System

Abstract

An object of a system according to an embodiment is to propose an optimal product based on a user's request and a reference image.SOLUTION: A system according to an embodiment includes a request reception unit, an image analysis unit, a product search unit, and a proposal unit. The request reception unit receives a user's request and a reference image. The image analysis unit analyzes the reference image received by the request reception unit. The product search unit searches for a product based on the information obtained by the request reception unit and the image analysis unit. The suggestion unit suggests the product retrieved by the product retrieval unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult for users to search the inventory of multiple select shops at once and find products that meet their needs.

[0005] The system according to the embodiment aims to propose optimal products based on user requests and reference images. [Means for solving the problem]

[0006] The system according to the embodiment includes a request receiving unit, an image analysis unit, a product search unit, and a suggestion unit. The request receiving unit receives user requests and reference images. The image analysis unit analyzes the reference images received by the request receiving unit. The product search unit searches for products based on information obtained by the request receiving unit and the image analysis unit. The suggestion unit suggests products searched for by the product search unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal products based on user requests and reference images. [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 product recommendation system according to an embodiment of the present invention is a system in which a generation AI searches the inventory of multiple select shops in one go based on a user's request and reference images, and suggests the most suitable products. This allows the product recommendation system to easily purchase items that suit the user, even if the user is unfamiliar with the brand or does not have to look at all of the websites.

[0029] A product recommendation system according to an embodiment includes a request receiving unit, an image analysis unit, a product search unit, and a suggestion unit. The request receiving unit receives user requests and reference images. For example, the user inputs specific requests, such as "I want a bag with this design," in text. Reference images can also be uploaded. This allows the user's specific needs to be communicated to the AI. The image analysis unit analyzes the reference images received by the request receiving unit. For example, the generation AI extracts design elements from the images to understand the user's preferences in detail. The product search unit searches for products based on the information obtained by the request receiving unit and the image analysis unit. For example, the generation AI searches the inventory of multiple select shops based on the user's request and finds products that match the criteria. The suggestion unit suggests products found by the product search unit to the user. For example, the generation AI selects items from the search results that best match the user's needs and suggests them to the user. This allows the product recommendation system according to an embodiment to suggest optimal products based on the user's request and reference images.

[0030] The image analysis unit performs image analysis on reference images to extract similar design features and understand the user's preferences in more detail. For example, the image analysis unit uses a generation AI to analyze reference images uploaded by the user and extract design elements within the image. For example, it analyzes features such as color, shape, and material to understand the user's preferences in detail. This allows for a detailed understanding of the user's preferences and more accurate suggestions to be made.

[0031] The request receiving unit automatically completes requests based on the user's past purchase history and browsing history, enabling more accurate searches. The request receiving unit, for example, analyzes the user's past purchase history, and the generation AI automatically completes the request. For example, the request is completed by inferring the user's preferences based on the design and brand of products purchased in the past. This completes requests based on the user's past behavioral history, improving search accuracy.

[0032] The request receiving unit can add a function that allows users to input requests by voice and convert them into text using voice recognition technology. For example, if a user inputs a request by voice, such as "I want a black leather tote bag," the generation AI converts the content into text and performs a search. This improves user convenience through voice input.

[0033] When a user inputs a request, the request receiving unit allows the generation AI to suggest related products in real time, improving the accuracy of the request. For example, when a user inputs a request, the request receiving unit allows the generation AI to suggest related products in real time. For example, if a user inputs "black leather tote bag," related products are automatically displayed. This improves the accuracy of the request through real-time suggestions.

[0034] The product search unit updates the inventory data of each select shop in real time, allowing searches to be performed based on the latest inventory information. For example, the generation AI updates the inventory data of each select shop in real time, allowing searches to be performed based on the latest inventory information. For example, the database is updated every time inventory changes, providing the latest information at all times. This allows searches to be performed based on the latest inventory information at all times through real-time updates.

[0035] The product search unit can combine multiple search algorithms to find the best product for a request. For example, the generation AI combines multiple search algorithms to find the best product for a user's request. For example, it combines algorithms such as text matching, image recognition, and natural language processing. By combining multiple search algorithms, search accuracy is improved.

