system

Abstract

We provide the system. [Solution] A system comprising: means for a user to take or upload photos of their clothing; means for the terminal to send the photos to a server; means for the server to analyze the photos and identify clothing information; means for registering the analyzed clothing information in a database; means for the user to input their current mood, date, and weather information; means for the terminal to send the information to the server; means for the server to generate styling based on the database and trend information; means for displaying the generated styling information on the terminal; means for the user to input feedback on the styling; means for the terminal to send the feedback to the server; and means for the server to accumulate the feedback and update the styling algorithm.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, it is becoming increasingly important for individual users to find styling that suits them. However, it takes a lot of time and effort for an individual to optimally combine their own clothing. Also, in order to keep up with changes in fashion, it is required to constantly keep track of the latest fashion trends and combine them with one's own clothing, which is not easy. Furthermore, it is difficult to style considering variables such as mood and weather, and many users are troubled by this. The present invention aims to solve these problems and help users easily find an optimal fashion style.

Means for Solving the Problems

[0005] The present invention provides a system comprising: means for a user to take or upload photos of their clothing; means for the terminal to transmit the photos to a server; means for the server to analyze the photos and identify clothing information; means for registering the analyzed clothing information in a database; means for the user to input their current mood, date, and weather information; means for the terminal to transmit the information to the server; means for the server to generate styling based on the database and trend information; means for displaying the generated styling information on the terminal; means for the user to input feedback on the styling; means for the terminal to transmit the feedback to the server; and means for the server to accumulate the feedback and update the styling algorithm. As a result, the user can easily receive suggestions for styling that is best suited to them, and through the feedback received, they can receive even more optimized suggestions.

[0006] A "user" refers to an individual who uses this system to receive fashion styling suggestions.

[0007] "Device" refers to electronic devices used by users, such as computers, smartphones, and tablets.

[0008] A "server" refers to a central computing system that receives data sent from terminals, processes it, and returns a response.

[0009] "Clothing" refers to clothing items (shirts, pants, jackets, etc.) owned by the user.

[0010] "Photo" refers to an image file that a user takes or uploads to provide image information about clothing.

[0011] A "database" refers to an information storage system used by a server to store and manage clothing information and user information that the server has analyzed.

[0012] "Mood" refers to the psychological state entered by the user (e.g., cheerful, calm, etc.).

[0013] "Date" refers to the date information entered by the user in the calendar.

[0014] "Weather" refers to the current weather information entered by the user (e.g., sunny, rainy, etc.).

[0015] "Trend information" refers to data on the latest fashion trends.

[0016] "Styling" refers to fashion coordination suggestions that combine the user's existing clothing with the latest trends.

[0017] "Feedback" refers to the evaluations and comments that users provide regarding the styling they have been offered.

[0018] An "algorithm" refers to a set of computational methods or procedures for solving a specific problem. [Brief explanation of the drawing]

[0019] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7]It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0020] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0021] First, the language used in the following description will be explained.

[0022] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0023] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0032] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0039] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0040] This invention relates to a system that allows users to register their clothing and suggests the optimal fashion style based on information such as mood, date, and weather. The system of this invention consists of a user terminal and a server. One embodiment of the present invention will be described below.

[0041] System Overview

[0042] 1. Register your clothing.

[0043] Users first register their clothing by taking photos of their clothes using the app. Alternatively, they can upload existing photos from their gallery. The device sends these photos to the server. The server analyzes the received photos using an image recognition algorithm to identify the type and characteristics of the clothing (e.g., shirt, pants, color, pattern, etc.). The analyzed clothing information is then registered in a database.

[0044] 2. Entering Information

[0045] Users input information such as their mood for the day, the date, and the weather through the app's interface. This information is then sent from the device to the server.

[0046] 3. Styling generation

[0047] The server retrieves the user's clothing information registered in the database, along with the latest trend information, and executes an algorithm that generates the optimal style considering the user's input. This algorithm suggests styling based on factors such as the user's mood, weather, and date.

[0048] 4. Styling display

[0049] The generated styling information is sent from the server to the terminal, which then displays the style to the user. The style includes detailed information about which items to combine and how. An image of the suggested style is also displayed.

[0050] 5. Receiving feedback and updating the algorithm

[0051] Users provide feedback on the suggested style, indicating whether they are satisfied or dissatisfied, through their device. This feedback information is sent to a server, which stores it in a database. The accumulated feedback is used to update the algorithm to improve future style suggestions.

[0052] Specific example

[0053] Clothing registration

[0054] Use your device's camera function to take a picture of a "red knit sweater." Alternatively, select a picture of "blue denim pants" that you have already taken from your gallery and upload it. This picture will be sent from your device to the server. The server will analyze the received picture using an image recognition algorithm and register it in its database as a "red knit sweater" or "blue denim pants."

[0055] Entering information

[0056] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into the app's input form. This information is then sent from the device to the server.

[0057] Styling generation and display

[0058] The server combines the user's registered "red knit sweater" and "blue denim pants" with the latest trend information to generate outfit ideas using the "red knit sweater" and "blue denim pants." This style suggestion is then sent to the device, which displays it to the user.

[0059] Receiving feedback

[0060] The user enters feedback such as, "I like this outfit." The device sends this feedback to the server, which stores it in a database. This feedback information is used to improve future styling algorithms.

[0061] As described above, the present invention provides a system that comprehensively utilizes the user's existing clothing and trend information to propose a fashion style that is individually optimized for each user.

[0062] The following describes the processing flow.

[0063] Step 1:

[0064] Users take photos of their own clothing or upload them from their gallery.

[0065] Step 2:

[0066] The device sends photos of clothing that have been taken or uploaded to the server.

[0067] Step 3:

[0068] The server uses an image recognition algorithm to analyze the received photos and identify the type of clothing (shirt, pants, jacket, etc.) and its characteristics (color, pattern, brand, etc.).

[0069] Step 4:

[0070] Based on the analysis results, the server registers clothing information in the database, associating it with the user ID.

[0071] Step 5:

[0072] The user enters their mood, date, and weather information into the app's input form.

[0073] Step 6:

[0074] The terminal sends the user's input information to the server.

[0075] Step 7:

[0076] The server retrieves information about the user's clothing collection from the database, and similarly retrieves the latest trend information.

[0077] Step 8:

[0078] The server runs a styling algorithm based on the user's mood, date, and weather information to generate the optimal style.

[0079] Step 9:

[0080] The server sends the generated styling information to the terminal.

[0081] Step 10:

[0082] The device displays the suggested style to the user along with detailed information (which items to combine and how).

[0083] Step 11:

[0084] Users provide feedback on styling suggestions, indicating whether they are satisfied or dissatisfied.

[0085] Step 12:

[0086] The device sends user feedback information to the server.

[0087] Step 13:

[0088] The server stores feedback information in a database and updates the styling algorithm to improve future styling suggestions.

[0089] (Example 1)

[0090] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] Traditionally, when users styled outfits using their existing wardrobe, they faced the challenge of having to decide for themselves which items to combine and how to combine them. Furthermore, they often lacked optimal suggestions that took into account fluctuating factors such as mood and weather, resulting in inconsistent styling quality. This, in particular, led to time-consuming and laborious styling processes, lowering user satisfaction, especially for busy individuals.

[0092] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0093] In this invention, the server includes means for a user to take or upload photos of their clothing; means for the terminal to transmit the photos to the server; means for the server to analyze the photos and identify clothing information; means for registering the analyzed clothing information in a database; means for the user to input their current mood, date, and weather information; means for the terminal to transmit the information to the server; means for the server to generate styling based on the database and trend information; means for the server to transmit the generated styling information to the terminal; means for the terminal to display the generated styling information; means for the user to input feedback on the styling; means for the terminal to transmit the feedback to the server; and means for the server to accumulate the feedback and update the styling algorithm. This makes it possible for the user to automatically receive suggestions for the optimal fashion style based on their clothing and changing factors.

[0094] A "user" refers to an individual who takes or uploads photos of clothing and enters information such as mood, date, and weather.

[0095] A "terminal" refers to an electronic device operated by a user that transmits photos taken and information entered to a server and displays styling information.

[0096] A "server" refers to a central computer system that analyzes data received from terminals, registers it in a database, generates styling, and sends it back to the terminals.

[0097] "Photographs" refer to still image data that users take or upload to represent their clothing.

[0098] "Analysis" refers to the process by which the server uses an image recognition algorithm to identify clothing information from the received photograph.

[0099] "Image recognition algorithms" refer to computer vision technologies used to recognize the type and characteristics of clothing from photographs.

[0100] "Clothing information" refers to characteristic data such as the type, color, and pattern of clothing identified from the analyzed photographs.

[0101] A "database" refers to a data storage system used to accumulate and manage analyzed clothing information and user input information.

[0102] "Mood" refers to the subjective feelings and sensations that users input to express their mental state on a given day.

[0103] "Date" refers to the year, month, and day of a specific date entered by the user.

[0104] "Weather information" refers to the weather conditions for the day (e.g., sunny, rainy, cloudy) entered by the user.

[0105] "Trend information" refers to data on the latest fashion trends and fashions.

[0106] "Styling" refers to the optimal clothing combination suggestions that the server generates based on database and trend information.

[0107] "Feedback" refers to information that users input, such as their impressions and evaluations of the suggested styling.

[0108] A "styling algorithm" refers to a program implemented on a server to generate the optimal styling based on the user's mood, date, and weather information.

[0109] This invention relates to a system that registers a user's clothing collection and suggests the optimal fashion style based on information such as mood, date, and weather. This system mainly consists of a user terminal and a server.

[0110] First, users register their clothing using an application installed on their device (e.g., a smartphone or tablet). Clothing can be registered by taking photos of the garments using the device's camera function, or by uploading existing photos from the device's gallery. These photos are then sent from the device to the server.

[0111] The server analyzes the received photos using image recognition algorithms (e.g., TENSORFLOW® or OpenCV). These algorithms allow the server to identify features such as the type, color, and pattern of clothing within the photos. The identified clothing information is then registered in a database.

[0112] Next, the user enters information such as their mood for the day, the date, and the weather through the app's interface. This information is sent from the device to the server. The server retrieves the clothing information registered by the user and the latest trend information, and runs a styling algorithm (e.g., Scikit-learn or Keras) based on the user's input. This algorithm takes into account the entered elements such as mood, weather, and date to generate the most suitable styling for the user.

[0113] The generated styling information is sent from the server to the terminal, which then presents the style to the user. The presented style includes detailed information on which items to combine and how to combine them. Style suggestion images are also displayed as needed.

[0114] The user provides feedback on the suggested style. This feedback is sent from the terminal to the server. The server stores this feedback information in a database and uses it to update the algorithm to improve future style suggestions.

[0115] Specific example

[0116] Clothing registration:

[0117] The user takes a picture of a "red knit sweater" using their smartphone camera, or selects and uploads an existing picture of "blue denim pants" from their gallery. This picture is sent from the device to the server. The server analyzes the received picture using an image recognition algorithm and registers it in the database as a "red knit sweater" or "blue denim pants."

[0118] Entering information:

[0119] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into the app's form. This information is then sent from the device to the server.

[0120] Styling generation and display:

[0121] The server retrieves the user's registered information for a "red knit sweater" and "blue denim pants," and runs a styling algorithm along with the latest trend information. The algorithm considers the input information, such as "cheerful mood," "October 16, 2023," and "sunny weather," to generate an outfit combining the "red knit sweater" and "blue denim pants." This outfit information is sent to the terminal, which then presents it to the user.

[0122] Receiving feedback:

[0123] The user enters feedback such as "I like this outfit," and the device sends that feedback to the server. The server stores this feedback information and uses it to improve future styling suggestions.

[0124] Thus, the present invention is a system that comprehensively utilizes the user's existing clothing and the latest fashion trend information to propose an individually optimized fashion style.

[0125] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0126] Step 1:

[0127] The user takes a photo of their clothes using the app on their device, or uploads an existing photo. The device then sends the taken or uploaded photo to the server.

[0128] Input: A photo of clothing taken or selected by the user.

[0129] Output: Photo data sent from the terminal to the server

[0130] Specific actions:

[0131] The user takes a picture of a "red knit sweater" using their smartphone's camera function and sends that picture to the server via the app.

[0132] Step 2:

[0133] The server analyzes the received photos using an image recognition algorithm (e.g., TensorFlow, OpenCV). The analysis results identify the type and characteristics of the clothing.

[0134] Input: Photo data of clothing received by the server

[0135] Output: Information on the type and characteristics of the analyzed clothing (e.g., shirt, pants, color, pattern, etc.)

[0136] Specific actions:

[0137] The server analyzes the received photo of a "red knit sweater" and uses an image recognition algorithm to identify the characteristic of a "red knit sweater."

[0138] Step 3:

[0139] The server registers the analyzed clothing information in a database.

[0140] Input: Information on the analyzed clothing

[0141] Output: Clothing information registered in the database

[0142] Specific actions:

[0143] The server saves information about the "red knit sweater" to the database and adds it to the user's clothing collection.

[0144] Step 4:

[0145] The user enters information such as their mood for the day, the date, and the weather through the app's interface. The device then sends the entered information to the server.

[0146] Input: Information entered by the user, such as mood, date, and weather.

[0147] Output: Mood, date, and weather information sent from the terminal to the server.

[0148] Specific actions:

[0149] The user enters "cheerful mood," "October 16, 2023," and "sunny" into the app's form and sends this information to the server.

[0150] Step 5:

[0151] The server retrieves the user's clothing information and the latest trend information registered in the database, and runs a styling algorithm (e.g., Scikit-learn, Keras) to generate the optimal style.

[0152] Input: Clothing information from the database, trend information, and user input information (mood, date, weather)

[0153] Output: Generated styling information

[0154] Specific actions:

[0155] Based on the "red knit sweater" and the latest trend information, the server generates the optimal outfit by taking into account the user's input information such as "cheerful mood," "October 16, 2023," and "sunny."

[0156] Step 6:

[0157] The server sends the generated styling information to the terminal. The terminal displays the received styling information to the user.

[0158] Input: Styling information generated by the server

[0159] Output: Styling information displayed on the terminal

[0160] Specific actions:

[0161] The server generates outfit information using a "red knit sweater" and "blue denim pants," which is then sent to the terminal, and the terminal presents this information to the user.

[0162] Step 7:

[0163] The user provides feedback on the suggested style through the app. The device then sends this feedback to the server.

[0164] Input: Feedback on the style entered by the user

[0165] Output: Feedback information sent from the terminal to the server

[0166] Specific actions:

[0167] The user enters feedback such as "I like this outfit" and sends it from their device to the server.

[0168] Step 8:

[0169] The server stores feedback information in a database and uses it to update algorithms in order to improve future styling suggestions.

[0170] Input: Feedback information sent from the terminal to the server

[0171] Output: Feedback information stored in the database, updated styling algorithms

[0172] Specific actions:

[0173] The server stores the feedback information it receives in a database and uses it to improve the styling algorithm.

[0174] (Application Example 1)

[0175] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0176] Existing fashion style suggestion systems fail to efficiently utilize users' existing clothing and trend information, making it difficult to suggest the most suitable fashion styles for individual users. Furthermore, algorithm improvements based on user feedback are insufficient. As a result, a major challenge is that users are not receiving the style suggestions they desire.

[0177] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0178] In this invention, the server includes means for the user to take or upload photos of their clothing using their smartphone with image recognition technology, means for analyzing the photos sent to the cloud server to identify clothing information, means for registering the analyzed clothing information in a database, means for the user to input their current mood, date, and weather information into their smartphone, means for the cloud server to generate an optimal styling based on the database and trend information, means for displaying the generated styling information on the smartphone, means for the user to input feedback on the styling into their smartphone, and means for the cloud server to accumulate the feedback and update the styling algorithm. This makes it possible to propose individually optimized fashion styles that reflect the user's clothing and trend information.

[0179] A "user" is an individual who uses the system to register their clothing collection and receive fashion style suggestions.

[0180] "Clothing on hand" refers to the various clothes and accessories that the user owns.

[0181] A "smartphone" is a portable information terminal that allows internet access and the use of various applications.

[0182] A "cloud server" is a remote server located on the internet that enables data processing and storage for a large number of users.

[0183] "Photos" refer to image data taken or uploaded using a user's smartphone.

[0184] "Image recognition technology" is a technology that analyzes image data to identify specific objects or features.

[0185] "Clothing information" refers to data that describes the type and characteristics of clothing identified using image recognition technology.

[0186] A "database" is an information system used to store and manage analyzed clothing information and other related data.

[0187] "Mood" refers to information that represents the user's psychological state and emotions on a given day.

[0188] "Date" refers to calendar information that indicates a specific day.

[0189] "Weather information" refers to data that shows the weather conditions for a given day.

[0190] "Trend information" refers to information about current trends and fashion developments.

[0191] "Styling" refers to fashion combinations and outfits generated based on the user's existing clothing and current trends.

[0192] "Feedback" refers to information that shows the evaluation and impressions that users give regarding the suggested styling.

[0193] An "algorithm" is a set of rules that define the steps or calculation methods for solving a specific problem.

[0194] This invention relates to a system that allows users to photograph or upload their clothing using their smartphones, and then suggests fashion styles based on those photos. Specific embodiments of this invention will be described below.

[0195] System Overview

[0196] 1. Register your clothing.

[0197] Users first register their clothing. To do this, they either take photos of their clothes using their smartphone's camera or upload photos they have already taken. The smartphone sends these photos to a cloud server. The cloud server uses image recognition technology to analyze the received photos and identify the type and characteristics of the clothing (e.g., shirt, pants, color, pattern, etc.). The analyzed clothing information is then registered in a database.

[0198] 2. Entering Information

[0199] Users input information such as their mood, the date, and the weather through their smartphone interface. This information is then sent from the smartphone to a cloud server.

[0200] 3. Styling generation

[0201] The cloud server retrieves the user's clothing information and the latest trend information registered in the database, and executes an algorithm to generate the optimal style, taking into account the user's input information. This algorithm suggests styling based on factors such as the user's mood, weather, and date.

[0202] 4. Styling display

[0203] The generated styling information is sent from the cloud server to the smartphone, which then displays the style to the user. The style includes detailed information on which items to combine and how. An image of the suggested style is also displayed.

[0204] 5. Receiving feedback and updating the algorithm

[0205] Users provide feedback on the suggested styles, indicating satisfaction or dissatisfaction, via their smartphones. This feedback information is sent to a cloud server, which stores it in a database. The accumulated feedback is used to update the algorithm to improve future style suggestions.

[0206] Hardware and software to be used

[0207] Hardware: Smartphones (iOS or Android®), cloud servers

[0208] software:

[0209] Camera function

[0210] Cloud servers (for example, Amazon Web Services or Google Cloud)

[0211] Image recognition algorithms (for example, models using TensorFlow or PyTorch)

[0212] Databases (for example, MySQL® or MongoDB)

[0213] Specific Examples

[0214] Clothing registration

[0215] The user takes a picture of a "red knit sweater" using their smartphone camera, or selects and uploads a picture of "blue denim pants" that they have already taken from their gallery. These photos are sent from the smartphone to a cloud server. The cloud server analyzes the received photos using image recognition technology and registers them in a database as "red knit sweater" or "blue denim pants."

[0216] Entering information

[0217] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into an input form on their smartphone. This information is then sent from the smartphone to a cloud server.

[0218] Styling generation and display

[0219] The cloud server combines the user's registered items, such as a "red knit sweater" and "blue denim pants," with the latest trend information to generate outfit ideas using the "red knit sweater" and "blue denim pants." This style suggestion is then sent to the user's smartphone, which displays it.

[0220] Receiving feedback

[0221] Users provide feedback such as, "I like this outfit." Their smartphones send this feedback to a cloud server, which stores it in a database. This feedback information is used to improve future styling algorithms.

[0222] Examples of prompt statements

[0223] "User's clothing: Red knit sweater, blue denim pants"

[0224] "Mood: Cheerful"

[0225] Date: October 20, 2023

[0226] "Weather: Sunny"

[0227] As described above, the present invention provides a system that comprehensively utilizes the user's existing clothing and trend information to propose a fashion style that is individually optimized for each user.

[0228] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0229] Step 1:

[0230] The user takes a photo of their clothing or uploads one from their gallery using their smartphone. The smartphone then sends the photo of the clothing taken or selected by the user as input to a cloud server. This transmitted image data is the input. The specific action the smartphone takes is to upload the image data to the cloud server.

[0231] Step 2:

[0232] The cloud server analyzes the received image data using image recognition technology. This image recognition technology uses TensorFlow or PyTorch. The input to the cloud server is image data of clothing sent by the user, and the output is the recognized type and characteristic information of the clothing. Specifically, the cloud server analyzes the image data and identifies clothing information such as "shirt" and "pants."

[0233] Step 3:

[0234] The cloud server registers the analyzed clothing information into a database. The input is the analyzed clothing information, and the output is the updated database. The specific action performed by the cloud server is to save the newly analyzed clothing information to the existing database.

[0235] Step 4:

[0236] The user uses their smartphone to input their mood, the date, and weather information for the day. Based on this input, the smartphone sends it to a cloud server. The input is the user's mood, the date, and the weather information, and the output is the information sent to the cloud server. Specifically, the user enters information into an input form, and the smartphone sends that information to the cloud server.

[0237] Step 5:

[0238] The cloud server retrieves the user's clothing information registered in the database, along with the latest trend information, and generates the optimal style considering the user's input information. In this step, the cloud server generates the style using an algorithm. The input is the user's mood, date, weather information, and clothing information, and the output is the generated styling information. Specifically, it retrieves the necessary information from the database and generates the style based on a calculation algorithm.

[0239] Step 6:

[0240] The cloud server sends the generated styling information to the smartphone. The smartphone receives this information and displays it to the user. The input is the styling information sent from the cloud server, and the output is the styling information displayed on the smartphone. The specific action performed by the smartphone is to visually display the received styling information to the user.

[0241] Step 7:

[0242] The user uses their smartphone to input feedback on the suggested style. This input feedback is sent from the smartphone to a cloud server. The input is the user's feedback information, and the output is the feedback information sent to the cloud server. Specifically, the user enters information into a feedback form, and the smartphone sends that information to the cloud server.

