System

Abstract

An object of a system according to an embodiment is to enable a user to efficiently find clothing that suits the user and propose coordination.SOLUTION: A system according to an embodiment includes AR glasses, a voice coordination module, a clothing registration module, a coordination suggestion module, an online shopping suggestion module, and a virtual dress-up module. The AR glasses are equipped with a generation AI. The voice coordination unit proposes coordination of clothes by voice using AR glasses. The clothes registration unit registers clothes that the user has. The coordination proposal unit immediately proposes coordination on the basis of the clothes registered by the clothes registration unit. The online shopping suggestion unit searches for and suggests clothing that suits the user on an online shopping site. The virtual dressing part virtually dresses clothes according to the mood of the day.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that the process for users to find clothes that suit them is complicated, making it difficult to efficiently suggest outfits.

[0005] The system according to the embodiment aims to enable a user to efficiently find clothes that suit the user and to suggest coordination ideas. [Means for solving the problem]

[0006] The system according to the embodiment includes AR glasses, a voice coordination unit, a clothing registration unit, a coordination suggestion unit, an online shopping suggestion unit, and a virtual dress-up unit. The AR glasses are equipped with a generation AI. The voice coordination unit suggests clothing coordination by voice using the AR glasses. The clothing registration unit registers clothing owned by the user. The coordination suggestion unit instantly suggests coordination based on the clothing registered by the clothing registration unit. The online shopping suggestion unit searches for and suggests clothing that suits the user on an online shopping site. The virtual dress-up unit virtually changes the user's clothing depending on the user's mood that day. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to efficiently find clothes that suit the user and suggests coordination ideas. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A clothing coordination support system according to an embodiment of the present invention is a system that uses AR glasses and utilizes a generative AI. When a user wears the AR glasses and looks into a mirror, the generative AI suggests clothing coordination via voice. By registering the user's existing clothes, the system can instantly suggest outfits that suit the user. It can also search for and suggest clothes on an online shopping site, allowing the user to purchase them on the spot, and allows the user to virtually dress up in clothes. This allows the clothing coordination support system to enable users to efficiently and enjoyably coordinate their clothing.

[0029] A clothing coordination support system according to an embodiment includes a voice coordination unit, a clothing registration unit, an online shopping suggestion unit, and a virtual dress-up unit. When a user wears AR glasses and looks into a mirror, the voice coordination unit uses a generation AI to suggest clothing coordination via voice. For example, the generation AI suggests, "A casual style suits you today. How about combining jeans with a white shirt?" The clothing registration unit registers the user's clothing in the system. For example, the user can take a photo of the clothing they own and upload it to the system, and the generation AI can suggest other items that go well with the clothing. The online shopping suggestion unit searches for clothing that suits the user on an online shopping site and suggests it for immediate purchase. For example, the suggestion suggests, "This jacket looks great on you. You can purchase it on the online shopping site." The virtual dress-up unit allows the user to virtually change their clothing depending on their mood that day. For example, when the user wears AR glasses and looks into a mirror, the generation AI generates a virtual image of the user dressed up in their clothes and presents it to the user. This allows the clothes coordination support system to enable the user to coordinate clothes efficiently and enjoyably.

[0030] The voice coordination unit can analyze the user's past coordination history and make suggestions that take into account changes in individual styles. For example, the voice coordination unit stores the history of coordinations chosen by the user in a database, and the generation AI analyzes changes in style based on that data. For example, it analyzes coordination history from the past year and makes suggestions that take into account changes in the user's preferences and trends. The voice coordination unit also suggests styles suitable for specific seasons or events based on the user's past coordination history. For example, it suggests styles that suit this summer based on past summer coordination. The voice coordination unit also analyzes the user's past coordination history, and if certain combinations of items or colors are common, it makes suggestions that take those trends into account. For example, it suggests new coordinations that suit the user based on the colors and items that the user often chooses. This makes it possible to make suggestions that take into account changes in the user's style.

[0031] The voice coordination unit can detect changes in the user's body shape in real time and suggest optimal outfits based on that. The voice coordination unit detects changes in the user's body shape in real time, for example, using sensors installed in the AR glasses. For example, it analyzes changes in weight and body fat percentage and suggests optimal outfits based on that. The voice coordination unit also periodically collects body shape data to detect changes in the user's body shape, and the generation AI suggests outfits based on that data. For example, it suggests optimal styles for each season based on monthly body shape data. The voice coordination unit also builds a system that detects changes in body shape in real time, and the generation AI suggests outfits based on that data. For example, it automatically updates the optimal outfits every time the body shape changes. This makes it possible to suggest optimal outfits that respond to the user's changes in body shape.

[0032] The voice coordination unit can detect the user's movements and posture and suggest outfits based on that. The voice coordination unit detects the user's movements and posture in real time, for example, using sensors installed in the AR glasses. For example, it detects whether the user is standing or sitting and suggests optimal outfits based on that. The voice coordination unit also periodically collects data to detect the user's movements and posture, and the generation AI suggests outfits based on that data. For example, if the user is exercising, it suggests styles that are easy to move in. The voice coordination unit also builds a system that detects movements and posture in real time, and the generation AI suggests outfits based on that data. For example, if the user is relaxed, it suggests a casual style. This makes it possible to coordinate outfits according to the user's movements and posture.

