system

Abstract

The system according to this embodiment aims to analyze images uploaded by users and efficiently search for and suggest products. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a search unit, and a suggestion unit. The reception unit receives an image uploaded by the user. The analysis unit analyzes the image uploaded by the reception unit and extracts the characteristics of the clothing. The search unit searches for products based on the characteristics extracted by the analysis unit. The suggestion unit suggests similar products if the search unit does not find a matching product.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for a user to find a specific piece of clothing they are looking for and efficient searching cannot be performed.

[0005] The system according to the embodiment aims to analyze an image uploaded by a user and efficiently search for and propose products.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a search unit, and a suggestion unit. The reception unit receives images uploaded by the user. The analysis unit analyzes the images uploaded by the reception unit and extracts the characteristics of the clothing. The search unit searches for products based on the characteristics extracted by the analysis unit. The suggestion unit suggests similar products if the search unit does not find a matching product. [Effects of the Invention]

[0007] The system according to this embodiment can analyze images uploaded by users and efficiently search for and suggest products. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that analyzes an image of clothing that a user is looking for or clothing worn by a celebrity, and finds the corresponding product. In this AI agent system, the user selects image search on an e-commerce site, and the AI ​​analyzes the uploaded image. The AI ​​extracts the features of the clothing in the image and identifies the brand of the clothing. If clothing of the identified brand exists on the e-commerce site, the product is presented to the user. If it does not exist, the AI ​​searches for similar products and suggests them to the user. This mechanism can solve the problem of not knowing the brand of clothing worn by celebrities or favorite idols seen on TV or social media, or the problem of wanting to quickly find affordable products similar to the clothing one wants. For example, a reception unit receives the image uploaded by the user. For example, an analysis unit analyzes the uploaded image and extracts the features of the clothing. For example, a search unit searches for products based on the extracted features. For example, a suggestion unit suggests similar products if the search unit does not find the corresponding product. In this way, the AI ​​agent system can analyze the image uploaded by the user and suggest the corresponding product or similar products.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, a search unit, and a suggestion unit. The reception unit receives images uploaded by the user. The reception unit can accept images in formats such as JPEG, PNG, and GIF. The reception unit saves the images uploaded by the user. The analysis unit analyzes the uploaded images and extracts the features of the clothing. The analysis unit extracts features such as the color, shape, material, and design of the clothing in the image. The analysis unit analyzes the features of the clothing in the image using AI, for example. The analysis unit extracts the features of the clothing using image recognition technology, for example. The search unit searches for products based on the extracted features. The search unit searches the product database on the e-commerce site based on the extracted features, for example. The search unit searches for products using AI, for example. The search unit performs keyword searches and filtered searches, for example. The suggestion unit suggests similar products if the search unit does not find a matching product. The suggestion unit, for example, suggests similar products if the search unit cannot find what the user is looking for. The suggestion unit suggests products based on similarities in design or price range, for example. The suggestion unit also uses AI to suggest similar products. This allows the AI ​​agent system to analyze images uploaded by the user and suggest relevant or similar products.

[0030] The reception unit receives images uploaded by users. The reception unit accepts images uploaded by users, for example. The reception unit can accept images in formats such as JPEG, PNG, and GIF. The reception unit also saves images uploaded by users. Specifically, when users upload images via a web interface or mobile application, the reception unit checks the image format and size to ensure it is in the appropriate format. For example, JPEG images have high compression and small file sizes, allowing for quick uploads. On the other hand, PNG images are uncompressed and support transparent backgrounds, making them suitable for certain applications. The reception unit takes these format differences into account and processes the images uploaded by users appropriately. Furthermore, the reception unit has a storage system for temporarily saving uploaded images, ensuring their secure storage. For example, by utilizing cloud storage, large amounts of image data can be managed efficiently and accessed quickly as needed. In addition, the reception unit can perform basic pre-processing on uploaded images. For example, it can resize, rotate, and crop images to prepare them for efficient analysis by the analysis unit. This means the reception department plays a crucial role in properly receiving, storing, and handing over images uploaded by users to the analysis department.

[0031] The analysis unit analyzes uploaded images and extracts features of clothing. For example, the analysis unit extracts features such as the color, shape, material, and design of clothing in the image. The analysis unit uses AI to analyze the features of clothing in images. Specifically, the analysis unit utilizes image recognition technology using deep learning to extract features of clothing in images with high accuracy. For example, it uses a convolutional neural network (CNN) to identify the color, shape, and pattern of clothing in an image. Because CNN processes the pixel information of an image layer by layer and extracts features, it can perform image analysis with very high accuracy. Furthermore, by using a model that has been trained on a large dataset of clothing images in advance, the analysis unit can accurately extract features of various types of clothing. For example, to extract color features, it analyzes RGB values ​​to identify the main colors. To extract shape features, it uses an edge detection algorithm to identify the outline of the clothing. To extract material features, it performs texture analysis to identify the texture and pattern of the fabric. To extract design features, it uses pattern recognition technology to identify specific design elements (e.g., stripes, dots, floral patterns, etc.). This allows the analysis unit to extract detailed features of the clothing from the uploaded image and generate data to provide to the search unit.

