System

Abstract

An object of a system according to an embodiment is to easily propose dressing of clothes owned by a user.SOLUTION: A system includes a photograph upload unit, an analysis unit, a proposal unit, and a link providing unit. The photograph upload unit uploads a photograph of clothing owned by the user. The analysis unit analyzes the photograph uploaded by the photograph upload unit. The proposal unit proposes stylish dressing based on the information analyzed by the analysis unit. The link providing unit provides a purchase link of the clothing proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is not easy to suggest ways to wear the clothes a user owns, making it difficult to dress stylishly.

[0005] The system according to the embodiment aims to easily suggest ways to wear clothes owned by a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo uploading unit, an analysis unit, a suggestion unit, and a link providing unit. The photo uploading unit uploads photos of clothes owned by the user. The analysis unit analyzes the photos uploaded by the photo uploading unit. The suggestion unit suggests fashionable outfits based on the information analyzed by the analysis unit. The link providing unit provides a purchase link for the clothes suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can easily suggest ways to wear clothes owned by a user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The fashion suggestion system according to an embodiment of the present invention allows users to upload photos of their own clothes, which are analyzed by a generation AI to suggest stylish outfits, and further suggests recommended clothes and provides a link to purchase them. As a result, the fashion suggestion system allows users to easily get suggestions for stylish outfits and also suggests recommended clothes, allowing them to enjoy fashion without any hassle.

[0029] A fashion suggestion system according to an embodiment includes a photo uploading unit, an analysis unit, a suggestion unit, and a link providing unit. The photo uploading unit uploads photos of clothes owned by a user. For example, photos taken with a smartphone or digital camera can be uploaded to the system. The photo uploading unit can also select and upload photos from cloud storage. The analysis unit analyzes the photos uploaded by the photo uploading unit. For example, the generation AI analyzes the type, color, and design of the clothes in the photos using image recognition technology. The generation AI can also analyze the material and texture of the clothes. The suggestion unit suggests fashionable outfits based on the information analyzed by the analysis unit. For example, the generation AI considers the user's body type and preferences to suggest combinations of shirts and pants or skirts and tops. The generation AI can also suggest outfits according to the season and weather. The link providing unit provides a purchase link for the clothes suggested by the suggestion unit. For example, the generation AI provides a link to an affiliated online shop, allowing the user to easily purchase the clothes. The link providing unit can also suggest clothes within a price range that suits the user's budget. As a result, the fashion suggestion system according to the embodiment allows the user to easily get suggestions for stylish outfits and also suggests recommended clothing, allowing the user to enjoy fashion without any hassle.

[0030] The analysis unit analyzes the material and texture of clothing from photos and can suggest outfits appropriate for the season and weather. For example, the generation AI analyzes the material of clothing from uploaded photos and identifies materials such as wool or cotton. This allows it to suggest seasonal outfits, such as wool sweaters for winter and cotton shirts for summer. The generation AI also analyzes the texture of clothing from photos and identifies shiny or matte materials, for example. This allows it to suggest shiny materials for formal occasions and matte materials for casual occasions. The generation AI also analyzes the thickness of clothing from photos and identifies thin or thick clothing, for example. This allows it to suggest weather-appropriate outfits, such as thick coats for cold days and light jackets for warm days. This allows it to suggest appropriate outfits appropriate for the season and weather.

[0031] The analysis unit analyzes the background information of the photo and can suggest a fashion style that suits the user's living environment. For example, the generation AI analyzes the buildings and scenery in the background of the photo to determine whether it is an urban or rural area. This allows it to suggest a modern style for urban areas and a casual style for rural areas. The generation AI also analyzes the means of transportation (e.g., bicycle or car) in the background of the photo to identify the user's living environment. This allows it to suggest comfortable clothing for commuting by bicycle and formal clothing for commuting by car. The generation AI also analyzes the plants and natural environment in the background of the photo to identify the user's living environment. This allows it to suggest a relaxed style for environments rich in nature and a sophisticated style for urban environments. This allows it to suggest an appropriate fashion style that suits the user's living environment.

[0032] The analysis unit can also analyze accessories or shoes that appear in the photo and suggest an overall outfit. For example, the analysis unit may have the generation AI analyze accessories (e.g., necklaces or bracelets) that appear in the photo and suggest clothing that matches them. For example, it may suggest cool-colored clothing for a silver necklace. The generation AI may also analyze shoes (e.g., sneakers or high heels) that appear in the photo and suggest clothing that matches them. For example, it may suggest casual clothing for sneakers and formal clothing for high heels. The generation AI may also analyze accessories such as bags and hats that appear in the photo and suggest an overall outfit that matches them. For example, it may suggest a relaxed style for a casual bag and an elegant style for a formal hat. This allows the generation AI to suggest an overall outfit that includes accessories and shoes.

