System

Abstract

An object of a system according to an embodiment is to efficiently perform coordination in which an item that a user has and an item that the user is considering purchasing are combined.SOLUTION: A system according to an embodiment includes an item acquisition unit, a coordination generation unit, and a recommendation unit. The item acquisition section acquires information about an item that the user has and an item that the user is considering purchasing. The coordination generation unit generates an image picture of coordination based on the information acquired by the item acquisition unit. The recommendation unit recommends a similar item on the basis of the coordinate image generated by the coordinate generation 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 technology has had the problem of making it difficult to efficiently coordinate items that a user owns with items that the user is considering purchasing.

[0005] The system according to the embodiment aims to efficiently coordinate outfits that combine items that a user has with items that the user is considering purchasing. [Means for solving the problem]

[0006] The system according to the embodiment includes an item acquisition unit, a coordinate generation unit, and a recommendation unit. The item acquisition unit acquires information about items the user owns and items the user is considering purchasing. The coordinate generation unit generates an image of a coordinated outfit based on the information acquired by the item acquisition unit. The recommendation unit recommends similar items based on the image of the coordinated outfit generated by the coordinate generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently coordinate outfits that combine items that a user has with items that the user is considering purchasing. [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) The total coordination system according to an embodiment of the present invention uses a generation AI to generate coordinated imagery based on items a user owns and items the user is considering purchasing, and recommends similar items. This allows the total coordination system to reduce the time users spend coordinating outfits and eliminate waste. It also allows users to gain new ideas for coordinating outfits.

[0029] A total coordination system according to an embodiment includes an item acquisition unit, a coordinate generation unit, and a recommendation unit. The item acquisition unit acquires information about items owned by a user and items the user is considering purchasing. For example, the item acquisition unit inputs information about clothing and accessories owned by the user. It can also acquire information about items in an online shopping cart or items registered on a wish list. The coordinate generation unit generates an image of an outfit based on the information acquired by the item acquisition unit. For example, the generation AI creates an outfit combining a jacket owned by the user with pants the user is considering purchasing, and displays the result as a 3D model. The generation AI can also generate an outfit taking into account the user's preferences and trends. The recommendation unit recommends similar items based on the image of the outfit generated by the coordinate generation unit. For example, it can suggest a shirt with a design or color similar to a shirt the user owns. The generation AI can also analyze the user's past purchase history and preferences to recommend more suitable items. This allows the total coordination system according to an embodiment to shorten the time the user spends on coordinating outfits and eliminate waste. It also allows the user to gain new outfit ideas.

[0030] The coordination generation unit can analyze the user's body shape and posture in real time and suggest optimal coordination based on that. For example, the coordination generation unit uses a generation AI to acquire the user's body shape data in real time and suggest optimal coordination based on that data. For example, the user takes a full-body photo of themselves using a smartphone camera and analyzes the image to identify their body shape. The unit also analyzes the user's posture in real time and suggests coordination based on that. For example, the unit takes a photo of the user standing or sitting with a camera and suggests clothing that suits that posture. The generation AI also analyzes the user's body shape and posture and generates a 3D model based on that data. For example, the unit dresses the 3D model up with items the user owns and displays the results. This allows for more personalized coordination by suggesting optimal coordination based on the user's body shape and posture.

[0031] The coordination generation unit can learn the user's past coordination history and generate image images that reflect the user's preferences and trends. For example, the generation AI of the coordination generation unit stores the user's past coordination history in a database and learns the user's preferences based on that data. For example, it analyzes the items and coordinations the user has selected in the past to understand preference trends. It also generates image images that reflect current fashion trends based on the user's past coordination history. For example, it combines past data with the latest trend information to suggest the optimal coordination for the user. The generation AI also learns the user's past coordination history and suggests new coordinations based on that data. For example, it suggests items with designs and colors similar to items the user has selected in the past. This allows the unit to learn the user's past coordination history and generate image images that reflect the user's preferences and trends, making it possible to create more personalized coordinations.

[0032] The coordination generation unit can suggest coordination for each scene based on the user's lifestyle and planned activities. In the coordination generation unit, for example, the generation AI analyzes the user's lifestyle data and suggests coordination for each scene based on that data. For example, it may suggest separate coordination for days when the user goes to work and for days off. It may also obtain the user's planned activities from a calendar app and suggest coordination that matches those plans. For example, it may suggest sportswear on days when the user plays sports. The generation AI may also analyze the user's lifestyle and planned activities and suggest optimal coordination based on that data. For example, it may suggest clothing suitable for travel on days when the user is going on a trip. This allows for more practical coordination by suggesting coordination for each scene based on the user's lifestyle and planned activities.