[0243] Step 8:

[0244] The cloud server stores the received feedback information in a database. It also updates the styling algorithm based on the accumulated feedback. The input is user feedback information, and the output is the updated styling algorithm. The specific action performed by the cloud server is to periodically update the algorithm to improve style suggestions for future use.

[0245] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0246] This invention relates to a system that allows users to register their clothing and suggests the optimal fashion style based on their mood, the date, the weather, and emotions recognized by an emotion engine. The system of this invention consists of a user terminal, a server, and an emotion engine. One embodiment of the present invention is described below.

[0247] System Overview

[0248] 1. Register your clothing.

[0249] Users first register their clothing by taking photos of their clothes using the app. Alternatively, they can upload existing photos from their gallery. The device sends these photos to the server. The server analyzes the received photos using an image recognition algorithm to identify the type and characteristics of the clothing (e.g., shirt, pants, color, pattern, etc.). The analyzed clothing information is then registered in a database.

[0250] 2. Entering Information

[0251] Users input information such as their mood for the day, the date, and the weather through the app's interface. This information is then sent from the device to the server.

[0252] 3. Recognition of emotions

[0253] The system further incorporates an emotion engine to recognize the user's emotions. This emotion engine analyzes the user's facial expressions and voice, and sends the results to the server. This emotion data, along with the user's input information, is used in the next step.

[0254] 4. Styling generation

[0255] The server retrieves the user's clothing information registered in the database, along with the latest trend information, and executes an algorithm that generates the optimal style considering the user's input information and emotional data. This algorithm suggests styling based on the user's mood, weather, date, and perceived emotions.

[0256] 5. Styling display

[0257] The generated styling information is sent from the server to the terminal, which then displays the style to the user. The style includes detailed information about which items to combine and how. An image of the suggested style is also displayed.

[0258] 6. Receiving feedback and updating the algorithm

[0259] Users provide feedback on the suggested style, indicating whether they are satisfied or dissatisfied, through their device. This feedback information is sent to a server, which stores it in a database. The accumulated feedback is used to update the algorithm to improve future style suggestions.

[0260] Specific example

[0261] Clothing registration

[0262] Use your device's camera function to take a picture of a "red knit sweater." Alternatively, select a picture of "blue denim pants" that you have already taken from your gallery and upload it. This picture will be sent from your device to the server. The server will analyze the received picture using an image recognition algorithm and register it in its database as a "red knit sweater" or "blue denim pants."

[0263] Entering information

[0264] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into the app's input form. This information is then sent from the device to the server.

[0265] Recognition of emotions

[0266] When a user uses the app, the emotion engine analyzes the user's facial expressions and voice to recognize emotions such as "joy" or "anxiety." This emotion data, along with other input information, is sent to the server.

[0267] Styling generation and display

[0268] The server generates outfit ideas using the "red knit sweater" and "blue denim pants" registered by the user, along with the latest trend information and the user's mood and emotional data. It then sends these style suggestions to the user's device, which displays them along with detailed information.

[0269] Receiving feedback

[0270] The user enters feedback such as, "I like this outfit." The device sends this feedback to the server, which stores it in a database. This feedback information is used to improve future styling algorithms.

[0271] As described above, the present invention provides a system that comprehensively utilizes the user's existing clothing and trend information, and proposes an individually optimized fashion style while also taking into account the user's emotions.

[0272] The following describes the processing flow.

[0273] Step 1:

[0274] Users use the app to take photos of their existing clothing, or upload existing photos from their gallery.

[0275] Step 2:

[0276] The device sends photos of clothing that have been taken or uploaded to the server.

[0277] Step 3:

[0278] The server uses an image recognition algorithm to analyze the received photos and identify the type and characteristics of the clothing (e.g., shirt, pants, color, pattern, etc.).

[0279] Step 4:

[0280] Based on the analysis results, the server associates the clothing information with the user ID and registers it in the database.

[0281] Step 5:

[0282] The user inputs their mood, date, and weather information into the input form of the app.

[0283] Step 6:

[0284] The terminal sends the user's input information to the server.

[0285] Step 7:

[0286] The terminal uses the emotion engine to analyze the user's expression and voice and generates emotion data.

[0287] Step 8:

[0288] The terminal sends the emotion data to the server.

[0289] Step 9:

[0290] The server retrieves the user's clothing information held in the database and also retrieves the latest trend information.

[0291] Step 10:

[0292] The server executes a styling algorithm based on the user's mood, date, weather information, and emotion data to generate an optimal style.

[0293] Step 11:

[0294] [[ID=5%6]] The server sends the generated styling information to the terminal.

[0295] Step 12:

[0296] The device displays the suggested style to the user along with detailed information (which items to combine and how).

[0297] Step 13:

[0298] Users provide feedback on styling suggestions, indicating whether they are satisfied or dissatisfied.

[0299] Step 14:

[0300] The device sends user feedback information to the server.

[0301] Step 15:

[0302] The server stores feedback information in a database and updates the styling algorithm to improve future styling suggestions.

[0303] (Example 2)

[0304] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0305] Conventional fashion suggestion systems only offered styling suggestions based on the user's existing clothing information and basic input, failing to consider subtle changes in the user's emotions. As a result, the styling suggestions were often unsatisfactory in terms of the user's mood and emotions, making it difficult to provide individually optimized suggestions.

[0306] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for incorporating an emotion engine for analyzing emotions from the user's facial expressions and voice, means for transmitting the emotion data analyzed by the emotion engine to the server, and means for generating styling by considering the emotion data by the server. Thereby, it becomes possible to propose an individually optimized fashion style based on the user's mood and emotions.

[0307] The "user" refers to a person who uses this system.

[0308] The "terminal" refers to an electronic device used by the user, including smartphones, tablets, personal computers, etc.

[0309] The "server" is the central processing unit of this system and refers to a device that receives, processes, stores, and transmits data.

[0310] The "photo" refers to image data taken or uploaded by the user to register clothing.

[0311] "Analysis" refers to the process of extracting necessary information from the photos and data received by the server.

[0312] "Clothing information" refers to information about the clothing registered by the user, such as the type, color, pattern, and features of the clothing.

[0313] The "database" refers to a structured storage system for the server to organize, store, and search information.

[0314] "Mood" refers to the mental state that the user is feeling at that time.

[0315] "Date" refers to information indicating specifically what year, month, and day it is.

[0316] "Weather information" refers to information indicating the weather of that day.

[0317] "Trend information" refers to information about the latest fashion and styles.

[0318] "Styling" refers to fashion combinations and outfits suggested based on the user's existing clothing.

[0319] "Feedback" refers to the evaluations and opinions that users give regarding the suggested styling.

[0320] An "emotion engine" refers to software or algorithms that analyze a user's facial expressions and voice to recognize their emotions.

[0321] "Emotional data" refers to data about a user's emotions that has been analyzed by the emotion engine.

[0322] An "image recognition algorithm" refers to an algorithm used to analyze photographs and image data to detect specific objects or features.

[0323] This invention relates to a system in which a user registers their clothing and the system suggests the optimal fashion style based on their mood, the date, the weather, and emotions recognized by an emotion engine. This system consists of a user terminal, a server, and an emotion engine.

[0324] System Configuration

[0325] Clothing registration

[0326] User:

[0327] Users first use the app to register their existing clothing. Within the app, they select the "Register Clothing" menu and register their clothing using the following method:

[0328] Use the device's camera function to take a picture of the clothing.

[0329] Upload existing photos from your gallery.

[0330] Terminal:

[0331] The device temporarily stores photos of the clothing and sends them to the server using an HTTP POST request.

[0332] server:

[0333] The server analyzes the received photos using image recognition algorithms such as the Google Cloud Vision API to identify the type and characteristics of the clothing. The results of this analysis are then registered in a database.

[0334] Entering information

[0335] User:

[0336] Users use an input form within the app to enter their mood for the day, the date, and weather information.

[0337] Terminal:

[0338] The terminal temporarily stores the entered information and sends it to the server using an HTTP POST request.

[0339] server:

[0340] The server registers the received information in the database.

[0341] Recognition of emotions

[0342] User:

[0343] While the user is using the app, the emotion engine analyzes the user's facial expressions and voice. Emotional data is collected when the user shows their facial expressions to the device's camera or speaks.

[0344] Terminal:

[0345] The device acquires facial expressions and voice data, stores it temporarily, and then sends it to the server using an HTTP POST request.

[0346] server:

[0347] The server uses an emotion engine to analyze facial expressions and voice data to identify the user's emotions. The analyzed emotion data is registered in a database. For example, Microsoft's Azure Cognitive Services can be used.

[0348] Styling generation

[0349] server:

[0350] The server retrieves information about the user's clothing collection, sentiment data, and the latest trend information registered in the database. The latest trend information can be obtained from fashion websites on the internet or APIs (for example, the Fashion Trends API).

[0351] server:

[0352] Machine learning algorithms (e.g., TensorFlow or PyTorch) are used to generate optimal styling based on the user's mood, date, weather information, and sentiment data. This generation process also takes past feedback data into consideration.

[0353] Styling display

[0354] server:

[0355] The server sends the generated styling information to the terminal.

[0356] Terminal:

[0357] The device displays the received styling information on the user interface. The displayed content includes images of clothing combinations and styles.

[0358] Receiving feedback

[0359] User:

[0360] Users can provide feedback on the suggested style, such as "satisfied" or "dissatisfied."

[0361] Terminal:

[0362] The device temporarily stores the feedback information and sends it to the server using an HTTP POST request.

[0363] server:

[0364] The server registers the feedback information in a database. This feedback information is used to update the algorithm and make future styling suggestions.

[0365] Specific example

[0366] Clothing registration

[0367] The user takes a picture of a "red knit sweater" using their device's camera function, or uploads a picture of "blue denim pants" from their gallery. This picture is sent from the device to the server. The server uses the Google Cloud Vision API to analyze the picture and register it in the database as a "red knit sweater" or "blue denim pants."

[0368] Entering information

[0369] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into the app's input form. This information is then sent from the device to the server.

[0370] Recognition of emotions

[0371] While the user is using the app, the emotion engine analyzes the user's facial expressions and voice to recognize emotions such as "joy" or "anxiety." This emotion data is sent to the server along with other information.

[0372] Styling generation and display

[0373] The server generates outfit ideas using the "red knit sweater" and "blue denim pants" based on the user's registered items, the latest trend information, and the user's mood and emotional data. This style suggestion is sent to the device, which displays it along with detailed information.

[0374] Receiving feedback

[0375] The user enters feedback such as "I like this outfit." The device sends this feedback to the server, which stores it in a database. This allows for future improvements to the styling algorithm.

[0376] Example prompts for a generative AI model

[0377] An example of a prompt message a user might enter is: "If the user's mood today is 'cheerful,' the date is 'October 16, 2023,' the weather is 'sunny,' and their emotion is 'joyful,' please suggest a fashion outfit using the 'red knit sweater' and 'blue denim pants' they already own."

[0378] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0379] Step 1: Register your clothing

[0380] User:

[0381] The user launches the app and selects the "Register Clothing" menu within the app. Here, the user registers clothing using the following method:

[0382] Use the device's camera function to take a picture of the clothing.

[0383] Upload existing photos from your gallery.

[0384] input:

[0385] Photographs of clothing taken or uploaded.

[0386] Terminal:

[0387] The device temporarily stores the captured or uploaded photo data. The photo data is then sent to the server.

[0388] output:

[0389] Temporarily saved photo data.

[0390] Step 2: Send and analyze photos

[0391] Terminal:

[0392] The device sends the temporarily stored photo data to the server using an HTTP POST request.

[0393] input:

[0394] Temporarily saved photo data.

[0395] server:

[0396] The server passes the received photo data to image recognition algorithms such as the Google Cloud Vision API, which analyze it to identify the type and characteristics of the clothing. This analysis provides information such as the type of clothing (e.g., shirt, pants), color, and pattern.

[0397] Data processing:

[0398] Photo analysis using image recognition algorithms.

[0399] output:

[0400] Analyzed clothing information.

[0401] Step 3: Register clothing information

[0402] server:

[0403] The server registers the clothing information analyzed in the previous step into the database.

[0404] input:

[0405] Analyzed clothing information.

[0406] output:

[0407] Clothing information registered in the database.

[0408] Step 4: Entering Information

[0409] User:

[0410] Users use an input form within the app to enter their mood for the day, the date, and weather information. Mood can include options such as "cheerful" or "calm."

[0411] input:

[0412] User-entered mood, date, and weather information.

[0413] Terminal:

[0414] The terminal temporarily stores the entered information and then sends it to the server using an HTTP POST request.

[0415] output:

[0416] Temporarily saved mood, date, and weather information.

[0417] Step 5: Submit and register your information.

[0418] Terminal:

[0419] The device sends temporarily stored mood, date, and weather information to the server using an HTTP POST request.

[0420] input:

[0421] Temporarily saved mood, date, and weather information.

[0422] server:

[0423] The server registers the received information in the database.

[0424] Data processing:

[0425] Receiving information and registering it in the database.

[0426] output:

[0427] Mood, date, and weather information registered in the database.

[0428] Step 6: Recognizing Emotions

[0429] User:

[0430] While the user is using the app, the emotion engine analyzes the user's facial expressions and voice. Facial and voice data is collected when the user shows their face to the device's camera or speaks.

[0431] input:

[0432] User's facial expressions and voice data.

[0433] Terminal:

[0434] The device acquires facial expression and voice data, stores it temporarily, and then sends it to the server using an HTTP POST request.

[0435] output:

[0436] Temporarily saved facial expression and audio data.

[0437] Step 7: Sending and analyzing emotional data

[0438] Terminal:

[0439] The terminal sends temporarily stored facial expression and audio data to the server using an HTTP POST request.

[0440] input:

[0441] Temporarily saved facial expression and audio data.

[0442] server:

[0443] The server uses an emotion engine to analyze this data and identify the user's emotions. For example, Microsoft's Azure Cognitive Services can be used.

[0444] Data processing:

[0445] Analysis of emotional data using an emotion engine.

[0446] output:

[0447] Analyzed emotion data.

[0448] Step 8: Registering emotion data

[0449] server:

[0450] The server registers the emotion data analyzed in the previous step into the database.

[0451] input:

[0452] Analyzed emotion data.

[0453] output:

[0454] Emotional data registered in the database.

[0455] Step 9: Styling Generation

[0456] server:

[0457] The server retrieves user clothing information, sentiment data, and the latest trend information registered in the database. The latest trend information can be obtained from fashion websites and APIs.

[0458] input:

[0459] Clothing information, sentiment data, and trend information obtained from a database.

[0460] server:

[0461] Machine learning algorithms (e.g., TensorFlow, PyTorch) are used to generate optimal styling based on user mood, date, weather information, and sentiment data. This generation process also takes past feedback data into consideration.

[0462] Data calculation:

[0463] Data analysis and styling generation using machine learning algorithms.

[0464] output:

[0465] The generated styling information.

[0466] Step 10: Display the styling

[0467] server:

[0468] The server sends the generated styling information to the terminal.

[0469] input:

[0470] The generated styling information.

[0471] Terminal:

[0472] The device displays the received styling information on the user interface. The displayed content includes images of clothing combinations and styles.

[0473] output:

[0474] Styling information displayed to the user.

[0475] Step 11: Receiving Feedback

[0476] User:

[0477] Users can provide feedback on the suggested style, such as "satisfied" or "dissatisfied."

[0478] input:

[0479] User feedback.

[0480] Terminal:

[0481] The device temporarily stores the feedback information and then sends it to the server using an HTTP POST request.

[0482] output:

[0483] Temporarily saved feedback information.

[0484] Step 12: Submit and register feedback data.

[0485] Terminal:

[0486] The terminal sends the temporarily stored feedback data to the server using an HTTP POST request.

[0487] input:

[0488] Temporarily stored feedback data.

[0489] server:

[0490] The server registers the feedback data in a database. This feedback information is used to update the algorithm and make future styling suggestions.

[0491] Data processing:

[0492] Receiving feedback data and registering it in the database.

[0493] output:

[0494] Feedback data registered in the database.

[0495] (Application Example 2)

[0496] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0497] Traditional fashion styling systems fail to consider user emotions, making it difficult to suggest the optimal styling based on the user's psychological state at any given time. Similarly, in the onboard experience, there was no technology to provide optimal service in real time based on passengers' emotions and interests, thereby improving comfort during the ride. Therefore, there is a need for an effective system that can increase user and passenger satisfaction.

[0498] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for a user to take or upload a photograph of their clothing; means for the terminal to transmit the photograph to the server; means for the server to analyze the photograph and identify clothing information; means for registering the analyzed clothing information in a database; means for the user to input their current mood, date, and weather information; means for the terminal to transmit the information to the server; means for the server to generate styling based on the database and trend information; means for displaying the generated styling information on the terminal; means for the user to input feedback on the styling; means for the terminal to transmit the feedback to the server; means for the server to accumulate the feedback and update the styling algorithm; means for passengers to register basic information and interests and for the optimal ride experience to be suggested based on their emotions and information; means for analyzing passengers' emotions using cameras and microphones installed in the vehicle and transmitting the data to a computer in the vehicle; and means for collecting feedback on the user's ride experience and improving future ride experience suggestions. This will enable the suggestion of styling based on the user's emotions and the provision of a comfortable ride experience that suits the passengers' emotions and interests.

[0499] - "Users" are the primary target audience for this system, who register their clothing, receive styling suggestions, and get suggestions for ride experiences.

[0500] A "terminal" is an electronic device that receives user input and transmits information to a server, and includes smartphones and tablets.

[0501] A "server" is a central processing unit that analyzes information sent by users and generates styling information and suggestions for the driving experience.

[0502] "Photos" refer to image data used by users to register their clothing items.

[0503] "Clothing information" refers to data about the types and characteristics of clothing registered by the user.

[0504] A "database" is an information aggregation system used to store and manage analyzed clothing information and feedback.

[0505] "Mood" refers to the psychological state a user is experiencing at that moment.

[0506] "Date" refers to the specific year, month, and day on which the user enters or receives information.

[0507] "Weather information" refers to data about current or future weather conditions.

[0508] "Trend information" refers to the latest information on current fashions and styling.

[0509] "Styling" refers to the clothing combinations and outfits suggested to the user.

[0510] "Generation" refers to the process by which the server concretizes styling and ride experience suggestions based on user information.

[0511] A "styling algorithm" is a numerical processing method used to calculate the optimal styling based on the user's emotions and input information.

[0512] "Passenger" refers to a user of an autonomous vehicle.

[0513] A "camera" is a video acquisition device used to analyze passengers' emotions.

[0514] A "microphone" is a voice input device that captures passengers' voices and uses them for emotion analysis.

[0515] A "computer" is a computing device that processes data acquired within a vehicle and generates suggestions.

[0516] "Riding experience" refers to the services and entertainment that passengers receive inside an autonomous vehicle.

[0517] This invention is a system that allows users to register their clothing and suggests the optimal fashion style based on their mood, the date, the weather, and emotions recognized by an emotion engine. As an example of the application of this invention, it further provides a system that suggests the optimal ride experience based on the passenger's emotions and interests. Embodiments of this invention will be described in detail below.

[0518] System Overview

[0519] Registration of clothing and passenger information

[0520] Users (passengers) first register their own clothing by taking photos of their clothes using their smartphone or tablet. Alternatively, they can upload existing photos from their gallery. Furthermore, passengers register their basic information and interests (e.g., music, movies, news, etc.).

[0521] The terminal sends these photos and information to the server. The server analyzes the received photos using an image recognition algorithm to identify the type and characteristics of the clothing, and the analyzed clothing information is registered in a database. Meanwhile, information about the passengers' interests is also stored on the server.

[0522] Entering information

[0523] Users input information such as their mood for the day, the date, and the weather through the app's interface. They can also input their mood during the ride and their plans for the day. This information is sent from the device to the server.

[0524] Recognition of emotions

[0525] The system further incorporates an emotion engine to recognize the emotions of users (passengers). Using cameras and microphones installed in the vehicle, it analyzes the emotions of passengers from their facial expressions and voices, and sends the results to a server. This emotion data, along with the user's input information, is used in the next step.

[0526] Styling and driving experience suggestions

[0527] The server retrieves the user's clothing information registered in the database, along with the latest trend information, and executes an algorithm that generates the optimal styling and ride experience, taking into account the user's (passenger's) input information and emotional data. This algorithm makes suggestions based on the user's mood, weather, date, and perceived emotions.

[0528] Display of proposals and feedback

[0529] The generated styling information and ride experience suggestions are sent from the server to the terminal, which then displays the suggestions to the user. The styling includes detailed information on which items to combine and how, while the ride experience includes suggestions such as music and videos.

[0530] Users can provide feedback on the suggested styling and driving experience. This feedback information is sent from the device to the server, which stores it in a database. The accumulated feedback is used to update the algorithm to improve future suggestions.

[0531] Specific example

[0532] Clothing registration and passenger information entry

[0533] The user takes a photo of a "red knit sweater" using their smartphone. The passenger also registers their preferences as basic information, indicating they like "relaxing music" and "action movies." These photos and information are then sent from the device to the server.

[0534] Information input and emotion recognition

[0535] The user uses the app's input form to enter information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny." Simultaneously, the vehicle's cameras and microphones analyze the "passenger's happiness" using an emotion engine and send the data to the server.

[0536] Creating Styling and Driving Experiences

[0537] The server generates styling based on registered items such as a "red knit sweater" and "blue denim pants," along with the latest trend information and user mood and emotional data. It also suggests the optimal ride experience based on passenger preferences for "relaxing music" and "action movies."

[0538] Display of proposed content and feedback

[0539] The generated styling and suggestions such as playing "relaxing music" or showing "action movies" are displayed on the terminal's screen. Users can input feedback such as "I like this outfit" or passengers can input feedback such as "The music selection was good," and the terminal sends this information to the server.

[0540] Example of a prompt

[0541] We will implement an emotion recognition algorithm using libraries such as Python, TensorFlow, and Keras.

[0542] Example code for capturing images from a camera and recognizing emotions:

[0543] with open(image_path, 'rb') as image:

[0544] detected_faces = face_client.face.detect_with_stream(image, return_face_attributes=[FaceAttributeType.emotion])

[0545] A flow for collecting feedback and improving the next proposal:

[0546] feedback = collect_feedback()

[0547] We save feedback in the system to improve future suggestions.

[0548] In this way, the present invention enables the proposal of styling based on the user's emotions and the provision of a comfortable riding experience that responds to the emotions and interests of passengers.

