System

Abstract

An object of a system according to an embodiment is to propose an optimal fashion style on the basis of the body type, preferences, and lifestyle of a user.SOLUTION: A system according to an embodiment includes a user information collector, a fashion database constructor, and a fashion style suggester. The user information collection unit collects information on the body shape, preferences, and lifestyle of the user. The fashion database construction unit constructs a fashion database on the basis of the user information collected by the user information collection unit. The fashion style proposing unit proposes an optimal fashion style to the user on the basis of the user information and the fashion database.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately proposed optimal fashion styles based on a user's body type, preferences, and lifestyle, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal fashion style based on the user's body type, preferences, and lifestyle. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information collection unit, a fashion database construction unit, and a fashion style suggestion unit. The user information collection unit collects information on a user's body type, preferences, and lifestyle. The fashion database construction unit constructs a fashion database based on the user information collected by the user information collection unit. The fashion style suggestion unit suggests an optimal fashion style for the user based on the user information and the fashion database. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable fashion style based on the user's body type, preferences, and lifestyle. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A fashion advice system according to an embodiment of the present invention uses generative AI to analyze the optimal fashion for an individual and provide advice, thereby enabling the fashion advice system to suggest the optimal fashion style based on the user's body type, preferences, and lifestyle.

[0029] A fashion advice system according to an embodiment includes a user information collection unit, a fashion database construction unit, and a fashion style suggestion unit. The user information collection unit collects information about a user's body type, preferences, and lifestyle. For example, the user information collection unit collects information such as height, weight, and body characteristics provided by the user. The user information collection unit can also collect information about the user's preferences (colors, styles, brands, etc.) and lifestyle (job, hobbies, special events, etc.). For example, a user may provide information such as, "I'm 170 cm tall, I like casual styles, I work in an office, and enjoy outdoor activities on weekends." The fashion database construction unit constructs a fashion database based on the user information collected by the user information collection unit. For example, the fashion database construction unit registers information such as a denim jacket and sneakers that are suitable for a casual style in the database. The fashion database construction unit can also register information about various fashion items (clothes, shoes, accessories, etc.), styles (casual, formal, sporty, etc.), and brands in the database. The fashion style suggestion unit suggests an optimal fashion style for the user based on the user information and the fashion database. For example, if a user provides information such as "I like casual styles and enjoy outdoor activities on weekends," the fashion style suggestion unit will suggest specific items such as "denim jacket, chinos, sneakers, and an outdoor hat." This allows the fashion advice system according to the embodiment to suggest an optimal fashion style based on the user's body type, preferences, and lifestyle.

[0030] The user information collection unit analyzes the user's past fashion history and social media posts to understand preferences and trends in more detail. The user information collection unit, for example, analyzes the user's past purchase history to identify the user's favorite brands and styles. For example, if the user purchases many items from a particular brand, it collects information on new products from that brand. The user information collection unit also analyzes the user's social media posts to understand the user's favorite styles from fashion-related comments and photos. For example, it identifies the styles and colors that the user frequently posts. The user information collection unit also analyzes the fashion worn at events or special occasions the user attended to understand preferences in specific situations. For example, it analyzes fashion worn at weddings and parties. This allows the user's preferences and trends to be understood in detail.

[0031] The user information collection unit can analyze movements and postures using a camera and collect body shape and physique characteristics in real time. The user information collection unit, for example, uses a camera to analyze the user's posture and movements and collect body shape and physique characteristics in real time. For example, it measures the user's shoulder width and waist position. The user information collection unit also analyzes the user's movements when trying on clothes and evaluates the fit and ease of movement. For example, it records the user's walking and sitting movements with a camera. The user information collection unit also identifies the optimal size and silhouette based on the user's body shape data. For example, it recommends a size based on the user's height and weight. This allows the user's body shape and physique characteristics to be collected in real time.

[0032] The fashion database construction unit collects detailed information about the materials and manufacturing methods of fashion items, and is able to prioritize the registration of eco-friendly items. For example, the fashion database construction unit collects material information about fashion items and registers items that use eco-friendly materials in the database. For example, items that use organic cotton or recycled materials are prioritized. The fashion database construction unit also collects information about manufacturing methods for fashion items and registers items that use environmentally friendly manufacturing methods in the database. For example, items that have a low-energy consumption manufacturing process are prioritized. The fashion database construction unit also collects information about eco-friendly brands and designers and registers those items in the database. For example, brands that promote sustainable fashion are prioritized. This allows eco-friendly items to be registered with priority.

[0033] The fashion database construction unit automatically generates combination patterns of fashion items and registers them in a database, thereby enabling more diverse style suggestions. The fashion database construction unit, for example, constructs a system that automatically generates combination patterns of fashion items and registers them in a database. For example, it automatically generates combinations of tops, bottoms, and accessories. The fashion database construction unit also automatically generates optimal combination patterns based on the user's preferences and body type and registers them in the database. For example, it suggests coordination that suits the user's style. The fashion database construction unit also automatically generates combination patterns according to seasons and trends and registers them in the database. For example, it suggests styles that combine trendy items for spring and summer. This enables more diverse style suggestions.

[0034] The fashion database construction unit integrates fashion styles from different cultures and regions into a database, enabling proposals from a global perspective. The fashion database construction unit, for example, collects fashion styles from different cultures and regions and integrates them into the database. For example, it collects fashion styles from Asia, Europe, and America. The fashion database construction unit also registers trends and popular items from each region in the database in order to make fashion proposals from a global perspective. For example, it analyzes fashion magazines and social media from each region. The fashion database construction unit also collects information on fashion events and shows from different cultures and regions and integrates it into the database. For example, it collects information on Paris Collection and New York Fashion Week. This makes it possible to make fashion proposals from a global perspective.