[0033] The voice coordination unit can add a social function to incorporate the opinions of the user's friends and family into the coordination proposal. The voice coordination unit adds a social function to incorporate the opinions of the user's friends and family into the coordination proposal. For example, the user can share the proposed coordination with friends and family and receive feedback. The voice coordination unit also uses the social function to build a system that allows the user's friends and family to comment on the coordination proposal in real time. For example, friends can "like" or leave comments on the proposed coordination. The voice coordination unit also adds a function to link with social networks to incorporate the opinions of friends and family into the coordination proposal. For example, the user can post the proposed coordination on a social networking site to widely solicit opinions. This makes it possible to coordinate outfits that incorporate the opinions of the user's friends and family.

[0034] The clothing registration unit can perform a detailed analysis of the materials and colors of clothing owned by the user and suggest optimal outfits based on that analysis. The clothing registration unit, for example, uses image analysis technology to perform a detailed analysis of the materials and colors of clothing registered by the user. For example, a photo of the clothing is uploaded and the materials and colors are automatically analyzed. The clothing registration unit also builds a system in which generative AI suggests optimal outfits based on the materials and colors of the clothing owned by the user. For example, it suggests outfits that combine items of the same material and color. The clothing registration unit also analyzes the materials and colors of the clothing registered by the user in detail and suggests outfits based on that data. For example, it suggests styles that combine items of different materials and colors. This makes it possible to suggest optimal outfits based on the materials and colors of the clothing owned by the user.

[0035] The clothing registration unit can suggest optimal outfits based on the user's lifestyle and daily activities. The clothing registration unit, for example, builds a system in which a generative AI suggests optimal outfits based on the user's lifestyle and daily activities. For example, it suggests styles that suit work or hobbies. The clothing registration unit also collects lifestyle data on users and suggests outfits based on that data. For example, it suggests styles that are easy to move in for users with active lifestyles. The clothing registration unit also develops a system in which a generative AI suggests optimal outfits based on the user's daily activities. For example, it suggests styles that suit how they commute or spend their days off. This makes it possible to suggest optimal outfits based on the user's lifestyle and daily activities.

[0036] The clothing registration unit can add clothing information about the user's friends and family to the registered clothing of the user's owned items and suggest coordinated outfits to share. For example, the clothing registration unit registers clothing information about the user's friends and family in the system and adds a function to suggest coordinated outfits to share. For example, it suggests coordinated outfits that combine the clothing of friends and family. The clothing registration unit also builds a system in which a generation AI suggests coordinated outfits to share based on the clothing information of friends and family. For example, it suggests matching styles for the whole family. The clothing registration unit also adds clothing information about the user's friends and family and suggests coordinated outfits to share based on that data. For example, it suggests outfits to wear when going out with friends. This makes it possible to suggest coordinated outfits to share based on the clothing information of the user's friends and family.

[0037] The clothing registration unit can add special suggestions according to seasons and events to the outfit suggestions. For example, the clothing registration unit inputs seasonal data and event information into the generation AI to make special outfit suggestions according to seasons and events. For example, it may suggest styles suitable for a summer beach party. The clothing registration unit also builds a system that makes outfit suggestions according to seasons and events. For example, it may suggest styles that suit special events such as Christmas and Halloween. The clothing registration unit also develops a system in which the generation AI automatically collects seasonal data and event information to make special outfit suggestions according to seasons and events. For example, it may suggest new styles to coincide with the change of seasons. This makes it possible to make special outfit suggestions according to seasons and events.

[0038] The online shopping suggestion unit can analyze a user's purchase history and suggest optimal clothing based on past purchasing trends. For example, the online shopping suggestion unit stores the user's past purchase history in a database, and the generation AI analyzes purchasing trends based on that data. For example, similar clothing is suggested based on the style and brand of items previously purchased. The online shopping suggestion unit also analyzes the user's preferences and trends based on the purchase history, building a system in which the generation AI suggests optimal clothing. For example, new items are suggested based on the colors and designs that the user frequently purchases. The online shopping suggestion unit also analyzes the user's purchase history and suggests clothing suitable for specific seasons or events. For example, styles that suit this summer are suggested based on past summer purchase history. This makes it possible to suggest optimal clothing based on the user's past purchasing trends.

[0039] The online shopping suggestion unit can analyze the user's body shape and size information in detail and suggest clothes of the optimal size based on that. The online shopping suggestion unit, for example, stores the user's body shape and size information in a database, and the generation AI suggests clothes of the optimal size based on that data. For example, it suggests clothes that fit based on the user's height, weight, and body shape. The online shopping suggestion unit also builds a system in which the generation AI suggests clothes of the optimal size based on the user's body shape and size information. For example, it provides customization options that match the user's body shape. The online shopping suggestion unit also analyzes the user's body shape and size information in detail and suggests clothes of the optimal size based on that data. For example, it suggests brands and designs that fit the user's body shape. This makes it possible to suggest clothes of the optimal size based on the user's body shape and size information.

[0040] The online shopping suggestion unit can add a review function to incorporate the opinions of the user's friends and family into suggestions made on the online shopping site. The online shopping suggestion unit, for example, adds a review function to the online shopping site to enable the user's friends and family to comment on and rate purchase suggestions. For example, friends can leave "likes" or comments on suggested clothing. The online shopping suggestion unit also uses the review function to build a system that enables the user's friends and family to provide feedback on purchase suggestions in real time. For example, family members can rate suggested clothing. The online shopping suggestion unit also adds a review function to the online shopping site to make purchase suggestions that incorporate the opinions of the user's friends and family. For example, friends can leave comments on suggested clothing and make purchase decisions based on those opinions. This makes it possible to make purchase suggestions that incorporate the opinions of the user's friends and family.

