhance its appeal.

[0053] The image correction unit can use the emotion estimation function to analyze the emotions toward an image uploaded by a user and perform image correction to elicit positive emotions. For example, the image correction unit analyzes the user's emotions toward an image uploaded by the generation AI and adjusts the brightness and contrast of the image to elicit positive emotions. For example, brightening a dark image and making the colors more vivid. The image correction unit also uses the emotion estimation function to analyze the emotions toward an image uploaded by a user and adjusts the color tone of the image to elicit positive emotions. For example, changing cool tones to warm tones. The image correction unit also analyzes the emotions toward an image by the generation AI and automatically adjusts the composition of the image to elicit positive emotions. For example, emphasizing the center of a product and blurring the background. In this way, image correction can be performed to elicit positive emotions.

[0054] The description generation unit can automatically generate a story or episode related to a product when an image is uploaded and add it to the description. For example, the generation AI in the description generation unit analyzes the uploaded image and automatically generates a story or episode related to the product. For example, it generates a description such as, "This bag is perfect for travel and has plenty of storage space." The description generation unit also analyzes the features of the image and automatically generates an episode related to the product. For example, it generates a description such as, "These shoes are used in marathons and are popular with many runners." The description generation unit also analyzes the background information of the image and automatically generates a story related to the product. For example, it generates a description such as, "This jewelry is often given on special anniversaries and symbolizes eternal love." This allows the product's appeal to be enhanced by adding stories and episodes related to the product.

[0055] The chatbot unit can analyze the past purchase history of a prospective purchaser and provide individually customized answers. In the chatbot unit, for example, the generation AI analyzes the past purchase history of a prospective purchaser and provides individually customized answers. For example, it generates an answer such as, "It is of the same quality as the product you purchased previously." In addition, the generation AI analyzes the purchase history of a prospective purchaser and provides related product information. For example, it generates an answer such as, "It can be used in combination with a product you previously purchased." In addition, the generation AI makes individually customized suggestions based on the past purchase history of a prospective purchaser. For example, it generates an answer such as, "It is the same brand as the product you purchased previously." In this way, the user experience can be improved by providing individually customized answers to prospective purchasers.

[0056] The chatbot unit can add a multilingual support function to respond according to the user's language and culture. The chatbot unit, for example, adds a multilingual support function that enables the chatbot to respond according to the user's language. For example, it supports multiple languages ​​such as English, French, and Chinese. The chatbot unit also adds a multilingual support function that enables the generation AI to respond according to the user's culture. For example, it generates answers that take cultural background into consideration. The chatbot unit also builds a system that adds a multilingual support function that enables the chatbot to respond according to the user's language and culture. For example, it automatically switches languages ​​based on the user's language settings. This makes it possible to improve the global user experience by responding according to the user's language and culture.

[0057] The chatbot unit can use the emotion estimation function to analyze the emotions of a prospective buyer and generate responses that elicit positive emotions. For example, the chatbot unit uses the emotion estimation function to analyze the emotions of a prospective buyer and generate responses that elicit positive emotions. For example, it generates a response such as, "This product has been highly rated by many users." The chatbot unit also uses a generation AI to analyze the emotions of a prospective buyer and make suggestions to elicit positive emotions. For example, it generates a response such as, "This product is available at a special discount." The chatbot unit also builds a system that analyzes the emotions of a prospective buyer based on the emotion estimation data and generates responses that elicit positive emotions. For example, it dynamically adjusts responses according to changes in the user's emotions. This makes it possible to analyze the emotions of a prospective buyer and elicit positive emotions, thereby improving the success rate of transactions.

[0058] The chatbot unit also supports voice input and can support interactions using voice recognition technology. The chatbot unit, for example, builds a system in which the chatbot supports voice input and supports voice interactions. For example, a user inputs a question by voice, and the chatbot responds by voice. The chatbot unit also uses a generation AI to analyze the voice input and support voice interactions. For example, when a user inquires about product information by voice, the chatbot responds by voice. The chatbot unit also adds a function to the chatbot to support voice input and support voice interactions. For example, the chatbot uses voice recognition technology to convert the user's voice into text and responds. This makes it possible to improve user convenience by supporting voice input.