[0549] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0550] Step 1:

[0551] Users use their smartphones or tablets to take photos of their clothing or upload them from their gallery. The input is a photo of clothing, which the device receives. The device then sends this photo to the server. The output is the completion of the photo data transmission to the server.

[0552] Step 2:

[0553] The server analyzes the received photo data. Using image recognition algorithms (e.g., TensorFlow, Keras), it identifies the types and characteristics of clothing in the photos. Data processing involves extracting features from the image data and converting it into a format suitable for database registration. The output is the registration of the identified clothing information into the database.

[0554] Step 3:

[0555] The user inputs information such as their mood for the day, the date, and the weather through the app's interface. This input can be text data or selected options, which the device receives. The device then sends this information to the server. The output indicates that the information has been successfully sent to the server.

[0556] Step 4:

[0557] The server acquires video and audio data in real time from cameras and microphones installed inside the vehicle. An emotion engine (e.g., Microsoft Azure Face API, Google Cloud Speech-to-Text) is used to analyze passengers' emotions from their facial expressions and voices. Data processing involves extracting and classifying emotion data. The output is the analyzed emotion data.

[0558] Step 5:

[0559] The server executes an algorithm that generates styling suggestions based on clothing information registered in the database, user input information, sentiment data, and the latest trend information. For data processing, it performs optimization using a machine learning model. The output is the generated styling information.

[0560] Step 6:

[0561] The server executes an algorithm to suggest the optimal ride experience based on the passenger's basic information and interests. It also takes emotional data into consideration to suggest entertainment such as music and movies. For data calculation, it integrates user profiles and real-time data to generate personalized suggestions. The output is a suggested ride experience.

[0562] Step 7:

[0563] The terminal displays generated styling information and ride experience suggestions to the user. The user reviews these suggestions. The input is suggestion data from the server, and the output is displayed on the user's device.

[0564] Step 8:

[0565] The user inputs feedback on the suggested styling and driving experience. This input can be text or a selection, which the device receives. The device then sends this feedback to the server. The output indicates that the feedback has been successfully sent to the server.

[0566] Step 9:

[0567] The server stores the received feedback in a database. The algorithm is updated using the feedback information to improve future suggestions. Data processing involves analyzing the feedback data and introducing a feedback loop into the algorithm. The output is the updated algorithm.

[0568] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0569] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0570] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0571] [Second Embodiment]

[0572] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0573] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0574] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0575] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0576] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0577] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0578] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0579] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0580] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0581] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0582] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0583] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0584] This invention relates to a system that allows users to register their clothing and suggests the optimal fashion style based on information such as mood, date, and weather. The system of this invention consists of a user terminal and a server. One embodiment of the present invention will be described below.

[0585] System Overview

[0586] 1. Register your clothing.

[0587] Users first register their clothing by taking photos of their clothes using the app. Alternatively, they can upload existing photos from their gallery. The device sends these photos to the server. The server analyzes the received photos using an image recognition algorithm to identify the type and characteristics of the clothing (e.g., shirt, pants, color, pattern, etc.). The analyzed clothing information is then registered in a database.

[0588] 2. Entering Information

[0589] Users input information such as their mood for the day, the date, and the weather through the app's interface. This information is then sent from the device to the server.

[0590] 3. Styling generation

[0591] The server retrieves the user's clothing information registered in the database, along with the latest trend information, and executes an algorithm that generates the optimal style considering the user's input. This algorithm suggests styling based on factors such as the user's mood, weather, and date.

[0592] 4. Styling display

[0593] The generated styling information is sent from the server to the terminal, which then displays the style to the user. The style includes detailed information about which items to combine and how. An image of the suggested style is also displayed.

[0594] 5. Receiving feedback and updating the algorithm

[0595] Users provide feedback on the suggested style, indicating whether they are satisfied or dissatisfied, through their device. This feedback information is sent to a server, which stores it in a database. The accumulated feedback is used to update the algorithm to improve future style suggestions.

[0596] Specific example

[0597] Clothing registration

[0598] Use your device's camera function to take a picture of a "red knit sweater." Alternatively, select a picture of "blue denim pants" that you have already taken from your gallery and upload it. This picture will be sent from your device to the server. The server will analyze the received picture using an image recognition algorithm and register it in its database as a "red knit sweater" or "blue denim pants."

[0599] Entering information

[0600] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into the app's input form. This information is then sent from the device to the server.

[0601] Styling generation and display

[0602] The server combines the user's registered "red knit sweater" and "blue denim pants" with the latest trend information to generate outfit ideas using the "red knit sweater" and "blue denim pants." This style suggestion is then sent to the device, which displays it to the user.

[0603] Receiving feedback

[0604] The user enters feedback such as, "I like this outfit." The device sends this feedback to the server, which stores it in a database. This feedback information is used to improve future styling algorithms.

[0605] As described above, the present invention provides a system that comprehensively utilizes the user's existing clothing and trend information to propose a fashion style that is individually optimized for each user.

[0606] The following describes the processing flow.

[0607] Step 1:

[0608] Users take photos of their own clothing or upload them from their gallery.

[0609] Step 2:

[0610] The device sends photos of clothing that have been taken or uploaded to the server.

[0611] Step 3:

[0612] The server uses an image recognition algorithm to analyze the received photos and identify the type of clothing (shirt, pants, jacket, etc.) and its characteristics (color, pattern, brand, etc.).

[0613] Step 4:

[0614] Based on the analysis results, the server registers clothing information in the database, associating it with the user ID.

[0615] Step 5:

[0616] The user enters their mood, date, and weather information into the app's input form.

[0617] Step 6:

[0618] The terminal sends the user's input information to the server.

[0619] Step 7:

[0620] The server retrieves information about the user's clothing collection from the database, and similarly retrieves the latest trend information.

[0621] Step 8:

[0622] The server runs a styling algorithm based on the user's mood, date, and weather information to generate the optimal style.

[0623] Step 9:

[0624] The server sends the generated styling information to the terminal.

[0625] Step 10:

[0626] The device displays the suggested style to the user along with detailed information (which items to combine and how).

[0627] Step 11:

[0628] Users provide feedback on styling suggestions, indicating whether they are satisfied or dissatisfied.

[0629] Step 12:

[0630] The device sends user feedback information to the server.

[0631] Step 13:

[0632] The server stores feedback information in a database and updates the styling algorithm to improve future styling suggestions.

[0633] (Example 1)

[0634] Next, we will describe Example 1. 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."

[0635] Traditionally, when users styled outfits using their existing wardrobe, they faced the challenge of having to decide for themselves which items to combine and how to combine them. Furthermore, they often lacked optimal suggestions that took into account fluctuating factors such as mood and weather, resulting in inconsistent styling quality. This, in particular, led to time-consuming and laborious styling processes, lowering user satisfaction, especially for busy individuals.

[0636] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0637] In this invention, the server includes means for a user to take or upload photos of their clothing; means for the terminal to transmit the photos to the server; means for the server to analyze the photos and identify clothing information; means for registering the analyzed clothing information in a database; means for the user to input their current mood, date, and weather information; means for the terminal to transmit the information to the server; means for the server to generate styling based on the database and trend information; means for the server to transmit the generated styling information to the terminal; means for the terminal to display the generated styling information; means for the user to input feedback on the styling; means for the terminal to transmit the feedback to the server; and means for the server to accumulate the feedback and update the styling algorithm. This makes it possible for the user to automatically receive suggestions for the optimal fashion style based on their clothing and changing factors.

[0638] A "user" refers to an individual who takes or uploads photos of clothing and enters information such as mood, date, and weather.

[0639] A "terminal" refers to an electronic device operated by a user that transmits photos taken and information entered to a server and displays styling information.

[0640] A "server" refers to a central computer system that analyzes data received from terminals, registers it in a database, generates styling, and sends it back to the terminals.

[0641] "Photographs" refer to still image data that users take or upload to represent their clothing.

[0642] "Analysis" refers to the process by which the server uses an image recognition algorithm to identify clothing information from the received photograph.

[0643] "Image recognition algorithms" refer to computer vision technologies used to recognize the type and characteristics of clothing from photographs.

[0644] "Clothing information" refers to characteristic data such as the type, color, and pattern of clothing identified from the analyzed photographs.

[0645] A "database" refers to a data storage system used to accumulate and manage analyzed clothing information and user input information.

[0646] "Mood" refers to the subjective feelings and sensations that users input to express their mental state on a given day.

[0647] "Date" refers to the year, month, and day of a specific date entered by the user.

[0648] "Weather information" refers to the weather conditions for the day (e.g., sunny, rainy, cloudy) entered by the user.

[0649] "Trend information" refers to data on the latest fashion trends and fashions.

[0650] "Styling" refers to the optimal clothing combination suggestions that the server generates based on database and trend information.

[0651] "Feedback" refers to information that users input, such as their impressions and evaluations of the suggested styling.

[0652] A "styling algorithm" refers to a program implemented on a server to generate the optimal styling based on the user's mood, date, and weather information.

[0653] This invention relates to a system that registers a user's clothing collection and suggests the optimal fashion style based on information such as mood, date, and weather. This system mainly consists of a user terminal and a server.

[0654] First, users register their clothing using an application installed on their device (e.g., a smartphone or tablet). Clothing can be registered by taking photos of the garments using the device's camera function, or by uploading existing photos from the device's gallery. These photos are then sent from the device to the server.

[0655] The server analyzes the received photos using image recognition algorithms (e.g., TensorFlow or OpenCV). These algorithms allow the server to identify features such as the type, color, and pattern of clothing within the photos. The identified clothing information is then registered in a database.

[0656] Next, the user enters information such as their mood for the day, the date, and the weather through the app's interface. This information is sent from the device to the server. The server retrieves the clothing information registered by the user and the latest trend information, and runs a styling algorithm (e.g., Scikit-learn or Keras) based on the user's input. This algorithm takes into account the entered elements such as mood, weather, and date to generate the most suitable styling for the user.

[0657] The generated styling information is sent from the server to the terminal, which then presents the style to the user. The presented style includes detailed information on which items to combine and how to combine them. Style suggestion images are also displayed as needed.

[0658] The user provides feedback on the suggested style. This feedback is sent from the terminal to the server. The server stores this feedback information in a database and uses it to update the algorithm to improve future style suggestions.

[0659] Specific example

[0660] Clothing registration:

[0661] The user takes a picture of a "red knit sweater" using their smartphone camera, or selects and uploads an existing picture of "blue denim pants" from their gallery. This picture is sent from the device to the server. The server analyzes the received picture using an image recognition algorithm and registers it in the database as a "red knit sweater" or "blue denim pants."

[0662] Entering information:

[0663] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into the app's form. This information is then sent from the device to the server.

[0664] Styling generation and display:

[0665] The server retrieves the user's registered information for a "red knit sweater" and "blue denim pants," and runs a styling algorithm along with the latest trend information. The algorithm considers the input information, such as "cheerful mood," "October 16, 2023," and "sunny weather," to generate an outfit combining the "red knit sweater" and "blue denim pants." This outfit information is sent to the terminal, which then presents it to the user.

[0666] Receiving feedback:

[0667] The user enters feedback such as "I like this outfit," and the device sends that feedback to the server. The server stores this feedback information and uses it to improve future styling suggestions.

[0668] Thus, the present invention is a system that comprehensively utilizes the user's existing clothing and the latest fashion trend information to propose an individually optimized fashion style.

[0669] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0670] Step 1:

[0671] The user takes a photo of their clothes using the app on their device, or uploads an existing photo. The device then sends the taken or uploaded photo to the server.

[0672] Input: A photo of clothing taken or selected by the user.

[0673] Output: Photo data sent from the terminal to the server

[0674] Specific actions:

[0675] The user takes a picture of a "red knit sweater" using their smartphone's camera function and sends that picture to the server via the app.

[0676] Step 2:

[0677] The server analyzes the received photos using an image recognition algorithm (e.g., TensorFlow, OpenCV). The analysis results identify the type and characteristics of the clothing.

[0678] Input: Photo data of clothing received by the server

[0679] Output: Information on the type and characteristics of the analyzed clothing (e.g., shirt, pants, color, pattern, etc.)

[0680] Specific actions:

[0681] The server analyzes the received photo of a "red knit sweater" and uses an image recognition algorithm to identify the characteristic of a "red knit sweater."

[0682] Step 3:

[0683] The server registers the analyzed clothing information in a database.

[0684] Input: Information on the analyzed clothing

[0685] Output: Clothing information registered in the database

[0686] Specific actions:

[0687] The server saves information about the "red knit sweater" to the database and adds it to the user's clothing collection.

[0688] Step 4:

[0689] The user enters information such as their mood for the day, the date, and the weather through the app's interface. The device then sends the entered information to the server.

[0690] Input: Information entered by the user, such as mood, date, and weather.

[0691] Output: Mood, date, and weather information sent from the terminal to the server.

[0692] Specific actions:

[0693] The user enters "cheerful mood," "October 16, 2023," and "sunny" into the app's form and sends this information to the server.

[0694] Step 5:

[0695] The server retrieves the user's clothing information and the latest trend information registered in the database, and runs a styling algorithm (e.g., Scikit-learn, Keras) to generate the optimal style.

[0696] Input: Clothing information from the database, trend information, and user input information (mood, date, weather)

[0697] Output: Generated styling information

[0698] Specific actions:

[0699] Based on the "red knit sweater" and the latest trend information, the server generates the optimal outfit by taking into account the user's input information: "cheerful mood," "October 16, 2023," and "sunny."

[0700] Step 6:

[0701] The server sends the generated styling information to the terminal. The terminal displays the received styling information to the user.

[0702] Input: Styling information generated by the server

[0703] Output: Styling information displayed on the terminal

[0704] Specific actions:

[0705] The server generates outfit information using a "red knit sweater" and "blue denim pants," which is then sent to the terminal, and the terminal presents this information to the user.

[0706] Step 7:

[0707] The user provides feedback on the suggested style through the app. The device then sends this feedback to the server.

[0708] Input: Feedback on the style entered by the user

[0709] Output: Feedback information sent from the terminal to the server

[0710] Specific actions:

[0711] The user enters feedback such as "I like this outfit" and sends it from their device to the server.

[0712] Step 8:

[0713] The server stores feedback information in a database and uses it to update algorithms in order to improve future styling suggestions.

[0714] Input: Feedback information sent from the terminal to the server

[0715] Output: Feedback information stored in the database, updated styling algorithms

[0716] Specific actions:

[0717] The server stores the feedback information it receives in a database and uses it to improve the styling algorithm.

[0718] (Application Example 1)

[0719] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0720] Existing fashion style suggestion systems fail to efficiently utilize users' existing clothing and trend information, making it difficult to suggest the most suitable fashion styles for individual users. Furthermore, algorithm improvements based on user feedback are insufficient. As a result, a major challenge is that users are not receiving the style suggestions they desire.

[0721] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0722] In this invention, the server includes means for the user to take or upload photos of their clothing using their smartphone with image recognition technology, means for analyzing the photos sent to the cloud server to identify clothing information, means for registering the analyzed clothing information in a database, means for the user to input their current mood, date, and weather information into their smartphone, means for the cloud server to generate an optimal styling based on the database and trend information, means for displaying the generated styling information on the smartphone, means for the user to input feedback on the styling into their smartphone, and means for the cloud server to accumulate the feedback and update the styling algorithm. This makes it possible to propose individually optimized fashion styles that reflect the user's clothing and trend information.

[0723] A "user" is an individual who uses the system to register their clothing collection and receive fashion style suggestions.

[0724] "Clothing on hand" refers to the various clothes and accessories that the user owns.

[0725] A "smartphone" is a portable information terminal that allows internet access and the use of various applications.

[0726] A "cloud server" is a remote server located on the internet that enables data processing and storage for a large number of users.

[0727] "Photos" refer to image data taken or uploaded using a user's smartphone.

[0728] "Image recognition technology" is a technology that analyzes image data to identify specific objects or features.

[0729] "Clothing information" refers to data that describes the type and characteristics of clothing identified using image recognition technology.

[0730] A "database" is an information system used to store and manage analyzed clothing information and other related data.

[0731] "Mood" refers to information that represents the user's psychological state and emotions on a given day.

[0732] "Date" refers to calendar information that indicates a specific day.

[0733] "Weather information" refers to data that shows the weather conditions for a given day.

[0734] "Trend information" refers to information about current trends and fashion developments.

[0735] "Styling" refers to fashion combinations and outfits generated based on the user's existing clothing and current trends.

[0736] "Feedback" refers to information that shows the evaluation and impressions that users give regarding the suggested styling.

[0737] An "algorithm" is a set of rules that define the steps or calculation methods for solving a specific problem.

[0738] This invention relates to a system that allows users to photograph or upload their clothing using their smartphones, and then suggests fashion styles based on those photos. Specific embodiments of this invention will be described below.

[0739] System Overview

[0740] 1. Register your clothing.

[0741] Users first register their clothing. To do this, they either take photos of their clothes using their smartphone's camera or upload photos they have already taken. The smartphone sends these photos to a cloud server. The cloud server uses image recognition technology to analyze the received photos and identify the type and characteristics of the clothing (e.g., shirt, pants, color, pattern, etc.). The analyzed clothing information is then registered in a database.

[0742] 2. Entering Information

[0743] Users input information such as their mood, the date, and the weather through their smartphone interface. This information is then sent from the smartphone to a cloud server.

[0744] 3. Styling generation

[0745] The cloud server retrieves the user's clothing information and the latest trend information registered in the database, and executes an algorithm to generate the optimal style, taking into account the user's input information. This algorithm suggests styling based on factors such as the user's mood, weather, and date.

[0746] 4. Styling display

[0747] The generated styling information is sent from the cloud server to the smartphone, which then displays the style to the user. The style includes detailed information on which items to combine and how. An image of the suggested style is also displayed.

[0748] 5. Receiving feedback and updating the algorithm

[0749] Users provide feedback on the suggested styles, indicating satisfaction or dissatisfaction, via their smartphones. This feedback information is sent to a cloud server, which stores it in a database. The accumulated feedback is used to update the algorithm to improve future style suggestions.

[0750] Hardware and software to be used

[0751] Hardware: Smartphones (iOS or Android), cloud servers

[0752] software:

[0753] Camera function

[0754] Cloud servers (for example, Amazon Web Services or Google Cloud)

[0755] Image recognition algorithms (for example, models using TensorFlow or PyTorch)

[0756] Database (for example, MySQL or MongoDB)

[0757] Specific Examples

[0758] Clothing registration

[0759] The user takes a picture of a "red knit sweater" using their smartphone camera, or selects and uploads a picture of "blue denim pants" that they have already taken from their gallery. These photos are sent from the smartphone to a cloud server. The cloud server analyzes the received photos using image recognition technology and registers them in a database as "red knit sweater" or "blue denim pants."

[0760] Entering information

[0761] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into an input form on their smartphone. This information is then sent from the smartphone to a cloud server.

[0762] Styling generation and display

[0763] The cloud server combines the user's registered items, such as a "red knit sweater" and "blue denim pants," with the latest trend information to generate outfit ideas using the "red knit sweater" and "blue denim pants." This style suggestion is then sent to the user's smartphone, which displays it.

[0764] Receiving feedback

[0765] Users provide feedback such as, "I like this outfit." Their smartphones send this feedback to a cloud server, which stores it in a database. This feedback information is used to improve future styling algorithms.

[0766] Examples of prompt statements

[0767] "User's clothing: Red knit sweater, blue denim pants"

[0768] "Mood: Cheerful"

[0769] Date: October 20, 2023

[0770] "Weather: Sunny"

[0771] As described above, the present invention provides a system that comprehensively utilizes the user's existing clothing and trend information to propose a fashion style that is individually optimized for each user.

[0772] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0773] Step 1:

[0774] The user takes a photo of their clothing or uploads one from their gallery using their smartphone. The smartphone then sends the photo of the clothing taken or selected by the user as input to a cloud server. This transmitted image data is the input. The specific action the smartphone takes is to upload the image data to the cloud server.

[0775] Step 2:

[0776] The cloud server analyzes the received image data using image recognition technology. This image recognition technology uses TensorFlow or PyTorch. The input to the cloud server is image data of clothing sent by the user, and the output is the recognized type and characteristic information of the clothing. Specifically, the cloud server analyzes the image data and identifies clothing information such as "shirt" and "pants."

[0777] Step 3:

[0778] The cloud server registers the analyzed clothing information into a database. The input is the analyzed clothing information, and the output is the updated database. The specific action performed by the cloud server is to save the newly analyzed clothing information to the existing database.

[0779] Step 4:

[0780] The user uses their smartphone to input their mood, the date, and weather information for the day. Based on this input, the smartphone sends it to a cloud server. The input is the user's mood, the date, and the weather information, and the output is the information sent to the cloud server. Specifically, the user enters information into an input form, and the smartphone sends that information to the cloud server.

[0781] Step 5:

[0782] The cloud server retrieves the user's clothing information registered in the database, along with the latest trend information, and generates the optimal style considering the user's input information. In this step, the cloud server generates the style using an algorithm. The input is the user's mood, date, weather information, and clothing information, and the output is the generated styling information. Specifically, it retrieves the necessary information from the database and generates the style based on a calculation algorithm.

[0783] Step 6:

[0784] The cloud server sends the generated styling information to the smartphone. The smartphone receives this information and displays it to the user. The input is the styling information sent from the cloud server, and the output is the styling information displayed on the smartphone. The specific action performed by the smartphone is to visually display the received styling information to the user.

[0785] Step 7:

[0786] The user uses their smartphone to input feedback on the suggested style. This input feedback is sent from the smartphone to a cloud server. The input is the user's feedback information, and the output is the feedback information sent to the cloud server. Specifically, the user enters information into a feedback form, and the smartphone sends that information to the cloud server.

[0787] Step 8:

[0788] The cloud server stores the received feedback information in a database. It also updates the styling algorithm based on the accumulated feedback. The input is user feedback information, and the output is the updated styling algorithm. The specific action performed by the cloud server is to periodically update the algorithm to improve style suggestions for future use.

[0789] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0790] This invention relates to a system that allows users to register their clothing and suggests the optimal fashion style based on their mood, the date, the weather, and emotions recognized by an emotion engine. The system of this invention consists of a user terminal, a server, and an emotion engine. One embodiment of the present invention is described below.

