system

Abstract

The system according to this embodiment aims to provide fashion suggestions tailored to the user's preferences and lifestyle. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a suggestion unit, a database unit, and a styling unit. The reception unit receives the user's photos and preferences. The analysis unit analyzes the information entered by the reception unit. The suggestion unit makes fashion suggestions based on the results of the analysis by the analysis unit. The database unit incorporates the latest trend information. The styling unit provides styling tailored to the user's lifestyle.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, fashion proposals tailored to individual user preferences and lifestyles have not been sufficiently made, leaving room for improvement.

[0005] The system according to the embodiment aims to make fashion proposals tailored to user preferences and lifestyles.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, a database unit, and a styling unit. The reception unit receives the user's photos and preferences as input. The analysis unit analyzes the information input by the reception unit. The suggestion unit makes fashion suggestions based on the results of the analysis by the analysis unit. The database unit incorporates the latest trend information. The styling unit provides styling tailored to the user's lifestyle. [Effects of the Invention]

[0007] The system according to this embodiment can provide fashion suggestions tailored to the user's preferences and lifestyle. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The fashion suggestion system according to an embodiment of the present invention is a system that analyzes a user's photos and preferences and provides personalized fashion suggestions. This fashion suggestion system allows the user to input their own photos and preferences, and the AI ​​analyzes this information to suggest the most suitable fashion style for the user. These suggestions reflect the latest trends and seasonality. Styling is also tailored to the user's lifestyle. This allows users to easily find fashion that suits them and go out with confidence. For example, when a user inputs their photos and preferences, they can also input information about their preferences and lifestyle. For instance, information is provided to users who prefer casual styles or those seeking styling for business settings, according to their individual needs. Next, the AI ​​analyzes the input information. The AI ​​utilizes deep learning-based image analysis technology to extract features from the user's photos. Furthermore, a recommendation system based on user preference data is used to suggest the most suitable fashion style for each individual user. For example, suggestions are made that match the user's face shape and body type, and reflect their preferred colors and designs. In addition, the AI ​​utilizes a real-time updated fashion database to provide suggestions that incorporate the latest trends. This allows users to always enjoy the latest fashion. For example, seasonal trends and information on popular brands and items are reflected in the suggestions. This system makes it easy for users to find fashion that suits them. For instance, when a user is attending a specific event, the AI ​​can suggest an appropriate style for that event. Furthermore, styling tailored to the user's lifestyle is also provided, allowing them to enjoy fashion with confidence in various everyday situations. This system has the effect of improving user satisfaction. For example, over 90% of users are satisfied with the suggested styles, and 70% of users reuse the service. Additionally, sharing on social media can lead to the acquisition of new customers. For example, a 20% increase in monthly users is predicted.Thus, an AI-powered fashion suggestion system is a groundbreaking solution for realizing a society where every user can enjoy fashion with confidence. This system analyzes the user's photos and preferences, enabling it to provide personalized fashion suggestions.

[0029] The fashion suggestion system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, a database unit, and a styling unit. The reception unit inputs the user's photos and preferences. The user's photos and preferences include, but are not limited to, full-body photos, face photos, color preferences, style preferences, etc. The reception unit can input information, for example, by the user uploading their own photos and answering a questionnaire about their preferences. The reception unit can also supplement the input by referring to the user's past fashion history. The analysis unit analyzes the information input by the reception unit. The analysis unit extracts features from the user's photos, for example, by utilizing image analysis technology using deep learning. The analysis unit analyzes, for example, the user's face shape and body type and generates data for suggesting appropriate fashion styles. The analysis unit also generates data for suggesting the optimal fashion style for each individual user using a recommendation system based on the user's preference data. The suggestion unit makes fashion suggestions based on the results analyzed by the analysis unit. The suggestion unit suggests styling that suits the user's face shape and body type, for example. The suggestion unit can, for example, make suggestions that reflect the user's preferred colors and designs. It can also suggest styling tailored to the user's lifestyle. The database unit incorporates the latest trend information. For example, the database unit utilizes a real-time updated fashion database to obtain the latest trend information. The database unit collects data from fashion magazines and online shops and reflects it in the database. The styling unit provides styling tailored to the user's lifestyle. For example, when a user is participating in a specific event, the styling unit suggests styling appropriate for that event. The styling unit can also suggest styling tailored to the user's occupation or hobbies. As a result, the fashion suggestion system according to this embodiment can analyze the user's photos and preferences and provide personalized fashion suggestions.

[0030] The reception desk inputs the user's photos and preferences. These include, but are not limited to, full-body photos, face photos, color preferences, and style preferences. For example, users can input information by uploading their photos and answering preference questionnaires. Specifically, users access a dedicated application or website using their smartphone or computer to upload photos. Full-body photos are used to understand the user's body type and height, while face photos are used to analyze facial features and shape. The preference questionnaire allows users to input detailed information such as their favorite colors, preferred fashion styles, usual clothing tendencies, and preferences for specific brands or designers. The reception desk can also supplement the input by referring to the user's past fashion history. For example, it can more accurately understand the user's preferences based on items the user has purchased in the past and a history of previous fashion suggestions. This allows the reception desk to collect detailed user information and generate basic data to provide to the analysis and suggestion departments.

[0031] The analysis unit analyzes the information entered by the reception unit. For example, the analysis unit uses advanced image analysis technology, such as deep learning, to extract features from the user's photographs. Specifically, it uses advanced image analysis algorithms such as convolutional neural networks (CNNs) to analyze body shape and height from full-body photographs and facial shape and features from face photographs. This generates data to suggest the most suitable fashion style for the user's body shape and facial shape. The analysis unit also uses a recommendation system based on user preference data to generate data to suggest the most suitable fashion style for each individual user. The recommendation system combines methods such as collaborative filtering and content-based filtering to analyze the user's past preferences and data from other users with similar preferences to recommend the most suitable fashion items. Furthermore, the analysis unit also considers information such as the user's occupation, hobbies, and daily activity patterns to suggest styling that suits the user's lifestyle and specific events. This allows the analysis unit to accurately grasp the user's detailed characteristics and preferences and generate data to provide the most suitable fashion suggestions for each individual user.

[0032] The Proposal Department makes fashion suggestions based on the results analyzed by the Analysis Department. For example, the Proposal Department suggests styling tailored to the user's face shape and body type. Specifically, it suggests the optimal hairstyle and accessories based on the user's face shape, and clothing with the optimal silhouette and design based on their body type. The Proposal Department can also make suggestions that reflect the user's preferred colors and designs. For example, based on the user's favorite colors, it suggests outfits incorporating those colors and selects items that match the user's preferred fashion style. Furthermore, the Proposal Department can also suggest styling tailored to the user's lifestyle. For example, it can make suggestions for various occasions, such as formal styles for business settings, casual everyday styles, and special styles for attending specific events. To present these suggestions to the user in a visually easy-to-understand manner, the Proposal Department provides image examples of the outfits and lists of specific items. In this way, the Proposal Department can make fashion suggestions tailored to the user's individual characteristics and preferences, helping the user find the style that is best suited to them.

[0033] The database department incorporates the latest trend information. For example, it obtains the latest trend information by utilizing a real-time updated fashion database. Specifically, it collects data from fashion magazines, online shops, and fashion shows and reflects it in the database. This allows the database department to always grasp the latest fashion trends and provide users with the most up-to-date information. Furthermore, the database department also accumulates historical fashion data and user preference data, providing a foundation for the analysis and proposal departments to utilize this data to make more accurate suggestions. For example, by analyzing historical fashion data, it is possible to understand the changes in trends during specific seasons or events and make appropriate suggestions to users. In addition, the database department collects user feedback and continuously updates the database content, enabling flexible responses to user needs. This allows the database department to integrate the latest trend information with user preference data, improving the accuracy and reliability of the entire system.

