system

Abstract

An object of a system according to an embodiment is to enable a user to efficiently find a product that the user wants.SOLUTION: A system includes a feature registration part, a retrieval part, and a pickup part. The feature registration unit registers a feature of what the user wants. The search unit searches for a product from an EC site based on the feature registered by the feature registration unit. The pickup section selects an optimum commodity from the commodities retrieved by the retrieval section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult for users to efficiently find the products they want.

[0005] The system according to the embodiment aims to enable users to efficiently find products they want. [Means for solving the problem]

[0006] The system according to the embodiment includes a feature registration unit, a search unit, and a pick-up unit. The feature registration unit registers the features of items desired by a user. The search unit searches for products on an e-commerce site based on the features registered by the feature registration unit. The pick-up unit selects the most suitable product from the products searched by the search unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to efficiently find products they want. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A recommendation system according to an embodiment of the present invention is a system that, when a user simply tells it what they want, finds out the product and periodically presents it to them, thereby enabling the user to find and purchase what they really want.

[0029] A recommendation system according to an embodiment includes a feature registration unit, a search unit, and a pick-up unit. The feature registration unit registers the features of items desired by a user. For example, a user can input information such as the product name, features, price range, and color. The search unit searches for products from e-commerce sites based on the features registered by the feature registration unit. For example, the generation AI searches for similar products from various e-commerce sites, such as official websites, Rakuten, Amazon, Mercari, and department stores. The pick-up unit selects the most suitable product from the products searched by the search unit. For example, the generation AI determines differences between Japanese and English names, brown and brown, medium and regular sizes, etc., and selects the most suitable product. This allows the recommendation system according to an embodiment to easily find what a user wants.

[0030] The feature registration unit accepts voice input, and the generation AI can analyze the voice data and convert it into text. The feature registration unit allows users to register the features of the item they want by voice, for example, using a smartphone or microphone. For example, if a user voice-inputs "red shoes, price range 5,000 to 10,000 yen, size M," the generation AI analyzes the voice data and converts it into text data. This allows users to easily register features by voice input.

[0031] The feature registration unit automatically imports past purchase history, and the generation AI can predict the features of desired items based on that data. For example, the feature registration unit automatically imports a user's past purchase history, and the generation AI analyzes that data to predict the features of desired items. For example, based on data on red shoes purchased in the past, the unit predicts that the user is looking for red shoes again. This makes it possible to predict the user's preferences based on past purchase history.

[0032] The feature registration unit allows users to upload an image and automatically extract the desired features from that image for the generative AI. For example, the feature registration unit allows users to upload an image taken with a smartphone or camera, and the generative AI analyzes the image to extract the desired features. For example, if an image of red shoes is uploaded, the generative AI will automatically extract features such as color, shape, and size. This allows features to be extracted automatically from images.

[0033] The feature registration unit analyzes the content of posts shared on SNS, and the generation AI can infer the characteristics of the desired item from that content. For example, if a user posts "I want red shoes," the generation AI will perform a search based on that information. This allows the generation AI to infer the characteristics of the desired item from the content of the SNS post.

[0034] The generation AI can use the API of each e-commerce site to obtain data in real time and perform searches based on the latest information. The generation AI can, for example, use the API of each e-commerce site to obtain product data in real time. For example, it can obtain the latest inventory and price information through the API of Rakuten or Amazon. This allows it to perform searches based on the latest information.

[0035] The generation AI can analyze the reviews and ratings of each e-commerce site and prioritize searching for highly reliable products. The generation AI can, for example, analyze the reviews and rating data of each e-commerce site and prioritize searching for highly reliable products. For example, it can evaluate reliability based on the number of reviews and rating scores. This allows it to prioritize searching for highly reliable products.

[0036] The generation AI can also search e-commerce sites in different languages ​​to provide international product information.The generation AI can also search e-commerce sites in different languages ​​to provide international product information.For example, it can obtain product information from e-commerce sites in English and Chinese.This makes it possible to provide international product information.

[0037] The generation AI can use the user's location information to include area-specific e-commerce sites and store information in the search results. For example, if the user is in Tokyo, the generation AI can display area-specific e-commerce sites and store information. This allows the user to provide area-specific information.