[0035] The fashion database construction unit updates price fluctuations and inventory information of fashion items in real time, allowing it to make economical suggestions. The fashion database construction unit, for example, builds a system that collects price fluctuations of fashion items in real time and reflects them in the database. For example, it updates sale information and price fluctuations in real time. The fashion database construction unit also collects inventory information of fashion items in real time and reflects it in the database. For example, it integrates inventory information from online shops and physical stores. The fashion database construction unit also develops a system that makes economical fashion suggestions based on price fluctuations and inventory information. For example, it prioritizes suggestions of items on sale or items with abundant stock. This makes it possible to make economical fashion suggestions.

[0036] The fashion style suggestion unit can analyze the user's past fashion selection history and make suggestions based on successful coordination patterns. The fashion style suggestion unit, for example, analyzes the user's past fashion selection history and identifies successful coordination patterns. For example, suggestions are made based on coordination that the user has given a high rating. The fashion style suggestion unit also suggests successful patterns for similar situations based on the user's past fashion selection history. For example, suggestions are made based on successful coordination at past events. The fashion style suggestion unit also analyzes the user's past fashion selection history and suggests successful patterns according to trends and seasons. For example, suggestions are made based on successful coordination for each season in the past. This allows suggestions to be made based on successful coordination patterns.

[0037] The fashion style suggestion unit generates a 3D model that matches the user's body type and posture, and can suggest the optimal style through virtual try-on. The fashion style suggestion unit, for example, generates a 3D model that matches the user's body type and posture, and suggests the optimal style through virtual try-on. For example, the user tries on items that fit the user's body type on the 3D model. The fashion style suggestion unit also checks the fit and appearance of the items selected by the user through virtual try-on and suggests the optimal style. For example, the suggestion is made based on the results of trying on the 3D model. The fashion style suggestion unit also suggests the optimal size and silhouette based on the user's body type and posture. For example, the size is recommended based on the results of trying on the 3D model. This makes it possible to suggest the optimal style that matches the user's body type and posture.

[0038] The fashion style suggestion unit can suggest styles according to different situations. The fashion style suggestion unit suggests styles according to different situations, such as work, dates, and travel. For example, it suggests a formal style for work and a casual style for dates. The fashion style suggestion unit also suggests styles according to events and situations in which the user will be attending. For example, it suggests styles suitable for special events such as weddings and parties. The fashion style suggestion unit also suggests styles according to everyday situations based on the user's lifestyle. For example, it suggests styles suitable for commuting or weekend outdoor activities. This makes it possible to suggest styles according to different situations.

[0039] The fashion style suggestion unit can make style suggestions that incorporate the opinions of the user's friends and family. The fashion style suggestion unit, for example, collects the opinions of the user's friends and family and makes style suggestions based on them. For example, it suggests styles that have been highly rated by friends and family. The fashion style suggestion unit also makes style suggestions that are suitable for events that the user's friends and family will be attending. For example, it suggests styles that are suitable for a family wedding or a friend's party. The fashion style suggestion unit also makes style suggestions that take into account the fashion styles of the user's friends and family. For example, it suggests brands and styles that friends and family like. This makes it possible to make style suggestions that incorporate the opinions of the user's friends and family.

[0040] The fashion advice providing unit can customize the content of advice based on the user's past fashion history and preferences. The fashion advice providing unit, for example, analyzes the user's past fashion history and customizes advice based on that data. For example, advice is provided based on styles that the user has given high ratings. The fashion advice providing unit also customizes the content of advice based on the user's preferences. For example, advice is provided that takes into account the user's favorite colors and brands. The fashion advice providing unit also provides advice for similar situations based on the user's past fashion selection history. For example, advice is provided based on successful coordination at past events. This makes it possible to provide customized advice based on the user's past history and preferences.

[0041] The fashion advice providing unit can provide explanations using illustrations and videos to make the advice visually easier to understand. For example, the fashion advice providing unit provides explanations using illustrations to make the content of the advice visually easier to understand. For example, it displays an illustration of an outfit. Furthermore, the fashion advice providing unit provides explanations using videos to make the content of the advice visually easier to understand. For example, it displays a video of an outfit. Furthermore, the fashion advice providing unit provides explanations using infographics to make the content of the advice visually easier to understand. For example, it illustrates the key points of outfit coordination. This makes it possible to provide advice that is visually easier to understand.

[0042] The fashion advice providing unit can provide advice according to different situations. The fashion advice providing unit provides advice according to different situations, such as work, dates, and travel. For example, it suggests a formal style for work and a casual style for dates. The fashion advice providing unit also provides advice according to the event or situation in which the user will be attending. For example, it provides advice suitable for special events such as weddings and parties. The fashion advice providing unit also provides advice according to everyday situations based on the user's lifestyle. For example, it provides advice suitable for commuting or weekend outdoor activities. This makes it possible to provide advice according to different situations.

