System

Abstract

An object of a system according to an embodiment is to propose optimal coordination by effectively utilizing items possessed by a user.SOLUTION: A system includes an acquisition unit, an analysis unit, a proposal unit, and a provision unit. The acquisition unit photographs an item held by a user. The analysis unit analyzes the moving image acquired by the acquisition unit and recognizes a feature of each item. The suggestion unit generates a coordination or fashion image on the basis of the feature recognized by the analysis unit. The provision unit provides the user with the coordination and the fashion image generated by the suggestion unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to effectively utilize the items a user owns and propose optimal outfits, so there is room for improvement.

[0005] The system according to the embodiment aims to effectively utilize items owned by a user and propose optimal coordination. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a suggestion unit, and a provision unit. The acquisition unit captures images of items held by a user. The analysis unit analyzes the video acquired by the acquisition unit and recognizes the characteristics of each item. The suggestion unit generates coordinated outfits and fashion images based on the characteristics recognized by the analysis unit. The provision unit provides the coordinated outfits and fashion images generated by the suggestion unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can effectively utilize items that a user has and suggest optimal coordination. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A fashion suggestion system according to an embodiment of the present invention allows a user to take a video of their personal items with their smartphone and then use AI to analyze the video to instantly suggest optimal outfits, fashion images, and new items that fit the user's budget. The fashion suggestion system effectively utilizes the user's personal items and easily achieves unique styling. For example, the fashion suggestion system allows a user to take a video of their clothes and shoes with their smartphone. The video is then input into AI. The AI ​​then analyzes the input video and recognizes the characteristics of each item. Based on the recognized item characteristics, the AI ​​generates optimal outfits and fashion images and also suggests new items that fit the user's budget. This allows users to effectively utilize their personal items and easily achieve unique styling. Furthermore, the AI ​​can learn the user's preferences and past outfit history to provide more personalized suggestions. This allows the fashion suggestion system to effectively utilize the user's personal items and easily achieve unique styling. For example, this system is extremely useful for users who are interested in fashion but lack confidence in their outfit coordination skills.

[0029] A fashion suggestion system according to an embodiment includes an acquisition unit, an analysis unit, a suggestion unit, and a provision unit. The acquisition unit captures images of items owned by a user. Examples of items owned by a user include, but are not limited to, clothing, accessories, and shoes. The acquisition unit acquires, for example, videos captured with a smartphone. The acquisition unit can also capture images using a digital camera or webcam. The acquisition unit also has a function for adjusting lighting conditions and shooting angles during capture. For example, the acquisition unit may encourage a user to capture images at a time when natural light is best. The acquisition unit may also guide a user to complete the capture as quickly as possible if the user is in a hurry. The acquisition unit may also provide an interface that emphasizes the enjoyment of capturing images if the user is excited. The analysis unit uses AI to analyze the video captured by the acquisition unit and recognize features of each item. Features include, for example, color, shape, and material, but are not limited to, examples. For example, the analysis unit recognizes color based on RGB values. The analysis unit may also recognize shape using contour extraction technology. The analysis unit may also recognize material using texture analysis technology. The suggestion unit uses AI to generate optimal outfits and fashion images based on the features recognized by the analysis unit. The suggestion unit generates outfits based on, for example, color combinations and style matches. The suggestion unit can also suggest new items based on the user's budget. For example, the suggestion unit may suggest items taking into account a price range and a budget limit. Furthermore, the suggestion unit can learn the user's preferences and past outfit history to provide more personalized suggestions. The provision unit provides the outfits and fashion images generated by the suggestion unit to the user. The provision unit displays information through, for example, a web application or a mobile application. The provision unit can also provide information via email or push notification. Furthermore, the provision unit can estimate the user's emotions and adjust the method of provision based on the estimated user's emotions. For example, if the user is relaxed, the provision unit selects a method of provision that includes detailed information.Furthermore, if the user is in a hurry, the providing unit can select a concise providing method that focuses on the main points. Furthermore, if the user is excited, the providing unit can select a visually appealing providing method. As a result, the fashion suggestion system according to the embodiment can effectively utilize the items the user owns and easily achieve unique styling. For example, this is very convenient for users who are interested in fashion but are not confident in coordinating outfits.

[0030] The acquisition unit can acquire videos taken by a user with a smartphone. The video taken with a smartphone includes, for example, resolution and frame rate, but is not limited to these examples. For example, the acquisition unit acquires videos taken with a smartphone at high resolution. The acquisition unit can also adjust the frame rate to acquire smoother videos. The acquisition unit can also automatically adjust the camera settings of the smartphone to acquire optimal videos. For example, the acquisition unit can automatically adjust the camera settings of the smartphone to acquire optimal videos. The acquisition unit can also automatically adjust the camera settings of the smartphone to acquire optimal videos. By acquiring videos taken by a user with a smartphone, the system can recognize items held by the user. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input videos taken with a smartphone to a generation AI and cause the generation AI to analyze the videos.

[0031] The analysis unit can recognize the color, shape, and material of each item in the video. The analysis unit can recognize color based on RGB values, for example. For example, the analysis unit can recognize color based on RGB values. The analysis unit can also recognize shape using contour extraction technology. For example, the analysis unit can recognize shape using contour extraction technology. The analysis unit can also recognize material using texture analysis technology. For example, the analysis unit can recognize material using texture analysis technology. This allows the system to understand the characteristics of each item by recognizing the color, shape, material, etc. of each item in the video. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, or can be performed without AI, for example. For example, the analysis unit can input the color, shape, and material of each item in the video to the generation AI and have the generation AI recognize the characteristics.

[0032] The suggestion unit can suggest new items based on the user's budget. The suggestion unit can suggest items, for example, taking into account a price range and a budget limit. For example, the suggestion unit can suggest items taking into account a price range and a budget limit. The suggestion unit can also suggest new items based on the user's budget. For example, the suggestion unit can suggest new items based on the user's budget. This allows the user to easily purchase new items by suggesting new items that fit the user's budget. Some or all of the above-described processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input the user's budget information into the generation AI and cause the generation AI to suggest items based on the budget.

[0033] The providing unit can learn the user's preferences and past coordination history and make personalized suggestions. The providing unit, for example, learns the user's preferences. For example, the providing unit learns the user's preferences. The providing unit can also learn past coordination history. For example, the providing unit learns past coordination history. The providing unit can also learn the user's preferences and past coordination history and make personalized suggestions. For example, the providing unit learns the user's preferences and past coordination history and makes personalized suggestions. By learning the user's preferences and past coordination history, more personalized suggestions can be made. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's preferences and past coordination history into a generation AI and cause the generation AI to make personalized suggestions.

[0034] The acquisition unit can analyze the user's past shooting history and select a shooting method. The acquisition unit, for example, analyzes the user's past shooting history. For example, the acquisition unit analyzes the user's past shooting history. The acquisition unit can also select a shooting method. For example, the acquisition unit selects a shooting method. The acquisition unit can also analyze the user's past shooting history and select a shooting method. For example, the acquisition unit can suggest shooting angles and backgrounds that the user has previously preferred. The acquisition unit can also suggest optimal shooting conditions by referring to the time periods and locations where the user has previously taken photos. The acquisition unit can also analyze and suggest the most successful shooting method from the user's past shooting history. In this way, the optimal shooting method can be suggested by analyzing the user's past shooting history. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past shooting history data into the generation AI and cause the generation AI to select the optimal shooting method.

[0035] The acquisition unit can perform filtering based on the user's current fashion trends and areas of interest when capturing an image. The acquisition unit, for example, identifies the user's current fashion trends. For example, the acquisition unit can identify the user's current fashion trends. The acquisition unit can also identify the user's areas of interest. For example, the acquisition unit can identify the user's areas of interest. The acquisition unit can also perform filtering based on the user's current fashion trends and areas of interest when capturing an image. For example, the acquisition unit can suggest a filter to use when capturing an image based on a fashion style that the user is currently interested in. The acquisition unit can also provide a filter that reflects the style of a brand or designer that the user is interested in. The acquisition unit can also suggest an optimal filter based on the user's past search history and purchase history. This allows for more appropriate capturing by filtering based on the user's current fashion trends and areas of interest. Some or all of the above-described processing by the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input data on the user's fashion trends and areas of interest into a generation AI and have the generation AI perform filtering.

