System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting user information; data analysis means for suggesting optimal clothes to a user based on the user information; means for obtaining weather information; and means for suggesting coordination based on the user information and the weather information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, choosing appropriate clothing amidst busy daily life is a significant burden for many people. Finding the right outfit is particularly difficult amid the overwhelming amount of fashion information available, resulting in stress. Furthermore, choosing the perfect outfit based on the weather and schedule can be difficult and requires a lot of time and effort. This can potentially reduce the quality of daily life. Therefore, the objective of this invention is to provide a system that suggests fashion items that match the user's individuality, reduces the stress of choosing clothes, and enables efficient outfit coordination. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including a means for collecting user information, a data analysis means for suggesting optimal outfits to the user based on the user information, a means for acquiring weather information, and a means for suggesting outfits based on the user information and the weather information. Furthermore, by including a means for learning from collected feedback information about the user's outfits and improving the accuracy of the suggestions, it is possible to make suggestions that are more suited to the user's individuality. Furthermore, by including a means for suggesting new items to purchase based on the user's body shape information and preferred style information, it is possible to enrich the user's fashion experience.

[0006] "User Information" refers to information about a user's personality and lifestyle, such as the user's body shape, preferred style, photos of items the user owns, and schedule information.

[0007] "Weather information" refers to weather data for a specified area and date and time, such as temperature, probability of precipitation, and wind speed.

[0008] "Data analysis means" refers to a program or device that performs analysis to provide optimal fashion suggestions based on collected user information and weather information.

[0009] "Coordination suggestions" refers to the act of presenting optimal clothing combinations based on the user's personality, the weather on the day, and their plans.

[0010] "Feedback information" refers to the evaluations and comments users have made on past suggestions, which the AI ​​learns from and uses to improve the accuracy of its suggestions.

[0011] "Suggesting the purchase of new items" refers to the act of suggesting new fashion items that the user does not currently own but that may suit the user's body shape and preferred style. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0013] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0016] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0017] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0018] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0025] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0032] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0033] This invention is a system that suggests optimal clothing based on user information, and supports daily clothing selection by taking into consideration weather information and the user's schedule information in particular. The main target of this invention is men and women in their 20s to 40s living in busy urban areas, and the aim is to make their daily lives more convenient and comfortable.

[0034] System configuration

[0035] server

[0036] 1. Collection of User Information

[0037] The server collects information provided by the user, such as body shape information, preferred style information, photos of items owned, and schedule information, and builds a database of the user's personality and lifestyle.

[0038] 2. Obtaining weather information

[0039] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the area specified by the user using an external weather API.

[0040] 3. Data analysis

[0041] The server's AI analyzes the collected user information and weather information to derive the optimal outfit combination based on the user's body type, preferences, and schedule, taking into account past outfit history and user feedback.

[0042] 4. Coordination Proposal Generation

[0043] The server then creates specific outfit suggestions based on the analysis results and delivers them to the user's device, including suggestions for combining items the user already owns and new items they should consider purchasing.

[0044] Terminal

[0045] 1. Providing an interface

[0046] The device provides an interface that allows users to easily input and update information about their body shape, preferred style, photos of items they own, and schedule information.

[0047] 2. Display of notifications

[0048] The device notifies the user of the coordination suggestions sent from the server, which are displayed as push notifications or in-app notifications.

[0049] 3. Gather feedback

[0050] The device collects user feedback (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future outfit suggestions.

[0051] 4. Purchase procedure assistance

[0052] The terminal provides an interface for the user to purchase the suggested new items and allows the user to transition to a fashion online shopping site via a purchase link from the server.

[0053] user

[0054] 1. Enter your information

[0055] Users enter and update their body shape information, preferred styles, photos of the items they own, and daily schedule information into the app on their device.

[0056] 2. Review and adoption of proposals

[0057] Users can check the outfit suggestions displayed on their device and choose to actually use them. If they like the new items suggested, they can purchase them through the fashion online shopping site.

[0058] 3. Providing Feedback

[0059] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[0060] Specific examples

[0061] A user has planned to meet a friend for lunch the next day. The user has entered their body type (height: 170cm, weight: 65kg), preferred style (casual), and photos of items they own (e.g., blue casual shirt, beige chinos). The weather forecast for the user's area is sunny with a temperature of 25°C.

[0062] 1. The server performs data analysis based on this information and suggests a combination of "a blue casual shirt and beige chino pants" that would be suitable for a casual lunch date.

[0063] 2. Additionally, users will also be suggested lightweight white sneakers that they have recently considered purchasing.

[0064] 3. The terminal notifies the user of these suggestions, and the user selects coordination according to the suggestions.

[0065] 4. If the user decides to purchase the suggested new sneakers, they can use the purchase link on their device to access a fashion online store and easily place their order.

[0066] As described above, the system of the present invention proposes optimal clothing that suits the user's personality, making daily clothing selection more efficient.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] Users can check and update their body shape information (e.g., height 170 cm, weight 65 kg), preferred style (e.g., casual), and photos of items they own (e.g., blue casual shirt, beige chino pants, etc.) that they have previously registered in the app on their device.

[0070] Step 2:

[0071] The user inputs the next day's schedule information (e.g., lunch with a friend) through the terminal, which is then reflected in the system.

[0072] Step 3:

[0073] The server receives the information entered by the user and stores it in a database, including the user's body type, preferred style, schedule, and items owned.

[0074] Step 4:

[0075] The server accesses an external weather API and obtains weather information (e.g., sunny, temperature 25 degrees) for the area and date and time specified by the user.

[0076] Step 5:

[0077] The server's AI begins analyzing data based on collected user information, weather information, and schedule information, and selects the optimal outfit, taking into account the user's past behavioral history and feedback.

[0078] Step 6:

[0079] Based on the results of the data analysis, the server determines the best outfit combination for the user, suggesting a blue casual shirt and beige chino pants.

[0080] Step 7:

[0081] The server also simultaneously suggests new items that the user does not own but would be suitable (e.g., lightweight white sneakers).

[0082] Step 8:

[0083] The device receives the coordination suggestions sent from the server and notifies the user via push notifications or in-app notifications.

[0084] Step 9:

[0085] The user checks the outfit suggestions displayed on the device and decides whether to adopt the suggested outfit. If the user likes the suggestion, they can adopt it.

[0086] Step 10:

[0087] When a user wants to purchase a new item, they access the online fashion store via a link on their device, complete the purchase, and confirm the order details to complete the purchase.

[0088] Step 11:

[0089] The user actually wears the suggested outfit and goes about their daily activities, after which they send feedback about their outfit to the server via their device.

[0090] Step 12:

[0091] The server receives the user's feedback information and stores it in a database, which is used to improve the accuracy of future suggestions.

[0092] Through the above processing steps, this system can improve the efficiency of the user's daily clothing selection and improve the quality of life.

[0093] Example 1

[0094] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0095] In modern society, many people living in busy urban areas spend a lot of time choosing their daily outfits. To solve this problem, there is a need for a system that can automatically suggest the best outfits for them, taking into account individual user information and weather conditions. It is also important to improve the accuracy of suggestions based on user feedback, and there is also a need for a function that supports the purchase of new items.

[0096] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0097] In this invention, the server includes means for collecting user information, data analysis means for suggesting optimal clothing to the user based on the user information, means for acquiring weather information, means for suggesting outfits based on the user information and the weather information, means for learning from the collected user feedback information and improving the accuracy of the suggestions, means for suggesting new items to purchase based on the user's body shape information and preferred style information, means for periodically acquiring local weather information using a weather API, means for collecting photos of items the user owns, means for collecting the user's schedule information, and means for transmitting the suggested clothing to the user terminal. This allows the user to select optimal clothing based on their individual lifestyle and weather conditions, improving the accuracy of suggestions and also supporting the purchase of new fashion items.

[0098] "User information" is data including the user's body shape information, preferred style, photos of items owned, and daily schedule information.

[0099] "Data analysis" is a process for suggesting optimal clothing for a user based on collected user information and weather information.

[0100] "Weather information" refers to meteorological data such as local temperature, precipitation probability, and wind speed obtained using an external weather API.

[0101] "Coordination suggestions" are suggestions for optimal outfit combinations generated based on user information and weather information.

[0102] "Feedback information" is data on the user's evaluation of the proposed outfit and points for improvement.

[0103] A "weather API" is an application programming interface for obtaining weather information via the Internet.

[0104] "Schedule information" is data about the schedules and events that a user will have on a particular day.

[0105] A "device" is an electronic device used by a user to enter information, review suggestions, or provide feedback.

[0106] A "purchase link" is a link that takes the user to a web page where they can purchase the suggested new fashion item.

[0107] This invention is a system that suggests optimal clothing based on user information, and supports daily clothing selection by taking into account weather information and the user's schedule information in particular. The main target of this invention is people living in busy urban areas, and its purpose is to make their daily lives more convenient and comfortable.

[0108] server

[0109] 1. Collection of User Information

[0110] The server collects information provided by the user via their device, such as body shape information, preferred style information, photos of items owned, and schedule information. This information is used to build a database of the user's personality and lifestyle. Specifically, the collected data is stored in the server's database and used for future suggestions.

[0111] 2. Obtaining weather information

[0112] The server periodically obtains weather information for the area specified by the user using an external weather API (e.g., OpenWeatherMap or Weatherstack). This information includes temperature, precipitation probability, wind speed, and other weather information. The obtained weather information is also stored in the server's database and used for analysis along with user information.

[0113] 3. Data analysis

[0114] The server's AI analyzes the collected user information and weather information, taking into account past outfit history and user feedback to derive the optimal outfit combinations based on the user's body type, preferences, and schedule.

[0115] 4. Coordination Proposal Generation

[0116] The server then creates specific outfit suggestions based on the AI ​​analysis results and delivers them to the user's device. The suggestions include combinations of items the user already owns and new items they should consider purchasing.

[0117] Terminal

[0118] 1. Providing an interface

[0119] The device provides an interface that allows users to easily input and update information about their body shape, preferred style, photos of items they own, and schedule information.

[0120] 2. Display of notifications

[0121] The device notifies the user of the coordination suggestions sent from the server, which are displayed as push notifications or in-app notifications.

[0122] 3. Gather feedback

[0123] The device collects feedback from users (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future suggestions.

[0124] 4. Purchase procedure assistance

[0125] The terminal provides an interface for the user to purchase the suggested new items and allows the user to transition to a fashion online shopping site via a purchase link from the server.

[0126] user

[0127] 1. Enter your information

[0128] Users enter and update their body shape information, preferred styles, photos of the items they own, and daily schedule information into the app on their device.

[0129] 2. Review and adoption of proposals

[0130] Users can check the outfit suggestions displayed on their device and choose to actually use them. If they like the new items suggested, they can purchase them through the fashion online shopping site.

[0131] 3. Providing Feedback

[0132] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[0133] Specific examples

[0134] A user has planned to meet a friend for lunch the next day. The user has entered their body type (height: 170cm, weight: 65kg), preferred style (casual), and photos of items they own (e.g., blue casual shirt, beige chinos). The weather forecast for the user's area is sunny with a temperature of 25°C.

[0135] 1. The server performs data analysis based on this information and suggests a combination of "a blue casual shirt and beige chino pants" that would be suitable for a casual lunch date.

[0136] 2. Additionally, users will also be suggested lightweight white sneakers that they have recently considered purchasing.

[0137] 3. The terminal notifies the user of these suggestions, and the user selects coordination according to the suggestions.

[0138] 4. If the user decides to purchase the suggested new sneakers, they can use the purchase link on their device to access a fashion online store and easily place their order.

[0139] Prompt Sentence Examples

[0140] "Please create a system that suggests the best outfits for users based on their personal information. Please also take into account weather and schedule information."

[0141] "Please suggest an appropriate outfit for a specific user (e.g., height 170cm, weight 65kg, casual style) for lunch the next day. The local weather forecast is sunny with a temperature of 25 degrees."

[0142] As described above, the system of the present invention proposes optimal clothing that suits the user's personality, making daily clothing selection more efficient.

[0143] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0144] Step 1:

[0145] Users input their body type, preferred style, photos of their belongings, and schedule information through the device's interface. This input data includes the user's height, weight, preferred fashion style, photos of clothes they own, and their schedule for a specific day. The input data is temporarily stored on the device.

[0146] Step 2:

[0147] The terminal sends the information entered by the user to the server. This includes the user's body shape information, preferred style, photos of items owned, and schedule information. The server stores the received information in a database and updates the user's profile. The output is the updated user profile data.

[0148] Step 3:

[0149] The server periodically retrieves weather information for the area specified by the user using an external weather API (e.g., OpenWeatherMap or Weatherstack). During this retrieval process, weather data such as temperature, precipitation probability, and wind speed for the specified area are collected. The retrieved weather data is also stored in a database, and the output is the latest weather information.

[0150] Step 4:

[0151] The server's AI performs data analysis based on collected user information and weather information. The input data is the user's profile information, weather information, and past outfit history. The AI ​​analyzes this data and derives the optimal outfit combination based on the user's body type, preferences, and schedule. The output is a recommendation of the optimal outfit.

[0152] Step 5:

[0153] The server generates specific outfit suggestions based on the results of the AI ​​analysis. This process also includes combinations of items the user already owns and new items they should consider purchasing. The generated outfit suggestions are sent to the user's device. The output is a specific outfit suggestion for the user.

[0154] Step 6:

[0155] The device notifies the user of the coordination suggestions sent from the server. This notification is displayed as a push notification or an in-app notification. The user receives the suggestion results on the device by checking the displayed suggestion. The output is a notification to the user.

[0156] Step 7:

[0157] The user checks the outfit suggestions displayed on the device and actually adopts the suggestions. If the user likes the suggested new items, they can purchase them through the fashion online shopping site. The input data is the suggested outfits and new items, and the output is the user's selection and purchasing behavior.

[0158] Step 8:

[0159] The device collects feedback from users. Users enter their feedback (ratings and areas for improvement) about the proposed outfits into the device, and this information is sent to the server. The server stores the received feedback in a database and uses it to improve the accuracy of the next proposal. The input data is the user's feedback information, and the output is an improved proposal algorithm.

[0160] (Application example 1)

[0161] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0162] In modern urban areas, many people living busy lives spend a lot of time and effort choosing their daily outfits. Furthermore, they must take into account weather information and the day's plans, making it difficult for many to efficiently choose the perfect outfit. Furthermore, there are issues with the effort required to actually find the suggested clothing items in physical stores and the complicated process of purchasing new items. There is a need for a system that can solve these issues and make users' lives more convenient and comfortable.

[0163] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0164] In this invention, the server includes means for collecting user information, data analysis means for suggesting optimal clothing to the user based on the user information, means for acquiring weather information, means for suggesting outfits based on the user information and the weather information, means for providing guidance for finding the suggested clothing items in a physical store, means for collecting feedback from the user and reflecting it in future suggestions, and means for supporting the process of purchasing the suggested new items. This not only enables the user to receive optimal clothing suggestions, but also makes it easier for the user to find and purchase clothing items in a physical store, thereby streamlining daily clothing selection.

[0165] The "means for collecting user information" is a function for acquiring information about the user's body shape, preferred style, photos of items owned, and schedule information, and storing the information in a database.

[0166] "Data analysis means" refers to algorithms and functions for deriving optimal clothing for a user based on collected user information and weather information.

[0167] "Means for obtaining weather information" refers to a function for periodically obtaining weather data for a specified area from an external API or other source and making it available within the system.

[0168] The "means for suggesting outfits" is a function for suggesting optimal outfit combinations to users based on user information and weather information.

[0169] "Means for providing in-store locating guidance" refers to navigation and guidance features that allow users to easily find the suggested clothing item in a physical store.

[0170] "Means of collecting feedback" is a function for obtaining user evaluations and opinions on proposals and reflecting them in future proposals.

[0171] A "checkout aid" is a link or interface that simplifies and assists the user in the process of purchasing a suggested new item.

[0172] This invention is a system that suggests optimal clothing based on user information and weather information. This system is mainly composed of three elements: a server, a terminal, and a user.

[0173] server

[0174] 1. Collection of User Information

[0175] The server collects the user's input data such as body shape, preferred style, photos of items owned, and schedule information. This information is stored in a database, which is then used to create a database based on the user's personality and lifestyle.

[0176] 2. Obtaining weather information

[0177] The server periodically retrieves weather information for the specified region using an external weather API (e.g., WeatherAPI), making the latest weather data available within the system.

[0178] 3. Data analysis

[0179] The server's AI analyzes the collected user information and weather information, taking into account past outfit history and user feedback. It then uses an algorithm to generate optimal outfit suggestions for the user.

[0180] 4. Coordination Proposal Generation

[0181] The server then creates specific outfit suggestions based on the results of the data analysis. These suggestions are then sent to the user's device. The suggestions include combinations of items the user already owns and new items they should consider purchasing.

[0182] Terminal

[0183] 1. Providing an interface

[0184] The device provides an interface that allows users to input and update information about their body shape, preferred style, photos of items they own, and schedule information. This interface is implemented as a smartphone app.

[0185] 2. Display of notifications

[0186] The device notifies the user of coordination suggestions sent from the server, including push notifications and in-app notifications.

[0187] 3. Gather feedback

[0188] The device collects user feedback (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future outfit suggestions.

[0189] 4. Purchase procedure assistance

[0190] The device provides an interface for users to purchase suggested new items, with a function that allows users to be redirected to a fashion online shopping site via a purchase link.

[0191] 5. In-store guidance

[0192] It provides a guide function to help users find suggested clothing items in physical stores, allowing them to easily find suggested items in physical stores.

[0193] user

[0194] 1. Enter your information

[0195] Users enter and update their body shape information, preferred styles, photos of the items they own, and daily schedule information into the app on their device.

[0196] 2. Review and adoption of proposals

[0197] Users can check the outfit suggestions displayed on their device and choose to actually use them. If they like the new items suggested, they can purchase them through the fashion online shopping site.

[0198] 3. Providing Feedback

[0199] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[0200] Specific examples

[0201] For example, suppose User A has plans to have lunch with a friend the next day. User A has previously entered his / her body type information (height 170cm, weight 65kg), preferred style (casual), and photos of items he / she owns (e.g., blue casual shirt, beige chino pants). The server performs data analysis based on the collected information and suggests a combination of "blue casual shirt and beige chino pants" that is suitable for a casual lunch. In addition, User A is also suggested a pair of lightweight white sneakers that he / she has recently been considering purchasing. The device notifies the user of these suggestions, and the user selects an outfit based on the suggestions.

[0202] Prompt Sentence Examples

[0203] Please use the following information to suggest the best outfit for you:

[0204] User information: Height 170cm, weight 65kg, casual style preference, list of items owned (blue casual shirt, beige chino pants)

[0205] Weather: Sunny, temperature 25 degrees

[0206] Planned: Lunch with a friend

[0207] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0208] Processing Steps

[0209] Step 1:

[0210] The user uses the device's app to enter information about their body shape, preferred style, photos of items they own, and schedule information.

[0211] Input: height, weight, preferred style, item photos, schedule

[0212] Output: These data are sent from the terminal to the server and stored in a database.

[0213] Specific behavior: The user enters the required information into the input form and clicks the submit button. The app executes an API request to send the entered data to the server.

[0214] Step 2:

[0215] The server uses a weather API to obtain weather information for the specified area.

[0216] Input: Region information

[0217] Output: Weather data such as temperature, precipitation probability, wind speed, etc.

[0218] Specific operation: The server periodically sends a request to the weather API to obtain the latest weather data and save it in the database.

[0219] Step 3:

[0220] The server's AI performs data analysis based on collected user information and weather information.

[0221] Input: User information, weather information, past coordination history, feedback information from users

[0222] Output: Recommendations for the best outfit for the user

[0223] How it works: The server's AI algorithm analyzes the input data and uses a generative AI model to generate the optimal outfit. The prompt is in the form "Please suggest the best outfit based on the following information: ..."

[0224] Step 4:

[0225] The server generates a coordination proposal and delivers it to the user's terminal.

[0226] Input: AI-generated outfit suggestions

[0227] Output: Coordination suggestions displayed on the device

[0228] What happens: The server sends the suggestion to the device using the notification API, and the device displays a push notification or in-app notification.

[0229] Step 5:

[0230] The user checks the outfit suggestions displayed on the device and enters feedback.

[0231] Input: Rating and improvements for the proposed outfit

[0232] Output: The feedback information is sent to the server and stored in a database.

[0233] Specific behavior: The user reviews the suggestion, enters their rating and improvements in the feedback form, and clicks the submit button. The app then makes an API request to send the feedback data to the server.

[0234] Step 6:

[0235] When users want to purchase a suggested new item, they access a fashion online shopping site via a purchase link on their device.

[0236] Input: Proposed new item

[0237] Output: Transition to fashion online shopping site and purchase procedure

[0238] Specific behavior: The user clicks on the purchase link and is redirected to a fashion online shopping site in a browser or in-app browser, where they proceed with the purchase.

[0239] Step 7:

[0240] The device provides guidance on how to find the suggested clothing items in physical stores.

[0241] Input: suggested item information, physical store location

[0242] Output: In-store navigation information

[0243] Specific operation: The device acquires the user's location information and displays information about physical stores where the suggested items are located. It also displays maps and routes to guide the user.