[0033] The analysis unit adds a function that allows users to specify a specific event when uploading a photo, and can suggest outfits appropriate for that event. For example, if a user specifies a wedding, the generation AI will suggest a coordination of formal dresses and suits. For example, it will suggest an elegant dress or tuxedo suitable for a wedding. If a user specifies a party, the generation AI will suggest a glamorous dress or a casual party style. For example, it will suggest a cocktail dress or casual party wear. If a user specifies a business meeting, the generation AI will suggest a coordination of professional suits or business casual. For example, it will suggest a business suit or business casual attire. This makes it possible to suggest outfits appropriate for a specific event.

[0034] The suggestion unit can analyze the user's fashion history and suggest optimal outfits based on past successes and failures. For example, the suggestion unit uses a generation AI to analyze the user's past fashion history and suggest optimal outfits based on outfits that have received high ratings in the past. For example, it uses outfits that have received many likes in the past as reference. The generation AI also analyzes the user's past fashion history and suggests optimal outfits that avoid past unsuccessful outfits. For example, it avoids outfits that have received low ratings in the past. The generation AI also analyzes the user's past fashion history and suggests optimal outfits according to the season or event. For example, it uses past summer outfits or wedding outfits as reference. This makes it possible to suggest optimal outfits based on past fashion history.

[0035] The suggestion unit can analyze the fashion styles of the user's friends or family and suggest outfits that take social networks into consideration. For example, the suggestion unit's generation AI analyzes the fashion styles of the user's friends and family and suggests similar styles. For example, it takes into consideration brands and designs that friends often wear. The generation AI also analyzes the user's social network and suggests outfits that are suitable for events with friends and family. For example, it suggests styles that are appropriate for a friend's wedding or a family gathering. The generation AI also analyzes the fashion history of the user's friends and family and suggests outfits that take common tastes and trends into consideration. For example, it suggests clothes from the same brand as a friend. This makes it possible to suggest outfits that take social networks into consideration.

[0036] The suggestion unit can analyze fashion styles from different cultures or regions and suggest new styles to the user. For example, the generation AI analyzes fashion styles from different cultures and suggests new styles to the user. For example, it suggests European classic styles or Asian modern styles. The generation AI also analyzes fashion trends from different regions and suggests new styles to the user. For example, it suggests New York street fashion or Parisian elegant styles. The generation AI also analyzes traditional fashion from different countries and suggests new styles to the user. For example, it suggests coordination that incorporates Japanese kimono styles or Indian saris. This makes it possible to suggest fashion styles from different cultures and regions.

[0037] The link providing unit can track changes in the user's body shape and suggest clothes that correspond to the changes in body shape. For example, the generation AI periodically tracks changes in the user's body shape and suggests the most suitable clothes based on, for example, weight gain or loss or changes in muscle mass. For example, if the user gains weight, loose-fitting clothes are suggested, and if the user loses weight, fitted clothes are suggested. The generation AI also analyzes changes in the user's body shape and suggests the most suitable clothes based on, for example, changes in waist or hip size. For example, if the waist becomes thinner, a tight skirt is suggested, and if the hips become larger, loose-fitting pants are suggested. The generation AI also tracks changes in the user's body shape and suggests the most suitable clothes based on, for example, changes in height or posture. For example, if the user grows taller, longer pants are suggested, and if the user's posture improves, fitted tops are suggested. This makes it possible to suggest clothes that correspond to the user's changes in body shape.

[0038] The link providing unit can analyze changes in the user's preferences and suggest clothes that suit new preferences based on past purchase history and ratings. In the link providing unit, for example, the generation AI analyzes the user's past purchase history and suggests clothes that suit new preferences based on, for example, the brands and designs of clothes purchased in the past. For example, it suggests new products from brands purchased in the past. The generation AI also analyzes the user's past ratings and suggests clothes that suit new preferences based on, for example, the characteristics of clothes that received high ratings. For example, it suggests new clothes based on highly rated designs and colors. The generation AI also analyzes changes in the user's preferences and suggests clothes that suit new preferences based on, for example, changes in seasons or trends. For example, it suggests new clothes that reflect seasonal trends. This makes it possible to suggest clothes that suit changes in the user's preferences.

[0039] The link providing unit can take the user's budget into consideration and suggest recommended clothes according to the price range. For example, the generation AI in the link providing unit analyzes the user's budget and suggests, for example, clothes in the low price range. For example, it suggests affordable brands or items on sale. The generation AI also analyzes the user's budget and suggests, for example, clothes in the mid-price range. For example, it suggests brands and items with good cost performance. The generation AI also analyzes the user's budget and suggests, for example, clothes in the high price range. For example, it suggests luxury brands or limited edition items. This makes it possible to suggest clothes according to the user's budget.