[0036] The acquisition unit can select a shooting means according to a user's input method when taking a photo. The acquisition unit, for example, identifies the user's input method. For example, the acquisition unit identifies the user's input method. The acquisition unit can also select a shooting means according to the input method. For example, the acquisition unit selects a shooting means according to the input method. The acquisition unit can also select a shooting means according to the user's input method when taking a photo. For example, if the user uses voice input, the acquisition unit guides the shooting procedure using voice guidance. Furthermore, if the user uses text input, the acquisition unit can provide detailed shooting procedures in text. Furthermore, if the user uses image input, the acquisition unit can suggest the optimal shooting means using image recognition technology. This allows for more appropriate shooting by selecting the optimal shooting means according to the user's input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's input method data to a generation AI and cause the generation AI to select the optimal shooting means.

[0037] The acquisition unit can prioritize capturing highly relevant items by taking into account the user's geographical location information when capturing an image. The acquisition unit, for example, acquires the user's geographical location information. For example, the acquisition unit acquires the user's geographical location information. The acquisition unit can also identify highly relevant items by taking into account the geographical location information. For example, the acquisition unit identifies highly relevant items by taking into account the geographical location information. The acquisition unit can also prioritize capturing highly relevant items by taking into account the user's geographical location information when capturing an image. For example, if the user is in a specific region, the acquisition unit can prioritize capturing items that are suitable for the climate and culture of that region. If the user is traveling, the acquisition unit can prioritize capturing items that are expected to be used at the travel destination. If the user is participating in a specific event, the acquisition unit can prioritize capturing items that are suitable for the event. As a result, by taking priority in capturing highly relevant items by taking into account the user's geographical location information, more appropriate items can be captured. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographic location information data into the generation AI and cause the generation AI to identify highly relevant items.

[0038] The acquisition unit can analyze the user's social media activity and capture related items when capturing images. The acquisition unit, for example, analyzes the user's social media activity. For example, the acquisition unit analyzes the user's social media activity. The acquisition unit can also identify related items based on the social media activity. For example, the acquisition unit identifies related items based on the social media activity. The acquisition unit can also analyze the user's social media activity and capture related items when capturing images. For example, the acquisition unit prioritizes capturing items that the user frequently posts on social media. The acquisition unit can also capture items that the user's social media followers are likely to be interested in. The acquisition unit can also analyze the user's social media trends and capture related items. In this way, the analysis of the user's social media activity allows capturing related items. Some or all of the above-described processing by the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity data to a generation AI and cause the generation AI to identify related items.

[0039] The acquisition unit can customize the shooting method by reflecting the user's past feedback when shooting. The acquisition unit, for example, collects the user's past feedback. For example, the acquisition unit collects the user's past feedback. The acquisition unit can also customize the shooting method based on the past feedback. For example, the acquisition unit customizes the shooting method based on the past feedback. The acquisition unit can also customize the shooting method by reflecting the user's past feedback when shooting. For example, the acquisition unit suggests an optimal shooting procedure based on shooting methods that the user has previously preferred. The acquisition unit can also analyze the user's past feedback and automatically adjust settings when shooting. The acquisition unit can also customize the shooting procedure to avoid shooting methods that the user has previously dissatisfied with. In this way, a more appropriate shooting method can be selected by reflecting the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into a generation AI and cause the generation AI to customize the shooting method.

[0040] The analysis unit can adjust the accuracy of the analysis based on the importance of the item during analysis. The analysis unit, for example, evaluates the importance of the item. For example, the analysis unit evaluates the importance of the item. The analysis unit can also adjust the accuracy of the analysis based on the importance. For example, the analysis unit adjusts the accuracy of the analysis based on the importance. The analysis unit can also adjust the accuracy of the analysis based on the importance of the item during analysis. For example, the analysis unit performs a detailed analysis of important items. The analysis unit can also perform a brief analysis of less important items. The analysis unit can also perform a detailed analysis of items in which the user is particularly interested. In this way, adjusting the accuracy of the analysis based on the importance of the item allows for more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input item importance data to the generation AI and cause the generation AI to adjust the accuracy of the analysis based on the importance.

[0041] The analysis unit can apply different analysis algorithms depending on the category of the item during analysis. The analysis unit, for example, identifies the category of the item. For example, the analysis unit identifies the category of the item. The analysis unit can also apply different analysis algorithms depending on the category. For example, the analysis unit can apply different analysis algorithms depending on the category. The analysis unit can also apply different analysis algorithms depending on the category of the item during analysis. For example, the analysis unit can apply an algorithm that emphasizes color and material to analyze clothes. The analysis unit can also apply an algorithm that emphasizes shape and design to analyze shoes. The analysis unit can also apply an algorithm that emphasizes detailed design and material to analyze accessories. In this way, by applying different analysis algorithms depending on the category of the item, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input item category data to the generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0042] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit, for example, collects the user's past analysis results. For example, the analysis unit collects the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the past analysis results. For example, the analysis unit can improve the accuracy of the analysis by referring to the past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past feedback. The analysis unit can also analyze the user's past analysis results and suggest an optimal analysis method. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the order of analysis based on the time when the items were photographed. The analysis unit, for example, identifies the time when the items were photographed. For example, the analysis unit identifies the time when the items were photographed. The analysis unit can also determine the order of analysis based on the time when the items were photographed. For example, the analysis unit can determine the order of analysis based on the time when the items were photographed. For example, the analysis unit can prioritize analyzing recently photographed items. The analysis unit can also prioritize analyzing items according to the season. The analysis unit can also prioritize analyzing items that the user plans to use for a specific event. In this way, determining the order of analysis based on the time when the items were photographed enables more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input item photography date data for the items to the generation AI and cause the generation AI to determine the order of analysis based on the time when the items were photographed.

[0044] The analysis unit can adjust the order of analysis based on the relevance of items during analysis. The analysis unit, for example, evaluates the relevance of items. For example, the analysis unit evaluates the relevance of items. The analysis unit can also adjust the order of analysis based on the relevance. For example, the analysis unit adjusts the order of analysis based on the relevance. The analysis unit can also adjust the order of analysis based on the relevance of items during analysis. For example, the analysis unit prioritizes analyzing items that are important in a coordination. The analysis unit can also prioritize analyzing items that the user is particularly interested in. The analysis unit can also adjust the order of analysis taking into account the relevance of items. In this way, adjusting the order of analysis based on the relevance of items enables more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input item relevance data to the generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0045] The analysis unit can adjust the use of analysis terms according to the user's level of expertise during analysis. The analysis unit, for example, identifies the user's level of expertise. For example, the analysis unit identifies the user's level of expertise. The analysis unit can also adjust the use of analysis terms according to the user's level of expertise during analysis. For example, the analysis unit can provide analysis results that use a lot of technical terms to a user who is knowledgeable about fashion. The analysis unit can also provide analysis results that are explained in simple terms to a user who is not knowledgeable about fashion. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to use analysis terms according to the level of expertise.

[0046] The suggestion unit can adjust the accuracy of the suggestion based on the importance of the item when making a suggestion. The suggestion unit, for example, evaluates the importance of the item. For example, the suggestion unit evaluates the importance of the item. The suggestion unit can also adjust the accuracy of the suggestion based on the importance. For example, the suggestion unit can adjust the accuracy of the suggestion based on the importance. The suggestion unit can also adjust the accuracy of the suggestion based on the importance of the item when making a suggestion. For example, the suggestion unit can make detailed suggestions for important items. The suggestion unit can also make brief suggestions for less important items. The suggestion unit can also make detailed suggestions for items in which the user is particularly interested. In this way, adjusting the accuracy of the suggestion based on the importance of the item allows for more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input item importance data to the generation AI and cause the generation AI to adjust the accuracy of the suggestion based on the importance.

