System

Abstract

An object of a system according to an embodiment is to enable even a user with insufficient digital skills to easily find and purchase an optimal product.SOLUTION: A system according to an embodiment includes a chat input unit, a photo analysis unit, an information collection unit, a recommendation unit, and a purchase support unit. The chat input unit receives a user's question or request in a chat format. The photo analysis unit analyzes the photo based on the request received by the chat input unit. The information collection unit collects information on the Internet on the basis of the information analyzed by the photo analysis unit. The recommendation unit recommends an optimum product based on the information collected by the information collection unit. The purchase support unit supports a purchase procedure of the commodity recommended by the recommendation unit.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, there was a problem in that non-users of e-commerce sites were unable to make a purchase due to a lack of digital skills and information.

[0005] The system according to the embodiment aims to enable even users who lack digital skills to easily find and purchase the most suitable products. [Means for solving the problem]

[0006] The system according to the embodiment includes a chat input unit, a photo analysis unit, an information collection unit, a recommendation unit, and a purchase support unit. The chat input unit accepts questions and requests from users in a chat format. The photo analysis unit analyzes photos based on the requests accepted by the chat input unit. The information collection unit collects information on the Internet based on the information analyzed by the photo analysis unit. The recommendation unit recommends optimal products based on the information collected by the information collection unit. The purchase support unit supports the purchase process for products recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows even users who lack digital skills to easily find and purchase the most suitable products. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The system for non-users of e-commerce sites according to an embodiment of the present invention is a system that allows users to communicate their questions and requests about what they want in chat format, or to import photos, and then recommends optimal products from the vast amount of information available on the Internet, allows users to purchase directly from links, and also supports site registration. As a result, the system for non-users of e-commerce sites eliminates barriers to purchase due to a lack of digital skills or information, and allows even non-users of e-commerce sites to easily search for and purchase products.

[0029] A system for non-users of e-commerce sites according to an embodiment includes a chat input unit, a photo analysis unit, an information collection unit, a recommendation unit, and a purchase support unit. The chat input unit accepts user questions and requests via chat. For example, a user can input a specific request, such as "I want a red bag within a budget of 10,000 yen." The photo analysis unit analyzes photos based on the request accepted by the chat input unit. For example, a user can upload photos of products they found in magazines or on the Internet, and a generation AI analyzes the photos to extract the product's features and style. The information collection unit collects information on the Internet based on the information analyzed by the photo analysis unit. For example, the generation AI collects and analyzes information such as online reviews, ratings, prices, and the user's style. The recommendation unit recommends optimal products based on the information collected by the information collection unit. For example, the generation AI presents multiple candidate products based on the user's request and information extracted from the photos. The purchase support unit supports the purchase process for products recommended by the recommendation unit. For example, the generation AI provides guidance to users when entering necessary information, allowing them to complete the registration process smoothly. This allows the system for non-users of e-commerce sites according to the embodiment to easily search for and purchase products, even for those who have never used an e-commerce site. For example, even elderly people who lack digital skills or users who are unfamiliar with e-commerce sites can find the perfect product by asking questions via chat or uploading photos.

[0030] The chat input unit analyzes the user's past chat history and learns the preferences and tendencies of each individual user, thereby supporting the input of more personalized questions and requests. The chat input unit, for example, analyzes the user's past chat history and learns preferences for specific brands and products. For example, for a user who has searched for a specific brand many times in the past, products from that brand are preferentially suggested. The chat input unit also analyzes the user's past chat history and learns the user's preferences and tendencies. For example, the chat input unit can analyze patterns of requests and questions entered by the user in the past and suggest more appropriate questions and requests in the next chat. The chat input unit also learns the user's preferences and tendencies based on the user's past chat history and supports the input of personalized questions and requests. For example, based on requests entered by the user in the past, related questions are automatically suggested in the next chat. This allows for more appropriate questions and requests to be supported based on the user's preferences and tendencies.

[0031] In the chat input unit, the generation AI automatically suggests related questions in response to requests entered by the user, enabling a more detailed understanding of the user's needs. For example, if a user enters, "I want a red bag," the generation AI automatically suggests related questions such as, "Which brand do you like?" and "What is your budget?" In addition, in the chat input unit, the generation AI automatically suggests related questions in response to requests entered by the user. For example, if a user enters, "I want a new smartphone," the generation AI suggests questions such as, "Which features are important?" and "What price range are you considering?" In addition, in the chat input unit, the generation AI suggests related questions based on requests entered by the user, enabling a more detailed understanding of the user's needs. For example, if a user enters, "I want a suitcase for travel," the generation AI suggests questions such as, "How much capacity do you need?" and "What material do you prefer?" This allows a more detailed understanding of the user's needs and suggests appropriate products.

[0032] The chat input unit can also accept chat-style questions and requests via voice input, and can analyze the user's requests using voice recognition technology. For example, when a user speaks, "I want a red bag for under 10,000 yen," the chat input unit converts the content into text using voice recognition technology, which the generation AI analyzes and suggests appropriate products. The chat input unit also analyzes the user's requests using voice input. For example, when a user speaks, "I want a new smartphone," the voice recognition technology converts the content into text, which the generation AI analyzes and suggests appropriate products. The chat input unit also analyzes the user's requests using voice input, which the generation AI suggests appropriate products. For example, when a user speaks, "I want a suitcase for travel," the voice recognition technology converts the content into text, which the generation AI analyzes and suggests appropriate products. This improves user convenience by supporting voice input.

[0033] The chat input unit allows the chatbot to automatically suggest videos of related products and services based on the user's input. For example, if a user inputs, "I want a red bag," the generation AI automatically suggests videos of related products. For example, it displays videos that include scenes of using a red bag and a detailed product description. The chat input unit also allows the generation AI to suggest videos of related products and services based on the user's input. For example, if a user inputs, "I want a new smartphone," the generation AI suggests smartphone review videos and demonstration videos of how to use them. The chat input unit also allows the generation AI to automatically suggest videos of related products and services based on the user's input. For example, if a user inputs, "I want a travel suitcase," the generation AI suggests videos of suitcase usage scenes and packing methods. This allows the user to deepen their understanding by suggesting videos of related products and services.

