System

Abstract

An object of a system according to an embodiment is to comprehensively analyze climate and culture data based on fashion preferences and position information of users and to select optimal fashion items.SOLUTION: A system includes an image recognition unit, a social graph analysis unit, a position information analysis unit, and a fashion item selection unit. The image recognition unit analyzes fashion preferences of the user. The social graph analysis unit analyzes data of a social graph of the user on the basis of the fashion preferences analyzed by the image recognition unit. The position information analysis unit analyzes climate data or culture data based on the position information of the user on the basis of the data analyzed by the social graph analysis unit. The fashion item selector selects fashion items most suitable for the user on the basis of the data analyzed by the position information analyzer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to fully integrate and analyze a user's fashion preferences and climate and cultural data based on location information to select the most suitable fashion items.

[0005] The system of the embodiment aims to comprehensively analyze the user's fashion preferences and climate and cultural data based on location information, and select the most suitable fashion items. [Means for solving the problem]

[0006] The system according to the embodiment includes an image recognition unit, a social graph analysis unit, a location information analysis unit, and a fashion item selection unit. The image recognition unit analyzes a user's fashion preferences. The social graph analysis unit analyzes the user's social graph data based on the fashion preferences analyzed by the image recognition unit. The location information analysis unit analyzes climate data or cultural data based on the user's location information based on the data analyzed by the social graph analysis unit. The fashion item selection unit selects fashion items that are optimal for the user based on the data analyzed by the location information analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively analyze the user's fashion preferences and climate and cultural data based on location information to select the most suitable fashion items. [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 suggestion system according to an embodiment of the present invention analyzes a user's fashion preferences and suggests optimal fashion items. This system integrates and analyzes image recognition technology, social graph data, and location-based climate and cultural data. This allows the fashion suggestion system to select fashion items tailored to the user, providing a new purchasing experience and ways to enjoy fashion, and supporting daily styling.

[0029] A fashion suggestion system according to an embodiment includes an image recognition unit, a social graph analysis unit, a location information analysis unit, and a fashion item selection unit. The image recognition unit analyzes a user's fashion preferences. For example, it analyzes images uploaded by the user and extracts features such as color, design, and brand. The image recognition unit can also analyze the content of the images using a generative AI (for example, a text generation AI or a multimodal generation AI). The social graph analysis unit analyzes the user's social graph data based on the fashion preferences analyzed by the image recognition unit. For example, it analyzes posts from fashion influencers and friends followed by the user to determine what fashions are popular. The location information analysis unit analyzes climate and cultural data based on the user's location information based on the data analyzed by the social graph analysis unit. For example, it determines the temperature and precipitation, seasonal events, traditional events, etc. in the area where the user lives. The fashion item selection unit selects the most suitable fashion items for the user based on the data analyzed by the location information analysis unit. For example, the system comprehensively considers the user's fashion preferences, social graph data, and climate and cultural data based on location information to propose outfits that suit the user. This allows the fashion suggestion system according to the embodiment to integrate and analyze the user's fashion preferences, social graph, and climate and cultural data based on location information to propose optimal fashion items.

[0030] The image recognition unit can perform a detailed analysis of at least one of the user's body type, skin color, and hairstyle characteristics, and suggest optimal fashion items based on that analysis. For example, the image recognition unit uses a generative AI to analyze the user's body type and suggest fashion items that suit that body type. For example, it selects designs and silhouettes that fit that body type. It can also analyze the user's skin color and suggest fashion items in shades that match that skin color. For example, it provides a color palette that matches the skin tone. It can also analyze the user's hairstyle and suggest fashion items that match the hairstyle. For example, it selects accessories and hats that match the hairstyle. This allows it to suggest optimal fashion items based on the user's body type, skin color, and hairstyle.

[0031] The image recognition unit analyzes the history of images uploaded by the user in the past, tracks changes in fashion preferences over time, and can predict future preferences. The image recognition unit, for example, analyzes images uploaded by the user in the past and tracks changes in fashion preferences. For example, it identifies changes in preferences by season. The generation AI also analyzes the user's image history and predicts preference trends. For example, it predicts future fashion preferences based on past data. The image recognition unit also analyzes the user's image history and visualizes changes in preferences. For example, it displays changes in preferences in a graph along a time axis. This makes it possible to track changes in the user's fashion preferences and predict future preferences.

[0032] The image recognition unit can analyze the user's home interior or lifestyle and suggest fashion items that match it. For example, the image recognition unit can analyze home interior images uploaded by the user and suggest fashion items that match the interior style. For example, it can select simple designs that go well with modern interiors. The generation AI can also analyze the user's lifestyle and suggest fashion items that suit the lifestyle. For example, it can select sporty items that suit an active lifestyle. It can also analyze images of the user's home interior and suggest fashion items that match the interior color and design. For example, it can select items that match the interior color palette. This makes it possible to suggest fashion items that match the user's interior and lifestyle.

[0033] The image recognition unit can also analyze the fashion preferences of friends or family based on images uploaded by the user, and suggest outfits that suit the entire group. For example, the image recognition unit analyzes images of friends and family uploaded by the user to understand the fashion preferences of the entire group. For example, it can suggest items that share common preferences. The generation AI also analyzes the fashion preferences of the user's friends and family and suggests outfits that suit the entire group. For example, it can suggest outfits that have a consistent look. The image recognition unit can also analyze the fashion preferences of friends and family based on images uploaded by the user, and suggest outfits that suit events for the entire group. For example, it can suggest outfits that are suitable for parties or trips. This makes it possible to suggest outfits that suit the entire group.

[0034] The social graph analysis unit also takes into account the user's past posts or comments, allowing for a more detailed understanding of interests. The social graph analysis unit, for example, analyzes the user's past posts and comments to understand interests in detail. For example, items may be suggested based on comments about a particular brand or design. The generative AI also analyzes the user's past posts and comments to understand interests in detail. For example, items may be selected based on frequently mentioned keywords. The system also analyzes past data in the user's social graph to understand interests in detail. For example, items may be suggested based on trends in past posts. This allows for a more detailed understanding of interests based on the user's past posts and comments.