[0038] The generation AI can analyze detailed product information and select the product that best suits the user's registered information. For example, the generation AI can analyze detailed product information and select the product that best suits the user's registered information. For example, it can select a product that meets the user's needs based on information such as materials, manufacturer, and usage. This allows the AI ​​to analyze detailed information and select the optimal product.

[0039] The generation AI can analyze product images and select the visually most suitable product based on the user's registered information. For example, the generation AI can analyze product images and select the visually most suitable product based on the user's registered information. For example, it can select products based on visual features such as color, design, and shape. This allows it to select the visually most suitable product.

[0040] The generation AI can analyze the user's past purchase history and prioritize pick up products similar to those purchased in the past. For example, the generation AI can analyze the user's past purchase history and prioritize pick up products similar to those purchased in the past. For example, it can prioritize displaying products of the same brand or design as the red shoes purchased in the past. This allows similar products to be prioritized based on the user's past purchase history.

[0041] Generative AI can analyze a user's social media activity and pick out products that the user is likely to be interested in. Generative AI can, for example, analyze a user's social media activity and pick out products that the user is likely to be interested in. For example, if a user posts, "I want red shoes," the generative AI will perform a search based on that information. This allows it to pick out products that the user is likely to be interested in based on social media activity.

[0042] The generation AI can analyze a user's purchase history and browsing history to recommend related products. For example, the generation AI can analyze a user's purchase history and recommend related products. For example, for a user who previously purchased red shoes, it can suggest related products such as a red bag or a red hat. This makes it possible to recommend related products based on purchase history and browsing history.

[0043] Generative AI can analyze product combination patterns and recommend related products that the user is likely to be interested in. For example, generative AI can analyze product combination patterns and recommend related products that the user is likely to be interested in. For example, it can suggest a combination of red shoes and a red bag. This makes it possible to recommend related products based on product combination patterns.

[0044] Generative AI can analyze a user's social media activity and recommend related products that the user may be interested in. Generative AI can, for example, analyze a user's social media activity and recommend related products that the user may be interested in. For example, if a user posts, "I want red shoes," the generative AI will suggest related products based on that information. This makes it possible to recommend related products that the user may be interested in based on social media activity.

[0045] Generative AI can recommend new related products by combining products from different categories. For example, generative AI can recommend new related products by combining products from different categories. For example, it can recommend a combination of red shoes and a red bag. This makes it possible to recommend new related products by combining products from different categories.

[0046] The generation AI can analyze the user's favorite registration data, learn the user's preferences, and improve the accuracy of recommendations. For example, if a user registers "red shoes" as a favorite, the generation AI can pick out "red shoes" that are even closer based on that information. This makes it possible to improve the accuracy of recommendations based on the favorite registration data.

[0047] The generation AI can recommend similar products based on the user's favorite registration data. For example, if a user registers "red shoes" as a favorite, the generation AI can pick out even more similar "red shoes" based on that information. This allows the AI ​​to recommend similar products based on the favorite registration data.

[0048] The generation AI can recommend products in different categories based on the user's favorite registration data. For example, if a user registers "red shoes" as a favorite, the generation AI can recommend red bags and red hats based on that information. This makes it possible to recommend products in different categories based on the favorite registration data.

[0049] The generation AI can analyze the preferences of other users based on the user's favorite registration data and make recommendations to users who share the same preferences. For example, the generation AI can analyze the preferences of other users based on the user's favorite registration data and make recommendations to users who share the same preferences. For example, to a user who has registered the same "red shoes" as a favorite, the generation AI can recommend products that other users who share the same preferences like. This allows recommendations to be made to users who share the same preferences based on the favorite registration data.

[0050] The generation AI can analyze user click data and prioritize displaying products with high click rates. The generation AI can, for example, analyze user click data and prioritize displaying products with high click rates. For example, it can prioritize displaying red shoes that many users have clicked on in the past. This allows products with high click rates to be prioritized.

[0051] The generation AI can improve recommendation accuracy by learning user preferences based on user click data. For example, if a user clicks on "red shoes," the generation AI can improve recommendation accuracy by learning user preferences based on user click data. For example, if a user clicks on "red shoes," the AI ​​can pick out "red shoes" that are even closer based on that information. This makes it possible to improve recommendation accuracy based on click data.

