System

Abstract

To provide a system that provides optimal coordination and beauty information based on user's individual fashion style and preferences.SOLUTION: A system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a coordination suggestion unit, a recommendation unit, and an explanation unit. The collection unit collects a user's past purchase history and photos of the user. The analysis unit analyzes the information collected by the collection unit, and analyzes the user's fashion style and preferences. The provision unit provides fashion trends and beauty information appropriate to the user based on the analysis results by the analysis unit. The coordination suggestion unit suggests coordination for items the user owns or for items the user is considering purchasing. The recommendation unit recommends fashion items that fit the user based on the analysis results by the analysis unit. The explanation unit explains how to apply makeup and style the user's hair.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have not adequately provided optimal coordination and beauty information based on a user's individual fashion style and preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal coordination and beauty information based on the individual fashion style and preferences of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a coordination suggestion unit, a recommendation unit, and an explanation unit. The collection unit collects the user's past purchase history and photos of the user. The analysis unit analyzes the information collected by the collection unit and analyzes the user's fashion style and preferences. The provision unit provides fashion trends and beauty information suited to the user based on the analysis results by the analysis unit. The coordination suggestion unit suggests coordination for items the user owns or items the user is considering purchasing. The recommendation unit recommends fashion items that fit the user based on the analysis results by the analysis unit. The explanation unit explains how to apply makeup and hairstyle. Effect of the Invention

[0007] The system according to the embodiment can provide optimal coordination and beauty information based on the individual fashion style and preferences of the user. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The fashion and beauty advisor AI assistant system according to the embodiment of the present invention is a system that provides advice on fashion and beauty to a user. The system collects the user's past purchase history and uploaded photos, analyzes this information, and analyzes the user's fashion style and preferences. Next, it collects the latest fashion trends and beauty information and provides it to the user. In addition, it proposes optimal coordination for items that the user owns and items that the user is considering purchasing. Furthermore, it provides advice on colors and coordination that suit the user based on the user's skin tone and preferences. It takes into account the user's body type and characteristics and recommends fashion items that fit. In addition, it suggests optimal beauty products according to the user's skin type, hair type, and specific concerns. It also provides a virtual try-on function that uses a camera to virtually match clothes and accessories to the user's actual appearance. In addition, it provides links to purchase sites and discount information based on preferences and suggested items. Finally, it provides a tutorial that explains how to apply makeup and hairstyle step by step. As a result, the fashion and beauty advisor AI assistant system can analyze the user's fashion style and preferences in detail and provide optimal coordination and beauty information.

[0029] The fashion and beauty advisor AI assistant system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a coordination suggestion unit, a recommendation unit, and an explanation unit. The collection unit collects the user's past purchase history and the user's photos. The user's past purchase history includes, for example, purchase date and time, purchased items, purchase frequency, etc., but is not limited to such examples. The user's photos include, for example, face photos, full-body photos, photos in specific situations, etc., but is not limited to such examples. The collection unit can also collect information from, for example, the user's social media account. The analysis unit analyzes the information collected by the collection unit and analyzes the user's fashion style and preferences. The analysis unit can improve the accuracy of the analysis by referring to the user's past fashion selections, for example, using AI. The provision unit provides fashion trends and beauty information according to the user based on the analysis results by the analysis unit. The provision unit can estimate the user's emotions, for example, using AI, and adjust the method of providing fashion trends and beauty information based on the estimated user's emotions. The coordination suggestion unit proposes optimal coordination for items the user owns or items the user is considering purchasing. The coordination suggestion unit can estimate the user's emotions using, for example, AI, and adjust the coordination suggestion method based on the estimated user's emotions. The recommendation unit recommends fashion items that fit the user based on the analysis results by the analysis unit. The recommendation unit can estimate the user's emotions using, for example, AI, and adjust the fashion item recommendation method based on the estimated user's emotions. The commentary unit provides a tutorial that explains how to do makeup and hairstyles step by step. The commentary unit can estimate the user's emotions using, for example, AI, and adjust the commentary method based on the estimated user's emotions. As a result, the fashion and beauty advisor AI assistant system according to the embodiment can analyze the user's fashion style and preferences in detail and provide optimal coordination and beauty information.

[0030] The collection unit collects the user's past purchase history and the user's photos. The user's past purchase history includes, for example, purchase date and time, purchased items, purchase frequency, etc., but is not limited to such examples. This makes it possible to grasp the user's tastes and trends regarding fashion and beauty. Furthermore, the user's photos include, but are not limited to such examples, face photos, full-body photos, photos in specific situations, etc. This makes it possible to grasp the user's body type, facial features, skin color, hairstyle, etc. in detail. The collection unit can also collect information from the user's social media account, for example. From the social media account, photos posted by the user, comments, likes, etc. can be collected, and the user's lifestyle and preferences can be understood more deeply. This allows the collection unit to build a foundation for centrally collecting various information about the user and providing it to the analysis unit and other departments. The collected data is stored in a cloud server and managed in an environment with security measures. This makes it possible to efficiently collect necessary information while protecting the user's privacy.

[0031] The analysis unit analyzes the information collected by the collection unit and analyzes the user's fashion style and preferences. Specifically, AI can be used to improve the accuracy of the analysis by referring to the user's past fashion choices. AI uses image recognition technology to analyze the user's photos and extract features such as face shape, skin color, hairstyle, and body type. It also analyzes purchase history to identify the user's preferred brands, colors, and styles. In addition, it analyzes data collected from social media to understand the user's lifestyle and interests. This allows the analysis unit to build a foundation for understanding the user's fashion and beauty preferences in detail and providing individually customized advice. The analysis unit can also respond to changes in the user's preferences and new trends by utilizing past data and trend information. For example, it can make fashion suggestions tailored to seasonal trends or specific events. This allows the analysis unit to flexibly respond to the user's needs and provide higher satisfaction.

[0032] The providing unit provides fashion trends and beauty information according to the user based on the analysis results by the analyzing unit. Specifically, the AI ​​can be used to estimate the user's emotions and adjust the method of providing fashion trends and beauty information based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit provides relaxing fashion and beauty information, and if the user wants to feel more confident, the providing unit suggests an elegant style that enhances confidence. The providing unit can improve the user's satisfaction by providing optimal information according to the user's emotions and situation. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the information provided. For example, the providing unit analyzes how the user responded to the provided information and reflects it in the next information provision. This allows the providing unit to always provide optimal information to the user and build a long-term relationship of trust.

[0033] The coordination suggestion unit proposes optimal coordination for items owned by the user or items the user is considering purchasing. Specifically, the coordination suggestion unit can use AI to estimate the user's emotions and adjust the coordination suggestion method based on the estimated user's emotions. For example, if the user plans to participate in a specific event, the coordination suggestion unit proposes an appropriate coordination for the event. In addition, the coordination suggestion unit can propose coordination according to the season and trends for items the user wears on a daily basis. The coordination suggestion unit manages a database of the user's owned items and automatically updates the optimal coordination when a new item is added. This allows the user to always receive the latest coordination suggestions. Furthermore, the coordination suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, the user can feedback the results of actually trying out the suggested coordination and adjust the next suggestion based on that information. This allows the coordination suggestion unit to always provide the optimal coordination to the user and maximize the fun of fashion.

[0034] The recommendation unit recommends fashion items that fit the user based on the analysis results by the analysis unit. Specifically, the recommendation unit can use AI to estimate the user's emotions and adjust the method of recommending fashion items based on the estimated user's emotions. For example, if the user feels like trying a new style, trend items and new brands are recommended, and if the user is looking for a sense of security, standard items and brands that the user has purchased in the past are recommended. The recommendation unit can improve user satisfaction by making individually customized recommendations based on the user's purchase history and preferences. Furthermore, the recommendation unit can collect user feedback and continuously improve the accuracy and effectiveness of the recommendation content. For example, the recommendation unit can feed back the results of the user's actual purchase of the recommended item and adjust the next recommendation based on that information. As a result, the recommendation unit can always recommend the most suitable fashion items to the user and increase the desire to purchase.

[0035] The commentary unit provides a step-by-step tutorial on how to apply makeup and hairstyles. Specifically, the commentary unit can use AI to estimate the user's emotions and adjust the method of explanation based on the estimated user's emotions. For example, if the user feels like relaxing, the commentary unit provides a relaxed pace, and if the user is in a hurry, the commentary unit provides a quick and effective method of applying makeup and hairstyles. The commentary unit can improve the user's satisfaction by providing a customized tutorial according to the user's skill level and needs. Furthermore, the commentary unit can collect user feedback and continuously improve the accuracy and effectiveness of the commentary content. For example, the commentary unit can provide feedback on the results of the user actually trying out the commentary and adjust the next commentary based on that information. As a result, the commentary unit can always provide the user with the most suitable method of makeup and hairstyles, maximizing the fun of beauty.

[0036] The advice unit can provide color and coordination advice based on the user's skin tone and preferences. The advice unit can, for example, analyze the user's skin tone and suggest colors that suit the user. For example, the advice unit can use AI to analyze the user's skin tone and suggest colors that suit the user. The advice unit can also provide coordination advice based on the user's preferences. For example, the advice unit can use AI to analyze the user's preferences and suggest the most suitable coordination for the user. This makes it possible to provide more appropriate color and coordination advice based on the user's skin tone and preferences.

[0037] The beauty product suggestion unit can suggest beauty products according to the user's skin type, hair type, and concerns. The beauty product suggestion unit, for example, analyzes the user's skin type and suggests the most suitable skin care product to the user. For example, the beauty product suggestion unit can use AI to analyze the user's skin type and suggest the most suitable skin care product to the user. The beauty product suggestion unit can also analyze the user's hair type and suggest the most suitable hair care product to the user. For example, the beauty product suggestion unit can use AI to analyze the user's hair type and suggest the most suitable hair care product to the user. Furthermore, the beauty product suggestion unit can also suggest beauty products based on the user's concerns. For example, the beauty product suggestion unit can use AI to analyze the user's concerns and suggest the most suitable beauty product to the user. This makes it possible to suggest the most suitable beauty product based on the user's skin type and hair type.

[0038] The virtual fitting section can use a camera to fit clothes and accessories to the user's actual appearance. The virtual fitting section, for example, uses a camera to take a picture of the user's appearance and virtually fits the clothes and accessories. For example, the virtual fitting section can use AI to analyze the user's appearance and virtually fit the clothes and accessories. The virtual fitting section can also provide a fitting simulation under different light conditions. For example, the virtual fitting section can use AI to provide a fitting simulation under different light conditions. Furthermore, the virtual fitting section can track changes in the user's body shape and provide feedback by comparing with past data. For example, the virtual fitting section can use AI to track changes in the user's body shape and provide feedback by comparing with past data. This allows the user to virtually try on clothes and accessories without actually trying them on.

[0039] The information providing unit can provide a link to a purchasing site for the recommended item. For example, the information providing unit can use AI to estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. The information providing unit can also improve the accuracy of providing information by referring to the user's past purchase history. For example, the information providing unit can use AI to analyze the user's past purchase history and improve the accuracy of providing information. Furthermore, the information providing unit can also apply different information providing algorithms based on the user's lifestyle and occupation. For example, the information providing unit can use AI to apply different information providing algorithms based on the user's lifestyle and occupation. This allows the user to easily purchase the recommended item.

[0040] The collection unit can collect information from the user's social media. For example, the collection unit can use AI to analyze the user's social media activities and collect related information. The collection unit can also estimate the user's emotions and determine the priority of the information to be collected based on the estimated user's emotions. For example, the collection unit can use AI to estimate the user's emotions and determine the priority of the information to be collected. Furthermore, the collection unit can preferentially collect highly relevant information in consideration of the user's geographical location information. For example, the collection unit can use AI to analyze the user's geographical location information and preferentially collect highly relevant information. As a result, by collecting information from the user's social media account, analysis can be performed based on more information.