[0034] The photo analysis unit uses image recognition technology to analyze photos and identify the material and texture of a product, thereby suggesting products that match the user's preferences. For example, the photo analysis unit analyzes a photo of a bag uploaded by a user and identifies the material and texture using image recognition technology. For example, the photo analysis unit identifies materials such as leather and canvas and suggests bags made of the same material. The photo analysis unit also uses image recognition technology to identify the material and texture of a product. For example, the photo analysis unit analyzes a photo of shoes uploaded by a user and identifies materials such as leather and suede. The photo analysis unit also uses image recognition technology to identify the material and texture of a product and suggest products that match the user's preferences. For example, the photo analysis unit analyzes a photo of a jacket uploaded by a user and identifies materials such as wool and cotton. In this way, the photo analysis unit can identify the material and texture of a product and suggest products that match the user's preferences.

[0035] The photo analysis unit can analyze background information contained in photos and recommend products suitable for the user's lifestyle and environment. The photo analysis unit, for example, analyzes background information of photos uploaded by the user and suggests products suitable for the lifestyle. For example, if an outdoor background is shown, outdoor equipment is suggested. The photo analysis unit also analyzes background information contained in photos and recommends products suitable for the user's lifestyle and environment. For example, it analyzes the style of furniture and interior decor shown in photos uploaded by the user and suggests products of the same style. The photo analysis unit also analyzes background information contained in photos and recommends products suitable for the user's lifestyle and environment. For example, it analyzes the scenery and locations shown in photos uploaded by the user and suggests products suitable for those locations. In this way, by analyzing background information, it is possible to suggest products suitable for the user's lifestyle and environment.

[0036] The photo analysis unit can incorporate the capture and analysis of photos into the capture and analysis of videos, thereby extracting product features from the videos. For example, the photo analysis unit analyzes videos of products uploaded by users and extracts product features from the videos. For example, it identifies the color and design of products featured in the videos and suggests products with the same features. The photo analysis unit also incorporates and analyzes videos, thereby extracting product features from the videos. For example, it analyzes videos of products uploaded by users and identifies the movement and usage of the products. The photo analysis unit also incorporates and analyzes videos, thereby extracting product features from the videos. For example, it analyzes videos of products uploaded by users and identifies the size and shape of the products. This allows for extracting product features from videos, thereby providing more detailed product information.

[0037] The photo analysis unit suggests product customization options to the user based on the photo analysis results, thereby enabling the provision of more personalized products. The photo analysis unit suggests product customization options based on, for example, the results of analyzing a photo uploaded by the user. For example, it provides an option to select the color and material of a bag. The photo analysis unit also suggests product customization options to the user based on the results of photo analysis. For example, it provides an option to select the design and size of shoes based on the results of analyzing a photo of shoes uploaded by the user. The photo analysis unit also suggests product customization options to the user based on the results of photo analysis. For example, it provides an option to select the color and material of a jacket based on the results of analyzing a photo of a jacket uploaded by the user. In this way, by suggesting customization options, it is possible to provide a more personalized product to the user.

[0038] The information collection unit can make more accurate product suggestions based on the information collected by the generation AI by comparing it with the user's past purchase history and browsing history. For example, the information collection unit may collect online reviews and ratings by the generation AI, compare them with the user's past purchase history, and suggest products of the same brand or category. For example, it may suggest new products of a brand that was previously purchased. The information collection unit also makes more accurate product suggestions based on the information collected by the generation AI by comparing it with the user's past purchase history and browsing history. For example, it may suggest products of the same category based on reviews and ratings of products that the user has previously viewed. The information collection unit also makes more accurate product suggestions based on the information collected by the generation AI by comparing it with the user's past purchase history and browsing history. For example, it may suggest new products of the same brand based on ratings of products that the user has previously purchased. This enables more accurate product suggestions based on the user's past purchase history and browsing history.

[0039] The information gathering unit can evaluate the reliability of specific brands and shops when gathering information online, and provide only highly reliable information to the user. For example, the generation AI in the information gathering unit evaluates the reliability of specific brands and shops, and provides only highly reliable information to the user. For example, the generation AI calculates a reliability score based on past reviews and ratings, and preferentially displays information with high scores. The information gathering unit also evaluates the reliability of specific brands and shops when gathering information online, and provides only highly reliable information to the user. For example, the generation AI evaluates the reliability of a brand or shop of a product that the user is considering purchasing, and provides highly reliable information. The information gathering unit also evaluates the reliability of specific brands and shops when gathering information online, and provides only highly reliable information to the user. For example, the generation AI evaluates the reliability based on past transaction history and ratings of the brand or shop, and provides highly reliable information. In this way, providing only highly reliable information can gain the trust of users.

[0040] The information collection unit expands its collection of information on the Internet to include user-generated content such as social media and blogs, thereby collecting data from a wider variety of information sources. For example, the generation AI in the information collection unit collects user-generated content from social media and blogs to provide a wider variety of information about products. For example, it analyzes Instagram posts and Twitter tweets to collect real opinions about products. The information collection unit also expands its collection of information on the Internet to include user-generated content such as social media and blogs to collect data from a wider variety of information sources. For example, it collects and analyzes social media posts and blog articles about products that users are considering purchasing. The information collection unit also expands its collection of information on the Internet to include user-generated content such as social media and blogs to collect data from a wider variety of information sources. For example, the generation AI collects user-generated content from social media and blogs to provide a wider variety of information about products. By collecting data from a wider variety of information sources, the range of information provided to users is expanded.

[0041] The information collection unit can suggest to the user how to use and maintain the product based on the collected information. For example, the information collection unit suggests to the user how to use the product based on the information collected by the generation AI. For example, it provides videos and articles explaining how to use and tips for the purchased product. The information collection unit also suggests to the user how to use and maintain the product based on the collected information. For example, it provides videos and articles explaining how to use and maintain a product that the user is considering purchasing. The information collection unit also suggests to the user how to use and maintain the product based on the collected information. For example, it suggests to the user how to use and maintain the product based on the information collected by the generation AI. This improves user convenience by suggesting how to use and maintain the product.

[0042] The recommendation unit can provide a comparison function for recommended products, allowing users to easily compare multiple products. The recommendation unit, for example, provides a comparison function for products recommended by the generation AI, allowing users to easily compare multiple products. For example, it displays a list of product features, prices, and ratings. The recommendation unit also provides a comparison function for recommended products, allowing users to easily compare multiple products. For example, it provides an interface for users to compare the features, prices, and ratings of products they are considering purchasing. The recommendation unit also provides a comparison function for recommended products, allowing users to easily compare multiple products. For example, it displays a list of the features, prices, and ratings of products recommended by the generation AI, allowing users to easily compare them. This makes it easier for users to make selections by making it easier to compare multiple products.