[0033] The coordination generation unit can automatically generate coordinations according to different seasons and weather conditions and suggest them to the user. For example, the generation AI of the coordination generation unit obtains current season and weather data and suggests coordinations based on that data. For example, it suggests warm clothing in winter and cool clothing in summer. It also obtains weather data for the user's location in real time and suggests coordinations that match the weather. For example, it suggests waterproof items for rainy days. The generation AI also automatically generates coordinations according to the season and weather and suggests them to the user. For example, it suggests floral items in spring and muted colored items in autumn. In this way, it is possible to automatically generate coordinations according to different seasons and weather conditions and suggest them to the user, making it possible to create coordinations that are appropriate for the season and weather.

[0034] The recommendation unit can analyze the materials and textures of items owned by the user and recommend items with similar materials and textures. For example, the recommendation unit uses a generation AI to analyze the material data of items owned by the user and recommend items with similar materials based on that data. For example, it can suggest wool items similar to a wool sweater owned by the user. It can also analyze the texture data of items owned by the user and recommend items with similar textures based on that data. For example, it can suggest silk items similar to a silk shirt owned by the user. The generation AI can also analyze the materials and textures of items owned by the user and recommend similar items based on that data. For example, it can suggest denim items similar to denim pants owned by the user. In this way, by analyzing the materials and textures of items owned by the user and recommending items with similar materials and textures, it can suggest more suitable items.

[0035] The recommendation unit learns the user's past purchase history and ratings, and can make more accurate recommendations. For example, the generation AI stores the user's past purchase history in a database and makes recommendations based on that data. For example, it may suggest items with similar designs or colors to items the user has previously purchased. It may also analyze the user's past rating data and make recommendations based on that data. For example, it may suggest items similar to items that the user has given a high rating. The generation AI may also learn the user's past purchase history and ratings, and make more accurate recommendations based on that data. For example, it may suggest items from the same brand as items the user has previously purchased. In this way, it can learn the user's past purchase history and ratings and make more accurate recommendations, thereby suggesting the best items for the user.

[0036] The recommendation unit can analyze items from different brands and designers across the board and recommend new brands and designers that match the user's preferences. For example, the generation AI analyzes items from different brands and recommends new brands that match the user's preferences. For example, if a user likes a particular brand, it will suggest brands with a similar style to that brand. It also analyzes items from different designers and recommends new designers that match the user's preferences. For example, if a user likes a particular designer, it will suggest designers with a similar style to that designer. It also analyzes items from different brands and designers across the board and recommends new brands and designers that match the user's preferences. For example, if a user likes a particular style, it will suggest new brands and designers that match that style. This allows the recommendation unit to analyze items from different brands and designers across the board and recommend new brands and designers that match the user's preferences, thereby expanding the user's choices.

[0037] The recommendation unit can analyze the colors and patterns of items owned by the user and suggest items of different colors and patterns. For example, the generation AI analyzes the color data of items owned by the user and suggests items of different colors based on that data. For example, it can suggest a red shirt for a blue shirt owned by the user. It can also analyze the pattern data of items owned by the user and suggest items of different patterns based on that data. For example, it can suggest a checked shirt for a striped shirt owned by the user. It can also analyze the colors and patterns of items owned by the user and suggest items of different colors and patterns based on that data. For example, it can suggest a patterned shirt for a plain shirt owned by the user. In this way, the generation AI can analyze the colors and patterns of items owned by the user and suggest items of different colors and patterns, thereby broadening the user's fashion options.

[0038] The coordination generation unit can propose coordination that combines the user's subjective preferences with the latest trends in the fashion industry. For example, the generation AI stores the user's subjective preferences in a database and proposes coordination that combines the latest fashion trends based on that data. For example, it incorporates the latest trends while taking into account the user's favorite colors and styles. The coordination generation unit also analyzes the user's subjective preferences and proposes coordination that reflects the latest trends in the fashion industry based on that data. For example, it incorporates the latest collections from the user's favorite brands and designers. The generation AI also proposes coordination that combines the user's subjective preferences with the latest trends in the fashion industry. For example, it proposes the latest trend items based on the user's favorite colors and styles. In this way, it is possible to provide attractive coordination to the user by proposing coordination that combines the user's subjective preferences with the latest trends in the fashion industry.

[0039] The coordination generation unit can incorporate evaluations from the user's friends and family to propose coordinations that reflect a more objective perspective. For example, the generation AI collects evaluation data from the user's friends and family and proposes coordinations based on that data. For example, it incorporates items that have been highly rated by the user's friends and family. It also collects evaluations from the user's friends and family in real time and proposes coordinations based on those evaluations. For example, it reflects the evaluations of friends and family on items selected by the user. It also incorporates the evaluations of the user's friends and family to propose coordinations that reflect a more objective perspective. For example, it incorporates the opinions of friends and family on items selected by the user. In this way, it is possible to incorporate the evaluations of the user's friends and family and propose coordinations that reflect a more objective perspective, thereby providing the user with highly reliable coordination.