[0041] The virtual fitting unit can track the changes in the user's body shape and provide a result by comparing with past data. For example, the virtual fitting unit can use AI to analyze the changes in the user's body shape and provide feedback by comparing with past data. The virtual fitting unit can also analyze the user's walking style and posture and provide feedback based on the analysis results. For example, the virtual fitting unit can use AI to analyze the user's walking style and posture and provide feedback. Furthermore, the virtual fitting unit can track the user's movements in real time and simulate the fit of clothes according to the movements. For example, the virtual fitting unit can use AI to analyze the user's movements and simulate the fit of clothes according to the movements. This allows the user to select more appropriate fashion items by tracking the changes in the user's body shape and providing appropriate feedback.

[0042] The advice unit can provide advice based on the user's occupation or lifestyle. The advice unit can, for example, analyze the user's occupation and provide advice on colors and coordination suitable for the occupation. For example, the advice unit can use AI to analyze the user's occupation and provide advice on colors and coordination suitable for the occupation. The advice unit can also analyze the user's lifestyle and provide advice on colors and coordination suitable for the lifestyle. For example, the advice unit can use AI to analyze the user's lifestyle and provide advice on colors and coordination suitable for the lifestyle. This makes it possible to provide more appropriate color and coordination advice based on the user's occupation and lifestyle.

[0043] The virtual fitting unit can analyze the walking style and posture of the user and provide a result based on the analysis result. The virtual fitting unit, for example, analyzes the walking style of the user and provides feedback based on the walking style. For example, the virtual fitting unit can use AI to analyze the walking style of the user and provide feedback based on the walking style. The virtual fitting unit can also analyze the posture of the user and provide feedback based on the posture. For example, the virtual fitting unit can use AI to analyze the posture of the user and provide feedback based on the posture. Furthermore, the virtual fitting unit can track the user's movements in real time and simulate the fit of the clothes according to the movements. For example, the virtual fitting unit can use AI to analyze the user's movements and simulate the fit of the clothes according to the movements. This allows more appropriate feedback to be provided based on the user's walking style and posture.

[0044] The commentary unit can detect a makeup mistake of the user and provide a step-by-step guide for correcting the detected mistake. For example, the commentary unit can use AI to detect a makeup mistake of the user and provide a step-by-step guide for correcting the detected mistake. The commentary unit can also improve the accuracy of the commentary by referring to the user's past makeup and hairstyle history. For example, the commentary unit can use AI to analyze the user's past makeup and hairstyle history and improve the accuracy of the commentary. This allows the user to apply more appropriate makeup by detecting a makeup mistake of the user and providing a guide for correcting it.

[0045] The virtual fitting unit can provide a simulation of trying on clothes under different lighting conditions. For example, the virtual fitting unit can use AI to provide a simulation of trying on clothes under different lighting conditions. The virtual fitting unit can also track changes in the user's body shape and provide feedback by comparing with past data. For example, the virtual fitting unit can use AI to track changes in the user's body shape and provide feedback by comparing with past data. This allows a more realistic fitting experience to be provided by providing a simulation of trying on clothes under different lighting conditions.

[0046] The analysis unit can analyze the walking style of the user and suggest shoes based on the analysis result. For example, the analysis unit can use AI to analyze the walking style of the user and suggest shoes based on the walking style. The analysis unit can also analyze the posture of the user and suggest shoes based on the posture. For example, the analysis unit can use AI to analyze the posture of the user and suggest shoes based on the posture. This makes it possible to suggest more appropriate shoes based on the walking style of the user.

[0047] The virtual fitting unit can track the user's movements in real time and simulate the fit of the clothes according to the movements. For example, the virtual fitting unit can use AI to analyze the user's movements and simulate the fit of the clothes according to the movements. The virtual fitting unit can also track the changes in the user's body shape and provide feedback by comparing with past data. For example, the virtual fitting unit can use AI to track the changes in the user's body shape and provide feedback by comparing with past data. This allows for a more realistic fitting experience by simulating the fit of the clothes according to the user's movements.

[0048] The collection unit can analyze the user's past purchase history and select the optimal collection method. For example, the collection unit can use AI to analyze the user's past purchase history and select the optimal collection method. The collection unit can also perform filtering based on the user's current living situation and areas of interest. For example, the collection unit can use AI to analyze the user's current living situation and areas of interest and perform filtering. This makes it possible to collect information more effectively by analyzing the user's past purchase history.

[0049] The collection unit can filter the purchase history and photos based on the user's current living situation and areas of interest when collecting the purchase history and photos. For example, the collection unit can use AI to analyze the user's current living situation and areas of interest and perform filtering. The collection unit can also estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, the collection unit can use AI to estimate the user's emotions and determine the priority of information to be collected. This makes it possible to collect more relevant information by filtering information based on the user's current living situation and areas of interest.

[0050] When collecting purchase history and photos, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the user. For example, the collection unit can use AI to analyze the geographical location information of the user and prioritize collecting highly relevant information. The collection unit can also perform filtering based on the user's current living situation and areas of interest. For example, the collection unit can use AI to analyze the user's current living situation and areas of interest and perform filtering. This allows more relevant information to be collected by taking into account the user's geographical location information.

[0051] The collection unit can analyze the user's social media activity and collect related information when collecting the purchase history and photos. For example, the collection unit can use AI to analyze the user's social media activity and collect related information. The collection unit can also estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, the collection unit can use AI to estimate the user's emotions and determine the priority of information to be collected. This makes it possible to collect more relevant information by analyzing the user's social media activity.

[0052] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past fashion selections. For example, the analysis unit can use AI to improve the accuracy of the analysis by referring to the user's past fashion selections. The analysis unit can also apply different analysis algorithms based on the user's lifestyle and occupation. For example, the analysis unit can use AI to analyze the user's lifestyle and occupation and apply different analysis algorithms. This allows for more accurate analysis by referring to the user's past fashion selections.

[0053] During the analysis, the analysis unit can apply different analysis algorithms based on the lifestyle and occupation of the user. For example, the analysis unit can use AI to analyze the lifestyle and occupation of the user and apply different analysis algorithms. The analysis unit can also improve the accuracy of the analysis by referring to the user's past fashion choices. For example, the analysis unit can use AI to improve the accuracy of the analysis by referring to the user's past fashion choices. This allows for a more appropriate analysis to be performed based on the user's lifestyle and occupation.

[0054] The analysis unit can perform the analysis while taking into account the geographical background of the user. For example, the analysis unit can use AI to analyze the geographical background of the user and perform the analysis. The analysis unit can also improve the accuracy of the analysis by referring to the user's past fashion selections. For example, the analysis unit can use AI to improve the accuracy of the analysis by referring to the user's past fashion selections. This allows for more appropriate analysis by taking into account the user's geographical background.

[0055] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's social media activity. For example, the analysis unit can use AI to improve the accuracy of the analysis by referring to the user's social media activity. The analysis unit can also perform the analysis taking into account the user's geographical background. For example, the analysis unit can use AI to analyze the user's geographical background and perform the analysis. This allows for more accurate analysis by referring to the user's social media activity.

[0056] The providing unit can optimize the content to be provided by referring to the user's past reactions at the time of providing the information. For example, the providing unit can optimize the content to be provided by referring to the user's past reactions using AI. The providing unit can also estimate the user's emotions and adjust the method of providing fashion trends and beauty information based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions using AI and adjust the method of providing fashion trends and beauty information. This makes it possible to provide more appropriate information by referring to the user's past reactions.

[0057] The providing unit can apply different information provision algorithms based on the user's lifestyle and occupation at the time of providing information. For example, the providing unit can use AI to analyze the user's lifestyle and occupation and apply different information provision algorithms. The providing unit can also optimize the provided content by referring to the user's past reactions. For example, the providing unit can optimize the provided content by using AI to refer to the user's past reactions. This makes it possible to provide more appropriate information based on the user's lifestyle and occupation.

[0058] The providing unit can provide information taking into consideration the geographical background of the user at the time of providing the information. For example, the providing unit can use AI to analyze the geographical background of the user and provide the information. The providing unit can also optimize the provided content by referring to the user's past responses. For example, the providing unit can use AI to optimize the provided content by referring to the user's past responses. This makes it possible to provide more appropriate information by taking into consideration the user's geographical background.

[0059] The providing unit can improve the relevance of the information by referring to the user's social media activity when providing the information. For example, the providing unit can improve the relevance of the information by referring to the user's social media activity using AI. The providing unit can also provide information by taking into account the user's geographical background. For example, the providing unit can use AI to analyze the user's geographical background and provide information. This makes it possible to provide more relevant information by referring to the user's social media activity.

[0060] The coordination suggestion unit can improve the accuracy of the proposal by referring to the user's past coordination history when making a proposal. For example, the coordination suggestion unit can improve the accuracy of the proposal by referring to the user's past coordination history using AI. The coordination suggestion unit can also apply different proposal algorithms based on the user's lifestyle and occupation. For example, the coordination suggestion unit can use AI to analyze the user's lifestyle and occupation and apply different proposal algorithms. This makes it possible to make more accurate proposals by referring to the user's past coordination history.

[0061] The coordination suggestion unit can apply different suggestion algorithms based on the user's lifestyle and occupation when making a suggestion. For example, the coordination suggestion unit can use AI to analyze the user's lifestyle and occupation and apply different suggestion algorithms. The coordination suggestion unit can also improve the accuracy of the suggestion by referring to the user's past coordination history. For example, the coordination suggestion unit can use AI to improve the accuracy of the suggestion by referring to the user's past coordination history. This enables more appropriate suggestions to be made based on the user's lifestyle and occupation.

[0062] The coordination suggestion unit can make a suggestion taking into consideration the geographical background of the user when making a suggestion. For example, the coordination suggestion unit can use AI to analyze the geographical background of the user and make a suggestion. The coordination suggestion unit can also improve the accuracy of the suggestion by referring to the user's past coordination history. For example, the coordination suggestion unit can use AI to improve the accuracy of the suggestion by referring to the user's past coordination history. This makes it possible to make more appropriate suggestions by taking into consideration the user's geographical background.

[0063] When making a proposal, the coordination suggestion unit can improve the accuracy of the proposal by referring to the user's social media activity. For example, the coordination suggestion unit can use AI to improve the accuracy of the proposal by referring to the user's social media activity. The coordination suggestion unit can also make a proposal by taking into account the user's geographical background. For example, the coordination suggestion unit can use AI to analyze the user's geographical background and make a proposal. This makes it possible to make more accurate proposals by referring to the user's social media activity.

[0064] The recommendation unit can improve the accuracy of the recommendation by referring to the user's past purchase history when making a recommendation. For example, the recommendation unit can improve the accuracy of the recommendation by using AI to refer to the user's past purchase history. The recommendation unit can also apply different recommendation algorithms based on the user's lifestyle and occupation. For example, the recommendation unit can use AI to analyze the user's lifestyle and occupation and apply different recommendation algorithms. This allows for more accurate recommendations by referring to the user's past purchase history.

[0065] The recommendation unit can apply different recommendation algorithms based on the user's lifestyle and occupation at the time of recommendation. For example, the recommendation unit can use AI to analyze the user's lifestyle and occupation and apply different recommendation algorithms. The recommendation unit can also improve the accuracy of the recommendation by referring to the user's past purchase history. For example, the recommendation unit can use AI to improve the accuracy of the recommendation by referring to the user's past purchase history. This enables more appropriate recommendations based on the user's lifestyle and occupation.

[0066] The recommendation unit can make recommendations taking into account the geographical background of the user when making recommendations. For example, the recommendation unit can use AI to analyze the geographical background of the user and make recommendations. The recommendation unit can also improve the accuracy of recommendations by referring to the user's past purchase history. For example, the recommendation unit can use AI to improve the accuracy of recommendations by referring to the user's past purchase history. This allows for more appropriate recommendations by taking into account the user's geographical background.