[0043] The recommendation unit can suggest customization options for recommended products, allowing the user to customize the products to their liking. For example, the recommendation unit suggests customization options for products recommended by the generation AI, allowing the user to customize the products to their liking. For example, it provides options to select the color and material of a bag. The recommendation unit also suggests customization options for recommended products, allowing the user to customize the products to their liking. For example, it provides options to select the design and functions of a product the user is considering purchasing. The recommendation unit also suggests customization options for recommended products, allowing the user to customize the products to their liking. For example, it provides options to select the color and material of a product recommended by the generation AI. In this way, by suggesting customization options, the user can customize the product to their liking.

[0044] The recommendation unit can suggest usage scenarios for recommended products, making it easier for users to imagine how to use the products. For example, the recommendation unit can suggest usage scenarios for products recommended by the generation AI, making it easier for users to imagine how to use the products. For example, it can introduce usage scenarios for bags and examples of coordination. The recommendation unit can also suggest usage scenarios for recommended products, making it easier for users to imagine how to use the products. For example, it can suggest usage scenarios and ways to utilize a product that the user is considering purchasing. The recommendation unit can also suggest usage scenarios for recommended products, making it easier for users to imagine how to use the products. For example, it can introduce usage scenarios and examples of coordination for the product recommended by the generation AI. In this way, by suggesting usage scenarios, it is easier for users to imagine how to use the products.

[0045] The purchase support department allows the generation AI to check the user's input in real time during the purchase process and provide immediate feedback if there are any errors or omissions. For example, the purchase support department allows the generation AI to check the user's input in real time during the purchase process and provide immediate feedback if there are any errors or omissions. For example, it may detect input errors in address or credit card information and prompt the user to correct them. The purchase support department also allows the generation AI to check the user's input in real time during the purchase process and provide immediate feedback if there are any errors or omissions. For example, if there is an error in the information entered by the user, it may immediately display a message prompting the user to correct it. The purchase support department also allows the generation AI to check the user's input in real time during the purchase process and provide immediate feedback if there are any errors or omissions. For example, if there is an error in the information entered by the user, it may immediately display a message prompting the user to correct it. This allows the purchase process to proceed smoothly by providing immediate feedback on any errors or omissions.

[0046] During the purchase process, the generation AI can refer to the user's past purchase history and suggest related products and accessories. During the purchase process, the generation AI can refer to the user's past purchase history and suggest related products and accessories. For example, it can suggest a wallet or key case that matches a bag purchased in the past. During the purchase process, the generation AI can refer to the user's past purchase history and suggest related products and accessories. For example, it can suggest related products and accessories based on the categories and brands of products that the user has purchased in the past. During the purchase process, the generation AI can refer to the user's past purchase history and suggest related products and accessories. For example, it can analyze the user's past purchase history and suggest related products and accessories. This can improve user satisfaction by suggesting related products and accessories based on the user's past purchase history.

[0047] During the purchase process, the generation AI can suggest to the user how to use and maintain the purchased product. During the purchase process, for example, the generation AI can suggest to the user how to use the purchased product. For example, it can introduce examples of how to use and coordinate a purchased bag. During the purchase process, the generation AI can also suggest to the user how to use and maintain the purchased product. For example, it can provide videos and articles that explain how to use and maintain a product that the user is considering purchasing. During the purchase process, the generation AI can also suggest to the user how to use and maintain the purchased product. For example, it can suggest to the user how to use and maintain the purchased product. This improves user convenience by suggesting how to use and maintain the purchased product.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] In the chat input section, the generation AI automatically suggests related questions in response to requests entered by the user, enabling a more detailed understanding of the user's needs. For example, if a user enters "I want a red bag," the generation AI automatically suggests related questions such as "What brand do you like?" and "What is your budget?". Similarly, if a user enters "I want a new smartphone," the generation AI suggests questions such as "What features are important?" and "What price range are you considering?". Furthermore, if a user enters "I want a suitcase for travel," the generation AI suggests questions such as "How much capacity do you need?" and "What material do you prefer?". This allows a more detailed understanding of the user's needs and suggests appropriate products.

[0050] The chat input unit can also accept chat-style questions and requests via voice input, and can analyze the user's requests using voice recognition technology. For example, if a user says, "I want a red bag for under 10,000 yen," the voice recognition technology converts the content into text, which the generation AI analyzes and suggests appropriate products. Similarly, if a user says, "I want a new smartphone," the voice recognition technology converts the content into text, which the generation AI analyzes and suggests appropriate products. Furthermore, if a user says, "I want a suitcase for travel," the voice recognition technology converts the content into text, which the generation AI analyzes and suggests appropriate products. This support for voice input improves user convenience.

[0051] The chat input section allows the chatbot to automatically suggest videos of related products and services based on the user's input. For example, if a user inputs "I want a red bag," the generation AI will automatically suggest videos of related products. For example, it will display videos showing how to use a red bag and a detailed product description. If a user inputs "I want a new smartphone," the generation AI will suggest smartphone review videos and videos demonstrating how to use the smartphone. Furthermore, if a user inputs "I want a suitcase for travel," the generation AI will suggest videos showing how to use the suitcase and how to pack it. This allows the user to deepen their understanding by suggesting videos of related products and services.

[0052] The photo analysis unit uses image recognition technology to analyze photos and identify the material and texture of a product, allowing it to suggest products that match the user's preferences. For example, it analyzes a photo of a bag uploaded by a user and uses image recognition technology to identify the material and texture. For example, it identifies materials such as leather and canvas and suggests bags made of the same material. It also analyzes a photo of shoes uploaded by a user and identifies materials such as leather and suede. It also analyzes a photo of a jacket uploaded by a user and identifies materials such as wool and cotton. This allows it to identify the material and texture of a product and suggest products that match the user's preferences.

[0053] The photo analysis unit analyzes background information contained in photos and can recommend products suitable for the user's lifestyle and environment. For example, it analyzes the background information of photos uploaded by the user and suggests products suitable for the lifestyle. For example, if the photo has an outdoor background, it suggests outdoor equipment. It also analyzes the style of furniture and interior decor shown in photos uploaded by the user and suggests products in the same style. It also analyzes the scenery and locations shown in photos uploaded by the user and suggests products suitable for that location. In this way, by analyzing background information, it is possible to suggest products suitable for the user's lifestyle and environment.