[0036] The product search unit can respond to requests in different languages ​​and search for products from a global perspective. For example, the generation AI can respond to requests in different languages ​​and search for products from a global perspective. For example, it can accept requests in multiple languages ​​such as English, French, and Chinese. This allows it to respond to different languages ​​and search for products from a global perspective.

[0037] The product search unit can simultaneously search for related accessories and coordinating items in response to a request and suggest a total outfit. For example, if a generation AI requests a bag, the product search unit can simultaneously search for related accessories and coordinating items in response to a user request. For example, if a bag is requested, related shoes and accessories will also be suggested. This improves user satisfaction by suggesting a total outfit.

[0038] The suggestion unit evaluates items suggested by the generation AI based on the user's past feedback, and can improve the accuracy of the suggestions. For example, the suggestion unit evaluates items suggested by the generation AI based on the user's past feedback. For example, the suggestion unit analyzes the characteristics of items that the user has given high ratings to in the past, and improves the accuracy of the suggestions. In this way, the accuracy of the suggestions is improved based on the user's past feedback.

[0039] The suggestion unit can simultaneously provide detailed information such as price and delivery terms for items suggested by the generation AI, thereby increasing the user's desire to purchase. For example, the suggestion unit simultaneously provides detailed information such as price and delivery terms for items suggested by the generation AI. For example, it displays information such as the product price, delivery time, and shipping fee. In this way, providing detailed information increases the user's desire to purchase.

[0040] The suggestion unit can provide customization options based on the user's preferences for items suggested by the generation AI. For example, the suggestion unit provides customization options based on the user's preferences for items suggested by the generation AI. For example, it displays options that allow customization of the color, size, material, etc. of the product. This improves the accuracy of suggestions by providing customization options based on the user's preferences.

[0041] The suggestion unit can add a sharing function to the items suggested by the generation AI to incorporate the opinions of the user's friends and family. For example, the suggestion unit adds a sharing function to the items suggested by the generation AI to incorporate the opinions of the user's friends and family. For example, the suggestion unit provides a function that allows the suggested items to be shared on social media or a messaging app. This makes it possible to incorporate the opinions of the user's friends and family through the sharing function.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] When a user enters a request, the request receiving unit can analyze the user's past social media posts to understand preferences and trends and complete the request. For example, it can analyze the fashion items and brands that the user frequently posts about on Instagram or Twitter and complete the request based on that. This makes it possible to complete requests based on the user's social media activity, resulting in more accurate product suggestions.

[0044] The image analysis unit can extract design elements from reference images that are not only tailored to the user's preferences, but also to the season or event. For example, cool colors and materials are extracted from a summer image, while warm design elements are extracted from a winter image. It can also analyze design elements suitable for specific events (such as weddings and parties). This allows for suggestions tailored to the season or event.

[0045] When a user inputs a request, the request receiving unit can suggest products suited to local trends and climates based on the user's current location information. For example, if the user is in a cold region, cold weather items will be suggested, and if the user is in a warm region, light clothing items will be suggested. Products suited to local fashion events and festivals will also be suggested. This makes it possible to suggest products that meet local needs.

[0046] When a user inputs a request, the request receiving unit can suggest products based on the user's health condition and lifestyle. For example, it can suggest sportswear to a user who prioritizes fitness, and comfortable loungewear to a user who wants to relax. It can also select materials based on allergy information. This makes it possible to suggest products that suit the user's lifestyle.

[0047] The suggestion unit can prioritize eco-friendly products in response to user requests. For example, it can suggest products made from recycled materials or products with environmentally friendly manufacturing processes. It can also provide detailed information about eco-friendly products and their impact on the environment. This makes it possible to suggest eco-friendly products.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The request reception unit accepts user requests and reference images. For example, a user can enter a specific request in text, such as "I want a bag with this design." It is also possible to upload reference images. This allows the user's specific needs to be communicated to the AI. Step 2: The image analysis unit analyzes the reference image received by the request reception unit. For example, the generation AI extracts design elements from the image and gains a detailed understanding of the user's preferences. Step 3: The product search unit searches for products based on the information obtained by the request reception unit and image analysis unit. For example, the generation AI searches the inventory of multiple select shops based on the user's request and finds products that match the conditions. Step 4: The suggestion unit suggests products found by the product search unit to the user. For example, the generation AI selects items from the search results that best match the user's needs and suggests them to the user.