[0067] The recommendation unit can improve the accuracy of the recommendation by referring to the user's social media activity when making a recommendation. For example, the recommendation unit can improve the accuracy of the recommendation by using AI to refer to the user's social media activity. The recommendation unit can also make a recommendation by taking into account the user's geographical background. For example, the recommendation unit can use AI to analyze the user's geographical background and make a recommendation. This allows for more accurate recommendations by referring to the user's social media activity.

[0068] When providing commentary, the commentary unit can improve the accuracy of the commentary by referring to the user's past makeup and hairstyle history. For example, the commentary unit can use AI to improve the accuracy of the commentary by referring to the user's past makeup and hairstyle history. The commentary unit can also estimate the user's emotions and adjust the method of commentary based on the estimated user's emotions. For example, the commentary unit can use AI to estimate the user's emotions and adjust the method of commentary. This allows for more accurate commentary by referring to the user's past makeup and hairstyle history.

[0069] The commentary unit can apply different commentary algorithms based on the user's lifestyle and occupation when providing commentary. For example, the commentary unit can use AI to analyze the user's lifestyle and occupation and apply different commentary algorithms. The commentary unit can also improve the accuracy of the commentary by referring to the user's past makeup and hairstyle history. For example, the commentary unit can use AI to improve the accuracy of the commentary by referring to the user's past makeup and hairstyle history. This enables a more appropriate commentary to be provided based on the user's lifestyle and occupation.

[0070] The commentary unit can provide commentary taking into consideration the geographical background of the user when providing commentary. For example, the commentary unit can use AI to analyze the geographical background of the user and provide commentary. The commentary unit can also improve the accuracy of the commentary by referring to the user's past makeup and hairstyle history. For example, the commentary unit can use AI to improve the accuracy of the commentary by referring to the user's past makeup and hairstyle history. This allows for a more appropriate commentary to be provided by taking into consideration the user's geographical background.

[0071] The commentary unit can improve the accuracy of the commentary by referring to the user's social media activity when providing commentary. For example, the commentary unit can use AI to improve the accuracy of the commentary by referring to the user's social media activity. The commentary unit can also provide commentary taking into account the user's geographical background. For example, the commentary unit can use AI to analyze the user's geographical background and provide commentary. This allows for more accurate commentary by referring to the user's social media activity.

[0072] When providing advice, the advice unit can improve the accuracy of the advice by referring to the user's past fashion selections. For example, the advice unit can use AI to improve the accuracy of the advice by referring to the user's past fashion selections. The advice unit can also apply different advice algorithms based on the user's lifestyle and occupation. For example, the advice unit can use AI to analyze the user's lifestyle and occupation and apply different advice algorithms. This allows for more accurate advice by referring to the user's past fashion selections.

[0073] When providing advice, the advice unit can apply different advice algorithms based on the user's lifestyle and occupation. For example, the advice unit can use AI to analyze the user's lifestyle and occupation and apply different advice algorithms. The advice unit can also improve the accuracy of advice by referring to the user's past fashion selections. For example, the advice unit can use AI to improve the accuracy of advice by referring to the user's past fashion selections. This enables more appropriate advice based on the user's lifestyle and occupation.

[0074] When providing advice, the advice unit can provide the advice by taking into consideration the geographical background of the user. For example, the advice unit can use AI to analyze the geographical background of the user and provide the advice. The advice unit can also improve the accuracy of the advice by referring to the user's past fashion selections. For example, the advice unit can use AI to improve the accuracy of the advice by referring to the user's past fashion selections. This makes it possible to provide more appropriate advice by taking into consideration the user's geographical background.

[0075] When providing advice, the advice unit can improve the accuracy of the advice by referring to the user's social media activity. For example, the advice unit can use AI to improve the accuracy of the advice by referring to the user's social media activity. The advice unit can also provide advice taking into account the user's geographical background. For example, the advice unit can use AI to analyze the user's geographical background and provide advice. This allows for more accurate advice by referring to the user's social media activity.

[0076] When making a suggestion, the beauty product suggestion unit can improve the accuracy of the suggestion by referring to the user's past beauty product use history. For example, the beauty product suggestion unit can use AI to improve the accuracy of the suggestion by referring to the user's past beauty product use history. In addition, the beauty product suggestion unit can apply different suggestion algorithms based on the user's lifestyle and occupation. For example, the beauty product suggestion unit can use AI to analyze the user's lifestyle and occupation and apply different suggestion algorithms. This allows for more accurate suggestions by referring to the user's past beauty product use history.

[0077] When making a suggestion, the beauty product suggestion unit can apply different suggestion algorithms based on the user's lifestyle and occupation. For example, the beauty product suggestion unit can use AI to analyze the user's lifestyle and occupation and apply different suggestion algorithms. In addition, the beauty product suggestion unit can also improve the accuracy of the suggestion by referring to the user's past beauty product use history. For example, the beauty product suggestion unit can use AI to improve the accuracy of the suggestion by referring to the user's past beauty product use history. This enables more appropriate suggestions to be made based on the user's lifestyle and occupation.

[0078] The beauty product suggestion unit can make suggestions taking into account the geographical background of the user when making suggestions. For example, the beauty product suggestion unit can use AI to analyze the geographical background of the user and make suggestions. The beauty product suggestion unit can also improve the accuracy of the suggestions by referring to the user's past beauty product use history. For example, the beauty product suggestion unit can use AI to improve the accuracy of the suggestions by referring to the user's past beauty product use history. This allows for more appropriate suggestions by taking into account the user's geographical background.

[0079] The beauty product suggestion unit can improve the accuracy of the suggestion by referring to the user's social media activity when making a suggestion. For example, the beauty product suggestion unit can improve the accuracy of the suggestion by using AI to refer to the user's social media activity. The beauty product suggestion unit can also make a suggestion by taking into account the user's geographical background. For example, the beauty product suggestion unit can use AI to analyze the user's geographical background and make a suggestion. This allows for more accurate suggestions by referring to the user's social media activity.

[0080] The virtual fitting unit can improve the accuracy of fitting by referring to the user's past fitting history when trying on clothes. For example, the virtual fitting unit can improve the accuracy of fitting by referring to the user's past fitting history using AI. The virtual fitting unit can also apply different fitting algorithms based on the user's lifestyle and occupation. For example, the virtual fitting unit can use AI to analyze the user's lifestyle and occupation and apply different fitting algorithms. This allows for more accurate fitting by referring to the user's past fitting history.

[0081] The virtual fitting unit can apply different fitting algorithms based on the user's lifestyle and occupation when trying on clothes. For example, the virtual fitting unit can use AI to analyze the user's lifestyle and occupation and apply different fitting algorithms. The virtual fitting unit can also improve the accuracy of trying on clothes by referring to the user's past fitting history. For example, the virtual fitting unit can improve the accuracy of trying on clothes by referring to the user's past fitting history by using AI. This enables a more appropriate fitting based on the user's lifestyle and occupation.

[0082] The virtual fitting unit can try on clothes while taking into account the geographical background of the user. For example, the virtual fitting unit can use AI to analyze the geographical background of the user and try on clothes. The virtual fitting unit can also improve the accuracy of the fitting by referring to the user's past fitting history. For example, the virtual fitting unit can use AI to improve the accuracy of the fitting by referring to the user's past fitting history. This allows for a more appropriate fitting by taking into account the user's geographical background.

[0083] The virtual fitting unit can improve the accuracy of fitting by referring to the user's social media activity when trying on clothes. For example, the virtual fitting unit can improve the accuracy of fitting by referring to the user's social media activity using AI. The virtual fitting unit can also try on clothes taking into account the user's geographical background. For example, the virtual fitting unit can analyze the user's geographical background using AI and try on clothes. This allows for more accurate fitting by referring to the user's social media activity.

[0084] When providing information, the information providing unit can improve the accuracy of the information provided by referring to the user's past purchase history. For example, the information providing unit can use AI to improve the accuracy of the information provided by referring to the user's past purchase history. The information providing unit can also apply different information providing algorithms based on the user's lifestyle and occupation. For example, the information providing unit can use AI to analyze the user's lifestyle and occupation and apply different information providing algorithms. This makes it possible to provide more accurate information by referring to the user's past purchase history.

[0085] The information providing unit can apply different information provision algorithms based on the user's lifestyle and occupation when providing information. For example, the information providing unit can use AI to analyze the user's lifestyle and occupation and apply different information provision algorithms. The information providing unit can also improve the accuracy of the information provision by referring to the user's past purchase history. For example, the information providing unit can use AI to improve the accuracy of the information provision by referring to the user's past purchase history. This makes it possible to provide more appropriate information based on the user's lifestyle and occupation.

[0086] When providing information, the information providing unit can provide the information while taking into consideration the geographical background of the user. For example, the information providing unit can use AI to analyze the geographical background of the user and provide the information. In addition, the information providing unit can also improve the accuracy of the information provided by referring to the user's past purchase history. For example, the information providing unit can use AI to improve the accuracy of the information provided by referring to the user's past purchase history. This makes it possible to provide more appropriate information by taking into consideration the user's geographical background.

[0087] When providing information, the information providing unit can improve the accuracy of the information provided by referring to the social media activity of the user. For example, the information providing unit can use AI to improve the accuracy of the information provided by referring to the social media activity of the user. The information providing unit can also provide information by taking into account the geographical background of the user. For example, the information providing unit can use AI to analyze the geographical background of the user and provide information. This makes it possible to provide more accurate information by referring to the social media activity of the user.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The system for providing advice on fashion and beauty to a user can also customize the advice based on the user's life events. For example, if the user has a special event such as a wedding or graduation coming up, the system can suggest fashion and beauty tips that are best suited to the event. The system can also provide advice tailored to a particular season or weather based on the user's life events. Furthermore, the system can also suggest fashion and beauty tips that are best suited to a particular culture or tradition based on the user's life events. This allows the user to receive advice that is best suited to a special event or situation.

[0090] The advice unit can also provide advice based on the user's health condition. For example, if the user has an allergy, the advice unit can suggest fashion items or beauty products that are allergy-friendly. Also, if the user has a specific health goal, the advice unit can provide fashion and beauty advice that matches the goal. Furthermore, the advice unit can also advise the user to avoid certain materials or ingredients based on the user's health condition. This allows the user to receive fashion and beauty advice that is health-friendly.

[0091] The beauty product suggestion unit can also suggest beauty products based on the user's environment. For example, it can suggest optimal skin care products and hair care products based on the climate and air quality of the area where the user lives. In addition, if the user is planning a trip, it can also suggest beauty products that match the environment of the travel destination. Furthermore, it can also suggest measures against specific environmental factors based on the user's living environment. In this way, the user can receive suggestions for environmentally friendly beauty products.

[0092] The virtual fitting unit can also predict changes in the user's body shape and provide a fitting simulation based on the future body shape. For example, if the user is on a diet or doing muscle training, the virtual fitting unit can predict the future body shape and provide a fitting simulation based on that body shape. Also, if the user is pregnant, the virtual fitting unit can predict the body shape after giving birth and provide a fitting simulation. Furthermore, it can predict future changes in body shape based on the user's age and provide a fitting simulation based on that body shape. This allows the user to select fashion items that match their future body shape.

[0093] The information providing unit can also adjust the timing of providing information based on the user's purchasing behavior. For example, if the user tends to do online shopping during a specific time period, information can be provided according to that time period. Also, if the user tends to do shopping on a specific day of the week, information can be provided according to that day of the week. Furthermore, information on specific events or sales can be provided based on the user's purchasing behavior. This allows the user to receive information at the optimal timing.

[0094] The process flow of the first embodiment will be briefly described below.