[0054] The information collection unit compares the information collected by the generation AI with the user's past purchase history and browsing history to make more accurate product suggestions. For example, the generation AI collects online reviews and ratings, compares them with the user's past purchase history, and suggests products of the same brand or category. For example, it suggests new products from a brand that the user has previously purchased. It also suggests products from the same category based on reviews and ratings of products the user has previously viewed. It also suggests new products from the same brand based on ratings of products the user has previously purchased. This enables more accurate product suggestions based on the user's past purchase history and browsing history.

[0055] The recommendation unit can provide a comparison function for recommended products, allowing users to easily compare multiple products. For example, the generation AI can provide a comparison function for products recommended by the generation AI, allowing users to easily compare multiple products. For example, it can display a list of product features, prices, and ratings. It can also provide an interface for users to compare the features, prices, and ratings of products they are considering purchasing. It can also display a list of the features, prices, and ratings of products recommended by the generation AI, allowing users to easily compare them. This makes it easier for users to make a selection by making it easier to compare multiple products.

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

[0057] Step 1: The chat input unit accepts questions and requests from users in chat format. For example, a user can input a specific request such as, "I want a red bag within a budget of 10,000 yen." Step 2: The photo analysis unit analyzes the photo based on the request received from the chat input unit. For example, a user can upload a photo of a product they found in a magazine or on the internet, and the generation AI will analyze the photo to extract the product's features and style. Step 3: The information collection unit collects information from the internet based on the information analyzed by the photo analysis unit. For example, the generation AI collects and analyzes information such as online reviews, ratings, prices, and user styles. Step 4: The recommendation unit recommends optimal products based on the information collected by the information collection unit. For example, the generation AI presents multiple candidate products based on the user's requests and information extracted from photos. Step 5: The purchase support department supports the purchase process for the products recommended by the recommendation department. For example, the generation AI provides guidance when the user enters the necessary information, allowing the user to complete the registration process smoothly.

[0058] (Example 2) The system for non-users of e-commerce sites according to an embodiment of the present invention is a system that allows users to communicate their questions and requests about what they want in chat format, or to import photos, and then recommends optimal products from the vast amount of information available on the Internet, allows users to purchase directly from links, and also supports site registration. As a result, the system for non-users of e-commerce sites eliminates barriers to purchase due to a lack of digital skills or information, and allows even non-users of e-commerce sites to easily search for and purchase products.

[0059] A system for non-users of e-commerce sites according to an embodiment includes a chat input unit, a photo analysis unit, an information collection unit, a recommendation unit, and a purchase support unit. The chat input unit accepts user questions and requests via chat. For example, a user can input a specific request, such as "I want a red bag within a budget of 10,000 yen." The photo analysis unit analyzes photos based on the request accepted by the chat input unit. For example, a user can upload photos of products they found in magazines or on the Internet, and a generation AI analyzes the photos to extract the product's features and style. The information collection unit collects information on the Internet based on the information analyzed by the photo analysis unit. For example, the generation AI collects and analyzes information such as online reviews, ratings, prices, and the user's style. The recommendation unit recommends optimal products based on the information collected by the information collection unit. For example, the generation AI presents multiple candidate products based on the user's request and information extracted from the photos. The purchase support unit supports the purchase process for products recommended by the recommendation unit. For example, the generation AI provides guidance to users when entering necessary information, allowing them to complete the registration process smoothly. This allows the system for non-users of e-commerce sites according to the embodiment to easily search for and purchase products, even for those who have never used an e-commerce site. For example, even elderly people who lack digital skills or users who are unfamiliar with e-commerce sites can find the perfect product by asking questions via chat or uploading photos.

[0060] The chat input unit analyzes the user's past chat history and learns the preferences and tendencies of each individual user, thereby supporting the input of more personalized questions and requests. The chat input unit, for example, analyzes the user's past chat history and learns preferences for specific brands and products. For example, for a user who has searched for a specific brand many times in the past, products from that brand are preferentially suggested. The chat input unit also analyzes the user's past chat history and learns the user's preferences and tendencies. For example, the chat input unit can analyze patterns of requests and questions entered by the user in the past and suggest more appropriate questions and requests in the next chat. The chat input unit also learns the user's preferences and tendencies based on the user's past chat history and supports the input of personalized questions and requests. For example, based on requests entered by the user in the past, related questions are automatically suggested in the next chat. This allows for more appropriate questions and requests to be supported based on the user's preferences and tendencies.

[0061] The chat input unit uses the generation AI to estimate the user's emotions in real time during chat, and can switch to a simpler question format if the user is feeling stressed. The chat input unit, for example, analyzes changes in the user's input speed and writing style to detect signs of stress. For example, if the user's input becomes slower or they start using shorter sentences more often, it presents simpler options. The chat input unit also uses the generation AI to estimate the user's emotions in real time, and switch to a simpler question format if the user is feeling stressed. For example, if the user is emotionally exhausted, it presents Yes / No questions. The chat input unit also uses the generation AI to estimate the user's emotions in real time, and adjust the difficulty of the questions if the user is feeling stressed. For example, if the user is feeling stressed, it prioritizes presenting easier questions. This allows the user to input questions and requests smoothly without feeling stressed.

[0062] In the chat input unit, the generation AI automatically suggests related questions in response to requests entered by the user, enabling a more detailed understanding of the user's needs. For example, if a user enters, "I want a red bag," the generation AI automatically suggests related questions such as, "Which brand do you like?" and "What is your budget?" In addition, in the chat input unit, the generation AI automatically suggests related questions in response to requests entered by the user. For example, if a user enters, "I want a new smartphone," the generation AI suggests questions such as, "Which features are important?" and "What price range are you considering?" In addition, in the chat input unit, the generation AI suggests related questions based on requests entered by the user, enabling a more detailed understanding of the user's needs. For example, if a user enters, "I want a suitcase for travel," the generation AI suggests questions such as, "How much capacity do you need?" and "What material do you prefer?" This allows a more detailed understanding of the user's needs and suggests appropriate products.

[0063] The chat input unit can also accept chat-style questions and requests via voice input, and can analyze the user's requests using voice recognition technology. For example, when a user speaks, "I want a red bag for under 10,000 yen," the chat input unit converts the content into text using voice recognition technology, which the generation AI analyzes and suggests appropriate products. The chat input unit also analyzes the user's requests using voice input. For example, when a user speaks, "I want a new smartphone," the voice recognition technology converts the content into text, which the generation AI analyzes and suggests appropriate products. The chat input unit also analyzes the user's requests using voice input, which the generation AI suggests appropriate products. For example, when a user speaks, "I want a suitcase for travel," the voice recognition technology converts the content into text, which the generation AI analyzes and suggests appropriate products. This improves user convenience by supporting voice input.