[0040] The link providing unit can take into consideration the user's environmental awareness and suggest clothes made from eco-friendly materials or brands. For example, the link providing unit has the generation AI analyze the user's environmental awareness and suggest clothes made from organic cotton or recycled materials, for example. For example, it suggests items from eco-friendly brands. The generation AI also analyzes the user's environmental awareness and suggests products with a low carbon footprint, for example. For example, it suggests clothes from brands with environmentally friendly manufacturing processes. The generation AI also analyzes the user's environmental awareness and suggests products that are fair trade certified, for example. For example, it suggests clothes from ethically produced brands. This makes it possible to suggest eco-friendly clothes that match the user's environmental awareness.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The suggestion unit can analyze the user's fashion history and suggest optimal outfits based on past successes and failures. For example, the suggestion unit uses a generation AI to analyze the user's past fashion history and suggest optimal outfits based on outfits that have received high ratings in the past. For example, it uses outfits that have received many likes in the past as reference. The generation AI also analyzes the user's past fashion history and suggests optimal outfits that avoid past unsuccessful outfits. For example, it avoids outfits that have received low ratings in the past. The generation AI also analyzes the user's past fashion history and suggests optimal outfits according to the season or event. For example, it uses past summer outfits or wedding outfits as reference. This makes it possible to suggest optimal outfits based on past fashion history.

[0043] The analysis unit can also analyze accessories or shoes that appear in the photo and suggest an overall outfit. For example, the analysis unit may have the generation AI analyze accessories (e.g., necklaces or bracelets) that appear in the photo and suggest clothing that matches them. For example, it may suggest cool-colored clothing for a silver necklace. The generation AI may also analyze shoes (e.g., sneakers or high heels) that appear in the photo and suggest clothing that matches them. For example, it may suggest casual clothing for sneakers and formal clothing for high heels. The generation AI may also analyze accessories such as bags and hats that appear in the photo and suggest an overall outfit that matches them. For example, it may suggest a relaxed style for a casual bag and an elegant style for a formal hat. This allows the generation AI to suggest an overall outfit that includes accessories and shoes.

[0044] The suggestion unit can analyze the fashion styles of the user's friends or family and suggest outfits that take social networks into consideration. For example, the suggestion unit's generation AI analyzes the fashion styles of the user's friends and family and suggests similar styles. For example, it takes into consideration brands and designs that friends often wear. The generation AI also analyzes the user's social network and suggests outfits that are suitable for events with friends and family. For example, it suggests styles that are appropriate for a friend's wedding or a family gathering. The generation AI also analyzes the fashion history of the user's friends and family and suggests outfits that take common tastes and trends into consideration. For example, it suggests clothes from the same brand as a friend. This makes it possible to suggest outfits that take social networks into consideration.

[0045] The link providing unit can track changes in the user's body shape and suggest clothes that correspond to the changes in body shape. For example, the generation AI periodically tracks changes in the user's body shape and suggests the most suitable clothes based on, for example, weight gain or loss or changes in muscle mass. For example, if the user gains weight, loose-fitting clothes are suggested, and if the user loses weight, fitted clothes are suggested. The generation AI also analyzes changes in the user's body shape and suggests the most suitable clothes based on, for example, changes in waist or hip size. For example, if the waist becomes thinner, a tight skirt is suggested, and if the hips become larger, loose-fitting pants are suggested. The generation AI also tracks changes in the user's body shape and suggests the most suitable clothes based on, for example, changes in height or posture. For example, if the user grows taller, longer pants are suggested, and if the user's posture improves, fitted tops are suggested. This makes it possible to suggest clothes that correspond to the user's changes in body shape.

[0046] The link providing unit can analyze changes in the user's preferences and suggest clothes that suit new preferences based on past purchase history and ratings. In the link providing unit, for example, the generation AI analyzes the user's past purchase history and suggests clothes that suit new preferences based on, for example, the brands and designs of clothes purchased in the past. For example, it suggests new products from brands purchased in the past. The generation AI also analyzes the user's past ratings and suggests clothes that suit new preferences based on, for example, the characteristics of clothes that received high ratings. For example, it suggests new clothes based on highly rated designs and colors. The generation AI also analyzes changes in the user's preferences and suggests clothes that suit new preferences based on, for example, changes in seasons or trends. For example, it suggests new clothes that reflect seasonal trends. This makes it possible to suggest clothes that suit changes in the user's preferences.

[0047] The link providing unit can take into consideration the user's environmental awareness and suggest clothes made from eco-friendly materials or brands. For example, the link providing unit has the generation AI analyze the user's environmental awareness and suggest clothes made from organic cotton or recycled materials, for example. For example, it suggests items from eco-friendly brands. The generation AI also analyzes the user's environmental awareness and suggests products with a low carbon footprint, for example. For example, it suggests clothes from brands with environmentally friendly manufacturing processes. The generation AI also analyzes the user's environmental awareness and suggests products that are fair trade certified, for example. For example, it suggests clothes from ethically produced brands. This makes it possible to suggest eco-friendly clothes that match the user's environmental awareness.