[0244] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0245] This invention is a system that suggests optimal clothing based on user information, particularly by combining weather information, the user's schedule information, and even the user's emotional information to support daily clothing selection. The main target audience of this invention is men and women in their 20s to 40s living in busy urban areas, and the aim is to make their daily lives more convenient and comfortable.

[0246] System configuration

[0247] server

[0248] 1. Collection of User Information

[0249] The server collects information provided by the user, such as body shape information, preferred style information, photos of items owned, and schedule information, and builds a database of the user's personality and lifestyle.

[0250] 2. Obtaining weather information

[0251] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the area specified by the user using an external weather API.

[0252] 3. Collecting emotional information

[0253] The server uses an emotion engine to analyze the user's facial expressions and voice, recognizing the user's emotions in real time.

[0254] 4. Data Analysis

[0255] The server's AI analyzes the collected user information, weather information, emotional information, and schedule information to derive the optimal outfit combination that matches the user's body type, preferences, and schedule, taking into account past outfit history and user feedback.

[0256] 5. Generating Coordination Proposals

[0257] The server then creates specific outfit suggestions based on the analysis results and delivers them to the user's device, including suggestions for combining items the user already owns and new items they should consider purchasing.

[0258] Terminal

[0259] 1. Providing an interface

[0260] The device provides an interface that allows users to easily input and update information about their body shape, preferred style, photos of their belongings, and schedule information. In addition, the device also has the ability to capture users' emotions in real time.

[0261] 2. Display of notifications

[0262] The device notifies the user of the coordination suggestions sent from the server, which are displayed as push notifications or in-app notifications.

[0263] 3. Gather feedback

[0264] The device collects user feedback (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future outfit suggestions.

[0265] 4. Purchase procedure assistance

[0266] The terminal provides an interface for the user to purchase the suggested new items and allows the user to transition to a fashion online shopping site via a purchase link from the server.

[0267] user

[0268] 1. Enter your information

[0269] Users can input and update their body shape, preferred style, photos of their belongings, and daily schedule information into the app on their device, and their emotions are reflected on the device in real time.

[0270] 2. Review and adoption of proposals

[0271] Users can check the outfit suggestions displayed on their device and choose to adopt them. If new items are suggested, they can consider purchasing them if necessary.

[0272] 3. Providing Feedback

[0273] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[0274] Specific examples

[0275] Let's say a user has lunch plans with a friend the next day. The user has previously entered information about their body type (height 170cm, weight 65kg), preferred style (casual), and photos of items they own (blue casual shirt, beige chino pants, etc.). The weather forecast for the user's area is sunny with a temperature of 25°C. The user is also excited about the lunch plans (positive emotion).

[0276] 1. The server performs data analysis based on this information and suggests a combination of "a blue casual shirt and beige chino pants" that would be suitable for a casual lunch date.

[0277] 2. Taking into account the user's emotional information, "red sneakers" that give an energetic impression are also suggested.

[0278] 3. The terminal notifies the user of these suggestions, and the user selects coordination according to the suggestions.

[0279] 4. If the user decides to purchase the suggested new sneakers, they can use the purchase link on their device to access the fashion online store and easily place their order.

[0280] As described above, the system of the present invention proposes optimal clothing that suits the user's personality, making daily clothing selection more efficient. In particular, by incorporating the user's real-time emotional information, it is possible to realize more personalized coordination.

[0281] The processing flow will be explained below.

[0282] Step 1:

[0283] Users can check and update their body shape information (e.g., height 170 cm, weight 65 kg), preferred style (e.g., casual), and photos of items they own (e.g., blue casual shirt, beige chino pants, etc.) that they have previously registered in the app on their device.

[0284] Step 2:

[0285] The user inputs the next day's schedule information (e.g., lunch with a friend) through the terminal, which is then reflected in the system.

[0286] Step 3:

[0287] The server receives the information entered by the user and stores it in a database, including the user's body type, preferred style, schedule, and items owned.

[0288] Step 4:

[0289] The server accesses an external weather API and obtains weather information (e.g., sunny, temperature 25 degrees) for the area and date and time specified by the user.

[0290] Step 5:

[0291] The server receives real-time emotional information provided by the user from the device, and the emotion engine analyzes the user's facial expressions and voice to generate emotional information.

[0292] Step 6:

[0293] The device updates the emotional information according to the user's real-time situation (e.g., looking forward to lunch plans) and sends it to the server.

[0294] Step 7:

[0295] The server's AI begins analyzing data based on the collected user information, weather information, emotional information, and schedule information, and then selects the optimal outfit, taking into account the user's behavioral history and feedback.

[0296] Step 8:

[0297] Based on the results of the data analysis, the server determines the optimal outfit combination for the user. In this case, it suggests a blue casual shirt and beige chino pants. It also suggests red sneakers to match the user's positive emotions.

[0298] Step 9:

[0299] The server also simultaneously suggests new items that the user does not own but would be suitable (e.g., red sneakers).

[0300] Step 10:

[0301] The device receives the coordination suggestions sent from the server and notifies the user via push notifications or in-app notifications.

[0302] Step 11:

[0303] The user checks the outfit suggestions displayed on the device and decides whether to adopt the suggested outfit. If the user likes the suggestion, they can adopt it.

[0304] Step 12:

[0305] When a user wants to purchase a new item, they access the online fashion store via a link on their device, complete the purchase, and confirm the order details to complete the purchase.

[0306] Step 13:

[0307] The user actually wears the suggested outfit and goes about their daily activities, after which they send feedback about their outfit to the server via their device.

[0308] Step 14:

[0309] The server receives the user's feedback information and stores it in a database, which is used to improve the accuracy of future suggestions.

[0310] Through these processing steps, this system can improve the efficiency of users' daily clothing selection and improve their quality of life. In particular, by utilizing the emotion engine, it is possible to provide more personalized suggestions based on the user's emotions.

[0311] Example 2

[0312] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0313] Conventional clothing recommendation systems are limited to suggestions based on the user's body shape and preferred style information, making it difficult to make personalized suggestions that take into account weather information and the user's emotional state. Furthermore, they lack the functionality to utilize feedback information to improve the accuracy of suggestions, which prevents them from fully increasing user satisfaction.

[0314] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0315] In this invention, the server includes means for collecting user information, means for acquiring weather information, means for collecting and analyzing emotional information, data analysis means, means for suggesting outfits based on the user information, weather information, and emotional information, means for learning collected feedback information about the user's clothing and improving the accuracy of suggestions, and means for suggesting new items to purchase based on the user's body shape information and preferred style information. This makes it possible to suggest optimal outfits that take into account the user's personality, lifestyle, weather, and emotional state.

[0316] "User information" is a collective term for data including the user's body shape information, preferred style information, photos of items owned, and daily schedule information.

[0317] The "data analysis means" refers to a hardware or software function that performs analysis based on collected user information, weather information, and emotional information to derive optimal clothing combinations.

[0318] "Weather information" refers to weather-related data such as temperature, precipitation probability, and wind speed obtained using external weather APIs.

[0319] "Emotional information" is data about the user's emotional state, recognized by analyzing the user's facial expressions and voice.

[0320] The "means for suggesting outfits" refers to a hardware or software function that has the ability to suggest specific outfit combinations based on the analysis results and deliver them to the user's device.

[0321] "Feedback information" refers to information collected from users, such as evaluations and areas for improvement regarding the proposed outfits.

[0322] "Means for improving the accuracy of suggestions" refers to algorithms or functions that learn from collected feedback information and improve the accuracy of future suggestions.

[0323] The "means for suggesting new items to purchase" is a function that suggests new items to the user based on the user's body shape information and preferred style information, and provides support for purchasing the items.

[0324] The present invention is a system that suggests optimal clothing based on user information, and in particular, supports daily clothing selection by combining weather information, user emotion information, and user schedule information. Specific embodiments for carrying out the present invention are described in detail below.

[0325] server

[0326] The server has the following features:

[0327] 1. Collection of User Information

[0328] User information includes the user's body shape information, preferred style information, photos of items owned, and daily schedule information. This information is provided by the user and sent to the server via the terminal. The server stores the received information in a database and manages it for each user. A commonly used database management system (DBMS) is used for this process.

[0329] 2. Obtaining weather information

[0330] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the area specified by the user using an external weather API (e.g., OpenWeatherMap API). The obtained weather information is saved for each user, and the latest information is kept.

[0331] 3. Collecting emotional information

[0332] The server uses an emotion analysis engine to analyze the user's facial expressions and voice to recognize their emotions in real time. The emotion engine uses commonly used machine learning or deep learning models. The analysis results are also stored in a database.

[0333] 4. Data Analysis

[0334] The server's AI analyzes the collected user information, weather information, emotional information, and schedule information to derive the optimal outfit combination based on the user's body shape, preferences, and schedule. Past outfit history and feedback from users are also taken into consideration. The AI ​​model uses a generative AI model to perform complex data analysis.

[0335] 5. Generating Coordination Proposals

[0336] The server then creates specific outfit suggestions based on the analysis results and delivers them to the user's device, including suggestions for combining items the user already owns and new items they should consider purchasing.

[0337] Specific examples

[0338] Let's say a user has lunch plans with a friend the next day. The user has previously entered information about their body type (height 170cm, weight 65kg), preferred style (casual), and photos of items they own (blue casual shirt, beige chino pants, etc.). The weather forecast for the user's area is sunny with a temperature of 25°C. The user is also excited about the lunch plans (positive emotion).

[0339] The server performs data analysis based on this information and suggests a combination of "a blue casual shirt and beige chino pants," suitable for a casual lunch date. Taking into account the user's emotional information, it also suggests "red sneakers," which give an energetic impression. The device notifies the user of these suggestions, and the user selects an outfit based on the suggestions. If the user decides to purchase the suggested new sneakers, they can use the purchase link on the device to access a fashion online shopping site and easily place an order.

[0340] Terminal

[0341] The terminal has the following features:

[0342] 1. Providing an interface

[0343] The device provides an interface that allows users to easily input and update information about their body shape, preferred style, photos of their belongings, and schedule information. The application is typically a smartphone app. The device also has the ability to capture the user's emotions in real time.

[0344] 2. Display of notifications

[0345] The device notifies the user of the coordination suggestions sent from the server, which are displayed as push notifications or in-app notifications.

[0346] 3. Gather feedback

[0347] The device collects user feedback (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future outfit suggestions.

[0348] 4. Purchase procedure assistance

[0349] The terminal provides an interface for the user to purchase the suggested new items and allows the user to transition to a fashion online shopping site via a purchase link from the server.

[0350] user

[0351] The user does the following:

[0352] 1. Enter your information

[0353] Users can input and update their body shape, preferred style, photos of their belongings, and daily schedule information into the app on their device, and their emotions are reflected on the device in real time.

[0354] 2. Review and adoption of proposals

[0355] Users can check the outfit suggestions displayed on their device and choose to adopt them. If new items are suggested, they can consider purchasing them if necessary.

[0356] 3. Providing Feedback

[0357] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[0358] In this way, the system of the present invention can suggest the best outfits to suit the user's personality, making daily outfit selection more efficient. In particular, by incorporating the user's real-time emotional information, more personalized outfits can be achieved.

[0359] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0360] Step 1:

[0361] Collection of User Information

[0362] Users log in to the app and enter their body type information (height, weight, etc.), preferred style information (casual, formal, etc.), photos of items they own, and daily schedule information.

[0363] Input: Body shape information, preferred style information, photos of items you own, daily schedule information

[0364] Data processing: The entered information is consolidated and stored in a separate database for each user.

[0365] Output: The consolidated user information is saved in the database.

[0366] Specific operation: The user enters their height (170cm) and weight (65kg) into the app's form, selects their lifestyle as "casual," takes a photo of an item they are carrying (a blue shirt), and enters their plans for the next day (lunch with a friend). The device sends this information to the server, which stores it in a database.

[0367] Step 2:

[0368] Obtaining weather information

[0369] The server calls an external weather API (for example, OpenWeatherMap API) and periodically obtains weather information (temperature, probability of precipitation, wind speed, etc.) for the area specified by the user.

[0370] Input: User's region information

[0371] Data processing: Weather information obtained from the weather API is saved for each user.

[0372] Output: The latest weather information for the user's location is stored in a database.

[0373] How it works: The server calls the weather API every morning at 7am and retrieves weather data such as "sunny, temperature 25 degrees." The server stores this information for each user.

[0374] Step 3:

[0375] Collecting emotional information

[0376] The device uses a camera and microphone to capture the user's facial expressions and voice and transmits them to an emotion analysis engine.

[0377] Input: User's facial expression and voice data

[0378] Data processing: The emotion analysis engine analyzes facial expressions and voice data to extract the user's emotional state (positive, negative, etc.).

[0379] Output: The analyzed emotion information is stored in a database.

[0380] How it works: When a user smiles at the device, the camera captures their facial expression and sends it to the emotion analysis engine. The engine interprets it as "positive" and sends the result to the server, which stores it in a database.

[0381] Step 4:

[0382] Data analysis

[0383] The server's AI combines and analyzes the collected user information, weather information, emotional information, and schedule information.

[0384] Input: User information, weather information, emotion information, schedule information

[0385] Data processing: Based on all collected data, a generative AI model is used to derive optimal outfit combinations.

[0386] Output: Specific outfit coordination suggestions guided by AI are generated.

[0387] Specific operation: The server's AI derives the combination of "a blue casual shirt and beige chino pants" based on the user's "height 170 cm, weight 65 kg," weather information "sunny, 25 degrees," emotional information "positive," and schedule information "lunch with a friend." It also suggests "red sneakers," which give an energetic impression.

[0388] Step 5:

[0389] Coordination proposal generation and notification

[0390] The server creates coordination suggestions based on the analysis results and delivers them to the user's device.

[0391] Input: AI-generated outfit suggestions

[0392] Data processing: Convert the proposal content into concrete text and images and send them to the device.

[0393] Output: Coordination suggestions are displayed on the terminal.

[0394] Specific operation: A notification such as "Today's outfit suggestion: A blue casual shirt and beige chino pants. We also recommend energetic red sneakers" will be displayed on the device.

[0395] Step 6:

[0396] Purchase procedure assistance

[0397] Users click on a link on their device to purchase the suggested new item.

[0398] Input: User's purchase click action

[0399] Data processing: The purchase link will take you to a fashion online shopping site and assist you with the purchase process.

[0400] Output: The user purchases the suggested new item.

[0401] Specific operation: The user clicks on the purchase link for "red sneakers" displayed on the device, and is redirected to the corresponding page on the fashion online shopping site to complete the order.

[0402] Step 7:

[0403] Collecting feedback

[0404] Users enter feedback about the suggested outfits into the app.

[0405] Input: Rating and improvements for the proposed outfit

[0406] Data processing: The collected feedback information is sent to the server and reflected in future proposals.

[0407] Output: A more accurate next proposal is generated.

[0408] Specific operation: The user rates the app as "very satisfied" and comments on the device that "I would like more casual suggestions." The device sends this to the server, and the feedback is saved in a database. The server analyzes this feedback and improves the accuracy of the next suggestion.

[0409] Based on the above processing steps, the program of the present invention can efficiently suggest optimal clothing that meets the user's needs.

[0410] (Application example 2)

[0411] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0412] Currently, choosing clothes is a time-consuming and laborious task in users' daily lives. For women and busy people in particular, thinking about how to coordinate their outfits every day can be very stressful. Furthermore, choosing appropriate clothing based on the weather, schedule, and emotions can be even more difficult. The purpose of this invention is to solve these problems and provide users with quick and appropriate clothing suggestions.

[0413] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user information, data analysis means for proposing optimal outfits to the user based on the user information, means for acquiring weather information, means for collecting and analyzing user emotion information, means for proposing outfits based on the user information, weather information, and emotion information, and visual display means for enabling virtual try-on. This allows the user to efficiently select everyday outfits and easily find optimal outfits while visually checking them.

[0414] "User information" refers to data including the user's body shape information, preferred style information, data on items owned, and schedule information.

[0415] "Data analysis means" refers to a technical means for analyzing and deriving optimal clothing for a user based on user information, weather information, and emotional information.

[0416] "Weather information" refers to local weather data such as temperature, probability of precipitation, and wind speed obtained using an external weather API.

[0417] "Emotional information" is data about the user's current emotional state, recognized by analyzing the user's facial expressions and voice.

[0418] "Visual display means" refers to a technical means that assists in visual confirmation of clothing to enable virtual try-on, and refers to devices such as smart glasses and head-mounted displays.

[0419] "Coordination suggestions" are suggestions for optimal clothing combinations presented to users based on collected and analyzed user information, weather information, and emotional information.

[0420] The present invention is a system that integrates user information, weather information, and emotional information to suggest optimal outfits. The system uses a server, a user terminal, and a visual display device such as smart glasses to provide users with quick and accurate outfit suggestions.

[0421] System configuration and operation

[0422] server

[0423] 1. Collection of User Information

[0424] The server collects the user's body shape information, preferred style information, data on items owned, and schedule information, and builds a database related to the user's lifestyle.

[0425] 2. Obtaining weather information

[0426] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the specified area using the weather API.

[0427] 3. Collection and analysis of emotional information

[0428] The server uses an emotion analysis engine to analyze the user's facial expressions and voice, recognizing the user's emotions in real time.

[0429] 4. Data Analysis

[0430] The server's AI analyzes data based on collected user information, weather information, emotional information, and schedule information to derive optimal outfit combinations, taking into account past outfit history and user feedback.

[0431] 5. Generating Coordination Proposals

[0432] The server generates specific outfit suggestions based on the analysis results and delivers them to the user's device, including suggestions for combining items the user already owns and new items they should consider purchasing.

[0433] User Device

[0434] 1. Providing an interface

[0435] The user device provides an interface that allows users to easily input and update information about their body shape, preferred style, items they own, and schedule information.The device also has the ability to capture users' emotions in real time.

[0436] 2. Display of notifications

[0437] The user's device will be notified of the coordination suggestions sent from the server, which will be displayed as a push notification or in-app notification.

[0438] 3. Gather feedback

[0439] The user device collects feedback from the user (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future suggestions.

[0440] 4. Purchase procedure assistance

[0441] The user terminal provides an interface for purchasing the proposed new items and assists the user in transitioning to a fashion online shopping site via a purchase link from the server.

[0442] visual display devices

[0443] 1. Providing virtual try-ons

[0444] Visual display devices such as smart glasses offer the ability to visually try on suggested outfits, allowing users to virtually try on the outfit and see how it will look in real life.

[0445] Specific examples

[0446] Let's say a user has lunch plans with a friend the next day. This user has previously entered information about their body type (height 170cm, weight 65kg), preferred style (casual), and photos of items they own (blue casual shirt, beige chino pants, etc.). The weather forecast for their area is sunny with a temperature of 25°C. Furthermore, this user is excited about the lunch plans (positive emotion).

[0447] The server analyzes the optimal outfit based on this information and generates a suggestion such as "a blue casual shirt and beige chino pants."

[0448] In addition, "red sneakers" are also suggested, as they reflect positive emotions and give an energetic impression.

[0449] The user's device will notify them of this suggestion, and the user will be able to visually confirm the suggestion in real time.

[0450] When a user wants to buy new sneakers, they can access a fashion online shopping site from their device and make the purchase smoothly.

[0451] Prompt Sentence Examples

[0452] Please suggest the best outfit for the user based on the following information:

[0453] 1. User information: Location: Tokyo, Height: 170cm, Weight: 65kg, Style preference: Casual

[0454] 2. Weather information: Clear skies, temperature 25°C

[0455] 3. Emotional information: Positive

[0456] Suggestion example:

[0457] Light clothing and bright colors are recommended.

[0458] In this way, the system of the present invention integrates a variety of information about the user to provide efficient and appropriate clothing suggestions.

[0459] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0460] Step 1: Collect user information

[0461] The server collects the user's body shape information (e.g., height, weight), preferred style information, photo data of items owned, and schedule information entered through the terminal. This information is stored in a user information database. The entered data is sent to the server and saved.

[0462] Input: Body shape information, style information, item photos, schedule information

[0463] Output: Data stored in the user information database

[0464] Step 2: Get weather information

[0465] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the area specified by the user using an external weather API. This data is stored in a weather database on the server.

[0466] Input: Area specification data

[0467] Output: Data stored in the weather information database

[0468] Step 3: Collecting and analyzing emotional information

[0469] The user captures facial expressions or voice in real time through the device and sends the data to the server, which uses an emotion analysis engine to analyze the user's emotions and store them in an emotion information database.

[0470] Input: facial expression data or voice data

[0471] Output: Analyzed data stored in the emotion information database

[0472] Step 4: Data analysis

[0473] The server integrates user information, weather information, emotional information, and schedule information, and analyzes the optimal outfit combination based on past outfit history and user feedback. Using an AI algorithm, it generates outfit suggestions based on data correlations and user preferences.

[0474] Input: User information, weather information, emotion information, schedule information

[0475] Output: Clothing suggestions based on analysis results

[0476] Step 5: Creating and notifying coordination proposals

[0477] The server creates specific outfit suggestions based on the analysis results and sends them to the user's device, which then displays the suggestions to the user as push notifications or in-app notifications.

[0478] Input: Analyzed clothing suggestion data

[0479] Output: Notification to user device

[0480] Step 6: Offer a virtual try-on

[0481] Users can visually try on the proposed outfits using smart glasses or other visual display devices. The terminal generates video data for the virtual try-on in real time and displays it on the smart glasses.