[0064] The chat input unit allows the chatbot to automatically suggest videos of related products and services based on the user's input. For example, if a user inputs, "I want a red bag," the generation AI automatically suggests videos of related products. For example, it displays videos that include scenes of using a red bag and a detailed product description. The chat input unit also allows the generation AI to suggest videos of related products and services based on the user's input. For example, if a user inputs, "I want a new smartphone," the generation AI suggests smartphone review videos and demonstration videos of how to use them. The chat input unit also allows the generation AI to automatically suggest videos of related products and services based on the user's input. For example, if a user inputs, "I want a travel suitcase," the generation AI suggests videos of suitcase usage scenes and packing methods. This allows the user to deepen their understanding by suggesting videos of related products and services.

[0065] The chat input unit uses the emotion estimation function to change the chatbot's response style according to the emotions the user feels during a chat, thereby realizing a more friendly dialogue. For example, if the user is feeling stressed, the chat input unit uses the emotion estimation function to change the chatbot's response style and conduct the dialogue in a more friendly tone. For example, by adding kind language or an encouraging message. The chat input unit also uses the emotion estimation function to change the chatbot's response style according to the emotions the user feels during a chat. For example, if the user is emotionally tired, the dialogue is conducted in a relaxed tone. The chat input unit also uses the emotion estimation function to change the chatbot's response style according to the emotions the user feels during a chat, thereby realizing a more friendly dialogue. For example, if the user is expressing positive emotions, the dialogue is conducted in a bright tone. In this way, a more friendly dialogue can be realized by providing a response style according to the user's emotions.

[0066] The photo analysis unit uses image recognition technology to analyze photos and identify the material and texture of a product, thereby suggesting products that match the user's preferences. For example, the photo analysis unit analyzes a photo of a bag uploaded by a user and identifies the material and texture using image recognition technology. For example, the photo analysis unit identifies materials such as leather and canvas and suggests bags made of the same material. The photo analysis unit also uses image recognition technology to identify the material and texture of a product. For example, the photo analysis unit analyzes a photo of shoes uploaded by a user and identifies materials such as leather and suede. The photo analysis unit also uses image recognition technology to identify the material and texture of a product and suggest products that match the user's preferences. For example, the photo analysis unit analyzes a photo of a jacket uploaded by a user and identifies materials such as wool and cotton. In this way, the photo analysis unit can identify the material and texture of a product and suggest products that match the user's preferences.

[0067] The photo analysis unit can analyze background information contained in photos and recommend products suitable for the user's lifestyle and environment. The photo analysis unit, for example, analyzes background information of photos uploaded by the user and suggests products suitable for the lifestyle. For example, if an outdoor background is shown, outdoor equipment is suggested. The photo analysis unit also analyzes background information contained in photos and recommends products suitable for the user's lifestyle and environment. For example, it analyzes the style of furniture and interior decor shown in photos uploaded by the user and suggests products of the same style. The photo analysis unit also analyzes background information contained in photos and recommends products suitable for the user's lifestyle and environment. For example, it analyzes the scenery and locations shown in photos uploaded by the user and suggests products suitable for those locations. In this way, by analyzing background information, it is possible to suggest products suitable for the user's lifestyle and environment.

[0068] The photo analysis unit can incorporate the capture and analysis of photos into the capture and analysis of videos, thereby extracting product features from the videos. For example, the photo analysis unit analyzes videos of products uploaded by users and extracts product features from the videos. For example, it identifies the color and design of products featured in the videos and suggests products with the same features. The photo analysis unit also incorporates and analyzes videos, thereby extracting product features from the videos. For example, it analyzes videos of products uploaded by users and identifies the movement and usage of the products. The photo analysis unit also incorporates and analyzes videos, thereby extracting product features from the videos. For example, it analyzes videos of products uploaded by users and identifies the size and shape of the products. This allows for extracting product features from videos, thereby providing more detailed product information.

[0069] The photo analysis unit suggests product customization options to the user based on the photo analysis results, thereby enabling the provision of more personalized products. The photo analysis unit suggests product customization options based on, for example, the results of analyzing a photo uploaded by the user. For example, it provides an option to select the color and material of a bag. The photo analysis unit also suggests product customization options to the user based on the results of photo analysis. For example, it provides an option to select the design and size of shoes based on the results of analyzing a photo of shoes uploaded by the user. The photo analysis unit also suggests product customization options to the user based on the results of photo analysis. For example, it provides an option to select the color and material of a jacket based on the results of analyzing a photo of a jacket uploaded by the user. In this way, by suggesting customization options, it is possible to provide a more personalized product to the user.

[0070] The photo analysis unit can use the emotion estimation function to monitor the user's emotional response in real time and adjust the content of the suggestions when proposing products based on the photo analysis results. The photo analysis unit, for example, monitors the user's emotional response in real time when uploading a photo and adjusts the content of the suggestions. For example, if the user has a positive response, it proposes products of the same style. The photo analysis unit also uses the emotion estimation function to monitor the user's emotional response in real time when proposing products based on the photo analysis results. For example, if the user has a negative response, it proposes products of a different style. The photo analysis unit also uses the emotion estimation function to monitor the user's emotional response in real time when proposing products based on the photo analysis results and adjust the content of the suggestions. For example, if the user is emotionally tired, it proposes products with a relaxed style. In this way, by adjusting the content of the suggestions based on the user's emotional response, it is possible to propose more appropriate products.

[0071] The information collection unit can make more accurate product suggestions based on the information collected by the generation AI by comparing it with the user's past purchase history and browsing history. For example, the information collection unit may collect online reviews and ratings by the generation AI, compare them with the user's past purchase history, and suggest products of the same brand or category. For example, it may suggest new products of a brand that was previously purchased. The information collection unit also makes more accurate product suggestions based on the information collected by the generation AI by comparing it with the user's past purchase history and browsing history. For example, it may suggest products of the same category based on reviews and ratings of products that the user has previously viewed. The information collection unit also makes more accurate product suggestions based on the information collected by the generation AI by comparing it with the user's past purchase history and browsing history. For example, it may suggest new products of the same brand based on ratings of products that the user has previously purchased. This enables more accurate product suggestions based on the user's past purchase history and browsing history.