[0043] The fashion advice providing unit can provide advice that incorporates the opinions of the user's friends and family. The fashion advice providing unit, for example, collects the opinions of the user's friends and family and provides advice based on them. For example, it suggests styles that have been highly rated by friends and family. The fashion advice providing unit also provides advice that is suitable for events that the user's friends and family will be attending. For example, it provides advice that is suitable for a family wedding or a friend's party. The fashion advice providing unit also provides advice that takes into account the fashion styles of the user's friends and family. For example, it suggests brands and styles that the friends and family like. This makes it possible to provide advice that incorporates the opinions of the user's friends and family.

[0044] The fashion item purchase support unit can provide the user with optimal purchase timing and sale information based on their purchase history and preferences. For example, the fashion item purchase support unit analyzes the user's purchase history and suggests the optimal purchase timing. For example, it predicts and notifies the user of sale times based on past purchase patterns. The fashion item purchase support unit also provides sale information based on the user's preferences. For example, it prioritizes notifying the user of sale information for brands and items that the user likes. The fashion item purchase support unit also integrates the user's purchase history with market sale information and suggests the optimal purchase timing. For example, it predicts and notifies the user of when a specific item will go on sale. This makes it possible to provide the user with optimal purchase timing and sale information.

[0045] The fashion item purchase support unit can perform a virtual try-on session before purchase, allowing the user to check the fit and style of an item. The fashion item purchase support unit, for example, builds a system that allows the user to perform a virtual try-on session before purchase, allowing the user to check the fit and style of an item. For example, the virtual try-on session is performed using a 3D model. The fashion item purchase support unit also supports the purchase by allowing the user to check the fit and appearance of the item selected by the user through the virtual try-on session. For example, the fashion item purchase support unit recommends a purchase based on the results of trying on the 3D model. The fashion item purchase support unit also suggests the optimal size and silhouette based on the user's body type and posture. For example, the unit recommends a size based on the results of trying on the 3D model. This allows the user to check the fit and style of the item before purchasing.

[0046] The fashion item purchasing support unit can compare prices from different online shops and brands and suggest the most suitable purchasing source. The fashion item purchasing support unit, for example, collects prices from different online shops and brands in real time and builds a system that suggests the most suitable purchasing source. For example, it compares prices for the same item and suggests the lowest price. The fashion item purchasing support unit also compares prices from multiple online shops and brands based on the user's preferences and suggests the most suitable purchasing source. For example, it prioritizes notifying the user of sale information for brands that the user likes. The fashion item purchasing support unit also suggests the most suitable purchasing source to the user based on the results of the price comparison. For example, it notifies the user of the shop where a specific item can be purchased at the lowest price. This makes it possible to suggest the most suitable purchasing source.

[0047] The fashion item purchasing support unit can provide purchasing support by taking into consideration the purchase history and opinions of the user's friends and family. The fashion item purchasing support unit, for example, collects the purchase history of the user's friends and family and provides purchasing support based on that. For example, it suggests items that friends and family have given high ratings. The fashion item purchasing support unit also collects the opinions of the user's friends and family and provides purchasing support based on that. For example, it suggests items recommended by friends and family. The fashion item purchasing support unit also collects reviews of items purchased by the user's friends and family and provides purchasing support based on that. For example, it prioritizes items that friends and family have given high ratings. This makes it possible to provide purchasing support by taking into consideration the opinions of the user's friends and family.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The user information collection unit can collect information about the user's health condition and activity level and reflect it in fashion suggestions. For example, if the user is connected to a fitness app, that data can be collected and used to suggest fashion items suited to an active lifestyle. Also, if the user is using a health management app, that data can be used to suggest items made from comfortable and healthy materials. Furthermore, items that emphasize ease of movement and breathability can be suggested depending on the user's activity level. This makes it possible to suggest fashion based on the user's health condition and activity level.

[0050] The fashion style suggestion unit can suggest styles that are specific to the user's occupation or industry. For example, if the user works in a creative industry, trends and styles that are appropriate for that industry can be suggested. If the user works in the financial industry, a formal and reliable style can be suggested. Furthermore, if the user works in the medical industry, a style that emphasizes functionality and cleanliness can be suggested. This makes it possible to suggest styles that are specific to the user's occupation or industry.

[0051] The fashion item purchasing support unit can provide purchasing support according to the user's budget. For example, it can suggest the most suitable items within the budget set by the user. If the user requests sale information, it can also provide that information and support purchasing within the budget. Furthermore, when the user purchases an expensive item, it can suggest an installment payment option. This makes it possible to provide purchasing support according to the user's budget.

[0052] The fashion item purchase support unit can provide purchase support based on the user's lifestyle. For example, if the user enjoys outdoor activities, it can suggest items suitable for that lifestyle (such as a waterproof jacket or trekking shoes). If the user works in an office, it can also suggest items suitable for that lifestyle (such as a business suit or formal shoes). Furthermore, if the user enjoys sports, it can suggest items suitable for that lifestyle (such as sportswear or running shoes). This makes it possible to provide purchase support based on the user's lifestyle.

[0053] The fashion item purchase support unit can provide customization options based on the user's preferences. For example, if the user prefers a particular color or design, customization options based on that preference can be suggested. Also, if the user prefers a particular material or brand, customization options based on that preference can be provided. Furthermore, if the user prefers a particular fit or size, customization options based on that preference can be suggested. This makes it possible to provide customization options based on the user's preferences.