[0052] The generation AI can recommend products in different categories based on the user's click data. For example, if a user clicks on "red shoes," the generation AI can recommend products in different categories based on that information. This makes it possible to recommend products in different categories based on click data.

[0053] The generation AI can analyze the preferences of other users based on the user's click data and make recommendations to users with common preferences. For example, the generation AI can analyze the preferences of other users based on the user's click data and make recommendations to users with common preferences. For example, to a user who clicked on the same "red shoes" search, it can recommend products that other users with common preferences like. This allows recommendations to be made to users with common preferences based on click data.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] Recommendation systems can also obtain a user's health data and suggest products based on their health condition. For example, based on data obtained from the user's fitness tracker, fitness-related products can be suggested to users who are not getting enough exercise. Sleep data can also be analyzed to suggest sleep aids to users with poor sleep quality. Furthermore, based on dietary records, it can also suggest health foods to users with an unbalanced nutritional intake. This makes it possible to suggest products based on the user's health condition.

[0056] Recommendation systems can also be equipped with a function that allows users to register their hobbies and interests. For example, if a user registers hobbies such as music, movies, or sports, the system can suggest products related to those hobbies. Also, if a user has a preference for a particular brand or designer, the system can suggest related products based on that information. Furthermore, if a user registers a travel destination or place they want to visit, the system can suggest products and services related to that area. This makes it possible to suggest products that match the user's hobbies and interests.

[0057] Recommendation systems can also be equipped with incentive functions to further increase users' motivation to purchase. For example, a system can be introduced where users can accumulate points when they purchase a specific product, and these points can be used for their next purchase. It is also possible to offer free shipping or discount coupons for purchases over a certain amount. It is also possible to introduce a system whereby when a user refers a friend, benefits are offered to both the referrer and the person referred. This can increase users' motivation to purchase.

[0058] Recommendation systems can also suggest products based on the user's life events. For example, if the user is about to get married, wedding-related products can be suggested. If the user is planning to move, furniture and home appliances necessary for the new home can be suggested. Furthermore, if the user is about to give birth, baby products can be suggested. This makes it possible to suggest products according to the user's life events.

[0059] Recommendation systems can also suggest products according to the season or trends based on the user's purchasing history. For example, they can suggest products recommended for this summer based on products purchased in the summer in the past. They can also analyze current fashion trends and suggest trendy products that match the user's preferences. They can also suggest products that match seasonal events (Christmas, Halloween, etc.). This makes it possible to suggest products according to the season and trends.

[0060] Recommendation systems can also suggest subscription services based on a user's purchasing history. For example, if a user has frequently purchased a particular brand of product in the past, they can suggest that brand's subscription service. Also, if a user regularly purchases consumable goods, they can suggest a regular purchase service. They can even suggest customizable subscription boxes tailored to the user's preferences. This makes it possible to suggest subscription services based on a user's purchasing history.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The feature registration unit registers the features of the item the user wants. For example, the user can enter information such as the product name, features, price range, and color. Step 2: The search unit searches for products from e-commerce sites based on the features registered by the feature registration unit. For example, the generation AI searches for similar products from various e-commerce sites, such as the official website, Rakuten, Amazon, Mercari, and department stores. Step 3: The picking section selects the most suitable product from the products found by the search section. For example, the generation AI distinguishes between Japanese and English names, brown and brown, medium and regular sizes, etc., and selects the most suitable product.

[0063] (Example 2) A recommendation system according to an embodiment of the present invention is a system that, when a user simply tells it what they want, finds out the product and periodically presents it to them, thereby enabling the user to find and purchase what they really want.

[0064] A recommendation system according to an embodiment includes a feature registration unit, a search unit, and a pick-up unit. The feature registration unit registers the features of items desired by a user. For example, a user can input information such as the product name, features, price range, and color. The search unit searches for products from e-commerce sites based on the features registered by the feature registration unit. For example, the generation AI searches for similar products from various e-commerce sites, such as official websites, Rakuten, Amazon, Mercari, and department stores. The pick-up unit selects the most suitable product from the products searched by the search unit. For example, the generation AI determines differences between Japanese and English names, brown and brown, medium and regular sizes, etc., and selects the most suitable product. This allows the recommendation system according to an embodiment to easily find what a user wants.