[0032] The search unit searches for products based on extracted features. For example, the search unit searches the product database on the e-commerce site based on the extracted features. The search unit also uses AI to search for products. Specifically, the search unit efficiently searches the product database on the e-commerce site using clothing feature data provided by the analysis unit. For example, the product database contains detailed information such as the color, shape, material, and design of each product, and the search unit matches this information with the feature data provided by the analysis unit. The search unit uses AI to match the feature data with the information in the product database. For example, it uses natural language processing (NLP) technology to analyze product descriptions and tag information to identify products that match the feature data. The search unit can also perform keyword searches and filtered searches. For example, if a user is looking for clothing of a specific color or design, the search unit searches the product database based on those keywords and lists the relevant products. Furthermore, the search unit can also provide personalized search results by considering the user's search history and purchase history. This allows the search unit to quickly and accurately find the products the user is looking for and generate information to provide to the suggestion unit.

[0033] The suggestion department suggests similar products when the search department cannot find the desired product. For example, if the search department fails to find a suitable product, the suggestion department suggests similar products. For example, the suggestion department suggests products based on similarity in design or price range. Specifically, the suggestion department uses an algorithm to identify similar products based on the search results provided by the search department. For example, to evaluate design similarity, it uses image recognition technology to compare the design patterns and colors of products. To evaluate price range similarity, it analyzes product price information to identify products that fit the user's budget. The suggestion department uses AI to suggest the most suitable product, taking into account the user's preferences and past purchase history. For example, it uses collaborative filtering technology to analyze the purchase history and evaluation data of other users and suggests products purchased by users with similar tastes. The suggestion department can also collect user feedback and continuously improve the accuracy of its suggestion algorithm. For example, by having users evaluate the suggested products, the suggestion algorithm can be adjusted based on that evaluation data to provide more accurate suggestions. This allows the suggestion department to suggest similar products even when the user cannot find the product they are looking for, thereby improving user satisfaction.

[0034] The reception unit can analyze a user's past upload history and select the optimal reception method. For example, the reception unit can analyze the time periods when a user frequently uploaded in the past and send notifications prompting uploads during those times. For example, the reception unit can prioritize suggesting upload methods the user has used in the past (camera, gallery, etc.). For example, the reception unit can prioritize receiving images of a specific category based on the user's past upload history. In this way, the optimal reception method can be selected by analyzing the user's past upload history. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's past upload history data into a generating AI and have the generating AI select the optimal reception method.

[0035] The reception desk can filter images based on the user's current fashion trends and areas of interest when they are uploaded. For example, the reception desk may prompt the user to upload only relevant images based on fashion items they have recently searched for. For example, the reception desk may analyze the user's social media activity and prioritize images related to fashion categories of interest. For example, the reception desk may refer to the user's past purchase history and prioritize images related to specific brands or styles. This allows users to upload highly relevant images by filtering based on their current fashion trends and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may input the user's social media activity data into a generating AI and have the generating AI perform the filtering of relevant images.

[0036] The reception unit can prioritize receiving images that are highly relevant to the user's geographical location when they upload images. For example, if the user is in a specific region, the reception unit will prioritize receiving images of fashion items related to that region. For example, if the user is traveling, the reception unit will prioritize receiving images based on the fashion trends of the travel destination. For example, based on the user's current location, the reception unit will prioritize receiving images related to local fashion events and trends. In this way, by considering the user's geographical location, highly relevant images can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of receiving highly relevant images.

[0037] The reception unit can analyze a user's social media activity when they upload an image and accept relevant images. For example, the reception unit may prioritize images related to fashion items the user has recently "liked." For example, the reception unit may prioritize images based on posts from influencers the user follows. For example, the reception unit may prioritize images in fashion categories of interest based on the user's comments and shares on social media. In this way, by analyzing the user's social media activity, relevant images can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit may input the user's social media activity data into a generating AI and have the generating AI perform the reception of relevant images.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the clothing during the analysis. For example, in the case of expensive brand clothing, the analysis unit performs a detailed analysis and describes the characteristics of the material and design. For example, in the case of general brand clothing, the analysis unit analyzes and displays only the basic characteristics. For example, in the case of clothing that the user is particularly interested in, the analysis unit performs a detailed analysis and provides relevant information. In this way, by adjusting the level of detail of the analysis based on the importance of the clothing, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input clothing importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the clothing category during analysis. For example, in the case of a dress, the analysis unit applies an analysis algorithm that emphasizes design and material characteristics. For example, in the case of casual wear, the analysis unit applies an analysis algorithm that emphasizes color and pattern characteristics. For example, in the case of sportswear, the analysis unit applies an analysis algorithm that emphasizes functionality and material characteristics. By applying different analysis algorithms depending on the clothing category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input clothing category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0040] The analysis unit can determine the priority of analysis based on when the clothing was uploaded. For example, the analysis unit will prioritize the analysis of the latest fashion items. For example, the analysis unit will set a lower priority for analysis of out-of-season fashion items. For example, the analysis unit will prioritize the analysis of items that the user has shown particular interest in. By determining the priority of analysis based on when the clothing was uploaded, the analysis unit can provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the clothing upload date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the clothing items during the analysis process. For example, the analysis unit may prioritize analyzing items related to images uploaded by the user. For example, the analysis unit may prioritize analyzing highly relevant items based on the user's past search history. For example, the analysis unit may prioritize analyzing highly relevant items based on the user's social media activity. By adjusting the order of analysis based on the relevance of the clothing items, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input clothing relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0042] The search unit can improve search accuracy by considering the interrelationships of clothing items during a search. For example, the search unit provides search results by considering the interrelationships of other items from the same brand. For example, the search unit provides search results by considering the interrelationships of other items from the same category. For example, the search unit prioritizes searching for highly relevant items based on the user's past search history. This improves search accuracy by considering the interrelationships of clothing items. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input clothing interrelationship data into a generating AI and have the generating AI perform the search accuracy improvement.