[0059] The chatbot unit can analyze buying and selling transactions and provide advice to improve the success rate of transactions. For example, the chatbot unit uses a generation AI to analyze buying and selling transactions and provide advice to improve the success rate of transactions. For example, the chatbot unit provides advice such as, "This product will be easier to sell if you lower the price a little." The chatbot unit also uses a generation AI to analyze past transaction data and make suggestions to improve the success rate. For example, the chatbot unit provides advice such as, "This product will be easier to sell if you add a photo." The chatbot unit also builds a system in which the generation AI analyzes buying and selling transactions in real time and provides advice to improve the success rate of transactions. For example, the chatbot unit provides advice such as, "This product will be easier to sell if you add more details to the description." By providing advice to improve the success rate of transactions, user satisfaction can be increased.

[0060] The chatbot unit can use the emotion estimation function to monitor the user's emotions in real time during buying and selling transactions and follow up at the appropriate time. For example, the chatbot unit can use the emotion estimation function to monitor the user's emotions in real time during buying and selling transactions and follow up at the appropriate time. For example, if the user feels anxious, it can send a message to reassure them. The chatbot unit also uses the generation AI to analyze the user's emotions during buying and selling transactions and follow up at the appropriate time. For example, if the user is excited, it can send a message encouraging them to make a purchase. The chatbot unit also builds a system based on the emotion estimation data to monitor the user's emotions in real time during buying and selling transactions and follow up at the appropriate time. For example, it can send a follow-up message depending on changes in the user's emotions. This allows the chatbot unit to monitor the user's emotions in real time and follow up at the appropriate time, thereby improving the success rate of transactions.

[0061] The advice providing unit can analyze sales data for each region, identify best-selling products specific to the region, and provide advice. For example, the generation AI analyzes sales data for each region, identifies best-selling products specific to the region, and provides advice. For example, the advice providing unit provides advice such as, "Winter coats sell well in this region." The advice providing unit also identifies products that are popular in a specific region based on the sales data for the region, and provides advice to the user. For example, the advice providing unit provides advice such as, "Outdoor goods are popular in this region." The advice providing unit also analyzes sales data for each region, identifies best-selling products according to the season or event, and provides advice. For example, the advice providing unit provides advice such as, "Yukatas sell well in this region during summer festivals." In this way, by analyzing sales data for each region, identifying best-selling products specific to the region, and providing advice, it is possible to increase sales opportunities for users.

[0062] The advice providing unit can generate advice that takes into account not only past sales data but also current market trends and seasonal factors. For example, the generation AI in the advice providing unit analyzes past sales data and current market trends and generates advice that also takes seasonal factors into account. For example, the advice providing unit provides advice such as, "Eco bags are currently trending, and they are especially popular in the summer." The generation AI also provides advice that takes seasonal factors into account based on past sales data and market trends. For example, the advice providing unit provides advice such as, "Boots sell well in the fall." The generation AI also analyzes market trends and seasonal factors in real time and generates advice by combining it with past sales data. For example, the advice providing unit provides advice such as, "Sportswear is currently trending, and it is especially popular in the spring." This allows the user to increase sales opportunities by providing advice that takes market trends and seasonal factors into account.

[0063] The advice providing unit can use the emotion estimation function to analyze the emotions of the user when receiving advice and provide advice to elicit positive emotions. The advice providing unit, for example, uses the emotion estimation function to analyze the emotions of the user when receiving advice and provide advice to elicit positive emotions. For example, it provides advice such as, "This product has been highly rated by many users." The advice providing unit also uses the generation AI to analyze the user's emotions and provide advice to elicit positive emotions. For example, it provides advice such as, "This product is available at a special discount." The advice providing unit also builds a system that analyzes the emotions of the user when receiving advice based on the emotion estimation data and provides advice to elicit positive emotions. For example, it dynamically adjusts the advice according to changes in the user's emotions. This makes it possible to analyze the user's emotions and provide advice that elicits positive emotions, thereby increasing user satisfaction.

[0064] The advice providing unit can analyze the user's past listing history and provide individually customized advice on products that are likely to sell. For example, the generation AI analyzes the user's past listing history and provides individually customized advice on products that are likely to sell. For example, the advice providing unit provides advice such as, "Items similar to products that were previously listed will sell well." The advice providing unit also analyzes the user's listing history and provides related product information. For example, the advice providing unit provides advice such as, "It is effective to sell this item in combination with products that were previously listed." The advice providing unit also provides individually customized suggestions based on the user's past listing history. For example, the advice providing unit provides advice such as, "Products in the same category as products that were previously listed will sell well." In this way, by analyzing the user's past listing history and providing individually customized advice, the user's sales opportunities can be increased.