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

[0049] Step 1: The photo upload section allows users to upload photos of their clothing. For example, users can upload photos taken with a smartphone or digital camera to the system. Users can also select and upload photos from cloud storage. Step 2: The analysis unit analyzes the photos uploaded by the photo upload unit. For example, the generation AI uses image recognition technology to analyze the type, color, and design of the clothes in the photo. It can also analyze the material and texture of the clothes. Step 3: The suggestion unit suggests stylish outfits based on the information analyzed by the analysis unit. For example, the generation AI takes into account the user's body type and preferences and suggests combinations of shirts and pants, or skirts and tops. It can also suggest outfits appropriate for the season and weather. Step 4: The link provider provides a link to purchase the clothes suggested by the suggester. For example, the generation AI can provide a link to an affiliated online shop, allowing the user to easily purchase the clothes. It can also suggest clothes in a price range that suits the user's budget.

[0050] (Example 2) The fashion suggestion system according to an embodiment of the present invention allows users to upload photos of their own clothes, which are analyzed by a generation AI to suggest stylish outfits, and further suggests recommended clothes and provides a link to purchase them. As a result, the fashion suggestion system allows users to easily get suggestions for stylish outfits and also suggests recommended clothes, allowing them to enjoy fashion without any hassle.

[0051] A fashion suggestion system according to an embodiment includes a photo uploading unit, an analysis unit, a suggestion unit, and a link providing unit. The photo uploading unit uploads photos of clothes owned by a user. For example, photos taken with a smartphone or digital camera can be uploaded to the system. The photo uploading unit can also select and upload photos from cloud storage. The analysis unit analyzes the photos uploaded by the photo uploading unit. For example, the generation AI analyzes the type, color, and design of the clothes in the photos using image recognition technology. The generation AI can also analyze the material and texture of the clothes. The suggestion unit suggests fashionable outfits based on the information analyzed by the analysis unit. For example, the generation AI considers the user's body type and preferences to suggest combinations of shirts and pants or skirts and tops. The generation AI can also suggest outfits according to the season and weather. The link providing unit provides a purchase link for the clothes suggested by the suggestion unit. For example, the generation AI provides a link to an affiliated online shop, allowing the user to easily purchase the clothes. The link providing unit can also suggest clothes within a price range that suits the user's budget. As a result, the fashion suggestion system according to the embodiment allows the user to easily get suggestions for stylish outfits and also suggests recommended clothing, allowing the user to enjoy fashion without any hassle.

[0052] The analysis unit analyzes the material and texture of clothing from photos and can suggest outfits appropriate for the season and weather. For example, the generation AI analyzes the material of clothing from uploaded photos and identifies materials such as wool or cotton. This allows it to suggest seasonal outfits, such as wool sweaters for winter and cotton shirts for summer. The generation AI also analyzes the texture of clothing from photos and identifies shiny or matte materials, for example. This allows it to suggest shiny materials for formal occasions and matte materials for casual occasions. The generation AI also analyzes the thickness of clothing from photos and identifies thin or thick clothing, for example. This allows it to suggest weather-appropriate outfits, such as thick coats for cold days and light jackets for warm days. This allows it to suggest appropriate outfits appropriate for the season and weather.

[0053] The analysis unit analyzes the background information of the photo and can suggest a fashion style that suits the user's living environment. For example, the generation AI analyzes the buildings and scenery in the background of the photo to determine whether it is an urban or rural area. This allows it to suggest a modern style for urban areas and a casual style for rural areas. The generation AI also analyzes the means of transportation (e.g., bicycle or car) in the background of the photo to identify the user's living environment. This allows it to suggest comfortable clothing for commuting by bicycle and formal clothing for commuting by car. The generation AI also analyzes the plants and natural environment in the background of the photo to identify the user's living environment. This allows it to suggest a relaxed style for environments rich in nature and a sophisticated style for urban environments. This allows it to suggest an appropriate fashion style that suits the user's living environment.

[0054] The analysis unit uses the emotion estimation function to analyze the user's emotion when uploading a photo and can suggest a relaxed or energetic style according to the emotion. For example, the analysis unit uses the emotion estimation function to analyze the user's facial expression when uploading a photo and suggests a casual style if the user is relaxed and an active style if the user is energetic. The emotion estimation function also analyzes the user's tone of voice when uploading a photo and suggests a relaxed style if the tone is calm and an energetic style if the tone is bright. The emotion estimation function also analyzes the user's posture when uploading a photo and suggests a casual style if the posture is relaxed and a sporty style if the posture is energetic. This makes it possible to suggest an appropriate style according to the user's emotion.