[0043] The search unit can perform searches while considering clothing brand information. For example, if the user specifies a particular brand, the search unit will prioritize searching for items from that brand. For example, the search unit will prioritize searching for items from brands that the user has previously purchased. For example, based on the user's social media activity, the search unit will prioritize searching for items from brands of interest. This allows for more appropriate search results by considering clothing brand information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input clothing brand information data into a generating AI and have the generating AI perform the search.

[0044] The search unit can perform searches while considering the geographical distribution of clothing. For example, the search unit can prioritize searching for items available at nearby stores based on the user's current location. For example, if the user is traveling, the search unit can prioritize searching for items available at stores in the user's travel destination. For example, based on the user's past purchase history, the search unit can prioritize searching for items popular in a particular region. By considering the geographical distribution of clothing, more appropriate search results can be provided. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input geographical distribution data of clothing into a generating AI and have the generating AI perform the search.

[0045] The search unit can improve the accuracy of its search by referring to relevant literature on clothing during the search process. For example, the search unit can search for relevant items by referring to articles in fashion magazines. For example, the search unit can search for trending items by referring to online fashion blogs. For example, the search unit can search based on information about specific materials and designs by referring to academic papers. In this way, the accuracy of the search can be improved by referring to relevant literature on clothing. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input relevant literature data on clothing into a generating AI and have the generating AI perform the search.

[0046] The suggestion unit can improve the accuracy of its suggestions by considering the interrelationships of clothing items. For example, the suggestion unit may suggest items considering their interrelationships with other items from the same brand. For example, the suggestion unit may suggest items considering their interrelationships with other items from the same category. For example, the suggestion unit may prioritize suggesting highly relevant items based on the user's past purchase history. This improves the accuracy of suggestions by considering the interrelationships of clothing items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit may input interrelationship data of clothing items into a generating AI and have the generating AI perform the suggestion.

[0047] The suggestion unit can make suggestions while considering clothing brand information. For example, if the user specifies a particular brand, the suggestion unit will prioritize suggesting items from that brand. For example, the suggestion unit will prioritize suggesting items from brands that the user has previously purchased. For example, based on the user's social media activity, the suggestion unit will prioritize suggesting items from brands of interest. This allows for the suggestion of more appropriate products by considering clothing brand information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input clothing brand information data into a generating AI and have the generating AI perform the suggestion.

[0048] The suggestion unit can make suggestions while considering the geographical distribution of clothing. For example, the suggestion unit can prioritize suggesting items that can be purchased at nearby stores based on the user's current location. For example, if the user is traveling, the suggestion unit can prioritize suggesting items that can be purchased at stores in the user's travel destination. For example, based on the user's past purchase history, the suggestion unit can prioritize suggesting items that are popular in a particular region. This allows for the suggestion of more appropriate products by considering the geographical distribution of clothing. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input geographical distribution data of clothing into a generating AI and have the generating AI perform the suggestion.

[0049] The suggestion unit can improve the accuracy of its suggestions by referring to relevant literature on clothing during the suggestion process. For example, the suggestion unit may suggest relevant items by referring to articles in fashion magazines. For example, the suggestion unit may suggest trendy items by referring to online fashion blogs. For example, the suggestion unit may suggest items based on information about specific materials and designs by referring to academic papers. In this way, the accuracy of suggestions can be improved by referring to relevant literature on clothing. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit may input relevant literature data on clothing into a generating AI and have the generating AI perform the suggestion.

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

[0051] The reception unit can receive user voice commands and use voice recognition technology to upload images. For example, if a user says the voice command "Upload an image," the reception unit will recognize the voice and automatically display the image upload interface. Furthermore, the reception unit can also select images from a specific folder based on the voice command. This allows users to upload images using only their voice, without using their hands, thus improving convenience.