[0035] The social graph analysis unit analyzes social graph data over time, tracks changes in fashion trends, and can predict future trends. The social graph analysis unit, for example, analyzes posted data in the social graph over time to track changes in fashion trends. For example, it identifies trends based on data from the past few years. The generative AI also analyzes social graph data over time to predict future trends. For example, it predicts future trends based on current trends. It also analyzes data in the user's social graph over time to visualize changes in trends. For example, it displays changes in trends in a graph along a time axis. This makes it possible to track changes in fashion trends based on social graph data and predict future trends.

[0036] The social graph analysis unit also integrates community data such as the user's workplace or school, and can suggest fashion items that suit the entire community. The social graph analysis unit, for example, analyzes community data such as the user's workplace or school, and suggests fashion items that suit the entire community. For example, it selects items that match the workplace dress code. The generation AI also analyzes the user's community data and suggests items that suit the entire community. For example, it selects items that are suitable for a school event. The user's community data is also integrated, and suggestions are made that suit the preferences of the entire community. For example, it selects items that share common interests. This makes it possible to suggest fashion items that suit the entire community based on community data such as the user's workplace or school.

[0037] The social graph analysis unit analyzes data from different cultural spheres in the user's social graph and can suggest fashion items that resonate across cultures. The social graph analysis unit, for example, analyzes data from different cultural spheres in the user's social graph and suggests fashion items that resonate across cultures. For example, it selects items that incorporate trends from different cultures. In addition, the generation AI analyzes data from different cultural spheres and suggests items that resonate across cultures. For example, it selects items that incorporate designs and colors from different cultures. In addition, it analyzes data from different cultures in the user's social graph and suggests items that resonate across cultures. For example, it selects items based on posts by fashion influencers from different cultures. This makes it possible to suggest fashion items that resonate across cultures based on data from different cultural spheres.

[0038] The location information analysis unit can perform a detailed analysis of climate data based on the user's location information and suggest fashion items that correspond to daily weather fluctuations. The location information analysis unit, for example, analyzes climate data based on the user's location information and suggests fashion items that correspond to daily weather fluctuations. For example, it selects appropriate items based on temperature and precipitation. The generation AI also performs a detailed analysis of climate data based on the user's location information and suggests items that correspond to weather fluctuations. For example, it suggests cold weather items for users living in cold regions. The generation AI also analyzes climate data based on the user's location information and suggests fashion items that correspond to weather fluctuations. For example, it suggests waterproof items for rainy days. This makes it possible to perform a detailed analysis of climate data based on the user's location information and suggest fashion items that correspond to daily weather fluctuations.

[0039] When analyzing cultural data based on location information, the location information analysis unit takes into account the history and traditional events of the region and can suggest fashion items that fit the cultural background. The location information analysis unit, for example, analyzes cultural data based on the user's location information and suggests fashion items that fit the history and traditional events of the region. For example, it selects items that are suitable for local festivals and events. The generation AI also analyzes the cultural data based on the user's location information and suggests items that fit the cultural background. For example, it selects items that incorporate traditional designs and colors of the region. The generation AI also analyzes the cultural data based on the user's location information and suggests fashion items that fit the cultural background. For example, it suggests items that are suitable for historical events of the region. This makes it possible to suggest fashion items that fit the cultural background by taking into account the history and traditional events of the region.

[0040] When analyzing data based on location information, the location information analysis unit also takes into account the user's movement history and can suggest fashion items suitable for travel or business trip destinations. The location information analysis unit, for example, analyzes the user's movement history and suggests fashion items suitable for travel or business trip destinations. For example, it selects items that suit the climate and culture of the travel destination. In addition, the generation AI analyzes the user's movement history and suggests items suitable for travel or business trip destinations. For example, it selects items suitable for business scenes at the business trip destination. In addition, it analyzes the user's movement history and suggests fashion items suitable for travel or business trip destinations. For example, it suggests items suitable for tourist spots at the travel destination. In this way, it is possible to suggest fashion items suitable for travel or business trip destinations taking the user's movement history into consideration.

[0041] When analyzing data based on location information, the location information analysis unit can also take into account the user's living environment and suggest fashion items that suit the environment. The location information analysis unit, for example, analyzes the user's living environment and suggests fashion items that suit the environment, such as urban, suburban, or rural areas. For example, it selects trendy items for urban areas and practical items for rural areas. The generation AI also analyzes the user's living environment and suggests items that suit the environment. For example, it selects items that are suitable for the natural environment of the suburbs. The generation AI also analyzes the user's living environment and suggests fashion items that suit the environment. For example, it suggests items that are suitable for business scenes in urban areas. This makes it possible to suggest fashion items that suit the environment by taking the user's living environment into consideration.

[0042] The location information analysis unit compares climate data based on the user's location information with the location information data of other users, and can suggest fashion items that resonate across regions. The location information analysis unit, for example, compares climate data based on the user's location information with the data of other users, and suggests fashion items that resonate across regions. For example, it selects items that are popular in regions with the same climate conditions. The generation AI also analyzes the user's location data and compares it with the data of other users to suggest items that resonate across regions. For example, it selects items that are preferred by users in the same region. The generation AI also compares climate data based on the user's location information with the data of other users, and suggests fashion items that resonate across regions. For example, it suggests items that are popular in regions with the same climate conditions. In this way, it is possible to suggest fashion items that resonate across regions by comparing climate data based on the user's location information with the data of other users.

[0043] The fashion item selection unit can analyze the user's past purchase history and evaluation data and select new fashion items based on items that the user has given high ratings. The fashion item selection unit, for example, analyzes the user's past purchase history and selects new fashion items based on items that the user has given high ratings. For example, it can suggest items of the same brand or design. The generation AI also analyzes the user's evaluation data and selects new items based on items that the user has given high ratings. For example, it can suggest items in colors or materials that the user has given high ratings. The generation AI also analyzes the user's past purchase history and evaluation data and selects new fashion items based on items that the user has given high ratings. For example, it can suggest items in a style that the user likes. This allows new fashion items to be selected based on the user's past purchase history and evaluation data.