[0054] The fashion item purchase support unit can provide recommendations based on the user's purchase history. For example, it can suggest related items based on items the user has purchased in the past. It can also suggest similar items based on items that the user has given high ratings to in the past. It can also provide recommendations based on the trends and seasons of items the user has purchased in the past. This makes it possible to provide recommendations based on the user's purchase history.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The user information collection unit collects information about the user's body type, preferences, and lifestyle. For example, information such as height, weight, body type characteristics, preferences (color, style, brand, etc.), and lifestyle (job, hobbies, special events, etc.) provided by the user is collected. Specifically, the user provides information such as, "I am 170 cm tall, I like casual styles, I work in an office, and I enjoy outdoor activities on the weekends." Step 2: The fashion database construction unit builds a fashion database based on the user information collected by the user information collection unit. For example, information such as denim jackets and sneakers are suitable for a casual style is registered in the database. Information on various fashion items (clothes, shoes, accessories, etc.), styles (casual, formal, sporty, etc.), and brands is also registered in the database. Step 3: The fashion style suggestion unit suggests the most suitable fashion style for the user based on the user information and the fashion database. For example, if the user provides information such as "I like casual styles and enjoy outdoor activities on weekends," the fashion style suggestion unit will suggest specific items such as "denim jacket, chinos, sneakers, and outdoor hat."

[0057] (Example 2) A fashion advice system according to an embodiment of the present invention uses generative AI to analyze the optimal fashion for an individual and provide advice, thereby enabling the fashion advice system to suggest the optimal fashion style based on the user's body type, preferences, and lifestyle.

[0058] A fashion advice system according to an embodiment includes a user information collection unit, a fashion database construction unit, and a fashion style suggestion unit. The user information collection unit collects information about a user's body type, preferences, and lifestyle. For example, the user information collection unit collects information such as height, weight, and body characteristics provided by the user. The user information collection unit can also collect information about the user's preferences (colors, styles, brands, etc.) and lifestyle (job, hobbies, special events, etc.). For example, a user may provide information such as, "I'm 170 cm tall, I like casual styles, I work in an office, and enjoy outdoor activities on weekends." The fashion database construction unit constructs a fashion database based on the user information collected by the user information collection unit. For example, the fashion database construction unit registers information such as a denim jacket and sneakers that are suitable for a casual style in the database. The fashion database construction unit can also register information about various fashion items (clothes, shoes, accessories, etc.), styles (casual, formal, sporty, etc.), and brands in the database. The fashion style suggestion unit suggests an optimal fashion style for the user based on the user information and the fashion database. For example, if a user provides information such as "I like casual styles and enjoy outdoor activities on weekends," the fashion style suggestion unit will suggest specific items such as "denim jacket, chinos, sneakers, and an outdoor hat." This allows the fashion advice system according to the embodiment to suggest an optimal fashion style based on the user's body type, preferences, and lifestyle.

[0059] The user information collection unit can use the emotion estimation function to analyze emotions toward fashion and prioritize collecting information that elicits positive emotions. For example, the user information collection unit analyzes the user's facial expressions and voice when selecting fashion items and prioritizes collecting items that indicate positive emotions. For example, it identifies items that make the user smile. The user information collection unit also analyzes the user's emotional responses to items previously purchased and collects items that tend to elicit positive emotions. For example, it prioritizes items that the user has given high ratings. The user information collection unit also collects emotional data in real time when the user tries on clothes and identifies items that elicit positive emotions. For example, it analyzes the user's facial expressions and voice when trying on clothes to calculate an emotion score. This makes it possible to prioritize collecting information that elicits positive emotions from the user.

[0060] The user information collection unit analyzes the user's past fashion history and social media posts to understand preferences and trends in more detail. The user information collection unit, for example, analyzes the user's past purchase history to identify the user's favorite brands and styles. For example, if the user purchases many items from a particular brand, it collects information on new products from that brand. The user information collection unit also analyzes the user's social media posts to understand the user's favorite styles from fashion-related comments and photos. For example, it identifies the styles and colors that the user frequently posts. The user information collection unit also analyzes the fashion worn at events or special occasions the user attended to understand preferences in specific situations. For example, it analyzes fashion worn at weddings and parties. This allows the user's preferences and trends to be understood in detail.

[0061] The user information collection unit can analyze movements and postures using a camera and collect body shape and physique characteristics in real time. The user information collection unit, for example, uses a camera to analyze the user's posture and movements and collect body shape and physique characteristics in real time. For example, it measures the user's shoulder width and waist position. The user information collection unit also analyzes the user's movements when trying on clothes and evaluates the fit and ease of movement. For example, it records the user's walking and sitting movements with a camera. The user information collection unit also identifies the optimal size and silhouette based on the user's body shape data. For example, it recommends a size based on the user's height and weight. This allows the user's body shape and physique characteristics to be collected in real time.

[0062] The fashion database construction unit can use the emotion estimation function to register the user's emotional reactions to fashion items in the database and build a database that takes emotional value into consideration. The fashion database construction unit, for example, analyzes the user's emotional reactions when looking at fashion items and registers the data in the database. For example, it identifies items that make the user smile. The fashion database construction unit also collects emotional data when the user tries on items and builds a database that takes emotional value into consideration. For example, it analyzes facial expressions and voices when trying on items to calculate an emotional score. The fashion database construction unit also registers the user's emotional reactions to items purchased in the database and makes suggestions that take emotional value into consideration. For example, it prioritizes items that the user has given high ratings to. This makes it possible to build a database that takes the user's emotional reactions into consideration.