[0047] The suggestion unit can apply different suggestion algorithms depending on the category of the item when making a suggestion. The suggestion unit, for example, identifies the category of the item. For example, the suggestion unit identifies the category of the item. The suggestion unit can also apply different suggestion algorithms depending on the category. For example, the suggestion unit can apply different suggestion algorithms depending on the category. The suggestion unit can also apply different suggestion algorithms depending on the category of the item when making a suggestion. For example, the suggestion unit can apply an algorithm that emphasizes color and material when suggesting clothes. The suggestion unit can also apply an algorithm that emphasizes shape and design when suggesting shoes. The suggestion unit can also apply an algorithm that emphasizes detailed design and material when suggesting accessories. In this way, applying different suggestion algorithms depending on the category of the item allows for more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input item category data to the generation AI and cause the generation AI to apply a suggestion algorithm depending on the category.

[0048] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. The suggestion unit, for example, collects the user's past suggestion results. For example, the suggestion unit collects the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the past suggestion results. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit adjusts the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past feedback. The suggestion unit can also analyze the user's past suggestion results and suggest an optimal suggestion method. As a result, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data to the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0049] The suggestion unit, when making suggestions, can determine the order of suggestions based on the dates when the items were photographed. The suggestion unit, for example, identifies the dates when the items were photographed. For example, the suggestion unit identifies the dates when the items were photographed. The suggestion unit can also determine the order of suggestions based on the dates when the items were photographed. For example, the suggestion unit can determine the order of suggestions based on the dates when the items were photographed. The suggestion unit can also determine the order of suggestions based on the dates when the items were photographed. For example, the suggestion unit can prioritize suggesting recently photographed items. The suggestion unit can also prioritize suggesting items according to the season. The suggestion unit can also prioritize suggesting items that the user plans to use for a specific event. In this way, determining the order of suggestions based on the dates when the items were photographed enables more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input item photography date data of the items to the generation AI and cause the generation AI to determine the order of suggestions based on the dates when the items were photographed.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the items when making suggestions. The suggestion unit, for example, evaluates the relevance of the items. For example, the suggestion unit evaluates the relevance of the items. The suggestion unit can also adjust the order of suggestions based on the relevance. For example, the suggestion unit adjusts the order of suggestions based on the relevance. The suggestion unit can also adjust the order of suggestions based on the relevance of the items when making suggestions. For example, the suggestion unit prioritizes suggesting items that are important in a coordination. The suggestion unit can also prioritize suggesting items in which the user is particularly interested. The suggestion unit can also adjust the order of suggestions taking the relevance of the items into consideration. In this way, adjusting the order of suggestions based on the relevance of the items enables more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input item relevance data to a generation AI and cause the generation AI to adjust the order of suggestions based on the relevance.

[0051] The suggestion unit can adjust the use of suggested terms according to the user's level of expertise when making a suggestion. The suggestion unit, for example, identifies the user's level of expertise. For example, the suggestion unit identifies the user's level of expertise. The suggestion unit can also adjust the use of suggested terms according to the user's level of expertise when making a suggestion. For example, the suggestion unit can provide suggestions that use a lot of technical terms to a user who is knowledgeable about fashion. The suggestion unit can also provide suggestions that are explained in simple terms to a user who is not knowledgeable about fashion. The suggestion unit can also adjust the way the suggestion is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the suggestion according to the user's level of expertise, more appropriate suggestions can be provided. Some or all of the above-described processing by the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data to the generation AI and cause the generation AI to use suggested terms according to the user's level of expertise.

[0052] The providing unit can select a providing method by analyzing the user's past providing history at the time of providing. The providing unit, for example, collects the user's past providing history. For example, the providing unit collects the user's past providing history. The providing unit can also select a providing method by analyzing the past providing history. For example, the providing unit selects a providing method by analyzing the past providing history. The providing unit can also select a providing method by analyzing the user's past providing history at the time of providing. For example, the providing unit selects an optimal providing means based on the user's preferred providing methods in the past. The providing unit can also analyze the user's past providing history and select the most effective providing method. The providing unit can also adjust the providing method by referring to the user's past feedback. In this way, the optimal providing method can be selected by analyzing the user's past providing history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past providing history data to the generation AI and cause the generation AI to select a providing method.

[0053] The providing unit can customize the provided content based on the user's current fashion trends and areas of interest at the time of providing the information. The providing unit, for example, identifies the user's current fashion trends. For example, the providing unit identifies the user's current fashion trends. The providing unit can also identify areas of interest. For example, the providing unit identifies areas of interest. The providing unit can also customize the provided content based on the user's current fashion trends and areas of interest at the time of providing the information. For example, the providing unit customizes the provided content based on the fashion style in which the user is currently interested. The providing unit can also provide information on brands and designers in which the user is interested. The providing unit can also customize the optimal provided content based on the user's past search history and purchase history. This allows for more appropriate information to be provided by customizing the provided content based on the user's current fashion trends and areas of interest. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input data on the user's fashion trends and areas of interest into a generation AI and cause the generation AI to customize the provided content.

[0054] The providing unit can improve the provision method by reflecting user feedback at the time of provision. The providing unit, for example, collects user feedback. For example, the providing unit collects user feedback. The providing unit can also improve the provision method based on the feedback. For example, the providing unit improves the provision method based on the feedback. The providing unit can also improve the provision method by reflecting user feedback at the time of provision. For example, the providing unit adjusts the provision method based on user feedback. The providing unit can also analyze past user feedback and select an optimal provision method. The providing unit can also customize the provision content by reflecting user feedback. In this way, the provision method is improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data to a generation AI and cause the generation AI to improve the provision method.

[0055] The providing unit can provide highly relevant items preferentially by taking into account the user's geographical location information when providing the items. The providing unit, for example, acquires the user's geographical location information. For example, the providing unit acquires the user's geographical location information. The providing unit can also identify highly relevant items by taking into account the geographical location information. For example, the providing unit identifies highly relevant items by taking into account the geographical location information. The providing unit can also provide highly relevant items preferentially by taking into account the user's geographical location information when providing the items. For example, if the user is in a specific region, the providing unit can preferentially provide items suitable for the climate and culture of the region. Furthermore, if the user is traveling, the providing unit can preferentially provide items intended for use at the travel destination. Furthermore, if the user is participating in a specific event, the providing unit can preferentially provide items suitable for the event. In this way, by preferentially providing highly relevant items by taking into account the user's geographical location information, more appropriate items can be provided. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographic location information data into the generating AI and cause the generating AI to identify highly relevant items.

[0056] The providing unit can analyze the user's social media activity and provide related items at the time of providing. The providing unit, for example, analyzes the user's social media activity. For example, the providing unit analyzes the user's social media activity. The providing unit can also identify related items based on the social media activity. For example, the providing unit identifies related items based on the social media activity. The providing unit can also analyze the user's social media activity and provide related items at the time of providing. For example, the providing unit can prioritize providing items that the user frequently posts on social media. The providing unit can also provide items that the user's social media followers are likely to be interested in. The providing unit can also analyze the user's social media trends and provide related items. In this way, related items can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data to a generation AI and cause the generation AI to identify related items.

[0057] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the content. The providing unit, for example, collects the user's past feedback. For example, the providing unit collects the user's past feedback. The providing unit can also customize the delivery method based on the past feedback. For example, the providing unit customizes the delivery method based on the past feedback. The providing unit can also customize the delivery method by reflecting the user's past feedback when providing the content. For example, the providing unit selects an optimal delivery means based on delivery methods preferred by the user in the past. The providing unit can also analyze the user's past delivery history and select the most effective delivery method. The providing unit can also adjust the delivery method by referring to the user's past feedback. In this way, the delivery method is customized by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the delivery method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The fashion suggestion system can also acquire lifestyle data about the user and reflect it in the suggestions. For example, the acquisition unit can acquire lifestyle data such as the user's exercise habits, diet, and work schedule. The analysis unit can analyze this data and make fashion suggestions that are optimal for the user's lifestyle. For example, for a user who exercises regularly, items made of materials and designs that are easy to move in can be suggested. Also, for a user with a busy work schedule, items made of materials that are wrinkle-resistant and easy to care for can be suggested. This makes it possible to make more practical and personalized fashion suggestions that are tailored to the user's lifestyle.