[0095] Step 1: The collection unit collects the user's past purchase history and the user's photos. The user's past purchase history includes, but is not limited to, purchase date and time, purchased items, purchase frequency, etc. The user's photos include, but are not limited to, face photos, full-body photos, photos in specific situations, etc. The collection unit can also collect information from, for example, the user's social media accounts. Step 2: The analysis unit analyzes the information collected by the collection unit and analyzes the user's fashion style and preferences. The analysis unit can use, for example, AI to improve the accuracy of the analysis by referring to the user's past fashion choices. Step 3: The providing unit provides fashion trends and beauty information suited to the user based on the analysis results by the analyzing unit. The providing unit can, for example, use AI to estimate the user's emotions and adjust the method of providing fashion trends and beauty information based on the estimated user's emotions. Step 4: The coordination suggestion unit suggests optimal coordination for items the user owns or items the user is considering purchasing. The coordination suggestion unit can, for example, use AI to estimate the user's emotions and adjust the method of suggesting coordination based on the estimated user's emotions. Step 5: The recommendation unit recommends fashion items that fit the user based on the analysis results by the analysis unit. The recommendation unit can, for example, use AI to estimate the user's emotions and adjust the method of recommending fashion items based on the estimated user's emotions. Step 6: The commentary unit provides a step-by-step tutorial on how to apply makeup and hairstyle. The commentary unit can, for example, use AI to estimate the user's emotions and adjust the method of explanation based on the estimated user's emotions.

[0096] (Example 2) The fashion and beauty advisor AI assistant system according to the embodiment of the present invention is a system that provides advice on fashion and beauty to a user. The system collects the user's past purchase history and uploaded photos, analyzes this information, and analyzes the user's fashion style and preferences. Next, it collects the latest fashion trends and beauty information and provides it to the user. In addition, it proposes optimal coordination for items that the user owns and items that the user is considering purchasing. Furthermore, it provides advice on colors and coordination that suit the user based on the user's skin tone and preferences. It takes into account the user's body type and characteristics and recommends fashion items that fit. In addition, it suggests optimal beauty products according to the user's skin type, hair type, and specific concerns. It also provides a virtual try-on function that uses a camera to virtually match clothes and accessories to the user's actual appearance. In addition, it provides links to purchase sites and discount information based on preferences and suggested items. Finally, it provides a tutorial that explains how to apply makeup and hairstyle step by step. As a result, the fashion and beauty advisor AI assistant system can analyze the user's fashion style and preferences in detail and provide optimal coordination and beauty information.

[0097] The fashion and beauty advisor AI assistant system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a coordination suggestion unit, a recommendation unit, and an explanation unit. The collection unit collects the user's past purchase history and the user's photos. The user's past purchase history includes, for example, purchase date and time, purchased items, purchase frequency, etc., but is not limited to such examples. The user's photos include, for example, face photos, full-body photos, photos in specific situations, etc., but is not limited to such examples. The collection unit can also collect information from, for example, the user's social media account. The analysis unit analyzes the information collected by the collection unit and analyzes the user's fashion style and preferences. The analysis unit can improve the accuracy of the analysis by referring to the user's past fashion selections, for example, using AI. The provision unit provides fashion trends and beauty information according to the user based on the analysis results by the analysis unit. The provision unit can estimate the user's emotions, for example, using AI, and adjust the method of providing fashion trends and beauty information based on the estimated user's emotions. The coordination suggestion unit proposes optimal coordination for items the user owns or items the user is considering purchasing. The coordination suggestion unit can estimate the user's emotions using, for example, AI, and adjust the coordination suggestion method based on the estimated user's emotions. The recommendation unit recommends fashion items that fit the user based on the analysis results by the analysis unit. The recommendation unit can estimate the user's emotions using, for example, AI, and adjust the fashion item recommendation method based on the estimated user's emotions. The commentary unit provides a tutorial that explains how to do makeup and hairstyles step by step. The commentary unit can estimate the user's emotions using, for example, AI, and adjust the commentary method based on the estimated user's emotions. As a result, the fashion and beauty advisor AI assistant system according to the embodiment can analyze the user's fashion style and preferences in detail and provide optimal coordination and beauty information.

[0098] The collection unit collects the user's past purchase history and the user's photos. The user's past purchase history includes, for example, purchase date and time, purchased items, purchase frequency, etc., but is not limited to such examples. This makes it possible to grasp the user's tastes and trends regarding fashion and beauty. Furthermore, the user's photos include, but are not limited to such examples, face photos, full-body photos, photos in specific situations, etc. This makes it possible to grasp the user's body type, facial features, skin color, hairstyle, etc. in detail. The collection unit can also collect information from the user's social media account, for example. From the social media account, photos posted by the user, comments, likes, etc. can be collected, and the user's lifestyle and preferences can be understood more deeply. This allows the collection unit to build a foundation for centrally collecting various information about the user and providing it to the analysis unit and other departments. The collected data is stored in a cloud server and managed in an environment with security measures. This makes it possible to efficiently collect necessary information while protecting the user's privacy.

[0099] The analysis unit analyzes the information collected by the collection unit and analyzes the user's fashion style and preferences. Specifically, AI can be used to improve the accuracy of the analysis by referring to the user's past fashion choices. AI uses image recognition technology to analyze the user's photos and extract features such as face shape, skin color, hairstyle, and body type. It also analyzes purchase history to identify the user's preferred brands, colors, and styles. In addition, it analyzes data collected from social media to understand the user's lifestyle and interests. This allows the analysis unit to build a foundation for understanding the user's fashion and beauty preferences in detail and providing individually customized advice. The analysis unit can also respond to changes in the user's preferences and new trends by utilizing past data and trend information. For example, it can make fashion suggestions tailored to seasonal trends or specific events. This allows the analysis unit to flexibly respond to the user's needs and provide higher satisfaction.

[0100] The providing unit provides fashion trends and beauty information according to the user based on the analysis results by the analyzing unit. Specifically, the AI ​​can be used to estimate the user's emotions and adjust the method of providing fashion trends and beauty information based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit provides relaxing fashion and beauty information, and if the user wants to feel more confident, the providing unit suggests an elegant style that enhances confidence. The providing unit can improve the user's satisfaction by providing optimal information according to the user's emotions and situation. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the information provided. For example, the providing unit analyzes how the user responded to the provided information and reflects it in the next information provision. This allows the providing unit to always provide optimal information to the user and build a long-term relationship of trust.

[0101] The coordination suggestion unit proposes optimal coordination for items owned by the user or items the user is considering purchasing. Specifically, the coordination suggestion unit can use AI to estimate the user's emotions and adjust the coordination suggestion method based on the estimated user's emotions. For example, if the user plans to participate in a specific event, the coordination suggestion unit proposes an appropriate coordination for the event. In addition, the coordination suggestion unit can propose coordination according to the season and trends for items the user wears on a daily basis. The coordination suggestion unit manages a database of the user's owned items and automatically updates the optimal coordination when a new item is added. This allows the user to always receive the latest coordination suggestions. Furthermore, the coordination suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, the user can feedback the results of actually trying out the suggested coordination and adjust the next suggestion based on that information. This allows the coordination suggestion unit to always provide the optimal coordination to the user and maximize the fun of fashion.

[0102] The recommendation unit recommends fashion items that fit the user based on the analysis results by the analysis unit. Specifically, the recommendation unit can use AI to estimate the user's emotions and adjust the method of recommending fashion items based on the estimated user's emotions. For example, if the user feels like trying a new style, trend items and new brands are recommended, and if the user is looking for a sense of security, standard items and brands that the user has purchased in the past are recommended. The recommendation unit can improve user satisfaction by making individually customized recommendations based on the user's purchase history and preferences. Furthermore, the recommendation unit can collect user feedback and continuously improve the accuracy and effectiveness of the recommendation content. For example, the recommendation unit can feed back the results of the user's actual purchase of the recommended item and adjust the next recommendation based on that information. As a result, the recommendation unit can always recommend the most suitable fashion items to the user and increase the desire to purchase.

[0103] The commentary unit provides a step-by-step tutorial on how to apply makeup and hairstyles. Specifically, the commentary unit can use AI to estimate the user's emotions and adjust the method of explanation based on the estimated user's emotions. For example, if the user feels like relaxing, the commentary unit provides a relaxed pace, and if the user is in a hurry, the commentary unit provides a quick and effective method of applying makeup and hairstyles. The commentary unit can improve the user's satisfaction by providing a customized tutorial according to the user's skill level and needs. Furthermore, the commentary unit can collect user feedback and continuously improve the accuracy and effectiveness of the commentary content. For example, the commentary unit can provide feedback on the results of the user actually trying out the commentary and adjust the next commentary based on that information. As a result, the commentary unit can always provide the user with the most suitable method of makeup and hairstyles, maximizing the fun of beauty.

[0104] The advice unit can provide color and coordination advice based on the user's skin tone and preferences. The advice unit can, for example, analyze the user's skin tone and suggest colors that suit the user. For example, the advice unit can use AI to analyze the user's skin tone and suggest colors that suit the user. The advice unit can also provide coordination advice based on the user's preferences. For example, the advice unit can use AI to analyze the user's preferences and suggest the most suitable coordination for the user. This makes it possible to provide more appropriate color and coordination advice based on the user's skin tone and preferences.

[0105] The beauty product suggestion unit can suggest beauty products according to the user's skin type, hair type, and concerns. The beauty product suggestion unit, for example, analyzes the user's skin type and suggests the most suitable skin care product to the user. For example, the beauty product suggestion unit can use AI to analyze the user's skin type and suggest the most suitable skin care product to the user. The beauty product suggestion unit can also analyze the user's hair type and suggest the most suitable hair care product to the user. For example, the beauty product suggestion unit can use AI to analyze the user's hair type and suggest the most suitable hair care product to the user. Furthermore, the beauty product suggestion unit can also suggest beauty products based on the user's concerns. For example, the beauty product suggestion unit can use AI to analyze the user's concerns and suggest the most suitable beauty product to the user. This makes it possible to suggest the most suitable beauty product based on the user's skin type and hair type.

[0106] The virtual fitting section can use a camera to fit clothes and accessories to the user's actual appearance. The virtual fitting section, for example, uses a camera to take a picture of the user's appearance and virtually fits the clothes and accessories. For example, the virtual fitting section can use AI to analyze the user's appearance and virtually fit the clothes and accessories. The virtual fitting section can also provide a fitting simulation under different light conditions. For example, the virtual fitting section can use AI to provide a fitting simulation under different light conditions. Furthermore, the virtual fitting section can track changes in the user's body shape and provide feedback by comparing with past data. For example, the virtual fitting section can use AI to track changes in the user's body shape and provide feedback by comparing with past data. This allows the user to virtually try on clothes and accessories without actually trying them on.

[0107] The information providing unit can provide a link to a purchasing site for the recommended item. For example, the information providing unit can use AI to estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. The information providing unit can also improve the accuracy of providing information by referring to the user's past purchase history. For example, the information providing unit can use AI to analyze the user's past purchase history and improve the accuracy of providing information. Furthermore, the information providing unit can also apply different information providing algorithms based on the user's lifestyle and occupation. For example, the information providing unit can use AI to apply different information providing algorithms based on the user's lifestyle and occupation. This allows the user to easily purchase the recommended item.

[0108] The collection unit can collect information from the user's social media. For example, the collection unit can use AI to analyze the user's social media activities and collect related information. The collection unit can also estimate the user's emotions and determine the priority of the information to be collected based on the estimated user's emotions. For example, the collection unit can use AI to estimate the user's emotions and determine the priority of the information to be collected. Furthermore, the collection unit can preferentially collect highly relevant information in consideration of the user's geographical location information. For example, the collection unit can use AI to analyze the user's geographical location information and preferentially collect highly relevant information. As a result, by collecting information from the user's social media account, analysis can be performed based on more information.