[0041] The online shopping suggestion unit can add special suggestions based on the user's lifestyle and daily activities to the purchasing suggestions. The online shopping suggestion unit, for example, builds a system in which a generation AI makes special purchasing suggestions based on the user's lifestyle and daily activities. For example, it suggests styles that suit work or hobbies. The online shopping suggestion unit also collects lifestyle data on users and makes purchasing suggestions based on that data. For example, it suggests styles that are easy to move in for users with active lifestyles. The online shopping suggestion unit also develops a system in which a generation AI makes special purchasing suggestions based on the user's daily activities. For example, it suggests styles that suit commutes and how people spend their days off. This makes it possible to make special purchasing suggestions based on the user's lifestyle and daily activities.

[0042] The virtual dress-up unit detects the user's body shape and posture in real time and can suggest optimal virtual outfits based on that. The virtual dress-up unit detects the user's body shape and posture in real time, for example, using sensors installed in AR glasses. For example, it detects whether the user is standing or sitting and suggests optimal virtual outfits based on that. The virtual dress-up unit also periodically collects data to detect the user's body shape and posture, and the generation AI suggests virtual outfits based on that data. For example, if the user is exercising, it suggests styles that are easy to move in. The virtual dress-up unit also detects the user's body shape and posture in real time, and the generation AI proposes virtual outfits based on that data. For example, if the user is relaxed, it suggests a casual style. This makes it possible to suggest optimal virtual outfits based on the user's body shape and posture.

[0043] The virtual dress-up unit can analyze the user's past virtual coordination history and make suggestions that take into account changes in individual styles. For example, the virtual dress-up unit stores the history of virtual coordinations chosen by the user in a database, and the generation AI analyzes style changes based on that data. For example, it can analyze the virtual coordination history from the past year and make suggestions that take into account changes in the user's preferences and trends. The virtual dress-up unit also suggests styles suitable for specific seasons or events based on the user's past virtual coordination history. For example, it can suggest styles that suit this summer based on virtual coordinations from past summers. The virtual dress-up unit also analyzes the user's past virtual coordination history and, if certain item or color combinations are common, makes suggestions that take those trends into account. For example, it can suggest new virtual coordinations that suit the user based on the colors and items the user frequently chooses. This enables optimal suggestions based on the user's past virtual coordination history.

[0044] The virtual dress-up unit can add a social function to incorporate the opinions of the user's friends and family into the virtual coordination. The virtual dress-up unit adds a social function to incorporate the opinions of the user's friends and family into the virtual coordination. For example, the user can share a proposed virtual coordination with friends and family and receive feedback. The virtual dress-up unit also uses the social function to build a system that allows the user's friends and family to comment on the virtual coordination in real time. For example, friends can "like" or leave comments on the proposed virtual coordination. The virtual dress-up unit also adds a function to link with a social network to incorporate the opinions of friends and family into the virtual coordination. For example, the user can post the proposed virtual coordination on a social networking site to widely solicit opinions. This makes it possible to propose virtual coordination that incorporates the opinions of the user's friends and family.

[0045] The virtual dress-up department can add special suggestions to virtual coordinations according to seasons and events. For example, the virtual dress-up department inputs seasonal data and event information into the generation AI to make special virtual coordination suggestions according to seasons and events. For example, it may suggest styles suitable for a summer beach party. The virtual dress-up department will also build a system to make virtual coordination suggestions according to seasons and events. For example, it may suggest styles to match special events such as Christmas and Halloween. The virtual dress-up department will also develop a system in which the generation AI automatically collects seasonal data and event information to make special virtual coordination suggestions according to seasons and events. For example, it may suggest new styles to coincide with the change of seasons. This will make it possible to make special virtual coordination suggestions according to seasons and events.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The voice coordination unit can also analyze the user's past coordination history and make optimal suggestions for specific events or situations. For example, it can suggest a style suitable for the next event based on the coordination history of weddings and parties the user has attended in the past. It can also suggest a similar style based on a particularly highly rated coordination chosen by the user in the past. For example, it can say, "This dress was well received in the past, so this time we will suggest accessories that go well with it." This makes it possible to suggest optimal coordination based on the user's past event history.

[0048] The voice coordination unit can not only detect changes in the user's body shape, but also suggest outfits that take the user's health condition into consideration. For example, if the user inputs the results of a health check into the system, the generation AI can suggest optimal outfits based on that data. For example, if the user is concerned about weight gain, it can suggest styles that will cover up their body shape. Also, if the user has started exercising, it can suggest sportswear that is easy to move in. This makes it possible to suggest optimal outfits that suit the user's health condition.

[0049] The voice coordination unit can not only detect the user's movements and posture, but also suggest outfits based on the user's activity level. For example, if the user spends most of the day sitting, it can suggest a comfortable style that is easy to move in. If the user is often active, it can suggest a sporty style. Furthermore, if the user plays a specific sport or activity, it can suggest outfits that are suitable for that activity. This makes it possible to suggest the optimal outfits according to the user's activity level.

[0050] The voice coordination unit not only adds a social function for incorporating the opinions of the user's friends and family, but can also analyze the coordination history of the user's friends and family and make suggestions based on that. For example, it can suggest similar styles based on the coordination chosen by the user's friends in the past. It can also suggest matching styles for the whole family based on the coordination chosen by the user's family for a specific event. This makes it possible to make optimal suggestions based on the coordination history of the user's friends and family.

[0051] The clothing registration unit not only analyzes the materials and colors of the user's clothing in detail, but also suggests outfits that take into account the user's preferences and trends. For example, if the user has a preference for a particular material or color, the unit can suggest new outfits based on that preference. It can also suggest trendy items that go well with the user's clothing, taking into account current fashion trends. This makes it possible to suggest optimal outfits based on the user's preferences and trends.