[0055] The analysis unit can also analyze accessories or shoes that appear in the photo and suggest an overall outfit. For example, the analysis unit may have the generation AI analyze accessories (e.g., necklaces or bracelets) that appear in the photo and suggest clothing that matches them. For example, it may suggest cool-colored clothing for a silver necklace. The generation AI may also analyze shoes (e.g., sneakers or high heels) that appear in the photo and suggest clothing that matches them. For example, it may suggest casual clothing for sneakers and formal clothing for high heels. The generation AI may also analyze accessories such as bags and hats that appear in the photo and suggest an overall outfit that matches them. For example, it may suggest a relaxed style for a casual bag and an elegant style for a formal hat. This allows the generation AI to suggest an overall outfit that includes accessories and shoes.

[0056] The analysis unit adds a function that allows users to specify a specific event when uploading a photo, and can suggest outfits appropriate for that event. For example, if a user specifies a wedding, the generation AI will suggest a coordination of formal dresses and suits. For example, it will suggest an elegant dress or tuxedo suitable for a wedding. If a user specifies a party, the generation AI will suggest a glamorous dress or a casual party style. For example, it will suggest a cocktail dress or casual party wear. If a user specifies a business meeting, the generation AI will suggest a coordination of professional suits or business casual. For example, it will suggest a business suit or business casual attire. This makes it possible to suggest outfits appropriate for a specific event.

[0057] The analysis unit can use the emotion estimation function to analyze the emotion a user expresses when uploading a photo and suggest clothes of a specific color or design based on that emotion. For example, the analysis unit can use the emotion estimation function to analyze the emotion a user expresses when uploading a photo, and if the emotion is positive, suggest clothes of bright colors or gorgeous designs. For example, suggest bright yellow or pink clothes. The emotion estimation function can also be used to analyze the emotion a user expresses when uploading a photo, and if the emotion is negative, suggest clothes of subdued colors or simple designs. For example, suggest navy or gray clothes. The emotion estimation function can also be used to analyze the emotion a user expresses when uploading a photo, and if the emotion is energetic, suggest clothes of vivid colors or bold designs. For example, suggest red or orange clothes. In this way, it is possible to suggest clothes of appropriate colors and designs based on the user's emotions.

[0058] The suggestion unit can analyze the user's fashion history and suggest optimal outfits based on past successes and failures. For example, the suggestion unit uses a generation AI to analyze the user's past fashion history and suggest optimal outfits based on outfits that have received high ratings in the past. For example, it uses outfits that have received many likes in the past as reference. The generation AI also analyzes the user's past fashion history and suggests optimal outfits that avoid past unsuccessful outfits. For example, it avoids outfits that have received low ratings in the past. The generation AI also analyzes the user's past fashion history and suggests optimal outfits according to the season or event. For example, it uses past summer outfits or wedding outfits as reference. This makes it possible to suggest optimal outfits based on past fashion history.

[0059] The suggestion unit can analyze the fashion styles of the user's friends or family and suggest outfits that take social networks into consideration. For example, the suggestion unit's generation AI analyzes the fashion styles of the user's friends and family and suggests similar styles. For example, it takes into consideration brands and designs that friends often wear. The generation AI also analyzes the user's social network and suggests outfits that are suitable for events with friends and family. For example, it suggests styles that are appropriate for a friend's wedding or a family gathering. The generation AI also analyzes the fashion history of the user's friends and family and suggests outfits that take common tastes and trends into consideration. For example, it suggests clothes from the same brand as a friend. This makes it possible to suggest outfits that take social networks into consideration.

[0060] The suggestion unit uses the emotion estimation function to suggest outfits that correspond to the user's mood and emotions, and can provide a style that will lift the user's mood. For example, the suggestion unit uses the emotion estimation function to analyze the user's current mood and suggest a casual style if the user wants to relax, or an active style if the user is energetic. The suggestion unit also uses the emotion estimation function to analyze the user's current emotions and suggest clothes in bright colors and designs to maintain positive emotions. For example, it suggests brightly colored tops and skirts with gorgeous designs. The suggestion unit also uses the emotion estimation function to analyze the user's current mood and suggest clothes from specific brands or designers to lift the user's mood. For example, it suggests new products from a brand that the user likes. In this way, it is possible to provide a style that lifts the user's mood.

[0061] The suggestion unit can analyze fashion styles from different cultures or regions and suggest new styles to the user. For example, the generation AI analyzes fashion styles from different cultures and suggests new styles to the user. For example, it suggests European classic styles or Asian modern styles. The generation AI also analyzes fashion trends from different regions and suggests new styles to the user. For example, it suggests New York street fashion or Parisian elegant styles. The generation AI also analyzes traditional fashion from different countries and suggests new styles to the user. For example, it suggests coordination that incorporates Japanese kimono styles or Indian saris. This makes it possible to suggest fashion styles from different cultures and regions.