[0034] The Styling Department provides styling tailored to the user's lifestyle. For example, when a user is attending a specific event, the Styling Department will suggest a suitable style for that event. Specifically, they will suggest styles for various occasions such as weddings, parties, and business meetings. They can also suggest styles based on the user's occupation and hobbies. For example, they will suggest a unique and individual style for users in creative professions, and a formal and sophisticated style for business professionals. Furthermore, the Styling Department utilizes data provided by the Analysis Department to create styles that match the user's body type and facial features. This allows them to suggest clothing with the optimal silhouette and design for the user. In addition, the Styling Department can create styles that reflect the user's preferred colors and designs. For example, based on the user's favorite colors, they will suggest coordinates incorporating those colors and select items that match the user's preferred fashion style. In this way, the Styling Department can provide styling tailored to the user's lifestyle and preferences, helping users find the style that is best suited to them.

[0035] The suggestion unit can propose styling tailored to the user's face shape and body type. For example, the suggestion unit can suggest appropriate hairstyles and accessories based on the user's face shape. For example, the suggestion unit can also suggest appropriate clothing silhouettes and sizes based on the user's body type. For example, the suggestion unit can use AI to perform face recognition and body type analysis in order to create styling tailored to the user's face shape and body type. This makes it possible to make more appropriate fashion suggestions by proposing styling tailored to the user's face shape and body type. Face shape and body type include, but are not limited to, round face, oval face, slender, and plump. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input data on the user's face shape and body type into a generating AI and have the generating AI execute styling suggestions.

[0036] The suggestion unit can make suggestions that reflect the user's preferred colors and designs. For example, the suggestion unit can suggest appropriate fashion items based on the user's preferred colors and designs. The suggestion unit can also make suggestions that reflect the user's preferred colors and designs based on the user's past purchase history or survey results. For example, the suggestion unit can use AI to analyze preference data in order to make suggestions that reflect the user's preferred colors and designs. This improves user satisfaction by making suggestions that reflect the user's preferred colors and designs. Preferred colors and designs include, but are not limited to, red, blue, green, simple, casual, and elegant. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user preference data into a generating AI and have the generating AI make fashion item suggestions.

[0037] The database unit can utilize a fashion database that is updated in real time. For example, the database unit can collect data from fashion magazines and online shops in real time and reflect it in the database. The database unit can also analyze data using AI to obtain the latest trend information. For example, the database unit can periodically update the fashion database to provide the latest information. This makes it possible to make suggestions that reflect the latest trend information by utilizing a fashion database that is updated in real time. A fashion database that is updated in real time may include, but is not limited to, the latest fashion items, popular brands, and seasonal trends. Some or all of the above processing in the database unit may be performed using AI or not. For example, the database unit can input fashion data into a generating AI and have the generating AI perform the database update.

[0038] The styling unit can provide styling tailored to the user's lifestyle. For example, the styling unit can suggest styling that suits the user's occupation and hobbies. For example, if the user has a casual lifestyle, the styling unit can suggest casual styling. For example, if the user is looking for styling for a business setting, the styling unit can suggest styling that is suitable for business. By providing styling tailored to the user's lifestyle, they can enjoy fashion with confidence in various everyday situations. Lifestyle includes, but is not limited to, work, casual, and formal wear. Some or all of the above processing in the styling unit may be performed using AI or not. For example, the styling unit can input the user's lifestyle data into a generating AI and have the generating AI generate styling suggestions.

[0039] The styling unit can suggest styling suitable for specific events. For example, when a user is attending a wedding, the styling unit can suggest styling suitable for a wedding. For example, when a user is attending a party, the styling unit can suggest styling suitable for a party. For example, when a user is attending a business meeting, the styling unit can suggest styling suitable for a business meeting. By suggesting styling suitable for specific events, users can enjoy fashion appropriate for the event. Specific events include, but are not limited to, weddings, parties, and business meetings. Some or all of the above processing in the styling unit may be performed using AI or not. For example, the styling unit can input event information into a generating AI and have the generating AI perform styling suggestions.

[0040] The reception desk can analyze the user's past fashion history and select the optimal input method. For example, the reception desk can suggest the most frequently used input method based on the user's past fashion history. The reception desk can also suggest an input method tailored to a specific season based on the user's past fashion history. The reception desk can also analyze the user's past fashion history and provide an auto-completion function to reduce the effort required for input. This allows the reception desk to provide the optimal input method by analyzing the user's past fashion history. Past fashion history includes, but is not limited to, purchase history and wear history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's fashion history data into a generating AI and have the generating AI select the optimal input method.

[0041] The reception desk can filter the input of photos and preferences based on the user's current fashion trends and areas of interest. For example, the reception desk can narrow down the input fields based on the fashion categories the user is currently interested in. The reception desk can also analyze the user's current fashion trends and prioritize the display of relevant input fields. The reception desk can also customize the sample images and examples displayed during input based on the user's areas of interest. This allows for the input of more appropriate information by filtering based on the user's current fashion trends and areas of interest. Current fashion trends and areas of interest include, but are not limited to, survey results and social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's fashion trend data into a generating AI and have the generating AI perform the filtering.

[0042] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when inputting photos and preferences. For example, the reception desk can prioritize inputting fashion information that matches the climate of the user's current location. The reception desk can also display input items that match local trends based on the user's geographical location. For example, the reception desk can input fashion information related to local events, taking the user's location into consideration. This allows for the priority input of highly relevant information by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's location data into a generating AI and have the generating AI determine the priority of the information.

[0043] The reception desk can analyze the user's social media activity and input relevant information when they input photos and preferences. For example, the reception desk can analyze the content of the user's social media posts and input relevant fashion information. The reception desk can also suggest input items by referencing the styles of influencers the user follows. The reception desk can also input information that matches the user's preferences based on their "likes" and comments on social media. In this way, relevant information can be input by analyzing the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI perform the information input.

[0044] The analysis unit can adjust the level of detail of the analysis based on the user's fashion history during the analysis. For example, if the user has preferred detailed analysis in the past, the analysis unit will provide detailed analysis results. The analysis unit can also provide concise analysis results if the user has preferred concise analysis in the past. The analysis unit can also provide analysis results with an appropriate level of detail based on the user's fashion history. In this way, by adjusting the level of detail of the analysis based on the user's fashion history, it is possible to provide analysis results that are appropriate for the user. Fashion history includes, but is not limited to, purchase history and wear history. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's fashion history data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the user's fashion category during analysis. For example, in the case of casual fashion, the analysis unit can apply an analysis algorithm specialized for casual wear. For example, in the case of business fashion, the analysis unit can also apply an analysis algorithm specialized for business wear. For example, in the case of sports fashion, the analysis unit can also apply an analysis algorithm specialized for sports wear. By applying different analysis algorithms depending on the user's fashion category, more appropriate analysis results can be provided. Fashion categories include, but are not limited to, casual, formal, and sports wear. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input fashion category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on when the user's fashion history was submitted. For example, the analysis unit may prioritize analyzing the user's most recently submitted fashion history. The analysis unit may also prioritize analyzing the user's fashion history submitted during a specific season. The analysis unit can also determine the priority of analysis based on when the user's fashion history was submitted. This allows for the provision of more appropriate analysis results by prioritizing analysis based on when the user's fashion history was submitted. The submission date of the fashion history includes, but is not limited to, the purchase date and the date of wear. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input fashion history submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the user's fashion relevance during the analysis. For example, the analysis unit may prioritize analyzing fashion categories that the user is interested in. The analysis unit can also adjust the order of analysis based on the user's fashion relevance. For example, the analysis unit can adjust the order of analysis according to the user's level of interest. By adjusting the order of analysis based on the user's fashion relevance, more appropriate analysis results can be provided. Fashion relevance includes, but is not limited to, item combinations and the degree of trend matching. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input fashion relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the fashion items. For example, it can provide detailed suggestions for important fashion items, while providing concise suggestions for less important items. The suggestion unit can also adjust the level of detail in its suggestions based on the importance of the fashion items. By adjusting the level of detail in suggestions based on the importance of the fashion items, it can provide appropriate suggestions to the user. The importance of fashion items includes, but is not limited to, factors such as trendiness and frequency of use. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input fashion item importance data into a generating AI and have the generating AI adjust the level of detail in its suggestions.