[0040] The coordinate generation unit can incorporate fashion styles from different cultures and regions to propose new styles to the user. For example, the generation AI analyzes fashion styles from different cultures and proposes new styles to the user based on that data. For example, it proposes items from different cultures that the user does not normally wear. It can also analyze fashion styles from different regions and propose new styles to the user based on that data. For example, it can incorporate trends from a region other than the one in which the user lives. The generation AI can also incorporate fashion styles from different cultures and regions to propose new styles to the user. For example, it can propose designs and colors from different cultures that the user would not normally choose. This can broaden the user's fashion options by incorporating fashion styles from different cultures and regions and proposing new styles to the user.

[0041] The coordination generation unit can suggest coordination suitable for specific situations based on the user's occupation and hobbies. In the coordination generation unit, for example, the generation AI analyzes the user's occupation data and suggests coordination suitable for specific situations based on that data. For example, if the user is a businessman, it suggests coordination suitable for business scenes. It also analyzes the user's hobby data and suggests coordination suitable for specific situations based on that data. For example, if the user likes the outdoors, it suggests coordination suitable for outdoor scenes. It also suggests coordination suitable for specific situations based on the user's occupation and hobbies. For example, if the user is an artist, it suggests coordination suitable for an art event. In this way, it is possible to provide practical coordination for the user by suggesting coordination suitable for specific situations based on the user's occupation and hobbies.

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

[0043] The coordination generation unit can also acquire biometric data such as the user's body temperature and heart rate in real time and suggest optimal coordination based on that data. For example, if the user's body temperature is high after exercise, it can suggest breathable clothing. Also, if the user's heart rate is high, it can suggest relaxing clothing. Furthermore, it can also suggest coordination according to the season and weather based on the user's biometric data. For example, it can suggest warm clothing to maintain body temperature in cold seasons. This makes it possible to create more personalized coordination based on the user's biometric data.

[0044] The coordination generation unit can learn the user's past coordination history and generate image images that reflect the user's preferences and trends. For example, it analyzes the items and coordinations the user has chosen in the past to understand preference trends. It also generates image images that reflect current fashion trends based on the user's past coordination history. For example, it combines past data with the latest trend information to suggest the most suitable coordination for the user. The generation AI also learns the user's past coordination history and suggests new coordinations based on that data. For example, it suggests items with designs and colors similar to items the user has chosen in the past. This allows the system to learn the user's past coordination history and generate image images that reflect the user's preferences and trends, enabling more personalized coordination.

[0045] The coordination generation unit can suggest coordinations for different occasions based on the user's lifestyle and planned activities. For example, the generation AI analyzes the user's lifestyle data and suggests coordinations for different occasions based on that data. For example, it can suggest separate coordinations for days when the user goes to work and for days off. It can also obtain the user's planned activities from a calendar app and suggest coordinations that match those plans. For example, it can suggest sportswear on days when the user plays sports. The generation AI can also analyze the user's lifestyle and planned activities and suggest optimal coordinations based on that data. For example, it can suggest clothing suitable for travel on days when the user is going on a trip. This allows for more practical coordination by suggesting coordinations for different occasions based on the user's lifestyle and planned activities.

[0046] The coordination generation unit can automatically generate coordinations according to different seasons and weather conditions and suggest them to the user. For example, the generation AI obtains current season and weather data and suggests coordinations based on that data. For example, it suggests warm clothing in winter and cool clothing in summer. It can also obtain weather data for the user's location in real time and suggest coordinations that match the weather. For example, it suggests waterproof items for rainy days. The generation AI can also automatically generate coordinations according to the season and weather and suggest them to the user. For example, it suggests floral items in spring and muted colored items in autumn. In this way, it is possible to automatically generate coordinations according to different seasons and weather conditions and suggest them to the user, making it possible to create coordinations that suit the season and weather.

[0047] The coordination generation unit can propose coordinations that combine the user's subjective preferences with the latest trends in the fashion industry. For example, the generation AI stores the user's subjective preferences in a database and proposes coordinations that combine the latest fashion trends based on that data. For example, it incorporates the latest trends while taking into account the user's favorite colors and styles. The generation AI can also analyze the user's subjective preferences and propose coordinations that reflect the latest trends in the fashion industry based on that data. For example, it can incorporate the latest collections from the user's favorite brands and designers. The generation AI can also propose coordinations that combine the user's subjective preferences with the latest trends in the fashion industry. For example, it can propose the latest trend items based on the user's favorite colors and styles. In this way, it is possible to provide attractive coordinations to the user by proposing coordinations that combine the user's subjective preferences with the latest trends in the fashion industry.

[0048] The coordination generation unit can suggest coordination suitable for specific situations based on the user's occupation and hobbies. For example, the generation AI analyzes the user's occupation data and suggests coordination suitable for specific situations based on that data. For example, if the user is a businessman, it suggests coordination suitable for business situations. It also analyzes the user's hobby data and suggests coordination suitable for specific situations based on that data. For example, if the user likes the outdoors, it suggests coordination suitable for outdoor scenes. It also suggests coordination suitable for specific situations based on the user's occupation and hobbies. For example, if the user is an artist, it suggests coordination suitable for an art event. In this way, it is possible to provide practical coordination for the user by suggesting coordination suitable for specific situations based on the user's occupation and hobbies.