[0060] The acquisition unit can acquire the user's health data and reflect it in the suggestions. For example, the acquisition unit can acquire health data such as the user's heart rate, sleep patterns, and stress level. The analysis unit can analyze this data and make fashion suggestions that are optimal for the user's health condition. For example, for a user with a high stress level, items with colors and materials that have a relaxing effect can be suggested. Also, for a user who is sleep-deprived, items that are comfortable to wear can be suggested. This makes it possible to suggest more comfortable and healthy fashion that is tailored to the user's health condition.

[0061] The fashion suggestion system can also acquire a user's purchasing history and reflect it in the suggestions. For example, the acquisition unit can acquire the user's past purchasing history. The analysis unit can analyze this data and make optimal fashion suggestions based on the user's purchasing trends. For example, for a user who likes items from a particular brand or designer, new items from that brand or designer can be suggested. Also, for a user who likes a particular color or style, items that match that color or style can be suggested. This makes it possible to make more personalized fashion suggestions that match the user's purchasing trends.

[0062] The fashion suggestion system can also acquire the user's social media activity and reflect it in the suggestions. For example, the acquisition unit can acquire the user's social media posts and followers' reactions. The analysis unit can analyze this data and make optimal fashion suggestions based on the user's social media activity. For example, it can suggest items and styles that the user often posts on social media. It can also suggest items that followers are likely to be interested in. This enables more personalized fashion suggestions to be made in line with the user's social media activity.

[0063] The fashion suggestion system can further acquire the user's geographical location information and reflect it in the suggestions. For example, the acquisition unit can acquire the user's current geographical location information. The analysis unit can analyze this data and make optimal fashion suggestions based on the user's geographical location. For example, it can suggest items suitable for the climate and culture of a particular region. It can also suggest items intended for use at travel destinations. This enables more practical and personalized fashion suggestions tailored to the user's geographical location.

[0064] The fashion suggestion system can also acquire the user's event participation information and reflect it in the suggestions. For example, the acquisition unit can acquire information about events the user plans to attend. The analysis unit can analyze this data and make optimal fashion suggestions based on the type of event and dress code. For example, elegant dresses and suits can be suggested for formal events, and relaxed style items can be suggested for casual events. This allows for more appropriate fashion suggestions tailored to the user's event participation information.

[0065] The fashion suggestion system can further acquire the user's seasonal fashion trends and reflect them in the suggestions. For example, the acquisition unit can acquire the user's past seasonal fashion trends. The analysis unit can analyze this data and make optimal fashion suggestions according to the season. For example, items made of cool materials and designs can be suggested in the summer, and items made of warm materials and layered styles can be suggested in the winter. This makes it possible to make more practical and personalized fashion suggestions that match the user's seasonal fashion trends.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The acquisition unit captures a photo of an item the user is holding. The item may include clothing, accessories, shoes, etc. The acquisition unit can capture videos taken with a smartphone, as well as photos taken with a digital camera or webcam. The acquisition unit also has a function to adjust the lighting conditions and shooting angle during shooting, and can encourage the user to take photos at times when natural light is best and guide the user to complete the photo shoot in a short amount of time. Furthermore, if the user is excited, the acquisition unit can provide an interface that emphasizes the fun of taking photos. Step 2: The analysis unit uses AI to analyze the video captured by the acquisition unit and recognize the characteristics of each item. These characteristics include color, shape, and material. For example, the analysis unit can recognize color based on RGB values, shape using contour extraction technology, and material using texture analysis technology. Step 3: The suggestion unit uses AI to generate optimal outfits and fashion images based on the features recognized by the analysis unit. The suggestion unit generates outfits based on color combinations and style matches, and can also suggest new items based on the user's budget. Furthermore, the suggestion unit can learn the user's preferences and past outfit history to make more personalized suggestions. Step 4: The providing unit provides the coordinated outfits and fashion images generated by the suggestion unit to the user. The providing unit can display the information through a web application or a mobile application, or can provide the information by email or push notification. Furthermore, the providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can select a method of providing information that includes detailed information, and if the user is in a hurry, the providing unit can select a concise method of providing information that focuses on the main points. Also, if the user is excited, the providing unit can select a visually appealing method of providing information.

[0068] (Example 2) A fashion suggestion system according to an embodiment of the present invention allows a user to take a video of their personal items with their smartphone and then use AI to analyze the video to instantly suggest optimal outfits, fashion images, and new items that fit the user's budget. The fashion suggestion system effectively utilizes the user's personal items and easily achieves unique styling. For example, the fashion suggestion system allows a user to take a video of their clothes and shoes with their smartphone. The video is then input into AI. The AI ​​then analyzes the input video and recognizes the characteristics of each item. Based on the recognized item characteristics, the AI ​​generates optimal outfits and fashion images and also suggests new items that fit the user's budget. This allows users to effectively utilize their personal items and easily achieve unique styling. Furthermore, the AI ​​can learn the user's preferences and past outfit history to provide more personalized suggestions. This allows the fashion suggestion system to effectively utilize the user's personal items and easily achieve unique styling. For example, this system is extremely useful for users who are interested in fashion but lack confidence in their outfit coordination skills.

[0069] A fashion suggestion system according to an embodiment includes an acquisition unit, an analysis unit, a suggestion unit, and a provision unit. The acquisition unit captures images of items owned by a user. Examples of items owned by a user include, but are not limited to, clothing, accessories, and shoes. The acquisition unit acquires, for example, videos captured with a smartphone. The acquisition unit can also capture images using a digital camera or webcam. The acquisition unit also has a function for adjusting lighting conditions and shooting angles during capture. For example, the acquisition unit may encourage a user to capture images at a time when natural light is best. The acquisition unit may also guide a user to complete the capture as quickly as possible if the user is in a hurry. The acquisition unit may also provide an interface that emphasizes the enjoyment of capturing images if the user is excited. The analysis unit uses AI to analyze the video captured by the acquisition unit and recognize features of each item. Features include, for example, color, shape, and material, but are not limited to, examples. For example, the analysis unit recognizes color based on RGB values. The analysis unit may also recognize shape using contour extraction technology. The analysis unit may also recognize material using texture analysis technology. The suggestion unit uses AI to generate optimal outfits and fashion images based on the features recognized by the analysis unit. The suggestion unit generates outfits based on, for example, color combinations and style matches. The suggestion unit can also suggest new items based on the user's budget. For example, the suggestion unit may suggest items taking into account a price range and a budget limit. Furthermore, the suggestion unit can learn the user's preferences and past outfit history to provide more personalized suggestions. The provision unit provides the outfits and fashion images generated by the suggestion unit to the user. The provision unit displays information through, for example, a web application or a mobile application. The provision unit can also provide information via email or push notification. Furthermore, the provision unit can estimate the user's emotions and adjust the method of provision based on the estimated user's emotions. For example, if the user is relaxed, the provision unit selects a method of provision that includes detailed information.Furthermore, if the user is in a hurry, the providing unit can select a concise providing method that focuses on the main points. Furthermore, if the user is excited, the providing unit can select a visually appealing providing method. As a result, the fashion suggestion system according to the embodiment can effectively utilize the items the user owns and easily achieve unique styling. For example, this is very convenient for users who are interested in fashion but are not confident in coordinating outfits.

[0070] The acquisition unit can acquire videos taken by a user with a smartphone. The video taken with a smartphone includes, for example, resolution and frame rate, but is not limited to these examples. For example, the acquisition unit acquires videos taken with a smartphone at high resolution. The acquisition unit can also adjust the frame rate to acquire smoother videos. The acquisition unit can also automatically adjust the camera settings of the smartphone to acquire optimal videos. For example, the acquisition unit can automatically adjust the camera settings of the smartphone to acquire optimal videos. The acquisition unit can also automatically adjust the camera settings of the smartphone to acquire optimal videos. By acquiring videos taken by a user with a smartphone, the system can recognize items held by the user. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input videos taken with a smartphone to a generation AI and cause the generation AI to analyze the videos.