[0049] The suggestion unit can apply different suggestion algorithms depending on the category of the fashion item when making suggestions. For example, in the case of casual fashion, the suggestion unit can apply a suggestion algorithm specialized for casual wear. For example, in the case of business fashion, the suggestion unit can also apply a suggestion algorithm specialized for business wear. For example, in the case of sports fashion, the suggestion unit can also apply a suggestion algorithm specialized for sports wear. By applying different suggestion algorithms depending on the category of the fashion item, the suggestion unit can provide appropriate suggestions to the user. Categories of fashion items include, but are not limited to, tops, bottoms, and accessories. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input fashion item category data into a generating AI and have the generating AI perform the application of the suggestion algorithm.

[0050] The proposal unit can determine the priority of proposals based on the submission date of the fashion items when making a proposal. For example, the proposal unit may prioritize recently submitted fashion items. The proposal unit may also prioritize fashion items submitted in a particular season. The proposal unit may also determine the priority of proposals based on the submission date of the fashion items. This allows the system to provide appropriate suggestions to users by prioritizing proposals based on the submission date of the fashion items. The submission date of fashion items includes, but is not limited to, the release date and purchase date. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input fashion item submission date data into a generating AI and have the generating AI determine the priority of proposals.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of fashion items when making suggestions. For example, the suggestion unit may prioritize suggesting fashion items that the user is interested in. The suggestion unit can also adjust the order of suggestions based on the relevance of fashion items. For example, the suggestion unit can adjust the order of suggestions according to the user's level of interest. By adjusting the order of suggestions based on the relevance of fashion items, the system can provide suggestions that are appropriate for the user. The relevance of fashion items includes, but is not limited to, combinations of items and the degree of trend matching. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input fashion item relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0052] The database unit can optimize the database by referring to past fashion data when updating the database. For example, the database unit optimizes the database based on past fashion data. The database unit can also determine the content of database updates by referring to past fashion data. For example, the database unit can analyze past fashion data and optimize the database. This allows the database unit to provide users with appropriate information by optimizing the database by referring to past fashion data. Past fashion data includes, but is not limited to, purchase history and wear history. Some or all of the above processing in the database unit may be performed using AI or not. For example, the database unit can input past fashion data into a generating AI and have the generating AI perform database optimization.

[0053] The database unit can apply different update algorithms to each fashion category when updating the database. For example, in the case of the casual fashion category, the database unit can apply an update algorithm specifically for casual wear. For example, in the case of the business fashion category, the database unit can also apply an update algorithm specifically for business wear. For example, in the case of the sports fashion category, the database unit can also apply an update algorithm specifically for sports wear. By applying different update algorithms to each fashion category, the database unit can provide users with appropriate information. Fashion categories include, but are not limited to, casual, formal, and sports wear. Some or all of the above processing in the database unit may be performed using AI or not. For example, the database unit can input fashion category data into a generating AI and have the generating AI execute the application of the update algorithm.

[0054] The database unit can determine the priority of the database based on the submission date of fashion items when updating the database. For example, the database unit may prioritize displaying recently submitted fashion items in the database. The database unit may also prioritize displaying fashion items submitted in a particular season in the database. The database unit may also determine the priority of the database based on the submission date of fashion items. This allows the database unit to provide users with appropriate information by prioritizing the database based on the submission date of fashion items. The submission date of fashion items includes, but is not limited to, the release date and purchase date. Some or all of the above processing in the database unit may be performed using AI or not. For example, the database unit may input fashion item submission date data into a generating AI and have the generating AI perform the determination of database priorities.

[0055] The database unit can adjust the order of the database based on the relevance of fashion items when updating the database. For example, the database unit can prioritize displaying fashion items that the user is interested in. The database unit can also adjust the order of the database based on the relevance of fashion items. For example, the database unit can adjust the order of the database according to the user's level of interest. This allows the database unit to provide users with appropriate information by adjusting the order of the database based on the relevance of fashion items. The relevance of fashion items includes, but is not limited to, item combinations and the degree of trend matching. Some or all of the above processing in the database unit may be performed using AI or not. For example, the database unit can input fashion item relevance data into a generating AI and have the generating AI perform the adjustment of the database order.

[0056] The styling unit can adjust the level of detail of the styling based on the user's lifestyle during the styling process. For example, if the user has a casual lifestyle, the styling unit will provide styling specifically tailored for casual occasions. For example, if the user is looking for styling for a business setting, the styling unit can also provide styling specifically tailored for business settings. The styling unit can also provide styling with an appropriate level of detail based on the user's lifestyle. This allows the styling unit to provide styling that is appropriate for the user by adjusting the level of detail based on the user's lifestyle. Lifestyle includes, but is not limited to, occupation, hobbies, and daily activities. Some or all of the above-described processes in the styling unit may be performed using AI or not. For example, the styling unit can input the user's lifestyle data into a generating AI and have the generating AI perform the adjustment of the level of detail of the styling.

[0057] The styling unit can apply different styling algorithms depending on the user's event category during styling. For example, for a casual event, the styling unit can apply a styling algorithm specifically for casual events. For example, for a business event, the styling unit can apply a styling algorithm specifically for business events. For example, for a sports event, the styling unit can apply a styling algorithm specifically for sports events. This allows the styling unit to provide appropriate styling to the user by applying different styling algorithms depending on the user's event category. Event categories include, but are not limited to, weddings, parties, and business meetings. Some or all of the above processing in the styling unit may be performed using AI or not. For example, the styling unit can input event category data into a generating AI and have the generating AI perform the application of the styling algorithm.

[0058] The styling unit can determine styling priorities based on when the user's lifestyle information was submitted. For example, the styling unit may prioritize the incorporation of recently submitted lifestyle information into the styling. The styling unit may also prioritize the incorporation of lifestyle information submitted during a particular season. The styling unit can also determine styling priorities based on when the user's lifestyle information was submitted. This allows the styling unit to provide the user with appropriate styling by prioritizing styling based on when the user's lifestyle information was submitted. Examples of lifestyle submission dates include, but are not limited to, the date the survey was conducted or when the user's lifestyle changed. Some or all of the above processing in the styling unit may be performed using AI or not. For example, the styling unit can input lifestyle submission date data into a generating AI and have the generating AI perform the determination of styling priorities.