[0065] The advice providing unit can integrate data from other flea market apps and online marketplaces to provide advice from a broader perspective. For example, the generation AI in the advice providing unit integrates data from other flea market apps and online marketplaces to provide advice from a broader perspective. For example, the advice providing unit provides advice such as, "Products that are popular on other platforms are also likely to sell here." The advice providing unit also analyzes data from multiple marketplaces, identifies common trends, and provides advice. For example, the advice providing unit provides advice such as, "Products that are best-selling on multiple platforms are also popular here." The advice providing unit also builds a system in which the generation AI provides advice from a broader perspective based on data from other flea market apps and online marketplaces. For example, the advice providing unit provides advice such as, "Use sales data from other platforms as a reference to identify products that are also likely to sell here." This allows the generation AI to integrate data from other flea market apps and online marketplaces and provide advice from a broader perspective, thereby increasing users' sales opportunities.

[0066] The advice providing unit can use the emotion estimation function to collect emotional reactions when a user receives advice and select advice that will garner the most empathy. The advice providing unit, for example, uses the emotion estimation function to collect emotional reactions when a user receives advice and selects advice that will garner the most empathy. For example, it prioritizes providing advice with a high number of positive emotional reactions. The advice providing unit also uses a generation AI to analyze the user's emotional reactions and select advice that will garner the most empathy. For example, it automatically selects advice with a high emotion score. The advice providing unit also builds a system that collects the user's emotional reactions based on the emotion estimation data and selects advice that will garner the most empathy. For example, it dynamically selects advice according to changes in the user's emotions. In this way, it is possible to increase user satisfaction by collecting the user's emotional reactions and providing advice that will garner the most empathy.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The flea market app system may further include a recommendation unit that analyzes a user's purchasing history and makes individually customized product recommendations. For example, the recommendation unit may recommend products similar to products previously purchased. The recommendation unit may also analyze a user's purchasing patterns and recommend products according to seasons or trends. Furthermore, the recommendation unit may recommend related products based on the user's interests. This may improve the user's purchasing experience and increase sales.

[0069] The flea market app system can further include a description adjustment unit that estimates the user's emotions and adjusts the product description based on the estimated emotions. For example, if the user is excited, the description can be made more detailed to emphasize the product's appeal. Alternatively, if the user is anxious, the description can be made more concise to emphasize the product's reliability. Furthermore, if the user shows interest, a related story or episode can be added to the description. This allows the system to provide a description that matches the user's emotions and increase their desire to purchase.

[0070] The flea market app system can further include a timing advice unit that analyzes user behavior data and advises the optimal listing timing. For example, the timing advice unit can advise the user that listing at a specific time of day or day of the week is more likely to sell based on past data. It can also suggest the optimal listing timing depending on the season or event. Furthermore, it can analyze the user's past listing history and identify and advise on the timing with the highest success rate. This can improve the user's listing success rate.

[0071] The flea market app system can further include a price adjustment unit that estimates the user's emotions and adjusts the price setting based on the estimated emotions. For example, if the user is excited, the price can be set a little higher. If the user is anxious, the price can be set a little lower. Furthermore, if the user shows interest, the price can be brought closer to the market price. This makes it possible to set prices according to the user's emotions and maximize sales.

[0072] The flea market app system may further include a bundle suggestion unit that suggests bundled sales of related products based on the user's purchasing history. For example, the bundle suggestion unit may suggest selling a set of products related to a previously purchased product. The bundle suggestion unit may also analyze the user's purchasing patterns and suggest bundled sales according to seasons and trends. Furthermore, the system may suggest selling related products in a bundle based on the user's interests. This may improve the user's purchasing experience and increase sales.

[0073] The flea market app system can further include an emotion response unit that estimates the user's emotions and adjusts the chatbot's response based on the estimated emotions. For example, if the user is excited, the chatbot's response can be made more proactive. Also, if the user is feeling anxious, the chatbot's response can be made more polite. Furthermore, if the user shows interest, the chatbot's response can be made more detailed. This allows the chatbot to respond according to the user's emotions and improve the user experience.