[0052] The search engine can analyze a user's past search history and provide the most relevant search results. For example, it can prioritize searching for related products based on keywords and categories the user has previously searched for. Furthermore, the search engine can suggest related products based on items the user has previously purchased. This allows the search engine to leverage the user's past search history to provide more relevant search results.

[0053] The suggestion function can prioritize products based on the user's geographical location. For example, if the user is in a specific region, it will prioritize suggesting products popular in that region. If the user is traveling, it will suggest products available in their travel destination. This allows for the suggestion of more relevant products by considering the user's geographical location.

[0054] The reception unit can receive user voice commands and use voice recognition technology to upload images. For example, if a user says the voice command "Upload an image," the reception unit will recognize the voice and automatically display the image upload interface. Furthermore, the reception unit can also select images from a specific folder based on the voice command. This allows users to upload images using only their voice, without using their hands, thus improving convenience.

[0055] The search engine can analyze a user's past search history and provide the most relevant search results. For example, it can prioritize searching for related products based on keywords and categories the user has previously searched for. Furthermore, the search engine can suggest related products based on items the user has previously purchased. This allows the search engine to leverage the user's past search history to provide more relevant search results.

[0056] The suggestion function can prioritize products based on the user's geographical location. For example, if the user is in a specific region, it will prioritize suggesting products popular in that region. If the user is traveling, it will suggest products available in their travel destination. This allows for the suggestion of more relevant products by considering the user's geographical location.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk accepts images uploaded by the user. For example, it accepts images in formats such as JPEG, PNG, and GIF, and saves the images uploaded by the user. Step 2: The analysis unit analyzes the uploaded image and extracts the features of the clothing. For example, it extracts features such as the color, shape, material, and design of the clothing in the image using AI and image recognition technology. Step 3: The search unit searches for products based on the extracted features. For example, it uses AI to search the product database within the e-commerce site based on the extracted features. Step 4: The suggestion unit suggests similar products if the search unit cannot find a matching product. For example, it uses AI to suggest products based on similarity in design or price range.

[0059] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that analyzes an image of clothing that a user is looking for or clothing worn by a celebrity, and finds the corresponding product. In this AI agent system, the user selects image search on an e-commerce site, and the AI ​​analyzes the uploaded image. The AI ​​extracts the features of the clothing in the image and identifies the brand of the clothing. If clothing of the identified brand exists on the e-commerce site, the product is presented to the user. If it does not exist, the AI ​​searches for similar products and suggests them to the user. This mechanism can solve the problem of not knowing the brand of clothing worn by celebrities or favorite idols seen on TV or social media, or the problem of wanting to quickly find affordable products similar to the clothing one wants. For example, a reception unit receives the image uploaded by the user. For example, an analysis unit analyzes the uploaded image and extracts the features of the clothing. For example, a search unit searches for products based on the extracted features. For example, a suggestion unit suggests similar products if the search unit does not find the corresponding product. In this way, the AI ​​agent system can analyze the image uploaded by the user and suggest the corresponding product or similar products.

[0060] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, a search unit, and a suggestion unit. The reception unit receives images uploaded by the user. The reception unit can accept images in formats such as JPEG, PNG, and GIF. The reception unit saves the images uploaded by the user. The analysis unit analyzes the uploaded images and extracts the features of the clothing. The analysis unit extracts features such as the color, shape, material, and design of the clothing in the image. The analysis unit analyzes the features of the clothing in the image using AI, for example. The analysis unit extracts the features of the clothing using image recognition technology, for example. The search unit searches for products based on the extracted features. The search unit searches the product database on the e-commerce site based on the extracted features, for example. The search unit searches for products using AI, for example. The search unit performs keyword searches and filtered searches, for example. The suggestion unit suggests similar products if the search unit does not find a matching product. The suggestion unit, for example, suggests similar products if the search unit cannot find what the user is looking for. The suggestion unit suggests products based on similarities in design or price range, for example. The suggestion unit also uses AI to suggest similar products. This allows the AI ​​agent system to analyze images uploaded by the user and suggest relevant or similar products.

[0061] The reception unit receives images uploaded by users. The reception unit accepts images uploaded by users, for example. The reception unit can accept images in formats such as JPEG, PNG, and GIF. The reception unit also saves images uploaded by users. Specifically, when users upload images via a web interface or mobile application, the reception unit checks the image format and size to ensure it is in the appropriate format. For example, JPEG images have high compression and small file sizes, allowing for quick uploads. On the other hand, PNG images are uncompressed and support transparent backgrounds, making them suitable for certain applications. The reception unit takes these format differences into account and processes the images uploaded by users appropriately. Furthermore, the reception unit has a storage system for temporarily saving uploaded images, ensuring their secure storage. For example, by utilizing cloud storage, large amounts of image data can be managed efficiently and accessed quickly as needed. In addition, the reception unit can perform basic pre-processing on uploaded images. For example, it can resize, rotate, and crop images to prepare them for efficient analysis by the analysis unit. This means the reception department plays a crucial role in properly receiving, storing, and handing over images uploaded by users to the analysis department.