[0063] The fashion database construction unit collects detailed information about the materials and manufacturing methods of fashion items, and is able to prioritize the registration of eco-friendly items. For example, the fashion database construction unit collects material information about fashion items and registers items that use eco-friendly materials in the database. For example, items that use organic cotton or recycled materials are prioritized. The fashion database construction unit also collects information about manufacturing methods for fashion items and registers items that use environmentally friendly manufacturing methods in the database. For example, items that have a low-energy consumption manufacturing process are prioritized. The fashion database construction unit also collects information about eco-friendly brands and designers and registers those items in the database. For example, brands that promote sustainable fashion are prioritized. This allows eco-friendly items to be registered with priority.

[0064] The fashion database construction unit automatically generates combination patterns of fashion items and registers them in a database, thereby enabling more diverse style suggestions. The fashion database construction unit, for example, constructs a system that automatically generates combination patterns of fashion items and registers them in a database. For example, it automatically generates combinations of tops, bottoms, and accessories. The fashion database construction unit also automatically generates optimal combination patterns based on the user's preferences and body type and registers them in the database. For example, it suggests coordination that suits the user's style. The fashion database construction unit also automatically generates combination patterns according to seasons and trends and registers them in the database. For example, it suggests styles that combine trendy items for spring and summer. This enables more diverse style suggestions.

[0065] The fashion database construction unit integrates fashion styles from different cultures and regions into a database, enabling proposals from a global perspective. The fashion database construction unit, for example, collects fashion styles from different cultures and regions and integrates them into the database. For example, it collects fashion styles from Asia, Europe, and America. The fashion database construction unit also registers trends and popular items from each region in the database in order to make fashion proposals from a global perspective. For example, it analyzes fashion magazines and social media from each region. The fashion database construction unit also collects information on fashion events and shows from different cultures and regions and integrates it into the database. For example, it collects information on Paris Collection and New York Fashion Week. This makes it possible to make fashion proposals from a global perspective.

[0066] The fashion database construction unit updates price fluctuations and inventory information of fashion items in real time, allowing it to make economical suggestions. The fashion database construction unit, for example, builds a system that collects price fluctuations of fashion items in real time and reflects them in the database. For example, it updates sale information and price fluctuations in real time. The fashion database construction unit also collects inventory information of fashion items in real time and reflects it in the database. For example, it integrates inventory information from online shops and physical stores. The fashion database construction unit also develops a system that makes economical fashion suggestions based on price fluctuations and inventory information. For example, it prioritizes suggestions of items on sale or items with abundant stock. This makes it possible to make economical fashion suggestions.

[0067] The fashion database construction unit uses the emotion estimation function to register the emotions that a user has toward a specific item in the database, and can preferentially suggest items that are likely to be emotionally relatable. The fashion database construction unit, for example, analyzes the emotions that a user has toward a specific item in real time and registers the data in the database. For example, it identifies items that make the user smile. The fashion database construction unit also uses the emotion estimation function to collect emotional data when the user tries on items and registers items that are likely to be emotionally relatable in the database. For example, it calculates an emotion score by analyzing facial expressions and voices when trying on items. The fashion database construction unit also registers the emotional reactions of the user to items purchased in the database and preferentially suggests items that are likely to be emotionally relatable. For example, it prioritizes items that the user has given high ratings. This makes it possible to preferentially suggest items that are likely to be emotionally relatable.

[0068] The fashion style suggestion unit can use the emotion estimation function to analyze the emotions the user feels toward proposed styles in real time and preferentially suggest styles that elicit positive emotions. For example, the fashion style suggestion unit can analyze the emotions the user feels toward proposed styles in real time and preferentially suggest styles that elicit positive emotions. For example, it can identify styles that make the user smile. The fashion style suggestion unit can also use the emotion estimation function to collect emotion data when the user tries on clothes and suggest styles that elicit positive emotions. For example, it can calculate an emotion score by analyzing facial expressions and voices when trying on clothes. The fashion style suggestion unit can also register the user's emotional reactions to previously proposed styles in a database and preferentially suggest styles that elicit positive emotions. For example, it can prioritize styles that the user has given high ratings. This allows the fashion style suggestion unit to preferentially suggest styles that elicit positive emotions.

[0069] The fashion style suggestion unit can analyze the user's past fashion selection history and make suggestions based on successful coordination patterns. The fashion style suggestion unit, for example, analyzes the user's past fashion selection history and identifies successful coordination patterns. For example, suggestions are made based on coordination that the user has given a high rating. The fashion style suggestion unit also suggests successful patterns for similar situations based on the user's past fashion selection history. For example, suggestions are made based on successful coordination at past events. The fashion style suggestion unit also analyzes the user's past fashion selection history and suggests successful patterns according to trends and seasons. For example, suggestions are made based on successful coordination for each season in the past. This allows suggestions to be made based on successful coordination patterns.

[0070] The fashion style suggestion unit generates a 3D model that matches the user's body type and posture, and can suggest the optimal style through virtual try-on. The fashion style suggestion unit, for example, generates a 3D model that matches the user's body type and posture, and suggests the optimal style through virtual try-on. For example, the user tries on items that fit the user's body type on the 3D model. The fashion style suggestion unit also checks the fit and appearance of the items selected by the user through virtual try-on and suggests the optimal style. For example, the suggestion is made based on the results of trying on the 3D model. The fashion style suggestion unit also suggests the optimal size and silhouette based on the user's body type and posture. For example, the size is recommended based on the results of trying on the 3D model. This makes it possible to suggest the optimal style that matches the user's body type and posture.