[0065] The feature registration unit accepts voice input, and the generation AI can analyze the voice data and convert it into text. The feature registration unit allows users to register the features of the item they want by voice, for example, using a smartphone or microphone. For example, if a user voice-inputs "red shoes, price range 5,000 to 10,000 yen, size M," the generation AI analyzes the voice data and converts it into text data. This allows users to easily register features by voice input.

[0066] The feature registration unit automatically imports past purchase history, and the generation AI can predict the features of desired items based on that data. For example, the feature registration unit automatically imports a user's past purchase history, and the generation AI analyzes that data to predict the features of desired items. For example, based on data on red shoes purchased in the past, the unit predicts that the user is looking for red shoes again. This makes it possible to predict the user's preferences based on past purchase history.

[0067] The feature registration unit uses the emotion estimation function to analyze the emotions expressed when the user enters what they want and can make suggestions to elicit positive emotions. For example, when the user enters what they want, the feature registration unit allows the generation AI to analyze their facial expressions and tone of voice to estimate their emotions. For example, if the user is smiling when entering their desired item, the generation AI will detect positive emotions and make suggestions to reinforce those emotions. This allows the system to make positive suggestions based on the user's emotions.

[0068] The feature registration unit allows users to upload an image and automatically extract the desired features from that image for the generative AI. For example, the feature registration unit allows users to upload an image taken with a smartphone or camera, and the generative AI analyzes the image to extract the desired features. For example, if an image of red shoes is uploaded, the generative AI will automatically extract features such as color, shape, and size. This allows features to be extracted automatically from images.

[0069] The feature registration unit analyzes the content of posts shared on SNS, and the generation AI can infer the characteristics of the desired item from that content. For example, if a user posts "I want red shoes," the generation AI will perform a search based on that information. This allows the generation AI to infer the characteristics of the desired item from the content of the SNS post.

[0070] The feature registration unit uses the emotion estimation function to analyze the emotions of users in real time when they input what they want, and can optimize the input content. For example, when a user inputs what they want, the feature registration unit uses the generation AI to analyze facial expressions and voice tone in real time to estimate emotions. For example, if the user is inputting with a smile, the generation AI detects positive emotions and makes suggestions to reinforce those emotions. This allows the input content to be optimized based on the user's emotions.

[0071] The generation AI can use the API of each e-commerce site to obtain data in real time and perform searches based on the latest information. The generation AI can, for example, use the API of each e-commerce site to obtain product data in real time. For example, it can obtain the latest inventory and price information through the API of Rakuten or Amazon. This allows it to perform searches based on the latest information.

[0072] The generation AI can analyze the reviews and ratings of each e-commerce site and prioritize searching for highly reliable products. The generation AI can, for example, analyze the reviews and rating data of each e-commerce site and prioritize searching for highly reliable products. For example, it can evaluate reliability based on the number of reviews and rating scores. This allows it to prioritize searching for highly reliable products.

[0073] Using its emotion estimation function, the generation AI can filter search results based on the user's emotions and prioritize products that evoke positive emotions. For example, the generation AI can analyze the user's emotions in real time and filter search results based on those emotions. For example, if the user has positive emotions, it will prioritize products that reinforce those emotions. This allows positive products to be prioritized based on the user's emotions.

[0074] The generation AI can also search e-commerce sites in different languages ​​to provide international product information.The generation AI can also search e-commerce sites in different languages ​​to provide international product information.For example, it can obtain product information from e-commerce sites in English and Chinese.This makes it possible to provide international product information.

[0075] The generation AI can use the user's location information to include area-specific e-commerce sites and store information in the search results. For example, if the user is in Tokyo, the generation AI can display area-specific e-commerce sites and store information. This allows the user to provide area-specific information.

[0076] Using its emotion estimation function, the generation AI can customize search results based on the user's emotions and prioritize the display of products that interest the user. For example, the generation AI can analyze the user's emotions in real time and customize search results based on those emotions. For example, if the user has positive emotions, products that reinforce those emotions will be prioritized. This allows products that interest the user to be displayed with priority based on their emotions.

[0077] The generation AI can analyze detailed product information and select the product that best suits the user's registered information. For example, the generation AI can analyze detailed product information and select the product that best suits the user's registered information. For example, it can select a product that meets the user's needs based on information such as materials, manufacturer, and usage. This allows the AI ​​to analyze detailed information and select the optimal product.