[0059] The styling unit can adjust the styling order based on the relevance of the user's lifestyle during the styling process. For example, the styling unit can prioritize reflecting lifestyle information that the user is interested in. The styling unit can also adjust the styling order based on the relevance of the user's lifestyle. For example, the styling unit can adjust the styling order according to the user's level of interest. This allows the styling unit to provide the user with appropriate styling by adjusting the styling order based on the relevance of the user's lifestyle. Lifestyle relevance includes, but is not limited to, the relevance of occupation and hobbies, or the relevance of daily activities and fashion. Some or all of the above processing in the styling unit may be performed using AI or not. For example, the styling unit can input lifestyle relevance data into a generating AI and have the generating AI perform the adjustment of the styling order.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can analyze a user's past fashion history and select the most suitable input method. For example, it can suggest the most frequently used input method based on the user's past fashion history. It can also suggest an input method tailored to a specific season based on the user's past fashion history. In this way, by analyzing the user's past fashion history, the system can provide the most suitable input method.

[0062] The reception desk can filter the input of photos and preferences based on the user's current fashion trends and areas of interest. For example, it can narrow down the input fields based on the fashion categories the user is currently interested in. It can also analyze the user's current fashion trends and prioritize displaying relevant input fields. This allows for more appropriate information to be entered by filtering based on the user's current fashion trends and areas of interest.

[0063] The analysis unit can adjust the level of detail in the analysis based on the user's fashion history. For example, if the user has previously preferred detailed analysis, it will provide detailed analysis results. If the user has previously preferred concise analysis, it can also provide concise analysis results. By adjusting the level of detail in the analysis based on the user's fashion history, the system can provide analysis results that are appropriate for the user.

[0064] The proposal department can adjust the level of detail in its proposals based on the importance of the fashion items. For example, it can provide detailed proposals for important fashion items, and concise proposals for less important items. By adjusting the level of detail based on the importance of the fashion items, it can provide users with appropriate suggestions.

[0065] The database unit can optimize the database by referring to past fashion data when updating it. For example, it can optimize the database based on past fashion data. It can also determine the content of database updates by referring to past fashion data. This allows the database to be optimized by referring to past fashion data, thereby providing users with appropriate information.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk inputs the user's photos and preferences. These include full-body photos, face photos, color preferences, style preferences, etc. The reception desk inputs information when the user uploads their photos and answers a questionnaire about their preferences. The reception desk can also supplement the input by referring to the user's past fashion history. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses deep learning-based image analysis technology to extract features from the user's photograph. For example, it analyzes the user's face shape and body type to generate data for suggesting appropriate fashion styles. The analysis unit also uses a recommendation system based on user preference data to generate data for suggesting the most suitable fashion styles for each individual user. Step 3: The proposal department makes fashion suggestions based on the results analyzed by the analysis department. The proposal department suggests styling tailored to the user's face shape and body type. Furthermore, it can make suggestions that reflect the user's preferred colors and designs. In addition, the proposal department can also suggest styling tailored to the user's lifestyle. Step 4: The database department incorporates the latest trend information. The database department uses a real-time updated fashion database to obtain the latest trend information. For example, it collects data from fashion magazines and online shops and reflects it in the database. Step 5: The styling department provides styling tailored to the user's lifestyle. When a user is attending a specific event, the styling department suggests styling appropriate for that event. Furthermore, they can also suggest styling based on the user's occupation and hobbies.

[0068] (Example of form 2) The fashion suggestion system according to an embodiment of the present invention is a system that analyzes a user's photos and preferences and provides personalized fashion suggestions. This fashion suggestion system allows the user to input their own photos and preferences, and the AI ​​analyzes this information to suggest the most suitable fashion style for the user. These suggestions reflect the latest trends and seasonality. Styling is also tailored to the user's lifestyle. This allows users to easily find fashion that suits them and go out with confidence. For example, when a user inputs their photos and preferences, they can also input information about their preferences and lifestyle. For instance, information is provided to users who prefer casual styles or those seeking styling for business settings, according to their individual needs. Next, the AI ​​analyzes the input information. The AI ​​utilizes deep learning-based image analysis technology to extract features from the user's photos. Furthermore, a recommendation system based on user preference data is used to suggest the most suitable fashion style for each individual user. For example, suggestions are made that match the user's face shape and body type, and reflect their preferred colors and designs. In addition, the AI ​​utilizes a real-time updated fashion database to provide suggestions that incorporate the latest trends. This allows users to always enjoy the latest fashion. For example, seasonal trends and information on popular brands and items are reflected in the suggestions. This system makes it easy for users to find fashion that suits them. For instance, when a user is attending a specific event, the AI ​​can suggest an appropriate style for that event. Furthermore, styling tailored to the user's lifestyle is also provided, allowing them to enjoy fashion with confidence in various everyday situations. This system has the effect of improving user satisfaction. For example, over 90% of users are satisfied with the suggested styles, and 70% of users reuse the service. Additionally, sharing on social media can lead to the acquisition of new customers. For example, a 20% increase in monthly users is predicted.Thus, an AI-powered fashion suggestion system is a groundbreaking solution for realizing a society where every user can enjoy fashion with confidence. This system analyzes the user's photos and preferences, enabling it to provide personalized fashion suggestions.

[0069] The fashion suggestion system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, a database unit, and a styling unit. The reception unit inputs the user's photos and preferences. The user's photos and preferences include, but are not limited to, full-body photos, face photos, color preferences, style preferences, etc. The reception unit can input information, for example, by the user uploading their own photos and answering a questionnaire about their preferences. The reception unit can also supplement the input by referring to the user's past fashion history. The analysis unit analyzes the information input by the reception unit. The analysis unit extracts features from the user's photos, for example, by utilizing image analysis technology using deep learning. The analysis unit analyzes, for example, the user's face shape and body type and generates data for suggesting appropriate fashion styles. The analysis unit also generates data for suggesting the optimal fashion style for each individual user using a recommendation system based on the user's preference data. The suggestion unit makes fashion suggestions based on the results analyzed by the analysis unit. The suggestion unit suggests styling that suits the user's face shape and body type, for example. The suggestion unit can, for example, make suggestions that reflect the user's preferred colors and designs. It can also suggest styling tailored to the user's lifestyle. The database unit incorporates the latest trend information. For example, the database unit utilizes a real-time updated fashion database to obtain the latest trend information. The database unit collects data from fashion magazines and online shops and reflects it in the database. The styling unit provides styling tailored to the user's lifestyle. For example, when a user is participating in a specific event, the styling unit suggests styling appropriate for that event. The styling unit can also suggest styling tailored to the user's occupation or hobbies. As a result, the fashion suggestion system according to this embodiment can analyze the user's photos and preferences and provide personalized fashion suggestions.

[0070] The reception desk inputs the user's photos and preferences. These include, but are not limited to, full-body photos, face photos, color preferences, and style preferences. For example, users can input information by uploading their photos and answering preference questionnaires. Specifically, users access a dedicated application or website using their smartphone or computer to upload photos. Full-body photos are used to understand the user's body type and height, while face photos are used to analyze facial features and shape. The preference questionnaire allows users to input detailed information such as their favorite colors, preferred fashion styles, usual clothing tendencies, and preferences for specific brands or designers. The reception desk can also supplement the input by referring to the user's past fashion history. For example, it can more accurately understand the user's preferences based on items the user has purchased in the past and a history of previous fashion suggestions. This allows the reception desk to collect detailed user information and generate basic data to provide to the analysis and suggestion departments.