[0049] The coordination generation unit can incorporate fashion styles from different cultures and regions to suggest new styles to the user. For example, the generation AI analyzes fashion styles from different cultures and suggests new styles to the user based on that data. For example, it can suggest items from different cultures that the user does not normally wear. It can also analyze fashion styles from different regions and suggest new styles to the user based on that data. For example, it can incorporate trends from a region other than the one the user lives in. The generation AI can also incorporate fashion styles from different cultures and regions to suggest new styles to the user. For example, it can suggest designs and colors from different cultures that the user would not normally choose. This can incorporate fashion styles from different cultures and regions to suggest new styles to the user, thereby broadening the user's fashion options.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The item acquisition unit acquires information about the items the user owns and the items they are considering purchasing. For example, the user enters information about the clothing and accessories they own. It can also acquire information about items in their online shopping cart or on their wish list. Step 2: The coordination generation unit generates an image of a coordination based on the information acquired by the item acquisition unit. For example, the generation AI creates a coordination that combines a jacket the user owns with pants the user is considering purchasing, and displays the result as a 3D model. The generation AI can also generate coordination taking into account the user's preferences and trends. Step 3: The recommendation unit recommends similar items based on the image of the outfit generated by the outfit generation unit. For example, it may suggest a shirt with a similar design or color to a shirt the user already owns. The generation AI can also analyze the user's past purchase history and preferences to recommend more suitable items.

[0052] (Example 2) The total coordination system according to an embodiment of the present invention uses a generation AI to generate coordinated imagery based on items a user owns and items the user is considering purchasing, and recommends similar items. This allows the total coordination system to reduce the time users spend coordinating outfits and eliminate waste. It also allows users to gain new ideas for coordinating outfits.

[0053] A total coordination system according to an embodiment includes an item acquisition unit, a coordinate generation unit, and a recommendation unit. The item acquisition unit acquires information about items owned by a user and items the user is considering purchasing. For example, the item acquisition unit inputs information about clothing and accessories owned by the user. It can also acquire information about items in an online shopping cart or items registered on a wish list. The coordinate generation unit generates an image of an outfit based on the information acquired by the item acquisition unit. For example, the generation AI creates an outfit combining a jacket owned by the user with pants the user is considering purchasing, and displays the result as a 3D model. The generation AI can also generate an outfit taking into account the user's preferences and trends. The recommendation unit recommends similar items based on the image of the outfit generated by the coordinate generation unit. For example, it can suggest a shirt with a design or color similar to a shirt the user owns. The generation AI can also analyze the user's past purchase history and preferences to recommend more suitable items. This allows the total coordination system according to an embodiment to shorten the time the user spends on coordinating outfits and eliminate waste. It also allows the user to gain new outfit ideas.

[0054] The coordination generation unit can analyze the user's body shape and posture in real time and suggest optimal coordination based on that. For example, the coordination generation unit uses a generation AI to acquire the user's body shape data in real time and suggest optimal coordination based on that data. For example, the user takes a full-body photo of themselves using a smartphone camera and analyzes the image to identify their body shape. The unit also analyzes the user's posture in real time and suggests coordination based on that. For example, the unit takes a photo of the user standing or sitting with a camera and suggests clothing that suits that posture. The generation AI also analyzes the user's body shape and posture and generates a 3D model based on that data. For example, the unit dresses the 3D model up with items the user owns and displays the results. This allows for more personalized coordination by suggesting optimal coordination based on the user's body shape and posture.

[0055] The coordination generation unit can learn the user's past coordination history and generate image images that reflect the user's preferences and trends. For example, the generation AI of the coordination generation unit stores the user's past coordination history in a database and learns the user's preferences based on that data. For example, it analyzes the items and coordinations the user has selected in the past to understand preference trends. It also generates image images that reflect current fashion trends based on the user's past coordination history. For example, it combines past data with the latest trend information to suggest the optimal coordination for the user. The generation AI also learns the user's past coordination history and suggests new coordinations based on that data. For example, it suggests items with designs and colors similar to items the user has selected in the past. This allows the unit to learn the user's past coordination history and generate image images that reflect the user's preferences and trends, making it possible to create more personalized coordinations.

[0056] The coordination generation unit can use the emotion estimation function to suggest coordination that matches the user's current mood and emotion. The coordination generation unit, for example, uses the emotion estimation function to analyze the user's current mood and suggest coordination that matches that mood. For example, when the user is in a relaxed mood, the coordination generation unit suggests a casual coordination. The coordination generation unit also acquires the user's emotion data in real time and suggests coordination based on that data. For example, when the user is feeling stressed, the coordination generation unit suggests relaxing clothing. The coordination generation unit also uses the emotion estimation function to suggest coordination based on the user's emotion. For example, when the user is in a happy mood, the coordination generation unit suggests brightly colored items. This makes it possible to suggest coordination that matches the user's current mood and emotion, thereby making it possible to create coordination that is more in line with the emotion.