[0078] The generation AI can analyze product images and select the visually most suitable product based on the user's registered information. For example, the generation AI can analyze product images and select the visually most suitable product based on the user's registered information. For example, it can select products based on visual features such as color, design, and shape. This allows it to select the visually most suitable product.

[0079] Using its emotion estimation function, the generative AI can adjust product selection criteria based on the user's emotions and select products that elicit positive emotions. For example, the generative AI can analyze the user's emotions in real time and adjust the product selection criteria based on those emotions. For example, if the user has positive emotions, it will select products that reinforce those emotions. This allows it to select positive products based on the user's emotions.

[0080] The generation AI can analyze the user's past purchase history and prioritize pick up products similar to those purchased in the past. For example, the generation AI can analyze the user's past purchase history and prioritize pick up products similar to those purchased in the past. For example, it can prioritize displaying products of the same brand or design as the red shoes purchased in the past. This allows similar products to be prioritized based on the user's past purchase history.

[0081] Generative AI can analyze a user's social media activity and pick out products that the user is likely to be interested in. Generative AI can, for example, analyze a user's social media activity and pick out products that the user is likely to be interested in. For example, if a user posts, "I want red shoes," the generative AI will perform a search based on that information. This allows it to pick out products that the user is likely to be interested in based on social media activity.

[0082] Using its emotion estimation function, the generative AI can adjust product selection criteria in real time based on the user's emotions and pick out the most suitable products. For example, the generative AI can analyze the user's emotions in real time and adjust the product selection criteria based on those emotions. For example, if the user has positive emotions, it will select products that reinforce those emotions. This allows it to pick out the most suitable products based on the user's emotions.

[0083] The generation AI can analyze a user's purchase history and browsing history to recommend related products. For example, the generation AI can analyze a user's purchase history and recommend related products. For example, for a user who previously purchased red shoes, it can suggest related products such as a red bag or a red hat. This makes it possible to recommend related products based on purchase history and browsing history.

[0084] Generative AI can analyze product combination patterns and recommend related products that the user is likely to be interested in. For example, generative AI can analyze product combination patterns and recommend related products that the user is likely to be interested in. For example, it can suggest a combination of red shoes and a red bag. This makes it possible to recommend related products based on product combination patterns.

[0085] Using its emotion estimation function, the generative AI can adjust related product recommendations based on the user's emotions and suggest products that elicit positive emotions. For example, the generative AI can analyze the user's emotions in real time and adjust related product recommendations based on those emotions. For example, if the user has positive emotions, it can suggest products that reinforce those emotions. This allows it to suggest positive related products based on the user's emotions.

[0086] Generative AI can analyze a user's social media activity and recommend related products that the user may be interested in. Generative AI can, for example, analyze a user's social media activity and recommend related products that the user may be interested in. For example, if a user posts, "I want red shoes," the generative AI will suggest related products based on that information. This makes it possible to recommend related products that the user may be interested in based on social media activity.

[0087] Generative AI can recommend new related products by combining products from different categories. For example, generative AI can recommend new related products by combining products from different categories. For example, it can recommend a combination of red shoes and a red bag. This makes it possible to recommend new related products by combining products from different categories.

[0088] Using its emotion estimation function, the generative AI can adjust related product recommendations in real time based on the user's emotions and suggest optimal products. For example, the generative AI can analyze the user's emotions in real time and adjust related product recommendations based on those emotions. For example, if the user has positive emotions, it can suggest products that reinforce those emotions. This allows it to suggest optimal related products based on the user's emotions.

[0089] The generation AI can analyze the user's favorite registration data, learn the user's preferences, and improve the accuracy of recommendations. For example, if a user registers "red shoes" as a favorite, the generation AI can pick out "red shoes" that are even closer based on that information. This makes it possible to improve the accuracy of recommendations based on the favorite registration data.

[0090] The generation AI can recommend similar products based on the user's favorite registration data. For example, if a user registers "red shoes" as a favorite, the generation AI can pick out even more similar "red shoes" based on that information. This allows the AI ​​to recommend similar products based on the favorite registration data.