[0071] The analysis unit can recognize the color, shape, and material of each item in the video. The analysis unit can recognize color based on RGB values, for example. For example, the analysis unit can recognize color based on RGB values. The analysis unit can also recognize shape using contour extraction technology. For example, the analysis unit can recognize shape using contour extraction technology. The analysis unit can also recognize material using texture analysis technology. For example, the analysis unit can recognize material using texture analysis technology. This allows the system to understand the characteristics of each item by recognizing the color, shape, material, etc. of each item in the video. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, or can be performed without AI, for example. For example, the analysis unit can input the color, shape, and material of each item in the video to the generation AI and have the generation AI recognize the characteristics.

[0072] The suggestion unit can suggest new items based on the user's budget. The suggestion unit can suggest items, for example, taking into account a price range and a budget limit. For example, the suggestion unit can suggest items taking into account a price range and a budget limit. The suggestion unit can also suggest new items based on the user's budget. For example, the suggestion unit can suggest new items based on the user's budget. This allows the user to easily purchase new items by suggesting new items that fit the user's budget. Some or all of the above-described processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input the user's budget information into the generation AI and cause the generation AI to suggest items based on the budget.

[0073] The providing unit can learn the user's preferences and past coordination history and make personalized suggestions. The providing unit, for example, learns the user's preferences. For example, the providing unit learns the user's preferences. The providing unit can also learn past coordination history. For example, the providing unit learns past coordination history. The providing unit can also learn the user's preferences and past coordination history and make personalized suggestions. For example, the providing unit learns the user's preferences and past coordination history and makes personalized suggestions. By learning the user's preferences and past coordination history, more personalized suggestions can be made. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's preferences and past coordination history into a generation AI and cause the generation AI to make personalized suggestions.

[0074] The acquisition unit can estimate the user's emotions and adjust the timing of shooting based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions. For example, the acquisition unit estimates the user's emotions. The acquisition unit can also adjust the timing of shooting based on the estimated user emotions. For example, the acquisition unit adjusts the timing of shooting based on the estimated user emotions. The acquisition unit can also estimate the user's emotions and adjust the timing of shooting based on the estimated user emotions. For example, if the user is relaxed, the acquisition unit can encourage the user to take a photo at a time when natural light is best. Furthermore, if the user is in a hurry, the acquisition unit can guide the user to complete the photo shoot as quickly as possible. Furthermore, if the user is excited, the acquisition unit can provide an interface that emphasizes the enjoyment of taking a photo. This allows the user to take a photo at a more appropriate time by adjusting the timing of shooting according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input user emotion data to the generation AI and cause the generation AI to adjust the timing of shooting based on the emotion.

[0075] The acquisition unit can analyze the user's past shooting history and select a shooting method. The acquisition unit, for example, analyzes the user's past shooting history. For example, the acquisition unit analyzes the user's past shooting history. The acquisition unit can also select a shooting method. For example, the acquisition unit selects a shooting method. The acquisition unit can also analyze the user's past shooting history and select a shooting method. For example, the acquisition unit can suggest shooting angles and backgrounds that the user has previously preferred. The acquisition unit can also suggest optimal shooting conditions by referring to the time periods and locations where the user has previously taken photos. The acquisition unit can also analyze and suggest the most successful shooting method from the user's past shooting history. In this way, the optimal shooting method can be suggested by analyzing the user's past shooting history. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past shooting history data into the generation AI and cause the generation AI to select the optimal shooting method.

[0076] The acquisition unit can perform filtering based on the user's current fashion trends and areas of interest when capturing an image. The acquisition unit, for example, identifies the user's current fashion trends. For example, the acquisition unit can identify the user's current fashion trends. The acquisition unit can also identify the user's areas of interest. For example, the acquisition unit can identify the user's areas of interest. The acquisition unit can also perform filtering based on the user's current fashion trends and areas of interest when capturing an image. For example, the acquisition unit can suggest a filter to use when capturing an image based on a fashion style that the user is currently interested in. The acquisition unit can also provide a filter that reflects the style of a brand or designer that the user is interested in. The acquisition unit can also suggest an optimal filter based on the user's past search history and purchase history. This allows for more appropriate capturing by filtering based on the user's current fashion trends and areas of interest. Some or all of the above-described processing by the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input data on the user's fashion trends and areas of interest into a generation AI and have the generation AI perform filtering.

[0077] The acquisition unit can select a shooting means according to a user's input method when taking a photo. The acquisition unit, for example, identifies the user's input method. For example, the acquisition unit identifies the user's input method. The acquisition unit can also select a shooting means according to the input method. For example, the acquisition unit selects a shooting means according to the input method. The acquisition unit can also select a shooting means according to the user's input method when taking a photo. For example, if the user uses voice input, the acquisition unit guides the shooting procedure using voice guidance. Furthermore, if the user uses text input, the acquisition unit can provide detailed shooting procedures in text. Furthermore, if the user uses image input, the acquisition unit can suggest the optimal shooting means using image recognition technology. This allows for more appropriate shooting by selecting the optimal shooting means according to the user's input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's input method data to a generation AI and cause the generation AI to select the optimal shooting means.

[0078] The acquisition unit can estimate the user's emotions and determine the priority of items to be photographed based on the estimated user's emotions. The acquisition unit, for example, estimates the user's emotions. The acquisition unit can also determine the priority of items to be photographed based on the estimated user's emotions. For example, the acquisition unit can determine the priority of items to be photographed based on the estimated user's emotions. The acquisition unit can also estimate the user's emotions and determine the priority of items to be photographed based on the estimated user's emotions. For example, if the user is excited, the acquisition unit can prioritize photographing the most noticeable item. If the user is relaxed, the acquisition unit can also photograph items taking into consideration the overall balance. If the user is in a hurry, the acquisition unit can prioritize photographing important items. This allows for more appropriate item photography by determining the priority of items to be photographed based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit may input user emotion data into the generation AI and cause the generation AI to determine the priority of items based on the emotion.

[0079] The acquisition unit can prioritize capturing highly relevant items by taking into account the user's geographical location information when capturing an image. The acquisition unit, for example, acquires the user's geographical location information. For example, the acquisition unit acquires the user's geographical location information. The acquisition unit can also identify highly relevant items by taking into account the geographical location information. For example, the acquisition unit identifies highly relevant items by taking into account the geographical location information. The acquisition unit can also prioritize capturing highly relevant items by taking into account the user's geographical location information when capturing an image. For example, if the user is in a specific region, the acquisition unit can prioritize capturing items that are suitable for the climate and culture of that region. If the user is traveling, the acquisition unit can prioritize capturing items that are expected to be used at the travel destination. If the user is participating in a specific event, the acquisition unit can prioritize capturing items that are suitable for the event. As a result, by taking priority in capturing highly relevant items by taking into account the user's geographical location information, more appropriate items can be captured. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographic location information data into the generation AI and cause the generation AI to identify highly relevant items.

[0080] The acquisition unit can analyze the user's social media activity and capture related items when capturing images. The acquisition unit, for example, analyzes the user's social media activity. For example, the acquisition unit analyzes the user's social media activity. The acquisition unit can also identify related items based on the social media activity. For example, the acquisition unit identifies related items based on the social media activity. The acquisition unit can also analyze the user's social media activity and capture related items when capturing images. For example, the acquisition unit prioritizes capturing items that the user frequently posts on social media. The acquisition unit can also capture items that the user's social media followers are likely to be interested in. The acquisition unit can also analyze the user's social media trends and capture related items. In this way, the analysis of the user's social media activity allows capturing related items. Some or all of the above-described processing by the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity data to a generation AI and cause the generation AI to identify related items.

[0081] The acquisition unit can customize the shooting method by reflecting the user's past feedback when shooting. The acquisition unit, for example, collects the user's past feedback. For example, the acquisition unit collects the user's past feedback. The acquisition unit can also customize the shooting method based on the past feedback. For example, the acquisition unit customizes the shooting method based on the past feedback. The acquisition unit can also customize the shooting method by reflecting the user's past feedback when shooting. For example, the acquisition unit suggests an optimal shooting procedure based on shooting methods that the user has previously preferred. The acquisition unit can also analyze the user's past feedback and automatically adjust settings when shooting. The acquisition unit can also customize the shooting procedure to avoid shooting methods that the user has previously dissatisfied with. In this way, a more appropriate shooting method can be selected by reflecting the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into a generation AI and cause the generation AI to customize the shooting method.