[0057] The coordination generation unit can suggest coordination for each scene based on the user's lifestyle and planned activities. In the coordination generation unit, for example, the generation AI analyzes the user's lifestyle data and suggests coordination for each scene based on that data. For example, it may suggest separate coordination for days when the user goes to work and for days off. It may also obtain the user's planned activities from a calendar app and suggest coordination that matches those plans. For example, it may suggest sportswear on days when the user plays sports. The generation AI may also analyze the user's lifestyle and planned activities and suggest optimal coordination based on that data. For example, it may suggest clothing suitable for travel on days when the user is going on a trip. This allows for more practical coordination by suggesting coordination for each scene based on the user's lifestyle and planned activities.

[0058] The coordination generation unit can automatically generate coordinations according to different seasons and weather conditions and suggest them to the user. For example, the generation AI of the coordination generation unit obtains current season and weather data and suggests coordinations based on that data. For example, it suggests warm clothing in winter and cool clothing in summer. It also obtains weather data for the user's location in real time and suggests coordinations that match the weather. For example, it suggests waterproof items for rainy days. The generation AI also automatically generates coordinations according to the season and weather and suggests them to the user. For example, it suggests floral items in spring and muted colored items in autumn. In this way, it is possible to automatically generate coordinations according to different seasons and weather conditions and suggest them to the user, making it possible to create coordinations that are appropriate for the season and weather.

[0059] The coordination generation unit can use the emotion estimation function to suggest coordination that takes into account the emotions the user feels toward a specific event or occasion. The coordination generation unit, for example, uses the emotion estimation function to analyze the emotions the user feels toward a specific event and suggests coordination that matches those emotions. For example, when the user is attending a wedding, formal attire is suggested. Also, based on the user's emotion data, coordination that is suitable for a specific event is suggested. For example, when the user is attending a party, glamorous attire is suggested. Also, using the emotion estimation function, coordination is suggested that takes into account the emotions the user feels toward a specific event or occasion. For example, when the user is going to an interview, clothing that makes the user feel confident is suggested. In this way, by suggesting coordination that takes into account the emotions the user feels toward a specific event or occasion, it is possible to create coordination that is more in line with emotions.

[0060] The recommendation unit can analyze the materials and textures of items owned by the user and recommend items with similar materials and textures. For example, the recommendation unit uses a generation AI to analyze the material data of items owned by the user and recommend items with similar materials based on that data. For example, it can suggest wool items similar to a wool sweater owned by the user. It can also analyze the texture data of items owned by the user and recommend items with similar textures based on that data. For example, it can suggest silk items similar to a silk shirt owned by the user. The generation AI can also analyze the materials and textures of items owned by the user and recommend similar items based on that data. For example, it can suggest denim items similar to denim pants owned by the user. In this way, by analyzing the materials and textures of items owned by the user and recommending items with similar materials and textures, it can suggest more suitable items.

[0061] The recommendation unit learns the user's past purchase history and ratings, and can make more accurate recommendations. For example, the generation AI stores the user's past purchase history in a database and makes recommendations based on that data. For example, it may suggest items with similar designs or colors to items the user has previously purchased. It may also analyze the user's past rating data and make recommendations based on that data. For example, it may suggest items similar to items that the user has given a high rating. The generation AI may also learn the user's past purchase history and ratings, and make more accurate recommendations based on that data. For example, it may suggest items from the same brand as items the user has previously purchased. In this way, it can learn the user's past purchase history and ratings and make more accurate recommendations, thereby suggesting the best items for the user.

[0062] The recommendation unit can use the emotion estimation function to recommend items that provide high emotional satisfaction based on the emotions a user has toward a specific item. The recommendation unit, for example, uses the emotion estimation function to analyze the emotions a user has toward a specific item and make recommendations based on those emotions. For example, if a user has positive emotions toward a specific item, the recommendation unit suggests items similar to that item. Furthermore, the recommendation unit recommends items that provide high emotional satisfaction based on the user's emotion data. For example, if a user has high satisfaction with a specific item, the recommendation unit suggests items similar to that item. Furthermore, the recommendation unit uses the emotion estimation function to make recommendations based on the emotions a user has toward a specific item. For example, if a user has a strong attachment to a specific item, the recommendation unit suggests items similar to that item. In this way, by recommending items that provide high emotional satisfaction based on the emotions a user has toward a specific item, it is possible to improve user satisfaction.

[0063] The recommendation unit can analyze items from different brands and designers across the board and recommend new brands and designers that match the user's preferences. For example, the generation AI analyzes items from different brands and recommends new brands that match the user's preferences. For example, if a user likes a particular brand, it will suggest brands with a similar style to that brand. It also analyzes items from different designers and recommends new designers that match the user's preferences. For example, if a user likes a particular designer, it will suggest designers with a similar style to that designer. It also analyzes items from different brands and designers across the board and recommends new brands and designers that match the user's preferences. For example, if a user likes a particular style, it will suggest new brands and designers that match that style. This allows the recommendation unit to analyze items from different brands and designers across the board and recommend new brands and designers that match the user's preferences, thereby expanding the user's choices.