[0062] The suggestion unit can use the emotion estimation function to suggest clothes from specific fashion brands or designers based on the user's emotions. For example, the suggestion unit uses the emotion estimation function to analyze the user's emotions, and if the user's emotions are positive, suggests clothes from fashion brands with bright colors and designs. For example, it suggests bright-colored tops and skirts with gorgeous designs. Also, the suggestion unit uses the emotion estimation function to analyze the user's emotions, and if the user's emotions are negative, suggests clothes from fashion brands with muted colors and simple designs. For example, it suggests navy or gray clothes. Also, the suggestion unit uses the emotion estimation function to analyze the user's emotions, and if the user's emotions are energetic, suggests clothes from designers with vivid colors and bold designs. For example, it suggests red or orange clothes. In this way, it is possible to suggest clothes from specific fashion brands or designers based on the user's emotions.

[0063] The link providing unit can track changes in the user's body shape and suggest clothes that correspond to the changes in body shape. For example, the generation AI periodically tracks changes in the user's body shape and suggests the most suitable clothes based on, for example, weight gain or loss or changes in muscle mass. For example, if the user gains weight, loose-fitting clothes are suggested, and if the user loses weight, fitted clothes are suggested. The generation AI also analyzes changes in the user's body shape and suggests the most suitable clothes based on, for example, changes in waist or hip size. For example, if the waist becomes thinner, a tight skirt is suggested, and if the hips become larger, loose-fitting pants are suggested. The generation AI also tracks changes in the user's body shape and suggests the most suitable clothes based on, for example, changes in height or posture. For example, if the user grows taller, longer pants are suggested, and if the user's posture improves, fitted tops are suggested. This makes it possible to suggest clothes that correspond to the user's changes in body shape.

[0064] The link providing unit can analyze changes in the user's preferences and suggest clothes that suit new preferences based on past purchase history and ratings. In the link providing unit, for example, the generation AI analyzes the user's past purchase history and suggests clothes that suit new preferences based on, for example, the brands and designs of clothes purchased in the past. For example, it suggests new products from brands purchased in the past. The generation AI also analyzes the user's past ratings and suggests clothes that suit new preferences based on, for example, the characteristics of clothes that received high ratings. For example, it suggests new clothes based on highly rated designs and colors. The generation AI also analyzes changes in the user's preferences and suggests clothes that suit new preferences based on, for example, changes in seasons or trends. For example, it suggests new clothes that reflect seasonal trends. This makes it possible to suggest clothes that suit changes in the user's preferences.

[0065] The link providing unit can use the emotion estimation function to suggest clothes suitable for a particular season or event based on the user's emotion and provide a purchase link. The link providing unit, for example, uses the emotion estimation function to analyze the user's emotion, and if the emotion is positive, suggests clothes with bright colors and designs and provides a purchase link. For example, it suggests bright-colored tops and skirts with gorgeous designs. The emotion estimation function can also be used to analyze the user's emotion, and if the emotion is negative, suggests clothes with subdued colors and simple designs and provides a purchase link. For example, it suggests navy or gray clothes. The emotion estimation function can also be used to analyze the user's emotion, and if the emotion is energetic, suggests clothes with vivid colors and bold designs and provides a purchase link. For example, it suggests red or orange clothes. In this way, it is possible to suggest clothes suitable for a particular season or event based on the user's emotion and provide a purchase link.

[0066] The link providing unit can take the user's budget into consideration and suggest recommended clothes according to the price range. For example, the generation AI in the link providing unit analyzes the user's budget and suggests, for example, clothes in the low price range. For example, it suggests affordable brands or items on sale. The generation AI also analyzes the user's budget and suggests, for example, clothes in the mid-price range. For example, it suggests brands and items with good cost performance. The generation AI also analyzes the user's budget and suggests, for example, clothes in the high price range. For example, it suggests luxury brands or limited edition items. This makes it possible to suggest clothes according to the user's budget.

[0067] The link providing unit can take into consideration the user's environmental awareness and suggest clothes made from eco-friendly materials or brands. For example, the link providing unit has the generation AI analyze the user's environmental awareness and suggest clothes made from organic cotton or recycled materials, for example. For example, it suggests items from eco-friendly brands. The generation AI also analyzes the user's environmental awareness and suggests products with a low carbon footprint, for example. For example, it suggests clothes from brands with environmentally friendly manufacturing processes. The generation AI also analyzes the user's environmental awareness and suggests products that are fair trade certified, for example. For example, it suggests clothes from ethically produced brands. This makes it possible to suggest eco-friendly clothes that match the user's environmental awareness.