[0050] (Example 2) The product recommendation system according to an embodiment of the present invention is a system in which a generation AI searches the inventory of multiple select shops in one go based on a user's request and reference images, and suggests the most suitable products. This allows the product recommendation system to easily purchase items that suit the user, even if the user is unfamiliar with the brand or does not have to look at all of the websites.

[0051] A product recommendation system according to an embodiment includes a request receiving unit, an image analysis unit, a product search unit, and a suggestion unit. The request receiving unit receives user requests and reference images. For example, the user inputs specific requests, such as "I want a bag with this design," in text. Reference images can also be uploaded. This allows the user's specific needs to be communicated to the AI. The image analysis unit analyzes the reference images received by the request receiving unit. For example, the generation AI extracts design elements from the images to understand the user's preferences in detail. The product search unit searches for products based on the information obtained by the request receiving unit and the image analysis unit. For example, the generation AI searches the inventory of multiple select shops based on the user's request and finds products that match the criteria. The suggestion unit suggests products found by the product search unit to the user. For example, the generation AI selects items from the search results that best match the user's needs and suggests them to the user. This allows the product recommendation system according to an embodiment to suggest optimal products based on the user's request and reference images.

[0052] The image analysis unit performs image analysis on reference images to extract similar design features and understand the user's preferences in more detail. For example, the image analysis unit uses a generation AI to analyze reference images uploaded by the user and extract design elements within the image. For example, it analyzes features such as color, shape, and material to understand the user's preferences in detail. This allows for a detailed understanding of the user's preferences and more accurate suggestions to be made.

[0053] The request receiving unit automatically completes requests based on the user's past purchase history and browsing history, enabling more accurate searches. The request receiving unit, for example, analyzes the user's past purchase history, and the generation AI automatically completes the request. For example, the request is completed by inferring the user's preferences based on the design and brand of products purchased in the past. This completes requests based on the user's past behavioral history, improving search accuracy.

[0054] The request receiving unit can add a function that allows users to input requests by voice and convert them into text using voice recognition technology. For example, if a user inputs a request by voice, such as "I want a black leather tote bag," the generation AI converts the content into text and performs a search. This improves user convenience through voice input.

[0055] When a user inputs a request, the request receiving unit allows the generation AI to suggest related products in real time, improving the accuracy of the request. For example, when a user inputs a request, the request receiving unit allows the generation AI to suggest related products in real time. For example, if a user inputs "black leather tote bag," related products are automatically displayed. This improves the accuracy of the request through real-time suggestions.

[0056] The request receiving unit can use the emotion estimation function to analyze the emotion a user feels when inputting a request in real time and provide an interface for eliciting positive emotions. The request receiving unit, for example, uses the emotion estimation function to analyze the emotion a user feels when inputting a request in real time. For example, the request receiving unit analyzes the user's facial expression and voice and calculates an emotion score. This makes it possible to elicit positive emotions from the user through emotion analysis.

[0057] The product search unit updates the inventory data of each select shop in real time, allowing searches to be performed based on the latest inventory information. For example, the generation AI updates the inventory data of each select shop in real time, allowing searches to be performed based on the latest inventory information. For example, the database is updated every time inventory changes, providing the latest information at all times. This allows searches to be performed based on the latest inventory information at all times through real-time updates.

[0058] The product search unit can combine multiple search algorithms to find the best product for a request. For example, the generation AI combines multiple search algorithms to find the best product for a user's request. For example, it combines algorithms such as text matching, image recognition, and natural language processing. By combining multiple search algorithms, search accuracy is improved.

[0059] The product search unit can use the emotion estimation function to analyze the emotion regarding the request and preferentially search for emotionally positive products. The product search unit, for example, uses the emotion estimation function to analyze the emotion regarding the user's request. For example, the product search unit analyzes the facial expression and voice when the user inputs the request and calculates an emotion score. This allows the emotion analysis to preferentially suggest positive products to the user.