[0052] The clothing registration unit not only suggests outfits based on the user's lifestyle and daily activities, but also makes suggestions that take the user's schedule into consideration. For example, if the user has an important meeting or event on a specific day, it can suggest a style suitable for that day. Also, if the user plans to relax on a day off, it can suggest a casual style. This makes it possible to suggest the best outfits to suit the user's schedule.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: When the user puts on the AR glasses and looks into the mirror, the AI ​​generator will suggest outfit coordination through voice. For example, the AI ​​generator might suggest, "A casual style suits you today. How about combining jeans with a white shirt?" Step 2: The clothing registration unit registers the user's clothing in the system. For example, the user can take a photo of the clothing they own and upload it to the system, and the generation AI will suggest other items that go well with the clothing. Step 3: The coordination suggestion unit immediately suggests coordinations based on the clothes registered by the clothes registration unit, allowing the user to quickly find coordinations that utilize clothes they already own. Step 4: The online shopping suggestion unit uses the generation AI to search online shopping sites for clothes that suit the user and suggest them for immediate purchase. For example, it might suggest, "This jacket looks great on you. You can buy it on the online shopping site." Step 5: The virtual dress-up unit allows the user to virtually change their clothes depending on their mood that day. For example, when the user wears the AR glasses and looks into the mirror, the AI ​​generates an image of the user virtually dressed in clothes and presents it to the user.

[0055] (Example 2) A clothing coordination support system according to an embodiment of the present invention is a system that uses AR glasses and utilizes a generative AI. When a user wears the AR glasses and looks into a mirror, the generative AI suggests clothing coordination via voice. By registering the user's existing clothes, the system can instantly suggest outfits that suit the user. It can also search for and suggest clothes on an online shopping site, allowing the user to purchase them on the spot, and allows the user to virtually dress up in clothes. This allows the clothing coordination support system to enable users to efficiently and enjoyably coordinate their clothing.

[0056] A clothing coordination support system according to an embodiment includes a voice coordination unit, a clothing registration unit, an online shopping suggestion unit, and a virtual dress-up unit. When a user wears AR glasses and looks into a mirror, the voice coordination unit uses a generation AI to suggest clothing coordination via voice. For example, the generation AI suggests, "A casual style suits you today. How about combining jeans with a white shirt?" The clothing registration unit registers the user's clothing in the system. For example, the user can take a photo of the clothing they own and upload it to the system, and the generation AI can suggest other items that go well with the clothing. The online shopping suggestion unit searches for clothing that suits the user on an online shopping site and suggests it for immediate purchase. For example, the suggestion suggests, "This jacket looks great on you. You can purchase it on the online shopping site." The virtual dress-up unit allows the user to virtually change their clothing depending on their mood that day. For example, when the user wears AR glasses and looks into a mirror, the generation AI generates a virtual image of the user dressed up in their clothes and presents it to the user. This allows the clothes coordination support system to enable the user to coordinate clothes efficiently and enjoyably.

[0057] The voice coordination unit can analyze the user's past coordination history and make suggestions that take into account changes in individual styles. For example, the voice coordination unit stores the history of coordinations chosen by the user in a database, and the generation AI analyzes changes in style based on that data. For example, it analyzes coordination history from the past year and makes suggestions that take into account changes in the user's preferences and trends. The voice coordination unit also suggests styles suitable for specific seasons or events based on the user's past coordination history. For example, it suggests styles that suit this summer based on past summer coordination. The voice coordination unit also analyzes the user's past coordination history, and if certain combinations of items or colors are common, it makes suggestions that take those trends into account. For example, it suggests new coordinations that suit the user based on the colors and items that the user often chooses. This makes it possible to make suggestions that take into account changes in the user's style.

[0058] The voice coordination unit can detect changes in the user's body shape in real time and suggest optimal outfits based on that. The voice coordination unit detects changes in the user's body shape in real time, for example, using sensors installed in the AR glasses. For example, it analyzes changes in weight and body fat percentage and suggests optimal outfits based on that. The voice coordination unit also periodically collects body shape data to detect changes in the user's body shape, and the generation AI suggests outfits based on that data. For example, it suggests optimal styles for each season based on monthly body shape data. The voice coordination unit also builds a system that detects changes in body shape in real time, and the generation AI suggests outfits based on that data. For example, it automatically updates the optimal outfits every time the body shape changes. This makes it possible to suggest optimal outfits that respond to the user's changes in body shape.

[0059] The voice coordination unit can use the emotion estimation function to estimate the user's mood for the day and suggest an outfit that matches the mood. For example, the voice coordination unit uses the emotion estimation function to analyze the user's facial expression and voice tone to estimate the user's mood for the day. For example, if the user is smiling, the voice coordination unit suggests an outfit in bright colors. The voice coordination unit also builds a system that suggests an outfit that matches the user's mood based on the user's emotion data. For example, if the user is feeling stressed, the voice coordination unit suggests a relaxing style. The voice coordination unit also uses the emotion estimation function to analyze the user's mood in real time and suggest an outfit based on that data. For example, if the user is feeling energetic, the voice coordination unit suggests an active style. This makes it possible to coordinate outfits according to the user's mood.