[0072] The information gathering unit can evaluate the reliability of specific brands and shops when gathering information online, and provide only highly reliable information to the user. For example, the generation AI in the information gathering unit evaluates the reliability of specific brands and shops, and provides only highly reliable information to the user. For example, the generation AI calculates a reliability score based on past reviews and ratings, and preferentially displays information with high scores. The information gathering unit also evaluates the reliability of specific brands and shops when gathering information online, and provides only highly reliable information to the user. For example, the generation AI evaluates the reliability of a brand or shop of a product that the user is considering purchasing, and provides highly reliable information. The information gathering unit also evaluates the reliability of specific brands and shops when gathering information online, and provides only highly reliable information to the user. For example, the generation AI evaluates the reliability based on past transaction history and ratings of the brand or shop, and provides highly reliable information. In this way, providing only highly reliable information can gain the trust of users.

[0073] The information collection unit can use the emotion estimation function to analyze the emotional tone of the collected reviews and ratings and prioritize to suggest products with a large number of positive reviews. For example, the information collection unit analyzes the emotional tone of the reviews and ratings collected by the generation AI and prioritizes to suggest products with a large number of positive reviews. For example, products with a high emotion score are displayed at the top of the list. The information collection unit also uses the emotion estimation function to analyze the emotional tone of the collected reviews and ratings and prioritize to suggest products with a large number of positive reviews. For example, reviews and ratings of products that a user is considering purchasing are analyzed and products with a large number of positive emotions are suggested. The information collection unit also uses the emotion estimation function to analyze the emotional tone of the collected reviews and ratings and prioritize to suggest products with a large number of positive reviews. For example, the generation AI analyzes the emotional tone of the reviews and ratings and displays products with a large number of positive emotions at the top of the list. This allows for prioritized suggestion of products with a large number of positive reviews, thereby improving user satisfaction.

[0074] The information collection unit expands its collection of information on the Internet to include user-generated content such as social media and blogs, thereby collecting data from a wider variety of information sources. For example, the generation AI in the information collection unit collects user-generated content from social media and blogs to provide a wider variety of information about products. For example, it analyzes Instagram posts and Twitter tweets to collect real opinions about products. The information collection unit also expands its collection of information on the Internet to include user-generated content such as social media and blogs to collect data from a wider variety of information sources. For example, it collects and analyzes social media posts and blog articles about products that users are considering purchasing. The information collection unit also expands its collection of information on the Internet to include user-generated content such as social media and blogs to collect data from a wider variety of information sources. For example, the generation AI collects user-generated content from social media and blogs to provide a wider variety of information about products. By collecting data from a wider variety of information sources, the range of information provided to users is expanded.

[0075] The information collection unit can suggest to the user how to use and maintain the product based on the collected information. For example, the information collection unit suggests to the user how to use the product based on the information collected by the generation AI. For example, it provides videos and articles explaining how to use and tips for the purchased product. The information collection unit also suggests to the user how to use and maintain the product based on the collected information. For example, it provides videos and articles explaining how to use and maintain a product that the user is considering purchasing. The information collection unit also suggests to the user how to use and maintain the product based on the collected information. For example, it suggests to the user how to use and maintain the product based on the information collected by the generation AI. This improves user convenience by suggesting how to use and maintain the product.

[0076] The information collection unit can use the emotion estimation function to analyze the user's emotional response to the collected information and prioritize displaying information that is likely to interest the user. The information collection unit, for example, analyzes the user's emotional response to the information collected by the generation AI in real time and prioritizes displaying information that is likely to interest the user. For example, information with a high number of positive emotional responses is displayed at the top of the list. The information collection unit also uses the emotion estimation function to analyze the user's emotional response to the collected information and prioritize displaying information that is likely to interest the user. For example, it analyzes reviews and ratings of products the user is considering purchasing and prioritizes displaying information with a high number of positive emotions. The information collection unit also uses the emotion estimation function to analyze the user's emotional response to the collected information and prioritize displaying information that is likely to interest the user. For example, it analyzes the user's emotional response to the information collected by the generation AI in real time and displays information that is likely to interest the user at the top of the list. This allows user satisfaction to be improved by preferentially displaying information that is likely to interest the user based on the user's emotional response.

[0077] The recommendation unit can provide a comparison function for recommended products, allowing users to easily compare multiple products. The recommendation unit, for example, provides a comparison function for products recommended by the generation AI, allowing users to easily compare multiple products. For example, it displays a list of product features, prices, and ratings. The recommendation unit also provides a comparison function for recommended products, allowing users to easily compare multiple products. For example, it provides an interface for users to compare the features, prices, and ratings of products they are considering purchasing. The recommendation unit also provides a comparison function for recommended products, allowing users to easily compare multiple products. For example, it displays a list of the features, prices, and ratings of products recommended by the generation AI, allowing users to easily compare them. This makes it easier for users to make selections by making it easier to compare multiple products.

[0078] The recommendation unit can use the emotion estimation function to monitor the user's emotional response to recommended products in real time and adjust the content of the suggestions. For example, the recommendation unit monitors the user's emotional response to products recommended by the generation AI in real time and adjusts the content of the suggestions. For example, if the user has a positive response, it suggests products of the same style. The recommendation unit also uses the emotion estimation function to monitor the user's emotional response to recommended products in real time and adjusts the content of the suggestions. For example, if the user has a negative response, it suggests products of a different style. The recommendation unit also uses the emotion estimation function to monitor the user's emotional response to recommended products in real time and adjusts the content of the suggestions. For example, if the user is emotionally tired, it suggests products with a relaxing style. In this way, by adjusting the content of the suggestions based on the user's emotional response, it is possible to suggest more appropriate products.

[0079] The recommendation unit can suggest customization options for recommended products, allowing the user to customize the products to their liking. For example, the recommendation unit suggests customization options for products recommended by the generation AI, allowing the user to customize the products to their liking. For example, it provides options to select the color and material of a bag. The recommendation unit also suggests customization options for recommended products, allowing the user to customize the products to their liking. For example, it provides options to select the design and functions of a product the user is considering purchasing. The recommendation unit also suggests customization options for recommended products, allowing the user to customize the products to their liking. For example, it provides options to select the color and material of a product recommended by the generation AI. In this way, by suggesting customization options, the user can customize the product to their liking.

[0080] The recommendation unit can suggest usage scenarios for recommended products, making it easier for users to imagine how to use the products. For example, the recommendation unit can suggest usage scenarios for products recommended by the generation AI, making it easier for users to imagine how to use the products. For example, it can introduce usage scenarios for bags and examples of coordination. The recommendation unit can also suggest usage scenarios for recommended products, making it easier for users to imagine how to use the products. For example, it can suggest usage scenarios and ways to utilize a product that the user is considering purchasing. The recommendation unit can also suggest usage scenarios for recommended products, making it easier for users to imagine how to use the products. For example, it can introduce usage scenarios and examples of coordination for the product recommended by the generation AI. In this way, by suggesting usage scenarios, it is easier for users to imagine how to use the products.