[0068] The link providing unit can use the emotion estimation function to suggest clothes of a specific color or design based on the user's emotion and provide a purchase link. The link providing unit, for example, uses the emotion estimation function to analyze the user's emotion, and if the emotion is positive, suggests clothes of a bright color or design and provides a purchase link. For example, it suggests bright-colored tops and skirts with gorgeous designs. Also, the emotion estimation function can be used to analyze the user's emotion, and if the emotion is negative, suggests clothes of a subdued color or a simple design and provides a purchase link. For example, it suggests navy or gray clothes. Also, the emotion estimation function can be used to analyze the user's emotion, and if the emotion is energetic, suggests clothes of a vivid color or a bold design and provides a purchase link. For example, it suggests red or orange clothes. In this way, it is possible to suggest clothes of a specific color or design based on the user's emotion and provide a purchase link.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The suggestion unit can analyze the user's fashion history and suggest optimal outfits based on past successes and failures. For example, the suggestion unit uses a generation AI to analyze the user's past fashion history and suggest optimal outfits based on outfits that have received high ratings in the past. For example, it uses outfits that have received many likes in the past as reference. The generation AI also analyzes the user's past fashion history and suggests optimal outfits that avoid past unsuccessful outfits. For example, it avoids outfits that have received low ratings in the past. The generation AI also analyzes the user's past fashion history and suggests optimal outfits according to the season or event. For example, it uses past summer outfits or wedding outfits as reference. This makes it possible to suggest optimal outfits based on past fashion history.

[0071] The analysis unit can also analyze accessories or shoes that appear in the photo and suggest an overall outfit. For example, the analysis unit may have the generation AI analyze accessories (e.g., necklaces or bracelets) that appear in the photo and suggest clothing that matches them. For example, it may suggest cool-colored clothing for a silver necklace. The generation AI may also analyze shoes (e.g., sneakers or high heels) that appear in the photo and suggest clothing that matches them. For example, it may suggest casual clothing for sneakers and formal clothing for high heels. The generation AI may also analyze accessories such as bags and hats that appear in the photo and suggest an overall outfit that matches them. For example, it may suggest a relaxed style for a casual bag and an elegant style for a formal hat. This allows the generation AI to suggest an overall outfit that includes accessories and shoes.

[0072] The analysis unit uses the emotion estimation function to analyze the user's emotion when uploading a photo and can suggest a relaxed or energetic style according to the emotion. For example, the analysis unit uses the emotion estimation function to analyze the user's facial expression when uploading a photo and suggests a casual style if the user is relaxed and an active style if the user is energetic. The emotion estimation function also analyzes the user's tone of voice when uploading a photo and suggests a relaxed style if the tone is calm and an energetic style if the tone is bright. The emotion estimation function also analyzes the user's posture when uploading a photo and suggests a casual style if the posture is relaxed and a sporty style if the posture is energetic. This makes it possible to suggest an appropriate style according to the user's emotion.

[0073] The suggestion unit can analyze the fashion styles of the user's friends or family and suggest outfits that take social networks into consideration. For example, the suggestion unit's generation AI analyzes the fashion styles of the user's friends and family and suggests similar styles. For example, it takes into consideration brands and designs that friends often wear. The generation AI also analyzes the user's social network and suggests outfits that are suitable for events with friends and family. For example, it suggests styles that are appropriate for a friend's wedding or a family gathering. The generation AI also analyzes the fashion history of the user's friends and family and suggests outfits that take common tastes and trends into consideration. For example, it suggests clothes from the same brand as a friend. This makes it possible to suggest outfits that take social networks into consideration.

[0074] The suggestion unit uses the emotion estimation function to suggest outfits that correspond to the user's mood and emotions, and can provide a style that will lift the user's mood. For example, the suggestion unit uses the emotion estimation function to analyze the user's current mood and suggest a casual style if the user wants to relax, or an active style if the user is energetic. The suggestion unit also uses the emotion estimation function to analyze the user's current emotions and suggest clothes in bright colors and designs to maintain positive emotions. For example, it suggests brightly colored tops and skirts with gorgeous designs. The suggestion unit also uses the emotion estimation function to analyze the user's current mood and suggest clothes from specific brands or designers to lift the user's mood. For example, it suggests new products from a brand that the user likes. In this way, it is possible to provide a style that lifts the user's mood.

[0075] The link providing unit can track changes in the user's body shape and suggest clothes that correspond to the changes in body shape. For example, the generation AI periodically tracks changes in the user's body shape and suggests the most suitable clothes based on, for example, weight gain or loss or changes in muscle mass. For example, if the user gains weight, loose-fitting clothes are suggested, and if the user loses weight, fitted clothes are suggested. The generation AI also analyzes changes in the user's body shape and suggests the most suitable clothes based on, for example, changes in waist or hip size. For example, if the waist becomes thinner, a tight skirt is suggested, and if the hips become larger, loose-fitting pants are suggested. The generation AI also tracks changes in the user's body shape and suggests the most suitable clothes based on, for example, changes in height or posture. For example, if the user grows taller, longer pants are suggested, and if the user's posture improves, fitted tops are suggested. This makes it possible to suggest clothes that correspond to the user's changes in body shape.