[0060] The voice coordination unit can detect the user's movements and posture and suggest outfits based on that. The voice coordination unit detects the user's movements and posture in real time, for example, using sensors installed in the AR glasses. For example, it detects whether the user is standing or sitting and suggests optimal outfits based on that. The voice coordination unit also periodically collects data to detect the user's movements and posture, and the generation AI suggests outfits based on that data. For example, if the user is exercising, it suggests styles that are easy to move in. The voice coordination unit also builds a system that detects movements and posture in real time, and the generation AI suggests outfits based on that data. For example, if the user is relaxed, it suggests a casual style. This makes it possible to coordinate outfits according to the user's movements and posture.

[0061] The voice coordination unit can add a social function to incorporate the opinions of the user's friends and family into the coordination proposal. The voice coordination unit adds a social function to incorporate the opinions of the user's friends and family into the coordination proposal. For example, the user can share the proposed coordination with friends and family and receive feedback. The voice coordination unit also uses the social function to build a system that allows the user's friends and family to comment on the coordination proposal in real time. For example, friends can "like" or leave comments on the proposed coordination. The voice coordination unit also adds a function to link with social networks to incorporate the opinions of friends and family into the coordination proposal. For example, the user can post the proposed coordination on a social networking site to widely solicit opinions. This makes it possible to coordinate outfits that incorporate the opinions of the user's friends and family.

[0062] The voice coordination unit uses the emotion estimation function to provide real-time feedback on the emotions the user feels toward a specific outfit, allowing it to make optimal suggestions. For example, the voice coordination unit uses the emotion estimation function to analyze the emotions the user feels toward a proposed outfit in real time. For example, if the user is smiling, the voice coordination unit recommends that outfit. The voice coordination unit also evaluates the proposed outfit based on the user's emotional response, building a system that provides optimal suggestions. For example, it prioritizes suggesting outfits for which the user expresses positive emotions. The voice coordination unit also uses the emotion estimation function to provide real-time feedback on the emotions the user feels toward a specific outfit, and adjusts the outfit based on that data. For example, if the user expresses dissatisfaction, the voice coordination unit suggests a different outfit. This makes it possible to suggest optimal outfits based on the user's emotions.

[0063] The clothing registration unit can perform a detailed analysis of the materials and colors of clothing owned by the user and suggest optimal outfits based on that analysis. The clothing registration unit, for example, uses image analysis technology to perform a detailed analysis of the materials and colors of clothing registered by the user. For example, a photo of the clothing is uploaded and the materials and colors are automatically analyzed. The clothing registration unit also builds a system in which generative AI suggests optimal outfits based on the materials and colors of the clothing owned by the user. For example, it suggests outfits that combine items of the same material and color. The clothing registration unit also analyzes the materials and colors of the clothing registered by the user in detail and suggests outfits based on that data. For example, it suggests styles that combine items of different materials and colors. This makes it possible to suggest optimal outfits based on the materials and colors of the clothing owned by the user.

[0064] The clothing registration unit can suggest optimal outfits based on the user's lifestyle and daily activities. The clothing registration unit, for example, builds a system in which a generative AI suggests optimal outfits based on the user's lifestyle and daily activities. For example, it suggests styles that suit work or hobbies. The clothing registration unit also collects lifestyle data on users and suggests outfits based on that data. For example, it suggests styles that are easy to move in for users with active lifestyles. The clothing registration unit also develops a system in which a generative AI suggests optimal outfits based on the user's daily activities. For example, it suggests styles that suit how they commute or spend their days off. This makes it possible to suggest optimal outfits based on the user's lifestyle and daily activities.

[0065] The clothing registration unit uses the emotion estimation function to analyze the emotions a user has toward specific clothing and can suggest outfits based on that. The clothing registration unit, for example, uses the emotion estimation function to analyze the emotions a user has toward registered clothing. For example, if a user expresses positive emotions toward specific clothing, it suggests outfits based on that clothing. The clothing registration unit also analyzes emotions toward specific clothing based on the user's emotion data and builds a system that suggests outfits based on that data. For example, it suggests outfits centered around clothing that the user likes. The clothing registration unit also uses the emotion estimation function to analyze the emotions a user has toward specific clothing in real time and suggests outfits based on that data. For example, it suggests new styles based on the user's favorite clothing. This makes it possible to suggest optimal outfits based on the user's emotions.

[0066] The clothing registration unit can add clothing information about the user's friends and family to the registered clothing of the user's owned items and suggest coordinated outfits to share. For example, the clothing registration unit registers clothing information about the user's friends and family in the system and adds a function to suggest coordinated outfits to share. For example, it suggests coordinated outfits that combine the clothing of friends and family. The clothing registration unit also builds a system in which a generation AI suggests coordinated outfits to share based on the clothing information of friends and family. For example, it suggests matching styles for the whole family. The clothing registration unit also adds clothing information about the user's friends and family and suggests coordinated outfits to share based on that data. For example, it suggests outfits to wear when going out with friends. This makes it possible to suggest coordinated outfits to share based on the clothing information of the user's friends and family.

[0067] The clothing registration unit can add special suggestions according to seasons and events to the outfit suggestions. For example, the clothing registration unit inputs seasonal data and event information into the generation AI to make special outfit suggestions according to seasons and events. For example, it may suggest styles suitable for a summer beach party. The clothing registration unit also builds a system that makes outfit suggestions according to seasons and events. For example, it may suggest styles that suit special events such as Christmas and Halloween. The clothing registration unit also develops a system in which the generation AI automatically collects seasonal data and event information to make special outfit suggestions according to seasons and events. For example, it may suggest new styles to coincide with the change of seasons. This makes it possible to make special outfit suggestions according to seasons and events.