[0060] The product search unit can respond to requests in different languages ​​and search for products from a global perspective. For example, the generation AI can respond to requests in different languages ​​and search for products from a global perspective. For example, it can accept requests in multiple languages ​​such as English, French, and Chinese. This allows it to respond to different languages ​​and search for products from a global perspective.

[0061] The product search unit can simultaneously search for related accessories and coordinating items in response to a request and suggest a total outfit. For example, if a generation AI requests a bag, the product search unit can simultaneously search for related accessories and coordinating items in response to a user request. For example, if a bag is requested, related shoes and accessories will also be suggested. This improves user satisfaction by suggesting a total outfit.

[0062] The suggestion unit evaluates items suggested by the generation AI based on the user's past feedback, and can improve the accuracy of the suggestions. For example, the suggestion unit evaluates items suggested by the generation AI based on the user's past feedback. For example, the suggestion unit analyzes the characteristics of items that the user has given high ratings to in the past, and improves the accuracy of the suggestions. In this way, the accuracy of the suggestions is improved based on the user's past feedback.

[0063] The suggestion unit can simultaneously provide detailed information such as price and delivery terms for items suggested by the generation AI, thereby increasing the user's desire to purchase. For example, the suggestion unit simultaneously provides detailed information such as price and delivery terms for items suggested by the generation AI. For example, it displays information such as the product price, delivery time, and shipping fee. In this way, providing detailed information increases the user's desire to purchase.

[0064] The suggestion unit can use the emotion estimation function to analyze emotions toward the suggested item and make suggestions to elicit positive emotions. The suggestion unit, for example, uses the emotion estimation function to analyze the user's emotions toward the suggested item. For example, the suggestion unit analyzes the user's facial expressions and voice when looking at the item and calculates an emotion score. This allows for emotion analysis to make positive suggestions to the user.

[0065] The suggestion unit can provide customization options based on the user's preferences for items suggested by the generation AI. For example, the suggestion unit provides customization options based on the user's preferences for items suggested by the generation AI. For example, it displays options that allow customization of the color, size, material, etc. of the product. This improves the accuracy of suggestions by providing customization options based on the user's preferences.

[0066] The suggestion unit can add a sharing function to the items suggested by the generation AI to incorporate the opinions of the user's friends and family. For example, the suggestion unit adds a sharing function to the items suggested by the generation AI to incorporate the opinions of the user's friends and family. For example, the suggestion unit provides a function that allows the suggested items to be shared on social media or a messaging app. This makes it possible to incorporate the opinions of the user's friends and family through the sharing function.

[0067] The suggestion unit can use the emotion estimation function to analyze the user's emotion toward the suggested item in real time and provide an interface for eliciting positive emotions. The suggestion unit, for example, uses the emotion estimation function to analyze the user's emotion toward the suggested item in real time. For example, the suggestion unit analyzes the user's facial expression and voice and calculates an emotion score. This allows the emotion analysis to make positive suggestions to the user.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] When a user enters a request, the request receiving unit can analyze the user's past social media posts to understand preferences and trends and complete the request. For example, it can analyze the fashion items and brands that the user frequently posts about on Instagram or Twitter and complete the request based on that. This makes it possible to complete requests based on the user's social media activity, resulting in more accurate product suggestions.

[0070] The image analysis unit can extract design elements from reference images that are not only tailored to the user's preferences, but also to the season or event. For example, cool colors and materials are extracted from a summer image, while warm design elements are extracted from a winter image. It can also analyze design elements suitable for specific events (such as weddings and parties). This allows for suggestions tailored to the season or event.

[0071] When a user inputs a request, the request receiving unit can suggest products suited to local trends and climates based on the user's current location information. For example, if the user is in a cold region, cold weather items will be suggested, and if the user is in a warm region, light clothing items will be suggested. Products suited to local fashion events and festivals will also be suggested. This makes it possible to suggest products that meet local needs.