[0062] The analysis unit analyzes uploaded images and extracts features of clothing. For example, the analysis unit extracts features such as the color, shape, material, and design of clothing in the image. The analysis unit uses AI to analyze the features of clothing in images. Specifically, the analysis unit utilizes image recognition technology using deep learning to extract features of clothing in images with high accuracy. For example, it uses a convolutional neural network (CNN) to identify the color, shape, and pattern of clothing in an image. Because CNN processes the pixel information of an image layer by layer and extracts features, it can perform image analysis with very high accuracy. Furthermore, by using a model that has been trained on a large dataset of clothing images in advance, the analysis unit can accurately extract features of various types of clothing. For example, to extract color features, it analyzes RGB values ​​to identify the main colors. To extract shape features, it uses an edge detection algorithm to identify the outline of the clothing. To extract material features, it performs texture analysis to identify the texture and pattern of the fabric. To extract design features, it uses pattern recognition technology to identify specific design elements (e.g., stripes, dots, floral patterns, etc.). This allows the analysis unit to extract detailed features of the clothing from the uploaded image and generate data to provide to the search unit.

[0063] The search unit searches for products based on extracted features. For example, the search unit searches the product database on the e-commerce site based on the extracted features. The search unit also uses AI to search for products. Specifically, the search unit efficiently searches the product database on the e-commerce site using clothing feature data provided by the analysis unit. For example, the product database contains detailed information such as the color, shape, material, and design of each product, and the search unit matches this information with the feature data provided by the analysis unit. The search unit uses AI to match the feature data with the information in the product database. For example, it uses natural language processing (NLP) technology to analyze product descriptions and tag information to identify products that match the feature data. The search unit can also perform keyword searches and filtered searches. For example, if a user is looking for clothing of a specific color or design, the search unit searches the product database based on those keywords and lists the relevant products. Furthermore, the search unit can also provide personalized search results by considering the user's search history and purchase history. This allows the search unit to quickly and accurately find the products the user is looking for and generate information to provide to the suggestion unit.

[0064] The suggestion department suggests similar products when the search department cannot find the desired product. For example, if the search department fails to find a suitable product, the suggestion department suggests similar products. For example, the suggestion department suggests products based on similarity in design or price range. Specifically, the suggestion department uses an algorithm to identify similar products based on the search results provided by the search department. For example, to evaluate design similarity, it uses image recognition technology to compare the design patterns and colors of products. To evaluate price range similarity, it analyzes product price information to identify products that fit the user's budget. The suggestion department uses AI to suggest the most suitable product, taking into account the user's preferences and past purchase history. For example, it uses collaborative filtering technology to analyze the purchase history and evaluation data of other users and suggests products purchased by users with similar tastes. The suggestion department can also collect user feedback and continuously improve the accuracy of its suggestion algorithm. For example, by having users evaluate the suggested products, the suggestion algorithm can be adjusted based on that evaluation data to provide more accurate suggestions. This allows the suggestion department to suggest similar products even when the user cannot find the product they are looking for, thereby improving user satisfaction.

[0065] The reception unit can estimate the user's emotions and adjust the timing of image uploads based on the estimated emotions. For example, if the user is excited, the reception unit simplifies the interface to allow immediate image uploads. For example, if the user is relaxed, the reception unit provides instructions for uploading images while displaying detailed explanations. For example, if the user is stressed, the reception unit offers the option to postpone image uploads. This allows for more appropriate image uploads by adjusting the timing of image uploads according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0066] The reception unit can analyze a user's past upload history and select the optimal reception method. For example, the reception unit can analyze the time periods when a user frequently uploaded in the past and send notifications prompting uploads during those times. For example, the reception unit can prioritize suggesting upload methods the user has used in the past (camera, gallery, etc.). For example, the reception unit can prioritize receiving images of a specific category based on the user's past upload history. In this way, the optimal reception method can be selected by analyzing the user's past upload history. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's past upload history data into a generating AI and have the generating AI select the optimal reception method.

[0067] The reception desk can filter images based on the user's current fashion trends and areas of interest when they are uploaded. For example, the reception desk may prompt the user to upload only relevant images based on fashion items they have recently searched for. For example, the reception desk may analyze the user's social media activity and prioritize images related to fashion categories of interest. For example, the reception desk may refer to the user's past purchase history and prioritize images related to specific brands or styles. This allows users to upload highly relevant images by filtering based on their current fashion trends and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may input the user's social media activity data into a generating AI and have the generating AI perform the filtering of relevant images.