[0109] The virtual fitting unit can track the changes in the user's body shape and provide a result by comparing with past data. For example, the virtual fitting unit can use AI to analyze the changes in the user's body shape and provide feedback by comparing with past data. The virtual fitting unit can also analyze the user's walking style and posture and provide feedback based on the analysis results. For example, the virtual fitting unit can use AI to analyze the user's walking style and posture and provide feedback. Furthermore, the virtual fitting unit can track the user's movements in real time and simulate the fit of clothes according to the movements. For example, the virtual fitting unit can use AI to analyze the user's movements and simulate the fit of clothes according to the movements. This allows the user to select more appropriate fashion items by tracking the changes in the user's body shape and providing appropriate feedback.

[0110] The advice unit can provide advice based on the user's occupation or lifestyle. The advice unit can, for example, analyze the user's occupation and provide advice on colors and coordination suitable for the occupation. For example, the advice unit can use AI to analyze the user's occupation and provide advice on colors and coordination suitable for the occupation. The advice unit can also analyze the user's lifestyle and provide advice on colors and coordination suitable for the lifestyle. For example, the advice unit can use AI to analyze the user's lifestyle and provide advice on colors and coordination suitable for the lifestyle. This makes it possible to provide more appropriate color and coordination advice based on the user's occupation and lifestyle.

[0111] The virtual fitting unit can analyze the walking style and posture of the user and provide a result based on the analysis result. The virtual fitting unit, for example, analyzes the walking style of the user and provides feedback based on the walking style. For example, the virtual fitting unit can use AI to analyze the walking style of the user and provide feedback based on the walking style. The virtual fitting unit can also analyze the posture of the user and provide feedback based on the posture. For example, the virtual fitting unit can use AI to analyze the posture of the user and provide feedback based on the posture. Furthermore, the virtual fitting unit can track the user's movements in real time and simulate the fit of the clothes according to the movements. For example, the virtual fitting unit can use AI to analyze the user's movements and simulate the fit of the clothes according to the movements. This allows more appropriate feedback to be provided based on the user's walking style and posture.

[0112] The advice unit can provide advice according to the emotional state of the user. The advice unit can, for example, analyze the emotional state of the user and provide color and coordination advice based on the emotional state. For example, the advice unit can use an emotion engine or a generation AI to analyze the emotional state of the user and provide color and coordination advice based on the emotional state. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This can provide more appropriate color and coordination advice based on the emotional state of the user.

[0113] The commentary unit can detect a makeup mistake of the user and provide a step-by-step guide for correcting the detected mistake. For example, the commentary unit can use AI to detect a makeup mistake of the user and provide a step-by-step guide for correcting the detected mistake. The commentary unit can also improve the accuracy of the commentary by referring to the user's past makeup and hairstyle history. For example, the commentary unit can use AI to analyze the user's past makeup and hairstyle history and improve the accuracy of the commentary. This allows the user to apply more appropriate makeup by detecting a makeup mistake of the user and providing a guide for correcting it.

[0114] The virtual fitting unit can provide a simulation of trying on clothes under different lighting conditions. For example, the virtual fitting unit can use AI to provide a simulation of trying on clothes under different lighting conditions. The virtual fitting unit can also track changes in the user's body shape and provide feedback by comparing with past data. For example, the virtual fitting unit can use AI to track changes in the user's body shape and provide feedback by comparing with past data. This allows a more realistic fitting experience to be provided by providing a simulation of trying on clothes under different lighting conditions.

[0115] The analysis unit can analyze the walking style of the user and suggest shoes based on the analysis result. For example, the analysis unit can use AI to analyze the walking style of the user and suggest shoes based on the walking style. The analysis unit can also analyze the posture of the user and suggest shoes based on the posture. For example, the analysis unit can use AI to analyze the posture of the user and suggest shoes based on the posture. This makes it possible to suggest more appropriate shoes based on the walking style of the user.

[0116] The virtual fitting unit can track the user's movements in real time and simulate the fit of the clothes according to the movements. For example, the virtual fitting unit can use AI to analyze the user's movements and simulate the fit of the clothes according to the movements. The virtual fitting unit can also track the changes in the user's body shape and provide feedback by comparing with past data. For example, the virtual fitting unit can use AI to track the changes in the user's body shape and provide feedback by comparing with past data. This allows for a more realistic fitting experience by simulating the fit of the clothes according to the user's movements.

[0117] The collection unit can estimate the user's emotions and adjust the timing of collecting purchase histories and photos based on the estimated user's emotions. For example, the collection unit can estimate the user's emotions using an emotion engine or a generation AI and adjust the timing of collecting purchase histories and photos based on the estimated user's emotions. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows information to be collected at a more appropriate timing by adjusting the collection timing based on the user's emotions.

[0118] The collection unit can analyze the user's past purchase history and select the optimal collection method. For example, the collection unit can use AI to analyze the user's past purchase history and select the optimal collection method. The collection unit can also perform filtering based on the user's current living situation and areas of interest. For example, the collection unit can use AI to analyze the user's current living situation and areas of interest and perform filtering. This makes it possible to collect information more effectively by analyzing the user's past purchase history.

[0119] The collection unit can filter the purchase history and photos based on the user's current living situation and areas of interest when collecting the purchase history and photos. For example, the collection unit can use AI to analyze the user's current living situation and areas of interest and perform filtering. The collection unit can also estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, the collection unit can use AI to estimate the user's emotions and determine the priority of information to be collected. This makes it possible to collect more relevant information by filtering information based on the user's current living situation and areas of interest.

[0120] The collection unit can estimate the user's emotion and determine the priority of information to be collected based on the estimated user's emotion. For example, the collection unit can estimate the user's emotion and determine the priority of information to be collected using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows more appropriate information to be collected by determining the priority of information based on the user's emotion.

[0121] When collecting purchase history and photos, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the user. For example, the collection unit can use AI to analyze the geographical location information of the user and prioritize collecting highly relevant information. The collection unit can also perform filtering based on the user's current living situation and areas of interest. For example, the collection unit can use AI to analyze the user's current living situation and areas of interest and perform filtering. This allows more relevant information to be collected by taking into account the user's geographical location information.

[0122] The collection unit can analyze the user's social media activity and collect related information when collecting the purchase history and photos. For example, the collection unit can use AI to analyze the user's social media activity and collect related information. The collection unit can also estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, the collection unit can use AI to estimate the user's emotions and determine the priority of information to be collected. This makes it possible to collect more relevant information by analyzing the user's social media activity.

[0123] The analysis unit can estimate the user's emotions and adjust the analysis method of the fashion style and preferences based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the analysis method of the fashion style and preferences using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the analysis method to be adjusted based on the user's emotions, thereby providing more appropriate analysis results.

[0124] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past fashion selections. For example, the analysis unit can use AI to improve the accuracy of the analysis by referring to the user's past fashion selections. The analysis unit can also apply different analysis algorithms based on the user's lifestyle and occupation. For example, the analysis unit can use AI to analyze the user's lifestyle and occupation and apply different analysis algorithms. This allows for more accurate analysis by referring to the user's past fashion selections.

[0125] During the analysis, the analysis unit can apply different analysis algorithms based on the lifestyle and occupation of the user. For example, the analysis unit can use AI to analyze the lifestyle and occupation of the user and apply different analysis algorithms. The analysis unit can also improve the accuracy of the analysis by referring to the user's past fashion choices. For example, the analysis unit can use AI to improve the accuracy of the analysis by referring to the user's past fashion choices. This allows for a more appropriate analysis to be performed based on the user's lifestyle and occupation.

[0126] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, the analysis unit can estimate the user's emotion using an emotion engine or a generation AI and adjust the display method of the analysis result. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows for more appropriate display by adjusting the display method based on the user's emotion.

[0127] The analysis unit can perform the analysis while taking into account the geographical background of the user. For example, the analysis unit can use AI to analyze the geographical background of the user and perform the analysis. The analysis unit can also improve the accuracy of the analysis by referring to the user's past fashion selections. For example, the analysis unit can use AI to improve the accuracy of the analysis by referring to the user's past fashion selections. This allows for more appropriate analysis by taking into account the user's geographical background.

[0128] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's social media activity. For example, the analysis unit can use AI to improve the accuracy of the analysis by referring to the user's social media activity. The analysis unit can also perform the analysis taking into account the user's geographical background. For example, the analysis unit can use AI to analyze the user's geographical background and perform the analysis. This allows for more accurate analysis by referring to the user's social media activity.

[0129] The providing unit can estimate the user's emotions and adjust the method of providing fashion trends and beauty information based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions using an emotion engine or a generation AI and adjust the method of providing fashion trends and beauty information. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to provide more appropriate information by adjusting the method of providing based on the user's emotions.

[0130] The providing unit can optimize the content to be provided by referring to the user's past reactions at the time of providing the information. For example, the providing unit can optimize the content to be provided by referring to the user's past reactions using AI. The providing unit can also estimate the user's emotions and adjust the method of providing fashion trends and beauty information based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions using AI and adjust the method of providing fashion trends and beauty information. This makes it possible to provide more appropriate information by referring to the user's past reactions.

[0131] The providing unit can apply different information provision algorithms based on the user's lifestyle and occupation at the time of providing information. For example, the providing unit can use AI to analyze the user's lifestyle and occupation and apply different information provision algorithms. The providing unit can also optimize the provided content by referring to the user's past reactions. For example, the providing unit can optimize the provided content by using AI to refer to the user's past reactions. This makes it possible to provide more appropriate information based on the user's lifestyle and occupation.

[0132] The providing unit can estimate the user's emotion and adjust the timing of providing information based on the estimated user's emotion. For example, the providing unit can estimate the user's emotion using an emotion engine or a generation AI and adjust the timing of providing information. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Thereby, by adjusting the timing of providing information based on the user's emotion, it is possible to provide information at a more appropriate timing.

[0133] The providing unit can provide information taking into consideration the geographical background of the user at the time of providing the information. For example, the providing unit can use AI to analyze the geographical background of the user and provide the information. The providing unit can also optimize the provided content by referring to the user's past responses. For example, the providing unit can use AI to optimize the provided content by referring to the user's past responses. This makes it possible to provide more appropriate information by taking into consideration the user's geographical background.

[0134] The providing unit can improve the relevance of the information by referring to the user's social media activity when providing the information. For example, the providing unit can improve the relevance of the information by referring to the user's social media activity using AI. The providing unit can also provide information by taking into account the user's geographical background. For example, the providing unit can use AI to analyze the user's geographical background and provide information. This makes it possible to provide more relevant information by referring to the user's social media activity.

[0135] The coordination suggestion unit can estimate the user's emotion and adjust the coordination suggestion method based on the estimated user's emotion. For example, the coordination suggestion unit can estimate the user's emotion and adjust the coordination suggestion method using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows more appropriate coordination suggestions to be made by adjusting the suggestion method based on the user's emotion.

[0136] The coordination suggestion unit can improve the accuracy of the proposal by referring to the user's past coordination history when making a proposal. For example, the coordination suggestion unit can improve the accuracy of the proposal by referring to the user's past coordination history using AI. The coordination suggestion unit can also apply different proposal algorithms based on the user's lifestyle and occupation. For example, the coordination suggestion unit can use AI to analyze the user's lifestyle and occupation and apply different proposal algorithms. This makes it possible to make more accurate proposals by referring to the user's past coordination history.

[0137] The coordination suggestion unit can apply different suggestion algorithms based on the user's lifestyle and occupation when making a suggestion. For example, the coordination suggestion unit can use AI to analyze the user's lifestyle and occupation and apply different suggestion algorithms. The coordination suggestion unit can also improve the accuracy of the suggestion by referring to the user's past coordination history. For example, the coordination suggestion unit can use AI to improve the accuracy of the suggestion by referring to the user's past coordination history. This enables more appropriate suggestions to be made based on the user's lifestyle and occupation.

[0138] The coordination suggestion unit can estimate the user's emotion and determine the priority of suggestions based on the estimated user's emotion. For example, the coordination suggestion unit can estimate the user's emotion and determine the priority of suggestions using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This enables more appropriate suggestions to be made by determining the priority of suggestions based on the user's emotion.