[0072] When a user inputs a request, the request receiving unit can suggest products based on the user's health condition and lifestyle. For example, it can suggest sportswear to a user who prioritizes fitness, and comfortable loungewear to a user who wants to relax. It can also select materials based on allergy information. This makes it possible to suggest products that suit the user's lifestyle.

[0073] When a user inputs a request, the request receiving unit can estimate the user's emotions and suggest relaxation items to reduce stress. For example, if the user is feeling stressed, the request receiving unit can suggest aroma products with a relaxing effect or comfortable loungewear. If the user is feeling positive, the request receiving unit can suggest fashionable items to further improve the user's mood. This makes it possible to suggest products that correspond to the user's emotions.

[0074] The product search unit can estimate the user's emotions and filter products based on the emotions. For example, if the user is feeling down, it will prioritize products with bright colors and designs to lift their spirits. On the other hand, if the user is excited, it will suggest products with calming designs. This makes it possible to suggest products according to the user's emotions.

[0075] The product search unit can estimate the user's emotions and provide options for customizing products based on the emotions. For example, if the user is happy, the unit can suggest customization options to further increase the user's joy. If the user is feeling anxious, the unit can suggest customization options to provide a sense of security. This allows customization according to the user's emotions.

[0076] The suggestion unit can estimate the user's emotions and change the presentation method of the suggested products based on the user's emotions. For example, if the user is excited, the unit can suggest products using dynamic animations and visual effects. On the other hand, if the user is relaxed, the unit can suggest products in a calm tone. This makes it possible to make a presentation that suits the user's emotions.

[0077] The suggestion unit can estimate the user's emotions and change the order of suggested products based on the user's emotions. For example, if the user is in a hurry, the most relevant product is suggested first. On the other hand, if the user is relaxed, products are suggested slowly. This makes it possible to suggest products according to the user's emotions.

[0078] The suggestion unit can prioritize eco-friendly products in response to user requests. For example, it can suggest products made from recycled materials or products with environmentally friendly manufacturing processes. It can also provide detailed information about eco-friendly products and their impact on the environment. This makes it possible to suggest eco-friendly products.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The request reception unit accepts user requests and reference images. For example, a user can enter a specific request in text, such as "I want a bag with this design." It is also possible to upload reference images. This allows the user's specific needs to be communicated to the AI. Step 2: The image analysis unit analyzes the reference image received by the request reception unit. For example, the generation AI extracts design elements from the image and gains a detailed understanding of the user's preferences. Step 3: The product search unit searches for products based on the information obtained by the request reception unit and image analysis unit. For example, the generation AI searches the inventory of multiple select shops based on the user's request and finds products that match the conditions. Step 4: The suggestion unit suggests products found by the product search unit to the user. For example, the generation AI selects items from the search results that best match the user's needs and suggests them to the user.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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 request receiving unit that receives user requests and reference images; an image analysis unit that analyzes the reference image received by the request receiving unit; a product search unit that searches for products based on the information obtained by the request receiving unit and the image analysis unit; a suggestion unit that suggests the product searched by the product search unit to the user. A system characterized by:

2. The image analysis unit Image analysis is performed on the reference image to extract features of similar designs and understand the user's preferences in more detail.

2. The system of claim 1.

3. The request receiving unit Adds a feature that allows users to input requests by voice and converts them into text using voice recognition technology.

2. The system of claim 1.

4. The product search unit Inventory data for each select shop is updated in real time, and searches are performed based on the latest inventory information.

2. The system of claim 1.

5. The proposal unit The items suggested by the generation AI are evaluated based on the user's past feedback to improve the accuracy of the suggestions.

2. The system of claim 1.

6. The request receiving unit Using emotion estimation functionality, we analyze the emotions of users when they input requests in real time and provide an interface that elicits positive emotions.

2. The system of claim 1.

7. The product search unit Using an emotion estimation function, the emotion associated with the request is analyzed, and emotionally positive products are searched for preferentially.

2. The system of claim 1.

8. The proposal unit Using emotion estimation function, analyze the emotions towards the suggested items and make suggestions to elicit positive emotions.

2. The system of claim 1.

Images