[0068] The reception unit can estimate the user's emotions and determine the priority of images to receive based on the estimated emotions. For example, if the user is excited, the reception unit will set a high priority for immediate analysis of uploaded images. If the user is relaxed, the reception unit will receive the image with the same priority as other images. If the user is stressed, the reception unit will offer the option to postpone receiving the image. This allows for the prioritization of more appropriate images by determining the priority of images to receive according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0069] The reception unit can prioritize receiving images that are highly relevant to the user's geographical location when they upload images. For example, if the user is in a specific region, the reception unit will prioritize receiving images of fashion items related to that region. For example, if the user is traveling, the reception unit will prioritize receiving images based on the fashion trends of the travel destination. For example, based on the user's current location, the reception unit will prioritize receiving images related to local fashion events and trends. In this way, by considering the user's geographical location, highly relevant images can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of receiving highly relevant images.

[0070] The reception unit can analyze a user's social media activity when they upload an image and accept relevant images. For example, the reception unit may prioritize images related to fashion items the user has recently "liked." For example, the reception unit may prioritize images based on posts from influencers the user follows. For example, the reception unit may prioritize images in fashion categories of interest based on the user's comments and shares on social media. In this way, by analyzing the user's social media activity, relevant images can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit may input the user's social media activity data into a generating AI and have the generating AI perform the reception of relevant images.

[0071] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is excited, the analysis unit can display the analysis results in a visually appealing graphic. For example, if the user is relaxed, the analysis unit can provide analysis results that include a detailed text explanation. For example, if the user is stressed, the analysis unit can display concise and to-the-point analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0072] The analysis unit can adjust the level of detail of the analysis based on the importance of the clothing during the analysis. For example, in the case of expensive brand clothing, the analysis unit performs a detailed analysis and describes the characteristics of the material and design. For example, in the case of general brand clothing, the analysis unit analyzes and displays only the basic characteristics. For example, in the case of clothing that the user is particularly interested in, the analysis unit performs a detailed analysis and provides relevant information. In this way, by adjusting the level of detail of the analysis based on the importance of the clothing, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input clothing importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0073] The analysis unit can apply different analysis algorithms depending on the clothing category during analysis. For example, in the case of a dress, the analysis unit applies an analysis algorithm that emphasizes design and material characteristics. For example, in the case of casual wear, the analysis unit applies an analysis algorithm that emphasizes color and pattern characteristics. For example, in the case of sportswear, the analysis unit applies an analysis algorithm that emphasizes functionality and material characteristics. By applying different analysis algorithms depending on the clothing category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input clothing category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0074] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is excited, the analysis unit provides a visually appealing analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0075] The analysis unit can determine the priority of analysis based on when the clothing was uploaded. For example, the analysis unit will prioritize the analysis of the latest fashion items. For example, the analysis unit will set a lower priority for analysis of out-of-season fashion items. For example, the analysis unit will prioritize the analysis of items that the user has shown particular interest in. By determining the priority of analysis based on when the clothing was uploaded, the analysis unit can provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the clothing upload date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0076] The analysis unit can adjust the order of analysis based on the relevance of the clothing items during the analysis process. For example, the analysis unit may prioritize analyzing items related to images uploaded by the user. For example, the analysis unit may prioritize analyzing highly relevant items based on the user's past search history. For example, the analysis unit may prioritize analyzing highly relevant items based on the user's social media activity. By adjusting the order of analysis based on the relevance of the clothing items, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input clothing relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0077] The search unit can estimate the user's emotions and adjust the search criteria based on the estimated emotions. For example, if the user is excited, the search unit will provide a wide range of search results. If the user is relaxed, the search unit will provide detailed search results. If the user is stressed, the search unit will provide concise and to-the-point search results. By adjusting the search criteria according to the user's emotions, more appropriate search results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0078] The search unit can improve search accuracy by considering the interrelationships of clothing items during a search. For example, the search unit provides search results by considering the interrelationships of other items from the same brand. For example, the search unit provides search results by considering the interrelationships of other items from the same category. For example, the search unit prioritizes searching for highly relevant items based on the user's past search history. This improves search accuracy by considering the interrelationships of clothing items. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input clothing interrelationship data into a generating AI and have the generating AI perform the search accuracy improvement.

[0079] The search unit can perform searches while considering clothing brand information. For example, if the user specifies a particular brand, the search unit will prioritize searching for items from that brand. For example, the search unit will prioritize searching for items from brands that the user has previously purchased. For example, based on the user's social media activity, the search unit will prioritize searching for items from brands of interest. This allows for more appropriate search results by considering clothing brand information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input clothing brand information data into a generating AI and have the generating AI perform the search.

[0080] The search unit can estimate the user's emotions and adjust the order in which search results are displayed based on the estimated emotions. For example, if the user is excited, the search unit will prioritize displaying visually appealing search results. If the user is relaxed, the search unit will prioritize displaying search results containing detailed information. If the user is stressed, the search unit will prioritize displaying concise and to-the-point search results. By adjusting the order in which search results are displayed according to the user's emotions, more appropriate search results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0081] The search unit can perform searches while considering the geographical distribution of clothing. For example, the search unit can prioritize searching for items available at nearby stores based on the user's current location. For example, if the user is traveling, the search unit can prioritize searching for items available at stores in the user's travel destination. For example, based on the user's past purchase history, the search unit can prioritize searching for items popular in a particular region. By considering the geographical distribution of clothing, more appropriate search results can be provided. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input geographical distribution data of clothing into a generating AI and have the generating AI perform the search.