[0482] Input: Clothing suggestion data

[0483] Output: Virtual fitting video on a visual display device

[0484] Step 7: Gather feedback

[0485] Users can input their evaluation of the suggested outfits and suggestions for improvement via their device and send them to the server, which then stores this information in a feedback information database and uses it to improve the accuracy of the next outfit suggestions.

[0486] Input: User feedback data

[0487] Output: Data stored in the feedback information database

[0488] Step 8: Assist with checkout

[0489] If the user decides to purchase the suggested new item, the device provides a purchase link, allowing the user to access the fashion online shopping site, where the server generates the necessary purchase information and assists in the purchase process.

[0490] Input: Purchase desired item data

[0491] Output: Access link to fashion online shopping site and data for purchase procedure

[0492] Through the above processing steps, the system of the present invention can support the user in choosing daily clothing in an efficient and personalized manner.

[0493] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0494] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0495] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0496] [Second embodiment]

[0497] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0498] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0499] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0500] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0501] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0502] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0503] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0504] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0505] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0506] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0507] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0508] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0509] This invention is a system that suggests optimal clothing based on user information, and supports daily clothing selection by taking into consideration weather information and the user's schedule information in particular. The main target of this invention is men and women in their 20s to 40s living in busy urban areas, and the aim is to make their daily lives more convenient and comfortable.

[0510] System configuration

[0511] server

[0512] 1. Collection of User Information

[0513] The server collects information provided by the user, such as body shape information, preferred style information, photos of items owned, and schedule information, and builds a database of the user's personality and lifestyle.

[0514] 2. Obtaining weather information

[0515] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the area specified by the user using an external weather API.

[0516] 3. Data analysis

[0517] The server's AI analyzes the collected user information and weather information to derive the optimal outfit combination based on the user's body type, preferences, and schedule, taking into account past outfit history and user feedback.

[0518] 4. Coordination Proposal Generation

[0519] The server then creates specific outfit suggestions based on the analysis results and delivers them to the user's device, including suggestions for combining items the user already owns and new items they should consider purchasing.

[0520] Terminal

[0521] 1. Providing an interface

[0522] The device provides an interface that allows users to easily input and update information about their body shape, preferred style, photos of items they own, and schedule information.

[0523] 2. Display of notifications

[0524] The device notifies the user of the coordination suggestions sent from the server, which are displayed as push notifications or in-app notifications.

[0525] 3. Gather feedback

[0526] The device collects user feedback (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future outfit suggestions.

[0527] 4. Purchase procedure assistance

[0528] The terminal provides an interface for the user to purchase the suggested new items and allows the user to transition to a fashion online shopping site via a purchase link from the server.

[0529] user

[0530] 1. Enter your information

[0531] Users enter and update their body shape information, preferred styles, photos of the items they own, and daily schedule information into the app on their device.

[0532] 2. Review and adoption of proposals

[0533] Users can check the outfit suggestions displayed on their device and choose to actually use them. If they like the new items suggested, they can purchase them through the fashion online shopping site.

[0534] 3. Providing Feedback

[0535] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[0536] Specific examples

[0537] A user has planned to meet a friend for lunch the next day. The user has entered their body type (height: 170cm, weight: 65kg), preferred style (casual), and photos of items they own (e.g., blue casual shirt, beige chinos). The weather forecast for the user's area is sunny with a temperature of 25°C.

[0538] 1. The server performs data analysis based on this information and suggests a combination of "a blue casual shirt and beige chino pants" that would be suitable for a casual lunch date.

[0539] 2. Additionally, users will also be suggested lightweight white sneakers that they have recently considered purchasing.

[0540] 3. The terminal notifies the user of these suggestions, and the user selects coordination according to the suggestions.

[0541] 4. If the user decides to purchase the suggested new sneakers, they can use the purchase link on their device to access a fashion online store and easily place their order.

[0542] As described above, the system of the present invention proposes optimal clothing that suits the user's personality, making daily clothing selection more efficient.

[0543] The processing flow will be explained below.

[0544] Step 1:

[0545] Users can check and update their body shape information (e.g., height 170 cm, weight 65 kg), preferred style (e.g., casual), and photos of items they own (e.g., blue casual shirt, beige chino pants, etc.) that they have previously registered in the app on their device.

[0546] Step 2:

[0547] The user inputs the next day's schedule information (e.g., lunch with a friend) through the terminal, which is then reflected in the system.

[0548] Step 3:

[0549] The server receives the information entered by the user and stores it in a database, including the user's body type, preferred style, schedule, and items owned.

[0550] Step 4:

[0551] The server accesses an external weather API and obtains weather information (e.g., sunny, temperature 25 degrees) for the area and date and time specified by the user.

[0552] Step 5:

[0553] The server's AI begins analyzing data based on collected user information, weather information, and schedule information, and selects the optimal outfit, taking into account the user's past behavioral history and feedback.

[0554] Step 6:

[0555] Based on the results of the data analysis, the server determines the best outfit combination for the user, suggesting a blue casual shirt and beige chino pants.

[0556] Step 7:

[0557] The server also simultaneously suggests new items that the user does not own but would be suitable (e.g., lightweight white sneakers).

[0558] Step 8:

[0559] The device receives the coordination suggestions sent from the server and notifies the user via push notifications or in-app notifications.

[0560] Step 9:

[0561] The user checks the outfit suggestions displayed on the device and decides whether to adopt the suggested outfit. If the user likes the suggestion, they can adopt it.

[0562] Step 10:

[0563] When a user wants to purchase a new item, they access the online fashion store via a link on their device, complete the purchase, and confirm the order details to complete the purchase.

[0564] Step 11:

[0565] The user actually wears the suggested outfit and goes about their daily activities, after which they send feedback about their outfit to the server via their device.

[0566] Step 12:

[0567] The server receives the user's feedback information and stores it in a database, which is used to improve the accuracy of future suggestions.

[0568] Through the above processing steps, this system can improve the efficiency of the user's daily clothing selection and improve the quality of life.

[0569] Example 1

[0570] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0571] In modern society, many people living in busy urban areas spend a lot of time choosing their daily outfits. To solve this problem, there is a need for a system that can automatically suggest the best outfits for them, taking into account individual user information and weather conditions. It is also important to improve the accuracy of suggestions based on user feedback, and there is also a need for a function that supports the purchase of new items.

[0572] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0573] In this invention, the server includes means for collecting user information, data analysis means for suggesting optimal clothing to the user based on the user information, means for acquiring weather information, means for suggesting outfits based on the user information and the weather information, means for learning from the collected user feedback information and improving the accuracy of the suggestions, means for suggesting new items to purchase based on the user's body shape information and preferred style information, means for periodically acquiring local weather information using a weather API, means for collecting photos of items the user owns, means for collecting the user's schedule information, and means for transmitting the suggested clothing to the user terminal. This allows the user to select optimal clothing based on their individual lifestyle and weather conditions, improving the accuracy of suggestions and also supporting the purchase of new fashion items.

[0574] "User information" is data including the user's body shape information, preferred style, photos of items owned, and daily schedule information.

[0575] "Data analysis" is a process for suggesting optimal clothing for a user based on collected user information and weather information.

[0576] "Weather information" refers to meteorological data such as local temperature, precipitation probability, and wind speed obtained using an external weather API.

[0577] "Coordination suggestions" are suggestions for optimal outfit combinations generated based on user information and weather information.

[0578] "Feedback information" is data on the user's evaluation of the proposed outfit and points for improvement.

[0579] A "weather API" is an application programming interface for obtaining weather information via the Internet.

[0580] "Schedule information" is data about the schedules and events that a user will have on a particular day.

[0581] A "device" is an electronic device used by a user to enter information, review suggestions, or provide feedback.

[0582] A "purchase link" is a link that takes the user to a web page where they can purchase the suggested new fashion item.

[0583] This invention is a system that suggests optimal clothing based on user information, and supports daily clothing selection by taking into account weather information and the user's schedule information in particular. The main target of this invention is people living in busy urban areas, and its purpose is to make their daily lives more convenient and comfortable.

[0584] server

[0585] 1. Collection of User Information

[0586] The server collects information provided by the user via their device, such as body shape information, preferred style information, photos of items owned, and schedule information. This information is used to build a database of the user's personality and lifestyle. Specifically, the collected data is stored in the server's database and used for future suggestions.

[0587] 2. Obtaining weather information

[0588] The server periodically obtains weather information for the area specified by the user using an external weather API (e.g., OpenWeatherMap or Weatherstack). This information includes temperature, precipitation probability, wind speed, and other weather information. The obtained weather information is also stored in the server's database and used for analysis along with user information.

[0589] 3. Data analysis

[0590] The server's AI analyzes the collected user information and weather information, taking into account past outfit history and user feedback to derive the optimal outfit combinations based on the user's body type, preferences, and schedule.

[0591] 4. Coordination Proposal Generation

[0592] The server then creates specific outfit suggestions based on the AI ​​analysis results and delivers them to the user's device. The suggestions include combinations of items the user already owns and new items they should consider purchasing.

[0593] Terminal

[0594] 1. Providing an interface

[0595] The device provides an interface that allows users to easily input and update information about their body shape, preferred style, photos of items they own, and schedule information.

[0596] 2. Display of notifications

[0597] The device notifies the user of the coordination suggestions sent from the server, which are displayed as push notifications or in-app notifications.

[0598] 3. Gather feedback

[0599] The device collects feedback from users (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future suggestions.

[0600] 4. Purchase procedure assistance

[0601] The terminal provides an interface for the user to purchase the suggested new items and allows the user to transition to a fashion online shopping site via a purchase link from the server.

[0602] user

[0603] 1. Enter your information

[0604] Users enter and update their body shape information, preferred styles, photos of the items they own, and daily schedule information into the app on their device.

[0605] 2. Review and adoption of proposals

[0606] Users can check the outfit suggestions displayed on their device and choose to actually use them. If they like the new items suggested, they can purchase them through the fashion online shopping site.

[0607] 3. Providing Feedback

[0608] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[0609] Specific examples

[0610] A user has planned to meet a friend for lunch the next day. The user has entered their body type (height: 170cm, weight: 65kg), preferred style (casual), and photos of items they own (e.g., blue casual shirt, beige chinos). The weather forecast for the user's area is sunny with a temperature of 25°C.

[0611] 1. The server performs data analysis based on this information and suggests a combination of "a blue casual shirt and beige chino pants" that would be suitable for a casual lunch date.

[0612] 2. Additionally, users will also be suggested lightweight white sneakers that they have recently considered purchasing.

[0613] 3. The terminal notifies the user of these suggestions, and the user selects coordination according to the suggestions.

[0614] 4. If the user decides to purchase the suggested new sneakers, they can use the purchase link on their device to access a fashion online store and easily place their order.

[0615] Prompt Sentence Examples

[0616] "Please create a system that suggests the best outfits for users based on their personal information. Please also take into account weather and schedule information."

[0617] "Please suggest an appropriate outfit for a specific user (e.g., height 170cm, weight 65kg, casual style) for lunch the next day. The local weather forecast is sunny with a temperature of 25 degrees."

[0618] As described above, the system of the present invention proposes optimal clothing that suits the user's personality, making daily clothing selection more efficient.

[0619] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0620] Step 1:

[0621] Users input their body type, preferred style, photos of their belongings, and schedule information through the device's interface. This input data includes the user's height, weight, preferred fashion style, photos of clothes they own, and their schedule for a specific day. The input data is temporarily stored on the device.

[0622] Step 2:

[0623] The terminal sends the information entered by the user to the server. This includes the user's body shape information, preferred style, photos of items owned, and schedule information. The server stores the received information in a database and updates the user's profile. The output is the updated user profile data.

[0624] Step 3:

[0625] The server periodically retrieves weather information for the area specified by the user using an external weather API (e.g., OpenWeatherMap or Weatherstack). During this retrieval process, weather data such as temperature, precipitation probability, and wind speed for the specified area are collected. The retrieved weather data is also stored in a database, and the output is the latest weather information.

[0626] Step 4:

[0627] The server's AI performs data analysis based on collected user information and weather information. The input data is the user's profile information, weather information, and past outfit history. The AI ​​analyzes this data and derives the optimal outfit combination based on the user's body type, preferences, and schedule. The output is a recommendation of the optimal outfit.

[0628] Step 5:

[0629] The server generates specific outfit suggestions based on the results of the AI ​​analysis. This process also includes combinations of items the user already owns and new items they should consider purchasing. The generated outfit suggestions are sent to the user's device. The output is a specific outfit suggestion for the user.

[0630] Step 6:

[0631] The device notifies the user of the coordination suggestions sent from the server. This notification is displayed as a push notification or an in-app notification. The user receives the suggestion results on the device by checking the displayed suggestion. The output is a notification to the user.

[0632] Step 7:

[0633] The user checks the outfit suggestions displayed on the device and actually adopts the suggestions. If the user likes the suggested new items, they can purchase them through the fashion online shopping site. The input data is the suggested outfits and new items, and the output is the user's selection and purchasing behavior.

[0634] Step 8:

[0635] The device collects feedback from users. Users enter their feedback (ratings and areas for improvement) about the proposed outfits into the device, and this information is sent to the server. The server stores the received feedback in a database and uses it to improve the accuracy of the next proposal. The input data is the user's feedback information, and the output is an improved proposal algorithm.

[0636] (Application example 1)

[0637] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0638] In modern urban areas, many people living busy lives spend a lot of time and effort choosing their daily outfits. Furthermore, they must take into account weather information and the day's plans, making it difficult for many to efficiently choose the perfect outfit. Furthermore, there are issues with the effort required to actually find the suggested clothing items in physical stores and the complicated process of purchasing new items. There is a need for a system that can solve these issues and make users' lives more convenient and comfortable.

[0639] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0640] In this invention, the server includes means for collecting user information, data analysis means for suggesting optimal clothing to the user based on the user information, means for acquiring weather information, means for suggesting outfits based on the user information and the weather information, means for providing guidance for finding the suggested clothing items in a physical store, means for collecting feedback from the user and reflecting it in future suggestions, and means for supporting the process of purchasing the suggested new items. This not only enables the user to receive optimal clothing suggestions, but also makes it easier for the user to find and purchase clothing items in a physical store, thereby streamlining daily clothing selection.

[0641] The "means for collecting user information" is a function for acquiring information about the user's body shape, preferred style, photos of items owned, and schedule information, and storing the information in a database.

[0642] "Data analysis means" refers to algorithms and functions for deriving optimal clothing for a user based on collected user information and weather information.

[0643] "Means for obtaining weather information" refers to a function for periodically obtaining weather data for a specified area from an external API or other source and making it available within the system.

[0644] The "means for suggesting outfits" is a function for suggesting optimal outfit combinations to users based on user information and weather information.

[0645] "Means for providing in-store locating guidance" refers to navigation and guidance features that allow users to easily find the suggested clothing item in a physical store.

[0646] "Means of collecting feedback" is a function for obtaining user evaluations and opinions on proposals and reflecting them in future proposals.

[0647] A "checkout aid" is a link or interface that simplifies and assists the user in the process of purchasing a suggested new item.

[0648] This invention is a system that suggests optimal clothing based on user information and weather information. This system is mainly composed of three elements: a server, a terminal, and a user.

[0649] server

[0650] 1. Collection of User Information

[0651] The server collects the user's input data such as body shape, preferred style, photos of items owned, and schedule information. This information is stored in a database, which is then used to create a database based on the user's personality and lifestyle.

[0652] 2. Obtaining weather information

[0653] The server periodically retrieves weather information for the specified region using an external weather API (e.g., WeatherAPI), making the latest weather data available within the system.

[0654] 3. Data analysis

[0655] The server's AI analyzes the collected user information and weather information, taking into account past outfit history and user feedback. It then uses an algorithm to generate optimal outfit suggestions for the user.

[0656] 4. Coordination Proposal Generation

[0657] The server then creates specific outfit suggestions based on the results of the data analysis. These suggestions are then sent to the user's device. The suggestions include combinations of items the user already owns and new items they should consider purchasing.

[0658] Terminal

[0659] 1. Providing an interface

[0660] The device provides an interface that allows users to input and update information about their body shape, preferred style, photos of items they own, and schedule information. This interface is implemented as a smartphone app.

[0661] 2. Display of notifications

[0662] The device notifies the user of coordination suggestions sent from the server, including push notifications and in-app notifications.

[0663] 3. Gather feedback

[0664] The device collects user feedback (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future outfit suggestions.

[0665] 4. Purchase procedure assistance

[0666] The device provides an interface for users to purchase suggested new items, with a function that allows users to be redirected to a fashion online shopping site via a purchase link.

[0667] 5. In-store guidance

[0668] It provides a guide function to help users find suggested clothing items in physical stores, allowing them to easily find suggested items in physical stores.

[0669] user

[0670] 1. Enter your information

[0671] Users enter and update their body shape information, preferred styles, photos of the items they own, and daily schedule information into the app on their device.

[0672] 2. Review and adoption of proposals

[0673] Users can check the outfit suggestions displayed on their device and choose to actually use them. If they like the new items suggested, they can purchase them through the fashion online shopping site.

[0674] 3. Providing Feedback

[0675] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[0676] Specific examples

[0677] For example, suppose User A has plans to have lunch with a friend the next day. User A has previously entered his / her body type information (height 170cm, weight 65kg), preferred style (casual), and photos of items he / she owns (e.g., blue casual shirt, beige chino pants). The server performs data analysis based on the collected information and suggests a combination of "blue casual shirt and beige chino pants" that is suitable for a casual lunch. In addition, User A is also suggested a pair of lightweight white sneakers that he / she has recently been considering purchasing. The device notifies the user of these suggestions, and the user selects an outfit based on the suggestions.

[0678] Prompt Sentence Examples

[0679] Please use the following information to suggest the best outfit for you:

[0680] User information: Height 170cm, weight 65kg, casual style preference, list of items owned (blue casual shirt, beige chino pants)

[0681] Weather: Sunny, temperature 25 degrees

[0682] Planned: Lunch with a friend

[0683] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0684] Processing Steps

[0685] Step 1:

[0686] The user uses the device's app to enter information about their body shape, preferred style, photos of items they own, and schedule information.

[0687] Input: height, weight, preferred style, item photos, schedule

[0688] Output: These data are sent from the terminal to the server and stored in a database.

[0689] Specific behavior: The user enters the required information into the input form and clicks the submit button. The app executes an API request to send the entered data to the server.

[0690] Step 2:

[0691] The server uses a weather API to obtain weather information for the specified area.

[0692] Input: Region information

[0693] Output: Weather data such as temperature, precipitation probability, wind speed, etc.

[0694] Specific operation: The server periodically sends a request to the weather API to obtain the latest weather data and save it in the database.

[0695] Step 3:

[0696] The server's AI performs data analysis based on collected user information and weather information.

[0697] Input: User information, weather information, past coordination history, feedback information from users

[0698] Output: Recommendations for the best outfit for the user

[0699] How it works: The server's AI algorithm analyzes the input data and uses a generative AI model to generate the optimal outfit. The prompt is in the form "Please suggest the best outfit based on the following information: ..."

[0700] Step 4:

[0701] The server generates a coordination proposal and delivers it to the user's terminal.

[0702] Input: AI-generated outfit suggestions

[0703] Output: Coordination suggestions displayed on the device

[0704] What happens: The server sends the suggestion to the device using the notification API, and the device displays a push notification or in-app notification.

[0705] Step 5:

[0706] The user checks the outfit suggestions displayed on the device and enters feedback.

[0707] Input: Rating and improvements for the proposed outfit

[0708] Output: The feedback information is sent to the server and stored in a database.

[0709] Specific behavior: The user reviews the suggestion, enters their rating and improvements in the feedback form, and clicks the submit button. The app then makes an API request to send the feedback data to the server.

[0710] Step 6:

[0711] When users want to purchase a suggested new item, they access a fashion online shopping site via a purchase link on their device.

[0712] Input: Proposed new item

[0713] Output: Transition to fashion online shopping site and purchase procedure

[0714] Specific behavior: The user clicks on the purchase link and is redirected to a fashion online shopping site in a browser or in-app browser, where they proceed with the purchase.

[0715] Step 7:

[0716] The device provides guidance on how to find the suggested clothing items in physical stores.

[0717] Input: suggested item information, physical store location

[0718] Output: In-store navigation information

[0719] Specific operation: The device acquires the user's location information and displays information about physical stores where the suggested items are located. It also displays maps and routes to guide the user.

[0720] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0721] This invention is a system that suggests optimal clothing based on user information, particularly by combining weather information, the user's schedule information, and even the user's emotional information to support daily clothing selection. The main target audience of this invention is men and women in their 20s to 40s living in busy urban areas, and the aim is to make their daily lives more convenient and comfortable.

[0722] System configuration

[0723] server

[0724] 1. Collection of User Information

[0725] The server collects information provided by the user, such as body shape information, preferred style information, photos of items owned, and schedule information, and builds a database of the user's personality and lifestyle.

[0726] 2. Obtaining weather information

[0727] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the area specified by the user using an external weather API.

[0728] 3. Collecting emotional information

[0729] The server uses an emotion engine to analyze the user's facial expressions and voice, recognizing the user's emotions in real time.

[0730] 4. Data Analysis

[0731] The server's AI analyzes the collected user information, weather information, emotional information, and schedule information to derive the optimal outfit combination that matches the user's body type, preferences, and schedule, taking into account past outfit history and user feedback.