[0091] Using its emotion estimation function, the generation AI can analyze the emotions users had when they registered favorites, improving the accuracy of product recommendations that elicit positive emotions. For example, the generation AI can analyze the emotions users had when they registered favorites in real time, and improve recommendation accuracy based on those emotions. For example, if a user has positive emotions, it can recommend products that reinforce those emotions. This makes it possible to improve recommendation accuracy based on the emotions users had when they registered favorites.

[0092] The generation AI can recommend products in different categories based on the user's favorite registration data. For example, if a user registers "red shoes" as a favorite, the generation AI can recommend red bags and red hats based on that information. This makes it possible to recommend products in different categories based on the favorite registration data.

[0093] The generation AI can analyze the preferences of other users based on the user's favorite registration data and make recommendations to users who share the same preferences. For example, the generation AI can analyze the preferences of other users based on the user's favorite registration data and make recommendations to users who share the same preferences. For example, to a user who has registered the same "red shoes" as a favorite, the generation AI can recommend products that other users who share the same preferences like. This allows recommendations to be made to users who share the same preferences based on the favorite registration data.

[0094] Using its emotion estimation function, the generation AI can analyze the emotions of users when they register favorites in real time and recommend optimal products. For example, the generation AI can analyze the emotions of users when they register favorites in real time and recommend optimal products based on those emotions. For example, if the user has positive emotions, it can recommend products that reinforce those emotions. This makes it possible to recommend optimal products based on the emotions of users when they register favorites.

[0095] The generation AI can analyze user click data and prioritize displaying products with high click rates. The generation AI can, for example, analyze user click data and prioritize displaying products with high click rates. For example, it can prioritize displaying red shoes that many users have clicked on in the past. This allows products with high click rates to be prioritized.

[0096] The generation AI can improve recommendation accuracy by learning user preferences based on user click data. For example, if a user clicks on "red shoes," the generation AI can improve recommendation accuracy by learning user preferences based on user click data. For example, if a user clicks on "red shoes," the AI ​​can pick out "red shoes" that are even closer based on that information. This makes it possible to improve recommendation accuracy based on click data.

[0097] Using its emotion estimation function, the generative AI can analyze the user's emotions at the time of click and optimize the display of products that elicit positive emotions. For example, the generative AI can analyze the user's emotions at the time of click in real time and optimize the display of products based on those emotions. For example, if the user has positive emotions, it will display products that reinforce those emotions. This makes it possible to optimize the display of positive products based on the emotions at the time of click.

[0098] The generation AI can recommend products in different categories based on the user's click data. For example, if a user clicks on "red shoes," the generation AI can recommend products in different categories based on that information. This makes it possible to recommend products in different categories based on click data.

[0099] The generation AI can analyze the preferences of other users based on the user's click data and make recommendations to users with common preferences. For example, the generation AI can analyze the preferences of other users based on the user's click data and make recommendations to users with common preferences. For example, to a user who clicked on the same "red shoes" search, it can recommend products that other users with common preferences like. This allows recommendations to be made to users with common preferences based on click data.

[0100] Using its emotion estimation function, the generation AI can analyze the user's emotions at the time of click in real time and recommend the most suitable product. For example, the generation AI can analyze the user's emotions at the time of click in real time and recommend the most suitable product based on those emotions. For example, if the user has positive emotions, it will recommend products that reinforce those emotions. This makes it possible to recommend the most suitable product based on the emotions at the time of click.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] Recommendation systems can also obtain a user's health data and suggest products based on their health condition. For example, based on data obtained from the user's fitness tracker, fitness-related products can be suggested to users who are not getting enough exercise. Sleep data can also be analyzed to suggest sleep aids to users with poor sleep quality. Furthermore, based on dietary records, it can also suggest health foods to users with an unbalanced nutritional intake. This makes it possible to suggest products based on the user's health condition.

[0103] Recommendation systems can also be equipped with a function that allows users to register their hobbies and interests. For example, if a user registers hobbies such as music, movies, or sports, the system can suggest products related to those hobbies. Also, if a user has a preference for a particular brand or designer, the system can suggest related products based on that information. Furthermore, if a user registers a travel destination or place they want to visit, the system can suggest products and services related to that area. This makes it possible to suggest products that match the user's hobbies and interests.