[0791] System Overview

[0792] 1. Register your clothing.

[0793] Users first register their clothing by taking photos of their clothes using the app. Alternatively, they can upload existing photos from their gallery. The device sends these photos to the server. The server analyzes the received photos using an image recognition algorithm to identify the type and characteristics of the clothing (e.g., shirt, pants, color, pattern, etc.). The analyzed clothing information is then registered in a database.

[0794] 2. Entering Information

[0795] Users input information such as their mood for the day, the date, and the weather through the app's interface. This information is then sent from the device to the server.

[0796] 3. Recognition of emotions

[0797] The system further incorporates an emotion engine to recognize the user's emotions. This emotion engine analyzes the user's facial expressions and voice, and sends the results to the server. This emotion data, along with the user's input information, is used in the next step.

[0798] 4. Styling generation

[0799] The server retrieves the user's clothing information registered in the database, along with the latest trend information, and executes an algorithm that generates the optimal style considering the user's input information and emotional data. This algorithm suggests styling based on the user's mood, weather, date, and perceived emotions.

[0800] 5. Styling display

[0801] The generated styling information is sent from the server to the terminal, which then displays the style to the user. The style includes detailed information about which items to combine and how. An image of the suggested style is also displayed.

[0802] 6. Receiving feedback and updating the algorithm

[0803] Users provide feedback on the suggested style, indicating whether they are satisfied or dissatisfied, through their device. This feedback information is sent to a server, which stores it in a database. The accumulated feedback is used to update the algorithm to improve future style suggestions.

[0804] Specific example

[0805] Clothing registration

[0806] Use your device's camera function to take a picture of a "red knit sweater." Alternatively, select a picture of "blue denim pants" that you have already taken from your gallery and upload it. This picture will be sent from your device to the server. The server will analyze the received picture using an image recognition algorithm and register it in its database as a "red knit sweater" or "blue denim pants."

[0807] Entering information

[0808] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into the app's input form. This information is then sent from the device to the server.

[0809] Recognition of emotions

[0810] When a user uses the app, the emotion engine analyzes the user's facial expressions and voice to recognize emotions such as "joy" or "anxiety." This emotion data, along with other input information, is sent to the server.

[0811] Styling generation and display

[0812] The server generates outfit ideas using the "red knit sweater" and "blue denim pants" registered by the user, along with the latest trend information and the user's mood and emotional data. It then sends these style suggestions to the user's device, which displays them along with detailed information.

[0813] Receiving feedback

[0814] The user enters feedback such as, "I like this outfit." The device sends this feedback to the server, which stores it in a database. This feedback information is used to improve future styling algorithms.

[0815] As described above, the present invention provides a system that comprehensively utilizes the user's existing clothing and trend information, and proposes an individually optimized fashion style while also considering the user's emotions.

[0816] The following describes the processing flow.

[0817] Step 1:

[0818] Users use the app to take photos of their existing clothing, or upload existing photos from their gallery.

[0819] Step 2:

[0820] The device sends photos of clothing that have been taken or uploaded to the server.

[0821] Step 3:

[0822] The server uses an image recognition algorithm to analyze the received photos and identify the type and characteristics of the clothing (e.g., shirt, pants, color, pattern, etc.).

[0823] Step 4:

[0824] Based on the analysis results, the server registers clothing information in the database, associating it with the user ID.

[0825] Step 5:

[0826] The user enters their mood, date, and weather information into the app's input form.

[0827] Step 6:

[0828] The terminal sends the user's input information to the server.

[0829] Step 7:

[0830] The device uses an emotion engine to analyze the user's facial expressions and voice, and generate emotion data.

[0831] Step 8:

[0832] The device sends emotional data to the server.

[0833] Step 9:

[0834] The server retrieves information about the user's clothing collection from the database, and similarly retrieves the latest trend information.

[0835] Step 10:

[0836] The server runs a styling algorithm based on the user's mood, date, weather information, and sentiment data to generate the optimal style.

[0837] Step 11:

[0838] The server sends the generated styling information to the terminal.

[0839] Step 12:

[0840] The device displays the suggested style to the user along with detailed information (which items to combine and how).

[0841] Step 13:

[0842] Users provide feedback on styling suggestions, indicating whether they are satisfied or dissatisfied.

[0843] Step 14:

[0844] The device sends user feedback information to the server.

[0845] Step 15:

[0846] The server stores feedback information in a database and updates the styling algorithm to improve future styling suggestions.

[0847] (Example 2)

[0848] Next, we will describe Example 2. 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".

[0849] Conventional fashion suggestion systems only offered styling suggestions based on the user's existing clothing information and basic input, failing to consider subtle changes in the user's emotions. As a result, the styling suggestions were often unsatisfactory in terms of the user's mood and emotions, making it difficult to provide individually optimized suggestions.

[0850] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for incorporating an emotion engine for analyzing emotions from the user's facial expressions and voice, means for transmitting the emotion data analyzed by the emotion engine to the server, and means for the server to generate styling considering the emotion data. This makes it possible to propose individually optimized fashion styles based on the user's mood and emotions.

[0851] "User" refers to anyone who uses this system.

[0852] "Device" refers to an electronic device used by a user, and includes smartphones, tablets, and personal computers.

[0853] A "server" refers to the central processing unit of this system, which is responsible for receiving, processing, storing, and transmitting data.

[0854] "Photos" refer to image data that users take or upload to register clothing items.

[0855] "Analysis" refers to the process by which a server extracts necessary information from the photos and data it receives.

[0856] "Clothing information" refers to information about clothing registered by the user, such as the type of clothing, color, pattern, and features.

[0857] A "database" refers to a structured storage system that allows a server to systematically manage, store, and retrieve information.

[0858] "Mood" refers to the mental state that the user is experiencing at that particular moment.

[0859] "Date" refers to information that specifically indicates the year, month, and day.

[0860] "Weather information" refers to information that indicates the weather conditions on a given day.

[0861] "Trend information" refers to information about the latest fashion and styles.

[0862] "Styling" refers to fashion combinations and outfits suggested based on the user's existing clothing.

[0863] "Feedback" refers to the evaluations and opinions that users give regarding the suggested styling.

[0864] An "emotion engine" refers to software or algorithms that analyze a user's facial expressions and voice to recognize their emotions.

[0865] "Emotional data" refers to data about a user's emotions that has been analyzed by the emotion engine.

[0866] An "image recognition algorithm" refers to an algorithm used to analyze photographs and image data to detect specific objects or features.

[0867] This invention relates to a system in which a user registers their clothing and the system suggests the optimal fashion style based on their mood, the date, the weather, and emotions recognized by an emotion engine. This system consists of a user terminal, a server, and an emotion engine.

[0868] System Configuration

[0869] Clothing registration

[0870] User:

[0871] Users first use the app to register their existing clothing. Within the app, they select the "Register Clothing" menu and register their clothing using the following method:

[0872] Use the device's camera function to take a picture of the clothing.

[0873] Upload existing photos from your gallery.

[0874] Terminal:

[0875] The device temporarily stores photos of the clothing and sends them to the server using an HTTP POST request.

[0876] server:

[0877] The server analyzes the received photos using image recognition algorithms such as the Google Cloud Vision API to identify the type and characteristics of the clothing. The results of this analysis are then registered in a database.

[0878] Entering information

[0879] User:

[0880] Users use an input form within the app to enter their mood for the day, the date, and weather information.

[0881] Terminal:

[0882] The terminal temporarily stores the entered information and sends it to the server using an HTTP POST request.

[0883] server:

[0884] The server registers the received information in the database.

[0885] Recognition of emotions

[0886] User:

[0887] While the user is using the app, the emotion engine analyzes the user's facial expressions and voice. Emotional data is collected when the user shows their facial expressions to the device's camera or speaks.

[0888] Terminal:

[0889] The device acquires facial expressions and voice data, stores it temporarily, and then sends it to the server using an HTTP POST request.

[0890] server:

[0891] The server uses an emotion engine to analyze facial expressions and voice data to identify the user's emotions. The analyzed emotion data is registered in a database. For example, Microsoft's Azure Cognitive Services can be used.

[0892] Styling generation

[0893] server:

[0894] The server retrieves information about the user's clothing collection, sentiment data, and the latest trend information registered in the database. The latest trend information can be obtained from fashion websites on the internet or APIs (for example, the Fashion Trends API).

[0895] server:

[0896] Machine learning algorithms (e.g., TensorFlow or PyTorch) are used to generate optimal styling based on the user's mood, date, weather information, and sentiment data. This generation process also takes past feedback data into consideration.

[0897] Styling display

[0898] server:

[0899] The server sends the generated styling information to the terminal.

[0900] Terminal:

[0901] The device displays the received styling information on the user interface. The displayed content includes images of clothing combinations and styles.

[0902] Receiving feedback

[0903] User:

[0904] Users can provide feedback on the suggested style, such as "satisfied" or "dissatisfied."

[0905] Terminal:

[0906] The device temporarily stores the feedback information and sends it to the server using an HTTP POST request.

[0907] server:

[0908] The server registers the feedback information in a database. This feedback information is used to update the algorithm and make future styling suggestions.

[0909] Specific example

[0910] Clothing registration

[0911] The user takes a picture of a "red knit sweater" using their device's camera function, or uploads a picture of "blue denim pants" from their gallery. This picture is sent from the device to the server. The server uses the Google Cloud Vision API to analyze the picture and register it in the database as a "red knit sweater" or "blue denim pants."

[0912] Entering information

[0913] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into the app's input form. This information is then sent from the device to the server.

[0914] Recognition of emotions

[0915] While the user is using the app, the emotion engine analyzes the user's facial expressions and voice to recognize emotions such as "joy" or "anxiety." This emotion data is sent to the server along with other information.

[0916] Styling generation and display

[0917] The server generates outfit ideas using the "red knit sweater" and "blue denim pants" based on the user's registered items, the latest trend information, and the user's mood and emotional data. This style suggestion is sent to the device, which displays it along with detailed information.

[0918] Receiving feedback

[0919] The user enters feedback such as "I like this outfit." The device sends this feedback to the server, which stores it in a database. This allows for future improvements to the styling algorithm.

[0920] Example prompts for a generative AI model

[0921] An example of a prompt message a user might enter is: "If the user's mood today is 'cheerful,' the date is 'October 16, 2023,' the weather is 'sunny,' and their emotion is 'joyful,' please suggest a fashion outfit using the 'red knit sweater' and 'blue denim pants' they already own."

[0922] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0923] Step 1: Register your clothing

[0924] User:

[0925] The user launches the app and selects the "Register Clothing" menu within the app. Here, the user registers clothing using the following method:

[0926] Use the device's camera function to take a picture of the clothing.

[0927] Upload existing photos from your gallery.

[0928] input:

[0929] Photographs of clothing taken or uploaded.

[0930] Terminal:

[0931] The device temporarily stores the captured or uploaded photo data. The photo data is then sent to the server.

[0932] output:

[0933] Temporarily saved photo data.

[0934] Step 2: Send and analyze photos

[0935] Terminal:

[0936] The device sends the temporarily stored photo data to the server using an HTTP POST request.

[0937] input:

[0938] Temporarily saved photo data.

[0939] server:

[0940] The server passes the received photo data to image recognition algorithms such as the Google Cloud Vision API, which analyze it to identify the type and characteristics of the clothing. This analysis provides information such as the type of clothing (e.g., shirt, pants), color, and pattern.

[0941] Data processing:

[0942] Photo analysis using image recognition algorithms.

[0943] output:

[0944] Analyzed clothing information.

[0945] Step 3: Register clothing information

[0946] server:

[0947] The server registers the clothing information analyzed in the previous step into the database.

[0948] input:

[0949] Analyzed clothing information.

[0950] output:

[0951] Clothing information registered in the database.

[0952] Step 4: Entering Information

[0953] User:

[0954] Users use an input form within the app to enter their mood for the day, the date, and weather information. Mood can include options such as "cheerful" or "calm."

[0955] input:

[0956] User-entered mood, date, and weather information.

[0957] Terminal:

[0958] The terminal temporarily stores the entered information and then sends it to the server using an HTTP POST request.

[0959] output:

[0960] Temporarily saved mood, date, and weather information.

[0961] Step 5: Submit and register your information.

[0962] Terminal:

[0963] The device sends temporarily stored mood, date, and weather information to the server using an HTTP POST request.

[0964] input:

[0965] Temporarily saved mood, date, and weather information.

[0966] server:

[0967] The server registers the received information in the database.

[0968] Data processing:

[0969] Receiving information and registering it in the database.

[0970] output:

[0971] Mood, date, and weather information registered in the database.

[0972] Step 6: Recognizing Emotions

[0973] User:

[0974] While the user is using the app, the emotion engine analyzes the user's facial expressions and voice. Facial and voice data is collected when the user shows their face to the device's camera or speaks.

[0975] input:

[0976] User's facial expressions and voice data.

[0977] Terminal:

[0978] The device acquires facial expression and voice data, stores it temporarily, and then sends it to the server using an HTTP POST request.

[0979] output:

[0980] Temporarily saved facial expression and audio data.

[0981] Step 7: Sending and analyzing emotional data

[0982] Terminal:

[0983] The terminal sends temporarily stored facial expression and audio data to the server using an HTTP POST request.

[0984] input:

[0985] Temporarily saved facial expression and audio data.

[0986] server:

[0987] The server uses an emotion engine to analyze this data and identify the user's emotions. For example, Microsoft's Azure Cognitive Services can be used.

[0988] Data processing:

[0989] Analysis of emotional data using an emotion engine.

[0990] output:

[0991] Analyzed emotion data.

[0992] Step 8: Registering emotion data

[0993] server:

[0994] The server registers the emotion data analyzed in the previous step into the database.

[0995] input:

[0996] Analyzed emotion data.

[0997] output:

[0998] Emotional data registered in the database.

[0999] Step 9: Styling Generation

[1000] server:

[1001] The server retrieves user clothing information, sentiment data, and the latest trend information registered in the database. The latest trend information can be obtained from fashion websites and APIs.

[1002] input:

[1003] Clothing information, sentiment data, and trend information obtained from a database.

[1004] server:

[1005] Machine learning algorithms (e.g., TensorFlow, PyTorch) are used to generate optimal styling based on user mood, date, weather information, and sentiment data. This generation process also takes past feedback data into consideration.

[1006] Data calculation:

[1007] Data analysis and styling generation using machine learning algorithms.

[1008] output:

[1009] The generated styling information.

[1010] Step 10: Display the styling

[1011] server:

[1012] The server sends the generated styling information to the terminal.

[1013] input:

[1014] The generated styling information.

[1015] Terminal:

[1016] The device displays the received styling information on the user interface. The displayed content includes images of clothing combinations and styles.

[1017] output:

[1018] Styling information displayed to the user.

[1019] Step 11: Receiving Feedback

[1020] User:

[1021] Users can provide feedback on the suggested style, such as "satisfied" or "dissatisfied."

[1022] input:

[1023] User feedback.

[1024] Terminal:

[1025] The device temporarily stores the feedback information and then sends it to the server using an HTTP POST request.

[1026] output:

[1027] Temporarily saved feedback information.

[1028] Step 12: Submit and register feedback data.

[1029] Terminal:

[1030] The terminal sends temporarily stored feedback data to the server using an HTTP POST request.

[1031] input:

[1032] Temporarily stored feedback data.

[1033] server:

[1034] The server registers the feedback data in a database. This feedback information is used to update the algorithm and make future styling suggestions.

[1035] Data processing:

[1036] Receiving feedback data and registering it in the database.

[1037] output:

[1038] Feedback data registered in the database.

[1039] (Application Example 2)

[1040] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1041] Traditional fashion styling systems fail to consider user emotions, making it difficult to suggest the optimal styling based on the user's psychological state at any given time. Similarly, in the onboard experience, there was no technology to provide optimal service in real time based on passengers' emotions and interests, thereby improving comfort during the ride. Therefore, there is a need for an effective system that can increase user and passenger satisfaction.

[1042] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for a user to take or upload a photograph of their clothing; means for the terminal to transmit the photograph to the server; means for the server to analyze the photograph and identify clothing information; means for registering the analyzed clothing information in a database; means for the user to input their current mood, date, and weather information; means for the terminal to transmit the information to the server; means for the server to generate styling based on the database and trend information; means for displaying the generated styling information on the terminal; means for the user to input feedback on the styling; means for the terminal to transmit the feedback to the server; means for the server to accumulate the feedback and update the styling algorithm; means for passengers to register basic information and interests and for the optimal ride experience to be suggested based on their emotions and information; means for analyzing passenger emotions using cameras and microphones installed in the vehicle and transmitting the data to a computer in the vehicle; and means for collecting feedback on the user's ride experience and improving future ride experience suggestions. This will enable the suggestion of styling based on the user's emotions and the provision of a comfortable ride experience that suits the passengers' emotions and interests.

[1043] - "Users" are the primary target audience for this system, who register their clothing, receive styling suggestions, and get suggestions for ride experiences.

[1044] A "terminal" is an electronic device that receives user input and transmits information to a server, and includes smartphones and tablets.

[1045] A "server" is a central processing unit that analyzes information sent by users and generates styling information and suggestions for the driving experience.

[1046] "Photos" refer to image data used by users to register their clothing items.

[1047] "Clothing information" refers to data about the types and characteristics of clothing registered by the user.

[1048] A "database" is an information aggregation system used to store and manage analyzed clothing information and feedback.

[1049] "Mood" refers to the psychological state a user is experiencing at that moment.

[1050] "Date" refers to the specific year, month, and day on which the user enters or receives information.

[1051] "Weather information" refers to data about current or future weather conditions.

[1052] "Trend information" refers to the latest information on current fashions and styling.

[1053] "Styling" refers to the clothing combinations and outfits suggested to the user.

[1054] "Generation" refers to the process by which the server concretizes styling and ride experience suggestions based on user information.

[1055] A "styling algorithm" is a numerical processing method used to calculate the optimal styling based on the user's emotions and input information.

[1056] "Passenger" refers to a user of an autonomous vehicle.

[1057] A "camera" is a video acquisition device used to analyze passengers' emotions.

[1058] A "microphone" is a voice input device that captures passengers' voices and uses them for emotion analysis.

[1059] A "computer" is a computing device that processes data acquired within a vehicle and generates suggestions.

[1060] "Riding experience" refers to the services and entertainment that passengers receive inside an autonomous vehicle.

[1061] This invention is a system that allows users to register their clothing and suggests the optimal fashion style based on their mood, the date, the weather, and emotions recognized by an emotion engine. As an example of the application of this invention, it further provides a system that suggests the optimal ride experience based on the passenger's emotions and interests. Embodiments of this invention will be described in detail below.

[1062] System Overview

[1063] Registration of clothing and passenger information

[1064] Users (passengers) first register their own clothing by taking photos of their clothes using their smartphone or tablet. Alternatively, they can upload existing photos from their gallery. Furthermore, passengers register their basic information and interests (e.g., music, movies, news, etc.).

[1065] The terminal sends these photos and information to the server. The server analyzes the received photos using an image recognition algorithm to identify the type and characteristics of the clothing, and the analyzed clothing information is registered in a database. Meanwhile, information about the passengers' interests is also stored on the server.

[1066] Entering information

[1067] Users input information such as their mood for the day, the date, and the weather through the app's interface. They can also input their mood during the ride and their plans for the day. This information is sent from the device to the server.

[1068] Recognition of emotions

[1069] The system further incorporates an emotion engine to recognize the emotions of users (passengers). Using cameras and microphones installed in the vehicle, it analyzes the emotions of passengers from their facial expressions and voices, and sends the results to a server. This emotion data, along with the user's input information, is used in the next step.

[1070] Styling and driving experience proposals

[1071] The server retrieves the user's clothing information registered in the database, along with the latest trend information, and executes an algorithm that generates the optimal styling and ride experience, taking into account the user's (passenger's) input information and emotional data. This algorithm makes suggestions based on the user's mood, weather, date, and perceived emotions.

[1072] Display of proposals and feedback

[1073] The generated styling information and ride experience suggestions are sent from the server to the terminal, which then displays the suggestions to the user. The styling includes detailed information on which items to combine and how, while the ride experience includes suggestions such as music and videos.

[1074] Users can provide feedback on the suggested styling and driving experience. This feedback information is sent from the device to the server, which stores it in a database. The accumulated feedback is used to update the algorithm to improve future suggestions.

[1075] Specific example

[1076] Clothing registration and passenger information entry

[1077] The user takes a photo of a "red knit sweater" using their smartphone. The passenger also registers their preferences as basic information, indicating they like "relaxing music" and "action movies." These photos and information are then sent from the device to the server.

[1078] Information input and emotion recognition

[1079] The user uses the app's input form to enter information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny." Simultaneously, the vehicle's cameras and microphones analyze the "passenger's happiness" using an emotion engine and send the data to the server.

[1080] Creating Styling and Driving Experiences

[1081] The server generates styling based on registered items such as a "red knit sweater" and "blue denim pants," along with the latest trend information and user mood and emotional data. It also suggests the optimal ride experience based on passenger preferences for "relaxing music" and "action movies."

[1082] Display of proposed content and feedback

[1083] The generated styling and suggestions such as playing "relaxing music" or showing "action movies" are displayed on the terminal's screen. Users can input feedback such as "I like this outfit" or passengers can input feedback such as "The music selection was good," and the terminal sends this information to the server.

[1084] Example of a prompt

[1085] We will implement an emotion recognition algorithm using libraries such as Python, TensorFlow, and Keras.

[1086] Example code for capturing images from a camera and recognizing emotions:

[1087] with open(image_path, 'rb') as image:

[1088] detected_faces = face_client.face.detect_with_stream(image, return_face_attributes=[FaceAttributeType.emotion])

[1089] A flow for collecting feedback and improving the next proposal:

[1090] feedback = collect_feedback()

[1091] We save feedback in the system to improve future suggestions.

[1092] In this way, the present invention enables the proposal of styling based on the user's emotions and the provision of a comfortable riding experience that responds to the emotions and interests of passengers.

[1093] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1094] Step 1:

[1095] Users use their smartphones or tablets to take photos of their clothing or upload them from their gallery. The input is a photo of the clothing, which the device receives. The device then sends this photo to the server. The output is the completion of the photo data transmission to the server.