[0732] 5. Generating Coordination Proposals

[0733] The server then creates specific outfit suggestions based on the analysis results and delivers them to the user's device, including suggestions for combining items the user already owns and new items they should consider purchasing.

[0734] Terminal

[0735] 1. Providing an interface

[0736] The device provides an interface that allows users to easily input and update information about their body shape, preferred style, photos of their belongings, and schedule information. In addition, the device also has the ability to capture users' emotions in real time.

[0737] 2. Display of notifications

[0738] The device notifies the user of the coordination suggestions sent from the server, which are displayed as push notifications or in-app notifications.

[0739] 3. Gather feedback

[0740] The device collects user feedback (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future outfit suggestions.

[0741] 4. Purchase procedure assistance

[0742] The terminal provides an interface for the user to purchase the suggested new items and allows the user to transition to a fashion online shopping site via a purchase link from the server.

[0743] user

[0744] 1. Enter your information

[0745] Users can input and update their body shape, preferred style, photos of their belongings, and daily schedule information into the app on their device, and their emotions are reflected on the device in real time.

[0746] 2. Review and adoption of proposals

[0747] Users can check the outfit suggestions displayed on their device and choose to adopt them. If new items are suggested, they can consider purchasing them if necessary.

[0748] 3. Providing Feedback

[0749] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[0750] Specific examples

[0751] Let's say a user has lunch plans with a friend the next day. The user has previously entered information about their body type (height 170cm, weight 65kg), preferred style (casual), and photos of items they own (blue casual shirt, beige chino pants, etc.). The weather forecast for the user's area is sunny with a temperature of 25°C. The user is also excited about the lunch plans (positive emotion).

[0752] 1. The server performs data analysis based on this information and suggests a combination of "a blue casual shirt and beige chino pants" that would be suitable for a casual lunch date.

[0753] 2. Taking into account the user's emotional information, "red sneakers" that give an energetic impression are also suggested.

[0754] 3. The terminal notifies the user of these suggestions, and the user selects coordination according to the suggestions.

[0755] 4. If the user decides to purchase the suggested new sneakers, they can use the purchase link on their device to access the fashion online store and easily place their order.

[0756] As described above, the system of the present invention proposes optimal clothing that suits the user's personality, making daily clothing selection more efficient. In particular, by incorporating the user's real-time emotional information, it is possible to realize more personalized coordination.

[0757] The processing flow will be explained below.

[0758] Step 1:

[0759] Users can check and update their body shape information (e.g., height 170 cm, weight 65 kg), preferred style (e.g., casual), and photos of items they own (e.g., blue casual shirt, beige chino pants, etc.) that they have previously registered in the app on their device.

[0760] Step 2:

[0761] The user inputs the next day's schedule information (e.g., lunch with a friend) through the terminal, which is then reflected in the system.

[0762] Step 3:

[0763] The server receives the information entered by the user and stores it in a database, including the user's body type, preferred style, schedule, and items owned.

[0764] Step 4:

[0765] The server accesses an external weather API and obtains weather information (e.g., sunny, temperature 25 degrees) for the area and date and time specified by the user.

[0766] Step 5:

[0767] The server receives real-time emotional information provided by the user from the device, and the emotion engine analyzes the user's facial expressions and voice to generate emotional information.

[0768] Step 6:

[0769] The device updates the emotional information according to the user's real-time situation (e.g., looking forward to lunch plans) and sends it to the server.

[0770] Step 7:

[0771] The server's AI begins analyzing data based on the collected user information, weather information, emotional information, and schedule information, and then selects the optimal outfit, taking into account the user's behavioral history and feedback.

[0772] Step 8:

[0773] Based on the results of the data analysis, the server determines the optimal outfit combination for the user. In this case, it suggests a blue casual shirt and beige chino pants. It also suggests red sneakers to match the user's positive emotions.

[0774] Step 9:

[0775] The server also simultaneously suggests new items that the user does not own but would be suitable (e.g., red sneakers).

[0776] Step 10:

[0777] The device receives the coordination suggestions sent from the server and notifies the user via push notifications or in-app notifications.

[0778] Step 11:

[0779] The user checks the outfit suggestions displayed on the device and decides whether to adopt the suggested outfit. If the user likes the suggestion, they can adopt it.

[0780] Step 12:

[0781] When a user wants to purchase a new item, they access the online fashion store via a link on their device, complete the purchase, and confirm the order details to complete the purchase.

[0782] Step 13:

[0783] The user actually wears the suggested outfit and goes about their daily activities, after which they send feedback about their outfit to the server via their device.

[0784] Step 14:

[0785] The server receives the user's feedback information and stores it in a database, which is used to improve the accuracy of future suggestions.

[0786] Through these processing steps, this system can improve the efficiency of users' daily clothing selection and improve their quality of life. In particular, by utilizing the emotion engine, it is possible to provide more personalized suggestions based on the user's emotions.

[0787] Example 2

[0788] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0789] Conventional clothing recommendation systems are limited to suggestions based on the user's body shape and preferred style information, making it difficult to make personalized suggestions that take into account weather information and the user's emotional state. Furthermore, they lack the functionality to utilize feedback information to improve the accuracy of suggestions, which prevents them from fully increasing user satisfaction.

[0790] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0791] In this invention, the server includes means for collecting user information, means for acquiring weather information, means for collecting and analyzing emotional information, data analysis means, means for suggesting outfits based on the user information, weather information, and emotional information, means for learning collected feedback information about the user's clothing and improving the accuracy of suggestions, and means for suggesting new items to purchase based on the user's body shape information and preferred style information. This makes it possible to suggest optimal outfits that take into account the user's personality, lifestyle, weather, and emotional state.

[0792] "User information" is a collective term for data including the user's body shape information, preferred style information, photos of items owned, and daily schedule information.

[0793] The "data analysis means" refers to a hardware or software function that performs analysis based on collected user information, weather information, and emotional information to derive optimal clothing combinations.

[0794] "Weather information" refers to weather-related data such as temperature, precipitation probability, and wind speed obtained using external weather APIs.

[0795] "Emotional information" is data about the user's emotional state, recognized by analyzing the user's facial expressions and voice.

[0796] The "means for suggesting outfits" refers to a hardware or software function that has the ability to suggest specific outfit combinations based on the analysis results and deliver them to the user's device.

[0797] "Feedback information" refers to information collected from users, such as evaluations and areas for improvement regarding the proposed outfits.

[0798] "Means for improving the accuracy of suggestions" refers to algorithms or functions that learn from collected feedback information and improve the accuracy of future suggestions.

[0799] The "means for suggesting new items to purchase" is a function that suggests new items to the user based on the user's body shape information and preferred style information, and provides support for purchasing the items.

[0800] The present invention is a system that suggests optimal clothing based on user information, and in particular, supports daily clothing selection by combining weather information, user emotion information, and user schedule information. Specific embodiments for carrying out the present invention are described in detail below.

[0801] server

[0802] The server has the following features:

[0803] 1. Collection of User Information

[0804] User information includes the user's body shape information, preferred style information, photos of items owned, and daily schedule information. This information is provided by the user and sent to the server via the terminal. The server stores the received information in a database and manages it for each user. A commonly used database management system (DBMS) is used for this process.

[0805] 2. Obtaining weather information

[0806] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the area specified by the user using an external weather API (e.g., OpenWeatherMap API). The obtained weather information is saved for each user, and the latest information is kept.

[0807] 3. Collecting emotional information

[0808] The server uses an emotion analysis engine to analyze the user's facial expressions and voice to recognize their emotions in real time. The emotion engine uses commonly used machine learning or deep learning models. The analysis results are also stored in a database.

[0809] 4. Data Analysis

[0810] The server's AI analyzes the collected user information, weather information, emotional information, and schedule information to derive the optimal outfit combination based on the user's body shape, preferences, and schedule. Past outfit history and feedback from users are also taken into consideration. The AI ​​model uses a generative AI model to perform complex data analysis.

[0811] 5. Generating Coordination Proposals

[0812] The server then creates specific outfit suggestions based on the analysis results and delivers them to the user's device, including suggestions for combining items the user already owns and new items they should consider purchasing.

[0813] Specific examples

[0814] Let's say a user has lunch plans with a friend the next day. The user has previously entered information about their body type (height 170cm, weight 65kg), preferred style (casual), and photos of items they own (blue casual shirt, beige chino pants, etc.). The weather forecast for the user's area is sunny with a temperature of 25°C. The user is also excited about the lunch plans (positive emotion).

[0815] The server performs data analysis based on this information and suggests a combination of "a blue casual shirt and beige chino pants," suitable for a casual lunch date. Taking into account the user's emotional information, it also suggests "red sneakers," which give an energetic impression. The device notifies the user of these suggestions, and the user selects an outfit based on the suggestions. If the user decides to purchase the suggested new sneakers, they can use the purchase link on the device to access a fashion online shopping site and easily place an order.

[0816] Terminal

[0817] The terminal has the following features:

[0818] 1. Providing an interface

[0819] The device provides an interface that allows users to easily input and update information about their body shape, preferred style, photos of their belongings, and schedule information. The application is typically a smartphone app. The device also has the ability to capture the user's emotions in real time.

[0820] 2. Display of notifications

[0821] The device notifies the user of the coordination suggestions sent from the server, which are displayed as push notifications or in-app notifications.

[0822] 3. Gather feedback

[0823] The device collects user feedback (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future outfit suggestions.

[0824] 4. Purchase procedure assistance

[0825] The terminal provides an interface for the user to purchase the suggested new items and allows the user to transition to a fashion online shopping site via a purchase link from the server.

[0826] user

[0827] The user does the following:

[0828] 1. Enter your information

[0829] Users can input and update their body shape, preferred style, photos of their belongings, and daily schedule information into the app on their device, and their emotions are reflected on the device in real time.

[0830] 2. Review and adoption of proposals

[0831] Users can check the outfit suggestions displayed on their device and choose to adopt them. If new items are suggested, they can consider purchasing them if necessary.

[0832] 3. Providing Feedback

[0833] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[0834] In this way, the system of the present invention can suggest the best outfits to suit the user's personality, making daily outfit selection more efficient. In particular, by incorporating the user's real-time emotional information, more personalized outfits can be achieved.

[0835] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0836] Step 1:

[0837] Collection of User Information

[0838] Users log in to the app and enter their body type information (height, weight, etc.), preferred style information (casual, formal, etc.), photos of items they own, and daily schedule information.

[0839] Input: Body shape information, preferred style information, photos of items you own, daily schedule information

[0840] Data processing: The entered information is consolidated and stored in a separate database for each user.

[0841] Output: The consolidated user information is saved in the database.

[0842] Specific operation: The user enters their height (170cm) and weight (65kg) into the app's form, selects their lifestyle as "casual," takes a photo of an item they are carrying (a blue shirt), and enters their plans for the next day (lunch with a friend). The device sends this information to the server, which stores it in a database.

[0843] Step 2:

[0844] Obtaining weather information

[0845] The server calls an external weather API (for example, OpenWeatherMap API) and periodically obtains weather information (temperature, probability of precipitation, wind speed, etc.) for the area specified by the user.

[0846] Input: User's region information

[0847] Data processing: Weather information obtained from the weather API is saved for each user.

[0848] Output: The latest weather information for the user's location is stored in a database.

[0849] How it works: The server calls the weather API every morning at 7am and retrieves weather data such as "sunny, temperature 25 degrees." The server stores this information for each user.

[0850] Step 3:

[0851] Collecting emotional information

[0852] The device uses a camera and microphone to capture the user's facial expressions and voice and transmits them to an emotion analysis engine.

[0853] Input: User's facial expression and voice data

[0854] Data processing: The emotion analysis engine analyzes facial expressions and voice data to extract the user's emotional state (positive, negative, etc.).

[0855] Output: The analyzed emotion information is stored in a database.

[0856] How it works: When a user smiles at the device, the camera captures their facial expression and sends it to the emotion analysis engine. The engine interprets it as "positive" and sends the result to the server, which stores it in a database.

[0857] Step 4:

[0858] Data analysis

[0859] The server's AI combines and analyzes the collected user information, weather information, emotional information, and schedule information.

[0860] Input: User information, weather information, emotion information, schedule information

[0861] Data processing: Based on all collected data, a generative AI model is used to derive optimal outfit combinations.

[0862] Output: Specific outfit coordination suggestions guided by AI are generated.

[0863] Specific operation: The server's AI derives the combination of "a blue casual shirt and beige chino pants" based on the user's "height 170 cm, weight 65 kg," weather information "sunny, 25 degrees," emotional information "positive," and schedule information "lunch with a friend." It also suggests "red sneakers," which give an energetic impression.

[0864] Step 5:

[0865] Coordination proposal generation and notification

[0866] The server creates coordination suggestions based on the analysis results and delivers them to the user's device.

[0867] Input: AI-generated outfit suggestions

[0868] Data processing: Convert the proposal content into concrete text and images and send them to the device.

[0869] Output: Coordination suggestions are displayed on the terminal.

[0870] Specific operation: A notification such as "Today's outfit suggestion: A blue casual shirt and beige chino pants. We also recommend energetic red sneakers" will be displayed on the device.

[0871] Step 6:

[0872] Purchase procedure assistance

[0873] Users click on a link on their device to purchase the suggested new item.

[0874] Input: User's purchase click action

[0875] Data processing: The purchase link will take you to a fashion online shopping site and assist you with the purchase process.

[0876] Output: The user purchases the suggested new item.

[0877] Specific operation: The user clicks on the purchase link for "red sneakers" displayed on the device, and is redirected to the corresponding page on the fashion online shopping site to complete the order.

[0878] Step 7:

[0879] Collecting feedback

[0880] Users enter feedback about the suggested outfits into the app.

[0881] Input: Rating and improvements for the proposed outfit

[0882] Data processing: The collected feedback information is sent to the server and reflected in future proposals.

[0883] Output: A more accurate next proposal is generated.

[0884] Specific operation: The user rates the app as "very satisfied" and comments on the device that "I would like more casual suggestions." The device sends this to the server, and the feedback is saved in a database. The server analyzes this feedback and improves the accuracy of the next suggestion.

[0885] Based on the above processing steps, the program of the present invention can efficiently suggest optimal clothing that meets the user's needs.

[0886] (Application example 2)

[0887] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0888] Currently, choosing clothes is a time-consuming and laborious task in users' daily lives. For women and busy people in particular, thinking about how to coordinate their outfits every day can be very stressful. Furthermore, choosing appropriate clothing based on the weather, schedule, and emotions can be even more difficult. The purpose of this invention is to solve these problems and provide users with quick and appropriate clothing suggestions.

[0889] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user information, data analysis means for proposing optimal outfits to the user based on the user information, means for acquiring weather information, means for collecting and analyzing user emotion information, means for proposing outfits based on the user information, weather information, and emotion information, and visual display means for enabling virtual try-on. This allows the user to efficiently select everyday outfits and easily find optimal outfits while visually checking them.

[0890] "User information" refers to data including the user's body shape information, preferred style information, data on items owned, and schedule information.

[0891] "Data analysis means" refers to a technical means for analyzing and deriving optimal clothing for a user based on user information, weather information, and emotional information.

[0892] "Weather information" refers to local weather data such as temperature, probability of precipitation, and wind speed obtained using an external weather API.

[0893] "Emotional information" is data about the user's current emotional state, recognized by analyzing the user's facial expressions and voice.

[0894] "Visual display means" refers to a technical means that assists in visual confirmation of clothing to enable virtual try-on, and refers to devices such as smart glasses and head-mounted displays.

[0895] "Coordination suggestions" are suggestions for optimal clothing combinations presented to users based on collected and analyzed user information, weather information, and emotional information.

[0896] The present invention is a system that integrates user information, weather information, and emotional information to suggest optimal outfits. The system uses a server, a user terminal, and a visual display device such as smart glasses to provide users with quick and accurate outfit suggestions.

[0897] System configuration and operation

[0898] server

[0899] 1. Collection of User Information

[0900] The server collects the user's body shape information, preferred style information, data on items owned, and schedule information, and builds a database related to the user's lifestyle.

[0901] 2. Obtaining weather information

[0902] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the specified area using the weather API.

[0903] 3. Collection and analysis of emotional information

[0904] The server uses an emotion analysis engine to analyze the user's facial expressions and voice, recognizing the user's emotions in real time.

[0905] 4. Data Analysis

[0906] The server's AI analyzes data based on collected user information, weather information, emotional information, and schedule information to derive optimal outfit combinations, taking into account past outfit history and user feedback.

[0907] 5. Generating Coordination Proposals

[0908] The server generates specific outfit suggestions based on the analysis results and delivers them to the user's device, including suggestions for combining items the user already owns and new items they should consider purchasing.

[0909] User Device

[0910] 1. Providing an interface

[0911] The user device provides an interface that allows users to easily input and update information about their body shape, preferred style, items they own, and schedule information.The device also has the ability to capture users' emotions in real time.

[0912] 2. Display of notifications

[0913] The user's device will be notified of the coordination suggestions sent from the server, which will be displayed as a push notification or in-app notification.

[0914] 3. Gather feedback

[0915] The user device collects feedback from the user (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future suggestions.

[0916] 4. Purchase procedure assistance

[0917] The user terminal provides an interface for purchasing the proposed new items and assists the user in transitioning to a fashion online shopping site via a purchase link from the server.

[0918] visual display devices

[0919] 1. Providing virtual try-ons

[0920] Visual display devices such as smart glasses offer the ability to visually try on suggested outfits, allowing users to virtually try on the outfit and see how it will look in real life.

[0921] Specific examples

[0922] Let's say a user has lunch plans with a friend the next day. This user has previously entered information about their body type (height 170cm, weight 65kg), preferred style (casual), and photos of items they own (blue casual shirt, beige chino pants, etc.). The weather forecast for their area is sunny with a temperature of 25°C. Furthermore, this user is excited about the lunch plans (positive emotion).

[0923] The server analyzes the optimal outfit based on this information and generates a suggestion such as "a blue casual shirt and beige chino pants."

[0924] In addition, "red sneakers" are also suggested, as they reflect positive emotions and give an energetic impression.

[0925] The user's device will notify them of this suggestion, and the user will be able to visually confirm the suggestion in real time.

[0926] When a user wants to buy new sneakers, they can access a fashion online shopping site from their device and make the purchase smoothly.

[0927] Prompt Sentence Examples

[0928] Please suggest the best outfit for the user based on the following information:

[0929] 1. User information: Location: Tokyo, Height: 170cm, Weight: 65kg, Style preference: Casual

[0930] 2. Weather information: Clear skies, temperature 25°C

[0931] 3. Emotional information: Positive

[0932] Suggestion example:

[0933] Light clothing and bright colors are recommended.

[0934] In this way, the system of the present invention integrates a variety of information about the user to provide efficient and appropriate clothing suggestions.

[0935] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0936] Step 1: Collect user information

[0937] The server collects the user's body shape information (e.g., height, weight), preferred style information, photo data of items owned, and schedule information entered through the terminal. This information is stored in a user information database. The entered data is sent to the server and saved.

[0938] Input: Body shape information, style information, item photos, schedule information

[0939] Output: Data stored in the user information database

[0940] Step 2: Get weather information

[0941] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the area specified by the user using an external weather API. This data is stored in a weather database on the server.

[0942] Input: Area specification data

[0943] Output: Data stored in the weather information database

[0944] Step 3: Collecting and analyzing emotional information

[0945] The user captures facial expressions or voice in real time through the device and sends the data to the server, which uses an emotion analysis engine to analyze the user's emotions and store them in an emotion information database.

[0946] Input: facial expression data or voice data

[0947] Output: Analyzed data stored in the emotion information database

[0948] Step 4: Data analysis

[0949] The server integrates user information, weather information, emotional information, and schedule information, and analyzes the optimal outfit combination based on past outfit history and user feedback. Using an AI algorithm, it generates outfit suggestions based on data correlations and user preferences.

[0950] Input: User information, weather information, emotion information, schedule information

[0951] Output: Clothing suggestions based on analysis results

[0952] Step 5: Creating and notifying coordination proposals

[0953] The server creates specific outfit suggestions based on the analysis results and sends them to the user's device, which then displays the suggestions to the user as push notifications or in-app notifications.

[0954] Input: Analyzed clothing suggestion data

[0955] Output: Notification to user device

[0956] Step 6: Offer a virtual try-on

[0957] Users can visually try on the proposed outfits using smart glasses or other visual display devices. The terminal generates video data for the virtual try-on in real time and displays it on the smart glasses.

[0958] Input: Clothing suggestion data

[0959] Output: Virtual fitting video on a visual display device

[0960] Step 7: Gather feedback

[0961] Users can input their evaluation of the suggested outfits and suggestions for improvement via their device and send them to the server, which then stores this information in a feedback information database and uses it to improve the accuracy of the next outfit suggestions.

[0962] Input: User feedback data

[0963] Output: Data stored in the feedback information database

[0964] Step 8: Assist with checkout

[0965] If the user decides to purchase the suggested new item, the device provides a purchase link, allowing the user to access the fashion online shopping site, where the server generates the necessary purchase information and assists in the purchase process.

[0966] Input: Purchase desired item data

[0967] Output: Access link to fashion online shopping site and data for purchase procedure

[0968] Through the above processing steps, the system of the present invention can support the user in choosing daily clothing in an efficient and personalized manner.