[0044] The fashion item selection unit can suggest fashion items suitable for everyday life, taking into consideration the user's lifestyle and activity patterns. The fashion item selection unit, for example, analyzes the user's lifestyle and suggests fashion items suitable for everyday life. For example, it selects sportswear that suits an active lifestyle. The generation AI also analyzes the user's activity pattern and suggests items suitable for everyday life. For example, it selects formal items suitable for business situations. The generation AI also analyzes the user's lifestyle and activity patterns and suggests fashion items suitable for everyday life. For example, it suggests casual items suitable for relaxing. This makes it possible to suggest fashion items suitable for everyday life based on the user's lifestyle and activity patterns.

[0045] The fashion item selection unit can predict changes in the user's fashion preferences and select items that match those future preferences. The fashion item selection unit, for example, predicts changes in the user's fashion preferences and selects items that match those future preferences. For example, it predicts future trends based on past data. The generation AI also analyzes changes in the user's preferences and selects items that match those future preferences. For example, it predicts future preferences based on current trends. It also predicts changes in the user's fashion preferences and selects items that match those future preferences. For example, it predicts future preferences based on past purchase history. This makes it possible to predict changes in the user's fashion preferences and select items that match those future preferences.

[0046] The fashion item selection unit also takes into consideration the user's hobbies and interests and can suggest fashion items that suit them. For example, the fashion item selection unit analyzes the user's hobbies and interests and suggests fashion items that suit them. For example, sportswear is suggested for a user whose hobby is sports. The generation AI also analyzes the user's interests and suggests items that suit them. For example, items suitable for music events are suggested for a user who likes music. The user's hobbies and interests are also analyzed and fashion items that suit them are suggested. For example, items suitable for art events are suggested for a user who likes art. In this way, it is possible to suggest fashion items that suit the user based on their hobbies and interests.

[0047] The fashion item selection unit can compare the user's fashion preferences with the preferences of other users and select items that are likely to be relatable. The fashion item selection unit, for example, compares the user's fashion preferences with the preferences of other users and selects items that are likely to be relatable. For example, it suggests items that users with the same preferences like. The generation AI also analyzes the user's preferences and selects items that are likely to be relatable by comparing them with the preferences of other users. For example, it suggests items that users who like the same trend would choose. The user's fashion preferences are also compared with the preferences of other users and select items that are likely to be relatable. For example, it suggests items that users who like the same brand would choose. In this way, it is possible to select items that are likely to be relatable by comparing the user's fashion preferences with the preferences of other users.

[0048] The fashion item selection unit analyzes the user's purchasing history and evaluation data, and can recreate purchasing experiences that the user has given a high rating. The fashion item selection unit, for example, analyzes the user's purchasing history and recreates purchasing experiences that the user has given a high rating. For example, it suggests purchasing experiences from the same brand or store. The generation AI also analyzes the user's evaluation data and recreates purchasing experiences that the user has given a high rating. For example, it recreates services and promotions that the user has given a high rating. The generation AI also analyzes the user's purchasing history and evaluation data and recreates purchasing experiences that the user has given a high rating. For example, it suggests the user's preferred purchasing process and payment method. This makes it possible to recreate purchasing experiences that the user has given a high rating based on the user's purchasing history and evaluation data.

[0049] The fashion item selection unit can cooperate with an online shopping platform to provide a virtual try-on function that allows the user to try on suggested items. The fashion item selection unit, for example, cooperates with an online shopping platform to provide a function that allows the user to virtually try on suggested items. For example, a try-on simulation is performed using a 3D model. In addition, the generation AI analyzes the user's body type data to provide the virtual try-on function. For example, a try-on simulation tailored to the user's body type is performed. In addition, in cooperation with an online shopping platform, a function that allows the user to virtually try on suggested items is provided. For example, a try-on simulation is performed in real time using AR technology. This makes it possible to provide a function that allows the user to virtually try on suggested items.

[0050] The fashion item selection unit can predict the user's purchasing behavior and make purchasing suggestions at the optimal timing. The fashion item selection unit, for example, analyzes the user's purchasing history, predicts purchasing behavior, and makes purchasing suggestions at the optimal timing. For example, suggestions can be made to coincide with seasonal changes or sales periods. In addition, the generation AI analyzes the user's purchasing behavior and makes purchasing suggestions at the optimal timing. For example, suggestions can be made to coincide with the user's birthday or special event. In addition, the user's purchasing history can be analyzed, predicting purchasing behavior and making purchasing suggestions at the optimal timing. For example, suggestions can be made based on past purchasing patterns. In this way, the user's purchasing behavior can be predicted and purchasing suggestions can be made at the optimal timing.

[0051] When providing a new purchasing experience, the fashion item selection unit can also suggest items related to the user's hobbies and interests (sports, music, art, etc.). The fashion item selection unit, for example, analyzes the user's hobbies and interests and suggests items related to them. For example, sports-related items are suggested for a user whose hobby is sports. The generation AI also analyzes the user's interests and suggests items related to them. For example, items related to music events are suggested for a user who likes music. The generation AI also analyzes the user's hobbies and interests and suggests items related to them. For example, items related to art events are suggested for a user who likes art. This makes it possible to provide a new purchasing experience by suggesting items related to the user's hobbies and interests.

[0052] The fashion item selection unit compares the user's purchasing history with the purchasing histories of other users to provide a purchasing experience that is easy to empathize with. The fashion item selection unit, for example, compares the user's purchasing history with the purchasing histories of other users to provide a purchasing experience that is easy to empathize with. For example, it suggests purchasing experiences preferred by users with the same tastes. In addition, the generation AI analyzes the user's purchasing history and compares it with the purchasing histories of other users to provide a purchasing experience that is easy to empathize with. For example, it suggests purchasing experiences selected by users who like the same trends. In addition, it compares the user's purchasing history with the purchasing histories of other users to provide a purchasing experience that is easy to empathize with. For example, it suggests purchasing experiences selected by users who like the same brand. In this way, it is possible to provide a purchasing experience that is easy to empathize with by comparing the user's purchasing history with the purchasing histories of other users.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The fashion suggestion system can also analyze the user's health data and suggest optimal fashion items based on the user's health condition. For example, it can analyze data such as the user's step count and heart rate to suggest sportswear suited to an active lifestyle. It can also analyze the user's sleep data to suggest casual items suitable for relaxing. It can also analyze the user's dietary data to suggest fashion items that support a healthy lifestyle. This makes it possible to suggest optimal fashion items based on the user's health condition.