[0071] The fashion style suggestion unit can suggest styles according to different situations. The fashion style suggestion unit suggests styles according to different situations, such as work, dates, and travel. For example, it suggests a formal style for work and a casual style for dates. The fashion style suggestion unit also suggests styles according to events and situations in which the user will be attending. For example, it suggests styles suitable for special events such as weddings and parties. The fashion style suggestion unit also suggests styles according to everyday situations based on the user's lifestyle. For example, it suggests styles suitable for commuting or weekend outdoor activities. This makes it possible to suggest styles according to different situations.

[0072] The fashion style suggestion unit can make style suggestions that incorporate the opinions of the user's friends and family. The fashion style suggestion unit, for example, collects the opinions of the user's friends and family and makes style suggestions based on them. For example, it suggests styles that have been highly rated by friends and family. The fashion style suggestion unit also makes style suggestions that are suitable for events that the user's friends and family will be attending. For example, it suggests styles that are suitable for a family wedding or a friend's party. The fashion style suggestion unit also makes style suggestions that take into account the fashion styles of the user's friends and family. For example, it suggests brands and styles that friends and family like. This makes it possible to make style suggestions that incorporate the opinions of the user's friends and family.

[0073] The fashion style suggestion unit can use the emotion estimation function to make style suggestions based on the emotions the user feels toward a specific event or situation. For example, the fashion style suggestion unit estimates the user's emotions toward a specific event (such as a wedding or a party) and makes style suggestions based on those emotions. For example, it can suggest a style that will ease tension at a wedding. The fashion style suggestion unit also collects the user's emotions toward a specific situation (such as work or a date) and makes style suggestions based on those emotions. For example, it can suggest a style that will increase confidence at work. The fashion style suggestion unit also collects emotional data from events the user has previously attended and makes style suggestions for similar events. For example, it can make suggestions based on emotions at past parties. This makes it possible to make style suggestions based on the user's emotions.

[0074] The fashion advice providing unit can use the emotion estimation function to analyze the emotions the user feels in response to advice and provide advice that elicits positive emotions. The fashion advice providing unit, for example, analyzes the emotions the user feels in response to advice in real time and provides advice that elicits positive emotions. For example, it identifies advice that makes the user smile. The fashion advice providing unit also uses the emotion estimation function to collect emotional data when the user receives advice and provides advice that elicits positive emotions. For example, it analyzes facial expressions and voice at the time of advice and calculates an emotion score. The fashion advice providing unit also registers the emotional reactions of the user to advice that they have received in the past in a database and provides advice that elicits positive emotions. For example, it prioritizes advice that the user has given a high rating. This makes it possible to provide advice that elicits positive emotions.

[0075] The fashion advice providing unit can customize the content of advice based on the user's past fashion history and preferences. The fashion advice providing unit, for example, analyzes the user's past fashion history and customizes advice based on that data. For example, advice is provided based on styles that the user has given high ratings. The fashion advice providing unit also customizes the content of advice based on the user's preferences. For example, advice is provided that takes into account the user's favorite colors and brands. The fashion advice providing unit also provides advice for similar situations based on the user's past fashion selection history. For example, advice is provided based on successful coordination at past events. This makes it possible to provide customized advice based on the user's past history and preferences.

[0076] The fashion advice providing unit can provide explanations using illustrations and videos to make the advice visually easier to understand. For example, the fashion advice providing unit provides explanations using illustrations to make the content of the advice visually easier to understand. For example, it displays an illustration of an outfit. Furthermore, the fashion advice providing unit provides explanations using videos to make the content of the advice visually easier to understand. For example, it displays a video of an outfit. Furthermore, the fashion advice providing unit provides explanations using infographics to make the content of the advice visually easier to understand. For example, it illustrates the key points of outfit coordination. This makes it possible to provide advice that is visually easier to understand.

[0077] The fashion advice providing unit can provide advice according to different situations. The fashion advice providing unit provides advice according to different situations, such as work, dates, and travel. For example, it suggests a formal style for work and a casual style for dates. The fashion advice providing unit also provides advice according to the event or situation in which the user will be attending. For example, it provides advice suitable for special events such as weddings and parties. The fashion advice providing unit also provides advice according to everyday situations based on the user's lifestyle. For example, it provides advice suitable for commuting or weekend outdoor activities. This makes it possible to provide advice according to different situations.

[0078] The fashion advice providing unit can provide advice that incorporates the opinions of the user's friends and family. The fashion advice providing unit, for example, collects the opinions of the user's friends and family and provides advice based on them. For example, it suggests styles that have been highly rated by friends and family. The fashion advice providing unit also provides advice that is suitable for events that the user's friends and family will be attending. For example, it provides advice that is suitable for a family wedding or a friend's party. The fashion advice providing unit also provides advice that takes into account the fashion styles of the user's friends and family. For example, it suggests brands and styles that the friends and family like. This makes it possible to provide advice that incorporates the opinions of the user's friends and family.

[0079] The fashion advice providing unit can use the emotion estimation function to provide advice based on the emotions the user feels toward a specific event or situation. For example, the fashion advice providing unit estimates the user's emotions toward a specific event (such as a wedding or a party) and provides advice based on those emotions. For example, it provides advice to ease tension at a wedding. The fashion advice providing unit also collects the user's emotions toward a specific situation (such as work or a date) and provides advice based on those emotions. For example, it provides advice to increase confidence at work. The fashion advice providing unit also collects emotion data from events the user has previously attended and provides advice for similar events. For example, it provides advice based on emotions at past parties. This makes it possible to provide advice based on the user's emotions.