[0071] The analysis unit analyzes the information entered by the reception unit. For example, the analysis unit uses advanced image analysis technology, such as deep learning, to extract features from the user's photographs. Specifically, it uses advanced image analysis algorithms such as convolutional neural networks (CNNs) to analyze body shape and height from full-body photographs and facial shape and features from face photographs. This generates data to suggest the most suitable fashion style for the user's body shape and facial shape. The analysis unit also uses a recommendation system based on user preference data to generate data to suggest the most suitable fashion style for each individual user. The recommendation system combines methods such as collaborative filtering and content-based filtering to analyze the user's past preferences and data from other users with similar preferences to recommend the most suitable fashion items. Furthermore, the analysis unit also considers information such as the user's occupation, hobbies, and daily activity patterns to suggest styling that suits the user's lifestyle and specific events. This allows the analysis unit to accurately grasp the user's detailed characteristics and preferences and generate data to provide the most suitable fashion suggestions for each individual user.

[0072] The Proposal Department makes fashion suggestions based on the results analyzed by the Analysis Department. For example, the Proposal Department suggests styling tailored to the user's face shape and body type. Specifically, it suggests the optimal hairstyle and accessories based on the user's face shape, and clothing with the optimal silhouette and design based on their body type. The Proposal Department can also make suggestions that reflect the user's preferred colors and designs. For example, based on the user's favorite colors, it suggests outfits incorporating those colors and selects items that match the user's preferred fashion style. Furthermore, the Proposal Department can also suggest styling tailored to the user's lifestyle. For example, it can make suggestions for various occasions, such as formal styles for business settings, casual everyday styles, and special styles for attending specific events. To present these suggestions to the user in a visually easy-to-understand manner, the Proposal Department provides image examples of the outfits and lists of specific items. In this way, the Proposal Department can make fashion suggestions tailored to the user's individual characteristics and preferences, helping the user find the style that is best suited to them.

[0073] The database department incorporates the latest trend information. For example, it obtains the latest trend information by utilizing a real-time updated fashion database. Specifically, it collects data from fashion magazines, online shops, and fashion shows and reflects it in the database. This allows the database department to always grasp the latest fashion trends and provide users with the most up-to-date information. Furthermore, the database department also accumulates historical fashion data and user preference data, providing a foundation for the analysis and proposal departments to utilize this data to make more accurate suggestions. For example, by analyzing historical fashion data, it is possible to understand the changes in trends during specific seasons or events and make appropriate suggestions to users. In addition, the database department collects user feedback and continuously updates the database content, enabling flexible responses to user needs. This allows the database department to integrate the latest trend information with user preference data, improving the accuracy and reliability of the entire system.

[0074] The Styling Department provides styling tailored to the user's lifestyle. For example, when a user is attending a specific event, the Styling Department will suggest a suitable style for that event. Specifically, they will suggest styles for various occasions such as weddings, parties, and business meetings. They can also suggest styles based on the user's occupation and hobbies. For example, they will suggest a unique and individual style for users in creative professions, and a formal and sophisticated style for business professionals. Furthermore, the Styling Department utilizes data provided by the Analysis Department to create styles that match the user's body type and facial features. This allows them to suggest clothing with the optimal silhouette and design for the user. In addition, the Styling Department can create styles that reflect the user's preferred colors and designs. For example, based on the user's favorite colors, they will suggest coordinates incorporating those colors and select items that match the user's preferred fashion style. In this way, the Styling Department can provide styling tailored to the user's lifestyle and preferences, helping users find the style that is best suited to them.

[0075] The suggestion unit can propose styling tailored to the user's face shape and body type. For example, the suggestion unit can suggest appropriate hairstyles and accessories based on the user's face shape. For example, the suggestion unit can also suggest appropriate clothing silhouettes and sizes based on the user's body type. For example, the suggestion unit can use AI to perform face recognition and body type analysis in order to create styling tailored to the user's face shape and body type. This makes it possible to make more appropriate fashion suggestions by proposing styling tailored to the user's face shape and body type. Face shape and body type include, but are not limited to, round face, oval face, slender, and plump. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input data on the user's face shape and body type into a generating AI and have the generating AI execute styling suggestions.

[0076] The suggestion unit can make suggestions that reflect the user's preferred colors and designs. For example, the suggestion unit can suggest appropriate fashion items based on the user's preferred colors and designs. The suggestion unit can also make suggestions that reflect the user's preferred colors and designs based on the user's past purchase history or survey results. For example, the suggestion unit can use AI to analyze preference data in order to make suggestions that reflect the user's preferred colors and designs. This improves user satisfaction by making suggestions that reflect the user's preferred colors and designs. Preferred colors and designs include, but are not limited to, red, blue, green, simple, casual, and elegant. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user preference data into a generating AI and have the generating AI make fashion item suggestions.

[0077] The database unit can utilize a fashion database that is updated in real time. For example, the database unit can collect data from fashion magazines and online shops in real time and reflect it in the database. The database unit can also analyze data using AI to obtain the latest trend information. For example, the database unit can periodically update the fashion database to provide the latest information. This makes it possible to make suggestions that reflect the latest trend information by utilizing a fashion database that is updated in real time. A fashion database that is updated in real time may include, but is not limited to, the latest fashion items, popular brands, and seasonal trends. Some or all of the above processing in the database unit may be performed using AI or not. For example, the database unit can input fashion data into a generating AI and have the generating AI perform the database update.

[0078] The styling unit can provide styling tailored to the user's lifestyle. For example, the styling unit can suggest styling that suits the user's occupation and hobbies. For example, if the user has a casual lifestyle, the styling unit can suggest casual styling. For example, if the user is looking for styling for a business setting, the styling unit can suggest styling that is suitable for business. By providing styling tailored to the user's lifestyle, they can enjoy fashion with confidence in various everyday situations. Lifestyle includes, but is not limited to, work, casual, and formal wear. Some or all of the above processing in the styling unit may be performed using AI or not. For example, the styling unit can input the user's lifestyle data into a generating AI and have the generating AI generate styling suggestions.

[0079] The styling unit can suggest styling suitable for specific events. For example, when a user is attending a wedding, the styling unit can suggest styling suitable for a wedding. For example, when a user is attending a party, the styling unit can suggest styling suitable for a party. For example, when a user is attending a business meeting, the styling unit can suggest styling suitable for a business meeting. By suggesting styling suitable for specific events, users can enjoy fashion appropriate for the event. Specific events include, but are not limited to, weddings, parties, and business meetings. Some or all of the above processing in the styling unit may be performed using AI or not. For example, the styling unit can input event information into a generating AI and have the generating AI perform styling suggestions.

[0080] The reception unit can estimate the user's emotions and adjust the timing of photo and preference input based on the estimated emotions. For example, if the user is relaxed, the reception unit can flexibly adjust the timing of prompts for photo and preference input. For example, if the user is stressed, the reception unit can simplify the input process to allow for quick completion. For example, if the user is excited, the reception unit can add interactive elements to make the input process more enjoyable. This reduces the user's burden by adjusting the input timing according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI adjust the input timing.

[0081] The reception desk can analyze the user's past fashion history and select the optimal input method. For example, the reception desk can suggest the most frequently used input method based on the user's past fashion history. The reception desk can also suggest an input method tailored to a specific season based on the user's past fashion history. The reception desk can also analyze the user's past fashion history and provide an auto-completion function to reduce the effort required for input. This allows the reception desk to provide the optimal input method by analyzing the user's past fashion history. Past fashion history includes, but is not limited to, purchase history and wear history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's fashion history data into a generating AI and have the generating AI select the optimal input method.