[0055] The fashion suggestion system can also analyze a user's musical preferences and suggest fashion items based on the music genre and artist. For example, for a user who likes rock music, rock-themed fashion items can be suggested. For a user who likes classical music, items with an elegant design can be suggested. Furthermore, it is possible to suggest fashion items that match the atmosphere of the playlists that the user frequently listens to. This makes it possible to suggest the most suitable fashion items based on the user's musical preferences.

[0056] The fashion suggestion system can also analyze a user's reading preferences and suggest fashion items based on the genre and author of the book. For example, for a user who likes mystery novels, items with a chic and mysterious design can be suggested. For a user who likes romance novels, items with a romantic design can be suggested. Furthermore, it is possible to suggest fashion items that match the atmosphere of the cover designs of books that the user often reads. This makes it possible to suggest the most suitable fashion items based on the user's reading preferences.

[0057] The fashion suggestion system can also analyze a user's travel history and suggest fashion items based on the places visited and experiences. For example, resort wear and swimwear can be suggested to a user who visited a beach resort. Outdoor wear and mountain climbing gear can also be suggested to a user who visited a mountainous area. Furthermore, it is possible to suggest fashion items that match the atmosphere of the city the user visited based on the fashion trends of that city. This makes it possible to suggest optimal fashion items based on the user's travel history.

[0058] The fashion suggestion system can also analyze a user's hobbies and special skills and suggest fashion items based on them. For example, for a user whose hobby is painting, it can suggest artsy fashion items. For a user whose special skill is cooking, it can also suggest aprons and casual wear suitable for kitchen work. It can also suggest fashion items suitable for the occasion based on hobby events or workshops that the user is participating in. This makes it possible to suggest optimal fashion items based on the user's hobbies and special skills.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The image recognition unit analyzes the user's fashion preferences. For example, it analyzes images uploaded by the user and extracts features such as color, design, and brand. The image recognition unit can also analyze the content of the image using generative AI (e.g., text generation AI or multimodal generation AI). Step 2: The social graph analysis unit analyzes the user's social graph data based on the fashion preferences analyzed by the image recognition unit. For example, it analyzes posts from fashion influencers and friends the user follows to understand what fashions are trending. Step 3: The location information analysis unit analyzes the climate and cultural data based on the user's location information, based on the data analyzed by the social graph analysis unit. For example, it identifies the temperature and precipitation in the user's area, as well as seasonal and traditional events. Step 4: The fashion item selection unit selects the most suitable fashion items for the user based on the data analyzed by the location information analysis unit. For example, it proposes a coordinated outfit that suits the user by comprehensively considering the user's fashion preferences, social graph data, and climate and cultural data based on location information.

[0061] (Example 2) A fashion suggestion system according to an embodiment of the present invention analyzes a user's fashion preferences and suggests optimal fashion items. This system integrates and analyzes image recognition technology, social graph data, and location-based climate and cultural data. This allows the fashion suggestion system to select fashion items tailored to the user, providing a new purchasing experience and ways to enjoy fashion, and supporting daily styling.

[0062] A fashion suggestion system according to an embodiment includes an image recognition unit, a social graph analysis unit, a location information analysis unit, and a fashion item selection unit. The image recognition unit analyzes a user's fashion preferences. For example, it analyzes images uploaded by the user and extracts features such as color, design, and brand. The image recognition unit can also analyze the content of the images using a generative AI (for example, a text generation AI or a multimodal generation AI). The social graph analysis unit analyzes the user's social graph data based on the fashion preferences analyzed by the image recognition unit. For example, it analyzes posts from fashion influencers and friends followed by the user to determine what fashions are popular. The location information analysis unit analyzes climate and cultural data based on the user's location information based on the data analyzed by the social graph analysis unit. For example, it determines the temperature and precipitation, seasonal events, traditional events, etc. in the area where the user lives. The fashion item selection unit selects the most suitable fashion items for the user based on the data analyzed by the location information analysis unit. For example, the system comprehensively considers the user's fashion preferences, social graph data, and climate and cultural data based on location information to propose outfits that suit the user. This allows the fashion suggestion system according to the embodiment to integrate and analyze the user's fashion preferences, social graph, and climate and cultural data based on location information to propose optimal fashion items.

[0063] The image recognition unit can infer emotions from a user's facial expressions and posture, and then analyze fashion preferences in more detail based on those emotions. For example, the image recognition unit analyzes facial expressions and posture from images uploaded by the user, and the generation AI infers those emotions. For example, it can detect smiles and relaxed postures and suggest fashion items that evoke positive emotions. The generation AI also analyzes the user's facial expressions and posture to track changes in emotion. For example, it can analyze changes in emotion when wearing specific fashion items to gain a detailed understanding of the user's preferences. It can also infer emotions from the user's facial expressions and posture, and analyze fashion preferences based on that emotional data. For example, it can analyze emotional responses to specific colors and designs to gain a detailed understanding of preferences. This allows for a detailed analysis of fashion preferences based on the user's emotions.

[0064] The image recognition unit can perform a detailed analysis of at least one of the user's body type, skin color, and hairstyle characteristics, and suggest optimal fashion items based on that analysis. For example, the image recognition unit uses a generative AI to analyze the user's body type and suggest fashion items that suit that body type. For example, it selects designs and silhouettes that fit that body type. It can also analyze the user's skin color and suggest fashion items in shades that match that skin color. For example, it provides a color palette that matches the skin tone. It can also analyze the user's hairstyle and suggest fashion items that match the hairstyle. For example, it selects accessories and hats that match the hairstyle. This allows it to suggest optimal fashion items based on the user's body type, skin color, and hairstyle.

[0065] The image recognition unit analyzes the history of images uploaded by the user in the past, tracks changes in fashion preferences over time, and can predict future preferences. The image recognition unit, for example, analyzes images uploaded by the user in the past and tracks changes in fashion preferences. For example, it identifies changes in preferences by season. The generation AI also analyzes the user's image history and predicts preference trends. For example, it predicts future fashion preferences based on past data. The image recognition unit also analyzes the user's image history and visualizes changes in preferences. For example, it displays changes in preferences in a graph along a time axis. This makes it possible to track changes in the user's fashion preferences and predict future preferences.