[0082] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. The analysis unit can also adjust the presentation method of the analysis based on the estimated user's emotion. For example, the analysis unit can adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit can also estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results that focus on the main points when the user is in a hurry. The analysis unit can also provide visually appealing analysis results when the user is excited. In this way, by adjusting the presentation method of the analysis according to the user's emotion, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and cause the generation AI to adjust the expression method of the analysis based on the emotion.

[0083] The analysis unit can adjust the accuracy of the analysis based on the importance of the item during analysis. The analysis unit, for example, evaluates the importance of the item. For example, the analysis unit evaluates the importance of the item. The analysis unit can also adjust the accuracy of the analysis based on the importance. For example, the analysis unit adjusts the accuracy of the analysis based on the importance. The analysis unit can also adjust the accuracy of the analysis based on the importance of the item during analysis. For example, the analysis unit performs a detailed analysis of important items. The analysis unit can also perform a brief analysis of less important items. The analysis unit can also perform a detailed analysis of items in which the user is particularly interested. In this way, adjusting the accuracy of the analysis based on the importance of the item allows for more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input item importance data to the generation AI and cause the generation AI to adjust the accuracy of the analysis based on the importance.

[0084] The analysis unit can apply different analysis algorithms depending on the category of the item during analysis. The analysis unit, for example, identifies the category of the item. For example, the analysis unit identifies the category of the item. The analysis unit can also apply different analysis algorithms depending on the category. For example, the analysis unit can apply different analysis algorithms depending on the category. The analysis unit can also apply different analysis algorithms depending on the category of the item during analysis. For example, the analysis unit can apply an algorithm that emphasizes color and material to analyze clothes. The analysis unit can also apply an algorithm that emphasizes shape and design to analyze shoes. The analysis unit can also apply an algorithm that emphasizes detailed design and material to analyze accessories. In this way, by applying different analysis algorithms depending on the category of the item, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input item category data to the generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0085] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit, for example, collects the user's past analysis results. For example, the analysis unit collects the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the past analysis results. For example, the analysis unit can improve the accuracy of the analysis by referring to the past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past feedback. The analysis unit can also analyze the user's past analysis results and suggest an optimal analysis method. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0086] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit estimates the user's emotion. The analysis unit can also adjust the length of the analysis based on the estimated user's emotion. For example, the analysis unit can adjust the length of the analysis based on the estimated user's emotion. The analysis unit can also estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, the analysis unit can provide a short and concise analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a visually appealing analysis result when the user is excited. Thus, by adjusting the length of the analysis according to the user's emotion, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and cause the generation AI to adjust the length of analysis based on the emotion.

[0087] During analysis, the analysis unit can determine the order of analysis based on the time when the items were photographed. The analysis unit, for example, identifies the time when the items were photographed. For example, the analysis unit identifies the time when the items were photographed. The analysis unit can also determine the order of analysis based on the time when the items were photographed. For example, the analysis unit can determine the order of analysis based on the time when the items were photographed. For example, the analysis unit can prioritize analyzing recently photographed items. The analysis unit can also prioritize analyzing items according to the season. The analysis unit can also prioritize analyzing items that the user plans to use for a specific event. In this way, determining the order of analysis based on the time when the items were photographed enables more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input item photography date data for the items to the generation AI and cause the generation AI to determine the order of analysis based on the time when the items were photographed.

[0088] The analysis unit can adjust the order of analysis based on the relevance of items during analysis. The analysis unit, for example, evaluates the relevance of items. For example, the analysis unit evaluates the relevance of items. The analysis unit can also adjust the order of analysis based on the relevance. For example, the analysis unit adjusts the order of analysis based on the relevance. The analysis unit can also adjust the order of analysis based on the relevance of items during analysis. For example, the analysis unit prioritizes analyzing items that are important in a coordination. The analysis unit can also prioritize analyzing items that the user is particularly interested in. The analysis unit can also adjust the order of analysis taking into account the relevance of items. In this way, adjusting the order of analysis based on the relevance of items enables more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input item relevance data to the generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0089] The analysis unit can adjust the use of analysis terms according to the user's level of expertise during analysis. The analysis unit, for example, identifies the user's level of expertise. For example, the analysis unit identifies the user's level of expertise. The analysis unit can also adjust the use of analysis terms according to the user's level of expertise during analysis. For example, the analysis unit can provide analysis results that use a lot of technical terms to a user who is knowledgeable about fashion. The analysis unit can also provide analysis results that are explained in simple terms to a user who is not knowledgeable about fashion. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to use analysis terms according to the level of expertise.

[0090] The suggestion unit can estimate a user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. The suggestion unit, for example, estimates a user's emotion. For example, the suggestion unit estimates a user's emotion. The suggestion unit can also adjust the way in which suggestions are expressed based on the estimated user's emotion. For example, the suggestion unit can adjust the way in which suggestions are expressed based on the estimated user's emotion. The suggestion unit can also estimate a user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. The suggestion unit can also provide concise suggestions that focus on the main points when the user is in a hurry. The suggestion unit can also provide visually appealing suggestions when the user is excited. In this way, by adjusting the way in which suggestions are expressed based on the user's emotion, more appropriate suggestions can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input user emotion data to the generation AI and cause the generation AI to adjust the way the suggestion is expressed based on the emotion.

[0091] The suggestion unit can adjust the accuracy of the suggestion based on the importance of the item when making a suggestion. The suggestion unit, for example, evaluates the importance of the item. For example, the suggestion unit evaluates the importance of the item. The suggestion unit can also adjust the accuracy of the suggestion based on the importance. For example, the suggestion unit can adjust the accuracy of the suggestion based on the importance. The suggestion unit can also adjust the accuracy of the suggestion based on the importance of the item when making a suggestion. For example, the suggestion unit can make detailed suggestions for important items. The suggestion unit can also make brief suggestions for less important items. The suggestion unit can also make detailed suggestions for items in which the user is particularly interested. In this way, adjusting the accuracy of the suggestion based on the importance of the item allows for more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input item importance data to the generation AI and cause the generation AI to adjust the accuracy of the suggestion based on the importance.

[0092] The suggestion unit can apply different suggestion algorithms depending on the category of the item when making a suggestion. The suggestion unit, for example, identifies the category of the item. For example, the suggestion unit identifies the category of the item. The suggestion unit can also apply different suggestion algorithms depending on the category. For example, the suggestion unit can apply different suggestion algorithms depending on the category. The suggestion unit can also apply different suggestion algorithms depending on the category of the item when making a suggestion. For example, the suggestion unit can apply an algorithm that emphasizes color and material when suggesting clothes. The suggestion unit can also apply an algorithm that emphasizes shape and design when suggesting shoes. The suggestion unit can also apply an algorithm that emphasizes detailed design and material when suggesting accessories. In this way, applying different suggestion algorithms depending on the category of the item allows for more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input item category data to the generation AI and cause the generation AI to apply a suggestion algorithm depending on the category.

[0093] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. The suggestion unit, for example, collects the user's past suggestion results. For example, the suggestion unit collects the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the past suggestion results. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit adjusts the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past feedback. The suggestion unit can also analyze the user's past suggestion results and suggest an optimal suggestion method. As a result, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data to the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0094] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. For example, the suggestion unit estimates the user's emotion. The suggestion unit can also adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit can also estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can provide a short and to-the-point suggestion when the user is in a hurry. The suggestion unit can also provide a detailed suggestion when the user is relaxed. The suggestion unit can also provide a visually appealing suggestion when the user is excited. In this way, by adjusting the length of the suggestion according to the user's emotion, more appropriate suggestions can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input user emotion data to the generation AI and cause the generation AI to adjust the length of the suggestion based on the emotion.

[0095] The suggestion unit, when making suggestions, can determine the order of suggestions based on the dates when the items were photographed. The suggestion unit, for example, identifies the dates when the items were photographed. For example, the suggestion unit identifies the dates when the items were photographed. The suggestion unit can also determine the order of suggestions based on the dates when the items were photographed. For example, the suggestion unit can determine the order of suggestions based on the dates when the items were photographed. The suggestion unit can also determine the order of suggestions based on the dates when the items were photographed. For example, the suggestion unit can prioritize suggesting recently photographed items. The suggestion unit can also prioritize suggesting items according to the season. The suggestion unit can also prioritize suggesting items that the user plans to use for a specific event. In this way, determining the order of suggestions based on the dates when the items were photographed enables more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input item photography date data of the items to the generation AI and cause the generation AI to determine the order of suggestions based on the dates when the items were photographed.