[0064] The recommendation unit can analyze the colors and patterns of items owned by the user and suggest items of different colors and patterns. For example, the generation AI analyzes the color data of items owned by the user and suggests items of different colors based on that data. For example, it can suggest a red shirt for a blue shirt owned by the user. It can also analyze the pattern data of items owned by the user and suggest items of different patterns based on that data. For example, it can suggest a checked shirt for a striped shirt owned by the user. It can also analyze the colors and patterns of items owned by the user and suggest items of different colors and patterns based on that data. For example, it can suggest a patterned shirt for a plain shirt owned by the user. In this way, the generation AI can analyze the colors and patterns of items owned by the user and suggest items of different colors and patterns, thereby broadening the user's fashion options.

[0065] The recommendation unit can use the emotion estimation function to recommend items that take into account the emotions a user has toward a particular season or event. The recommendation unit, for example, uses the emotion estimation function to analyze the emotions a user has toward a particular season and recommend items that match those emotions. For example, if a user has positive emotions toward summer, items suitable for summer are suggested. Furthermore, based on the user's emotion data, items that take into account the emotions a user has toward a particular event are recommended. For example, if a user has special emotions toward Christmas, items suitable for Christmas are suggested. Furthermore, using the emotion estimation function, items that take into account the emotions a user has toward a particular season or event are suggested. For example, if a user has calm emotions toward autumn, items suitable for autumn are suggested. In this way, by recommending items that take into account the emotions a user has toward a particular season or event, it is possible to suggest items that are in line with the user's emotions.

[0066] The coordination generation unit can propose coordination that combines the user's subjective preferences with the latest trends in the fashion industry. For example, the generation AI stores the user's subjective preferences in a database and proposes coordination that combines the latest fashion trends based on that data. For example, it incorporates the latest trends while taking into account the user's favorite colors and styles. The coordination generation unit also analyzes the user's subjective preferences and proposes coordination that reflects the latest trends in the fashion industry based on that data. For example, it incorporates the latest collections from the user's favorite brands and designers. The generation AI also proposes coordination that combines the user's subjective preferences with the latest trends in the fashion industry. For example, it proposes the latest trend items based on the user's favorite colors and styles. In this way, it is possible to provide attractive coordination to the user by proposing coordination that combines the user's subjective preferences with the latest trends in the fashion industry.

[0067] The coordination generation unit can incorporate evaluations from the user's friends and family to propose coordinations that reflect a more objective perspective. For example, the generation AI collects evaluation data from the user's friends and family and proposes coordinations based on that data. For example, it incorporates items that have been highly rated by the user's friends and family. It also collects evaluations from the user's friends and family in real time and proposes coordinations based on those evaluations. For example, it reflects the evaluations of friends and family on items selected by the user. It also incorporates the evaluations of the user's friends and family to propose coordinations that reflect a more objective perspective. For example, it incorporates the opinions of friends and family on items selected by the user. In this way, it is possible to incorporate the evaluations of the user's friends and family and propose coordinations that reflect a more objective perspective, thereby providing the user with highly reliable coordination.

[0068] The coordinate generation unit can use the emotion estimation function to compare the user's emotions with those of other users and propose a coordinate that is highly relatable. The coordinate generation unit, for example, uses the emotion estimation function to collect user emotion data and compare it with the emotions of other users based on that data. For example, the emotion the user has toward a specific item is compared with that of other users to propose a coordinate that is highly relatable. Also, based on the user's emotion data, the coordinate generation unit compares it with the emotions of other users to propose a coordinate that is highly relatable. For example, the emotion the user has toward a specific style is compared with that of other users to propose a style that is highly relatable. Also, using the emotion estimation function, the coordinate generation unit compares the user's emotions with those of other users to propose a coordinate that is highly relatable. For example, the emotion the user has toward a specific color or design is compared with that of other users to propose an item that is highly relatable. In this way, by comparing the user's emotions with that of other users and proposing a coordinate that is highly relatable, it is possible to provide a coordinate that is highly relatable to the user.

[0069] The coordinate generation unit can incorporate fashion styles from different cultures and regions to propose new styles to the user. For example, the generation AI analyzes fashion styles from different cultures and proposes new styles to the user based on that data. For example, it proposes items from different cultures that the user does not normally wear. It can also analyze fashion styles from different regions and propose new styles to the user based on that data. For example, it can incorporate trends from a region other than the one in which the user lives. The generation AI can also incorporate fashion styles from different cultures and regions to propose new styles to the user. For example, it can propose designs and colors from different cultures that the user would not normally choose. This can broaden the user's fashion options by incorporating fashion styles from different cultures and regions and proposing new styles to the user.