[0068] The clothing registration unit can use the emotion estimation function to suggest outfits that take into account the emotions the user feels toward a specific event or situation. For example, the clothing registration unit uses the emotion estimation function to analyze the emotions the user feels toward a specific event or situation. For example, if the user expresses positive emotions toward an event they are looking forward to, the clothing registration unit suggests outfits suitable for that event. The clothing registration unit also analyzes the emotions toward a specific event or situation based on the user's emotion data, and builds a system that suggests outfits based on that data. For example, if the user is nervous, the clothing registration unit suggests a relaxed style. The clothing registration unit also uses the emotion estimation function to analyze the emotions the user feels toward a specific event or situation in real time, and suggests outfits based on that data. For example, if the user is excited, the clothing registration unit suggests a flashy style. This makes it possible to suggest outfits that correspond to events and situations based on the user's emotions.

[0069] The online shopping suggestion unit can analyze a user's purchase history and suggest optimal clothing based on past purchasing trends. For example, the online shopping suggestion unit stores the user's past purchase history in a database, and the generation AI analyzes purchasing trends based on that data. For example, similar clothing is suggested based on the style and brand of items previously purchased. The online shopping suggestion unit also analyzes the user's preferences and trends based on the purchase history, building a system in which the generation AI suggests optimal clothing. For example, new items are suggested based on the colors and designs that the user frequently purchases. The online shopping suggestion unit also analyzes the user's purchase history and suggests clothing suitable for specific seasons or events. For example, styles that suit this summer are suggested based on past summer purchase history. This makes it possible to suggest optimal clothing based on the user's past purchasing trends.

[0070] The online shopping suggestion unit can analyze the user's body shape and size information in detail and suggest clothes of the optimal size based on that. The online shopping suggestion unit, for example, stores the user's body shape and size information in a database, and the generation AI suggests clothes of the optimal size based on that data. For example, it suggests clothes that fit based on the user's height, weight, and body shape. The online shopping suggestion unit also builds a system in which the generation AI suggests clothes of the optimal size based on the user's body shape and size information. For example, it provides customization options that match the user's body shape. The online shopping suggestion unit also analyzes the user's body shape and size information in detail and suggests clothes of the optimal size based on that data. For example, it suggests brands and designs that fit the user's body shape. This makes it possible to suggest clothes of the optimal size based on the user's body shape and size information.

[0071] The online shopping suggestion unit can use the emotion estimation function to analyze the emotion a user has toward specific clothing and make purchase suggestions based on that. The online shopping suggestion unit, for example, uses the emotion estimation function to analyze the emotion a user has toward specific clothing. For example, it preferentially suggests clothing for which the user has expressed positive emotions. The online shopping suggestion unit also builds a system that analyzes the emotion a user has toward specific clothing based on the user's emotion data and makes purchase suggestions based on that data. For example, it suggests new items based on the user's favorite styles and designs. The online shopping suggestion unit also uses the emotion estimation function to analyze the emotion a user has toward specific clothing in real time and makes purchase suggestions based on that data. For example, if the user is excited, it suggests clothing suitable for a special event. This makes it possible to make optimal purchase suggestions based on the user's emotions.

[0072] The online shopping suggestion unit can add a review function to incorporate the opinions of the user's friends and family into suggestions made on the online shopping site. The online shopping suggestion unit, for example, adds a review function to the online shopping site to enable the user's friends and family to comment on and rate purchase suggestions. For example, friends can leave "likes" or comments on suggested clothing. The online shopping suggestion unit also uses the review function to build a system that enables the user's friends and family to provide feedback on purchase suggestions in real time. For example, family members can rate suggested clothing. The online shopping suggestion unit also adds a review function to the online shopping site to make purchase suggestions that incorporate the opinions of the user's friends and family. For example, friends can leave comments on suggested clothing and make purchase decisions based on those opinions. This makes it possible to make purchase suggestions that incorporate the opinions of the user's friends and family.

[0073] The online shopping suggestion unit can add special suggestions based on the user's lifestyle and daily activities to the purchasing suggestions. The online shopping suggestion unit, for example, builds a system in which a generation AI makes special purchasing suggestions based on the user's lifestyle and daily activities. For example, it suggests styles that suit work or hobbies. The online shopping suggestion unit also collects lifestyle data on users and makes purchasing suggestions based on that data. For example, it suggests styles that are easy to move in for users with active lifestyles. The online shopping suggestion unit also develops a system in which a generation AI makes special purchasing suggestions based on the user's daily activities. For example, it suggests styles that suit commutes and how people spend their days off. This makes it possible to make special purchasing suggestions based on the user's lifestyle and daily activities.

[0074] The online shopping suggestion unit can use the emotion estimation function to make suggestions that take into account the emotions a user has toward a specific brand or design. For example, the online shopping suggestion unit uses the emotion estimation function to analyze the emotions a user has toward a specific brand or design. For example, it preferentially suggests brands and designs for which the user has expressed positive emotions. The online shopping suggestion unit also analyzes emotions toward a specific brand or design based on user emotion data and builds a system that makes purchase suggestions based on the data. For example, it suggests new items based on a brand or design that the user likes. The online shopping suggestion unit also uses the emotion estimation function to analyze the emotions a user has toward a specific brand or design in real time and makes purchase suggestions based on the data. For example, if the user is excited, it suggests brands and designs that are suitable for a special event. This makes it possible to suggest brands and designs based on the user's emotions.