[0139] The coordination suggestion unit can make a suggestion taking into consideration the geographical background of the user when making a suggestion. For example, the coordination suggestion unit can use AI to analyze the geographical background of the user and make a suggestion. The coordination suggestion unit can also improve the accuracy of the suggestion by referring to the user's past coordination history. For example, the coordination suggestion unit can use AI to improve the accuracy of the suggestion by referring to the user's past coordination history. This makes it possible to make more appropriate suggestions by taking into consideration the user's geographical background.

[0140] When making a proposal, the coordination suggestion unit can improve the accuracy of the proposal by referring to the user's social media activity. For example, the coordination suggestion unit can use AI to improve the accuracy of the proposal by referring to the user's social media activity. The coordination suggestion unit can also make a proposal by taking into account the user's geographical background. For example, the coordination suggestion unit can use AI to analyze the user's geographical background and make a proposal. This makes it possible to make more accurate proposals by referring to the user's social media activity.

[0141] The recommendation unit can estimate the user's emotion and adjust the fashion item recommendation method based on the estimated user's emotion. For example, the recommendation unit can estimate the user's emotion and adjust the fashion item recommendation method using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to recommend more appropriate fashion items by adjusting the recommendation method based on the user's emotion.

[0142] The recommendation unit can improve the accuracy of the recommendation by referring to the user's past purchase history when making a recommendation. For example, the recommendation unit can improve the accuracy of the recommendation by using AI to refer to the user's past purchase history. The recommendation unit can also apply different recommendation algorithms based on the user's lifestyle and occupation. For example, the recommendation unit can use AI to analyze the user's lifestyle and occupation and apply different recommendation algorithms. This allows for more accurate recommendations by referring to the user's past purchase history.

[0143] The recommendation unit can apply different recommendation algorithms based on the user's lifestyle and occupation at the time of recommendation. For example, the recommendation unit can use AI to analyze the user's lifestyle and occupation and apply different recommendation algorithms. The recommendation unit can also improve the accuracy of the recommendation by referring to the user's past purchase history. For example, the recommendation unit can use AI to improve the accuracy of the recommendation by referring to the user's past purchase history. This enables more appropriate recommendations based on the user's lifestyle and occupation.

[0144] The recommendation unit can estimate the user's emotion and determine the priority of recommendations based on the estimated user's emotion. For example, the recommendation unit can estimate the user's emotion and determine the priority of recommendations using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This enables more appropriate recommendations by determining the priority of recommendations based on the user's emotion.

[0145] The recommendation unit can make recommendations taking into account the geographical background of the user when making recommendations. For example, the recommendation unit can use AI to analyze the geographical background of the user and make recommendations. The recommendation unit can also improve the accuracy of recommendations by referring to the user's past purchase history. For example, the recommendation unit can use AI to improve the accuracy of recommendations by referring to the user's past purchase history. This allows for more appropriate recommendations by taking into account the user's geographical background.

[0146] The recommendation unit can improve the accuracy of the recommendation by referring to the user's social media activity when making a recommendation. For example, the recommendation unit can improve the accuracy of the recommendation by using AI to refer to the user's social media activity. The recommendation unit can also make a recommendation by taking into account the user's geographical background. For example, the recommendation unit can use AI to analyze the user's geographical background and make a recommendation. This allows for more accurate recommendations by referring to the user's social media activity.

[0147] The commentary unit can estimate the user's emotions and adjust the commentary method based on the estimated user's emotions. For example, the commentary unit can estimate the user's emotions and adjust the commentary method using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows for a more appropriate commentary by adjusting the commentary method based on the user's emotions.

[0148] When providing commentary, the commentary unit can improve the accuracy of the commentary by referring to the user's past makeup and hairstyle history. For example, the commentary unit can use AI to improve the accuracy of the commentary by referring to the user's past makeup and hairstyle history. The commentary unit can also estimate the user's emotions and adjust the method of commentary based on the estimated user's emotions. For example, the commentary unit can use AI to estimate the user's emotions and adjust the method of commentary. This allows for more accurate commentary by referring to the user's past makeup and hairstyle history.

[0149] The commentary unit can apply different commentary algorithms based on the user's lifestyle and occupation when providing commentary. For example, the commentary unit can use AI to analyze the user's lifestyle and occupation and apply different commentary algorithms. The commentary unit can also improve the accuracy of the commentary by referring to the user's past makeup and hairstyle history. For example, the commentary unit can use AI to improve the accuracy of the commentary by referring to the user's past makeup and hairstyle history. This enables a more appropriate commentary to be provided based on the user's lifestyle and occupation.

[0150] The commentary unit can estimate the user's emotions and determine the priority of the commentary based on the estimated user's emotions. For example, the commentary unit can estimate the user's emotions and determine the priority of the commentary using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This enables a more appropriate commentary to be provided by determining the priority of the commentary based on the user's emotions.

[0151] The commentary unit can provide commentary taking into consideration the geographical background of the user when providing commentary. For example, the commentary unit can use AI to analyze the geographical background of the user and provide commentary. The commentary unit can also improve the accuracy of the commentary by referring to the user's past makeup and hairstyle history. For example, the commentary unit can use AI to improve the accuracy of the commentary by referring to the user's past makeup and hairstyle history. This allows for a more appropriate commentary to be provided by taking into consideration the user's geographical background.

[0152] The commentary unit can improve the accuracy of the commentary by referring to the user's social media activity when providing commentary. For example, the commentary unit can use AI to improve the accuracy of the commentary by referring to the user's social media activity. The commentary unit can also provide commentary taking into account the user's geographical background. For example, the commentary unit can use AI to analyze the user's geographical background and provide commentary. This allows for more accurate commentary by referring to the user's social media activity.

[0153] The advice unit can estimate the user's emotions and adjust the color and coordination advice method based on the estimated user's emotions. For example, the advice unit can estimate the user's emotions and adjust the color and coordination advice method using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to provide more appropriate color and coordination advice by adjusting the advice method based on the user's emotions.

[0154] When providing advice, the advice unit can improve the accuracy of the advice by referring to the user's past fashion selections. For example, the advice unit can use AI to improve the accuracy of the advice by referring to the user's past fashion selections. The advice unit can also apply different advice algorithms based on the user's lifestyle and occupation. For example, the advice unit can use AI to analyze the user's lifestyle and occupation and apply different advice algorithms. This allows for more accurate advice by referring to the user's past fashion selections.

[0155] When providing advice, the advice unit can apply different advice algorithms based on the user's lifestyle and occupation. For example, the advice unit can use AI to analyze the user's lifestyle and occupation and apply different advice algorithms. The advice unit can also improve the accuracy of advice by referring to the user's past fashion selections. For example, the advice unit can use AI to improve the accuracy of advice by referring to the user's past fashion selections. This enables more appropriate advice based on the user's lifestyle and occupation.

[0156] The advice unit can estimate the user's emotion and determine the priority of advice based on the estimated user's emotion. For example, the advice unit can estimate the user's emotion and determine the priority of advice using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This enables more appropriate advice to be provided by determining the priority of advice based on the user's emotion.

[0157] When providing advice, the advice unit can provide the advice by taking into consideration the geographical background of the user. For example, the advice unit can use AI to analyze the geographical background of the user and provide the advice. The advice unit can also improve the accuracy of the advice by referring to the user's past fashion selections. For example, the advice unit can use AI to improve the accuracy of the advice by referring to the user's past fashion selections. This makes it possible to provide more appropriate advice by taking into consideration the user's geographical background.

[0158] When providing advice, the advice unit can improve the accuracy of the advice by referring to the user's social media activity. For example, the advice unit can use AI to improve the accuracy of the advice by referring to the user's social media activity. The advice unit can also provide advice taking into account the user's geographical background. For example, the advice unit can use AI to analyze the user's geographical background and provide advice. This allows for more accurate advice by referring to the user's social media activity.

[0159] The beauty product suggestion unit can estimate the user's emotion and adjust the beauty product suggestion method based on the estimated user's emotion. For example, the beauty product suggestion unit can estimate the user's emotion and adjust the beauty product suggestion method using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows more appropriate beauty product suggestions to be made by adjusting the suggestion method based on the user's emotion.

[0160] When making a suggestion, the beauty product suggestion unit can improve the accuracy of the suggestion by referring to the user's past beauty product use history. For example, the beauty product suggestion unit can use AI to improve the accuracy of the suggestion by referring to the user's past beauty product use history. In addition, the beauty product suggestion unit can apply different suggestion algorithms based on the user's lifestyle and occupation. For example, the beauty product suggestion unit can use AI to analyze the user's lifestyle and occupation and apply different suggestion algorithms. This allows for more accurate suggestions by referring to the user's past beauty product use history.

[0161] When making a suggestion, the beauty product suggestion unit can apply different suggestion algorithms based on the user's lifestyle and occupation. For example, the beauty product suggestion unit can use AI to analyze the user's lifestyle and occupation and apply different suggestion algorithms. In addition, the beauty product suggestion unit can also improve the accuracy of the suggestion by referring to the user's past beauty product use history. For example, the beauty product suggestion unit can use AI to improve the accuracy of the suggestion by referring to the user's past beauty product use history. This enables more appropriate suggestions to be made based on the user's lifestyle and occupation.

[0162] The beauty product suggestion unit can estimate the user's emotion and determine the priority of suggestions based on the estimated user's emotion. For example, the beauty product suggestion unit can estimate the user's emotion and determine the priority of suggestions using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This enables more appropriate suggestions to be made by determining the priority of suggestions based on the user's emotion.

[0163] The beauty product suggestion unit can make suggestions taking into account the geographical background of the user when making suggestions. For example, the beauty product suggestion unit can use AI to analyze the geographical background of the user and make suggestions. The beauty product suggestion unit can also improve the accuracy of the suggestions by referring to the user's past beauty product use history. For example, the beauty product suggestion unit can use AI to improve the accuracy of the suggestions by referring to the user's past beauty product use history. This allows for more appropriate suggestions by taking into account the user's geographical background.

[0164] The beauty product suggestion unit can improve the accuracy of the suggestion by referring to the user's social media activity when making a suggestion. For example, the beauty product suggestion unit can improve the accuracy of the suggestion by using AI to refer to the user's social media activity. The beauty product suggestion unit can also make a suggestion by taking into account the user's geographical background. For example, the beauty product suggestion unit can use AI to analyze the user's geographical background and make a suggestion. This allows for more accurate suggestions by referring to the user's social media activity.

[0165] The virtual fitting unit can estimate the user's emotions and adjust the virtual fitting method based on the estimated user's emotions. For example, the virtual fitting unit can estimate the user's emotions and adjust the virtual fitting method using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows for a more appropriate virtual fitting by adjusting the fitting method based on the user's emotions.

[0166] The virtual fitting unit can improve the accuracy of fitting by referring to the user's past fitting history when trying on clothes. For example, the virtual fitting unit can improve the accuracy of fitting by referring to the user's past fitting history using AI. The virtual fitting unit can also apply different fitting algorithms based on the user's lifestyle and occupation. For example, the virtual fitting unit can use AI to analyze the user's lifestyle and occupation and apply different fitting algorithms. This allows for more accurate fitting by referring to the user's past fitting history.

[0167] The virtual fitting unit can apply different fitting algorithms based on the user's lifestyle and occupation when trying on clothes. For example, the virtual fitting unit can use AI to analyze the user's lifestyle and occupation and apply different fitting algorithms. The virtual fitting unit can also improve the accuracy of trying on clothes by referring to the user's past fitting history. For example, the virtual fitting unit can improve the accuracy of trying on clothes by referring to the user's past fitting history by using AI. This enables a more appropriate fitting based on the user's lifestyle and occupation.

[0168] The virtual fitting unit can estimate the user's emotions and determine the priorities of trying on based on the estimated user's emotions. For example, the virtual fitting unit can estimate the user's emotions and determine the priorities of trying on using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows for more appropriate trying on by determining the priorities of trying on based on the user's emotions.