[0080] The fashion item purchase support unit uses the emotion estimation function to analyze the emotions a user has toward an item they are considering purchasing, and can provide purchase support that draws out positive emotions. The fashion item purchase support unit, for example, analyzes in real time the emotions a user has toward an item they are considering purchasing, and provides purchase support that draws out positive emotions. For example, it identifies items that make the user smile. The fashion item purchase support unit also uses the emotion estimation function to collect emotional data when the user tries on items, and provides purchase support that draws out positive emotions. For example, it analyzes facial expressions and voices when trying on items to calculate an emotion score. The fashion item purchase support unit also registers the emotional reactions of the user to items they have previously purchased in a database, and provides purchase support that draws out positive emotions. For example, it prioritizes items that the user has given high ratings to. This makes it possible to provide purchase support that draws out positive emotions.

[0081] The fashion item purchase support unit can provide the user with optimal purchase timing and sale information based on their purchase history and preferences. For example, the fashion item purchase support unit analyzes the user's purchase history and suggests the optimal purchase timing. For example, it predicts and notifies the user of sale times based on past purchase patterns. The fashion item purchase support unit also provides sale information based on the user's preferences. For example, it prioritizes notifying the user of sale information for brands and items that the user likes. The fashion item purchase support unit also integrates the user's purchase history with market sale information and suggests the optimal purchase timing. For example, it predicts and notifies the user of when a specific item will go on sale. This makes it possible to provide the user with optimal purchase timing and sale information.

[0082] The fashion item purchase support unit can perform a virtual try-on session before purchase, allowing the user to check the fit and style of an item. The fashion item purchase support unit, for example, builds a system that allows the user to perform a virtual try-on session before purchase, allowing the user to check the fit and style of an item. For example, the virtual try-on session is performed using a 3D model. The fashion item purchase support unit also supports the purchase by allowing the user to check the fit and appearance of the item selected by the user through the virtual try-on session. For example, the fashion item purchase support unit recommends a purchase based on the results of trying on the 3D model. The fashion item purchase support unit also suggests the optimal size and silhouette based on the user's body type and posture. For example, the unit recommends a size based on the results of trying on the 3D model. This allows the user to check the fit and style of the item before purchasing.

[0083] The fashion item purchasing support unit can compare prices from different online shops and brands and suggest the most suitable purchasing source. The fashion item purchasing support unit, for example, collects prices from different online shops and brands in real time and builds a system that suggests the most suitable purchasing source. For example, it compares prices for the same item and suggests the lowest price. The fashion item purchasing support unit also compares prices from multiple online shops and brands based on the user's preferences and suggests the most suitable purchasing source. For example, it prioritizes notifying the user of sale information for brands that the user likes. The fashion item purchasing support unit also suggests the most suitable purchasing source to the user based on the results of the price comparison. For example, it notifies the user of the shop where a specific item can be purchased at the lowest price. This makes it possible to suggest the most suitable purchasing source.

[0084] The fashion item purchasing support unit can provide purchasing support by taking into consideration the purchase history and opinions of the user's friends and family. The fashion item purchasing support unit, for example, collects the purchase history of the user's friends and family and provides purchasing support based on that. For example, it suggests items that friends and family have given high ratings. The fashion item purchasing support unit also collects the opinions of the user's friends and family and provides purchasing support based on that. For example, it suggests items recommended by friends and family. The fashion item purchasing support unit also collects reviews of items purchased by the user's friends and family and provides purchasing support based on that. For example, it prioritizes items that friends and family have given high ratings. This makes it possible to provide purchasing support by taking into consideration the opinions of the user's friends and family.

[0085] The fashion item purchasing support unit can use the emotion estimation function to provide purchasing support based on the emotions the user feels toward a specific event or situation. For example, the fashion item purchasing support unit estimates the user's emotions toward a specific event (such as a wedding or party) and provides purchasing support based on those emotions. For example, it can suggest items that will ease tension at a wedding. The fashion item purchasing support unit also collects the user's emotions toward a specific situation (such as work or a date) and provides purchasing support based on those emotions. For example, it can suggest items that will increase confidence at work. The fashion item purchasing support unit also collects emotional data from events the user has previously attended and provides purchasing support for similar events. For example, it can suggest items based on the emotions felt at past parties. This makes it possible to provide purchasing support based on the user's emotions.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The user information collection unit can collect information about the user's health condition and activity level and reflect it in fashion suggestions. For example, if the user is connected to a fitness app, that data can be collected and used to suggest fashion items suited to an active lifestyle. Also, if the user is using a health management app, that data can be used to suggest items made from comfortable and healthy materials. Furthermore, items that emphasize ease of movement and breathability can be suggested depending on the user's activity level. This makes it possible to suggest fashion based on the user's health condition and activity level.

[0088] The fashion database construction unit can estimate the user's emotions and register fashion items that evoke specific emotions in the database. For example, if the user feels like relaxing, items that evoke that emotion (e.g., pajamas or loungewear made of soft materials) can be registered in the database. Also, if the user feels like feeling confident, items that evoke that emotion (e.g., formal suits or luxurious accessories) can be registered in the database. Furthermore, if the user wants to feel happy, items that evoke that emotion (e.g., colorful designs or unique accessories) can be registered in the database. This makes it possible to suggest fashion items based on the user's emotions.