[0075] The virtual dress-up unit detects the user's body shape and posture in real time and can suggest optimal virtual outfits based on that. The virtual dress-up unit detects the user's body shape and posture in real time, for example, using sensors installed in AR glasses. For example, it detects whether the user is standing or sitting and suggests optimal virtual outfits based on that. The virtual dress-up unit also periodically collects data to detect the user's body shape and posture, and the generation AI suggests virtual outfits based on that data. For example, if the user is exercising, it suggests styles that are easy to move in. The virtual dress-up unit also detects the user's body shape and posture in real time, and the generation AI proposes virtual outfits based on that data. For example, if the user is relaxed, it suggests a casual style. This makes it possible to suggest optimal virtual outfits based on the user's body shape and posture.

[0076] The virtual dress-up unit can analyze the user's past virtual coordination history and make suggestions that take into account changes in individual styles. For example, the virtual dress-up unit stores the history of virtual coordinations chosen by the user in a database, and the generation AI analyzes style changes based on that data. For example, it can analyze the virtual coordination history from the past year and make suggestions that take into account changes in the user's preferences and trends. The virtual dress-up unit also suggests styles suitable for specific seasons or events based on the user's past virtual coordination history. For example, it can suggest styles that suit this summer based on virtual coordinations from past summers. The virtual dress-up unit also analyzes the user's past virtual coordination history and, if certain item or color combinations are common, makes suggestions that take those trends into account. For example, it can suggest new virtual coordinations that suit the user based on the colors and items the user frequently chooses. This enables optimal suggestions based on the user's past virtual coordination history.

[0077] The virtual dress-up unit can use the emotion estimation function to estimate the user's mood for the day and suggest a virtual outfit that matches the mood. For example, the virtual dress-up unit uses the emotion estimation function to analyze the user's facial expression and voice tone to estimate the user's mood for the day. For example, if the user is smiling, the virtual dress-up unit suggests a bright-colored virtual outfit. The virtual dress-up unit also builds a system that suggests a virtual outfit that matches the user's mood based on the user's emotion data. For example, if the user is feeling stressed, the virtual dress-up unit suggests a relaxing style. The virtual dress-up unit also uses the emotion estimation function to analyze the user's mood in real time and suggest a virtual outfit based on that data. For example, if the user is feeling energetic, the virtual dress-up unit suggests an active style. This makes it possible to suggest optimal virtual outfits based on the user's mood.

[0078] The virtual dress-up unit can add a social function to incorporate the opinions of the user's friends and family into the virtual coordination. The virtual dress-up unit adds a social function to incorporate the opinions of the user's friends and family into the virtual coordination. For example, the user can share a proposed virtual coordination with friends and family and receive feedback. The virtual dress-up unit also uses the social function to build a system that allows the user's friends and family to comment on the virtual coordination in real time. For example, friends can "like" or leave comments on the proposed virtual coordination. The virtual dress-up unit also adds a function to link with a social network to incorporate the opinions of friends and family into the virtual coordination. For example, the user can post the proposed virtual coordination on a social networking site to widely solicit opinions. This makes it possible to propose virtual coordination that incorporates the opinions of the user's friends and family.

[0079] The virtual dress-up department can add special suggestions to virtual coordinations according to seasons and events. For example, the virtual dress-up department inputs seasonal data and event information into the generation AI to make special virtual coordination suggestions according to seasons and events. For example, it may suggest styles suitable for a summer beach party. The virtual dress-up department will also build a system to make virtual coordination suggestions according to seasons and events. For example, it may suggest styles to match special events such as Christmas and Halloween. The virtual dress-up department will also develop a system in which the generation AI automatically collects seasonal data and event information to make special virtual coordination suggestions according to seasons and events. For example, it may suggest new styles to coincide with the change of seasons. This will make it possible to make special virtual coordination suggestions according to seasons and events.

[0080] The virtual dress-up unit uses an emotion estimation function to provide real-time feedback on the emotions the user feels toward a specific virtual outfit, allowing it to make optimal suggestions. For example, the virtual dress-up unit uses the emotion estimation function to analyze the emotions the user feels toward a proposed virtual outfit in real time. For example, if the user is smiling, the virtual outfit is recommended. The virtual dress-up unit also evaluates the proposed virtual outfit based on the user's emotional response, building a system that provides optimal suggestions. For example, it prioritizes suggesting virtual outfits for which the user expresses positive emotions. The virtual dress-up unit also uses the emotion estimation function to provide real-time feedback on the emotions the user feels toward a specific virtual outfit, and adjusts the virtual outfit based on that data. For example, if the user expresses dissatisfaction, a different virtual outfit is suggested. This makes it possible to suggest optimal virtual outfits based on the user's emotions.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The voice coordination unit can analyze the user's tone of voice and speaking style to estimate the user's stress level and suggest relaxing outfits based on that. For example, if the user sounds tired, the AI ​​can suggest, "A relaxed, casual style would be good today. How about combining sweatpants with a loose-fitting top?" On the other hand, if the user sounds energetic, the AI ​​can suggest an active style. For example, it could suggest, "You look energetic today. How about staying active with a sporty style?" This makes it possible to suggest outfits based on the user's tone of voice.

[0083] The voice coordination unit can also analyze the user's past coordination history and make optimal suggestions for specific events or situations. For example, it can suggest a style suitable for the next event based on the coordination history of weddings and parties the user has attended in the past. It can also suggest a similar style based on a particularly highly rated coordination chosen by the user in the past. For example, it can say, "This dress was well received in the past, so this time we will suggest accessories that go well with it." This makes it possible to suggest optimal coordination based on the user's past event history.