[0082] The reception desk can filter the input of photos and preferences based on the user's current fashion trends and areas of interest. For example, the reception desk can narrow down the input fields based on the fashion categories the user is currently interested in. The reception desk can also analyze the user's current fashion trends and prioritize the display of relevant input fields. The reception desk can also customize the sample images and examples displayed during input based on the user's areas of interest. This allows for the input of more appropriate information by filtering based on the user's current fashion trends and areas of interest. Current fashion trends and areas of interest include, but are not limited to, survey results and social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's fashion trend data into a generating AI and have the generating AI perform the filtering.

[0083] The reception desk can estimate the user's emotions and determine the priority of the information to be entered based on the estimated emotions. For example, if the user is in a hurry, the reception desk may prioritize the input of important information. If the user is relaxed, the reception desk may also encourage the user to enter detailed information. If the user is stressed, the reception desk may also allow the user to enter only the minimum amount of information. This reduces the user's burden by determining the priority of the information to be entered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's emotion data into a generative AI and have the generative AI determine the priority of the information.

[0084] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when inputting photos and preferences. For example, the reception desk can prioritize inputting fashion information that matches the climate of the user's current location. The reception desk can also display input items that match local trends based on the user's geographical location. For example, the reception desk can input fashion information related to local events, taking the user's location into consideration. This allows for the priority input of highly relevant information by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's location data into a generating AI and have the generating AI determine the priority of the information.

[0085] The reception desk can analyze the user's social media activity and input relevant information when they input photos and preferences. For example, the reception desk can analyze the content of the user's social media posts and input relevant fashion information. The reception desk can also suggest input items by referencing the styles of influencers the user follows. The reception desk can also input information that matches the user's preferences based on their "likes" and comments on social media. In this way, relevant information can be input by analyzing the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI perform the information input.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. For example, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on the user's fashion history during the analysis. For example, if the user has preferred detailed analysis in the past, the analysis unit will provide detailed analysis results. The analysis unit can also provide concise analysis results if the user has preferred concise analysis in the past. The analysis unit can also provide analysis results with an appropriate level of detail based on the user's fashion history. In this way, by adjusting the level of detail of the analysis based on the user's fashion history, it is possible to provide analysis results that are appropriate for the user. Fashion history includes, but is not limited to, purchase history and wear history. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's fashion history data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the user's fashion category during analysis. For example, in the case of casual fashion, the analysis unit can apply an analysis algorithm specialized for casual wear. For example, in the case of business fashion, the analysis unit can also apply an analysis algorithm specialized for business wear. For example, in the case of sports fashion, the analysis unit can also apply an analysis algorithm specialized for sports wear. By applying different analysis algorithms depending on the user's fashion category, more appropriate analysis results can be provided. Fashion categories include, but are not limited to, casual, formal, and sports wear. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input fashion category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is excited, the analysis unit can also provide a visually appealing analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with an appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on when the user's fashion history was submitted. For example, the analysis unit may prioritize analyzing the user's most recently submitted fashion history. The analysis unit may also prioritize analyzing the user's fashion history submitted during a specific season. The analysis unit can also determine the priority of analysis based on when the user's fashion history was submitted. This allows for the provision of more appropriate analysis results by prioritizing analysis based on when the user's fashion history was submitted. The submission date of the fashion history includes, but is not limited to, the purchase date and the date of wear. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input fashion history submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the user's fashion relevance during the analysis. For example, the analysis unit may prioritize analyzing fashion categories that the user is interested in. The analysis unit can also adjust the order of analysis based on the user's fashion relevance. For example, the analysis unit can adjust the order of analysis according to the user's level of interest. By adjusting the order of analysis based on the user's fashion relevance, more appropriate analysis results can be provided. Fashion relevance includes, but is not limited to, item combinations and the degree of trend matching. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input fashion relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0092] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. If the user is excited, the suggestion unit can provide visually appealing suggestions. By adjusting the way it presents suggestions according to the user's emotions, it can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way it presents its suggestions.

[0093] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. If the user is excited, the suggestion unit can provide visually appealing suggestions. By adjusting the way it presents suggestions according to the user's emotions, it can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way it presents its suggestions.

[0094] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the fashion items. For example, it can provide detailed suggestions for important fashion items, while providing concise suggestions for less important items. The suggestion unit can also adjust the level of detail in its suggestions based on the importance of the fashion items. By adjusting the level of detail in suggestions based on the importance of the fashion items, it can provide appropriate suggestions to the user. The importance of fashion items includes, but is not limited to, factors such as trendiness and frequency of use. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input fashion item importance data into a generating AI and have the generating AI adjust the level of detail in its suggestions.

[0095] The suggestion unit can apply different suggestion algorithms depending on the category of the fashion item when making suggestions. For example, in the case of casual fashion, the suggestion unit can apply a suggestion algorithm specialized for casual wear. For example, in the case of business fashion, the suggestion unit can also apply a suggestion algorithm specialized for business wear. For example, in the case of sports fashion, the suggestion unit can also apply a suggestion algorithm specialized for sports wear. By applying different suggestion algorithms depending on the category of the fashion item, the suggestion unit can provide appropriate suggestions to the user. Categories of fashion items include, but are not limited to, tops, bottoms, and accessories. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input fashion item category data into a generating AI and have the generating AI perform the application of the suggestion algorithm.

[0096] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, for example, the suggestion unit can provide detailed suggestions. If the user is excited, for example, the suggestion unit can provide visually appealing suggestions. By adjusting the length of suggestions according to the user's emotions, the suggestion unit can provide suggestions that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0097] The proposal unit can determine the priority of proposals based on the submission date of the fashion items when making a proposal. For example, the proposal unit may prioritize recently submitted fashion items. The proposal unit may also prioritize fashion items submitted in a particular season. The proposal unit may also determine the priority of proposals based on the submission date of the fashion items. This allows the system to provide appropriate suggestions to users by prioritizing proposals based on the submission date of the fashion items. The submission date of fashion items includes, but is not limited to, the release date and purchase date. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input fashion item submission date data into a generating AI and have the generating AI determine the priority of proposals.

[0098] The suggestion unit can adjust the order of suggestions based on the relevance of fashion items when making suggestions. For example, the suggestion unit may prioritize suggesting fashion items that the user is interested in. The suggestion unit can also adjust the order of suggestions based on the relevance of fashion items. For example, the suggestion unit can adjust the order of suggestions according to the user's level of interest. By adjusting the order of suggestions based on the relevance of fashion items, the system can provide suggestions that are appropriate for the user. The relevance of fashion items includes, but is not limited to, combinations of items and the degree of trend matching. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input fashion item relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0099] The database unit can estimate the user's emotions and adjust the database update frequency based on the estimated emotions. For example, the database unit can increase the database update frequency if the user uses the database frequently. For example, the database unit can decrease the database update frequency if the user uses the database infrequently. The database unit can also adjust the database update frequency according to the user's emotions. This allows the system to provide users with appropriate information by adjusting the database update frequency according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the database unit may be performed using AI or not. For example, the database unit can input user emotion data into a generative AI and have the generative AI adjust the database update frequency.

[0100] The database unit can optimize the database by referring to past fashion data when updating the database. For example, the database unit optimizes the database based on past fashion data. The database unit can also determine the content of database updates by referring to past fashion data. For example, the database unit can analyze past fashion data and optimize the database. This allows the database unit to provide users with appropriate information by optimizing the database by referring to past fashion data. Past fashion data includes, but is not limited to, purchase history and wear history. Some or all of the above processing in the database unit may be performed using AI or not. For example, the database unit can input past fashion data into a generating AI and have the generating AI perform database optimization.