[1096] Step 2:

[1097] The server analyzes the received photo data. Using image recognition algorithms (e.g., TensorFlow, Keras), it identifies the types and characteristics of clothing in the photos. Data processing involves extracting features from the image data and converting it into a format suitable for database registration. The output is the registration of the identified clothing information into the database.

[1098] Step 3:

[1099] The user inputs information such as their mood for the day, the date, and the weather through the app's interface. This input can be text data or selected options, which the device receives. The device then sends this information to the server. The output indicates that the information has been successfully sent to the server.

[1100] Step 4:

[1101] The server acquires video and audio data in real time from cameras and microphones installed inside the vehicle. An emotion engine (e.g., Microsoft Azure Face API, Google Cloud Speech-to-Text) is used to analyze passengers' emotions from their facial expressions and voices. Data processing involves extracting and classifying emotion data. The output is the analyzed emotion data.

[1102] Step 5:

[1103] The server executes an algorithm that generates styling suggestions based on clothing information registered in the database, user input information, sentiment data, and the latest trend information. For data processing, it performs optimization using a machine learning model. The output is the generated styling information.

[1104] Step 6:

[1105] The server executes an algorithm to suggest the optimal ride experience based on the passenger's basic information and interests. It also takes emotional data into consideration to suggest entertainment such as music and movies. For data calculation, it integrates user profiles and real-time data to generate personalized suggestions. The output is a suggested ride experience.

[1106] Step 7:

[1107] The terminal displays generated styling information and ride experience suggestions to the user. The user reviews these suggestions. The input is suggestion data from the server, and the output is displayed on the user's device.

[1108] Step 8:

[1109] The user inputs feedback on the suggested styling and driving experience. This input can be text or a selection, which the device receives. The device then sends this feedback to the server. The output indicates that the feedback has been successfully sent to the server.

[1110] Step 9:

[1111] The server stores the received feedback in a database. The algorithm is updated using the feedback information to improve future suggestions. Data processing involves analyzing the feedback data and introducing a feedback loop into the algorithm. The output is the updated algorithm.

[1112] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1113] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1114] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1115] [Third Embodiment]

[1116] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1117] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1119] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1120] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1123] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1124] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1125] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1126] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1127] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1128] This invention relates to a system that allows users to register their clothing and suggests the optimal fashion style based on information such as mood, date, and weather. The system of this invention consists of a user terminal and a server. One embodiment of the present invention will be described below.

[1129] System Overview

[1130] 1. Register your clothing.

[1131] Users first register their clothing by taking photos of their clothes using the app. Alternatively, they can upload existing photos from their gallery. The device sends these photos to the server. The server analyzes the received photos using an image recognition algorithm to identify the type and characteristics of the clothing (e.g., shirt, pants, color, pattern, etc.). The analyzed clothing information is then registered in a database.

[1132] 2. Entering Information

[1133] Users input information such as their mood for the day, the date, and the weather through the app's interface. This information is then sent from the device to the server.

[1134] 3. Styling generation

[1135] The server retrieves the user's clothing information registered in the database, along with the latest trend information, and executes an algorithm that generates the optimal style considering the user's input. This algorithm suggests styling based on factors such as the user's mood, weather, and date.

[1136] 4. Styling display

[1137] The generated styling information is sent from the server to the terminal, which then displays the style to the user. The style includes detailed information about which items to combine and how. An image of the suggested style is also displayed.

[1138] 5. Receiving feedback and updating the algorithm

[1139] Users provide feedback on the suggested style, indicating whether they are satisfied or dissatisfied, through their device. This feedback information is sent to a server, which stores it in a database. The accumulated feedback is used to update the algorithm to improve future style suggestions.

[1140] Specific example

[1141] Clothing registration

[1142] Use your device's camera function to take a picture of a "red knit sweater." Alternatively, select a picture of "blue denim pants" that you have already taken from your gallery and upload it. This picture will be sent from your device to the server. The server will analyze the received picture using an image recognition algorithm and register it in its database as a "red knit sweater" or "blue denim pants."

[1143] Entering information

[1144] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into the app's input form. This information is then sent from the device to the server.

[1145] Styling generation and display

[1146] The server combines the user's registered "red knit sweater" and "blue denim pants" with the latest trend information to generate outfit ideas using the "red knit sweater" and "blue denim pants." This style suggestion is then sent to the device, which displays it to the user.

[1147] Receiving feedback

[1148] The user enters feedback such as, "I like this outfit." The device sends this feedback to the server, which stores it in a database. This feedback information is used to improve future styling algorithms.

[1149] As described above, the present invention provides a system that comprehensively utilizes the user's existing clothing and trend information to propose a fashion style that is individually optimized for each user.

[1150] The following describes the processing flow.

[1151] Step 1:

[1152] Users take photos of their own clothing or upload them from their gallery.

[1153] Step 2:

[1154] The device sends photos of clothing that have been taken or uploaded to the server.

[1155] Step 3:

[1156] The server uses an image recognition algorithm to analyze the received photos and identify the type of clothing (shirt, pants, jacket, etc.) and its characteristics (color, pattern, brand, etc.).

[1157] Step 4:

[1158] Based on the analysis results, the server registers clothing information in the database, associating it with the user ID.

[1159] Step 5:

[1160] The user enters their mood, date, and weather information into the app's input form.

[1161] Step 6:

[1162] The terminal sends the user's input information to the server.

[1163] Step 7:

[1164] The server retrieves information about the user's clothing collection from the database, and similarly retrieves the latest trend information.

[1165] Step 8:

[1166] The server runs a styling algorithm based on the user's mood, date, and weather information to generate the optimal style.

[1167] Step 9:

[1168] The server sends the generated styling information to the terminal.

[1169] Step 10:

[1170] The device displays the suggested style to the user along with detailed information (which items to combine and how).

[1171] Step 11:

[1172] Users provide feedback on styling suggestions, indicating whether they are satisfied or dissatisfied.

[1173] Step 12:

[1174] The device sends user feedback information to the server.

[1175] Step 13:

[1176] The server stores feedback information in a database and updates the styling algorithm to improve future styling suggestions.

[1177] (Example 1)

[1178] Next, we will describe Example 1. 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."

[1179] Traditionally, when users styled outfits using their existing wardrobe, they faced the challenge of having to decide for themselves which items to combine and how to combine them. Furthermore, they often lacked optimal suggestions that took into account fluctuating factors such as mood and weather, resulting in inconsistent styling quality. This, in particular, led to time-consuming and laborious styling processes, lowering user satisfaction, especially for busy individuals.

[1180] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1181] In this invention, the server includes means for a user to take or upload photos of their clothing; means for the terminal to transmit the photos to the server; means for the server to analyze the photos and identify clothing information; means for registering the analyzed clothing information in a database; means for the user to input their current mood, date, and weather information; means for the terminal to transmit the information to the server; means for the server to generate styling based on the database and trend information; means for the server to transmit the generated styling information to the terminal; means for the terminal to display the generated styling information; means for the user to input feedback on the styling; means for the terminal to transmit the feedback to the server; and means for the server to accumulate the feedback and update the styling algorithm. This makes it possible for the user to automatically receive suggestions for the optimal fashion style based on their clothing and changing factors.

[1182] A "user" refers to an individual who takes or uploads photos of clothing and enters information such as mood, date, and weather.

[1183] A "terminal" refers to an electronic device operated by a user that transmits photos taken and information entered to a server and displays styling information.

[1184] A "server" refers to a central computer system that analyzes data received from terminals, registers it in a database, generates styling, and sends it back to the terminals.

[1185] "Photographs" refer to still image data that users take or upload to represent their clothing.

[1186] "Analysis" refers to the process by which the server uses an image recognition algorithm to identify clothing information from the received photograph.

[1187] "Image recognition algorithms" refer to computer vision technologies used to recognize the type and characteristics of clothing from photographs.

[1188] "Clothing information" refers to characteristic data such as the type, color, and pattern of clothing identified from the analyzed photographs.

[1189] A "database" refers to a data storage system used to accumulate and manage analyzed clothing information and user input information.

[1190] "Mood" refers to the subjective feelings and sensations that users input to express their mental state on a given day.

[1191] "Date" refers to the year, month, and day of a specific date entered by the user.

[1192] "Weather information" refers to the weather conditions for the day (e.g., sunny, rainy, cloudy) entered by the user.

[1193] "Trend information" refers to data on the latest fashion trends and fashions.

[1194] "Styling" refers to the optimal clothing combination suggestions that the server generates based on database and trend information.

[1195] "Feedback" refers to information that users input, such as their impressions and evaluations of the suggested styling.

[1196] A "styling algorithm" refers to a program implemented on a server to generate the optimal styling based on the user's mood, date, and weather information.

[1197] This invention relates to a system that registers a user's clothing collection and suggests the optimal fashion style based on information such as mood, date, and weather. This system mainly consists of a user terminal and a server.

[1198] First, users register their clothing using an application installed on their device (e.g., a smartphone or tablet). Clothing can be registered by taking photos of the garments using the device's camera function, or by uploading existing photos from the device's gallery. These photos are then sent from the device to the server.

[1199] The server analyzes the received photos using image recognition algorithms (e.g., TensorFlow or OpenCV). These algorithms allow the server to identify features such as the type, color, and pattern of clothing within the photos. The identified clothing information is then registered in a database.

[1200] Next, the user enters information such as their mood for the day, the date, and the weather through the app's interface. This information is sent from the device to the server. The server retrieves the clothing information registered by the user and the latest trend information, and runs a styling algorithm (e.g., Scikit-learn or Keras) based on the user's input. This algorithm takes into account the entered elements such as mood, weather, and date to generate the most suitable styling for the user.

[1201] The generated styling information is sent from the server to the terminal, which then presents the style to the user. The presented style includes detailed information on which items to combine and how to combine them. Style suggestion images are also displayed as needed.

[1202] The user provides feedback on the suggested style. This feedback is sent from the terminal to the server. The server stores this feedback information in a database and uses it to update the algorithm to improve future style suggestions.

[1203] Specific example

[1204] Clothing registration:

[1205] The user takes a picture of a "red knit sweater" using their smartphone camera, or selects and uploads an existing picture of "blue denim pants" from their gallery. This picture is sent from the device to the server. The server analyzes the received picture using an image recognition algorithm and registers it in the database as a "red knit sweater" or "blue denim pants."

[1206] Entering information:

[1207] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into the app's form. This information is then sent from the device to the server.

[1208] Styling generation and display:

[1209] The server retrieves the user's registered information on a "red knit sweater" and "blue denim pants," and runs a styling algorithm along with the latest trend information. The algorithm considers the input information, such as "cheerful mood," "October 16, 2023," and "sunny weather," to generate an outfit combining the "red knit sweater" and "blue denim pants." This outfit information is sent to the terminal, which then presents it to the user.

[1210] Receiving feedback:

[1211] The user enters feedback such as "I like this outfit," and the device sends that feedback to the server. The server stores this feedback information and uses it to improve future styling suggestions.

[1212] Thus, the present invention is a system that comprehensively utilizes the user's existing clothing and the latest fashion trend information to propose an individually optimized fashion style.

[1213] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1214] Step 1:

[1215] The user takes a photo of their clothes using the app on their device, or uploads an existing photo. The device then sends the taken or uploaded photo to the server.

[1216] Input: A photo of clothing taken or selected by the user.

[1217] Output: Photo data sent from the terminal to the server

[1218] Specific actions:

[1219] The user takes a picture of a "red knit sweater" using their smartphone's camera function and sends that picture to the server via the app.

[1220] Step 2:

[1221] The server analyzes the received photos using an image recognition algorithm (e.g., TensorFlow, OpenCV). The analysis results identify the type and characteristics of the clothing.

[1222] Input: Photo data of clothing received by the server

[1223] Output: Information on the type and characteristics of the analyzed clothing (e.g., shirt, pants, color, pattern, etc.)

[1224] Specific actions:

[1225] The server analyzes the received photo of a "red knit sweater" and uses an image recognition algorithm to identify the characteristic of a "red knit sweater."

[1226] Step 3:

[1227] The server registers the analyzed clothing information in a database.

[1228] Input: Information on the analyzed clothing

[1229] Output: Clothing information registered in the database

[1230] Specific actions:

[1231] The server saves information about the "red knit sweater" to the database and adds it to the user's clothing collection.

[1232] Step 4:

[1233] The user enters information such as their mood for the day, the date, and the weather through the app's interface. The device then sends the entered information to the server.

[1234] Input: Information entered by the user, such as mood, date, and weather.

[1235] Output: Mood, date, and weather information sent from the terminal to the server.

[1236] Specific actions:

[1237] The user enters "cheerful mood," "October 16, 2023," and "sunny" into the app's form and sends this information to the server.

[1238] Step 5:

[1239] The server retrieves the user's clothing information and the latest trend information registered in the database, and runs a styling algorithm (e.g., Scikit-learn, Keras) to generate the optimal style.

[1240] Input: Clothing information from the database, trend information, and user input information (mood, date, weather)

[1241] Output: Generated styling information

[1242] Specific actions:

[1243] Based on the "red knit sweater" and the latest trend information, the server generates the optimal outfit by taking into account the user's input information: "cheerful mood," "October 16, 2023," and "sunny."

[1244] Step 6:

[1245] The server sends the generated styling information to the terminal. The terminal displays the received styling information to the user.

[1246] Input: Styling information generated by the server

[1247] Output: Styling information displayed on the terminal

[1248] Specific actions:

[1249] The server generates outfit information using a "red knit sweater" and "blue denim pants," which is then sent to the terminal, and the terminal presents this information to the user.

[1250] Step 7:

[1251] The user provides feedback on the suggested style through the app. The device then sends this feedback to the server.

[1252] Input: Feedback on the style entered by the user

[1253] Output: Feedback information sent from the terminal to the server

[1254] Specific actions:

[1255] The user enters feedback such as "I like this outfit" and sends it from their device to the server.

[1256] Step 8:

[1257] The server stores feedback information in a database and uses it to update algorithms in order to improve future styling suggestions.

[1258] Input: Feedback information sent from the terminal to the server

[1259] Output: Feedback information stored in the database, updated styling algorithms

[1260] Specific actions:

[1261] The server stores the feedback information it receives in a database and uses it to improve the styling algorithm.

[1262] (Application Example 1)

[1263] Next, we will explain Application Example 1. In the following explanation, 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."

[1264] Existing fashion style suggestion systems fail to efficiently utilize users' existing clothing and trend information, making it difficult to suggest the most suitable fashion styles for individual users. Furthermore, algorithm improvements based on user feedback are insufficient. As a result, a major challenge is that users are not receiving the style suggestions they desire.

[1265] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1266] In this invention, the server includes means for the user to take or upload photos of their clothing using their smartphone with image recognition technology, means for analyzing the photos sent to the cloud server to identify clothing information, means for registering the analyzed clothing information in a database, means for the user to input their current mood, date, and weather information into their smartphone, means for the cloud server to generate an optimal styling based on the database and trend information, means for displaying the generated styling information on the smartphone, means for the user to input feedback on the styling into their smartphone, and means for the cloud server to accumulate the feedback and update the styling algorithm. This makes it possible to propose individually optimized fashion styles that reflect the user's clothing and trend information.

[1267] A "user" is an individual who uses the system to register their clothing collection and receive fashion style suggestions.

[1268] "Clothing on hand" refers to the various clothes and accessories that the user owns.

[1269] A "smartphone" is a portable information terminal that allows internet access and the use of various applications.

[1270] A "cloud server" is a remote server located on the internet that enables data processing and storage for a large number of users.

[1271] "Photos" refer to image data taken or uploaded using a user's smartphone.

[1272] "Image recognition technology" is a technology that analyzes image data to identify specific objects or features.

[1273] "Clothing information" refers to data that describes the type and characteristics of clothing identified using image recognition technology.

[1274] A "database" is an information system used to store and manage analyzed clothing information and other related data.

[1275] "Mood" refers to information that represents the user's psychological state and emotions on a given day.

[1276] "Date" refers to calendar information that indicates a specific day.

[1277] "Weather information" refers to data that shows the weather conditions for a given day.

[1278] "Trend information" refers to information about current trends and fashion developments.

[1279] "Styling" refers to fashion combinations and outfits generated based on the user's existing clothing and current trends.

[1280] "Feedback" refers to information that shows the evaluation and impressions that users give regarding the suggested styling.

[1281] An "algorithm" is a set of rules that define the steps or calculation methods for solving a specific problem.

[1282] This invention relates to a system that allows users to photograph or upload their clothing using their smartphones, and then suggests fashion styles based on those photos. Specific embodiments of this invention will be described below.

[1283] System Overview

[1284] 1. Register your clothing.

[1285] Users first register their clothing. To do this, they either take photos of their clothes using their smartphone's camera or upload photos they have already taken. The smartphone sends these photos to a cloud server. The cloud server uses image recognition technology to analyze the received photos and identify the type and characteristics of the clothing (e.g., shirt, pants, color, pattern, etc.). The analyzed clothing information is then registered in a database.

[1286] 2. Entering Information

[1287] Users input information such as their mood, the date, and the weather through their smartphone interface. This information is then sent from the smartphone to a cloud server.

[1288] 3. Styling generation

[1289] The cloud server retrieves the user's clothing information and the latest trend information registered in the database, and executes an algorithm to generate the optimal style, taking into account the user's input information. This algorithm suggests styling based on factors such as the user's mood, weather, and date.

[1290] 4. Styling display

[1291] The generated styling information is sent from the cloud server to the smartphone, which then displays the style to the user. The style includes detailed information on which items to combine and how. An image of the suggested style is also displayed.

[1292] 5. Receiving feedback and updating the algorithm

[1293] Users provide feedback on the suggested styles, indicating satisfaction or dissatisfaction, via their smartphones. This feedback information is sent to a cloud server, which stores it in a database. The accumulated feedback is used to update the algorithm to improve future style suggestions.

[1294] Hardware and software to be used

[1295] Hardware: Smartphones (iOS or Android), cloud servers

[1296] software:

[1297] Camera function

[1298] Cloud servers (for example, Amazon Web Services or Google Cloud)

[1299] Image recognition algorithms (for example, models using TensorFlow or PyTorch)

[1300] Database (for example, MySQL or MongoDB)

[1301] Specific Examples

[1302] Clothing registration

[1303] The user takes a picture of a "red knit sweater" using their smartphone camera, or selects and uploads a picture of "blue denim pants" that they have already taken from their gallery. These photos are sent from the smartphone to a cloud server. The cloud server analyzes the received photos using image recognition technology and registers them in a database as "red knit sweater" or "blue denim pants."

[1304] Entering information

[1305] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into an input form on their smartphone. This information is then sent from the smartphone to a cloud server.

[1306] Styling generation and display

[1307] The cloud server combines the user's registered items, such as a "red knit sweater" and "blue denim pants," with the latest trend information to generate outfit ideas using the "red knit sweater" and "blue denim pants." This style suggestion is then sent to the user's smartphone, which displays it.

[1308] Receiving feedback

[1309] Users provide feedback such as, "I like this outfit." Their smartphones send this feedback to a cloud server, which stores it in a database. This feedback information is used to improve future styling algorithms.

[1310] Examples of prompt statements

[1311] "User's clothing: Red knit sweater, blue denim pants"

[1312] "Mood: Cheerful"

[1313] Date: October 20, 2023

[1314] "Weather: Sunny"

[1315] As described above, the present invention provides a system that comprehensively utilizes the user's existing clothing and trend information to propose a fashion style that is individually optimized for each user.

[1316] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1317] Step 1:

[1318] The user takes a photo of their clothing or uploads one from their gallery using their smartphone. The smartphone then sends the photo of the clothing taken or selected by the user as input to a cloud server. This transmitted image data is the input. The specific action the smartphone takes is to upload the image data to the cloud server.

[1319] Step 2:

[1320] The cloud server analyzes the received image data using image recognition technology. This image recognition technology uses TensorFlow or PyTorch. The input to the cloud server is image data of clothing sent by the user, and the output is the recognized type and characteristic information of the clothing. Specifically, the cloud server analyzes the image data and identifies clothing information such as "shirt" and "pants."

[1321] Step 3:

[1322] The cloud server registers the analyzed clothing information into a database. The input is the analyzed clothing information, and the output is the updated database. The specific action performed by the cloud server is to save the newly analyzed clothing information to the existing database.

[1323] Step 4:

[1324] The user uses their smartphone to input their mood, the date, and weather information for the day. Based on this input, the smartphone sends it to a cloud server. The input is the user's mood, the date, and the weather information, and the output is the information sent to the cloud server. Specifically, the user enters information into an input form, and the smartphone sends that information to the cloud server.

[1325] Step 5:

[1326] The cloud server retrieves the user's clothing information registered in the database, along with the latest trend information, and generates the optimal style considering the user's input information. In this step, the cloud server generates the style using an algorithm. The input is the user's mood, date, weather information, and clothing information, and the output is the generated styling information. Specifically, it retrieves the necessary information from the database and generates the style based on a calculation algorithm.

[1327] Step 6:

[1328] The cloud server sends the generated styling information to the smartphone. The smartphone receives this information and displays it to the user. The input is the styling information sent from the cloud server, and the output is the styling information displayed on the smartphone. The specific action performed by the smartphone is to visually display the received styling information to the user.

[1329] Step 7:

[1330] The user uses their smartphone to input feedback on the suggested style. This input feedback is sent from the smartphone to a cloud server. The input is the user's feedback information, and the output is the feedback information sent to the cloud server. Specifically, the user enters information into a feedback form, and the smartphone sends that information to the cloud server.

[1331] Step 8:

[1332] The cloud server stores the received feedback information in a database. It also updates the styling algorithm based on the accumulated feedback. The input is user feedback information, and the output is the updated styling algorithm. The specific action performed by the cloud server is to periodically update the algorithm to improve style suggestions for future use.

[1333] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1334] This invention relates to a system that allows users to register their clothing and suggests the optimal fashion style based on their mood, the date, the weather, and emotions recognized by an emotion engine. The system of this invention consists of a user terminal, a server, and an emotion engine. One embodiment of the present invention is described below.

[1335] System Overview

[1336] 1. Register your clothing.

[1337] Users first register their clothing by taking photos of their clothes using the app. Alternatively, they can upload existing photos from their gallery. The device sends these photos to the server. The server analyzes the received photos using an image recognition algorithm to identify the type and characteristics of the clothing (e.g., shirt, pants, color, pattern, etc.). The analyzed clothing information is then registered in a database.