[0084] The voice coordination unit can not only detect changes in the user's body shape, but also suggest outfits that take the user's health condition into consideration. For example, if the user inputs the results of a health check into the system, the generation AI can suggest optimal outfits based on that data. For example, if the user is concerned about weight gain, it can suggest styles that will cover up their body shape. Also, if the user has started exercising, it can suggest sportswear that is easy to move in. This makes it possible to suggest optimal outfits that suit the user's health condition.

[0085] The voice coordination unit uses its emotion estimation function to analyze not only the user's mood for the day, but also their long-term emotional trends, and can suggest outfits based on these. For example, if the user has been feeling stressed for the past few weeks, the generation AI can suggest a relaxing style. Alternatively, if the user has been feeling positive for a long period of time, it can suggest outfits with bright colors and designs. This makes it possible to suggest optimal outfits based on the user's long-term emotional trends.

[0086] The voice coordination unit can not only detect the user's movements and posture, but also suggest outfits based on the user's activity level. For example, if the user spends most of the day sitting, it can suggest a comfortable style that is easy to move in. If the user is often active, it can suggest a sporty style. Furthermore, if the user plays a specific sport or activity, it can suggest outfits that are suitable for that activity. This makes it possible to suggest the optimal outfits according to the user's activity level.

[0087] The voice coordination unit not only adds a social function for incorporating the opinions of the user's friends and family, but can also analyze the coordination history of the user's friends and family and make suggestions based on that. For example, it can suggest similar styles based on the coordination chosen by the user's friends in the past. It can also suggest matching styles for the whole family based on the coordination chosen by the user's family for a specific event. This makes it possible to make optimal suggestions based on the coordination history of the user's friends and family.

[0088] The voice coordination unit uses its emotion estimation function to not only provide real-time feedback on the user's emotions toward a particular outfit, but also track changes in the user's emotions and adjust suggestions based on those emotions. For example, if the user expresses positive emotions toward a particular outfit, a new suggestion will be made based on that style. On the other hand, if the user expresses negative emotions, a different style can be suggested. This makes it possible to suggest optimal outfits in response to changes in the user's emotions.

[0089] The clothing registration unit not only analyzes the materials and colors of the user's clothing in detail, but also suggests outfits that take into account the user's preferences and trends. For example, if the user has a preference for a particular material or color, the unit can suggest new outfits based on that preference. It can also suggest trendy items that go well with the user's clothing, taking into account current fashion trends. This makes it possible to suggest optimal outfits based on the user's preferences and trends.

[0090] The clothing registration unit not only suggests outfits based on the user's lifestyle and daily activities, but also makes suggestions that take the user's schedule into consideration. For example, if the user has an important meeting or event on a specific day, it can suggest a style suitable for that day. Also, if the user plans to relax on a day off, it can suggest a casual style. This makes it possible to suggest the best outfits to suit the user's schedule.

[0091] The clothing registration unit uses the emotion estimation function to not only analyze the emotions a user has toward a particular piece of clothing, but also track changes in the user's emotions and adjust suggestions based on those emotions. For example, if a user expresses positive emotions toward a particular piece of clothing, the unit can suggest a new outfit based on that clothing. On the other hand, if the user expresses negative emotions, the unit can suggest a new outfit based on a different piece of clothing. This makes it possible to suggest optimal outfits in response to changes in the user's emotions.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: When the user puts on the AR glasses and looks into the mirror, the AI ​​generator will suggest outfit coordination through voice. For example, the AI ​​generator might suggest, "A casual style suits you today. How about combining jeans with a white shirt?" Step 2: The clothing registration unit registers the user's clothing in the system. For example, the user can take a photo of the clothing they own and upload it to the system, and the generation AI will suggest other items that go well with the clothing. Step 3: The coordination suggestion unit immediately suggests coordinations based on the clothes registered by the clothes registration unit, allowing the user to quickly find coordinations that utilize clothes they already own. Step 4: The online shopping suggestion unit uses the generation AI to search online shopping sites for clothes that suit the user and suggest them for immediate purchase. For example, it might suggest, "This jacket looks great on you. You can buy it on the online shopping site." Step 5: The virtual dress-up unit allows the user to virtually change their clothes depending on their mood that day. For example, when the user wears the AR glasses and looks into the mirror, the AI ​​generates an image of the user virtually dressed in clothes and presents it to the user.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, a 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] 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]

[0161] 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. AR glasses equipped with generative AI, a voice coordination unit that uses the AR glasses to suggest outfit coordination by voice; a clothes registration unit for registering clothes owned by the user; a coordinate suggestion unit that instantly suggests coordinates based on the clothes registered by the clothes registration unit; an online shopping suggestion unit that searches for and suggests clothes that suit the user on an online shopping site; A virtual dress-up unit that virtually changes clothes depending on the mood of the day. A system characterized by:

2. The voice coordinating unit Analyze the user's past coordination history and make suggestions that take into account changes in individual styles 2. The system of claim 1.

3. The voice coordinating unit Detecting changes in the user's body shape in real time and suggesting optimal outfits based on that.

2. The system of claim 1.

4. The voice coordinating unit Estimate the user's mood for the day and suggest outfits that match that mood 2. The system of claim 1.

5. The voice coordinating unit Detecting the user's movements and posture and suggesting outfits based on them 2. The system of claim 1.

6. The voice coordinating unit Add a social feature to incorporate the opinions of the user's friends and family into outfit suggestions.

2. The system of claim 1.

7. The voice coordinating unit The user's feelings about a particular outfit are fed back in real time, and optimal suggestions are made.

2. The system of claim 1.

8. The clothes registration unit It analyzes the materials and colors of your clothes in detail and suggests the best outfits based on that information.

2. The system of claim 1.

Images