[0076] The link providing unit can use the emotion estimation function to suggest clothes suitable for a particular season or event based on the user's emotion and provide a purchase link. The link providing unit, for example, uses the emotion estimation function to analyze the user's emotion, and if the emotion is positive, suggests clothes with bright colors and designs and provides a purchase link. For example, it suggests bright-colored tops and skirts with gorgeous designs. The emotion estimation function can also be used to analyze the user's emotion, and if the emotion is negative, suggests clothes with subdued colors and simple designs and provides a purchase link. For example, it suggests navy or gray clothes. The emotion estimation function can also be used to analyze the user's emotion, and if the emotion is energetic, suggests clothes with vivid colors and bold designs and provides a purchase link. For example, it suggests red or orange clothes. In this way, it is possible to suggest clothes suitable for a particular season or event based on the user's emotion and provide a purchase link.

[0077] The link providing unit can analyze changes in the user's preferences and suggest clothes that suit new preferences based on past purchase history and ratings. In the link providing unit, for example, the generation AI analyzes the user's past purchase history and suggests clothes that suit new preferences based on, for example, the brands and designs of clothes purchased in the past. For example, it suggests new products from brands purchased in the past. The generation AI also analyzes the user's past ratings and suggests clothes that suit new preferences based on, for example, the characteristics of clothes that received high ratings. For example, it suggests new clothes based on highly rated designs and colors. The generation AI also analyzes changes in the user's preferences and suggests clothes that suit new preferences based on, for example, changes in seasons or trends. For example, it suggests new clothes that reflect seasonal trends. This makes it possible to suggest clothes that suit changes in the user's preferences.

[0078] The link providing unit can take into consideration the user's environmental awareness and suggest clothes made from eco-friendly materials or brands. For example, the link providing unit has the generation AI analyze the user's environmental awareness and suggest clothes made from organic cotton or recycled materials, for example. For example, it suggests items from eco-friendly brands. The generation AI also analyzes the user's environmental awareness and suggests products with a low carbon footprint, for example. For example, it suggests clothes from brands with environmentally friendly manufacturing processes. The generation AI also analyzes the user's environmental awareness and suggests products that are fair trade certified, for example. For example, it suggests clothes from ethically produced brands. This makes it possible to suggest eco-friendly clothes that match the user's environmental awareness.

[0079] The link providing unit can use the emotion estimation function to suggest clothes of a specific color or design based on the user's emotion and provide a purchase link. The link providing unit, for example, uses the emotion estimation function to analyze the user's emotion, and if the emotion is positive, suggests clothes of a bright color or design and provides a purchase link. For example, it suggests bright-colored tops and skirts with gorgeous designs. Also, the emotion estimation function can be used to analyze the user's emotion, and if the emotion is negative, suggests clothes of a subdued color or a simple design and provides a purchase link. For example, it suggests navy or gray clothes. Also, the emotion estimation function can be used to analyze the user's emotion, and if the emotion is energetic, suggests clothes of a vivid color or a bold design and provides a purchase link. For example, it suggests red or orange clothes. In this way, it is possible to suggest clothes of a specific color or design based on the user's emotion and provide a purchase link.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The photo upload section allows users to upload photos of their clothing. For example, users can upload photos taken with a smartphone or digital camera to the system. Users can also select and upload photos from cloud storage. Step 2: The analysis unit analyzes the photos uploaded by the photo upload unit. For example, the generation AI uses image recognition technology to analyze the type, color, and design of the clothes in the photo. It can also analyze the material and texture of the clothes. Step 3: The suggestion unit suggests stylish outfits based on the information analyzed by the analysis unit. For example, the generation AI takes into account the user's body type and preferences and suggests combinations of shirts and pants, or skirts and tops. It can also suggest outfits appropriate for the season and weather. Step 4: The link provider provides a link to purchase the clothes suggested by the suggester. For example, the generation AI can provide a link to an affiliated online shop, allowing the user to easily purchase the clothes. It can also suggest clothes in a price range that suits the user's budget.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 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.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] 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]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a photo upload section for uploading photos of clothes owned by the user; an analysis unit that analyzes the photos uploaded by the photo upload unit; a suggestion unit that suggests fashionable outfits based on the information analyzed by the analysis unit; a link providing unit that provides a purchase link for the clothes suggested by the suggestion unit. A system characterized by:

2. The analysis unit Analyze the material and texture of the clothing from the photo and suggest outfits that suit the season and weather.

2. The system of claim 1.

3. The analysis unit Analyzing the background information of the photo and proposing a fashion style that suits the user's living environment 2. The system of claim 1.

4. The analysis unit Analyzing the user's emotions when uploading the photo and suggesting a relaxed or energetic style according to the emotions 2. The system of claim 1.

5. The analysis unit The accessories or shoes in the photo are also analyzed to suggest an overall outfit.

2. The system of claim 1.

6. The analysis unit Add a function that allows the user to specify an event when uploading the photo, and suggest the outfit suitable for the event.

2. The system of claim 1.

7. The analysis unit Analyzing the emotion of the user when uploading the photo, and suggesting the clothes of a specific color or design based on the emotion.

2. The system of claim 1.

8. The proposal unit Analyze the user's fashion history and suggest the most suitable outfit based on successes and failures 2. The system of claim 1.

Images