[0969] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0970] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0971] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0972] [Third embodiment]

[0973] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0974] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0975] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0976] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0977] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0978] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0979] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0980] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0981] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0982] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0983] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0984] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0985] This invention is a system that suggests optimal clothing based on user information, and supports daily clothing selection by taking into consideration weather information and the user's schedule information in particular. The main target of this invention is men and women in their 20s to 40s living in busy urban areas, and the aim is to make their daily lives more convenient and comfortable.

[0986] System configuration

[0987] server

[0988] 1. Collection of User Information

[0989] The server collects information provided by the user, such as body shape information, preferred style information, photos of items owned, and schedule information, and builds a database of the user's personality and lifestyle.

[0990] 2. Obtaining weather information

[0991] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the area specified by the user using an external weather API.

[0992] 3. Data analysis

[0993] The server's AI analyzes the collected user information and weather information to derive the optimal outfit combination based on the user's body type, preferences, and schedule, taking into account past outfit history and user feedback.

[0994] 4. Coordination Proposal Generation

[0995] The server then creates specific outfit suggestions based on the analysis results and delivers them to the user's device, including suggestions for combining items the user already owns and new items they should consider purchasing.

[0996] Terminal

[0997] 1. Providing an interface

[0998] The device provides an interface that allows users to easily input and update information about their body shape, preferred style, photos of items they own, and schedule information.

[0999] 2. Display of notifications

[1000] The device notifies the user of the coordination suggestions sent from the server, which are displayed as push notifications or in-app notifications.

[1001] 3. Gather feedback

[1002] The device collects user feedback (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future outfit suggestions.

[1003] 4. Purchase procedure assistance

[1004] The terminal provides an interface for the user to purchase the suggested new items and allows the user to transition to a fashion online shopping site via a purchase link from the server.

[1005] user

[1006] 1. Enter your information

[1007] Users enter and update their body shape information, preferred styles, photos of the items they own, and daily schedule information into the app on their device.

[1008] 2. Review and adoption of proposals

[1009] Users can check the outfit suggestions displayed on their device and choose to actually use them. If they like the new items suggested, they can purchase them through the fashion online shopping site.

[1010] 3. Providing Feedback

[1011] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[1012] Specific examples

[1013] A user has planned to meet a friend for lunch the next day. The user has entered their body type (height: 170cm, weight: 65kg), preferred style (casual), and photos of items they own (e.g., blue casual shirt, beige chinos). The weather forecast for the user's area is sunny with a temperature of 25°C.

[1014] 1. The server performs data analysis based on this information and suggests a combination of "a blue casual shirt and beige chino pants" that would be suitable for a casual lunch date.

[1015] 2. Additionally, users will also be suggested lightweight white sneakers that they have recently considered purchasing.

[1016] 3. The terminal notifies the user of these suggestions, and the user selects coordination according to the suggestions.

[1017] 4. If the user decides to purchase the suggested new sneakers, they can use the purchase link on their device to access a fashion online store and easily place their order.

[1018] As described above, the system of the present invention proposes optimal clothing that suits the user's personality, making daily clothing selection more efficient.

[1019] The processing flow will be explained below.

[1020] Step 1:

[1021] Users can check and update their body shape information (e.g., height 170 cm, weight 65 kg), preferred style (e.g., casual), and photos of items they own (e.g., blue casual shirt, beige chino pants, etc.) that they have previously registered in the app on their device.

[1022] Step 2:

[1023] The user inputs the next day's schedule information (e.g., lunch with a friend) through the terminal, which is then reflected in the system.

[1024] Step 3:

[1025] The server receives the information entered by the user and stores it in a database, including the user's body type, preferred style, schedule, and items owned.

[1026] Step 4:

[1027] The server accesses an external weather API and obtains weather information (e.g., sunny, temperature 25 degrees) for the area and date and time specified by the user.

[1028] Step 5:

[1029] The server's AI begins analyzing data based on collected user information, weather information, and schedule information, and selects the optimal outfit, taking into account the user's past behavioral history and feedback.

[1030] Step 6:

[1031] Based on the results of the data analysis, the server determines the best outfit combination for the user, suggesting a blue casual shirt and beige chino pants.

[1032] Step 7:

[1033] The server also simultaneously suggests new items that the user does not own but would be suitable (e.g., lightweight white sneakers).

[1034] Step 8:

[1035] The device receives the coordination suggestions sent from the server and notifies the user via push notifications or in-app notifications.

[1036] Step 9:

[1037] The user checks the outfit suggestions displayed on the device and decides whether to adopt the suggested outfit. If the user likes the suggestion, they can adopt it.

[1038] Step 10:

[1039] When a user wants to purchase a new item, they access the online fashion store via a link on their device, complete the purchase, and confirm the order details to complete the purchase.

[1040] Step 11:

[1041] The user actually wears the suggested outfit and goes about their daily activities, after which they send feedback about their outfit to the server via their device.

[1042] Step 12:

[1043] The server receives the user's feedback information and stores it in a database, which is used to improve the accuracy of future suggestions.

[1044] Through the above processing steps, this system can improve the efficiency of the user's daily clothing selection and improve the quality of life.

[1045] Example 1

[1046] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1047] In modern society, many people living in busy urban areas spend a lot of time choosing their daily outfits. To solve this problem, there is a need for a system that can automatically suggest the best outfits for them, taking into account individual user information and weather conditions. It is also important to improve the accuracy of suggestions based on user feedback, and there is also a need for a function that supports the purchase of new items.

[1048] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1049] In this invention, the server includes means for collecting user information, data analysis means for suggesting optimal clothing to the user based on the user information, means for acquiring weather information, means for suggesting outfits based on the user information and the weather information, means for learning from the collected user feedback information and improving the accuracy of the suggestions, means for suggesting new items to purchase based on the user's body shape information and preferred style information, means for periodically acquiring local weather information using a weather API, means for collecting photos of items the user owns, means for collecting the user's schedule information, and means for transmitting the suggested clothing to the user terminal. This allows the user to select optimal clothing based on their individual lifestyle and weather conditions, improving the accuracy of suggestions and also supporting the purchase of new fashion items.

[1050] "User information" is data including the user's body shape information, preferred style, photos of items owned, and daily schedule information.

[1051] "Data analysis" is a process for suggesting optimal clothing for a user based on collected user information and weather information.

[1052] "Weather information" refers to meteorological data such as local temperature, precipitation probability, and wind speed obtained using an external weather API.

[1053] "Coordination suggestions" are suggestions for optimal outfit combinations generated based on user information and weather information.

[1054] "Feedback information" is data on the user's evaluation of the proposed outfit and points for improvement.

[1055] A "weather API" is an application programming interface for obtaining weather information via the Internet.

[1056] "Schedule information" is data about the schedules and events that a user will have on a particular day.

[1057] A "device" is an electronic device used by a user to enter information, review suggestions, or provide feedback.

[1058] A "purchase link" is a link that takes the user to a web page where they can purchase the suggested new fashion item.

[1059] This invention is a system that suggests optimal clothing based on user information, and supports daily clothing selection by taking into account weather information and the user's schedule information in particular. The main target of this invention is people living in busy urban areas, and its purpose is to make their daily lives more convenient and comfortable.

[1060] server

[1061] 1. Collection of User Information

[1062] The server collects information provided by the user via their device, such as body shape information, preferred style information, photos of items owned, and schedule information. This information is used to build a database of the user's personality and lifestyle. Specifically, the collected data is stored in the server's database and used for future suggestions.

[1063] 2. Obtaining weather information

[1064] The server periodically obtains weather information for the area specified by the user using an external weather API (e.g., OpenWeatherMap or Weatherstack). This information includes temperature, precipitation probability, wind speed, and other weather information. The obtained weather information is also stored in the server's database and used for analysis along with user information.

[1065] 3. Data analysis

[1066] The server's AI analyzes the collected user information and weather information, taking into account past outfit history and user feedback to derive the optimal outfit combinations based on the user's body type, preferences, and schedule.

[1067] 4. Coordination Proposal Generation

[1068] The server then creates specific outfit suggestions based on the AI ​​analysis results and delivers them to the user's device. The suggestions include combinations of items the user already owns and new items they should consider purchasing.

[1069] Terminal

[1070] 1. Providing an interface

[1071] The device provides an interface that allows users to easily input and update information about their body shape, preferred style, photos of items they own, and schedule information.

[1072] 2. Display of notifications

[1073] The device notifies the user of the coordination suggestions sent from the server, which are displayed as push notifications or in-app notifications.

[1074] 3. Gather feedback

[1075] The device collects feedback from users (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future suggestions.

[1076] 4. Purchase procedure assistance

[1077] The terminal provides an interface for the user to purchase the suggested new items and allows the user to transition to a fashion online shopping site via a purchase link from the server.

[1078] user

[1079] 1. Enter your information

[1080] Users enter and update their body shape information, preferred styles, photos of the items they own, and daily schedule information into the app on their device.

[1081] 2. Review and adoption of proposals

[1082] Users can check the outfit suggestions displayed on their device and choose to actually use them. If they like the new items suggested, they can purchase them through the fashion online shopping site.

[1083] 3. Providing Feedback

[1084] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[1085] Specific examples

[1086] A user has planned to meet a friend for lunch the next day. The user has entered their body type (height: 170cm, weight: 65kg), preferred style (casual), and photos of items they own (e.g., blue casual shirt, beige chinos). The weather forecast for the user's area is sunny with a temperature of 25°C.

[1087] 1. The server performs data analysis based on this information and suggests a combination of "a blue casual shirt and beige chino pants" that would be suitable for a casual lunch date.

[1088] 2. Additionally, users will also be suggested lightweight white sneakers that they have recently considered purchasing.

[1089] 3. The terminal notifies the user of these suggestions, and the user selects coordination according to the suggestions.

[1090] 4. If the user decides to purchase the suggested new sneakers, they can use the purchase link on their device to access a fashion online store and easily place their order.

[1091] Prompt Sentence Examples

[1092] "Please create a system that suggests the best outfits for users based on their personal information. Please also take into account weather and schedule information."

[1093] "Please suggest an appropriate outfit for a specific user (e.g., height 170cm, weight 65kg, casual style) for lunch the next day. The local weather forecast is sunny with a temperature of 25 degrees."

[1094] As described above, the system of the present invention proposes optimal clothing that suits the user's personality, making daily clothing selection more efficient.

[1095] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1096] Step 1:

[1097] Users input their body type, preferred style, photos of their belongings, and schedule information through the device's interface. This input data includes the user's height, weight, preferred fashion style, photos of clothes they own, and their schedule for a specific day. The input data is temporarily stored on the device.

[1098] Step 2:

[1099] The terminal sends the information entered by the user to the server. This includes the user's body shape information, preferred style, photos of items owned, and schedule information. The server stores the received information in a database and updates the user's profile. The output is the updated user profile data.

[1100] Step 3:

[1101] The server periodically retrieves weather information for the area specified by the user using an external weather API (e.g., OpenWeatherMap or Weatherstack). During this retrieval process, weather data such as temperature, precipitation probability, and wind speed for the specified area are collected. The retrieved weather data is also stored in a database, and the output is the latest weather information.

[1102] Step 4:

[1103] The server's AI performs data analysis based on collected user information and weather information. The input data is the user's profile information, weather information, and past outfit history. The AI ​​analyzes this data and derives the optimal outfit combination based on the user's body type, preferences, and schedule. The output is a recommendation of the optimal outfit.

[1104] Step 5:

[1105] The server generates specific outfit suggestions based on the results of the AI ​​analysis. This process also includes combinations of items the user already owns and new items they should consider purchasing. The generated outfit suggestions are sent to the user's device. The output is a specific outfit suggestion for the user.

[1106] Step 6:

[1107] The device notifies the user of the coordination suggestions sent from the server. This notification is displayed as a push notification or an in-app notification. The user receives the suggestion results on the device by checking the displayed suggestion. The output is a notification to the user.

[1108] Step 7:

[1109] The user checks the outfit suggestions displayed on the device and actually adopts the suggestions. If the user likes the suggested new items, they can purchase them through the fashion online shopping site. The input data is the suggested outfits and new items, and the output is the user's selection and purchasing behavior.

[1110] Step 8:

[1111] The device collects feedback from users. Users enter their feedback (ratings and areas for improvement) about the proposed outfits into the device, and this information is sent to the server. The server stores the received feedback in a database and uses it to improve the accuracy of the next proposal. The input data is the user's feedback information, and the output is an improved proposal algorithm.

[1112] (Application example 1)

[1113] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1114] In modern urban areas, many people living busy lives spend a lot of time and effort choosing their daily outfits. Furthermore, they must take into account weather information and the day's plans, making it difficult for many to efficiently choose the perfect outfit. Furthermore, there are issues with the effort required to actually find the suggested clothing items in physical stores and the complicated process of purchasing new items. There is a need for a system that can solve these issues and make users' lives more convenient and comfortable.

[1115] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1116] In this invention, the server includes means for collecting user information, data analysis means for suggesting optimal clothing to the user based on the user information, means for acquiring weather information, means for suggesting outfits based on the user information and the weather information, means for providing guidance for finding the suggested clothing items in a physical store, means for collecting feedback from the user and reflecting it in future suggestions, and means for supporting the process of purchasing the suggested new items. This not only enables the user to receive optimal clothing suggestions, but also makes it easier for the user to find and purchase clothing items in a physical store, thereby streamlining daily clothing selection.

[1117] The "means for collecting user information" is a function for acquiring information about the user's body shape, preferred style, photos of items owned, and schedule information, and storing the information in a database.

[1118] "Data analysis means" refers to algorithms and functions for deriving optimal clothing for a user based on collected user information and weather information.

[1119] "Means for obtaining weather information" refers to a function for periodically obtaining weather data for a specified area from an external API or other source and making it available within the system.

[1120] The "means for suggesting outfits" is a function for suggesting optimal outfit combinations to users based on user information and weather information.

[1121] "Means for providing in-store locating guidance" refers to navigation and guidance features that allow users to easily find the suggested clothing item in a physical store.

[1122] "Means of collecting feedback" is a function for obtaining user evaluations and opinions on proposals and reflecting them in future proposals.

[1123] A "checkout aid" is a link or interface that simplifies and assists the user in the process of purchasing a suggested new item.

[1124] This invention is a system that suggests optimal clothing based on user information and weather information. This system is mainly composed of three elements: a server, a terminal, and a user.

[1125] server

[1126] 1. Collection of User Information

[1127] The server collects the user's input data such as body shape, preferred style, photos of items owned, and schedule information. This information is stored in a database, which is then used to create a database based on the user's personality and lifestyle.

[1128] 2. Obtaining weather information

[1129] The server periodically retrieves weather information for the specified region using an external weather API (e.g., WeatherAPI), making the latest weather data available within the system.

[1130] 3. Data analysis

[1131] The server's AI analyzes the collected user information and weather information, taking into account past outfit history and user feedback. It then uses an algorithm to generate optimal outfit suggestions for the user.

[1132] 4. Coordination Proposal Generation

[1133] The server then creates specific outfit suggestions based on the results of the data analysis. These suggestions are then sent to the user's device. The suggestions include combinations of items the user already owns and new items they should consider purchasing.

[1134] Terminal

[1135] 1. Providing an interface

[1136] The device provides an interface that allows users to input and update information about their body shape, preferred style, photos of items they own, and schedule information. This interface is implemented as a smartphone app.

[1137] 2. Display of notifications

[1138] The device notifies the user of coordination suggestions sent from the server, including push notifications and in-app notifications.

[1139] 3. Gather feedback

[1140] The device collects user feedback (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future outfit suggestions.

[1141] 4. Purchase procedure assistance

[1142] The device provides an interface for users to purchase suggested new items, with a function that allows users to be redirected to a fashion online shopping site via a purchase link.

[1143] 5. In-store guidance

[1144] It provides a guide function to help users find suggested clothing items in physical stores, allowing them to easily find suggested items in physical stores.

[1145] user

[1146] 1. Enter your information

[1147] Users enter and update their body shape information, preferred styles, photos of the items they own, and daily schedule information into the app on their device.

[1148] 2. Review and adoption of proposals

[1149] Users can check the outfit suggestions displayed on their device and choose to actually use them. If they like the new items suggested, they can purchase them through the fashion online shopping site.

[1150] 3. Providing Feedback

[1151] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[1152] Specific examples

[1153] For example, suppose User A has plans to have lunch with a friend the next day. User A has previously entered his / her body type information (height 170cm, weight 65kg), preferred style (casual), and photos of items he / she owns (e.g., blue casual shirt, beige chino pants). The server performs data analysis based on the collected information and suggests a combination of "blue casual shirt and beige chino pants" that is suitable for a casual lunch. In addition, User A is also suggested a pair of lightweight white sneakers that he / she has recently been considering purchasing. The device notifies the user of these suggestions, and the user selects an outfit based on the suggestions.

[1154] Prompt Sentence Examples

[1155] Please use the following information to suggest the best outfit for you:

[1156] User information: Height 170cm, weight 65kg, casual style preference, list of items owned (blue casual shirt, beige chino pants)

[1157] Weather: Sunny, temperature 25 degrees

[1158] Planned: Lunch with a friend

[1159] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1160] Processing Steps

[1161] Step 1:

[1162] The user uses the device's app to enter information about their body shape, preferred style, photos of items they own, and schedule information.

[1163] Input: height, weight, preferred style, item photos, schedule

[1164] Output: These data are sent from the terminal to the server and stored in a database.

[1165] Specific behavior: The user enters the required information into the input form and clicks the submit button. The app executes an API request to send the entered data to the server.

[1166] Step 2:

[1167] The server uses a weather API to obtain weather information for the specified area.

[1168] Input: Region information

[1169] Output: Weather data such as temperature, precipitation probability, wind speed, etc.

[1170] Specific operation: The server periodically sends a request to the weather API to obtain the latest weather data and save it in the database.

[1171] Step 3:

[1172] The server's AI performs data analysis based on collected user information and weather information.

[1173] Input: User information, weather information, past coordination history, feedback information from users

[1174] Output: Recommendations for the best outfit for the user

[1175] How it works: The server's AI algorithm analyzes the input data and uses a generative AI model to generate the optimal outfit. The prompt is in the form "Please suggest the best outfit based on the following information: ..."

[1176] Step 4:

[1177] The server generates a coordination proposal and delivers it to the user's terminal.

[1178] Input: AI-generated outfit suggestions

[1179] Output: Coordination suggestions displayed on the device

[1180] What happens: The server sends the suggestion to the device using the notification API, and the device displays a push notification or in-app notification.

[1181] Step 5:

[1182] The user checks the outfit suggestions displayed on the device and enters feedback.

[1183] Input: Rating and improvements for the proposed outfit

[1184] Output: The feedback information is sent to the server and stored in a database.

[1185] Specific behavior: The user reviews the suggestion, enters their rating and improvements in the feedback form, and clicks the submit button. The app then makes an API request to send the feedback data to the server.

[1186] Step 6:

[1187] When users want to purchase a suggested new item, they access a fashion online shopping site via a purchase link on their device.

[1188] Input: Proposed new item

[1189] Output: Transition to fashion online shopping site and purchase procedure

[1190] Specific behavior: The user clicks on the purchase link and is redirected to a fashion online shopping site in a browser or in-app browser, where they proceed with the purchase.

[1191] Step 7:

[1192] The device provides guidance on how to find the suggested clothing items in physical stores.

[1193] Input: suggested item information, physical store location

[1194] Output: In-store navigation information

[1195] Specific operation: The device acquires the user's location information and displays information about physical stores where the suggested items are located. It also displays maps and routes to guide the user.

[1196] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1197] This invention is a system that suggests optimal clothing based on user information, particularly by combining weather information, the user's schedule information, and even the user's emotional information to support daily clothing selection. The main target audience of this invention is men and women in their 20s to 40s living in busy urban areas, and the aim is to make their daily lives more convenient and comfortable.

[1198] System configuration

[1199] server

[1200] 1. Collection of User Information

[1201] The server collects information provided by the user, such as body shape information, preferred style information, photos of items owned, and schedule information, and builds a database of the user's personality and lifestyle.

[1202] 2. Obtaining weather information

[1203] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the area specified by the user using an external weather API.

[1204] 3. Collecting emotional information

[1205] The server uses an emotion engine to analyze the user's facial expressions and voice, recognizing the user's emotions in real time.

[1206] 4. Data Analysis

[1207] The server's AI analyzes the collected user information, weather information, emotional information, and schedule information to derive the optimal outfit combination that matches the user's body type, preferences, and schedule, taking into account past outfit history and user feedback.

[1208] 5. Generating Coordination Proposals

[1209] The server then creates specific outfit suggestions based on the analysis results and delivers them to the user's device, including suggestions for combining items the user already owns and new items they should consider purchasing.

[1210] Terminal

[1211] 1. Providing an interface

[1212] The device provides an interface that allows users to easily input and update information about their body shape, preferred style, photos of their belongings, and schedule information. In addition, the device also has the ability to capture users' emotions in real time.

[1213] 2. Display of notifications

[1214] The device notifies the user of the coordination suggestions sent from the server, which are displayed as push notifications or in-app notifications.

[1215] 3. Gather feedback

[1216] The device collects user feedback (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future outfit suggestions.

[1217] 4. Purchase procedure assistance

[1218] The terminal provides an interface for the user to purchase the suggested new items and allows the user to transition to a fashion online shopping site via a purchase link from the server.

[1219] user

[1220] 1. Enter your information

[1221] Users can input and update their body shape, preferred style, photos of their belongings, and daily schedule information into the app on their device, and their emotions are reflected on the device in real time.