[0104] Recommendation systems can also be equipped with incentive functions to further increase users' motivation to purchase. For example, a system can be introduced where users can accumulate points when they purchase a specific product, and these points can be used for their next purchase. It is also possible to offer free shipping or discount coupons for purchases over a certain amount. It is also possible to introduce a system whereby when a user refers a friend, benefits are offered to both the referrer and the person referred. This can increase users' motivation to purchase.

[0105] The recommendation system can also estimate the user's emotions and suggest products based on the estimated emotions. For example, if the user is feeling stressed, it can suggest products that have a relaxing effect. If the user is excited, it can also suggest products that will further increase that excitement. Furthermore, if the user is sad, it can also suggest positive products to lift the user's spirits. This makes it possible to suggest products that correspond to the user's emotions.

[0106] Recommendation systems can also suggest products based on the user's life events. For example, if the user is about to get married, wedding-related products can be suggested. If the user is planning to move, furniture and home appliances necessary for the new home can be suggested. Furthermore, if the user is about to give birth, baby products can be suggested. This makes it possible to suggest products according to the user's life events.

[0107] Recommendation systems can also estimate the user's emotions and adjust the display order of products based on the estimated emotions. For example, if a user has positive emotions, products that reinforce those emotions will be displayed at the top. Conversely, if a user has negative emotions, products that alleviate those emotions can be displayed at the top. Furthermore, it is possible to customize product descriptions and images according to the user's emotions. This makes it possible to display products optimally according to the user's emotions.

[0108] Recommendation systems can also suggest products according to the season or trends based on the user's purchasing history. For example, they can suggest products recommended for this summer based on products purchased in the summer in the past. They can also analyze current fashion trends and suggest trendy products that match the user's preferences. They can also suggest products that match seasonal events (Christmas, Halloween, etc.). This makes it possible to suggest products according to the season and trends.

[0109] The recommendation system can also estimate the user's emotions and customize product descriptions based on the estimated emotions. For example, if the user has positive emotions, it can use positive expressions that reinforce those emotions. On the other hand, if the user has negative emotions, it can use expressions that alleviate those emotions. It can also change product images and videos according to the user's emotions. This makes it possible to provide optimal product descriptions that match the user's emotions.

[0110] Recommendation systems can also suggest subscription services based on a user's purchasing history. For example, if a user has frequently purchased a particular brand of product in the past, they can suggest that brand's subscription service. Also, if a user regularly purchases consumable goods, they can suggest a regular purchase service. They can even suggest customizable subscription boxes tailored to the user's preferences. This makes it possible to suggest subscription services based on a user's purchasing history.

[0111] The recommendation system can further estimate the user's emotions and adjust the frequency of product recommendations based on the estimated emotions. For example, if the user has positive emotions, the recommendation frequency can be increased to maintain that emotion. On the other hand, if the user has negative emotions, the recommendation frequency can be decreased to alleviate those emotions. Furthermore, it is also possible to adjust the timing of recommendations according to the user's emotions. This makes it possible to optimize the recommendation frequency according to the user's emotions.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The feature registration unit registers the features of the item the user wants. For example, the user can enter information such as the product name, features, price range, and color. Step 2: The search unit searches for products from e-commerce sites based on the features registered by the feature registration unit. For example, the generation AI searches for similar products from various e-commerce sites, such as the official website, Rakuten, Amazon, Mercari, and department stores. Step 3: The picking section selects the most suitable product from the products found by the search section. For example, the generation AI distinguishes between Japanese and English names, brown and brown, medium and regular sizes, etc., and selects the most suitable product.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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, in order to avoid confusion and to 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.

[0180] 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]

[0181] 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 feature registration unit for registering features of items desired by users; a search unit that searches for products from an EC site based on the features registered by the feature registration unit; a pick-up unit that selects an optimal product from the products searched by the search unit. A system characterized by:

2. The feature registration unit Accepts voice input, and the generative AI analyzes the voice data and converts it into text 2. The system of claim 1.

3. The generating AI is Use the API of each e-commerce site to obtain data in real time and perform searches based on the latest information.

2. The system of claim 1.

4. The generating AI is Analyze product details and select the product that best suits the user's registration details 2. The system of claim 1.

5. The generating AI is Analyze users' purchase and browsing history to recommend related products 2. The system of claim 1.

6. The feature registration unit Analyzes emotions when users input what they want and makes suggestions to elicit positive emotions 2. The system of claim 1.

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