[0066] The image recognition unit can analyze the user's home interior or lifestyle and suggest fashion items that match it. For example, the image recognition unit can analyze home interior images uploaded by the user and suggest fashion items that match the interior style. For example, it can select simple designs that go well with modern interiors. The generation AI can also analyze the user's lifestyle and suggest fashion items that suit the lifestyle. For example, it can select sporty items that suit an active lifestyle. It can also analyze images of the user's home interior and suggest fashion items that match the interior color and design. For example, it can select items that match the interior color palette. This makes it possible to suggest fashion items that match the user's interior and lifestyle.

[0067] The image recognition unit can also analyze the fashion preferences of friends or family based on images uploaded by the user, and suggest outfits that suit the entire group. For example, the image recognition unit analyzes images of friends and family uploaded by the user to understand the fashion preferences of the entire group. For example, it can suggest items that share common preferences. The generation AI also analyzes the fashion preferences of the user's friends and family and suggests outfits that suit the entire group. For example, it can suggest outfits that have a consistent look. The image recognition unit can also analyze the fashion preferences of friends and family based on images uploaded by the user, and suggest outfits that suit events for the entire group. For example, it can suggest outfits that are suitable for parties or trips. This makes it possible to suggest outfits that suit the entire group.

[0068] The image recognition unit uses the emotion estimation function to analyze the emotions of users when they upload images in real time and suggest fashion items that elicit positive emotions. For example, the image recognition unit analyzes the facial expression of a user when they upload an image and suggests fashion items that elicit positive emotions. For example, it suggests brightly colored items based on an image of a smiling face. The generative AI also analyzes the user's emotions in real time and suggests items that elicit positive emotions. For example, it suggests items made of comfortable materials based on a relaxed facial expression. The image recognition unit also analyzes the emotions of a user when they upload an image and suggests fashion items based on that emotion. For example, it suggests trendy items based on an excited facial expression. This makes it possible to suggest fashion items that elicit positive emotions based on the user's emotions.

[0069] The social graph analysis unit analyzes the flow of emotions within a user's social graph and can suggest fashion items that are likely to resonate emotionally. The social graph analysis unit, for example, analyzes posts and comments within a user's social graph to understand the flow of emotions. For example, it suggests fashion items based on posts with a lot of positive emotions. The generation AI also analyzes the flow of emotions within the social graph and suggests fashion items that are likely to resonate emotionally. For example, it selects items that have a lot of positive reactions from friends. It also analyzes emotional data within the user's social graph and suggests items that are likely to resonate emotionally. For example, it selects items based on posts with a high emotional score. This makes it possible to suggest fashion items that are likely to resonate based on the flow of emotions within the user's social graph.

[0070] The social graph analysis unit also takes into account the user's past posts or comments, allowing for a more detailed understanding of interests. The social graph analysis unit, for example, analyzes the user's past posts and comments to understand interests in detail. For example, items may be suggested based on comments about a particular brand or design. The generative AI also analyzes the user's past posts and comments to understand interests in detail. For example, items may be selected based on frequently mentioned keywords. The system also analyzes past data in the user's social graph to understand interests in detail. For example, items may be suggested based on trends in past posts. This allows for a more detailed understanding of interests based on the user's past posts and comments.

[0071] The social graph analysis unit analyzes social graph data over time, tracks changes in fashion trends, and can predict future trends. The social graph analysis unit, for example, analyzes posted data in the social graph over time to track changes in fashion trends. For example, it identifies trends based on data from the past few years. The generative AI also analyzes social graph data over time to predict future trends. For example, it predicts future trends based on current trends. It also analyzes data in the user's social graph over time to visualize changes in trends. For example, it displays changes in trends in a graph along a time axis. This makes it possible to track changes in fashion trends based on social graph data and predict future trends.

[0072] The social graph analysis unit also integrates community data such as the user's workplace or school, and can suggest fashion items that suit the entire community. The social graph analysis unit, for example, analyzes community data such as the user's workplace or school, and suggests fashion items that suit the entire community. For example, it selects items that match the workplace dress code. The generation AI also analyzes the user's community data and suggests items that suit the entire community. For example, it selects items that are suitable for a school event. The user's community data is also integrated, and suggestions are made that suit the preferences of the entire community. For example, it selects items that share common interests. This makes it possible to suggest fashion items that suit the entire community based on community data such as the user's workplace or school.

[0073] The social graph analysis unit analyzes data from different cultural spheres in the user's social graph and can suggest fashion items that resonate across cultures. The social graph analysis unit, for example, analyzes data from different cultural spheres in the user's social graph and suggests fashion items that resonate across cultures. For example, it selects items that incorporate trends from different cultures. In addition, the generation AI analyzes data from different cultural spheres and suggests items that resonate across cultures. For example, it selects items that incorporate designs and colors from different cultures. In addition, it analyzes data from different cultures in the user's social graph and suggests items that resonate across cultures. For example, it selects items based on posts by fashion influencers from different cultures. This makes it possible to suggest fashion items that resonate across cultures based on data from different cultural spheres.

[0074] The social graph analysis unit uses the emotion estimation function to analyze the emotional connections in the user's social graph and suggest fashion items that have a positive emotional impact. The social graph analysis unit, for example, analyzes the emotional connections in the user's social graph and suggests items that have a positive impact. For example, items are selected based on posts with a high number of positive emotions. The generation AI also analyzes the emotional connections in the social graph and suggests items that have a positive impact. For example, items that have a high number of positive reactions from friends are selected. The emotion data in the user's social graph is also analyzed and items that have a positive impact are suggested. For example, items are selected based on posts with a high emotion score. This makes it possible to suggest fashion items that have a positive impact based on the emotional connections in the user's social graph.

[0075] The location information analysis unit can perform a detailed analysis of climate data based on the user's location information and suggest fashion items that correspond to daily weather fluctuations. The location information analysis unit, for example, analyzes climate data based on the user's location information and suggests fashion items that correspond to daily weather fluctuations. For example, it selects appropriate items based on temperature and precipitation. The generation AI also performs a detailed analysis of climate data based on the user's location information and suggests items that correspond to weather fluctuations. For example, it suggests cold weather items for users living in cold regions. The generation AI also analyzes climate data based on the user's location information and suggests fashion items that correspond to weather fluctuations. For example, it suggests waterproof items for rainy days. This makes it possible to perform a detailed analysis of climate data based on the user's location information and suggest fashion items that correspond to daily weather fluctuations.