[1222] 2. Review and adoption of proposals

[1223] Users can check the outfit suggestions displayed on their device and choose to adopt them. If new items are suggested, they can consider purchasing them if necessary.

[1224] 3. Providing Feedback

[1225] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[1226] Specific examples

[1227] Let's say a user has lunch plans with a friend the next day. The user has previously entered information about their body type (height 170cm, weight 65kg), preferred style (casual), and photos of items they own (blue casual shirt, beige chino pants, etc.). The weather forecast for the user's area is sunny with a temperature of 25°C. The user is also excited about the lunch plans (positive emotion).

[1228] 1. The server performs data analysis based on this information and suggests a combination of "a blue casual shirt and beige chino pants" that would be suitable for a casual lunch date.

[1229] 2. Taking into account the user's emotional information, "red sneakers" that give an energetic impression are also suggested.

[1230] 3. The terminal notifies the user of these suggestions, and the user selects coordination according to the suggestions.

[1231] 4. If the user decides to purchase the suggested new sneakers, they can use the purchase link on their device to access the fashion online store and easily place their order.

[1232] As described above, the system of the present invention proposes optimal clothing that suits the user's personality, making daily clothing selection more efficient. In particular, by incorporating the user's real-time emotional information, it is possible to realize more personalized coordination.

[1233] The processing flow will be explained below.

[1234] Step 1:

[1235] Users can check and update their body shape information (e.g., height 170 cm, weight 65 kg), preferred style (e.g., casual), and photos of items they own (e.g., blue casual shirt, beige chino pants, etc.) that they have previously registered in the app on their device.

[1236] Step 2:

[1237] The user inputs the next day's schedule information (e.g., lunch with a friend) through the terminal, which is then reflected in the system.

[1238] Step 3:

[1239] The server receives the information entered by the user and stores it in a database, including the user's body type, preferred style, schedule, and items owned.

[1240] Step 4:

[1241] The server accesses an external weather API and obtains weather information (e.g., sunny, temperature 25 degrees) for the area and date and time specified by the user.

[1242] Step 5:

[1243] The server receives real-time emotional information provided by the user from the device, and the emotion engine analyzes the user's facial expressions and voice to generate emotional information.

[1244] Step 6:

[1245] The device updates the emotional information according to the user's real-time situation (e.g., looking forward to lunch plans) and sends it to the server.

[1246] Step 7:

[1247] The server's AI begins analyzing data based on the collected user information, weather information, emotional information, and schedule information, and then selects the optimal outfit, taking into account the user's behavioral history and feedback.

[1248] Step 8:

[1249] Based on the results of the data analysis, the server determines the optimal outfit combination for the user. In this case, it suggests a blue casual shirt and beige chino pants. It also suggests red sneakers to match the user's positive emotions.

[1250] Step 9:

[1251] The server also simultaneously suggests new items that the user does not own but would be suitable (e.g., red sneakers).

[1252] Step 10:

[1253] The device receives the coordination suggestions sent from the server and notifies the user via push notifications or in-app notifications.

[1254] Step 11:

[1255] The user checks the outfit suggestions displayed on the device and decides whether to adopt the suggested outfit. If the user likes the suggestion, they can adopt it.

[1256] Step 12:

[1257] When a user wants to purchase a new item, they access the online fashion store via a link on their device, complete the purchase, and confirm the order details to complete the purchase.

[1258] Step 13:

[1259] The user actually wears the suggested outfit and goes about their daily activities, after which they send feedback about their outfit to the server via their device.

[1260] Step 14:

[1261] The server receives the user's feedback information and stores it in a database, which is used to improve the accuracy of future suggestions.

[1262] Through these processing steps, this system can improve the efficiency of users' daily clothing selection and improve their quality of life. In particular, by utilizing the emotion engine, it is possible to provide more personalized suggestions based on the user's emotions.

[1263] Example 2

[1264] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1265] Conventional clothing recommendation systems are limited to suggestions based on the user's body shape and preferred style information, making it difficult to make personalized suggestions that take into account weather information and the user's emotional state. Furthermore, they lack the functionality to utilize feedback information to improve the accuracy of suggestions, which prevents them from fully increasing user satisfaction.

[1266] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1267] In this invention, the server includes means for collecting user information, means for acquiring weather information, means for collecting and analyzing emotional information, data analysis means, means for suggesting outfits based on the user information, weather information, and emotional information, means for learning collected feedback information about the user's clothing and improving the accuracy of suggestions, and means for suggesting new items to purchase based on the user's body shape information and preferred style information. This makes it possible to suggest optimal outfits that take into account the user's personality, lifestyle, weather, and emotional state.

[1268] "User information" is a collective term for data including the user's body shape information, preferred style information, photos of items owned, and daily schedule information.

[1269] The "data analysis means" refers to a hardware or software function that performs analysis based on collected user information, weather information, and emotional information to derive optimal clothing combinations.

[1270] "Weather information" refers to weather-related data such as temperature, precipitation probability, and wind speed obtained using external weather APIs.

[1271] "Emotional information" is data about the user's emotional state, recognized by analyzing the user's facial expressions and voice.

[1272] The "means for suggesting outfits" refers to a hardware or software function that has the ability to suggest specific outfit combinations based on the analysis results and deliver them to the user's device.

[1273] "Feedback information" refers to information collected from users, such as evaluations and areas for improvement regarding the proposed outfits.

[1274] "Means for improving the accuracy of suggestions" refers to algorithms or functions that learn from collected feedback information and improve the accuracy of future suggestions.

[1275] The "means for suggesting new items to purchase" is a function that suggests new items to the user based on the user's body shape information and preferred style information, and provides support for purchasing the items.

[1276] The present invention is a system that suggests optimal clothing based on user information, and in particular, supports daily clothing selection by combining weather information, user emotion information, and user schedule information. Specific embodiments for carrying out the present invention are described in detail below.

[1277] server

[1278] The server has the following features:

[1279] 1. Collection of User Information

[1280] User information includes the user's body shape information, preferred style information, photos of items owned, and daily schedule information. This information is provided by the user and sent to the server via the terminal. The server stores the received information in a database and manages it for each user. A commonly used database management system (DBMS) is used for this process.

[1281] 2. Obtaining weather information

[1282] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the area specified by the user using an external weather API (e.g., OpenWeatherMap API). The obtained weather information is saved for each user, and the latest information is kept.

[1283] 3. Collecting emotional information

[1284] The server uses an emotion analysis engine to analyze the user's facial expressions and voice to recognize their emotions in real time. The emotion engine uses commonly used machine learning or deep learning models. The analysis results are also stored in a database.

[1285] 4. Data Analysis

[1286] The server's AI analyzes the collected user information, weather information, emotional information, and schedule information to derive the optimal outfit combination based on the user's body shape, preferences, and schedule. Past outfit history and feedback from users are also taken into consideration. The AI ​​model uses a generative AI model to perform complex data analysis.

[1287] 5. Generating Coordination Proposals

[1288] The server then creates specific outfit suggestions based on the analysis results and delivers them to the user's device, including suggestions for combining items the user already owns and new items they should consider purchasing.

[1289] Specific examples

[1290] Let's say a user has lunch plans with a friend the next day. The user has previously entered information about their body type (height 170cm, weight 65kg), preferred style (casual), and photos of items they own (blue casual shirt, beige chino pants, etc.). The weather forecast for the user's area is sunny with a temperature of 25°C. The user is also excited about the lunch plans (positive emotion).

[1291] The server performs data analysis based on this information and suggests a combination of "a blue casual shirt and beige chino pants," suitable for a casual lunch date. Taking into account the user's emotional information, it also suggests "red sneakers," which give an energetic impression. The device notifies the user of these suggestions, and the user selects an outfit based on the suggestions. If the user decides to purchase the suggested new sneakers, they can use the purchase link on the device to access a fashion online shopping site and easily place an order.

[1292] Terminal

[1293] The terminal has the following features:

[1294] 1. Providing an interface

[1295] The device provides an interface that allows users to easily input and update information about their body shape, preferred style, photos of their belongings, and schedule information. The application is typically a smartphone app. The device also has the ability to capture the user's emotions in real time.

[1296] 2. Display of notifications

[1297] The device notifies the user of the coordination suggestions sent from the server, which are displayed as push notifications or in-app notifications.

[1298] 3. Gather feedback

[1299] The device collects user feedback (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future outfit suggestions.

[1300] 4. Purchase procedure assistance

[1301] The terminal provides an interface for the user to purchase the suggested new items and allows the user to transition to a fashion online shopping site via a purchase link from the server.

[1302] user

[1303] The user does the following:

[1304] 1. Enter your information

[1305] Users can input and update their body shape, preferred style, photos of their belongings, and daily schedule information into the app on their device, and their emotions are reflected on the device in real time.

[1306] 2. Review and adoption of proposals

[1307] Users can check the outfit suggestions displayed on their device and choose to adopt them. If new items are suggested, they can consider purchasing them if necessary.

[1308] 3. Providing Feedback

[1309] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[1310] In this way, the system of the present invention can suggest the best outfits to suit the user's personality, making daily outfit selection more efficient. In particular, by incorporating the user's real-time emotional information, more personalized outfits can be achieved.

[1311] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1312] Step 1:

[1313] Collection of User Information

[1314] Users log in to the app and enter their body type information (height, weight, etc.), preferred style information (casual, formal, etc.), photos of items they own, and daily schedule information.

[1315] Input: Body shape information, preferred style information, photos of items you own, daily schedule information

[1316] Data processing: The entered information is consolidated and stored in a separate database for each user.

[1317] Output: The consolidated user information is saved in the database.

[1318] Specific operation: The user enters their height (170cm) and weight (65kg) into the app's form, selects their lifestyle as "casual," takes a photo of an item they are carrying (a blue shirt), and enters their plans for the next day (lunch with a friend). The device sends this information to the server, which stores it in a database.

[1319] Step 2:

[1320] Obtaining weather information

[1321] The server calls an external weather API (for example, OpenWeatherMap API) and periodically obtains weather information (temperature, probability of precipitation, wind speed, etc.) for the area specified by the user.

[1322] Input: User's region information

[1323] Data processing: Weather information obtained from the weather API is saved for each user.

[1324] Output: The latest weather information for the user's location is stored in a database.

[1325] How it works: The server calls the weather API every morning at 7am and retrieves weather data such as "sunny, temperature 25 degrees." The server stores this information for each user.

[1326] Step 3:

[1327] Collecting emotional information

[1328] The device uses a camera and microphone to capture the user's facial expressions and voice and transmits them to an emotion analysis engine.

[1329] Input: User's facial expression and voice data

[1330] Data processing: The emotion analysis engine analyzes facial expressions and voice data to extract the user's emotional state (positive, negative, etc.).

[1331] Output: The analyzed emotion information is stored in a database.

[1332] How it works: When a user smiles at the device, the camera captures their facial expression and sends it to the emotion analysis engine. The engine interprets it as "positive" and sends the result to the server, which stores it in a database.

[1333] Step 4:

[1334] Data analysis

[1335] The server's AI combines and analyzes the collected user information, weather information, emotional information, and schedule information.

[1336] Input: User information, weather information, emotion information, schedule information

[1337] Data processing: Based on all collected data, a generative AI model is used to derive optimal outfit combinations.

[1338] Output: Specific outfit coordination suggestions guided by AI are generated.

[1339] Specific operation: The server's AI derives the combination of "a blue casual shirt and beige chino pants" based on the user's "height 170 cm, weight 65 kg," weather information "sunny, 25 degrees," emotional information "positive," and schedule information "lunch with a friend." It also suggests "red sneakers," which give an energetic impression.

[1340] Step 5:

[1341] Coordination proposal generation and notification

[1342] The server creates coordination suggestions based on the analysis results and delivers them to the user's device.

[1343] Input: AI-generated outfit suggestions

[1344] Data processing: Convert the proposal content into concrete text and images and send them to the device.

[1345] Output: Coordination suggestions are displayed on the terminal.

[1346] Specific operation: A notification such as "Today's outfit suggestion: A blue casual shirt and beige chino pants. We also recommend energetic red sneakers" will be displayed on the device.

[1347] Step 6:

[1348] Purchase procedure assistance

[1349] Users click on a link on their device to purchase the suggested new item.

[1350] Input: User's purchase click action

[1351] Data processing: The purchase link will take you to a fashion online shopping site and assist you with the purchase process.

[1352] Output: The user purchases the suggested new item.

[1353] Specific operation: The user clicks on the purchase link for "red sneakers" displayed on the device, and is redirected to the corresponding page on the fashion online shopping site to complete the order.

[1354] Step 7:

[1355] Collecting feedback

[1356] Users enter feedback about the suggested outfits into the app.

[1357] Input: Rating and improvements for the proposed outfit

[1358] Data processing: The collected feedback information is sent to the server and reflected in future proposals.

[1359] Output: A more accurate next proposal is generated.

[1360] Specific operation: The user rates the app as "very satisfied" and comments on the device that "I would like more casual suggestions." The device sends this to the server, and the feedback is saved in a database. The server analyzes this feedback and improves the accuracy of the next suggestion.

[1361] Based on the above processing steps, the program of the present invention can efficiently suggest optimal clothing that meets the user's needs.

[1362] (Application example 2)

[1363] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1364] Currently, choosing clothes is a time-consuming and laborious task in users' daily lives. For women and busy people in particular, thinking about how to coordinate their outfits every day can be very stressful. Furthermore, choosing appropriate clothing based on the weather, schedule, and emotions can be even more difficult. The purpose of this invention is to solve these problems and provide users with quick and appropriate clothing suggestions.

[1365] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user information, data analysis means for proposing optimal outfits to the user based on the user information, means for acquiring weather information, means for collecting and analyzing user emotion information, means for proposing outfits based on the user information, weather information, and emotion information, and visual display means for enabling virtual try-on. This allows the user to efficiently select everyday outfits and easily find optimal outfits while visually checking them.

[1366] "User information" refers to data including the user's body shape information, preferred style information, data on items owned, and schedule information.

[1367] "Data analysis means" refers to a technical means for analyzing and deriving optimal clothing for a user based on user information, weather information, and emotional information.

[1368] "Weather information" refers to local weather data such as temperature, probability of precipitation, and wind speed obtained using an external weather API.

[1369] "Emotional information" is data about the user's current emotional state, recognized by analyzing the user's facial expressions and voice.

[1370] "Visual display means" refers to a technical means that assists in visual confirmation of clothing to enable virtual try-on, and refers to devices such as smart glasses and head-mounted displays.

[1371] "Coordination suggestions" are suggestions for optimal clothing combinations presented to users based on collected and analyzed user information, weather information, and emotional information.

[1372] The present invention is a system that integrates user information, weather information, and emotional information to suggest optimal outfits. The system uses a server, a user terminal, and a visual display device such as smart glasses to provide users with quick and accurate outfit suggestions.

[1373] System configuration and operation

[1374] server

[1375] 1. Collection of User Information

[1376] The server collects the user's body shape information, preferred style information, data on items owned, and schedule information, and builds a database related to the user's lifestyle.

[1377] 2. Obtaining weather information

[1378] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the specified area using the weather API.

[1379] 3. Collection and analysis of emotional information

[1380] The server uses an emotion analysis engine to analyze the user's facial expressions and voice, recognizing the user's emotions in real time.

[1381] 4. Data Analysis

[1382] The server's AI analyzes data based on collected user information, weather information, emotional information, and schedule information to derive optimal outfit combinations, taking into account past outfit history and user feedback.

[1383] 5. Generating Coordination Proposals

[1384] The server generates specific outfit suggestions based on the analysis results and delivers them to the user's device, including suggestions for combining items the user already owns and new items they should consider purchasing.

[1385] User Device

[1386] 1. Providing an interface

[1387] The user device provides an interface that allows users to easily input and update information about their body shape, preferred style, items they own, and schedule information.The device also has the ability to capture users' emotions in real time.

[1388] 2. Display of notifications

[1389] The user's device will be notified of the coordination suggestions sent from the server, which will be displayed as a push notification or in-app notification.

[1390] 3. Gather feedback

[1391] The user device collects feedback from the user (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future suggestions.

[1392] 4. Purchase procedure assistance

[1393] The user terminal provides an interface for purchasing the proposed new items and assists the user in transitioning to a fashion online shopping site via a purchase link from the server.

[1394] visual display devices

[1395] 1. Providing virtual try-ons

[1396] Visual display devices such as smart glasses offer the ability to visually try on suggested outfits, allowing users to virtually try on the outfit and see how it will look in real life.

[1397] Specific examples

[1398] Let's say a user has lunch plans with a friend the next day. This user has previously entered information about their body type (height 170cm, weight 65kg), preferred style (casual), and photos of items they own (blue casual shirt, beige chino pants, etc.). The weather forecast for their area is sunny with a temperature of 25°C. Furthermore, this user is excited about the lunch plans (positive emotion).

[1399] The server analyzes the optimal outfit based on this information and generates a suggestion such as "a blue casual shirt and beige chino pants."

[1400] In addition, "red sneakers" are also suggested, as they reflect positive emotions and give an energetic impression.

[1401] The user's device will notify them of this suggestion, and the user will be able to visually confirm the suggestion in real time.

[1402] When a user wants to buy new sneakers, they can access a fashion online shopping site from their device and make the purchase smoothly.

[1403] Prompt Sentence Examples

[1404] Please suggest the best outfit for the user based on the following information:

[1405] 1. User information: Location: Tokyo, Height: 170cm, Weight: 65kg, Style preference: Casual

[1406] 2. Weather information: Clear skies, temperature 25°C

[1407] 3. Emotional information: Positive

[1408] Suggestion example:

[1409] Light clothing and bright colors are recommended.

[1410] In this way, the system of the present invention integrates a variety of information about the user to provide efficient and appropriate clothing suggestions.

[1411] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1412] Step 1: Collect user information

[1413] The server collects the user's body shape information (e.g., height, weight), preferred style information, photo data of items owned, and schedule information entered through the terminal. This information is stored in a user information database. The entered data is sent to the server and saved.

[1414] Input: Body shape information, style information, item photos, schedule information

[1415] Output: Data stored in the user information database

[1416] Step 2: Get weather information

[1417] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the area specified by the user using an external weather API. This data is stored in a weather database on the server.

[1418] Input: Area specification data

[1419] Output: Data stored in the weather information database

[1420] Step 3: Collecting and analyzing emotional information

[1421] The user captures facial expressions or voice in real time through the device and sends the data to the server, which uses an emotion analysis engine to analyze the user's emotions and store them in an emotion information database.

[1422] Input: facial expression data or voice data

[1423] Output: Analyzed data stored in the emotion information database

[1424] Step 4: Data analysis

[1425] The server integrates user information, weather information, emotional information, and schedule information, and analyzes the optimal outfit combination based on past outfit history and user feedback. Using an AI algorithm, it generates outfit suggestions based on data correlations and user preferences.

[1426] Input: User information, weather information, emotion information, schedule information

[1427] Output: Clothing suggestions based on analysis results

[1428] Step 5: Creating and notifying coordination proposals

[1429] The server creates specific outfit suggestions based on the analysis results and sends them to the user's device, which then displays the suggestions to the user as push notifications or in-app notifications.

[1430] Input: Analyzed clothing suggestion data

[1431] Output: Notification to user device

[1432] Step 6: Offer a virtual try-on

[1433] Users can visually try on the proposed outfits using smart glasses or other visual display devices. The terminal generates video data for the virtual try-on in real time and displays it on the smart glasses.

[1434] Input: Clothing suggestion data

[1435] Output: Virtual fitting video on a visual display device

[1436] Step 7: Gather feedback

[1437] Users can input their evaluation of the suggested outfits and suggestions for improvement via their device and send them to the server, which then stores this information in a feedback information database and uses it to improve the accuracy of the next outfit suggestions.

[1438] Input: User feedback data

[1439] Output: Data stored in the feedback information database

[1440] Step 8: Assist with checkout

[1441] If the user decides to purchase the suggested new item, the device provides a purchase link, allowing the user to access the fashion online shopping site, where the server generates the necessary purchase information and assists in the purchase process.

[1442] Input: Purchase desired item data

[1443] Output: Access link to fashion online shopping site and data for purchase procedure

[1444] Through the above processing steps, the system of the present invention can support the user in choosing daily clothing in an efficient and personalized manner.

[1445] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1446] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1447] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1448] [Fourth embodiment]

[1449] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1450] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1451] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1452] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1453] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1454] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1455] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1456] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1457] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1458] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1459] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1460] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1461] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1462] This invention is a system that suggests optimal clothing based on user information, and supports daily clothing selection by taking into consideration weather information and the user's schedule information in particular. The main target of this invention is men and women in their 20s to 40s living in busy urban areas, and the aim is to make their daily lives more convenient and comfortable.

[1463] System configuration

[1464] server

[1465] 1. Collection of User Information

[1466] The server collects information provided by the user, such as body shape information, preferred style information, photos of items owned, and schedule information, and builds a database of the user's personality and lifestyle.

[1467] 2. Obtaining weather information

[1468] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the area specified by the user using an external weather API.

[1469] 3. Data analysis

[1470] The server's AI analyzes the collected user information and weather information to derive the optimal outfit combination based on the user's body type, preferences, and schedule, taking into account past outfit history and user feedback.