[0081] The recommendation unit can use the emotion estimation function to analyze the user's emotional response to recommended products and prioritize suggesting products that elicit positive emotions. For example, the recommendation unit analyzes the user's emotional response to products recommended by the generation AI and prioritizes suggesting products that elicit positive emotions. For example, products with high emotion scores are displayed at the top of the list. The recommendation unit also uses the emotion estimation function to analyze the user's emotional response to recommended products and prioritize suggesting products that elicit positive emotions. For example, the recommendation unit analyzes reviews and ratings of products the user is considering purchasing and prioritizes suggesting products that elicit a large number of positive emotions. The recommendation unit also uses the emotion estimation function to analyze the user's emotional response to recommended products and prioritize suggesting products that elicit positive emotions. For example, the recommendation unit analyzes the emotion scores of products recommended by the generation AI and displays products with a large number of positive emotions at the top of the list. This allows for prioritized suggestion of products that elicit positive emotions, thereby improving user satisfaction.

[0082] The purchase support department allows the generation AI to check the user's input in real time during the purchase process and provide immediate feedback if there are any errors or omissions. For example, the purchase support department allows the generation AI to check the user's input in real time during the purchase process and provide immediate feedback if there are any errors or omissions. For example, it may detect input errors in address or credit card information and prompt the user to correct them. The purchase support department also allows the generation AI to check the user's input in real time during the purchase process and provide immediate feedback if there are any errors or omissions. For example, if there is an error in the information entered by the user, it may immediately display a message prompting the user to correct it. The purchase support department also allows the generation AI to check the user's input in real time during the purchase process and provide immediate feedback if there are any errors or omissions. For example, if there is an error in the information entered by the user, it may immediately display a message prompting the user to correct it. This allows the purchase process to proceed smoothly by providing immediate feedback on any errors or omissions.

[0083] During the purchase process, the generation AI can refer to the user's past purchase history and suggest related products and accessories. During the purchase process, the generation AI can refer to the user's past purchase history and suggest related products and accessories. For example, it can suggest a wallet or key case that matches a bag purchased in the past. During the purchase process, the generation AI can refer to the user's past purchase history and suggest related products and accessories. For example, it can suggest related products and accessories based on the categories and brands of products that the user has purchased in the past. During the purchase process, the generation AI can refer to the user's past purchase history and suggest related products and accessories. For example, it can analyze the user's past purchase history and suggest related products and accessories. This can improve user satisfaction by suggesting related products and accessories based on the user's past purchase history.

[0084] The purchase support department can use the emotion estimation function to monitor the user's emotions during the purchase process and make suggestions to simplify the process if the user is feeling stressed. For example, the purchase support department can use the generation AI to monitor the user's emotions during the purchase process and make suggestions to simplify the process if the user is feeling stressed. For example, it can provide a one-click purchase option. The purchase support department can also use the emotion estimation function to monitor the user's emotions during the purchase process and make suggestions to simplify the process if the user is feeling stressed. For example, it can present a simple option if the user is taking a long time to enter information. The purchase support department can also use the emotion estimation function to monitor the user's emotions during the purchase process and make suggestions to simplify the process if the user is feeling stressed. For example, it can provide an option to simplify the process if the user is emotionally exhausted. This allows the user to complete the purchase process smoothly without feeling stressed.

[0085] During the purchase process, the generation AI can suggest to the user how to use and maintain the purchased product. During the purchase process, for example, the generation AI can suggest to the user how to use the purchased product. For example, it can introduce examples of how to use and coordinate a purchased bag. During the purchase process, the generation AI can also suggest to the user how to use and maintain the purchased product. For example, it can provide videos and articles that explain how to use and maintain a product that the user is considering purchasing. During the purchase process, the generation AI can also suggest to the user how to use and maintain the purchased product. For example, it can suggest to the user how to use and maintain the purchased product. This improves user convenience by suggesting how to use and maintain the purchased product.

[0086] The purchase support unit can use the emotion estimation function to analyze the user's emotional response during the purchase process and provide an incentive (e.g., a discount coupon) to elicit positive emotions. For example, the purchase support unit uses the generation AI to analyze the user's emotional response during the purchase process and provide an incentive to elicit positive emotions. For example, if the purchase process is proceeding smoothly, a discount coupon is provided. The purchase support unit can also use the emotion estimation function to analyze the user's emotional response during the purchase process and provide an incentive to elicit positive emotions. For example, if the user shows positive emotions during the purchase process, a point reward is provided. The purchase support unit can also use the emotion estimation function to analyze the user's emotional response during the purchase process and provide an incentive to elicit positive emotions. For example, the generation AI can analyze the user's emotional response during the purchase process and provide an incentive to elicit positive emotions. In this way, by providing an incentive to elicit positive emotions, user satisfaction can be improved.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] In the chat input section, the generation AI automatically suggests related questions in response to requests entered by the user, enabling a more detailed understanding of the user's needs. For example, if a user enters "I want a red bag," the generation AI automatically suggests related questions such as "What brand do you like?" and "What is your budget?". Similarly, if a user enters "I want a new smartphone," the generation AI suggests questions such as "What features are important?" and "What price range are you considering?". Furthermore, if a user enters "I want a suitcase for travel," the generation AI suggests questions such as "How much capacity do you need?" and "What material do you prefer?". This allows a more detailed understanding of the user's needs and suggests appropriate products.

[0089] The chat input unit can also accept chat-style questions and requests via voice input, and can analyze the user's requests using voice recognition technology. For example, if a user says, "I want a red bag for under 10,000 yen," the voice recognition technology converts the content into text, which the generation AI analyzes and suggests appropriate products. Similarly, if a user says, "I want a new smartphone," the voice recognition technology converts the content into text, which the generation AI analyzes and suggests appropriate products. Furthermore, if a user says, "I want a suitcase for travel," the voice recognition technology converts the content into text, which the generation AI analyzes and suggests appropriate products. This support for voice input improves user convenience.