[0070] The coordination generation unit can suggest coordination suitable for specific situations based on the user's occupation and hobbies. In the coordination generation unit, for example, the generation AI analyzes the user's occupation data and suggests coordination suitable for specific situations based on that data. For example, if the user is a businessman, it suggests coordination suitable for business scenes. It also analyzes the user's hobby data and suggests coordination suitable for specific situations based on that data. For example, if the user likes the outdoors, it suggests coordination suitable for outdoor scenes. It also suggests coordination suitable for specific situations based on the user's occupation and hobbies. For example, if the user is an artist, it suggests coordination suitable for an art event. In this way, it is possible to provide practical coordination for the user by suggesting coordination suitable for specific situations based on the user's occupation and hobbies.

[0071] The coordination generation unit can use the emotion estimation function to propose coordination that takes into account the emotions the user feels toward specific music or movies. The coordination generation unit, for example, uses the emotion estimation function to analyze the emotions the user feels toward specific music and proposes coordination that matches those emotions. For example, the coordination generation unit proposes coordination that matches the music the user listens to when they want to relax. Furthermore, the coordination generation unit proposes coordination that takes into account the emotions the user feels toward a specific movie based on the user's emotion data. For example, the coordination generation unit proposes coordination that matches the emotions the user feels toward a moving movie. Furthermore, the coordination generation unit uses the emotion estimation function to propose coordination that takes into account the emotions the user feels toward specific music or movies. For example, the coordination generation unit proposes coordination that matches the emotions the user feels toward energetic music. In this way, by proposing coordination that takes into account the emotions the user feels toward specific music or movies, it is possible to provide coordination that is in line with the user's emotions.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The coordination generation unit can also acquire biometric data such as the user's body temperature and heart rate in real time and suggest optimal coordination based on that data. For example, if the user's body temperature is high after exercise, it can suggest breathable clothing. Also, if the user's heart rate is high, it can suggest relaxing clothing. Furthermore, it can also suggest coordination according to the season and weather based on the user's biometric data. For example, it can suggest warm clothing to maintain body temperature in cold seasons. This makes it possible to create more personalized coordination based on the user's biometric data.

[0074] The coordination generation unit can learn the user's past coordination history and generate image images that reflect the user's preferences and trends. For example, it analyzes the items and coordinations the user has chosen in the past to understand preference trends. It also generates image images that reflect current fashion trends based on the user's past coordination history. For example, it combines past data with the latest trend information to suggest the most suitable coordination for the user. The generation AI also learns the user's past coordination history and suggests new coordinations based on that data. For example, it suggests items with designs and colors similar to items the user has chosen in the past. This allows the system to learn the user's past coordination history and generate image images that reflect the user's preferences and trends, enabling more personalized coordination.

[0075] The coordination generation unit can suggest coordinations for different occasions based on the user's lifestyle and planned activities. For example, the generation AI analyzes the user's lifestyle data and suggests coordinations for different occasions based on that data. For example, it can suggest separate coordinations for days when the user goes to work and for days off. It can also obtain the user's planned activities from a calendar app and suggest coordinations that match those plans. For example, it can suggest sportswear on days when the user plays sports. The generation AI can also analyze the user's lifestyle and planned activities and suggest optimal coordinations based on that data. For example, it can suggest clothing suitable for travel on days when the user is going on a trip. This allows for more practical coordination by suggesting coordinations for different occasions based on the user's lifestyle and planned activities.

[0076] The coordination generation unit can automatically generate coordinations according to different seasons and weather conditions and suggest them to the user. For example, the generation AI obtains current season and weather data and suggests coordinations based on that data. For example, it suggests warm clothing in winter and cool clothing in summer. It can also obtain weather data for the user's location in real time and suggest coordinations that match the weather. For example, it suggests waterproof items for rainy days. The generation AI can also automatically generate coordinations according to the season and weather and suggest them to the user. For example, it suggests floral items in spring and muted colored items in autumn. In this way, it is possible to automatically generate coordinations according to different seasons and weather conditions and suggest them to the user, making it possible to create coordinations that suit the season and weather.

[0077] The coordination generation unit can use the emotion estimation function to suggest coordination that matches the user's current mood and emotions. For example, the emotion estimation function can be used to analyze the user's current mood and suggest coordination that matches that mood. For example, when the user is in a relaxed mood, a casual coordination can be suggested. In addition, the coordination generation unit can obtain the user's emotion data in real time and suggest coordination based on that data. For example, when the user is feeling stressed, relaxing clothing can be suggested. In addition, the emotion estimation function can be used to suggest coordination based on the user's emotions. For example, when the user is in a happy mood, bright colored items can be suggested. In this way, by suggesting coordination that matches the user's current mood and emotions, it becomes possible to create coordination that is more in line with emotions.