[0076] When analyzing cultural data based on location information, the location information analysis unit takes into account the history and traditional events of the region and can suggest fashion items that fit the cultural background. The location information analysis unit, for example, analyzes cultural data based on the user's location information and suggests fashion items that fit the history and traditional events of the region. For example, it selects items that are suitable for local festivals and events. The generation AI also analyzes the cultural data based on the user's location information and suggests items that fit the cultural background. For example, it selects items that incorporate traditional designs and colors of the region. The generation AI also analyzes the cultural data based on the user's location information and suggests fashion items that fit the cultural background. For example, it suggests items that are suitable for historical events of the region. This makes it possible to suggest fashion items that fit the cultural background by taking into account the history and traditional events of the region.

[0077] When analyzing data based on location information, the location information analysis unit also takes into account the user's movement history and can suggest fashion items suitable for travel or business trip destinations. The location information analysis unit, for example, analyzes the user's movement history and suggests fashion items suitable for travel or business trip destinations. For example, it selects items that suit the climate and culture of the travel destination. In addition, the generation AI analyzes the user's movement history and suggests items suitable for travel or business trip destinations. For example, it selects items suitable for business scenes at the business trip destination. In addition, it analyzes the user's movement history and suggests fashion items suitable for travel or business trip destinations. For example, it suggests items suitable for tourist spots at the travel destination. In this way, it is possible to suggest fashion items suitable for travel or business trip destinations taking the user's movement history into consideration.

[0078] When analyzing data based on location information, the location information analysis unit can also take into account the user's living environment and suggest fashion items that suit the environment. The location information analysis unit, for example, analyzes the user's living environment and suggests fashion items that suit the environment, such as urban, suburban, or rural areas. For example, it selects trendy items for urban areas and practical items for rural areas. The generation AI also analyzes the user's living environment and suggests items that suit the environment. For example, it selects items that are suitable for the natural environment of the suburbs. The generation AI also analyzes the user's living environment and suggests fashion items that suit the environment. For example, it suggests items that are suitable for business scenes in urban areas. This makes it possible to suggest fashion items that suit the environment by taking the user's living environment into consideration.

[0079] The location information analysis unit compares climate data based on the user's location information with the location information data of other users, and can suggest fashion items that resonate across regions. The location information analysis unit, for example, compares climate data based on the user's location information with the data of other users, and suggests fashion items that resonate across regions. For example, it selects items that are popular in regions with the same climate conditions. The generation AI also analyzes the user's location data and compares it with the data of other users to suggest items that resonate across regions. For example, it selects items that are preferred by users in the same region. The generation AI also compares climate data based on the user's location information with the data of other users, and suggests fashion items that resonate across regions. For example, it suggests items that are popular in regions with the same climate conditions. In this way, it is possible to suggest fashion items that resonate across regions by comparing climate data based on the user's location information with the data of other users.

[0080] The location information analysis unit can use the emotion estimation function to analyze the emotions a user feels when they are in a specific area and suggest fashion items based on those emotions. The location information analysis unit, for example, analyzes the emotions a user feels when they are in a specific area and suggests fashion items based on those emotions. For example, items are selected based on positive emotions felt at a travel destination. The generation AI also analyzes the user's emotions and suggests items based on the emotions felt when they are in a specific area. For example, items are selected based on relaxed emotions felt at a business trip destination. The generation AI also analyzes the emotions a user feels when they are in a specific area and suggests fashion items based on those emotions. For example, items are suggested based on excited emotions felt at a tourist spot. This makes it possible to suggest fashion items based on the emotions a user feels when they are in a specific area.

[0081] The fashion item selection unit can analyze the user's past purchase history and evaluation data and select new fashion items based on items that the user has given high ratings. The fashion item selection unit, for example, analyzes the user's past purchase history and selects new fashion items based on items that the user has given high ratings. For example, it can suggest items of the same brand or design. The generation AI also analyzes the user's evaluation data and selects new items based on items that the user has given high ratings. For example, it can suggest items in colors or materials that the user has given high ratings. The generation AI also analyzes the user's past purchase history and evaluation data and selects new fashion items based on items that the user has given high ratings. For example, it can suggest items in a style that the user likes. This allows new fashion items to be selected based on the user's past purchase history and evaluation data.

[0082] The fashion item selection unit can suggest fashion items suitable for everyday life, taking into consideration the user's lifestyle and activity patterns. The fashion item selection unit, for example, analyzes the user's lifestyle and suggests fashion items suitable for everyday life. For example, it selects sportswear that suits an active lifestyle. The generation AI also analyzes the user's activity pattern and suggests items suitable for everyday life. For example, it selects formal items suitable for business situations. The generation AI also analyzes the user's lifestyle and activity patterns and suggests fashion items suitable for everyday life. For example, it suggests casual items suitable for relaxing. This makes it possible to suggest fashion items suitable for everyday life based on the user's lifestyle and activity patterns.

[0083] The fashion item selection unit can predict changes in the user's fashion preferences and select items that match those future preferences. The fashion item selection unit, for example, predicts changes in the user's fashion preferences and selects items that match those future preferences. For example, it predicts future trends based on past data. The generation AI also analyzes changes in the user's preferences and selects items that match those future preferences. For example, it predicts future preferences based on current trends. It also predicts changes in the user's fashion preferences and selects items that match those future preferences. For example, it predicts future preferences based on past purchase history. This makes it possible to predict changes in the user's fashion preferences and select items that match those future preferences.

[0084] The fashion item selection unit also takes into consideration the user's hobbies and interests and can suggest fashion items that suit them. For example, the fashion item selection unit analyzes the user's hobbies and interests and suggests fashion items that suit them. For example, sportswear is suggested for a user whose hobby is sports. The generation AI also analyzes the user's interests and suggests items that suit them. For example, items suitable for music events are suggested for a user who likes music. The user's hobbies and interests are also analyzed and fashion items that suit them are suggested. For example, items suitable for art events are suggested for a user who likes art. In this way, it is possible to suggest fashion items that suit the user based on their hobbies and interests.