[1471] 4. Coordination Proposal Generation

[1472] The server then creates specific outfit suggestions based on the analysis results and delivers them to the user's device, including suggestions for combining items the user already owns and new items they should consider purchasing.

[1473] Terminal

[1474] 1. Providing an interface

[1475] The device provides an interface that allows users to easily input and update information about their body shape, preferred style, photos of items they own, and schedule information.

[1476] 2. Display of notifications

[1477] The device notifies the user of the coordination suggestions sent from the server, which are displayed as push notifications or in-app notifications.

[1478] 3. Gather feedback

[1479] The device collects user feedback (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future outfit suggestions.

[1480] 4. Purchase procedure assistance

[1481] The terminal provides an interface for the user to purchase the suggested new items and allows the user to transition to a fashion online shopping site via a purchase link from the server.

[1482] user

[1483] 1. Enter your information

[1484] Users enter and update their body shape information, preferred styles, photos of the items they own, and daily schedule information into the app on their device.

[1485] 2. Review and adoption of proposals

[1486] Users can check the outfit suggestions displayed on their device and choose to actually use them. If they like the new items suggested, they can purchase them through the fashion online shopping site.

[1487] 3. Providing Feedback

[1488] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[1489] Specific examples

[1490] A user has planned to meet a friend for lunch the next day. The user has entered their body type (height: 170cm, weight: 65kg), preferred style (casual), and photos of items they own (e.g., blue casual shirt, beige chinos). The weather forecast for the user's area is sunny with a temperature of 25°C.

[1491] 1. The server performs data analysis based on this information and suggests a combination of "a blue casual shirt and beige chino pants" that would be suitable for a casual lunch date.

[1492] 2. Additionally, users will also be suggested lightweight white sneakers that they have recently considered purchasing.

[1493] 3. The terminal notifies the user of these suggestions, and the user selects coordination according to the suggestions.

[1494] 4. If the user decides to purchase the suggested new sneakers, they can use the purchase link on their device to access a fashion online store and easily place their order.

[1495] As described above, the system of the present invention proposes optimal clothing that suits the user's personality, making daily clothing selection more efficient.

[1496] The processing flow will be explained below.

[1497] Step 1:

[1498] Users can check and update their body shape information (e.g., height 170 cm, weight 65 kg), preferred style (e.g., casual), and photos of items they own (e.g., blue casual shirt, beige chino pants, etc.) that they have previously registered in the app on their device.

[1499] Step 2:

[1500] The user inputs the next day's schedule information (e.g., lunch with a friend) through the terminal, which is then reflected in the system.

[1501] Step 3:

[1502] The server receives the information entered by the user and stores it in a database, including the user's body type, preferred style, schedule, and items owned.

[1503] Step 4:

[1504] The server accesses an external weather API and obtains weather information (e.g., sunny, temperature 25 degrees) for the area and date and time specified by the user.

[1505] Step 5:

[1506] The server's AI begins analyzing data based on collected user information, weather information, and schedule information, and selects the optimal outfit, taking into account the user's past behavioral history and feedback.

[1507] Step 6:

[1508] Based on the results of the data analysis, the server determines the best outfit combination for the user, suggesting a blue casual shirt and beige chino pants.

[1509] Step 7:

[1510] The server also simultaneously suggests new items that the user does not own but would be suitable (e.g., lightweight white sneakers).

[1511] Step 8:

[1512] The device receives the coordination suggestions sent from the server and notifies the user via push notifications or in-app notifications.

[1513] Step 9:

[1514] The user checks the outfit suggestions displayed on the device and decides whether to adopt the suggested outfit. If the user likes the suggestion, they can adopt it.

[1515] Step 10:

[1516] When a user wants to purchase a new item, they access the online fashion store via a link on their device, complete the purchase, and confirm the order details to complete the purchase.

[1517] Step 11:

[1518] The user actually wears the suggested outfit and goes about their daily activities, after which they send feedback about their outfit to the server via their device.

[1519] Step 12:

[1520] The server receives the user's feedback information and stores it in a database, which is used to improve the accuracy of future suggestions.

[1521] Through the above processing steps, this system can improve the efficiency of the user's daily clothing selection and improve the quality of life.

[1522] Example 1

[1523] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1524] In modern society, many people living in busy urban areas spend a lot of time choosing their daily outfits. To solve this problem, there is a need for a system that can automatically suggest the best outfits for them, taking into account individual user information and weather conditions. It is also important to improve the accuracy of suggestions based on user feedback, and there is also a need for a function that supports the purchase of new items.

[1525] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1526] In this invention, the server includes means for collecting user information, data analysis means for suggesting optimal clothing to the user based on the user information, means for acquiring weather information, means for suggesting outfits based on the user information and the weather information, means for learning from the collected user feedback information and improving the accuracy of the suggestions, means for suggesting new items to purchase based on the user's body shape information and preferred style information, means for periodically acquiring local weather information using a weather API, means for collecting photos of items the user owns, means for collecting the user's schedule information, and means for transmitting the suggested clothing to the user terminal. This allows the user to select optimal clothing based on their individual lifestyle and weather conditions, improving the accuracy of suggestions and also supporting the purchase of new fashion items.

[1527] "User information" is data including the user's body shape information, preferred style, photos of items owned, and daily schedule information.

[1528] "Data analysis" is a process for suggesting optimal clothing for a user based on collected user information and weather information.

[1529] "Weather information" refers to meteorological data such as local temperature, precipitation probability, and wind speed obtained using an external weather API.

[1530] "Coordination suggestions" are suggestions for optimal outfit combinations generated based on user information and weather information.

[1531] "Feedback information" is data on the user's evaluation of the proposed outfit and points for improvement.

[1532] A "weather API" is an application programming interface for obtaining weather information via the Internet.

[1533] "Schedule information" is data about the schedules and events that a user will have on a particular day.

[1534] A "device" is an electronic device used by a user to enter information, review suggestions, or provide feedback.

[1535] A "purchase link" is a link that takes the user to a web page where they can purchase the suggested new fashion item.

[1536] This invention is a system that suggests optimal clothing based on user information, and supports daily clothing selection by taking into account weather information and the user's schedule information in particular. The main target of this invention is people living in busy urban areas, and its purpose is to make their daily lives more convenient and comfortable.

[1537] server

[1538] 1. Collection of User Information

[1539] The server collects information provided by the user via their device, such as body shape information, preferred style information, photos of items owned, and schedule information. This information is used to build a database of the user's personality and lifestyle. Specifically, the collected data is stored in the server's database and used for future suggestions.

[1540] 2. Obtaining weather information

[1541] The server periodically obtains weather information for the area specified by the user using an external weather API (e.g., OpenWeatherMap or Weatherstack). This information includes temperature, precipitation probability, wind speed, and other weather information. The obtained weather information is also stored in the server's database and used for analysis along with user information.

[1542] 3. Data analysis

[1543] The server's AI analyzes the collected user information and weather information, taking into account past outfit history and user feedback to derive the optimal outfit combinations based on the user's body type, preferences, and schedule.

[1544] 4. Coordination Proposal Generation

[1545] The server then creates specific outfit suggestions based on the AI ​​analysis results and delivers them to the user's device. The suggestions include combinations of items the user already owns and new items they should consider purchasing.

[1546] Terminal

[1547] 1. Providing an interface

[1548] The device provides an interface that allows users to easily input and update information about their body shape, preferred style, photos of items they own, and schedule information.

[1549] 2. Display of notifications

[1550] The device notifies the user of the coordination suggestions sent from the server, which are displayed as push notifications or in-app notifications.

[1551] 3. Gather feedback

[1552] The device collects feedback from users (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future suggestions.

[1553] 4. Purchase procedure assistance

[1554] The terminal provides an interface for the user to purchase the suggested new items and allows the user to transition to a fashion online shopping site via a purchase link from the server.

[1555] user

[1556] 1. Enter your information

[1557] Users enter and update their body shape information, preferred styles, photos of the items they own, and daily schedule information into the app on their device.

[1558] 2. Review and adoption of proposals

[1559] Users can check the outfit suggestions displayed on their device and choose to actually use them. If they like the new items suggested, they can purchase them through the fashion online shopping site.

[1560] 3. Providing Feedback

[1561] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[1562] Specific examples

[1563] A user has planned to meet a friend for lunch the next day. The user has entered their body type (height: 170cm, weight: 65kg), preferred style (casual), and photos of items they own (e.g., blue casual shirt, beige chinos). The weather forecast for the user's area is sunny with a temperature of 25°C.

[1564] 1. The server performs data analysis based on this information and suggests a combination of "a blue casual shirt and beige chino pants" that would be suitable for a casual lunch date.

[1565] 2. Additionally, users will also be suggested lightweight white sneakers that they have recently considered purchasing.

[1566] 3. The terminal notifies the user of these suggestions, and the user selects coordination according to the suggestions.

[1567] 4. If the user decides to purchase the suggested new sneakers, they can use the purchase link on their device to access a fashion online store and easily place their order.

[1568] Prompt Sentence Examples

[1569] "Please create a system that suggests the best outfits for users based on their personal information. Please also take into account weather and schedule information."

[1570] "Please suggest an appropriate outfit for a specific user (e.g., height 170cm, weight 65kg, casual style) for lunch the next day. The local weather forecast is sunny with a temperature of 25 degrees."

[1571] As described above, the system of the present invention proposes optimal clothing that suits the user's personality, making daily clothing selection more efficient.

[1572] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1573] Step 1:

[1574] Users input their body type, preferred style, photos of their belongings, and schedule information through the device's interface. This input data includes the user's height, weight, preferred fashion style, photos of clothes they own, and their schedule for a specific day. The input data is temporarily stored on the device.

[1575] Step 2:

[1576] The terminal sends the information entered by the user to the server. This includes the user's body shape information, preferred style, photos of items owned, and schedule information. The server stores the received information in a database and updates the user's profile. The output is the updated user profile data.

[1577] Step 3:

[1578] The server periodically retrieves weather information for the area specified by the user using an external weather API (e.g., OpenWeatherMap or Weatherstack). During this retrieval process, weather data such as temperature, precipitation probability, and wind speed for the specified area are collected. The retrieved weather data is also stored in a database, and the output is the latest weather information.

[1579] Step 4:

[1580] The server's AI performs data analysis based on collected user information and weather information. The input data is the user's profile information, weather information, and past outfit history. The AI ​​analyzes this data and derives the optimal outfit combination based on the user's body type, preferences, and schedule. The output is a recommendation of the optimal outfit.

[1581] Step 5:

[1582] The server generates specific outfit suggestions based on the results of the AI ​​analysis. This process also includes combinations of items the user already owns and new items they should consider purchasing. The generated outfit suggestions are sent to the user's device. The output is a specific outfit suggestion for the user.

[1583] Step 6:

[1584] The device notifies the user of the coordination suggestions sent from the server. This notification is displayed as a push notification or an in-app notification. The user receives the suggestion results on the device by checking the displayed suggestion. The output is a notification to the user.

[1585] Step 7:

[1586] The user checks the outfit suggestions displayed on the device and actually adopts the suggestions. If the user likes the suggested new items, they can purchase them through the fashion online shopping site. The input data is the suggested outfits and new items, and the output is the user's selection and purchasing behavior.

[1587] Step 8:

[1588] The device collects feedback from users. Users enter their feedback (ratings and areas for improvement) about the proposed outfits into the device, and this information is sent to the server. The server stores the received feedback in a database and uses it to improve the accuracy of the next proposal. The input data is the user's feedback information, and the output is an improved proposal algorithm.

[1589] (Application example 1)

[1590] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1591] In modern urban areas, many people living busy lives spend a lot of time and effort choosing their daily outfits. Furthermore, they must take into account weather information and the day's plans, making it difficult for many to efficiently choose the perfect outfit. Furthermore, there are issues with the effort required to actually find the suggested clothing items in physical stores and the complicated process of purchasing new items. There is a need for a system that can solve these issues and make users' lives more convenient and comfortable.

[1592] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1593] In this invention, the server includes means for collecting user information, data analysis means for suggesting optimal clothing to the user based on the user information, means for acquiring weather information, means for suggesting outfits based on the user information and the weather information, means for providing guidance for finding the suggested clothing items in a physical store, means for collecting feedback from the user and reflecting it in future suggestions, and means for supporting the process of purchasing the suggested new items. This not only enables the user to receive optimal clothing suggestions, but also makes it easier for the user to find and purchase clothing items in a physical store, thereby streamlining daily clothing selection.

[1594] The "means for collecting user information" is a function for acquiring information about the user's body shape, preferred style, photos of items owned, and schedule information, and storing the information in a database.

[1595] "Data analysis means" refers to algorithms and functions for deriving optimal clothing for a user based on collected user information and weather information.

[1596] "Means for obtaining weather information" refers to a function for periodically obtaining weather data for a specified area from an external API or other source and making it available within the system.

[1597] The "means for suggesting outfits" is a function for suggesting optimal outfit combinations to users based on user information and weather information.

[1598] "Means for providing in-store locating guidance" refers to navigation and guidance features that allow users to easily find the suggested clothing item in a physical store.

[1599] "Means of collecting feedback" is a function for obtaining user evaluations and opinions on proposals and reflecting them in future proposals.

[1600] A "checkout aid" is a link or interface that simplifies and assists the user in the process of purchasing a suggested new item.

[1601] This invention is a system that suggests optimal clothing based on user information and weather information. This system is mainly composed of three elements: a server, a terminal, and a user.

[1602] server

[1603] 1. Collection of User Information

[1604] The server collects the user's input data such as body shape, preferred style, photos of items owned, and schedule information. This information is stored in a database, which is then used to create a database based on the user's personality and lifestyle.

[1605] 2. Obtaining weather information

[1606] The server periodically retrieves weather information for the specified region using an external weather API (e.g., WeatherAPI), making the latest weather data available within the system.

[1607] 3. Data analysis

[1608] The server's AI analyzes the collected user information and weather information, taking into account past outfit history and user feedback. It then uses an algorithm to generate optimal outfit suggestions for the user.

[1609] 4. Coordination Proposal Generation

[1610] The server then creates specific outfit suggestions based on the results of the data analysis. These suggestions are then sent to the user's device. The suggestions include combinations of items the user already owns and new items they should consider purchasing.

[1611] Terminal

[1612] 1. Providing an interface

[1613] The device provides an interface that allows users to input and update information about their body shape, preferred style, photos of items they own, and schedule information. This interface is implemented as a smartphone app.

[1614] 2. Display of notifications

[1615] The device notifies the user of coordination suggestions sent from the server, including push notifications and in-app notifications.

[1616] 3. Gather feedback

[1617] The device collects user feedback (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future outfit suggestions.

[1618] 4. Purchase procedure assistance

[1619] The device provides an interface for users to purchase suggested new items, with a function that allows users to be redirected to a fashion online shopping site via a purchase link.

[1620] 5. In-store guidance

[1621] It provides a guide function to help users find suggested clothing items in physical stores, allowing them to easily find suggested items in physical stores.

[1622] user

[1623] 1. Enter your information

[1624] Users enter and update their body shape information, preferred styles, photos of the items they own, and daily schedule information into the app on their device.

[1625] 2. Review and adoption of proposals

[1626] Users can check the outfit suggestions displayed on their device and choose to actually use them. If they like the new items suggested, they can purchase them through the fashion online shopping site.

[1627] 3. Providing Feedback

[1628] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[1629] Specific examples

[1630] For example, suppose User A has plans to have lunch with a friend the next day. User A has previously entered his / her body type information (height 170cm, weight 65kg), preferred style (casual), and photos of items he / she owns (e.g., blue casual shirt, beige chino pants). The server performs data analysis based on the collected information and suggests a combination of "blue casual shirt and beige chino pants" that is suitable for a casual lunch. In addition, User A is also suggested a pair of lightweight white sneakers that he / she has recently been considering purchasing. The device notifies the user of these suggestions, and the user selects an outfit based on the suggestions.

[1631] Prompt Sentence Examples

[1632] Please use the following information to suggest the best outfit for you:

[1633] User information: Height 170cm, weight 65kg, casual style preference, list of items owned (blue casual shirt, beige chino pants)

[1634] Weather: Sunny, temperature 25 degrees

[1635] Planned: Lunch with a friend

[1636] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1637] Processing Steps

[1638] Step 1:

[1639] The user uses the device's app to enter information about their body shape, preferred style, photos of items they own, and schedule information.

[1640] Input: height, weight, preferred style, item photos, schedule

[1641] Output: These data are sent from the terminal to the server and stored in a database.

[1642] Specific behavior: The user enters the required information into the input form and clicks the submit button. The app executes an API request to send the entered data to the server.

[1643] Step 2:

[1644] The server uses a weather API to obtain weather information for the specified area.

[1645] Input: Region information

[1646] Output: Weather data such as temperature, precipitation probability, wind speed, etc.

[1647] Specific operation: The server periodically sends a request to the weather API to obtain the latest weather data and save it in the database.

[1648] Step 3:

[1649] The server's AI performs data analysis based on collected user information and weather information.

[1650] Input: User information, weather information, past coordination history, feedback information from users

[1651] Output: Recommendations for the best outfit for the user

[1652] How it works: The server's AI algorithm analyzes the input data and uses a generative AI model to generate the optimal outfit. The prompt is in the form "Please suggest the best outfit based on the following information: ..."

[1653] Step 4:

[1654] The server generates a coordination proposal and delivers it to the user's terminal.

[1655] Input: AI-generated outfit suggestions

[1656] Output: Coordination suggestions displayed on the device

[1657] What happens: The server sends the suggestion to the device using the notification API, and the device displays a push notification or in-app notification.

[1658] Step 5:

[1659] The user checks the outfit suggestions displayed on the device and enters feedback.

[1660] Input: Rating and improvements for the proposed outfit

[1661] Output: The feedback information is sent to the server and stored in a database.

[1662] Specific behavior: The user reviews the suggestion, enters their rating and improvements in the feedback form, and clicks the submit button. The app then makes an API request to send the feedback data to the server.

[1663] Step 6:

[1664] When users want to purchase a suggested new item, they access a fashion online shopping site via a purchase link on their device.

[1665] Input: Proposed new item

[1666] Output: Transition to fashion online shopping site and purchase procedure

[1667] Specific behavior: The user clicks on the purchase link and is redirected to a fashion online shopping site in a browser or in-app browser, where they proceed with the purchase.

[1668] Step 7:

[1669] The device provides guidance on how to find the suggested clothing items in physical stores.

[1670] Input: suggested item information, physical store location

[1671] Output: In-store navigation information

[1672] Specific operation: The device acquires the user's location information and displays information about physical stores where the suggested items are located. It also displays maps and routes to guide the user.

[1673] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1674] This invention is a system that suggests optimal clothing based on user information, particularly by combining weather information, the user's schedule information, and even the user's emotional information to support daily clothing selection. The main target audience of this invention is men and women in their 20s to 40s living in busy urban areas, and the aim is to make their daily lives more convenient and comfortable.

[1675] System configuration

[1676] server

[1677] 1. Collection of User Information

[1678] The server collects information provided by the user, such as body shape information, preferred style information, photos of items owned, and schedule information, and builds a database of the user's personality and lifestyle.

[1679] 2. Obtaining weather information

[1680] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the area specified by the user using an external weather API.

[1681] 3. Collecting emotional information

[1682] The server uses an emotion engine to analyze the user's facial expressions and voice, recognizing the user's emotions in real time.

[1683] 4. Data Analysis

[1684] The server's AI analyzes the collected user information, weather information, emotional information, and schedule information to derive the optimal outfit combination that matches the user's body type, preferences, and schedule, taking into account past outfit history and user feedback.

[1685] 5. Generating Coordination Proposals

[1686] The server then creates specific outfit suggestions based on the analysis results and delivers them to the user's device, including suggestions for combining items the user already owns and new items they should consider purchasing.

[1687] Terminal

[1688] 1. Providing an interface

[1689] The device provides an interface that allows users to easily input and update information about their body shape, preferred style, photos of their belongings, and schedule information. In addition, the device also has the ability to capture users' emotions in real time.

[1690] 2. Display of notifications

[1691] The device notifies the user of the coordination suggestions sent from the server, which are displayed as push notifications or in-app notifications.

[1692] 3. Gather feedback

[1693] The device collects user feedback (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future outfit suggestions.

[1694] 4. Purchase procedure assistance

[1695] The terminal provides an interface for the user to purchase the suggested new items and allows the user to transition to a fashion online shopping site via a purchase link from the server.

[1696] user

[1697] 1. Enter your information

[1698] Users can input and update their body shape, preferred style, photos of their belongings, and daily schedule information into the app on their device, and their emotions are reflected on the device in real time.

[1699] 2. Review and adoption of proposals

[1700] Users can check the outfit suggestions displayed on their device and choose to adopt them. If new items are suggested, they can consider purchasing them if necessary.

[1701] 3. Providing Feedback

[1702] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[1703] Specific examples

[1704] Let's say a user has lunch plans with a friend the next day. The user has previously entered information about their body type (height 170cm, weight 65kg), preferred style (casual), and photos of items they own (blue casual shirt, beige chino pants, etc.). The weather forecast for the user's area is sunny with a temperature of 25°C. The user is also excited about the lunch plans (positive emotion).

[1705] 1. The server performs data analysis based on this information and suggests a combination of "a blue casual shirt and beige chino pants" that would be suitable for a casual lunch date.

[1706] 2. Taking into account the user's emotional information, "red sneakers" that give an energetic impression are also suggested.