[0082] The search unit can improve the accuracy of its search by referring to relevant literature on clothing during the search process. For example, the search unit can search for relevant items by referring to articles in fashion magazines. For example, the search unit can search for trending items by referring to online fashion blogs. For example, the search unit can search based on information about specific materials and designs by referring to academic papers. In this way, the accuracy of the search can be improved by referring to relevant literature on clothing. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input relevant literature data on clothing into a generating AI and have the generating AI perform the search.

[0083] The suggestion unit can estimate the user's emotions and determine the priority of products to suggest based on the estimated emotions. For example, if the user is excited, the suggestion unit will prioritize suggesting visually appealing products. For example, if the user is relaxed, the suggestion unit will prioritize suggesting products that contain detailed information. For example, if the user is stressed, the suggestion unit will prioritize suggesting concise and to-the-point products. This allows for the suggestion of more appropriate products by prioritizing products according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0084] The suggestion unit can improve the accuracy of its suggestions by considering the interrelationships of clothing items. For example, the suggestion unit may suggest items considering their interrelationships with other items from the same brand. For example, the suggestion unit may suggest items considering their interrelationships with other items from the same category. For example, the suggestion unit may prioritize suggesting highly relevant items based on the user's past purchase history. This improves the accuracy of suggestions by considering the interrelationships of clothing items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit may input interrelationship data of clothing items into a generating AI and have the generating AI perform the suggestion.

[0085] The suggestion unit can make suggestions while considering clothing brand information. For example, if the user specifies a particular brand, the suggestion unit will prioritize suggesting items from that brand. For example, the suggestion unit will prioritize suggesting items from brands that the user has previously purchased. For example, based on the user's social media activity, the suggestion unit will prioritize suggesting items from brands of interest. This allows for the suggestion of more appropriate products by considering clothing brand information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input clothing brand information data into a generating AI and have the generating AI perform the suggestion.

[0086] The suggestion unit can estimate the user's emotions and adjust the way suggested products are displayed based on the estimated emotions. For example, if the user is excited, the suggestion unit provides a visually appealing display method. If the user is relaxed, the suggestion unit provides a display method that includes detailed information. If the user is stressed, the suggestion unit provides a concise and to-the-point display method. By adjusting the product display method according to the user's emotions, more appropriate products can be suggested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0087] The suggestion unit can make suggestions while considering the geographical distribution of clothing. For example, the suggestion unit can prioritize suggesting items that can be purchased at nearby stores based on the user's current location. For example, if the user is traveling, the suggestion unit can prioritize suggesting items that can be purchased at stores in the user's travel destination. For example, based on the user's past purchase history, the suggestion unit can prioritize suggesting items that are popular in a particular region. This allows for the suggestion of more appropriate products by considering the geographical distribution of clothing. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input geographical distribution data of clothing into a generating AI and have the generating AI perform the suggestion.

[0088] The suggestion unit can improve the accuracy of its suggestions by referring to relevant literature on clothing during the suggestion process. For example, the suggestion unit may suggest relevant items by referring to articles in fashion magazines. For example, the suggestion unit may suggest trendy items by referring to online fashion blogs. For example, the suggestion unit may suggest items based on information about specific materials and designs by referring to academic papers. In this way, the accuracy of suggestions can be improved by referring to relevant literature on clothing. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit may input relevant literature data on clothing into a generating AI and have the generating AI perform the suggestion.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The reception unit can receive user voice commands and use voice recognition technology to upload images. For example, if a user says the voice command "Upload an image," the reception unit will recognize the voice and automatically display the image upload interface. Furthermore, the reception unit can also select images from a specific folder based on the voice command. This allows users to upload images using only their voice, without using their hands, thus improving convenience.

[0091] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is excited, the analysis unit performs a rapid analysis and provides results immediately. If the user is relaxed, the analysis unit performs a detailed analysis and provides more information. If the user is stressed, the analysis unit provides a concise and to-the-point analysis result. In this way, by adjusting the accuracy of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0092] The search engine can analyze a user's past search history and provide the most relevant search results. For example, it can prioritize searching for related products based on keywords and categories the user has previously searched for. Furthermore, the search engine can suggest related products based on items the user has previously purchased. This allows the search engine to leverage the user's past search history to provide more relevant search results.

[0093] The suggestion function can estimate the user's emotions and adjust the way suggested products are displayed based on those emotions. For example, if the user is excited, it provides a visually appealing display. If the user is relaxed, it provides a display that includes detailed information. If the user is stressed, it provides a concise and to-the-point display. By adjusting the product display according to the user's emotions, it can suggest more appropriate products.