[0096] The suggestion unit can adjust the order of suggestions based on the relevance of the items when making suggestions. The suggestion unit, for example, evaluates the relevance of the items. For example, the suggestion unit evaluates the relevance of the items. The suggestion unit can also adjust the order of suggestions based on the relevance. For example, the suggestion unit adjusts the order of suggestions based on the relevance. The suggestion unit can also adjust the order of suggestions based on the relevance of the items when making suggestions. For example, the suggestion unit prioritizes suggesting items that are important in a coordination. The suggestion unit can also prioritize suggesting items in which the user is particularly interested. The suggestion unit can also adjust the order of suggestions taking the relevance of the items into consideration. In this way, adjusting the order of suggestions based on the relevance of the items enables more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input item relevance data to a generation AI and cause the generation AI to adjust the order of suggestions based on the relevance.

[0097] The suggestion unit can adjust the use of suggested terms according to the user's level of expertise when making a suggestion. The suggestion unit, for example, identifies the user's level of expertise. For example, the suggestion unit identifies the user's level of expertise. The suggestion unit can also adjust the use of suggested terms according to the user's level of expertise when making a suggestion. For example, the suggestion unit can provide suggestions that use a lot of technical terms to a user who is knowledgeable about fashion. The suggestion unit can also provide suggestions that are explained in simple terms to a user who is not knowledgeable about fashion. The suggestion unit can also adjust the way the suggestion is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the suggestion according to the user's level of expertise, more appropriate suggestions can be provided. Some or all of the above-described processing by the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data to the generation AI and cause the generation AI to use suggested terms according to the user's level of expertise.

[0098] The providing unit can estimate the user's emotion and adjust the method of providing information based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. For example, the providing unit estimates the user's emotion. The providing unit can also adjust the method of providing information based on the estimated user's emotion. For example, the providing unit adjusts the method of providing information based on the estimated user's emotion. The providing unit can also estimate the user's emotion and adjust the method of providing information based on the estimated user's emotion. For example, if the user is relaxed, the providing unit can select a method of providing information that includes detailed information. If the user is in a hurry, the providing unit can select a concise method of providing information that focuses on the main points. If the user is excited, the providing unit can select a visually appealing method of providing information. This allows for more appropriate information provision by adjusting the method of providing information according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generating AI and cause the generating AI to adjust the providing method based on the emotion.

[0099] The providing unit can select a providing method by analyzing the user's past providing history at the time of providing. The providing unit, for example, collects the user's past providing history. For example, the providing unit collects the user's past providing history. The providing unit can also select a providing method by analyzing the past providing history. For example, the providing unit selects a providing method by analyzing the past providing history. The providing unit can also select a providing method by analyzing the user's past providing history at the time of providing. For example, the providing unit selects an optimal providing means based on the user's preferred providing methods in the past. The providing unit can also analyze the user's past providing history and select the most effective providing method. The providing unit can also adjust the providing method by referring to the user's past feedback. In this way, the optimal providing method can be selected by analyzing the user's past providing history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past providing history data to the generation AI and cause the generation AI to select a providing method.

[0100] The providing unit can customize the provided content based on the user's current fashion trends and areas of interest at the time of providing the information. The providing unit, for example, identifies the user's current fashion trends. For example, the providing unit identifies the user's current fashion trends. The providing unit can also identify areas of interest. For example, the providing unit identifies areas of interest. The providing unit can also customize the provided content based on the user's current fashion trends and areas of interest at the time of providing the information. For example, the providing unit customizes the provided content based on the fashion style in which the user is currently interested. The providing unit can also provide information on brands and designers in which the user is interested. The providing unit can also customize the optimal provided content based on the user's past search history and purchase history. This allows for more appropriate information to be provided by customizing the provided content based on the user's current fashion trends and areas of interest. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input data on the user's fashion trends and areas of interest into a generation AI and cause the generation AI to customize the provided content.

[0101] The providing unit can improve the provision method by reflecting user feedback at the time of provision. The providing unit, for example, collects user feedback. For example, the providing unit collects user feedback. The providing unit can also improve the provision method based on the feedback. For example, the providing unit improves the provision method based on the feedback. The providing unit can also improve the provision method by reflecting user feedback at the time of provision. For example, the providing unit adjusts the provision method based on user feedback. The providing unit can also analyze past user feedback and select an optimal provision method. The providing unit can also customize the provision content by reflecting user feedback. In this way, the provision method is improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data to a generation AI and cause the generation AI to improve the provision method.

[0102] The providing unit can estimate the user's emotions and determine the priority of items to be provided based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. The providing unit can also determine the priority of items to be provided based on the estimated user's emotions. For example, the providing unit determines the priority of items to be provided based on the estimated user's emotions. The providing unit can also estimate the user's emotions and determine the priority of items to be provided based on the estimated user's emotions. For example, if the user is excited, the providing unit can prioritize providing the most eye-catching item. Furthermore, if the user is relaxed, the providing unit can also provide items taking into consideration the overall balance. Furthermore, if the user is in a hurry, the providing unit can prioritize providing important items. Thus, by determining the priority of items to be provided based on the user's emotions, more appropriate items can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generating AI and cause the generating AI to determine the priority of items based on the emotion.

[0103] The providing unit can provide highly relevant items preferentially by taking into account the user's geographical location information when providing the items. The providing unit, for example, acquires the user's geographical location information. For example, the providing unit acquires the user's geographical location information. The providing unit can also identify highly relevant items by taking into account the geographical location information. For example, the providing unit identifies highly relevant items by taking into account the geographical location information. The providing unit can also provide highly relevant items preferentially by taking into account the user's geographical location information when providing the items. For example, if the user is in a specific region, the providing unit can preferentially provide items suitable for the climate and culture of the region. Furthermore, if the user is traveling, the providing unit can preferentially provide items intended for use at the travel destination. Furthermore, if the user is participating in a specific event, the providing unit can preferentially provide items suitable for the event. In this way, by preferentially providing highly relevant items by taking into account the user's geographical location information, more appropriate items can be provided. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographic location information data into the generating AI and cause the generating AI to identify highly relevant items.

[0104] The providing unit can analyze the user's social media activity and provide related items at the time of providing. The providing unit, for example, analyzes the user's social media activity. For example, the providing unit analyzes the user's social media activity. The providing unit can also identify related items based on the social media activity. For example, the providing unit identifies related items based on the social media activity. The providing unit can also analyze the user's social media activity and provide related items at the time of providing. For example, the providing unit can prioritize providing items that the user frequently posts on social media. The providing unit can also provide items that the user's social media followers are likely to be interested in. The providing unit can also analyze the user's social media trends and provide related items. In this way, related items can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data to a generation AI and cause the generation AI to identify related items.