[1707] 3. The terminal notifies the user of these suggestions, and the user selects coordination according to the suggestions.

[1708] 4. If the user decides to purchase the suggested new sneakers, they can use the purchase link on their device to access the fashion online store and easily place their order.

[1709] As described above, the system of the present invention proposes optimal clothing that suits the user's personality, making daily clothing selection more efficient. In particular, by incorporating the user's real-time emotional information, it is possible to realize more personalized coordination.

[1710] The processing flow will be explained below.

[1711] Step 1:

[1712] Users can check and update their body shape information (e.g., height 170 cm, weight 65 kg), preferred style (e.g., casual), and photos of items they own (e.g., blue casual shirt, beige chino pants, etc.) that they have previously registered in the app on their device.

[1713] Step 2:

[1714] The user inputs the next day's schedule information (e.g., lunch with a friend) through the terminal, which is then reflected in the system.

[1715] Step 3:

[1716] The server receives the information entered by the user and stores it in a database, including the user's body type, preferred style, schedule, and items owned.

[1717] Step 4:

[1718] The server accesses an external weather API and obtains weather information (e.g., sunny, temperature 25 degrees) for the area and date and time specified by the user.

[1719] Step 5:

[1720] The server receives real-time emotional information provided by the user from the device, and the emotion engine analyzes the user's facial expressions and voice to generate emotional information.

[1721] Step 6:

[1722] The device updates the emotional information according to the user's real-time situation (e.g., looking forward to lunch plans) and sends it to the server.

[1723] Step 7:

[1724] The server's AI begins analyzing data based on the collected user information, weather information, emotional information, and schedule information, and then selects the optimal outfit, taking into account the user's behavioral history and feedback.

[1725] Step 8:

[1726] Based on the results of the data analysis, the server determines the optimal outfit combination for the user. In this case, it suggests a blue casual shirt and beige chino pants. It also suggests red sneakers to match the user's positive emotions.

[1727] Step 9:

[1728] The server also simultaneously suggests new items that the user does not own but would be suitable (e.g., red sneakers).

[1729] Step 10:

[1730] The device receives the coordination suggestions sent from the server and notifies the user via push notifications or in-app notifications.

[1731] Step 11:

[1732] The user checks the outfit suggestions displayed on the device and decides whether to adopt the suggested outfit. If the user likes the suggestion, they can adopt it.

[1733] Step 12:

[1734] When a user wants to purchase a new item, they access the online fashion store via a link on their device, complete the purchase, and confirm the order details to complete the purchase.

[1735] Step 13:

[1736] The user actually wears the suggested outfit and goes about their daily activities, after which they send feedback about their outfit to the server via their device.

[1737] Step 14:

[1738] The server receives the user's feedback information and stores it in a database, which is used to improve the accuracy of future suggestions.

[1739] Through these processing steps, this system can improve the efficiency of users' daily clothing selection and improve their quality of life. In particular, by utilizing the emotion engine, it is possible to provide more personalized suggestions based on the user's emotions.

[1740] Example 2

[1741] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1742] Conventional clothing recommendation systems are limited to suggestions based on the user's body shape and preferred style information, making it difficult to make personalized suggestions that take into account weather information and the user's emotional state. Furthermore, they lack the functionality to utilize feedback information to improve the accuracy of suggestions, which prevents them from fully increasing user satisfaction.

[1743] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1744] In this invention, the server includes means for collecting user information, means for acquiring weather information, means for collecting and analyzing emotional information, data analysis means, means for suggesting outfits based on the user information, weather information, and emotional information, means for learning collected feedback information about the user's clothing and improving the accuracy of suggestions, and means for suggesting new items to purchase based on the user's body shape information and preferred style information. This makes it possible to suggest optimal outfits that take into account the user's personality, lifestyle, weather, and emotional state.

[1745] "User information" is a collective term for data including the user's body shape information, preferred style information, photos of items owned, and daily schedule information.

[1746] The "data analysis means" refers to a hardware or software function that performs analysis based on collected user information, weather information, and emotional information to derive optimal clothing combinations.

[1747] "Weather information" refers to weather-related data such as temperature, precipitation probability, and wind speed obtained using external weather APIs.

[1748] "Emotional information" is data about the user's emotional state, recognized by analyzing the user's facial expressions and voice.

[1749] The "means for suggesting outfits" refers to a hardware or software function that has the ability to suggest specific outfit combinations based on the analysis results and deliver them to the user's device.

[1750] "Feedback information" refers to information collected from users, such as evaluations and areas for improvement regarding the proposed outfits.

[1751] "Means for improving the accuracy of suggestions" refers to algorithms or functions that learn from collected feedback information and improve the accuracy of future suggestions.

[1752] The "means for suggesting new items to purchase" is a function that suggests new items to the user based on the user's body shape information and preferred style information, and provides support for purchasing the items.

[1753] The present invention is a system that suggests optimal clothing based on user information, and in particular, supports daily clothing selection by combining weather information, user emotion information, and user schedule information. Specific embodiments for carrying out the present invention are described in detail below.

[1754] server

[1755] The server has the following features:

[1756] 1. Collection of User Information

[1757] User information includes the user's body shape information, preferred style information, photos of items owned, and daily schedule information. This information is provided by the user and sent to the server via the terminal. The server stores the received information in a database and manages it for each user. A commonly used database management system (DBMS) is used for this process.

[1758] 2. Obtaining weather information

[1759] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the area specified by the user using an external weather API (e.g., OpenWeatherMap API). The obtained weather information is saved for each user, and the latest information is kept.

[1760] 3. Collecting emotional information

[1761] The server uses an emotion analysis engine to analyze the user's facial expressions and voice to recognize their emotions in real time. The emotion engine uses commonly used machine learning or deep learning models. The analysis results are also stored in a database.

[1762] 4. Data Analysis

[1763] The server's AI analyzes the collected user information, weather information, emotional information, and schedule information to derive the optimal outfit combination based on the user's body shape, preferences, and schedule. Past outfit history and feedback from users are also taken into consideration. The AI ​​model uses a generative AI model to perform complex data analysis.

[1764] 5. Generating Coordination Proposals

[1765] The server then creates specific outfit suggestions based on the analysis results and delivers them to the user's device, including suggestions for combining items the user already owns and new items they should consider purchasing.

[1766] Specific examples

[1767] Let's say a user has lunch plans with a friend the next day. The user has previously entered information about their body type (height 170cm, weight 65kg), preferred style (casual), and photos of items they own (blue casual shirt, beige chino pants, etc.). The weather forecast for the user's area is sunny with a temperature of 25°C. The user is also excited about the lunch plans (positive emotion).

[1768] The server performs data analysis based on this information and suggests a combination of "a blue casual shirt and beige chino pants," suitable for a casual lunch date. Taking into account the user's emotional information, it also suggests "red sneakers," which give an energetic impression. The device notifies the user of these suggestions, and the user selects an outfit based on the suggestions. If the user decides to purchase the suggested new sneakers, they can use the purchase link on the device to access a fashion online shopping site and easily place an order.

[1769] Terminal

[1770] The terminal has the following features:

[1771] 1. Providing an interface

[1772] The device provides an interface that allows users to easily input and update information about their body shape, preferred style, photos of their belongings, and schedule information. The application is typically a smartphone app. The device also has the ability to capture the user's emotions in real time.

[1773] 2. Display of notifications

[1774] The device notifies the user of the coordination suggestions sent from the server, which are displayed as push notifications or in-app notifications.

[1775] 3. Gather feedback

[1776] The device collects user feedback (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future outfit suggestions.

[1777] 4. Purchase procedure assistance

[1778] The terminal provides an interface for the user to purchase the suggested new items and allows the user to transition to a fashion online shopping site via a purchase link from the server.

[1779] user

[1780] The user does the following:

[1781] 1. Enter your information

[1782] Users can input and update their body shape, preferred style, photos of their belongings, and daily schedule information into the app on their device, and their emotions are reflected on the device in real time.

[1783] 2. Review and adoption of proposals

[1784] Users can check the outfit suggestions displayed on their device and choose to adopt them. If new items are suggested, they can consider purchasing them if necessary.

[1785] 3. Providing Feedback

[1786] Users can provide feedback on the suggested outfits through their devices and send it to the server, which will help make the next suggestions more personalized.

[1787] In this way, the system of the present invention can suggest the best outfits to suit the user's personality, making daily outfit selection more efficient. In particular, by incorporating the user's real-time emotional information, more personalized outfits can be achieved.

[1788] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1789] Step 1:

[1790] Collection of User Information

[1791] Users log in to the app and enter their body type information (height, weight, etc.), preferred style information (casual, formal, etc.), photos of items they own, and daily schedule information.

[1792] Input: Body shape information, preferred style information, photos of items you own, daily schedule information

[1793] Data processing: The entered information is consolidated and stored in a separate database for each user.

[1794] Output: The consolidated user information is saved in the database.

[1795] Specific operation: The user enters their height (170cm) and weight (65kg) into the app's form, selects their lifestyle as "casual," takes a photo of an item they are carrying (a blue shirt), and enters their plans for the next day (lunch with a friend). The device sends this information to the server, which stores it in a database.

[1796] Step 2:

[1797] Obtaining weather information

[1798] The server calls an external weather API (for example, OpenWeatherMap API) and periodically obtains weather information (temperature, probability of precipitation, wind speed, etc.) for the area specified by the user.

[1799] Input: User's region information

[1800] Data processing: Weather information obtained from the weather API is saved for each user.

[1801] Output: The latest weather information for the user's location is stored in a database.

[1802] How it works: The server calls the weather API every morning at 7am and retrieves weather data such as "sunny, temperature 25 degrees." The server stores this information for each user.

[1803] Step 3:

[1804] Collecting emotional information

[1805] The device uses a camera and microphone to capture the user's facial expressions and voice and transmits them to an emotion analysis engine.

[1806] Input: User's facial expression and voice data

[1807] Data processing: The emotion analysis engine analyzes facial expressions and voice data to extract the user's emotional state (positive, negative, etc.).

[1808] Output: The analyzed emotion information is stored in a database.

[1809] How it works: When a user smiles at the device, the camera captures their facial expression and sends it to the emotion analysis engine. The engine interprets it as "positive" and sends the result to the server, which stores it in a database.

[1810] Step 4:

[1811] Data analysis

[1812] The server's AI combines and analyzes the collected user information, weather information, emotional information, and schedule information.

[1813] Input: User information, weather information, emotion information, schedule information

[1814] Data processing: Based on all collected data, a generative AI model is used to derive optimal outfit combinations.

[1815] Output: Specific outfit coordination suggestions guided by AI are generated.

[1816] Specific operation: The server's AI derives the combination of "a blue casual shirt and beige chino pants" based on the user's "height 170 cm, weight 65 kg," weather information "sunny, 25 degrees," emotional information "positive," and schedule information "lunch with a friend." It also suggests "red sneakers," which give an energetic impression.

[1817] Step 5:

[1818] Coordination proposal generation and notification

[1819] The server creates coordination suggestions based on the analysis results and delivers them to the user's device.

[1820] Input: AI-generated outfit suggestions

[1821] Data processing: Convert the proposal content into concrete text and images and send them to the device.

[1822] Output: Coordination suggestions are displayed on the terminal.

[1823] Specific operation: A notification such as "Today's outfit suggestion: A blue casual shirt and beige chino pants. We also recommend energetic red sneakers" will be displayed on the device.

[1824] Step 6:

[1825] Purchase procedure assistance

[1826] Users click on a link on their device to purchase the suggested new item.

[1827] Input: User's purchase click action

[1828] Data processing: The purchase link will take you to a fashion online shopping site and assist you with the purchase process.

[1829] Output: The user purchases the suggested new item.

[1830] Specific operation: The user clicks on the purchase link for "red sneakers" displayed on the device, and is redirected to the corresponding page on the fashion online shopping site to complete the order.

[1831] Step 7:

[1832] Collecting feedback

[1833] Users enter feedback about the suggested outfits into the app.

[1834] Input: Rating and improvements for the proposed outfit

[1835] Data processing: The collected feedback information is sent to the server and reflected in future proposals.

[1836] Output: A more accurate next proposal is generated.

[1837] Specific operation: The user rates the app as "very satisfied" and comments on the device that "I would like more casual suggestions." The device sends this to the server, and the feedback is saved in a database. The server analyzes this feedback and improves the accuracy of the next suggestion.

[1838] Based on the above processing steps, the program of the present invention can efficiently suggest optimal clothing that meets the user's needs.

[1839] (Application example 2)

[1840] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1841] Currently, choosing clothes is a time-consuming and laborious task in users' daily lives. For women and busy people in particular, thinking about how to coordinate their outfits every day can be very stressful. Furthermore, choosing appropriate clothing based on the weather, schedule, and emotions can be even more difficult. The purpose of this invention is to solve these problems and provide users with quick and appropriate clothing suggestions.

[1842] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user information, data analysis means for proposing optimal outfits to the user based on the user information, means for acquiring weather information, means for collecting and analyzing user emotion information, means for proposing outfits based on the user information, weather information, and emotion information, and visual display means for enabling virtual try-on. This allows the user to efficiently select everyday outfits and easily find optimal outfits while visually checking them.

[1843] "User information" refers to data including the user's body shape information, preferred style information, data on items owned, and schedule information.

[1844] "Data analysis means" refers to a technical means for analyzing and deriving optimal clothing for a user based on user information, weather information, and emotional information.

[1845] "Weather information" refers to local weather data such as temperature, probability of precipitation, and wind speed obtained using an external weather API.

[1846] "Emotional information" is data about the user's current emotional state, recognized by analyzing the user's facial expressions and voice.

[1847] "Visual display means" refers to a technical means that assists in visual confirmation of clothing to enable virtual try-on, and refers to devices such as smart glasses and head-mounted displays.

[1848] "Coordination suggestions" are suggestions for optimal clothing combinations presented to users based on collected and analyzed user information, weather information, and emotional information.

[1849] The present invention is a system that integrates user information, weather information, and emotional information to suggest optimal outfits. The system uses a server, a user terminal, and a visual display device such as smart glasses to provide users with quick and accurate outfit suggestions.

[1850] System configuration and operation

[1851] server

[1852] 1. Collection of User Information

[1853] The server collects the user's body shape information, preferred style information, data on items owned, and schedule information, and builds a database related to the user's lifestyle.

[1854] 2. Obtaining weather information

[1855] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the specified area using the weather API.

[1856] 3. Collection and analysis of emotional information

[1857] The server uses an emotion analysis engine to analyze the user's facial expressions and voice, recognizing the user's emotions in real time.

[1858] 4. Data Analysis

[1859] The server's AI analyzes data based on collected user information, weather information, emotional information, and schedule information to derive optimal outfit combinations, taking into account past outfit history and user feedback.

[1860] 5. Generating Coordination Proposals

[1861] The server generates specific outfit suggestions based on the analysis results and delivers them to the user's device, including suggestions for combining items the user already owns and new items they should consider purchasing.

[1862] User Device

[1863] 1. Providing an interface

[1864] The user device provides an interface that allows users to easily input and update information about their body shape, preferred style, items they own, and schedule information.The device also has the ability to capture users' emotions in real time.

[1865] 2. Display of notifications

[1866] The user's device will be notified of the coordination suggestions sent from the server, which will be displayed as a push notification or in-app notification.

[1867] 3. Gather feedback

[1868] The user device collects feedback from the user (ratings of the proposed outfits and areas for improvement) and sends it to the server. This feedback is used to improve the accuracy of future suggestions.

[1869] 4. Purchase procedure assistance

[1870] The user terminal provides an interface for purchasing the proposed new items and assists the user in transitioning to a fashion online shopping site via a purchase link from the server.

[1871] visual display devices

[1872] 1. Providing virtual try-ons

[1873] Visual display devices such as smart glasses offer the ability to visually try on suggested outfits, allowing users to virtually try on the outfit and see how it will look in real life.

[1874] Specific examples

[1875] Let's say a user has lunch plans with a friend the next day. This user has previously entered information about their body type (height 170cm, weight 65kg), preferred style (casual), and photos of items they own (blue casual shirt, beige chino pants, etc.). The weather forecast for their area is sunny with a temperature of 25°C. Furthermore, this user is excited about the lunch plans (positive emotion).

[1876] The server analyzes the optimal outfit based on this information and generates a suggestion such as "a blue casual shirt and beige chino pants."

[1877] In addition, "red sneakers" are also suggested, as they reflect positive emotions and give an energetic impression.

[1878] The user's device will notify them of this suggestion, and the user will be able to visually confirm the suggestion in real time.

[1879] When a user wants to buy new sneakers, they can access a fashion online shopping site from their device and make the purchase smoothly.

[1880] Prompt Sentence Examples

[1881] Please suggest the best outfit for the user based on the following information:

[1882] 1. User information: Location: Tokyo, Height: 170cm, Weight: 65kg, Style preference: Casual

[1883] 2. Weather information: Clear skies, temperature 25°C

[1884] 3. Emotional information: Positive

[1885] Suggestion example:

[1886] Light clothing and bright colors are recommended.

[1887] In this way, the system of the present invention integrates a variety of information about the user to provide efficient and appropriate clothing suggestions.

[1888] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1889] Step 1: Collect user information

[1890] The server collects the user's body shape information (e.g., height, weight), preferred style information, photo data of items owned, and schedule information entered through the terminal. This information is stored in a user information database. The entered data is sent to the server and saved.

[1891] Input: Body shape information, style information, item photos, schedule information

[1892] Output: Data stored in the user information database

[1893] Step 2: Get weather information

[1894] The server periodically obtains weather information (e.g., temperature, probability of precipitation, wind speed, etc.) for the area specified by the user using an external weather API. This data is stored in a weather database on the server.

[1895] Input: Area specification data

[1896] Output: Data stored in the weather information database

[1897] Step 3: Collecting and analyzing emotional information

[1898] The user captures facial expressions or voice in real time through the device and sends the data to the server, which uses an emotion analysis engine to analyze the user's emotions and store them in an emotion information database.

[1899] Input: facial expression data or voice data

[1900] Output: Analyzed data stored in the emotion information database

[1901] Step 4: Data analysis

[1902] The server integrates user information, weather information, emotional information, and schedule information, and analyzes the optimal outfit combination based on past outfit history and user feedback. Using an AI algorithm, it generates outfit suggestions based on data correlations and user preferences.

[1903] Input: User information, weather information, emotion information, schedule information

[1904] Output: Clothing suggestions based on analysis results

[1905] Step 5: Creating and notifying coordination proposals

[1906] The server creates specific outfit suggestions based on the analysis results and sends them to the user's device, which then displays the suggestions to the user as push notifications or in-app notifications.

[1907] Input: Analyzed clothing suggestion data

[1908] Output: Notification to user device

[1909] Step 6: Offer a virtual try-on

[1910] Users can visually try on the proposed outfits using smart glasses or other visual display devices. The terminal generates video data for the virtual try-on in real time and displays it on the smart glasses.

[1911] Input: Clothing suggestion data

[1912] Output: Virtual fitting video on a visual display device

[1913] Step 7: Gather feedback

[1914] Users can input their evaluation of the suggested outfits and suggestions for improvement via their device and send them to the server, which then stores this information in a feedback information database and uses it to improve the accuracy of the next outfit suggestions.

[1915] Input: User feedback data

[1916] Output: Data stored in the feedback information database

[1917] Step 8: Assist with checkout

[1918] If the user decides to purchase the suggested new item, the device provides a purchase link, allowing the user to access the fashion online shopping site, where the server generates the necessary purchase information and assists in the purchase process.

[1919] Input: Purchase desired item data

[1920] Output: Access link to fashion online shopping site and data for purchase procedure

[1921] Through the above processing steps, the system of the present invention can support the user in choosing daily clothing in an efficient and personalized manner.

[1922] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1923] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1924] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1925] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1926] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1927] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1928] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1929] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1930] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1931] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1932] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1933] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1934] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1935] 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.

[1936] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1937] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1938] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1939] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1940] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1941] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1942] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1943] The following is further disclosed regarding the above embodiment.

[1944] (Claim 1)

[1945] the means by which user information is collected;

[1946] A data analysis means for suggesting optimal clothing to the user based on the user information;

[1947] A means for obtaining weather information;

[1948] The system includes a means for suggesting outfits based on the user information and weather information.

[1949] (Claim 2)

[1950] The system of claim 1, further comprising means for learning from collected feedback information about the user's clothing and improving the accuracy of the suggestions.

[1951] (Claim 3)

[1952] 10. The system of claim 1, further comprising: means for suggesting new items to purchase based on the user's body shape information and preferred style information.

[1953] "Example 1"

[1954] (Claim 1)

[1955] the means by which user information is collected;

[1956] A data analysis means for suggesting optimal clothing to the user based on the user information;

[1957] A means for obtaining weather information;

[1958] a means for suggesting outfits based on the user information and weather information;

[1959] A means for learning from collected user feedback information and improving the accuracy of suggestions;

[1960] A means for suggesting new items ...

Claims

1. the means by which user information is collected; A data analysis means for suggesting optimal clothing to the user based on the user information; A means for obtaining weather information; The system includes a means for suggesting outfits based on the user information and weather information.

2. The system according to claim 1 , further comprising means for learning from collected feedback information about the user's clothing and improving the accuracy of the suggestions.

3. 10. The system of claim 1, further comprising means for suggesting new items to purchase based on the user's body shape information and preferred style information.

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