[0078] The coordination generation unit can propose coordinations that combine the user's subjective preferences with the latest trends in the fashion industry. For example, the generation AI stores the user's subjective preferences in a database and proposes coordinations that combine the latest fashion trends based on that data. For example, it incorporates the latest trends while taking into account the user's favorite colors and styles. The generation AI can also analyze the user's subjective preferences and propose coordinations that reflect the latest trends in the fashion industry based on that data. For example, it can incorporate the latest collections from the user's favorite brands and designers. The generation AI can also propose coordinations that combine the user's subjective preferences with the latest trends in the fashion industry. For example, it can propose the latest trend items based on the user's favorite colors and styles. In this way, it is possible to provide attractive coordinations to the user by proposing coordinations that combine the user's subjective preferences with the latest trends in the fashion industry.

[0079] The coordination generation unit can use the emotion estimation function to suggest coordination that takes into account the emotions the user feels toward a specific event or occasion. For example, the emotion estimation function can be used to analyze the emotions the user feels toward a specific event and suggest coordination that matches those emotions. For example, when the user is attending a wedding, formal attire can be suggested. Also, based on the user's emotion data, coordination that is suitable for a specific event can be suggested. For example, when the user is attending a party, glamorous attire can be suggested. Also, the emotion estimation function can be used to suggest coordination that takes into account the emotions the user feels toward a specific event or occasion. For example, when the user is going to an interview, clothing that makes the user feel confident can be suggested. In this way, by suggesting coordination that takes into account the emotions the user feels toward a specific event or occasion, it becomes possible to create coordination that is more in line with emotions.

[0080] The coordination generation unit can suggest coordination suitable for specific situations based on the user's occupation and hobbies. For example, the generation AI analyzes the user's occupation data and suggests coordination suitable for specific situations based on that data. For example, if the user is a businessman, it suggests coordination suitable for business situations. It also analyzes the user's hobby data and suggests coordination suitable for specific situations based on that data. For example, if the user likes the outdoors, it suggests coordination suitable for outdoor scenes. It also suggests coordination suitable for specific situations based on the user's occupation and hobbies. For example, if the user is an artist, it suggests coordination suitable for an art event. In this way, it is possible to provide practical coordination for the user by suggesting coordination suitable for specific situations based on the user's occupation and hobbies.

[0081] The coordination generation unit can use the emotion estimation function to suggest coordination that takes into account the emotions the user feels in response to specific music or movies. For example, the emotion estimation function can be used to analyze the emotions the user feels in response to specific music and suggest coordination that matches those emotions. For example, the coordination generation unit can suggest coordination that matches the music the user listens to when they want to relax. Furthermore, the coordination generation unit can suggest coordination that takes into account the emotions the user feels in response to a specific movie based on the user's emotion data. For example, the coordination generation unit can suggest coordination that matches the emotions the user feels in response to specific music or movies. For example, the coordination generation unit can suggest coordination that matches the emotions the user feels when listening to energetic music. In this way, by suggesting coordination that takes into account the emotions the user feels in response to specific music or movies, it is possible to provide coordination that is in line with the user's emotions.

[0082] The coordination generation unit can incorporate fashion styles from different cultures and regions to suggest new styles to the user. For example, the generation AI analyzes fashion styles from different cultures and suggests new styles to the user based on that data. For example, it can suggest items from different cultures that the user does not normally wear. It can also analyze fashion styles from different regions and suggest new styles to the user based on that data. For example, it can incorporate trends from a region other than the one the user lives in. The generation AI can also incorporate fashion styles from different cultures and regions to suggest new styles to the user. For example, it can suggest designs and colors from different cultures that the user would not normally choose. This can incorporate fashion styles from different cultures and regions to suggest new styles to the user, thereby broadening the user's fashion options.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The item acquisition unit acquires information about the items the user owns and the items they are considering purchasing. For example, the user enters information about the clothing and accessories they own. It can also acquire information about items in their online shopping cart or on their wish list. Step 2: The coordination generation unit generates an image of a coordination based on the information acquired by the item acquisition unit. For example, the generation AI creates a coordination that combines a jacket the user owns with pants the user is considering purchasing, and displays the result as a 3D model. The generation AI can also generate coordination taking into account the user's preferences and trends. Step 3: The recommendation unit recommends similar items based on the image of the outfit generated by the outfit generation unit. For example, it may suggest a shirt with a similar design or color to a shirt the user already owns. The generation AI can also analyze the user's past purchase history and preferences to recommend more suitable items.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 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.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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 AI 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 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 item acquisition unit that acquires information about items the user owns and items the user is considering purchasing; a coordinate creation unit that creates an image of a coordinated outfit based on the information acquired by the item acquisition unit; a recommendation unit that recommends similar items based on the coordinated image generated by the coordinate generation unit. A system characterized by:

2. The coordinate generation unit Analyze the user's body shape and posture in real time and suggest the best outfit based on that.

2. The system of claim 1.

3. The coordinate generation unit The system learns the user's past coordination history and generates an image that reflects the user's preferences and trends.

2. The system of claim 1.

4. The coordinate generation unit Proposing outfits that match the user's current mood and emotions 2. The system of claim 1.

5. The coordinate generation unit Proposing outfits for different occasions based on the user's lifestyle and planned activities 2. The system of claim 1.

Images