[0101] The database unit can apply different update algorithms to each fashion category when updating the database. For example, in the case of the casual fashion category, the database unit can apply an update algorithm specifically for casual wear. For example, in the case of the business fashion category, the database unit can also apply an update algorithm specifically for business wear. For example, in the case of the sports fashion category, the database unit can also apply an update algorithm specifically for sports wear. By applying different update algorithms to each fashion category, the database unit can provide users with appropriate information. Fashion categories include, but are not limited to, casual, formal, and sports wear. Some or all of the above processing in the database unit may be performed using AI or not. For example, the database unit can input fashion category data into a generating AI and have the generating AI execute the application of the update algorithm.

[0102] The database unit can estimate the user's emotions and adjust how the database is displayed based on the estimated emotions. For example, if the user is relaxed, the database unit can provide a detailed database display. If the user is in a hurry, for example, the database unit can provide a concise database display that gets straight to the point. If the user is excited, for example, the database unit can provide a visually appealing database display. In this way, by adjusting how the database is displayed according to the user's emotions, the system can provide the user with appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the database unit may be performed using AI or not. For example, the database unit can input user emotion data into a generative AI and have the generative AI adjust how the database is displayed.

[0103] The database unit can determine the priority of the database based on the submission date of fashion items when updating the database. For example, the database unit may prioritize displaying recently submitted fashion items in the database. The database unit may also prioritize displaying fashion items submitted in a particular season in the database. The database unit may also determine the priority of the database based on the submission date of fashion items. This allows the database unit to provide users with appropriate information by prioritizing the database based on the submission date of fashion items. The submission date of fashion items includes, but is not limited to, the release date and purchase date. Some or all of the above processing in the database unit may be performed using AI or not. For example, the database unit may input fashion item submission date data into a generating AI and have the generating AI perform the determination of database priorities.

[0104] The database unit can adjust the order of the database based on the relevance of fashion items when updating the database. For example, the database unit can prioritize displaying fashion items that the user is interested in. The database unit can also adjust the order of the database based on the relevance of fashion items. For example, the database unit can adjust the order of the database according to the user's level of interest. This allows the database unit to provide users with appropriate information by adjusting the order of the database based on the relevance of fashion items. The relevance of fashion items includes, but is not limited to, item combinations and the degree of trend matching. Some or all of the above processing in the database unit may be performed using AI or not. For example, the database unit can input fashion item relevance data into a generating AI and have the generating AI perform the adjustment of the database order.

[0105] The styling unit can estimate the user's emotions and adjust the styling expression based on the estimated emotions. For example, if the user is relaxed, the styling unit can provide detailed styling. If the user is in a hurry, the styling unit can also provide concise styling that gets straight to the point. If the user is excited, the styling unit can also provide visually appealing styling. In this way, by adjusting the styling expression according to the user's emotions, the styling can be provided that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the styling unit may be performed using AI or not. For example, the styling unit can input user emotion data into the generative AI and have the generative AI perform adjustments to the styling expression.

[0106] The styling unit can adjust the level of detail of the styling based on the user's lifestyle during the styling process. For example, if the user has a casual lifestyle, the styling unit will provide styling specifically tailored for casual occasions. For example, if the user is looking for styling for a business setting, the styling unit can also provide styling specifically tailored for business settings. The styling unit can also provide styling with an appropriate level of detail based on the user's lifestyle. This allows the styling unit to provide styling that is appropriate for the user by adjusting the level of detail based on the user's lifestyle. Lifestyle includes, but is not limited to, occupation, hobbies, and daily activities. Some or all of the above-described processes in the styling unit may be performed using AI or not. For example, the styling unit can input the user's lifestyle data into a generating AI and have the generating AI perform the adjustment of the level of detail of the styling.

[0107] The styling unit can apply different styling algorithms depending on the user's event category during styling. For example, for a casual event, the styling unit can apply a styling algorithm specifically for casual events. For example, for a business event, the styling unit can apply a styling algorithm specifically for business events. For example, for a sports event, the styling unit can apply a styling algorithm specifically for sports events. This allows the styling unit to provide appropriate styling to the user by applying different styling algorithms depending on the user's event category. Event categories include, but are not limited to, weddings, parties, and business meetings. Some or all of the above processing in the styling unit may be performed using AI or not. For example, the styling unit can input event category data into a generating AI and have the generating AI perform the application of the styling algorithm.

[0108] The styling unit can estimate the user's emotions and adjust the length of the styling based on the estimated emotions. For example, if the user is in a hurry, the styling unit can provide a short, concise style. For example, if the user is relaxed, the styling unit can provide a detailed style. For example, if the user is excited, the styling unit can provide a visually appealing style. In this way, by adjusting the length of the styling according to the user's emotions, the styling can be provided that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the styling unit may be performed using AI or not. For example, the styling unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the styling length.

[0109] The styling unit can determine styling priorities based on when the user's lifestyle information was submitted. For example, the styling unit may prioritize the incorporation of recently submitted lifestyle information into the styling. The styling unit may also prioritize the incorporation of lifestyle information submitted during a particular season. The styling unit can also determine styling priorities based on when the user's lifestyle information was submitted. This allows the styling unit to provide the user with appropriate styling by prioritizing styling based on when the user's lifestyle information was submitted. Examples of lifestyle submission dates include, but are not limited to, the date the survey was conducted or when the user's lifestyle changed. Some or all of the above processing in the styling unit may be performed using AI or not. For example, the styling unit can input lifestyle submission date data into a generating AI and have the generating AI perform the determination of styling priorities.

[0110] The styling unit can adjust the styling order based on the relevance of the user's lifestyle during the styling process. For example, the styling unit can prioritize reflecting lifestyle information that the user is interested in. The styling unit can also adjust the styling order based on the relevance of the user's lifestyle. For example, the styling unit can adjust the styling order according to the user's level of interest. This allows the styling unit to provide the user with appropriate styling by adjusting the styling order based on the relevance of the user's lifestyle. Lifestyle relevance includes, but is not limited to, the relevance of occupation and hobbies, or the relevance of daily activities and fashion. Some or all of the above processing in the styling unit may be performed using AI or not. For example, the styling unit can input lifestyle relevance data into a generating AI and have the generating AI perform the adjustment of the styling order.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The reception system can estimate the user's emotions and adjust the timing of photo and preference input based on those estimates. For example, if the user is relaxed, it can flexibly adjust the timing of prompts for photo and preference input. If the user is stressed, the input process can be simplified to allow for quick completion. If the user is excited, interactive elements can be added to make the input process more enjoyable. By adjusting the input timing according to the user's emotions, the system can reduce the user's burden.

[0113] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. If the user is excited, it can provide visually appealing analysis results. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0114] The proposal function can estimate the user's emotions and adjust the way the proposal is presented based on those emotions. For example, if the user is relaxed, it can provide a detailed proposal. If the user is in a hurry, it can provide a concise proposal that gets straight to the point. If the user is excited, it can provide a visually appealing proposal. By adjusting the way the proposal is presented according to the user's emotions, it can provide proposals that are easy for the user to understand.

[0115] The database unit can estimate user sentiment and adjust the database update frequency based on that estimation. For example, if a user uses the database frequently, the update frequency can be increased. If a user uses it infrequently, the update frequency can be decreased. By adjusting the database update frequency according to user sentiment, the system can provide users with information that is appropriate for them.

[0116] The styling unit can estimate the user's emotions and adjust the styling presentation based on those emotions. For example, if the user is relaxed, it can provide detailed styling. If the user is in a hurry, it can provide concise styling that gets straight to the point. If the user is excited, it can provide visually appealing styling. In this way, by adjusting the styling presentation according to the user's emotions, it can provide styling that is appropriate for the user.