[0089] The fashion style suggestion unit can suggest styles that are specific to the user's occupation or industry. For example, if the user works in a creative industry, trends and styles that are appropriate for that industry can be suggested. If the user works in the financial industry, a formal and reliable style can be suggested. Furthermore, if the user works in the medical industry, a style that emphasizes functionality and cleanliness can be suggested. This makes it possible to suggest styles that are specific to the user's occupation or industry.

[0090] The fashion style suggestion unit can estimate the user's emotions and suggest a style that brings out a specific emotion. For example, if the user feels like relaxing, it can suggest a style that brings out that emotion (e.g., a casual, relaxed outfit). Also, if the user feels like feeling confident, it can suggest a style that brings out that emotion (e.g., a formal, sophisticated outfit). Furthermore, if the user wants to feel fun, it can suggest a style that brings out that emotion (e.g., a colorful, playful outfit). This makes it possible to suggest styles based on the user's emotions.

[0091] The fashion advice providing unit can estimate the user's emotions and provide advice that elicits a specific emotion. For example, if the user feels like relaxing, advice to elicit that emotion (e.g., choosing items with relaxed materials and designs) can be provided. Also, if the user feels like feeling confident, advice to elicit that emotion (e.g., choosing formal and sophisticated items) can be provided. Furthermore, if the user wants to feel fun, advice to elicit that emotion (e.g., choosing colorful and unique items) can be provided. This makes it possible to provide advice based on the user's emotions.

[0092] The fashion item purchase support unit can estimate the user's emotions and provide purchase support that brings out specific emotions. For example, if the user feels like relaxing, it can suggest items that bring out that emotion (e.g., pajamas or relaxation wear made of soft materials). Also, if the user feels like feeling confident, it can suggest items that bring out that emotion (e.g., formal suits or luxurious accessories). Furthermore, if the user wants to feel happy, it can suggest items that bring out that emotion (e.g., colorful designs or unique accessories). This makes it possible to provide purchase support based on the user's emotions.

[0093] The fashion item purchasing support unit can provide purchasing support according to the user's budget. For example, it can suggest the most suitable items within the budget set by the user. If the user requests sale information, it can also provide that information and support purchasing within the budget. Furthermore, when the user purchases an expensive item, it can suggest an installment payment option. This makes it possible to provide purchasing support according to the user's budget.

[0094] The fashion item purchase support unit can provide purchase support based on the user's lifestyle. For example, if the user enjoys outdoor activities, it can suggest items suitable for that lifestyle (such as a waterproof jacket or trekking shoes). If the user works in an office, it can also suggest items suitable for that lifestyle (such as a business suit or formal shoes). Furthermore, if the user enjoys sports, it can suggest items suitable for that lifestyle (such as sportswear or running shoes). This makes it possible to provide purchase support based on the user's lifestyle.

[0095] The fashion item purchase support unit can provide customization options based on the user's preferences. For example, if the user prefers a particular color or design, customization options based on that preference can be suggested. Also, if the user prefers a particular material or brand, customization options based on that preference can be provided. Furthermore, if the user prefers a particular fit or size, customization options based on that preference can be suggested. This makes it possible to provide customization options based on the user's preferences.

[0096] The fashion item purchase support unit can provide recommendations based on the user's purchase history. For example, it can suggest related items based on items the user has purchased in the past. It can also suggest similar items based on items that the user has given high ratings to in the past. It can also provide recommendations based on the trends and seasons of items the user has purchased in the past. This makes it possible to provide recommendations based on the user's purchase history.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The user information collection unit collects information about the user's body type, preferences, and lifestyle. For example, information such as height, weight, body type characteristics, preferences (color, style, brand, etc.), and lifestyle (job, hobbies, special events, etc.) provided by the user is collected. Specifically, the user provides information such as, "I am 170 cm tall, I like casual styles, I work in an office, and I enjoy outdoor activities on the weekends." Step 2: The fashion database construction unit builds a fashion database based on the user information collected by the user information collection unit. For example, information such as denim jackets and sneakers are suitable for a casual style is registered in the database. Information on various fashion items (clothes, shoes, accessories, etc.), styles (casual, formal, sporty, etc.), and brands is also registered in the database. Step 3: The fashion style suggestion unit suggests the most suitable fashion style for the user based on the user information and the fashion database. For example, if the user provides information such as "I like casual styles and enjoy outdoor activities on weekends," the fashion style suggestion unit will suggest specific items such as "denim jacket, chinos, sneakers, and outdoor hat."

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 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 user information collection unit that collects information on the user's body type, preferences, and lifestyle; a fashion database construction unit that constructs a fashion database based on the user information collected by the user information collection unit; a fashion style suggestion unit that suggests an optimal fashion style to the user based on the user information and the fashion database. A system characterized by:

2. The user information collection unit Movements and postures are analyzed using a camera, and the characteristics of the body shape and physique are collected in real time.

2. The system of claim 1.

3. The fashion database construction unit By automatically generating the above combination patterns of fashion items and registering them in a database, it is possible to propose a wider variety of styles.

2. The system of claim 1.

4. The fashion style suggestion unit The 3D model is generated according to the body shape and posture of the user, and the optimal style is suggested through virtual try-on.

2. The system of claim 1.

5. The Fashion Item Purchasing Support Department Analyze the emotions the user has about the item they are considering purchasing, and provide purchasing support that elicits positive emotions.

2. The system of claim 1.

6. The user information collection unit Analyze emotions towards fashion and prioritize collecting information that evokes positive emotions 2. The system of claim 1.

Images