[0085] The fashion item selection unit can compare the user's fashion preferences with the preferences of other users and select items that are likely to be relatable. The fashion item selection unit, for example, compares the user's fashion preferences with the preferences of other users and selects items that are likely to be relatable. For example, it suggests items that users with the same preferences like. The generation AI also analyzes the user's preferences and selects items that are likely to be relatable by comparing them with the preferences of other users. For example, it suggests items that users who like the same trend would choose. The user's fashion preferences are also compared with the preferences of other users and select items that are likely to be relatable. For example, it suggests items that users who like the same brand would choose. In this way, it is possible to select items that are likely to be relatable by comparing the user's fashion preferences with the preferences of other users.

[0086] The fashion item selection unit can use the emotion estimation function to analyze the emotion a user has toward a specific fashion item and select items based on that emotion. For example, the fashion item selection unit analyzes the emotion a user has toward a specific fashion item and selects items based on that emotion. For example, it suggests items that elicit positive emotions. Furthermore, the generation AI analyzes the user's emotions and selects items based on the emotion toward a specific item. For example, it suggests items that elicit relaxed emotions. Furthermore, it analyzes the emotion a user has toward a specific fashion item and selects items based on that emotion. For example, it suggests items that elicit excited emotions. In this way, items can be selected based on the emotion a user has toward a specific fashion item.

[0087] The fashion item selection unit analyzes the user's purchasing history and evaluation data, and can recreate purchasing experiences that the user has given a high rating. The fashion item selection unit, for example, analyzes the user's purchasing history and recreates purchasing experiences that the user has given a high rating. For example, it suggests purchasing experiences from the same brand or store. The generation AI also analyzes the user's evaluation data and recreates purchasing experiences that the user has given a high rating. For example, it recreates services and promotions that the user has given a high rating. The generation AI also analyzes the user's purchasing history and evaluation data and recreates purchasing experiences that the user has given a high rating. For example, it suggests the user's preferred purchasing process and payment method. This makes it possible to recreate purchasing experiences that the user has given a high rating based on the user's purchasing history and evaluation data.

[0088] The fashion item selection unit can cooperate with an online shopping platform to provide a virtual try-on function that allows the user to try on suggested items. The fashion item selection unit, for example, cooperates with an online shopping platform to provide a function that allows the user to virtually try on suggested items. For example, a try-on simulation is performed using a 3D model. In addition, the generation AI analyzes the user's body type data to provide the virtual try-on function. For example, a try-on simulation tailored to the user's body type is performed. In addition, in cooperation with an online shopping platform, a function that allows the user to virtually try on suggested items is provided. For example, a try-on simulation is performed in real time using AR technology. This makes it possible to provide a function that allows the user to virtually try on suggested items.

[0089] The fashion item selection unit can predict the user's purchasing behavior and make purchasing suggestions at the optimal timing. The fashion item selection unit, for example, analyzes the user's purchasing history, predicts purchasing behavior, and makes purchasing suggestions at the optimal timing. For example, suggestions can be made to coincide with seasonal changes or sales periods. In addition, the generation AI analyzes the user's purchasing behavior and makes purchasing suggestions at the optimal timing. For example, suggestions can be made to coincide with the user's birthday or special event. In addition, the user's purchasing history can be analyzed, predicting purchasing behavior and making purchasing suggestions at the optimal timing. For example, suggestions can be made based on past purchasing patterns. In this way, the user's purchasing behavior can be predicted and purchasing suggestions can be made at the optimal timing.

[0090] When providing a new purchasing experience, the fashion item selection unit can also suggest items related to the user's hobbies and interests (sports, music, art, etc.). The fashion item selection unit, for example, analyzes the user's hobbies and interests and suggests items related to them. For example, sports-related items are suggested for a user whose hobby is sports. The generation AI also analyzes the user's interests and suggests items related to them. For example, items related to music events are suggested for a user who likes music. The generation AI also analyzes the user's hobbies and interests and suggests items related to them. For example, items related to art events are suggested for a user who likes art. This makes it possible to provide a new purchasing experience by suggesting items related to the user's hobbies and interests.

[0091] The fashion item selection unit compares the user's purchasing history with the purchasing histories of other users to provide a purchasing experience that is easy to empathize with. The fashion item selection unit, for example, compares the user's purchasing history with the purchasing histories of other users to provide a purchasing experience that is easy to empathize with. For example, it suggests purchasing experiences preferred by users with the same tastes. In addition, the generation AI analyzes the user's purchasing history and compares it with the purchasing histories of other users to provide a purchasing experience that is easy to empathize with. For example, it suggests purchasing experiences selected by users who like the same trends. In addition, it compares the user's purchasing history with the purchasing histories of other users to provide a purchasing experience that is easy to empathize with. For example, it suggests purchasing experiences selected by users who like the same brand. In this way, it is possible to provide a purchasing experience that is easy to empathize with by comparing the user's purchasing history with the purchasing histories of other users.

[0092] The fashion item selection unit can use the emotion estimation function to analyze the emotions a user has toward a specific purchasing experience and provide a purchasing experience based on those emotions. The fashion item selection unit, for example, analyzes the emotions a user has toward a specific purchasing experience and provides a purchasing experience based on those emotions. For example, it suggests a purchasing experience that elicits positive emotions. The generation AI also analyzes the user's emotions and provides a purchasing experience based on the emotions they have toward a specific purchasing experience. For example, it suggests a purchasing experience that elicits relaxed emotions. The generation AI also analyzes the emotions a user has toward a specific purchasing experience and provides a purchasing experience based on those emotions. For example, it suggests a purchasing experience that elicits excited emotions. This makes it possible to provide a purchasing experience based on the emotions a user has toward a specific purchasing experience.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The fashion suggestion system can also analyze the user's health data and suggest optimal fashion items based on the user's health condition. For example, it can analyze data such as the user's step count and heart rate to suggest sportswear suited to an active lifestyle. It can also analyze the user's sleep data to suggest casual items suitable for relaxing. It can also analyze the user's dietary data to suggest fashion items that support a healthy lifestyle. This makes it possible to suggest optimal fashion items based on the user's health condition.