[0090] The chat input section allows the chatbot to automatically suggest videos of related products and services based on the user's input. For example, if a user inputs "I want a red bag," the generation AI will automatically suggest videos of related products. For example, it will display videos showing how to use a red bag and a detailed product description. If a user inputs "I want a new smartphone," the generation AI will suggest smartphone review videos and videos demonstrating how to use the smartphone. Furthermore, if a user inputs "I want a suitcase for travel," the generation AI will suggest videos showing how to use the suitcase and how to pack it. This allows the user to deepen their understanding by suggesting videos of related products and services.

[0091] The chat input unit can use the emotion estimation function to change the chatbot's response style according to the emotions the user feels during the chat, thereby realizing a more friendly dialogue. For example, if the user is feeling stressed, the emotion estimation function can be used to change the chatbot's response style and engage in a more friendly dialogue. For example, gentle language or encouraging messages can be added. Also, if the user is emotionally exhausted, the dialogue can be conducted in a relaxed tone. Furthermore, if the user is expressing positive emotions, the dialogue can be conducted in a bright tone. In this way, a more friendly dialogue can be realized by providing a response style according to the user's emotions.

[0092] The photo analysis unit uses image recognition technology to analyze photos and identify the material and texture of a product, allowing it to suggest products that match the user's preferences. For example, it analyzes a photo of a bag uploaded by a user and uses image recognition technology to identify the material and texture. For example, it identifies materials such as leather and canvas and suggests bags made of the same material. It also analyzes a photo of shoes uploaded by a user and identifies materials such as leather and suede. It also analyzes a photo of a jacket uploaded by a user and identifies materials such as wool and cotton. This allows it to identify the material and texture of a product and suggest products that match the user's preferences.

[0093] The photo analysis unit analyzes background information contained in photos and can recommend products suitable for the user's lifestyle and environment. For example, it analyzes the background information of photos uploaded by the user and suggests products suitable for the lifestyle. For example, if the photo has an outdoor background, it suggests outdoor equipment. It also analyzes the style of furniture and interior decor shown in photos uploaded by the user and suggests products in the same style. It also analyzes the scenery and locations shown in photos uploaded by the user and suggests products suitable for that location. In this way, by analyzing background information, it is possible to suggest products suitable for the user's lifestyle and environment.

[0094] The photo analysis unit can use the emotion estimation function to monitor the user's emotional response in real time and adjust the content of the suggestions when suggesting products based on the photo analysis results. For example, the emotional response when the user uploads a photo can be monitored in real time and the content of the suggestions can be adjusted. For example, if the user has a positive response, a product of the same style can be suggested. On the other hand, if the user has a negative response, a product of a different style can be suggested. Furthermore, if the user is emotionally exhausted, a product with a relaxed style can be suggested. In this way, the content of the suggestions can be adjusted based on the user's emotional response, making it possible to suggest more appropriate products.

[0095] The information collection unit compares the information collected by the generation AI with the user's past purchase history and browsing history to make more accurate product suggestions. For example, the generation AI collects online reviews and ratings, compares them with the user's past purchase history, and suggests products of the same brand or category. For example, it suggests new products from a brand that the user has previously purchased. It also suggests products from the same category based on reviews and ratings of products the user has previously viewed. It also suggests new products from the same brand based on ratings of products the user has previously purchased. This enables more accurate product suggestions based on the user's past purchase history and browsing history.

[0096] The information collection unit can use the emotion estimation function to analyze the emotional tone of the collected reviews and ratings, and prioritize suggesting products with many positive reviews. For example, the generation AI can analyze the emotional tone of the collected reviews and ratings, and prioritize suggesting products with many positive reviews. For example, products with high emotion scores can be displayed at the top of the list. The generation AI can also analyze the reviews and ratings of products that the user is considering purchasing, and suggest products with many positive emotions. Furthermore, the generation AI can analyze the emotional tone of the reviews and ratings, and display products with many positive emotions at the top of the list. This can improve user satisfaction by prioritizing the suggestion of products with many positive reviews.

[0097] The recommendation unit can provide a comparison function for recommended products, allowing users to easily compare multiple products. For example, the generation AI can provide a comparison function for products recommended by the generation AI, allowing users to easily compare multiple products. For example, it can display a list of product features, prices, and ratings. It can also provide an interface for users to compare the features, prices, and ratings of products they are considering purchasing. It can also display a list of the features, prices, and ratings of products recommended by the generation AI, allowing users to easily compare them. This makes it easier for users to make a selection by making it easier to compare multiple products.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The chat input unit accepts questions and requests from users in chat format. For example, a user can input a specific request such as, "I want a red bag within a budget of 10,000 yen." Step 2: The photo analysis unit analyzes the photo based on the request received from the chat input unit. For example, a user can upload a photo of a product they found in a magazine or on the internet, and the generation AI will analyze the photo to extract the product's features and style. Step 3: The information collection unit collects information from the internet based on the information analyzed by the photo analysis unit. For example, the generation AI collects and analyzes information such as online reviews, ratings, prices, and user styles. Step 4: The recommendation unit recommends optimal products based on the information collected by the information collection unit. For example, the generation AI presents multiple candidate products based on the user's requests and information extracted from photos. Step 5: The purchase support department supports the purchase process for the products recommended by the recommendation department. For example, the generation AI provides guidance when the user enters the necessary information, allowing the user to complete the registration process smoothly.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a chat input unit that accepts questions and requests from users in chat format; a photo analysis unit that analyzes a photo based on the request received by the chat input unit; an information collection unit that collects information on the Internet based on the information analyzed by the photo analysis unit; a recommendation unit that recommends optimal products based on the information collected by the information collection unit; a purchase support unit that supports the purchase procedure of the product recommended by the recommendation unit. A system characterized by:

2. The chat input unit Chat-style questions and requests can also be entered via voice, and the user's requests are analyzed using voice recognition technology.

2. The system of claim 1.

3. The photo analysis unit In analyzing photos, image recognition technology is used to identify the material and texture of the product, and then products that match the user's preferences are suggested.

2. The system of claim 1.

4. The information collecting unit Based on the information collected by the generation AI, it compares it with the user's past purchase history and browsing history to make more accurate product suggestions.

2. The system of claim 1.

5. The recommendation unit The recommendation algorithm incorporates the user's past purchase and browsing history to provide more personalized product suggestions.

2. The system of claim 1.

6. The purchasing support department Monitor the user's emotions during the purchase process, and if the user feels stressed, make suggestions to simplify the process.

2. The system of claim 1.

7. The chat input unit During the chat, the generation AI estimates the user's emotions in real time, and if the user feels stressed, it switches to a simpler question format.

2. The system of claim 1.

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