[0094] The suggestion function can prioritize products based on the user's geographical location. For example, if the user is in a specific region, it will prioritize suggesting products popular in that region. If the user is traveling, it will suggest products available in their travel destination. This allows for the suggestion of more relevant products by considering the user's geographical location.

[0095] The reception unit can receive user voice commands and use voice recognition technology to upload images. For example, if a user says the voice command "Upload an image," the reception unit will recognize the voice and automatically display the image upload interface. Furthermore, the reception unit can also select images from a specific folder based on the voice command. This allows users to upload images using only their voice, without using their hands, thus improving convenience.

[0096] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is excited, the analysis unit performs a rapid analysis and provides results immediately. If the user is relaxed, the analysis unit performs a detailed analysis and provides more information. If the user is stressed, the analysis unit provides a concise and to-the-point analysis result. In this way, by adjusting the accuracy of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0097] The search engine can analyze a user's past search history and provide the most relevant search results. For example, it can prioritize searching for related products based on keywords and categories the user has previously searched for. Furthermore, the search engine can suggest related products based on items the user has previously purchased. This allows the search engine to leverage the user's past search history to provide more relevant search results.

[0098] The suggestion function can estimate the user's emotions and adjust the way suggested products are displayed based on those emotions. For example, if the user is excited, it provides a visually appealing display. If the user is relaxed, it provides a display that includes detailed information. If the user is stressed, it provides a concise and to-the-point display. By adjusting the product display according to the user's emotions, it can suggest more appropriate products.

[0099] The suggestion function can prioritize products based on the user's geographical location. For example, if the user is in a specific region, it will prioritize suggesting products popular in that region. If the user is traveling, it will suggest products available in their travel destination. This allows for the suggestion of more relevant products by considering the user's geographical location.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The reception desk accepts images uploaded by the user. For example, it accepts images in formats such as JPEG, PNG, and GIF, and saves the images uploaded by the user. Step 2: The analysis unit analyzes the uploaded image and extracts the features of the clothing. For example, it extracts features such as the color, shape, material, and design of the clothing in the image using AI and image recognition technology. Step 3: The search unit searches for products based on the extracted features. For example, it uses AI to search the product database within the e-commerce site based on the extracted features. Step 4: The suggestion unit suggests similar products if the search unit cannot find a matching product. For example, it uses AI to suggest products based on similarity in design or price range.

[0102] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0104] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0105] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives images uploaded by the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the uploaded image and extracts the features of the clothing. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches for products based on the extracted features. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and suggests similar products when the search unit does not find a matching product. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0107] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

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

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

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

[0112] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0113] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0114] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0115] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0116] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0119] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives images uploaded by the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the uploaded image and extracts clothing features. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches for products based on the extracted features. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and suggests similar products when the search unit does not find a matching product. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0129] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives images uploaded by the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the uploaded image and extracts the features of the clothing. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches for products based on the extracted features. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and suggests similar products when the search unit does not find a matching product. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] As shown in Figure 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.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0146] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and suggestion unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives images uploaded by the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the uploaded image and extracts the features of the clothing. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and searches for products based on the extracted features. The suggestion unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and suggests similar products when the search unit does not find a matching product. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0155] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0157] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0158] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0159] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0163] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0164] 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.

[0165] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0166] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0167] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0168] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0170] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0171] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0172] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0173] (Note 1) A reception area where users upload images, The reception unit analyzes the uploaded image and extracts the characteristics of the clothing, A search unit searches for products based on the features extracted by the analysis unit, The system includes a suggestion unit that suggests similar products if the search unit does not find a matching product. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of image uploads based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Analyze the user's past upload history and select the optimal acceptance method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When uploading images, filtering is performed based on the user's current fashion trends and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of images to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When uploading images, the system prioritizes accepting images that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When an image is uploaded, the system analyzes the user's social media activity and accepts relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of each garment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the clothing category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the clothing items were uploaded. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the clothing. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, It estimates user sentiment and adjusts search criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, When searching, consider the relationships between clothing items to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, When searching, take clothing brand information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, It estimates the user's sentiment and adjusts the order in which search results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned search unit, When searching, consider the geographical distribution of clothing. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned search unit, When searching, refer to related literature on clothing to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggested products based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, consider the interrelationships between clothing items to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, take into account the clothing brand information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, We estimate the user's emotions and adjust how suggested products are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, take into account the geographical distribution of clothing. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, refer to relevant literature on clothing to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users upload images, The reception unit analyzes the uploaded image and extracts the characteristics of the clothing, A search unit searches for products based on the features extracted by the analysis unit, The system includes a suggestion unit that suggests similar products if the search unit does not find a matching product. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of image uploads based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past upload history and select the optimal acceptance method. The system according to feature 1.

4. The aforementioned reception unit is When uploading images, filtering is performed based on the user's current fashion trends and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of images to accept based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When uploading images, the system prioritizes accepting images that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When an image is uploaded, the system analyzes the user's social media activity and accepts relevant images. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

Images