[0074] The flea market app system may further include a priority display unit that prioritizes the display of products from a specific brand or category based on the user's purchasing history. For example, the priority display unit may prioritize the display of products from a brand that the user has previously purchased. The priority display unit may also analyze the user's purchasing patterns and prioritize the display of products from a specific category. Furthermore, related products may be prioritized based on the user's interests. This may improve the user's purchasing experience and increase sales.

[0075] The flea market app system can further include an emotion hashtag generation unit that estimates the user's emotion and generates hashtags based on the estimated emotion. For example, if the user is excited, a positive hashtag can be generated. If the user is feeling anxious, a hashtag that gives a sense of security can be generated. Furthermore, if the user is interested, an interest-inducing hashtag can be generated. This allows hashtags to be generated according to the user's emotion, increasing the appeal of products.

[0076] The flea market app system can further include a delivery suggestion unit that suggests the optimal delivery method based on user behavior data. For example, the delivery suggestion unit suggests the optimal delivery method based on past delivery history. It can also suggest the optimal delivery method based on the user's location and delivery destination. Furthermore, it can analyze the user's purchase history and preferentially suggest specific delivery methods. This can improve the user's delivery experience and increase satisfaction.

[0077] The flea market app system can further include an emotion advice unit that estimates the user's emotion and provides advice based on the estimated emotion. For example, if the user is excited, proactive advice can be provided. Also, if the user is feeling anxious, advice that gives a sense of security can be provided. Furthermore, if the user shows interest, detailed advice can be provided. In this way, advice can be provided that corresponds to the user's emotion, and user satisfaction can be increased.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The image analysis unit analyzes the image uploaded by the user. For example, it can use object recognition technology to identify objects in the image, or face recognition technology to identify people in the image. It can also use texture analysis technology to analyze the texture of the image. Step 2: The description generator generates a description based on the image analyzed by the image analyzer. For example, the description can be generated using natural language generation technology, template-based generation technology, or generation AI. Step 3: The pricing unit sets the price based on the description generated by the description generation unit. For example, the price can be set by referring to the market price, taking into account the balance between supply and demand, or using a generation AI. Step 4: The image correction unit corrects the image analyzed by the image analysis unit. For example, the image may be corrected using a color correction technique to adjust the color tone of the image, a noise reduction technique to remove noise from the image, or a sharpness adjustment technique to adjust the clarity of the image. Step 5: The hashtag generator generates hashtags based on the images analyzed by the image analyzer. For example, hashtags can be generated using keyword extraction technology, trend analysis technology, or generation AI. Step 6: The chatbot supports the buying and selling process. For example, it can answer user questions using natural language understanding technology, manage user interactions using dialogue management technology, and answer user questions using FAQ technology. Step 7: The advice providing unit provides advice on products that are likely to sell. For example, it can identify products that are likely to sell using data analysis technology, provide advice based on expert knowledge, or provide advice based on user feedback.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 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. an image analysis unit that analyzes images uploaded by users; a description generation unit that generates a description based on the image analyzed by the image analysis unit; a price setting unit that sets a price based on the description generated by the description generation unit; an image correction unit that corrects the image analyzed by the image analysis unit; a hashtag generation unit that generates a hashtag based on the image analyzed by the image analysis unit; A chatbot section that supports buying and selling transactions, An advice providing unit that provides advice on products that are likely to sell well. A system characterized by:

2. The image analysis unit Identifying the material or brand of the product from the image and generating the detailed description based on that 2. The system of claim 1.

3. The image correction unit Add an image editing feature to automatically remove the background of the image and highlight only the product.

2. The system of claim 1.

4. The explanation generation unit When uploading an image, a story or episode related to the product is automatically generated and added to the description.

2. The system of claim 1.

5. The chatbot unit Analyze prospective buyers' past purchase history to provide personalized answers 2. The system of claim 1.

6. The advice providing unit Analyze regional sales data to identify and advise on best-selling products specific to each region 2. The system of claim 1.

7. The image correction unit Analyzing the emotions felt by the user regarding the image uploaded by the user and correcting the image to elicit positive emotions 2. The system of claim 1.

8. The chatbot unit Analyze the buyer's emotions and generate responses that elicit positive emotions 2. The system of claim 1.

Images