[1338] 2. Entering Information

[1339] Users input information such as their mood for the day, the date, and the weather through the app's interface. This information is then sent from the device to the server.

[1340] 3. Recognition of emotions

[1341] The system further incorporates an emotion engine to recognize the user's emotions. This emotion engine analyzes the user's facial expressions and voice, and sends the results to the server. This emotion data, along with the user's input information, is used in the next step.

[1342] 4. Styling generation

[1343] The server retrieves the user's clothing information registered in the database, along with the latest trend information, and executes an algorithm that generates the optimal style considering the user's input information and emotional data. This algorithm suggests styling based on the user's mood, weather, date, and perceived emotions.

[1344] 5. Styling display

[1345] The generated styling information is sent from the server to the terminal, which then displays the style to the user. The style includes detailed information about which items to combine and how. An image of the suggested style is also displayed.

[1346] 6. Receiving feedback and updating the algorithm

[1347] Users provide feedback on the suggested style, indicating whether they are satisfied or dissatisfied, through their device. This feedback information is sent to a server, which stores it in a database. The accumulated feedback is used to update the algorithm to improve future style suggestions.

[1348] Specific example

[1349] Clothing registration

[1350] Use your device's camera function to take a picture of a "red knit sweater." Alternatively, select a picture of "blue denim pants" that you have already taken from your gallery and upload it. This picture will be sent from your device to the server. The server will analyze the received picture using an image recognition algorithm and register it in its database as a "red knit sweater" or "blue denim pants."

[1351] Entering information

[1352] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into the app's input form. This information is then sent from the device to the server.

[1353] Recognition of emotions

[1354] When a user uses the app, the emotion engine analyzes the user's facial expressions and voice to recognize emotions such as "joy" or "anxiety." This emotion data is sent to the server along with other input information.

[1355] Styling generation and display

[1356] The server generates outfit ideas using the "red knit sweater" and "blue denim pants" registered by the user, along with the latest trend information and the user's mood and emotional data. It then sends these style suggestions to the user's device, which displays them along with detailed information.

[1357] Receiving feedback

[1358] The user enters feedback such as, "I like this outfit." The device sends this feedback to the server, which stores it in a database. This feedback information is used to improve future styling algorithms.

[1359] As described above, the present invention provides a system that comprehensively utilizes the user's existing clothing and trend information, and proposes an individually optimized fashion style while also considering the user's emotions.

[1360] The following describes the processing flow.

[1361] Step 1:

[1362] Users use the app to take photos of their existing clothing, or upload existing photos from their gallery.

[1363] Step 2:

[1364] The device sends photos of clothing that have been taken or uploaded to the server.

[1365] Step 3:

[1366] The server uses an image recognition algorithm to analyze the received photos and identify the type and characteristics of the clothing (e.g., shirt, pants, color, pattern, etc.).

[1367] Step 4:

[1368] Based on the analysis results, the server registers clothing information in the database, associating it with the user ID.

[1369] Step 5:

[1370] The user enters their mood, date, and weather information into the app's input form.

[1371] Step 6:

[1372] The terminal sends the user's input information to the server.

[1373] Step 7:

[1374] The device uses an emotion engine to analyze the user's facial expressions and voice, and generate emotion data.

[1375] Step 8:

[1376] The device sends emotional data to the server.

[1377] Step 9:

[1378] The server retrieves information about the user's clothing collection from the database, and similarly retrieves the latest trend information.

[1379] Step 10:

[1380] The server runs a styling algorithm based on the user's mood, date, weather information, and sentiment data to generate the optimal style.

[1381] Step 11:

[1382] The server sends the generated styling information to the terminal.

[1383] Step 12:

[1384] The device displays the suggested style to the user along with detailed information (which items to combine and how).

[1385] Step 13:

[1386] Users provide feedback on styling suggestions, indicating whether they are satisfied or dissatisfied.

[1387] Step 14:

[1388] The device sends user feedback information to the server.

[1389] Step 15:

[1390] The server stores feedback information in a database and updates the styling algorithm to improve future styling suggestions.

[1391] (Example 2)

[1392] Next, we will describe Example 2. 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."

[1393] Conventional fashion suggestion systems only offered styling suggestions based on the user's existing clothing information and basic input, failing to consider subtle changes in the user's emotions. As a result, the styling suggestions were often unsatisfactory in terms of the user's mood and emotions, making it difficult to provide individually optimized suggestions.

[1394] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for incorporating an emotion engine for analyzing emotions from the user's facial expressions and voice, means for transmitting the emotion data analyzed by the emotion engine to the server, and means for the server to generate styling considering the emotion data. This makes it possible to propose individually optimized fashion styles based on the user's mood and emotions.

[1395] "User" refers to anyone who uses this system.

[1396] "Device" refers to an electronic device used by a user, and includes smartphones, tablets, and personal computers.

[1397] A "server" refers to the central processing unit of this system, which is responsible for receiving, processing, storing, and transmitting data.

[1398] "Photos" refer to image data that users take or upload to register clothing items.

[1399] "Analysis" refers to the process by which a server extracts necessary information from the photos and data it receives.

[1400] "Clothing information" refers to information about clothing registered by the user, such as the type of clothing, color, pattern, and features.

[1401] A "database" refers to a structured storage system that allows a server to systematically manage, store, and retrieve information.

[1402] "Mood" refers to the mental state that the user is experiencing at that particular moment.

[1403] "Date" refers to information that specifically indicates the year, month, and day.

[1404] "Weather information" refers to information that indicates the weather conditions on a given day.

[1405] "Trend information" refers to information about the latest fashion and styles.

[1406] "Styling" refers to fashion combinations and outfits suggested based on the user's existing clothing.

[1407] "Feedback" refers to the evaluations and opinions that users give regarding the suggested styling.

[1408] An "emotion engine" refers to software or algorithms that analyze a user's facial expressions and voice to recognize their emotions.

[1409] "Emotional data" refers to data about a user's emotions that has been analyzed by the emotion engine.

[1410] An "image recognition algorithm" refers to an algorithm used to analyze photographs and image data to detect specific objects or features.

[1411] This invention relates to a system in which a user registers their clothing and the system suggests the optimal fashion style based on their mood, the date, the weather, and emotions recognized by an emotion engine. This system consists of a user terminal, a server, and an emotion engine.

[1412] System Configuration

[1413] Clothing registration

[1414] User:

[1415] Users first use the app to register their existing clothing. Within the app, they select the "Register Clothing" menu and register their clothing using the following method:

[1416] Use the device's camera function to take a picture of the clothing.

[1417] Upload existing photos from your gallery.

[1418] Terminal:

[1419] The device temporarily stores photos of the clothing and sends them to the server using an HTTP POST request.

[1420] server:

[1421] The server analyzes the received photos using image recognition algorithms such as the Google Cloud Vision API to identify the type and characteristics of the clothing. The results of this analysis are then registered in a database.

[1422] Entering information

[1423] User:

[1424] Users use an input form within the app to enter their mood for the day, the date, and weather information.

[1425] Terminal:

[1426] The terminal temporarily stores the entered information and sends it to the server using an HTTP POST request.

[1427] server:

[1428] The server registers the received information in the database.

[1429] Recognition of emotions

[1430] User:

[1431] While the user is using the app, the emotion engine analyzes the user's facial expressions and voice. Emotional data is collected when the user shows their facial expressions to the device's camera or speaks.

[1432] Terminal:

[1433] The device acquires facial expressions and voice data, stores it temporarily, and then sends it to the server using an HTTP POST request.

[1434] server:

[1435] The server uses an emotion engine to analyze facial expressions and voice data to identify the user's emotions. The analyzed emotion data is registered in a database. For example, Microsoft's Azure Cognitive Services can be used.

[1436] Styling generation

[1437] server:

[1438] The server retrieves information about the user's clothing collection, sentiment data, and the latest trend information registered in the database. The latest trend information can be obtained from fashion websites on the internet or APIs (for example, the Fashion Trends API).

[1439] server:

[1440] Machine learning algorithms (e.g., TensorFlow or PyTorch) are used to generate optimal styling based on the user's mood, date, weather information, and sentiment data. This generation process also takes past feedback data into consideration.

[1441] Styling display

[1442] server:

[1443] The server sends the generated styling information to the terminal.

[1444] Terminal:

[1445] The device displays the received styling information on the user interface. The displayed content includes images of clothing combinations and styles.

[1446] Receiving feedback

[1447] User:

[1448] Users can provide feedback on the suggested style, such as "satisfied" or "dissatisfied."

[1449] Terminal:

[1450] The device temporarily stores the feedback information and sends it to the server using an HTTP POST request.

[1451] server:

[1452] The server registers the feedback information in a database. This feedback information is used to update the algorithm and make future styling suggestions.

[1453] Specific example

[1454] Clothing registration

[1455] The user takes a picture of a "red knit sweater" using their device's camera function, or uploads a picture of "blue denim pants" from their gallery. This picture is sent from the device to the server. The server uses the Google Cloud Vision API to analyze the picture and register it in the database as a "red knit sweater" or "blue denim pants."

[1456] Entering information

[1457] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into the app's input form. This information is then sent from the device to the server.

[1458] Recognition of emotions

[1459] While the user is using the app, the emotion engine analyzes the user's facial expressions and voice to recognize emotions such as "joy" or "anxiety." This emotion data is sent to the server along with other information.

[1460] Styling generation and display

[1461] The server generates outfit ideas using the "red knit sweater" and "blue denim pants" based on the user's registered items, the latest trend information, and the user's mood and emotional data. This style suggestion is sent to the device, which displays it along with detailed information.

[1462] Receiving feedback

[1463] The user enters feedback such as "I like this outfit." The device sends this feedback to the server, which stores it in a database. This allows for future improvements to the styling algorithm.

[1464] Example prompts for a generative AI model

[1465] An example of a prompt message a user might enter is: "If the user's mood today is 'cheerful,' the date is 'October 16, 2023,' the weather is 'sunny,' and their emotion is 'joyful,' please suggest a fashion outfit using the 'red knit sweater' and 'blue denim pants' they already own."

[1466] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1467] Step 1: Register your clothing

[1468] User:

[1469] The user launches the app and selects the "Register Clothing" menu within the app. Here, the user registers clothing using the following method:

[1470] Use the device's camera function to take a picture of the clothing.

[1471] Upload existing photos from your gallery.

[1472] input:

[1473] Photographs of clothing taken or uploaded.

[1474] Terminal:

[1475] The device temporarily stores the captured or uploaded photo data. The photo data is then sent to the server.

[1476] output:

[1477] Temporarily saved photo data.

[1478] Step 2: Send and analyze photos

[1479] Terminal:

[1480] The device sends the temporarily stored photo data to the server using an HTTP POST request.

[1481] input:

[1482] Temporarily saved photo data.

[1483] server:

[1484] The server passes the received photo data to image recognition algorithms such as the Google Cloud Vision API, which analyze it to identify the type and characteristics of the clothing. This analysis provides information such as the type of clothing (e.g., shirt, pants), color, and pattern.

[1485] Data processing:

[1486] Photo analysis using image recognition algorithms.

[1487] output:

[1488] Analyzed clothing information.

[1489] Step 3: Register clothing information

[1490] server:

[1491] The server registers the clothing information analyzed in the previous step into the database.

[1492] input:

[1493] Analyzed clothing information.

[1494] output:

[1495] Clothing information registered in the database.

[1496] Step 4: Entering Information

[1497] User:

[1498] Users use an input form within the app to enter their mood for the day, the date, and weather information. Mood can include options such as "cheerful" or "calm."

[1499] input:

[1500] User-entered mood, date, and weather information.

[1501] Terminal:

[1502] The terminal temporarily stores the entered information and then sends it to the server using an HTTP POST request.

[1503] output:

[1504] Temporarily saved mood, date, and weather information.

[1505] Step 5: Submit and register your information.

[1506] Terminal:

[1507] The device sends temporarily stored mood, date, and weather information to the server using an HTTP POST request.

[1508] input:

[1509] Temporarily saved mood, date, and weather information.

[1510] server:

[1511] The server registers the received information in the database.

[1512] Data processing:

[1513] Receiving information and registering it in the database.

[1514] output:

[1515] Mood, date, and weather information registered in the database.

[1516] Step 6: Recognizing Emotions

[1517] User:

[1518] While the user is using the app, the emotion engine analyzes the user's facial expressions and voice. Facial and voice data is collected when the user shows their face to the device's camera or speaks.

[1519] input:

[1520] User's facial expressions and voice data.

[1521] Terminal:

[1522] The device acquires facial expression and voice data, stores it temporarily, and then sends it to the server using an HTTP POST request.

[1523] output:

[1524] Temporarily saved facial expression and audio data.

[1525] Step 7: Sending and analyzing emotional data

[1526] Terminal:

[1527] The terminal sends temporarily stored facial expression and audio data to the server using an HTTP POST request.

[1528] input:

[1529] Temporarily saved facial expression and audio data.

[1530] server:

[1531] The server uses an emotion engine to analyze this data and identify the user's emotions. For example, Microsoft's Azure Cognitive Services can be used.

[1532] Data processing:

[1533] Analysis of emotional data using an emotion engine.

[1534] output:

[1535] Analyzed emotion data.

[1536] Step 8: Registering emotion data

[1537] server:

[1538] The server registers the emotion data analyzed in the previous step into the database.

[1539] input:

[1540] Analyzed emotion data.

[1541] output:

[1542] Emotional data registered in the database.

[1543] Step 9: Styling Generation

[1544] server:

[1545] The server retrieves user clothing information, sentiment data, and the latest trend information registered in the database. The latest trend information can be obtained from fashion websites and APIs.

[1546] input:

[1547] Clothing information, sentiment data, and trend information obtained from a database.

[1548] server:

[1549] Machine learning algorithms (e.g., TensorFlow, PyTorch) are used to generate optimal styling based on user mood, date, weather information, and sentiment data. This generation process also takes past feedback data into consideration.

[1550] Data calculation:

[1551] Data analysis and styling generation using machine learning algorithms.

[1552] output:

[1553] The generated styling information.

[1554] Step 10: Display the styling

[1555] server:

[1556] The server sends the generated styling information to the terminal.

[1557] input:

[1558] The generated styling information.

[1559] Terminal:

[1560] The device displays the received styling information on the user interface. The displayed content includes images of clothing combinations and styles.

[1561] output:

[1562] Styling information displayed to the user.

[1563] Step 11: Receiving Feedback

[1564] User:

[1565] Users can provide feedback on the suggested style, such as "satisfied" or "dissatisfied."

[1566] input:

[1567] User feedback.

[1568] Terminal:

[1569] The device temporarily stores the feedback information and then sends it to the server using an HTTP POST request.

[1570] output:

[1571] Temporarily saved feedback information.

[1572] Step 12: Submit and register feedback data.

[1573] Terminal:

[1574] The terminal sends temporarily stored feedback data to the server using an HTTP POST request.

[1575] input:

[1576] Temporarily stored feedback data.

[1577] server:

[1578] The server registers the feedback data in a database. This feedback information is used to update the algorithm and make future styling suggestions.

[1579] Data processing:

[1580] Receiving feedback data and registering it in the database.

[1581] output:

[1582] Feedback data registered in the database.

[1583] (Application Example 2)

[1584] Next, we will explain application example 2. In the following explanation, 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."

[1585] Traditional fashion styling systems fail to consider user emotions, making it difficult to suggest the optimal styling based on the user's psychological state at any given time. Similarly, in the onboard experience, there was no technology to provide optimal service in real time based on passengers' emotions and interests, thereby improving comfort during the ride. Therefore, there is a need for an effective system that can increase user and passenger satisfaction.

[1586] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for a user to take or upload a photograph of their clothing; means for the terminal to transmit the photograph to the server; means for the server to analyze the photograph and identify clothing information; means for registering the analyzed clothing information in a database; means for the user to input their current mood, date, and weather information; means for the terminal to transmit the information to the server; means for the server to generate styling based on the database and trend information; means for displaying the generated styling information on the terminal; means for the user to input feedback on the styling; means for the terminal to transmit the feedback to the server; means for the server to accumulate the feedback and update the styling algorithm; means for passengers to register basic information and interests and for the optimal ride experience to be suggested based on their emotions and information; means for analyzing passenger emotions using cameras and microphones installed in the vehicle and transmitting the data to a computer in the vehicle; and means for collecting feedback on the user's ride experience and improving future ride experience suggestions. This will enable the suggestion of styling based on the user's emotions and the provision of a comfortable ride experience that suits the passengers' emotions and interests.

[1587] - "Users" are the primary target audience for this system, who register their clothing, receive styling suggestions, and get suggestions for ride experiences.

[1588] A "terminal" is an electronic device that receives user input and transmits information to a server, and includes smartphones and tablets.

[1589] A "server" is a central processing unit that analyzes information sent by users and generates styling information and suggestions for the driving experience.

[1590] "Photos" refer to image data used by users to register their clothing items.

[1591] "Clothing information" refers to data about the types and characteristics of clothing registered by the user.

[1592] A "database" is an information aggregation system used to store and manage analyzed clothing information and feedback.

[1593] "Mood" refers to the psychological state a user is experiencing at that moment.

[1594] "Date" refers to the specific year, month, and day on which the user enters or receives information.

[1595] "Weather information" refers to data about current or future weather conditions.

[1596] "Trend information" refers to the latest information on current fashions and styling.

[1597] "Styling" refers to the clothing combinations and outfits suggested to the user.

[1598] "Generation" refers to the process by which the server concretizes styling and ride experience suggestions based on user information.

[1599] A "styling algorithm" is a numerical processing method used to calculate the optimal styling based on the user's emotions and input information.

[1600] "Passenger" refers to a user of an autonomous vehicle.

[1601] A "camera" is a video acquisition device used to analyze passengers' emotions.

[1602] A "microphone" is a voice input device that captures passengers' voices and uses them for emotion analysis.

[1603] A "computer" is a computing device that processes data acquired within a vehicle and generates suggestions.

[1604] "Riding experience" refers to the services and entertainment that passengers receive inside an autonomous vehicle.

[1605] This invention is a system that allows users to register their clothing and suggests the optimal fashion style based on their mood, the date, the weather, and emotions recognized by an emotion engine. As an example of the application of this invention, it further provides a system that suggests the optimal ride experience based on the passenger's emotions and interests. Embodiments of this invention will be described in detail below.

[1606] System Overview

[1607] Registration of clothing and passenger information

[1608] Users (passengers) first register their own clothing by taking photos of their clothes using their smartphone or tablet. Alternatively, they can upload existing photos from their gallery. Furthermore, passengers register their basic information and interests (e.g., music, movies, news, etc.).

[1609] The terminal sends these photos and information to the server. The server analyzes the received photos using an image recognition algorithm to identify the type and characteristics of the clothing, and the analyzed clothing information is registered in a database. Meanwhile, information about the passengers' interests is also stored on the server.

[1610] Entering information

[1611] Users input information such as their mood for the day, the date, and the weather through the app's interface. They can also input their mood during the ride and their plans for the day. This information is sent from the device to the server.

[1612] Recognition of emotions

[1613] The system further incorporates an emotion engine to recognize the emotions of users (passengers). Using cameras and microphones installed in the vehicle, it analyzes the emotions of passengers from their facial expressions and voices, and sends the results to a server. This emotion data, along with the user's input information, is used in the next step.

[1614] Styling and driving experience proposals

[1615] The server retrieves the user's clothing information registered in the database, along with the latest trend information, and executes an algorithm that generates the optimal styling and ride experience, taking into account the user's (passenger's) input information and emotional data. This algorithm makes suggestions based on the user's mood, weather, date, and perceived emotions.

[1616] Display of proposals and feedback

[1617] The generated styling information and ride experience suggestions are sent from the server to the terminal, which then displays the suggestions to the user. The styling includes detailed information on which items to combine and how, while the ride experience includes suggestions such as music and videos.

[1618] Users can provide feedback on the suggested styling and driving experience. This feedback information is sent from the device to the server, which stores it in a database. The accumulated feedback is used to update the algorithm to improve future suggestions.

[1619] Specific example

[1620] Clothing registration and passenger information entry

[1621] The user takes a photo of a "red knit sweater" using their smartphone. The passenger also registers their preferences as basic information, indicating they like "relaxing music" and "action movies." These photos and information are then sent from the device to the server.

[1622] Information input and emotion recognition

[1623] The user uses the app's input form to enter information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny." Simultaneously, the vehicle's cameras and microphones analyze the "passenger's happiness" using an emotion engine and send the data to the server.

[1624] Creating Styling and Driving Experiences

[1625] The server generates styling based on registered items such as a "red knit sweater" and "blue denim pants," along with the latest trend information and user mood and emotional data. It also suggests the optimal ride experience based on passenger preferences for "relaxing music" and "action movies."

[1626] Display of proposed content and feedback

[1627] The generated styling and suggestions such as playing "relaxing music" or showing "action movies" are displayed on the terminal's screen. Users can input feedback such as "I like this outfit" or passengers can input feedback such as "The music selection was good," and the terminal sends this information to the server.

[1628] Example of a prompt

[1629] We will implement an emotion recognition algorithm using libraries such as Python, TensorFlow, and Keras.

[1630] Example code for capturing images from a camera and recognizing emotions:

[1631] with open(image_path, 'rb') as image:

[1632] detected_faces = face_client.face.detect_with_stream(image, return_face_attributes=[FaceAttributeType.emotion])

[1633] A flow for collecting feedback and improving the next proposal:

[1634] feedback = collect_feedback()

[1635] We save feedback in the system to improve future suggestions.

[1636] In this way, the present invention enables the proposal of styling based on the user's emotions and the provision of a comfortable riding experience that responds to the emotions and interests of passengers.

[1637] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1638] Step 1:

[1639] Users use their smartphones or tablets to take photos of their clothing or upload them from their gallery. The input is a photo of the clothing, which the device receives. The device then sends this photo to the server. The output is the completion of the photo data transmission to the server.

[1640] Step 2:

[1641] The server analyzes the received photo data. Using image recognition algorithms (e.g., TensorFlow, Keras), it identifies the types and characteristics of clothing in the photos. Data processing involves extracting features from the image data and converting it into a format suitable for database registration. The output is the registration of the identified clothing information into the database.