[0105] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the content. The providing unit, for example, collects the user's past feedback. For example, the providing unit collects the user's past feedback. The providing unit can also customize the delivery method based on the past feedback. For example, the providing unit customizes the delivery method based on the past feedback. The providing unit can also customize the delivery method by reflecting the user's past feedback when providing the content. For example, the providing unit selects an optimal delivery means based on delivery methods preferred by the user in the past. The providing unit can also analyze the user's past delivery history and select the most effective delivery method. The providing unit can also adjust the delivery method by referring to the user's past feedback. In this way, the delivery method is customized by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the delivery method. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, analysis unit, suggestion unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit can capture images of items held by the user via the camera 42 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the video transmitted from the acquisition unit, and recognizes the characteristics of each item. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and generates optimal coordination and fashion images based on the characteristics recognized by the analysis unit. The provision unit is realized by the control unit 46A of the smart device 14, and provides the coordination and fashion images generated by the suggestion unit to the user. === Hard Collateral 1-2 === Each of the multiple elements including the acquisition unit, analysis unit, suggestion unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit can capture images of items held by the user via the camera 42 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes videos transmitted from the acquisition unit, and recognizes the characteristics of each item. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and generates optimal coordination and fashion images based on the characteristics recognized by the analysis unit. The provision unit is realized by the control unit 46A of the smart glasses 214, and provides the coordination and fashion images generated by the suggestion unit to the user. === Hard Collateral 1-3 === Each of the multiple elements including the acquisition unit, analysis unit, suggestion unit, and provision unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit can capture images of items held by the user via the camera 42 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the video transmitted from the acquisition unit, and recognizes the characteristics of each item. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and generates optimal coordination and fashion images based on the characteristics recognized by the analysis unit. The provision unit is realized by the control unit 46A of the headset terminal 314, and provides the coordination and fashion images generated by the suggestion unit to the user. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, analysis unit, suggestion unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit can capture images of items held by the user via the camera 42 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the video transmitted from the acquisition unit, and recognizes the characteristics of each item. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and generates optimal coordination and fashion images based on the characteristics recognized by the analysis unit. The provision unit is realized by the control unit 46A of the robot 414, and provides the coordination and fashion images generated by the suggestion unit to the user.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The fashion suggestion system can also acquire lifestyle data about the user and reflect it in the suggestions. For example, the acquisition unit can acquire lifestyle data such as the user's exercise habits, diet, and work schedule. The analysis unit can analyze this data and make fashion suggestions that are optimal for the user's lifestyle. For example, for a user who exercises regularly, items made of materials and designs that are easy to move in can be suggested. Also, for a user with a busy work schedule, items made of materials that are wrinkle-resistant and easy to care for can be suggested. This makes it possible to make more practical and personalized fashion suggestions that are tailored to the user's lifestyle.

[0108] The acquisition unit can acquire the user's health data and reflect it in the suggestions. For example, the acquisition unit can acquire health data such as the user's heart rate, sleep patterns, and stress level. The analysis unit can analyze this data and make fashion suggestions that are optimal for the user's health condition. For example, for a user with a high stress level, items with colors and materials that have a relaxing effect can be suggested. Also, for a user who is sleep-deprived, items that are comfortable to wear can be suggested. This makes it possible to suggest more comfortable and healthy fashion that is tailored to the user's health condition.

[0109] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is excited, the analysis unit can prioritize analyzing the most prominent items. Also, if the user is relaxed, the analysis unit can analyze items taking into consideration the overall balance. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing important items. In this way, by determining the analysis priority according to the user's emotions, more appropriate analysis can be performed.

[0110] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can select the timing to make detailed suggestions. If the user is in a hurry, the suggestion unit can select the timing to make concise suggestions. Furthermore, if the user is excited, the suggestion unit can select the timing to make visually appealing suggestions. In this way, more appropriate suggestions can be made by adjusting the timing of suggestions according to the user's emotions.

[0111] The providing unit can estimate the user's emotions and adjust the amount of information to be provided based on the estimated user's emotions. For example, the providing unit can provide detailed information when the user is relaxed. Also, when the user is in a hurry, the providing unit can provide concise information that focuses on the main points. Furthermore, when the user is excited, the providing unit can provide visually appealing information. In this way, by adjusting the amount of information to be provided according to the user's emotions, more appropriate information can be provided.

[0112] The fashion suggestion system can also acquire a user's purchasing history and reflect it in the suggestions. For example, the acquisition unit can acquire the user's past purchasing history. The analysis unit can analyze this data and make optimal fashion suggestions based on the user's purchasing trends. For example, for a user who likes items from a particular brand or designer, new items from that brand or designer can be suggested. Also, for a user who likes a particular color or style, items that match that color or style can be suggested. This makes it possible to make more personalized fashion suggestions that match the user's purchasing trends.

[0113] The fashion suggestion system can also acquire the user's social media activity and reflect it in the suggestions. For example, the acquisition unit can acquire the user's social media posts and followers' reactions. The analysis unit can analyze this data and make optimal fashion suggestions based on the user's social media activity. For example, it can suggest items and styles that the user often posts on social media. It can also suggest items that followers are likely to be interested in. This enables more personalized fashion suggestions to be made in line with the user's social media activity.

[0114] The fashion suggestion system can further acquire the user's geographical location information and reflect it in the suggestions. For example, the acquisition unit can acquire the user's current geographical location information. The analysis unit can analyze this data and make optimal fashion suggestions based on the user's geographical location. For example, it can suggest items suitable for the climate and culture of a particular region. It can also suggest items intended for use at travel destinations. This enables more practical and personalized fashion suggestions tailored to the user's geographical location.

[0115] The fashion suggestion system can also acquire the user's event participation information and reflect it in the suggestions. For example, the acquisition unit can acquire information about events the user plans to attend. The analysis unit can analyze this data and make optimal fashion suggestions based on the type of event and dress code. For example, elegant dresses and suits can be suggested for formal events, and relaxed style items can be suggested for casual events. This allows for more appropriate fashion suggestions tailored to the user's event participation information.

[0116] The fashion suggestion system can further acquire the user's seasonal fashion trends and reflect them in the suggestions. For example, the acquisition unit can acquire the user's past seasonal fashion trends. The analysis unit can analyze this data and make optimal fashion suggestions according to the season. For example, items made of cool materials and designs can be suggested in the summer, and items made of warm materials and layered styles can be suggested in the winter. This makes it possible to make more practical and personalized fashion suggestions that match the user's seasonal fashion trends.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The acquisition unit captures a photo of an item the user is holding. The item may include clothing, accessories, shoes, etc. The acquisition unit can capture videos taken with a smartphone, as well as photos taken with a digital camera or webcam. The acquisition unit also has a function to adjust the lighting conditions and shooting angle during shooting, and can encourage the user to take photos at times when natural light is best and guide the user to complete the photo shoot in a short amount of time. Furthermore, if the user is excited, the acquisition unit can provide an interface that emphasizes the fun of taking photos. Step 2: The analysis unit uses AI to analyze the video captured by the acquisition unit and recognize the characteristics of each item. These characteristics include color, shape, and material. For example, the analysis unit can recognize color based on RGB values, shape using contour extraction technology, and material using texture analysis technology. Step 3: The suggestion unit uses AI to generate optimal outfits and fashion images based on the features recognized by the analysis unit. The suggestion unit generates outfits based on color combinations and style matches, and can also suggest new items based on the user's budget. Furthermore, the suggestion unit can learn the user's preferences and past outfit history to make more personalized suggestions. Step 4: The providing unit provides the coordinated outfits and fashion images generated by the suggestion unit to the user. The providing unit can display the information through a web application or a mobile application, or can provide the information by email or push notification. Furthermore, the providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can select a method of providing information that includes detailed information, and if the user is in a hurry, the providing unit can select a concise method of providing information that focuses on the main points. Also, if the user is excited, the providing unit can select a visually appealing method of providing information.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that takes a photograph of an item held by a user; an analysis unit that analyzes the video acquired by the acquisition unit and recognizes features of each item; a suggestion unit that generates coordinate and fashion images based on the features recognized by the analysis unit; a providing unit that provides a user with coordinate and fashion images generated by the suggestion unit; Equipped with A system characterized by:

2. The acquisition unit Obtain videos taken by users on their smartphones 2. The system of claim 1.

3. The analysis unit Recognize the color, shape, and material of each item in the video 2. The system of claim 1.

4. The proposal unit Suggest new items based on the user's budget 2. The system of claim 1.

5. The providing unit Learns user preferences and past outfit history to provide personalized suggestions 2. The system of claim 1.

6. The acquisition unit Estimate the user's emotions and adjust the timing of taking photos based on the estimated user emotions.

2. The system of claim 1.

7. The acquisition unit Analyze the user's past photography history and select the photography method 2. The system of claim 1.

8. The acquisition unit When taking a photo, it filters based on the user's current fashion trends and interests.

2. The system of claim 1.

9. The acquisition unit When taking a photograph, the photographing means is selected according to the user's input method.

2. The system of claim 1.

10. The acquisition unit Estimate the user's emotions and prioritize the items to be photographed based on the estimated user emotions.

2. The system of claim 1.

Images