[0169] The virtual fitting unit can try on clothes while taking into account the geographical background of the user. For example, the virtual fitting unit can use AI to analyze the geographical background of the user and try on clothes. The virtual fitting unit can also improve the accuracy of the fitting by referring to the user's past fitting history. For example, the virtual fitting unit can use AI to improve the accuracy of the fitting by referring to the user's past fitting history. This allows for a more appropriate fitting by taking into account the user's geographical background.

[0170] The virtual fitting unit can improve the accuracy of fitting by referring to the user's social media activity when trying on clothes. For example, the virtual fitting unit can improve the accuracy of fitting by referring to the user's social media activity using AI. The virtual fitting unit can also try on clothes taking into account the user's geographical background. For example, the virtual fitting unit can analyze the user's geographical background using AI and try on clothes. This allows for more accurate fitting by referring to the user's social media activity.

[0171] The information providing unit can estimate the user's emotion and adjust the method of providing information based on the estimated user's emotion. For example, the information providing unit can estimate the user's emotion using an emotion engine or a generation AI and adjust the method of providing information. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to provide more appropriate information by adjusting the method of providing information based on the user's emotion.

[0172] When providing information, the information providing unit can improve the accuracy of the information provided by referring to the user's past purchase history. For example, the information providing unit can use AI to improve the accuracy of the information provided by referring to the user's past purchase history. The information providing unit can also apply different information providing algorithms based on the user's lifestyle and occupation. For example, the information providing unit can use AI to analyze the user's lifestyle and occupation and apply different information providing algorithms. This makes it possible to provide more accurate information by referring to the user's past purchase history.

[0173] The information providing unit can apply different information provision algorithms based on the user's lifestyle and occupation when providing information. For example, the information providing unit can use AI to analyze the user's lifestyle and occupation and apply different information provision algorithms. The information providing unit can also improve the accuracy of the information provision by referring to the user's past purchase history. For example, the information providing unit can use AI to improve the accuracy of the information provision by referring to the user's past purchase history. This makes it possible to provide more appropriate information based on the user's lifestyle and occupation.

[0174] The information providing unit can estimate the user's emotion and determine the priority of information provision based on the estimated user's emotion. For example, the information providing unit can estimate the user's emotion and determine the priority of information provision using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This enables more appropriate information provision by determining the priority of information provision based on the user's emotion.

[0175] When providing information, the information providing unit can provide the information while taking into consideration the geographical background of the user. For example, the information providing unit can use AI to analyze the geographical background of the user and provide the information. In addition, the information providing unit can also improve the accuracy of the information provided by referring to the user's past purchase history. For example, the information providing unit can use AI to improve the accuracy of the information provided by referring to the user's past purchase history. This makes it possible to provide more appropriate information by taking into consideration the user's geographical background.

[0176] When providing information, the information providing unit can improve the accuracy of the information provided by referring to the social media activity of the user. For example, the information providing unit can use AI to improve the accuracy of the information provided by referring to the social media activity of the user. The information providing unit can also provide information by taking into account the geographical background of the user. For example, the information providing unit can use AI to analyze the geographical background of the user and provide information. This makes it possible to provide more accurate information by referring to the social media activity of the user.

[0177] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0178] The system for providing advice on fashion and beauty to a user can also customize the advice based on the user's life events. For example, if the user has a special event such as a wedding or graduation coming up, the system can suggest fashion and beauty tips that are best suited to the event. The system can also provide advice tailored to a particular season or weather based on the user's life events. Furthermore, the system can also suggest fashion and beauty tips that are best suited to a particular culture or tradition based on the user's life events. This allows the user to receive advice that is best suited to a special event or situation.

[0179] The advice unit can also provide advice based on the user's health condition. For example, if the user has an allergy, the advice unit can suggest fashion items or beauty products that are allergy-friendly. Also, if the user has a specific health goal, the advice unit can provide fashion and beauty advice that matches the goal. Furthermore, the advice unit can also advise the user to avoid certain materials or ingredients based on the user's health condition. This allows the user to receive fashion and beauty advice that is health-friendly.

[0180] The beauty product suggestion unit can also suggest beauty products based on the user's environment. For example, it can suggest optimal skin care products and hair care products based on the climate and air quality of the area where the user lives. In addition, if the user is planning a trip, it can also suggest beauty products that match the environment of the travel destination. Furthermore, it can also suggest measures against specific environmental factors based on the user's living environment. In this way, the user can receive suggestions for environmentally friendly beauty products.

[0181] The virtual fitting unit can also predict changes in the user's body shape and provide a fitting simulation based on the future body shape. For example, if the user is on a diet or doing muscle training, the virtual fitting unit can predict the future body shape and provide a fitting simulation based on that body shape. Also, if the user is pregnant, the virtual fitting unit can predict the body shape after giving birth and provide a fitting simulation. Furthermore, it can predict future changes in body shape based on the user's age and provide a fitting simulation based on that body shape. This allows the user to select fashion items that match their future body shape.

[0182] The information providing unit can also adjust the timing of providing information based on the user's purchasing behavior. For example, if the user tends to do online shopping during a specific time period, information can be provided according to that time period. Also, if the user tends to do shopping on a specific day of the week, information can be provided according to that day of the week. Furthermore, information on specific events or sales can be provided based on the user's purchasing behavior. This allows the user to receive information at the optimal timing.

[0183] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting information about relaxing fashion items and beauty products. Also, if the user is feeling happy, the collection unit can collect information about items with bright colors and designs that match the user's emotions. Furthermore, if the user is tired, the collection unit can prioritize collecting information about refreshing beauty products. In this way, optimal information can be collected based on the user's emotions.

[0184] The analysis unit can estimate the user's emotions and adjust the analysis method of the fashion style and preferences based on the estimated user's emotions. For example, if the user is depressed, the analysis unit can suggest styles and colors that will cheer the user up. Also, if the user is excited, the analysis unit can suggest bold designs and trends that match the user's emotions. Furthermore, if the user is relaxed, the analysis unit can suggest relaxing styles and materials that match the user's emotions. This enables more appropriate analysis of fashion styles and preferences based on the user's emotions.

[0185] The providing unit can estimate the user's emotions and adjust the method of providing fashion trends and beauty information based on the estimated user's emotions. For example, if the user is tired, the providing unit can provide refreshing beauty information and relaxing fashion item information. Also, if the user is excited, the providing unit can provide the latest trend information and bold design item information that matches the user's emotions. Furthermore, if the user is depressed, the providing unit can provide information on bright colors and designs that will cheer up the user. This makes it possible to provide optimal information based on the user's emotions.

[0186] The coordination suggestion unit can estimate the user's emotions and adjust the coordination suggestion method based on the estimated user's emotions. For example, if the user is feeling stressed, the coordination suggestion unit can suggest coordination of relaxing styles and materials. Also, if the user is feeling happy, it can suggest coordination of bright colors and designs that match the user's emotions. Furthermore, if the user is tired, the coordination suggestion unit can suggest coordination of refreshing styles and items. This makes it possible to suggest optimal coordination based on the user's emotions.

[0187] The beauty product suggestion unit can estimate the user's emotions and adjust the beauty product suggestion method based on the estimated user's emotions. For example, if the user is feeling stressed, the beauty product suggestion unit can suggest relaxing skin care products or aroma products. Also, if the user is feeling happy, the beauty product suggestion unit can suggest beauty products with bright colors and scents that match the user's emotions. Furthermore, if the user is tired, the beauty product suggestion unit can suggest refreshing hair care products or body care products. This makes it possible to suggest optimal beauty products based on the user's emotions.

[0188] The process flow of the second embodiment will be briefly described below.

[0189] Step 1: The collection unit collects the user's past purchase history and the user's photos. The user's past purchase history includes, but is not limited to, purchase date and time, purchased items, purchase frequency, etc. The user's photos include, but are not limited to, face photos, full-body photos, photos in specific situations, etc. The collection unit can also collect information from, for example, the user's social media accounts. Step 2: The analysis unit analyzes the information collected by the collection unit and analyzes the user's fashion style and preferences. The analysis unit can use, for example, AI to improve the accuracy of the analysis by referring to the user's past fashion choices. Step 3: The providing unit provides fashion trends and beauty information suited to the user based on the analysis results by the analyzing unit. The providing unit can, for example, use AI to estimate the user's emotions and adjust the method of providing fashion trends and beauty information based on the estimated user's emotions. Step 4: The coordination suggestion unit suggests optimal coordination for items the user owns or items the user is considering purchasing. The coordination suggestion unit can, for example, use AI to estimate the user's emotions and adjust the method of suggesting coordination based on the estimated user's emotions. Step 5: The recommendation unit recommends fashion items that fit the user based on the analysis results by the analysis unit. The recommendation unit can, for example, use AI to estimate the user's emotions and adjust the method of recommending fashion items based on the estimated user's emotions. Step 6: The commentary unit provides a step-by-step tutorial on how to apply makeup and hairstyle. The commentary unit can, for example, use AI to estimate the user's emotions and adjust the method of explanation based on the estimated user's emotions.

[0190] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0191] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0192] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0193] For example, the collection unit can collect photos and social media information of the user using the camera 42 and the communication I / F 44 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to analyze the user's fashion style and preferences. The provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and provides fashion trends and beauty information according to the user. The coordination suggestion unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and proposes optimal coordination for the user's owned items and items under consideration for purchase. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12, and recommends fashion items that fit the user. The commentary unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and explains how to apply makeup and hairstyle step by step. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0194] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0195] 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.

[0196] 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, the RAM 30, and the 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.

[0197] 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.

[0198] 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 the voice according to instructions from the processor 46.

[0199] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0200] 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 performed in a secure state.

[0201] Fig. 4 shows an example of 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.

[0202] 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.

[0203] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0204] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0205] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0206] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0207] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0208] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0209] For example, the collection unit can collect photos and social media information of the user using the camera 42 and the communication I / F 44 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to analyze the user's fashion style and preferences. The provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and provides fashion trends and beauty information according to the user. The coordination suggestion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and proposes optimal coordination for the user's owned items and items under consideration for purchase. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12, and recommends fashion items that fit the user. The commentary unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and explains how to apply makeup and hairstyle step by step. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0210] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0211] 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.

[0212] 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, the RAM 30, and the 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.

[0213] 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.

[0214] 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 the voice according to instructions from the processor 46.

[0215] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0216] 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 performed in a secure state.

[0217] 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.

[0218] 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.

[0219] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0220] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0221] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0222] 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 voice indicating a user input for 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.

[0223] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0224] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0225] For example, the collection unit can collect photos and social media information of the user using the camera 42 and the communication I / F 44 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to analyze the user's fashion style and preferences. The provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and provides fashion trends and beauty information according to the user. The coordination suggestion unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and proposes optimal coordination for items owned by the user and items considered for purchase. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12, and recommends fashion items that fit the user. The commentary unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and explains how to apply makeup and hairstyle step by step. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0226] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0227] 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.

[0228] 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, the RAM 30, and the 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.

[0229] 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.

[0230] 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 the voice according to instructions from the processor 46.

[0231] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0232] 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 performed in a secure state.

[0233] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0234] 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.

[0235] 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.

[0236] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0237] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0238] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0239] 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 a voice indicating a user input for 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.

[0240] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0241] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0242] For example, the collection unit can collect user photos and social media information using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to analyze the user's fashion style and preferences. The provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides fashion trends and beauty information according to the user. The coordination suggestion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and proposes optimal coordination for the user's owned items and items under consideration for purchase. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12, and recommends fashion items that fit the user. The commentary unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and explains how to apply makeup and hairstyle step by step. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0243] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0244] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0245] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0246] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0247] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0248] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0249] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0250] 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.

[0251] 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 Universal Serial Bus (USB) 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.

[0252] In addition, 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 upon request from the data processing device 12.

[0253] 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.