[1642] Step 3:

[1643] The user inputs information such as their mood for the day, the date, and the weather through the app's interface. This input can be text data or selected options, which the device receives. The device then sends this information to the server. The output indicates that the information has been successfully sent to the server.

[1644] Step 4:

[1645] The server acquires video and audio data in real time from cameras and microphones installed inside the vehicle. An emotion engine (e.g., Microsoft Azure Face API, Google Cloud Speech-to-Text) is used to analyze passengers' emotions from their facial expressions and voices. Data processing involves extracting and classifying emotion data. The output is the analyzed emotion data.

[1646] Step 5:

[1647] The server executes an algorithm that generates styling suggestions based on clothing information registered in the database, user input information, sentiment data, and the latest trend information. For data processing, it performs optimization using a machine learning model. The output is the generated styling information.

[1648] Step 6:

[1649] The server executes an algorithm to suggest the optimal ride experience based on the passenger's basic information and interests. It also takes emotional data into consideration to suggest entertainment such as music and movies. For data calculation, it integrates user profiles and real-time data to generate personalized suggestions. The output is a suggested ride experience.

[1650] Step 7:

[1651] The terminal displays generated styling information and ride experience suggestions to the user. The user reviews these suggestions. The input is suggestion data from the server, and the output is displayed on the user's device.

[1652] Step 8:

[1653] The user inputs feedback on the suggested styling and driving experience. This input can be text or a selection, which the device receives. The device then sends this feedback to the server. The output indicates that the feedback has been successfully sent to the server.

[1654] Step 9:

[1655] The server stores the received feedback in a database. The algorithm is updated using the feedback information to improve future suggestions. Data processing involves analyzing the feedback data and introducing a feedback loop into the algorithm. The output is the updated algorithm.

[1656] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1657] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1658] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1659] [Fourth Embodiment]

[1660] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1661] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1662] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1663] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1664] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1665] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1666] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1667] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1668] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1669] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1670] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1671] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1672] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1673] This invention relates to a system that allows users to register their clothing and suggests the optimal fashion style based on information such as mood, date, and weather. The system of this invention consists of a user terminal and a server. One embodiment of the present invention will be described below.

[1674] System Overview

[1675] 1. Register your clothing.

[1676] Users first register their clothing by taking photos of their clothes using the app. Alternatively, they can upload existing photos from their gallery. The device sends these photos to the server. The server analyzes the received photos using an image recognition algorithm to identify the type and characteristics of the clothing (e.g., shirt, pants, color, pattern, etc.). The analyzed clothing information is then registered in a database.

[1677] 2. Entering Information

[1678] Users input information such as their mood for the day, the date, and the weather through the app's interface. This information is then sent from the device to the server.

[1679] 3. Styling generation

[1680] The server retrieves the user's clothing information registered in the database, along with the latest trend information, and executes an algorithm that generates the optimal style considering the user's input. This algorithm suggests styling based on factors such as the user's mood, weather, and date.

[1681] 4. Styling display

[1682] The generated styling information is sent from the server to the terminal, which then displays the style to the user. The style includes detailed information about which items to combine and how. An image of the suggested style is also displayed.

[1683] 5. Receiving feedback and updating the algorithm

[1684] Users provide feedback on the suggested style, indicating whether they are satisfied or dissatisfied, through their device. This feedback information is sent to a server, which stores it in a database. The accumulated feedback is used to update the algorithm to improve future style suggestions.

[1685] Specific example

[1686] Clothing registration

[1687] Use your device's camera function to take a picture of a "red knit sweater." Alternatively, select a picture of "blue denim pants" that you have already taken from your gallery and upload it. This picture will be sent from your device to the server. The server will analyze the received picture using an image recognition algorithm and register it in its database as a "red knit sweater" or "blue denim pants."

[1688] Entering information

[1689] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into the app's input form. This information is then sent from the device to the server.

[1690] Styling generation and display

[1691] The server combines the user's registered "red knit sweater" and "blue denim pants" with the latest trend information to generate outfit ideas using the "red knit sweater" and "blue denim pants." This style suggestion is then sent to the device, which displays it to the user.

[1692] Receiving feedback

[1693] The user enters feedback such as, "I like this outfit." The device sends this feedback to the server, which stores it in a database. This feedback information is used to improve future styling algorithms.

[1694] As described above, the present invention provides a system that comprehensively utilizes the user's existing clothing and trend information to propose a fashion style that is individually optimized for each user.

[1695] The following describes the processing flow.

[1696] Step 1:

[1697] Users take photos of their own clothing or upload them from their gallery.

[1698] Step 2:

[1699] The device sends photos of clothing that have been taken or uploaded to the server.

[1700] Step 3:

[1701] The server uses an image recognition algorithm to analyze the received photos and identify the type of clothing (shirt, pants, jacket, etc.) and its characteristics (color, pattern, brand, etc.).

[1702] Step 4:

[1703] Based on the analysis results, the server registers clothing information in the database, associating it with the user ID.

[1704] Step 5:

[1705] The user enters their mood, date, and weather information into the app's input form.

[1706] Step 6:

[1707] The terminal sends the user's input information to the server.

[1708] Step 7:

[1709] The server retrieves information about the user's clothing collection from the database, and similarly retrieves the latest trend information.

[1710] Step 8:

[1711] The server runs a styling algorithm based on the user's mood, date, and weather information to generate the optimal style.

[1712] Step 9:

[1713] The server sends the generated styling information to the terminal.

[1714] Step 10:

[1715] The device displays the suggested style to the user along with detailed information (which items to combine and how).

[1716] Step 11:

[1717] Users provide feedback on styling suggestions, indicating whether they are satisfied or dissatisfied.

[1718] Step 12:

[1719] The device sends user feedback information to the server.

[1720] Step 13:

[1721] The server stores feedback information in a database and updates the styling algorithm to improve future styling suggestions.

[1722] (Example 1)

[1723] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1724] Traditionally, when users styled outfits using their existing wardrobe, they faced the challenge of having to decide for themselves which items to combine and how to combine them. Furthermore, they often lacked optimal suggestions that took into account fluctuating factors such as mood and weather, resulting in inconsistent styling quality. This, in particular, led to time-consuming and laborious styling processes, lowering user satisfaction, especially for busy individuals.

[1725] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1726] In this invention, the server includes means for a user to take or upload photos of their clothing; means for the terminal to transmit the photos to the server; means for the server to analyze the photos and identify clothing information; means for registering the analyzed clothing information in a database; means for the user to input their current mood, date, and weather information; means for the terminal to transmit the information to the server; means for the server to generate styling based on the database and trend information; means for the server to transmit the generated styling information to the terminal; means for the terminal to display the generated styling information; means for the user to input feedback on the styling; means for the terminal to transmit the feedback to the server; and means for the server to accumulate the feedback and update the styling algorithm. This makes it possible for the user to automatically receive suggestions for the optimal fashion style based on their clothing and changing factors.

[1727] A "user" refers to an individual who takes or uploads photos of clothing and enters information such as mood, date, and weather.

[1728] A "terminal" refers to an electronic device operated by a user that transmits photos taken and information entered to a server and displays styling information.

[1729] A "server" refers to a central computer system that analyzes data received from terminals, registers it in a database, generates styling, and sends it back to the terminals.

[1730] "Photographs" refer to still image data that users take or upload to represent their clothing.

[1731] "Analysis" refers to the process by which the server uses an image recognition algorithm to identify clothing information from the received photograph.

[1732] "Image recognition algorithms" refer to computer vision technologies used to recognize the type and characteristics of clothing from photographs.

[1733] "Clothing information" refers to characteristic data such as the type, color, and pattern of clothing identified from the analyzed photographs.

[1734] A "database" refers to a data storage system used to accumulate and manage analyzed clothing information and user input information.

[1735] "Mood" refers to the subjective feelings and sensations that users input to express their mental state on a given day.

[1736] "Date" refers to the year, month, and day of a specific date entered by the user.

[1737] "Weather information" refers to the weather conditions for the day (e.g., sunny, rainy, cloudy) entered by the user.

[1738] "Trend information" refers to data on the latest fashion trends and fashions.

[1739] "Styling" refers to the optimal clothing combination suggestions that the server generates based on database and trend information.

[1740] "Feedback" refers to information that users input, such as their impressions and evaluations of the suggested styling.

[1741] A "styling algorithm" refers to a program implemented on a server to generate the optimal styling based on the user's mood, date, and weather information.

[1742] This invention relates to a system that registers a user's clothing collection and suggests the optimal fashion style based on information such as mood, date, and weather. This system mainly consists of a user terminal and a server.

[1743] First, users register their clothing using an application installed on their device (e.g., a smartphone or tablet). Clothing can be registered by taking photos of the garments using the device's camera function, or by uploading existing photos from the device's gallery. These photos are then sent from the device to the server.

[1744] The server analyzes the received photos using image recognition algorithms (e.g., TensorFlow or OpenCV). These algorithms allow the server to identify features such as the type, color, and pattern of clothing within the photos. The identified clothing information is then registered in a database.

[1745] Next, the user enters information such as their mood for the day, the date, and the weather through the app's interface. This information is sent from the device to the server. The server retrieves the clothing information registered by the user and the latest trend information, and runs a styling algorithm (e.g., Scikit-learn or Keras) based on the user's input. This algorithm takes into account the entered elements such as mood, weather, and date to generate the most suitable styling for the user.

[1746] The generated styling information is sent from the server to the terminal, which then presents the style to the user. The presented style includes detailed information on which items to combine and how to combine them. Style suggestion images are also displayed as needed.

[1747] The user provides feedback on the suggested style. This feedback is sent from the terminal to the server. The server stores this feedback information in a database and uses it to update the algorithm to improve future style suggestions.

[1748] Specific example

[1749] Clothing registration:

[1750] The user takes a picture of a "red knit sweater" using their smartphone camera, or selects and uploads an existing picture of "blue denim pants" from their gallery. This picture is sent from the device to the server. The server analyzes the received picture using an image recognition algorithm and registers it in the database as a "red knit sweater" or "blue denim pants."

[1751] Entering information:

[1752] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into the app's form. This information is then sent from the device to the server.

[1753] Styling generation and display:

[1754] The server retrieves the user's registered information for a "red knit sweater" and "blue denim pants," and runs a styling algorithm along with the latest trend information. The algorithm considers the input information, such as "cheerful mood," "October 16, 2023," and "sunny weather," to generate an outfit combining the "red knit sweater" and "blue denim pants." This outfit information is sent to the terminal, which then presents it to the user.

[1755] Receiving feedback:

[1756] The user enters feedback such as "I like this outfit," and the device sends that feedback to the server. The server stores this feedback information and uses it to improve future styling suggestions.

[1757] Thus, the present invention is a system that comprehensively utilizes the user's existing clothing and the latest fashion trend information to propose an individually optimized fashion style.

[1758] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1759] Step 1:

[1760] The user takes a photo of their clothes using the app on their device, or uploads an existing photo. The device then sends the taken or uploaded photo to the server.

[1761] Input: A photo of clothing taken or selected by the user.

[1762] Output: Photo data sent from the terminal to the server

[1763] Specific actions:

[1764] The user takes a picture of a "red knit sweater" using their smartphone's camera function and sends that picture to the server via the app.

[1765] Step 2:

[1766] The server analyzes the received photos using an image recognition algorithm (e.g., TensorFlow, OpenCV). The analysis results identify the type and characteristics of the clothing.

[1767] Input: Photo data of clothing received by the server

[1768] Output: Information on the type and characteristics of the analyzed clothing (e.g., shirt, pants, color, pattern, etc.)

[1769] Specific actions:

[1770] The server analyzes the received photo of a "red knit sweater" and uses an image recognition algorithm to identify the characteristic of a "red knit sweater."

[1771] Step 3:

[1772] The server registers the analyzed clothing information in a database.

[1773] Input: Information on the analyzed clothing

[1774] Output: Clothing information registered in the database

[1775] Specific actions:

[1776] The server saves information about the "red knit sweater" to the database and adds it to the user's clothing collection.

[1777] Step 4:

[1778] The user enters information such as their mood for the day, the date, and the weather through the app's interface. The device then sends the entered information to the server.

[1779] Input: Information entered by the user, such as mood, date, and weather.

[1780] Output: Mood, date, and weather information sent from the terminal to the server.

[1781] Specific actions:

[1782] The user enters "cheerful mood," "October 16, 2023," and "sunny" into the app's form and sends this information to the server.

[1783] Step 5:

[1784] The server retrieves the user's clothing information and the latest trend information registered in the database, and runs a styling algorithm (e.g., Scikit-learn, Keras) to generate the optimal style.

[1785] Input: Clothing information from the database, trend information, and user input information (mood, date, weather)

[1786] Output: Generated styling information

[1787] Specific actions:

[1788] Based on the "red knit sweater" and the latest trend information, the server generates the optimal outfit by taking into account the user's input information: "cheerful mood," "October 16, 2023," and "sunny."

[1789] Step 6:

[1790] The server sends the generated styling information to the terminal. The terminal displays the received styling information to the user.

[1791] Input: Styling information generated by the server

[1792] Output: Styling information displayed on the terminal

[1793] Specific actions:

[1794] The server generates outfit information using a "red knit sweater" and "blue denim pants," which is then sent to the terminal, and the terminal presents this information to the user.

[1795] Step 7:

[1796] The user provides feedback on the suggested style through the app. The device then sends this feedback to the server.

[1797] Input: Feedback on the style entered by the user

[1798] Output: Feedback information sent from the terminal to the server

[1799] Specific actions:

[1800] The user enters feedback such as "I like this outfit" and sends it from their device to the server.

[1801] Step 8:

[1802] The server stores feedback information in a database and uses it to update algorithms in order to improve future styling suggestions.

[1803] Input: Feedback information sent from the terminal to the server

[1804] Output: Feedback information stored in the database, updated styling algorithms

[1805] Specific actions:

[1806] The server stores the feedback information it receives in a database and uses it to improve the styling algorithm.

[1807] (Application Example 1)

[1808] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1809] Existing fashion style suggestion systems fail to efficiently utilize users' existing clothing and trend information, making it difficult to suggest the most suitable fashion styles for individual users. Furthermore, algorithm improvements based on user feedback are insufficient. As a result, a major challenge is that users are not receiving the style suggestions they desire.

[1810] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1811] In this invention, the server includes means for the user to take or upload photos of their clothing using their smartphone with image recognition technology, means for analyzing the photos sent to the cloud server to identify clothing information, means for registering the analyzed clothing information in a database, means for the user to input their current mood, date, and weather information into their smartphone, means for the cloud server to generate an optimal styling based on the database and trend information, means for displaying the generated styling information on the smartphone, means for the user to input feedback on the styling into their smartphone, and means for the cloud server to accumulate the feedback and update the styling algorithm. This makes it possible to propose individually optimized fashion styles that reflect the user's clothing and trend information.

[1812] A "user" is an individual who uses the system to register their clothing collection and receive fashion style suggestions.

[1813] "Clothing on hand" refers to the various clothes and accessories that the user owns.

[1814] A "smartphone" is a portable information terminal that allows internet access and the use of various applications.

[1815] A "cloud server" is a remote server located on the internet that enables data processing and storage for a large number of users.

[1816] "Photos" refer to image data taken or uploaded using a user's smartphone.

[1817] "Image recognition technology" is a technology that analyzes image data to identify specific objects or features.

[1818] "Clothing information" refers to data that describes the type and characteristics of clothing identified using image recognition technology.

[1819] A "database" is an information system used to store and manage analyzed clothing information and other related data.

[1820] "Mood" refers to information that represents the user's psychological state and emotions on a given day.

[1821] "Date" refers to calendar information that indicates a specific day.

[1822] "Weather information" refers to data that shows the weather conditions for a given day.

[1823] "Trend information" refers to information about current trends and fashion developments.

[1824] "Styling" refers to fashion combinations and outfits generated based on the user's existing clothing and current trends.

[1825] "Feedback" refers to information that shows the evaluation and impressions that users give regarding the suggested styling.

[1826] An "algorithm" is a set of rules that define the steps or calculation methods for solving a specific problem.

[1827] This invention relates to a system that allows users to photograph or upload their clothing using their smartphones, and then suggests fashion styles based on those photos. Specific embodiments of this invention will be described below.

[1828] System Overview

[1829] 1. Register your clothing.

[1830] Users first register their clothing. To do this, they either take photos of their clothes using their smartphone's camera or upload photos they have already taken. The smartphone sends these photos to a cloud server. The cloud server uses image recognition technology to analyze the received photos and identify the type and characteristics of the clothing (e.g., shirt, pants, color, pattern, etc.). The analyzed clothing information is then registered in a database.

[1831] 2. Entering Information

[1832] Users input information such as their mood, the date, and the weather through their smartphone interface. This information is then sent from the smartphone to a cloud server.

[1833] 3. Styling generation

[1834] The cloud server retrieves the user's clothing information and the latest trend information registered in the database, and executes an algorithm to generate the optimal style, taking into account the user's input information. This algorithm suggests styling based on factors such as the user's mood, weather, and date.

[1835] 4. Styling display

[1836] The generated styling information is sent from the cloud server to the smartphone, which then displays the style to the user. The style includes detailed information on which items to combine and how. An image of the suggested style is also displayed.

[1837] 5. Receiving feedback and updating the algorithm

[1838] Users provide feedback on the suggested styles, indicating satisfaction or dissatisfaction, via their smartphones. This feedback information is sent to a cloud server, which stores it in a database. The accumulated feedback is used to update the algorithm to improve future style suggestions.

[1839] Hardware and software to be used

[1840] Hardware: Smartphones (iOS or Android), cloud servers

[1841] software:

[1842] Camera function

[1843] Cloud servers (for example, Amazon Web Services or Google Cloud)

[1844] Image recognition algorithms (for example, models using TensorFlow or PyTorch)

[1845] Database (for example, MySQL or MongoDB)

[1846] Specific Examples

[1847] Clothing registration

[1848] The user takes a picture of a "red knit sweater" using their smartphone camera, or selects and uploads a picture of "blue denim pants" that they have already taken from their gallery. These photos are sent from the smartphone to a cloud server. The cloud server analyzes the received photos using image recognition technology and registers them in a database as "red knit sweater" or "blue denim pants."

[1849] Entering information

[1850] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into an input form on their smartphone. This information is then sent from the smartphone to a cloud server.

[1851] Styling generation and display

[1852] The cloud server combines the user's registered items, such as a "red knit sweater" and "blue denim pants," with the latest trend information to generate outfit ideas using the "red knit sweater" and "blue denim pants." This style suggestion is then sent to the user's smartphone, which displays it.

[1853] Receiving feedback

[1854] Users provide feedback such as, "I like this outfit." Their smartphone sends this feedback to a cloud server, which stores it in a database. This feedback information is used to improve future styling algorithms.

[1855] Examples of prompt statements

[1856] "User's clothing: Red knit sweater, blue denim pants"

[1857] "Mood: Cheerful"

[1858] Date: October 20, 2023

[1859] "Weather: Sunny"

[1860] As described above, the present invention provides a system that comprehensively utilizes the user's existing clothing and trend information to propose a fashion style that is individually optimized for each user.

[1861] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1862] Step 1:

[1863] The user takes a photo of their clothing or uploads one from their gallery using their smartphone. The smartphone then sends the photo of the clothing taken or selected by the user as input to a cloud server. This transmitted image data is the input. The specific action the smartphone takes is to upload the image data to the cloud server.

[1864] Step 2:

[1865] The cloud server analyzes the received image data using image recognition technology. This image recognition technology uses TensorFlow or PyTorch. The input to the cloud server is image data of clothing sent by the user, and the output is the recognized type and characteristic information of the clothing. Specifically, the cloud server analyzes the image data and identifies clothing information such as "shirt" and "pants."

[1866] Step 3:

[1867] The cloud server registers the analyzed clothing information into a database. The input is the analyzed clothing information, and the output is the updated database. The specific action performed by the cloud server is to save the newly analyzed clothing information to the existing database.

[1868] Step 4:

[1869] The user uses their smartphone to input their mood, the date, and weather information for the day. Based on this input, the smartphone sends it to a cloud server. The input is the user's mood, the date, and the weather information, and the output is the information sent to the cloud server. Specifically, the user enters information into an input form, and the smartphone sends that information to the cloud server.

[1870] Step 5:

[1871] The cloud server retrieves the user's clothing information registered in the database, along with the latest trend information, and generates the optimal style considering the user's input information. In this step, the cloud server generates the style using an algorithm. The input is the user's mood, date, weather information, and clothing information, and the output is the generated styling information. Specifically, it retrieves the necessary information from the database and generates the style based on a calculation algorithm.

[1872] Step 6:

[1873] The cloud server sends the generated styling information to the smartphone. The smartphone receives this information and displays it to the user. The input is the styling information sent from the cloud server, and the output is the styling information displayed on the smartphone. The specific action performed by the smartphone is to visually display the received styling information to the user.

[1874] Step 7:

[1875] The user uses their smartphone to input feedback on the suggested style. This input feedback is sent from the smartphone to a cloud server. The input is the user's feedback information, and the output is the feedback information sent to the cloud server. Specifically, the user enters information into a feedback form, and the smartphone sends that information to the cloud server.

[1876] Step 8:

[1877] The cloud server stores the received feedback information in a database. It also updates the styling algorithm based on the accumulated feedback. The input is user feedback information, and the output is the updated styling algorithm. The specific action performed by the cloud server is to periodically update the algorithm to improve style suggestions for future use.

[1878] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1879] This invention relates to a system that allows users to register their clothing and suggests the optimal fashion style based on their mood, the date, the weather, and emotions recognized by an emotion engine. The system of this invention consists of a user terminal, a server, and an emotion engine. One embodiment of the present invention is described below.

[1880] System Overview

[1881] 1. Register your clothing.

[1882] Users first register their clothing by taking photos of their clothes using the app. Alternatively, they can upload existing photos from their gallery. The device sends these photos to the server. The server analyzes the received photos using an image recognition algorithm to identify the type and characteristics of the clothing (e.g., shirt, pants, color, pattern, etc.). The analyzed clothing information is then registered in a database.

[1883] 2. Entering Information

[1884] Users input information such as their mood for the day, the date, and the weather through the app's interface. This information is then sent from the device to the server.