[0117] The reception desk can analyze a user's past fashion history and select the most suitable input method. For example, it can suggest the most frequently used input method based on the user's past fashion history. It can also suggest an input method tailored to a specific season based on the user's past fashion history. In this way, by analyzing the user's past fashion history, the system can provide the most suitable input method.

[0118] The reception desk can filter the input of photos and preferences based on the user's current fashion trends and areas of interest. For example, it can narrow down the input fields based on the fashion categories the user is currently interested in. It can also analyze the user's current fashion trends and prioritize displaying relevant input fields. This allows for more appropriate information to be entered by filtering based on the user's current fashion trends and areas of interest.

[0119] The analysis unit can adjust the level of detail in the analysis based on the user's fashion history. For example, if the user has previously preferred detailed analysis, it will provide detailed analysis results. If the user has previously preferred concise analysis, it can also provide concise analysis results. By adjusting the level of detail in the analysis based on the user's fashion history, the system can provide analysis results that are appropriate for the user.

[0120] The proposal department can adjust the level of detail in its proposals based on the importance of the fashion items. For example, it can provide detailed proposals for important fashion items, and concise proposals for less important items. By adjusting the level of detail based on the importance of the fashion items, it can provide users with appropriate suggestions.

[0121] The database unit can optimize the database by referring to past fashion data when updating it. For example, it can optimize the database based on past fashion data. It can also determine the content of database updates by referring to past fashion data. This allows the database to be optimized by referring to past fashion data, thereby providing users with appropriate information.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The reception desk inputs the user's photos and preferences. These include full-body photos, face photos, color preferences, style preferences, etc. The reception desk inputs information when the user uploads their photos and answers a questionnaire about their preferences. The reception desk can also supplement the input by referring to the user's past fashion history. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses deep learning-based image analysis technology to extract features from the user's photograph. For example, it analyzes the user's face shape and body type to generate data for suggesting appropriate fashion styles. The analysis unit also uses a recommendation system based on user preference data to generate data for suggesting the most suitable fashion styles for each individual user. Step 3: The proposal department makes fashion suggestions based on the results analyzed by the analysis department. The proposal department suggests styling tailored to the user's face shape and body type. Furthermore, it can make suggestions that reflect the user's preferred colors and designs. In addition, the proposal department can also suggest styling tailored to the user's lifestyle. Step 4: The database department incorporates the latest trend information. The database department uses a real-time updated fashion database to obtain the latest trend information. For example, it collects data from fashion magazines and online shops and reflects it in the database. Step 5: The styling department provides styling tailored to the user's lifestyle. When a user is attending a specific event, the styling department suggests styling appropriate for that event. Furthermore, they can also suggest styling based on the user's occupation and hobbies.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, database unit, and styling unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, which inputs the user's photos and preferences. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which extracts features from the user's photos using deep learning-based image analysis technology. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which makes fashion suggestions based on the analysis results. The database unit is implemented by the database 24 of the data processing unit 12, which incorporates the latest trend information. The styling unit is implemented by the control unit 46A of the smart device 14, which styles the user according to their lifestyle. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, database unit, and styling unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, which inputs the user's photos and preferences. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which extracts features from the user's photos using deep learning-based image analysis technology. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12, which makes fashion suggestions based on the analysis results. The database unit is implemented by the database 24 of the data processing unit 12, which incorporates the latest trend information. The styling unit is implemented by the control unit 46A of the smart glasses 214, which styles the user according to their lifestyle. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, database unit, and styling unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, which inputs the user's photos and preferences. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which extracts features from the user's photos using deep learning-based image analysis technology. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which makes fashion suggestions based on the analysis results. The database unit is implemented by, for example, the database 24 of the data processing unit 12, which incorporates the latest trend information. The styling unit is implemented by, for example, the control unit 46A of the headset terminal 314, which performs styling tailored to the user's lifestyle. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, database unit, and styling unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, which inputs the user's photos and preferences. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which extracts features from the user's photos using deep learning-based image analysis technology. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which makes fashion suggestions based on the analysis results. The database unit is implemented by, for example, the database 24 of the data processing unit 12, which incorporates the latest trend information. The styling unit is implemented by, for example, the control unit 46A of the robot 414, which performs styling tailored to the user's lifestyle. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0177] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0187] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) A reception area where users input their photos and preferences, An analysis unit analyzes the information input by the reception unit, A proposal unit that makes fashion suggestions based on the results of the analysis performed by the aforementioned analysis unit, The database department incorporates the latest trend information, It includes a styling unit that allows for styling tailored to the user's lifestyle. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We propose styling tailored to the user's face shape and body type. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We provide suggestions that reflect the user's preferred colors and designs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned database unit is Utilizing a fashion database that is updated in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned styling section is Styling tailored to the user's lifestyle The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned styling section is We suggest styling suitable for specific events. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of photo and preference input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past fashion history and selects the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users input photos and preferences, the system filters them based on their current fashion trends and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input photos and preferences, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input photos and preferences, the system analyzes their social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the user's fashion history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the user's fashion category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the system prioritizes analysis based on when the user submitted their fashion history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the user's fashion relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the fashion items. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of the fashion item. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of their submission. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the fashion items. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned database unit is The system estimates user sentiment and adjusts the database update frequency based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned database unit is When updating the database, the database is optimized by referencing past fashion data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned database unit is When updating the database, different update algorithms are applied to each fashion category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned database unit is It estimates the user's emotions and adjusts how the database is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned database unit is When updating the database, the database priority is determined based on when the fashion items were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned database unit is When updating the database, the database order is adjusted based on the relevance of fashion items. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned styling section is It estimates the user's emotions and adjusts the styling expression based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned styling section is During styling, the level of detail in the styling is adjusted based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned styling section is During styling, different styling algorithms are applied depending on the user's event category. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned styling section is It estimates the user's emotions and adjusts the length of the styling based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned styling section is During the styling process, prioritizing styling based on when the user's lifestyle was submitted is determined. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned styling section is During styling, the styling order is adjusted based on the relevance of the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception unit that inputs a user's photo and preferences, An analysis unit that analyzes the information input by the reception unit, A proposal unit that makes a fashion proposal based on the result analyzed by the analysis unit, A database unit that incorporates the latest trend information, A styling unit that performs styling according to the user's lifestyle, and comprising A system characterized by this.

2. The proposal unit Proposes styling that suits the shape of the user's face and body type The system according to claim 1, characterized by this.

3. The proposal unit Makes a proposal that reflects the user's favorite colors and designs The system according to claim 1, characterized by this.

4. The database unit Utilizes a fashion database that is updated in real time The system according to claim 1, characterized by this.

5. The styling unit Performs styling according to the user's lifestyle The system according to claim 1, characterized by this.

6. The styling unit Proposes styling suitable for a specific event The system according to claim 1, characterized by this.

7. The reception unit Estimates the user's emotions and adjusts the timing of photo and preference input based on the estimated user's emotions The system according to claim 1, characterized by this.

8. The reception unit Analyzes the user's past fashion history and selects the optimal input method The system according to claim 1, characterized by this.

9. The reception unit Performs filtering based on the user's current fashion trends and areas of interest when inputting photos and preferences The system according to claim 1, characterized by this.

10. The reception unit Estimates the user's emotions and determines the priority of the information to be input based on the estimated user's emotions The system according to claim 1, characterized by this.

Images