[0254] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0255] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0256] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0257] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0258] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0259] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0260] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0261] (Appendix 1) A collection unit that collects a user's past purchase history and a user's photograph; an analysis unit that analyzes the information collected by the collection unit and analyzes the user's fashion style and preferences; a providing unit that provides fashion trend and beauty information according to the user based on the analysis result by the analysis unit; A coordination suggestion unit that suggests coordination for an item owned by the user or an item that the user is considering purchasing; a recommendation unit that recommends fashion items that suit the user based on the analysis result by the analysis unit; An explanation section that explains how to apply makeup and style a hairstyle. A system characterized by: (Appendix 2) An advice unit that provides color and coordination advice based on the user's skin tone and preferences is provided. 2. The system of claim 1, (Appendix 3) Equipped with a beauty product suggestion section that suggests beauty products according to the user's skin type, hair type, and concerns 2. The system of claim 1, (Appendix 4) A virtual fitting section is provided that uses a camera to match the user's clothes and accessories to their actual appearance. 2. The system of claim 1, (Appendix 5) The recommendation unit includes an information providing unit that provides links to purchasing sites related to the recommended items. 2. The system of claim 1, (Appendix 6) The collecting unit includes: Collect information from users' social media 2. The system of claim 1, (Appendix 7) The virtual fitting section includes: Tracks changes in the user's body shape and provides results by comparing with past data 5. The system of claim 4. (Appendix 8) The advice unit is Give advice based on the user's occupation or lifestyle 3. The system of claim 2, (Appendix 9) The virtual fitting section includes: Analyze the walking style and posture of the user and provide results based on the analysis 5. The system of claim 4. (Appendix 10) The advice unit is Providing advice according to the user's emotional state 3. The system of claim 2, (Appendix 11) The commentary section includes: Detect user make mistakes and provide step-by-step guidance to fix them 2. The system of claim 1, (Appendix 12) The virtual fitting section includes: Provides simulation of fitting under different lighting conditions 5. The system of claim 4. (Appendix 13) The analysis unit includes: Analyzes the user's walking style and makes shoe recommendations based on the analysis results 2. The system of claim 1, (Appendix 14) The virtual fitting section includes: Tracks user movements in real time and simulates how clothes fit based on the user's movements 5. The system of claim 4. (Appendix 15) The collecting unit includes: Infer user emotions and adjust the timing of collecting purchase history and photos based on the estimated user emotions 2. The system of claim 1, (Appendix 16) The collecting unit includes: Analyze the user's past purchase history and select the optimal collection method 2. The system of claim 1, (Appendix 17) The collecting unit includes: Filter purchase history and photos based on the user's current life situation and interests 2. The system of claim 1, (Appendix 18) The collecting unit includes: Estimate user emotions and prioritize information to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 19) The collecting unit includes: When collecting purchase history and photos, the system takes into account the user's geographic location information to prioritize collection of relevant information. 2. The system of claim 1, (Appendix 20) The collecting unit includes: Analyze users' social media activity and collect related information when collecting purchase history and photos 2. The system of claim 1, (Appendix 21) The analysis unit includes: Inferring user emotions and adjusting the analysis method of fashion style and preferences based on the estimated user emotions 2. The system of claim 1, (Appendix 22) The analysis unit includes: During analysis, the user's past fashion choices are referenced to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 23) The analysis unit includes: During analysis, apply different analysis algorithms based on the user's lifestyle and occupation 2. The system of claim 1, (Appendix 24) The analysis unit includes: Infer user sentiment and adjust how analysis results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 25) The analysis unit includes: During analysis, the geographical location of the user is taken into account. 2. The system of claim 1, (Appendix 26) The analysis unit includes: During analysis, refer to users' social media activity to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 27) The providing unit is Estimate user emotions and adjust the way fashion trends and beauty information are provided based on the estimated user emotions 2. The system of claim 1, (Appendix 28) The providing unit is When providing content, optimize the content by referring to the user's past responses. 2. The system of claim 1, (Appendix 29) The providing unit is When serving, different information serving algorithms are applied based on the user's lifestyle and occupation. 2. The system of claim 1, (Appendix 30) The providing unit is Estimate the user's emotions and adjust the timing of providing information based on the estimated user's emotions 2. The system of claim 1, (Appendix 31) The providing unit is When providing information, it takes into account the user's geographical context 2. The system of claim 1, (Appendix 32) The providing unit is When providing information, we refer to your social media activity to improve the relevance of the information 2. The system of claim 1, (Appendix 33) The coordination suggestion unit, Estimate the user's emotions and adjust the coordination suggestion method based on the estimated user's emotions. 2. The system of claim 1, (Appendix 34) The coordination suggestion unit, When making suggestions, the system improves the accuracy of the suggestions by referring to the user's past coordination history. 2. The system of claim 1, (Appendix 35) The coordination suggestion unit, When making suggestions, different suggestion algorithms are applied based on the user's lifestyle and occupation. 2. The system of claim 1, (Appendix 36) The coordination suggestion unit, Estimate user sentiment and prioritize suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 37) The coordination suggestion unit, When making suggestions, the geographical context of the user is taken into account. 2. The system of claim 1, (Appendix 38) The coordination suggestion unit, When making suggestions, the app will look at the user's social media activity to improve the accuracy of the suggestions. 2. The system of claim 1, (Appendix 39) The recommendation unit, Estimating user emotions and adjusting the fashion item recommendation method based on the estimated user emotions 2. The system of claim 1, (Appendix 40) The recommendation unit, When making recommendations, the accuracy of the recommendations is improved by referring to the user's past purchase history. 2. The system of claim 1, (Appendix 41) The recommendation unit, When making recommendations, different recommendation algorithms are applied based on the user's lifestyle and occupation. 2. The system of claim 1, (Appendix 42) The recommendation unit, Estimate user emotions and prioritize recommendations based on the estimated user emotions 2. The system of claim 1, (Appendix 43) The recommendation unit, When making recommendations, the geographical background of the user is taken into account. 2. The system of claim 1, (Appendix 44) The recommendation unit, When making recommendations, the system will refer to the user's social media activity to improve the accuracy of the recommendations. 2. The system of claim 1, (Appendix 45) The commentary section includes: Estimate the user's emotions and adjust the commentary method based on the estimated user's emotions. 2. The system of claim 1, (Appendix 46) The commentary section includes: When providing an explanation, the system improves the accuracy of the explanation by referring to the user's past makeup and hairstyle history. 2. The system of claim 1, (Appendix 47) The commentary section includes: When providing commentary, apply different commentary algorithms based on the user's lifestyle and occupation. 2. The system of claim 1, (Appendix 48) The commentary section includes: Estimate user emotions and prioritize commentaries based on the estimated user emotions 2. The system of claim 1, (Appendix 49) The commentary section includes: When providing explanations, the geographical background of the user is taken into consideration. 2. The system of claim 1, (Appendix 50) The commentary section includes: When providing commentary, the accuracy of the commentary can be improved by referring to the user's social media activity. 2. The system of claim 1, (Appendix 51) The advice unit, To estimate a user's emotion and adjust a color and coordination advice method based on the estimated user's emotion 3. The system of claim 2, (Appendix 52) The advice unit, When providing advice, the system refers to the user's past fashion choices to improve the accuracy of the advice. 3. The system of claim 2, (Appendix 53) The advice unit, When giving advice, apply different advice algorithms based on the user's lifestyle and occupation 3. The system of claim 2, (Appendix 54) The advice unit, Estimate the user's emotions and prioritize advice based on the estimated user emotions. 3. The system of claim 2, (Appendix 55) The advice unit, When giving advice, the advice is given taking into account the user's geographical background. 3. The system of claim 2, (Appendix 56) The advice unit, When providing advice, the system refers to the user's social media activity to improve the accuracy of the advice. 3. The system of claim 2, (Appendix 57) The beauty product suggestion department includes: Estimating a user's emotions and adjusting a beauty product suggestion method based on the estimated user's emotions 4. The system of claim 3. (Appendix 58) The beauty product suggestion department includes: When making suggestions, the system will refer to the user's past beauty product usage history to improve the accuracy of the suggestions. 4. The system of claim 3. (Appendix 59) The beauty product suggestion department includes: When making suggestions, different suggestion algorithms are applied based on the user's lifestyle and occupation. 4. The system of claim 3. (Appendix 60) The beauty product suggestion department includes: Estimate user sentiment and prioritize suggestions based on the estimated user sentiment 4. The system of claim 3. (Appendix 61) The beauty product suggestion department includes: When making suggestions, the geographical context of the user is taken into account. 4. The system of claim 3. (Appendix 62) The beauty product suggestion department includes: When making suggestions, the app will look at the user's social media activity to improve the accuracy of the suggestions. 4. The system of claim 3. (Appendix 63) The virtual fitting section includes: Estimating a user's emotion and adjusting a virtual try-on method based on the estimated user's emotion 5. The system of claim 4. (Appendix 64) The virtual fitting section includes: When trying on clothes, the accuracy of the fitting can be improved by referring to the user's past fitting history. 5. The system of claim 4. (Appendix 65) The virtual fitting section includes: When trying on clothes, apply different fitting algorithms based on the user's lifestyle and occupation. 5. The system of claim 4. (Appendix 66) The virtual fitting section includes: Estimate the user's emotions and determine the priority of fitting items based on the estimated user's emotions. 5. The system of claim 4. (Appendix 67) The virtual fitting section includes: When trying on clothes, the user's geographical background is taken into account 5. The system of claim 4. (Appendix 68) The virtual fitting section includes: When trying on clothes, the system references the user's social media activity to improve the accuracy of the try-on. 5. The system of claim 4. (Appendix 69) The information providing unit is Estimate the user's emotions and adjust the way information is provided based on the estimated user's emotions. 6. The system of claim 5, (Appendix 70) The information providing unit is When providing information, improve the accuracy of the information provided by referring to the user's past purchase history 6. The system of claim 5, (Appendix 71) The information providing unit is When providing information, different information provision algorithms are applied based on the user's lifestyle and occupation. 6. The system of claim 5, (Appendix 72) The information providing unit is Estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions. 6. The system of claim 5, (Appendix 73) The information providing unit is When providing information, provide information taking into account the user's geographical background. 6. The system of claim 5, (Appendix 74) The information providing unit is When providing information, the accuracy of the information provided will be improved by referring to the user's social media activity. 6. The system of claim 5, [Explanation of symbols]

[0262] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects a user's past purchase history and a photo of the user; an analysis unit that analyzes the information collected by the collection unit and analyzes the user's fashion style and preferences; a providing unit that provides fashion trend and beauty information according to the user based on the analysis result by the analysis unit; A coordination suggestion unit that suggests coordination for an item owned by the user or an item that the user is considering purchasing; a recommendation unit that recommends fashion items that suit the user based on the analysis result by the analysis unit; An explanation section that explains how to apply makeup and style a hairstyle. A system characterized by:

2. An advice unit that gives advice on colors and coordination based on the skin tone and preferences of the user.

2. The system of claim 1 .

3. A beauty product suggestion unit that suggests beauty products according to the user's skin type, hair type, and concerns is provided.

2. The system of claim 1 .

4. A virtual fitting section is provided that uses a camera to match the user's clothes and accessories to their actual appearance.

2. The system of claim 1 .

5. The recommendation unit includes an information providing unit that provides a link to a purchasing site for the recommended item.

2. The system of claim 1 .

6. The collecting unit includes: Collect information from the user's social media 2. The system of claim 1 .

7. The virtual fitting section includes: Tracking the user's body shape changes and comparing with past data to provide results 5. The system of claim 4.

8. The advice unit, Providing advice based on the user's occupation or lifestyle 3. The system of claim 2.

9. The virtual fitting section includes: Analyzing the walking style and posture of the user and providing results based on the analysis.

5. The system of claim 4.

10. The advice unit is Providing advice according to the emotional state of the user 3. The system of claim 2.

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