[0095] The fashion suggestion system can also analyze a user's musical preferences and suggest fashion items based on the music genre and artist. For example, for a user who likes rock music, rock-themed fashion items can be suggested. For a user who likes classical music, items with an elegant design can be suggested. Furthermore, it is possible to suggest fashion items that match the atmosphere of the playlists that the user frequently listens to. This makes it possible to suggest the most suitable fashion items based on the user's musical preferences.

[0096] The fashion suggestion system can also analyze a user's reading preferences and suggest fashion items based on the genre and author of the book. For example, for a user who likes mystery novels, items with a chic and mysterious design can be suggested. For a user who likes romance novels, items with a romantic design can be suggested. Furthermore, it is possible to suggest fashion items that match the atmosphere of the cover designs of books that the user often reads. This makes it possible to suggest the most suitable fashion items based on the user's reading preferences.

[0097] The fashion suggestion system can also analyze a user's travel history and suggest fashion items based on the places visited and experiences. For example, resort wear and swimwear can be suggested to a user who visited a beach resort. Outdoor wear and mountain climbing gear can also be suggested to a user who visited a mountainous area. Furthermore, it is possible to suggest fashion items that match the atmosphere of the city the user visited based on the fashion trends of that city. This makes it possible to suggest optimal fashion items based on the user's travel history.

[0098] The fashion suggestion system can also analyze a user's hobbies and special skills and suggest fashion items based on them. For example, for a user whose hobby is painting, it can suggest artsy fashion items. For a user whose special skill is cooking, it can also suggest aprons and casual wear suitable for kitchen work. It can also suggest fashion items suitable for the occasion based on hobby events or workshops that the user is participating in. This makes it possible to suggest optimal fashion items based on the user's hobbies and special skills.

[0099] The fashion suggestion system can also estimate the user's emotions and suggest fashion items with a relaxing effect based on the estimated emotions. For example, if the user is feeling stressed, items with materials and designs that have a relaxing effect can be suggested. Also, if the user is tired, items that are comfortable to wear can be suggested. Furthermore, if the user feels like relaxing, it is also possible to suggest items with colors and patterns that have a relaxing effect. In this way, fashion items with a relaxing effect can be suggested based on the user's emotions.

[0100] The fashion suggestion system can also estimate the user's emotions and suggest fashion items that will increase motivation based on the estimated emotions. For example, if the user feels like they want to be motivated, items with designs and colors that will increase motivation can be suggested. Also, if the user feels like they want to feel more confident, items with a style that will bring out confidence can be suggested. Furthermore, if the user feels like they want to feel positive, it is also possible to suggest items that will elicit positive emotions. In this way, fashion items that will increase motivation can be suggested based on the user's emotions.

[0101] The fashion suggestion system can also estimate the user's emotions and suggest fashion items suitable for special events based on the estimated emotions. For example, if the user is excited, the system can suggest glamorous items suitable for parties and events. If the user is nervous, the system can suggest items with a relaxing effect. Furthermore, if the user is feeling like having fun, the system can suggest items with a design that will bring out a happy mood. In this way, the system can suggest fashion items suitable for special events based on the user's emotions.

[0102] The fashion suggestion system can also estimate the user's emotions and suggest seasonal fashion items based on the estimated emotions. For example, if the user is feeling happy about the arrival of spring, items with bright spring colors can be suggested. If the user is tired of the summer heat, items made of cool materials can be suggested. Furthermore, if the user wants to enjoy the calm atmosphere of autumn, items with autumnal colors and designs can be suggested. In this way, seasonal fashion items can be suggested based on the user's emotions.

[0103] The fashion suggestion system can also estimate the user's emotions and suggest fashion items suitable for specific situations based on the estimated emotions. For example, if the user is nervous about going on a date, items that will make the user feel confident can be suggested. If the user is anxious about a business presentation, items that will give a professional impression can be suggested. Furthermore, if the user wants to relax when attending a casual gathering with friends, items that have a relaxing effect can be suggested. In this way, fashion items suitable for specific situations can be suggested based on the user's emotions.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The image recognition unit analyzes the user's fashion preferences. For example, it analyzes images uploaded by the user and extracts features such as color, design, and brand. The image recognition unit can also analyze the content of the image using generative AI (e.g., text generation AI or multimodal generation AI). Step 2: The social graph analysis unit analyzes the user's social graph data based on the fashion preferences analyzed by the image recognition unit. For example, it analyzes posts from fashion influencers and friends the user follows to understand what fashions are trending. Step 3: The location information analysis unit analyzes the climate and cultural data based on the user's location information, based on the data analyzed by the social graph analysis unit. For example, it identifies the temperature and precipitation in the user's area, as well as seasonal and traditional events. Step 4: The fashion item selection unit selects the most suitable fashion items for the user based on the data analyzed by the location information analysis unit. For example, it proposes a coordinated outfit that suits the user by comprehensively considering the user's fashion preferences, social graph data, and climate and cultural data based on location information.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0142] The 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.

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

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image recognition unit that analyzes a user's fashion preferences; a social graph analysis unit that analyzes data of a user's social graph based on the fashion preferences analyzed by the image recognition unit; a location information analysis unit that analyzes climate data or culture data based on the user's location information based on the data analyzed by the social graph analysis unit; a fashion item selection unit that selects the most suitable fashion item for the user based on the data analyzed by the location information analysis unit. A system characterized by:

2. The image recognition unit Analyzing the user's home interior or lifestyle and suggesting fashion items that suit them 2. The system of claim 1.

3. The social graph analysis unit Analyze the flow of emotions within the user's social graph and suggest fashion items that are likely to resonate with the user emotionally.

2. The system of claim 1.

4. The location information analysis unit The system analyzes weather data based on the user's location information in detail and suggests fashion items that correspond to daily weather changes.

2. The system of claim 1.

5. The fashion item selection unit The past purchase history and evaluation data of the user are analyzed, and new fashion items are selected based on items that the user has given high ratings to.

2. The system of claim 1.

6. The image recognition unit Emotions of the user are estimated from their facial expressions and postures, and fashion preferences are analyzed in more detail based on the emotions.

2. The system of claim 1.

Images