[1885] 3. Recognition of emotions

[1886] The system further incorporates an emotion engine to recognize the user's emotions. This emotion engine analyzes the user's facial expressions and voice, and sends the results to the server. This emotion data, along with the user's input information, is used in the next step.

[1887] 4. Styling generation

[1888] The server retrieves the user's clothing information registered in the database, along with the latest trend information, and executes an algorithm that generates the optimal style considering the user's input information and emotional data. This algorithm suggests styling based on the user's mood, weather, date, and perceived emotions.

[1889] 5. Styling display

[1890] The generated styling information is sent from the server to the terminal, which then displays the style to the user. The style includes detailed information about which items to combine and how. An image of the suggested style is also displayed.

[1891] 6. Receiving feedback and updating the algorithm

[1892] Users provide feedback on the suggested style, indicating whether they are satisfied or dissatisfied, through their device. This feedback information is sent to a server, which stores it in a database. The accumulated feedback is used to update the algorithm to improve future style suggestions.

[1893] Specific example

[1894] Clothing registration

[1895] Use your device's camera function to take a picture of a "red knit sweater." Alternatively, select a picture of "blue denim pants" that you have already taken from your gallery and upload it. This picture will be sent from your device to the server. The server will analyze the received picture using an image recognition algorithm and register it in its database as a "red knit sweater" or "blue denim pants."

[1896] Entering information

[1897] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into the app's input form. This information is then sent from the device to the server.

[1898] Recognition of emotions

[1899] When a user uses the app, the emotion engine analyzes the user's facial expressions and voice to recognize emotions such as "joy" or "anxiety." This emotion data is sent to the server along with other input information.

[1900] Styling generation and display

[1901] The server generates outfit ideas using the "red knit sweater" and "blue denim pants" registered by the user, along with the latest trend information and the user's mood and emotional data. It then sends these style suggestions to the user's device, which displays them along with detailed information.

[1902] Receiving feedback

[1903] The user enters feedback such as, "I like this outfit." The device sends this feedback to the server, which stores it in a database. This feedback information is used to improve future styling algorithms.

[1904] As described above, the present invention provides a system that comprehensively utilizes the user's existing clothing and trend information, and proposes an individually optimized fashion style that also takes into account the user's emotions.

[1905] The following describes the processing flow.

[1906] Step 1:

[1907] Users use the app to take photos of their existing clothing, or upload existing photos from their gallery.

[1908] Step 2:

[1909] The device sends photos of clothing that have been taken or uploaded to the server.

[1910] Step 3:

[1911] The server uses an image recognition algorithm to analyze the received photos and identify the type and characteristics of the clothing (e.g., shirt, pants, color, pattern, etc.).

[1912] Step 4:

[1913] Based on the analysis results, the server registers clothing information in the database, associating it with the user ID.

[1914] Step 5:

[1915] The user enters their mood, date, and weather information into the app's input form.

[1916] Step 6:

[1917] The terminal sends the user's input information to the server.

[1918] Step 7:

[1919] The device uses an emotion engine to analyze the user's facial expressions and voice, and generate emotion data.

[1920] Step 8:

[1921] The device sends emotional data to the server.

[1922] Step 9:

[1923] The server retrieves information about the user's clothing collection from the database, and similarly retrieves the latest trend information.

[1924] Step 10:

[1925] The server runs a styling algorithm based on the user's mood, date, weather information, and sentiment data to generate the optimal style.

[1926] Step 11:

[1927] The server sends the generated styling information to the terminal.

[1928] Step 12:

[1929] The device displays the suggested style to the user along with detailed information (which items to combine and how).

[1930] Step 13:

[1931] Users provide feedback on styling suggestions, indicating whether they are satisfied or dissatisfied.

[1932] Step 14:

[1933] The device sends user feedback information to the server.

[1934] Step 15:

[1935] The server stores feedback information in a database and updates the styling algorithm to improve future styling suggestions.

[1936] (Example 2)

[1937] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1938] Conventional fashion suggestion systems only offered styling suggestions based on the user's existing clothing information and basic input, failing to consider subtle changes in the user's emotions. As a result, the styling suggestions were often unsatisfactory in terms of the user's mood and emotions, making it difficult to provide individually optimized suggestions.

[1939] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for incorporating an emotion engine for analyzing emotions from the user's facial expressions and voice, means for transmitting the emotion data analyzed by the emotion engine to the server, and means for the server to generate styling considering the emotion data. This makes it possible to propose individually optimized fashion styles based on the user's mood and emotions.

[1940] "User" refers to anyone who uses this system.

[1941] "Device" refers to an electronic device used by a user, and includes smartphones, tablets, and personal computers.

[1942] A "server" refers to the central processing unit of this system, which is responsible for receiving, processing, storing, and transmitting data.

[1943] "Photos" refer to image data that users take or upload to register clothing items.

[1944] "Analysis" refers to the process by which a server extracts necessary information from the photos and data it receives.

[1945] "Clothing information" refers to information about clothing registered by the user, such as the type of clothing, color, pattern, and features.

[1946] A "database" refers to a structured storage system that allows a server to systematically manage, store, and retrieve information.

[1947] "Mood" refers to the mental state that the user is experiencing at that particular moment.

[1948] "Date" refers to information that specifically indicates the year, month, and day.

[1949] "Weather information" refers to information that indicates the weather conditions on a given day.

[1950] "Trend information" refers to information about the latest fashion and styles.

[1951] "Styling" refers to fashion combinations and outfits suggested based on the user's existing clothing.

[1952] "Feedback" refers to the evaluations and opinions that users give regarding the suggested styling.

[1953] An "emotion engine" refers to software or algorithms that analyze a user's facial expressions and voice to recognize their emotions.

[1954] "Emotional data" refers to data about a user's emotions that has been analyzed by the emotion engine.

[1955] An "image recognition algorithm" refers to an algorithm used to analyze photographs and image data to detect specific objects or features.

[1956] This invention relates to a system in which a user registers their clothing and the system suggests the optimal fashion style based on their mood, the date, the weather, and emotions recognized by an emotion engine. This system consists of a user terminal, a server, and an emotion engine.

[1957] System Configuration

[1958] Clothing registration

[1959] User:

[1960] Users first use the app to register their clothing. Within the app, they select the "Register Clothing" menu and register their clothing using the following method:

[1961] Use the device's camera function to take a picture of the clothing.

[1962] Upload existing photos from your gallery.

[1963] Terminal:

[1964] The device temporarily stores photos of the clothing and sends them to the server using an HTTP POST request.

[1965] server:

[1966] The server analyzes the received photos using image recognition algorithms such as the Google Cloud Vision API to identify the type and characteristics of the clothing. The results of this analysis are then registered in a database.

[1967] Entering information

[1968] User:

[1969] Users use an input form within the app to enter their mood for the day, the date, and weather information.

[1970] Terminal:

[1971] The terminal temporarily stores the entered information and sends it to the server using an HTTP POST request.

[1972] server:

[1973] The server registers the received information in the database.

[1974] Recognition of emotions

[1975] User:

[1976] While the user is using the app, the emotion engine analyzes the user's facial expressions and voice. Emotional data is collected when the user shows their facial expressions to the device's camera or speaks.

[1977] Terminal:

[1978] The device acquires facial expressions and voice data, stores it temporarily, and then sends it to the server using an HTTP POST request.

[1979] server:

[1980] The server uses an emotion engine to analyze facial expressions and voice data to identify the user's emotions. The analyzed emotion data is registered in a database. For example, Microsoft's Azure Cognitive Services can be used.

[1981] Styling generation

[1982] server:

[1983] The server retrieves information about the user's clothing collection, sentiment data, and the latest trend information registered in the database. The latest trend information can be obtained from fashion websites on the internet or APIs (for example, the Fashion Trends API).

[1984] server:

[1985] Machine learning algorithms (e.g., TensorFlow or PyTorch) are used to generate optimal styling based on the user's mood, date, weather information, and sentiment data. This generation process also takes past feedback data into consideration.

[1986] Styling display

[1987] server:

[1988] The server sends the generated styling information to the terminal.

[1989] Terminal:

[1990] The device displays the received styling information on the user interface. The displayed content includes images of clothing combinations and styles.

[1991] Receiving feedback

[1992] User:

[1993] Users can provide feedback on the suggested style, such as "satisfied" or "dissatisfied."

[1994] Terminal:

[1995] The device temporarily stores the feedback information and sends it to the server using an HTTP POST request.

[1996] server:

[1997] The server registers the feedback information in a database. This feedback information is used to update the algorithm and make future styling suggestions.

[1998] Specific example

[1999] Clothing registration

[2000] The user takes a picture of a "red knit sweater" using their device's camera function, or uploads a picture of "blue denim pants" from their gallery. This picture is sent from the device to the server. The server uses the Google Cloud Vision API to analyze the picture and register it in the database as a "red knit sweater" or "blue denim pants."

[2001] Entering information

[2002] The user enters information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny" into the app's input form. This information is then sent from the device to the server.

[2003] Recognition of emotions

[2004] While the user is using the app, the emotion engine analyzes the user's facial expressions and voice to recognize emotions such as "joy" or "anxiety." This emotion data is sent to the server along with other information.

[2005] Styling generation and display

[2006] The server generates outfit ideas using the "red knit sweater" and "blue denim pants" based on the user's registered items, the latest trend information, and the user's mood and emotional data. This style suggestion is sent to the device, which displays it along with detailed information.

[2007] Receiving feedback

[2008] The user enters feedback such as "I like this outfit." The device sends this feedback to the server, which stores it in a database. This allows for future improvements to the styling algorithm.

[2009] Example prompts for a generative AI model

[2010] An example of a prompt message a user might enter is: "If the user's mood today is 'cheerful,' the date is 'October 16, 2023,' the weather is 'sunny,' and their emotion is 'joyful,' please suggest a fashion outfit using the 'red knit sweater' and 'blue denim pants' they already own."

[2011] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2012] Step 1: Register your clothing

[2013] User:

[2014] The user launches the app and selects the "Register Clothing" menu within the app. Here, the user registers clothing using the following method:

[2015] Use the device's camera function to take a picture of the clothing.

[2016] Upload existing photos from your gallery.

[2017] input:

[2018] Photographs of clothing taken or uploaded.

[2019] Terminal:

[2020] The device temporarily stores the captured or uploaded photo data. The photo data is then sent to the server.

[2021] output:

[2022] Temporarily saved photo data.

[2023] Step 2: Send and analyze photos

[2024] Terminal:

[2025] The device sends the temporarily stored photo data to the server using an HTTP POST request.

[2026] input:

[2027] Temporarily saved photo data.

[2028] server:

[2029] The server passes the received photo data to image recognition algorithms such as the Google Cloud Vision API, which analyze it to identify the type and characteristics of the clothing. This analysis provides information such as the type of clothing (e.g., shirt, pants), color, and pattern.

[2030] Data processing:

[2031] Photo analysis using image recognition algorithms.

[2032] output:

[2033] Analyzed clothing information.

[2034] Step 3: Register clothing information

[2035] server:

[2036] The server registers the clothing information analyzed in the previous step into the database.

[2037] input:

[2038] Analyzed clothing information.

[2039] output:

[2040] Clothing information registered in the database.

[2041] Step 4: Entering Information

[2042] User:

[2043] Users use an input form within the app to enter their mood for the day, the date, and weather information. Mood can include options such as "cheerful" or "calm."

[2044] input:

[2045] User-entered mood, date, and weather information.

[2046] Terminal:

[2047] The terminal temporarily stores the entered information and then sends it to the server using an HTTP POST request.

[2048] output:

[2049] Temporarily saved mood, date, and weather information.

[2050] Step 5: Submit and register your information.

[2051] Terminal:

[2052] The device sends temporarily stored mood, date, and weather information to the server using an HTTP POST request.

[2053] input:

[2054] Temporarily saved mood, date, and weather information.

[2055] server:

[2056] The server registers the received information in the database.

[2057] Data processing:

[2058] Receiving information and registering it in the database.

[2059] output:

[2060] Mood, date, and weather information registered in the database.

[2061] Step 6: Recognizing Emotions

[2062] User:

[2063] While the user is using the app, the emotion engine analyzes the user's facial expressions and voice. Facial and voice data is collected when the user shows their face to the device's camera or speaks something.

[2064] input:

[2065] User's facial expressions and voice data.

[2066] Terminal:

[2067] The device acquires facial expression and voice data, stores it temporarily, and then sends it to the server using an HTTP POST request.

[2068] output:

[2069] Temporarily saved facial expression and audio data.

[2070] Step 7: Sending and analyzing emotional data

[2071] Terminal:

[2072] The terminal sends temporarily stored facial expression and audio data to the server using an HTTP POST request.

[2073] input:

[2074] Temporarily saved facial expression and audio data.

[2075] server:

[2076] The server uses an emotion engine to analyze this data and identify the user's emotions. For example, Microsoft's Azure Cognitive Services can be used.

[2077] Data processing:

[2078] Analysis of emotional data using an emotion engine.

[2079] output:

[2080] Analyzed emotion data.

[2081] Step 8: Registering emotion data

[2082] server:

[2083] The server registers the emotion data analyzed in the previous step into the database.

[2084] input:

[2085] Analyzed emotion data.

[2086] output:

[2087] Emotional data registered in the database.

[2088] Step 9: Styling Generation

[2089] server:

[2090] The server retrieves user clothing information, sentiment data, and the latest trend information registered in the database. The latest trend information can be obtained from fashion websites and APIs.

[2091] input:

[2092] Clothing information, sentiment data, and trend information obtained from a database.

[2093] server:

[2094] Machine learning algorithms (e.g., TensorFlow, PyTorch) are used to generate optimal styling based on user mood, date, weather information, and sentiment data. This generation process also takes past feedback data into consideration.

[2095] Data calculation:

[2096] Data analysis and styling generation using machine learning algorithms.

[2097] output:

[2098] The generated styling information.

[2099] Step 10: Display the styling

[2100] server:

[2101] The server sends the generated styling information to the terminal.

[2102] input:

[2103] The generated styling information.

[2104] Terminal:

[2105] The device displays the received styling information on the user interface. The displayed content includes images of clothing combinations and styles.

[2106] output:

[2107] Styling information displayed to the user.

[2108] Step 11: Receiving Feedback

[2109] User:

[2110] Users can provide feedback on the suggested style, such as "satisfied" or "dissatisfied."

[2111] input:

[2112] User feedback.

[2113] Terminal:

[2114] The device temporarily stores the feedback information and then sends it to the server using an HTTP POST request.

[2115] output:

[2116] Temporarily saved feedback information.

[2117] Step 12: Submit and register feedback data.

[2118] Terminal:

[2119] The terminal sends temporarily stored feedback data to the server using an HTTP POST request.

[2120] input:

[2121] Temporarily stored feedback data.

[2122] server:

[2123] The server registers the feedback data in a database. This feedback information is used to update the algorithm and make future styling suggestions.

[2124] Data processing:

[2125] Receiving feedback data and registering it in the database.

[2126] output:

[2127] Feedback data registered in the database.

[2128] (Application Example 2)

[2129] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2130] Traditional fashion styling systems fail to consider user emotions, making it difficult to suggest the optimal styling based on the user's psychological state at any given time. Similarly, in the onboard experience, there was no technology to provide optimal service in real time based on passengers' emotions and interests, thereby improving comfort during the ride. Therefore, there is a need for an effective system that can increase user and passenger satisfaction.

[2131] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for a user to take or upload a photograph of their clothing; means for the terminal to transmit the photograph to the server; means for the server to analyze the photograph and identify clothing information; means for registering the analyzed clothing information in a database; means for the user to input their current mood, date, and weather information; means for the terminal to transmit the information to the server; means for the server to generate styling based on the database and trend information; means for displaying the generated styling information on the terminal; means for the user to input feedback on the styling; means for the terminal to transmit the feedback to the server; means for the server to accumulate the feedback and update the styling algorithm; means for passengers to register basic information and interests and for the optimal ride experience to be suggested based on their emotions and information; means for analyzing passenger emotions using cameras and microphones installed in the vehicle and transmitting the data to a computer in the vehicle; and means for collecting feedback on the user's ride experience and improving future ride experience suggestions. This will enable the suggestion of styling based on the user's emotions and the provision of a comfortable ride experience that suits the passengers' emotions and interests.

[2132] - "Users" are the primary target audience for this system, who register their clothing, receive styling suggestions, and get suggestions for ride experiences.

[2133] A "terminal" is an electronic device that receives user input and transmits information to a server, and includes smartphones and tablets.

[2134] A "server" is a central processing unit that analyzes information sent by users and generates styling information and suggestions for the driving experience.

[2135] "Photos" refer to image data used by users to register their clothing items.

[2136] "Clothing information" refers to data about the types and characteristics of clothing registered by the user.

[2137] A "database" is an information aggregation system used to store and manage analyzed clothing information and feedback.

[2138] "Mood" refers to the psychological state a user is experiencing at that moment.

[2139] "Date" refers to the specific year, month, and day on which the user enters or receives information.

[2140] "Weather information" refers to data about current or future weather conditions.

[2141] "Trend information" refers to the latest information on current fashions and styling.

[2142] "Styling" refers to the clothing combinations and outfits suggested to the user.

[2143] "Generation" refers to the process by which the server concretizes styling and ride experience suggestions based on user information.

[2144] A "styling algorithm" is a numerical processing method used to calculate the optimal styling based on the user's emotions and input information.

[2145] "Passenger" refers to a user of an autonomous vehicle.

[2146] A "camera" is a video acquisition device used to analyze passengers' emotions.

[2147] A "microphone" is a voice input device that captures passengers' voices and uses them for emotion analysis.

[2148] A "computer" is a computing device that processes data acquired within a vehicle and generates suggestions.

[2149] "Riding experience" refers to the services and entertainment that passengers receive inside an autonomous vehicle.

[2150] This invention is a system that allows users to register their clothing and suggests the optimal fashion style based on their mood, the date, the weather, and emotions recognized by an emotion engine. As an example of the application of this invention, it further provides a system that suggests the optimal ride experience based on the passenger's emotions and interests. Embodiments of this invention will be described in detail below.

[2151] System Overview

[2152] Registration of clothing and passenger information

[2153] Users (passengers) first register their own clothing by taking photos of their clothes using their smartphone or tablet. Alternatively, they can upload existing photos from their gallery. Furthermore, passengers register their basic information and interests (e.g., music, movies, news, etc.).

[2154] The terminal sends these photos and information to the server. The server analyzes the received photos using an image recognition algorithm to identify the type and characteristics of the clothing, and the analyzed clothing information is registered in a database. Meanwhile, information about the passengers' interests is also stored on the server.

[2155] Entering information

[2156] Users input information such as their mood for the day, the date, and the weather through the app's interface. They can also input their mood during the ride and their plans for the day. This information is sent from the device to the server.

[2157] Recognition of emotions

[2158] The system further incorporates an emotion engine to recognize the emotions of users (passengers). Using cameras and microphones installed in the vehicle, it analyzes the emotions of passengers from their facial expressions and voices, and sends the results to a server. This emotion data, along with the user's input information, is used in the next step.

[2159] Styling and driving experience suggestions

[2160] The server retrieves the user's clothing information registered in the database, along with the latest trend information, and executes an algorithm that generates the optimal styling and ride experience, taking into account the user's (passenger's) input information and emotional data. This algorithm makes suggestions based on the user's mood, weather, date, and perceived emotions.

[2161] Display of proposals and feedback

[2162] The generated styling information and ride experience suggestions are sent from the server to the terminal, which then displays the suggestions to the user. The styling includes detailed information on which items to combine and how, while the ride experience includes suggestions such as music and videos.

[2163] Users can provide feedback on the suggested styling and driving experience. This feedback information is sent from the device to the server, which stores it in a database. The accumulated feedback is used to update the algorithm to improve future suggestions.

[2164] Specific example

[2165] Clothing registration and passenger information entry

[2166] The user takes a photo of a "red knit sweater" using their smartphone. The passenger also registers their preferences as basic information, indicating they like "relaxing music" and "action movies." These photos and information are then sent from the device to the server.

[2167] Information input and emotion recognition

[2168] The user uses the app's input form to enter information such as "I'm in a good mood today," "October 16, 2023," and "The weather is sunny." Simultaneously, the vehicle's cameras and microphones analyze the "passenger's happiness" using an emotion engine and send the data to the server.

[2169] Creating Styling and Driving Experiences

[2170] The server generates styling based on registered items such as a "red knit sweater" and "blue denim pants," along with the latest trend information and user mood and emotional data. It also suggests the optimal ride experience based on passenger preferences for "relaxing music" and "action movies."

[2171] Display of proposed content and feedback

[2172] The generated styling and suggestions such as playing "relaxing music" or showing "action movies" are displayed on the terminal's screen. Users can input feedback such as "I like this outfit" or passengers can input feedback such as "The music selection was good," and the terminal sends this information to the server.

[2173] Example of a prompt

[2174] We will implement an emotion recognition algorithm using libraries such as Python, TensorFlow, and Keras.

[2175] Example code for capturing images from a camera and recognizing emotions:

[2176] with open(image_path, 'rb') as image:

[2177] detected_faces = face_client.face.detect_with_stream(image, return_face_attributes=[FaceAttributeType.emotion])

[2178] A flow for collecting feedback and improving the next proposal:

[2179] feedback = collect_feedback()

[2180] We save feedback in the system to improve future suggestions.

[2181] In this way, the present invention enables the proposal of styling based on the user's emotions and the provision of a comfortable riding experience that responds to the emotions and interests of passengers.

[2182] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2183] Step 1:

[2184] Users use their smartphones or tablets to take photos of their clothing or upload them from their gallery. The input is a photo of clothing, which the device receives. The device then sends this photo to the server. The output is the completion of the photo data transmission to the server.

[2185] Step 2:

[2186] The server analyzes the received photo data. Using image recognition algorithms (e.g., TensorFlow, Keras), it identifies the types and characteristics of clothing in the photos. Data processing involves extracting features from the image data and converting it into a format suitable for database registration. The output is the registration of the identified clothing information into the database.

[2187] Step 3:

[2188] The user inputs information such as their mood for the day, the date, and the weather through the app's interface. This input can be text data or selected options, which the device receives. The device then sends this information to the server. The output indicates that the information has been successfully sent to the server.

[2189] Step 4:

[2190] The server acquires video and audio data in real time from cameras and microphones installed inside the vehicle. An emotion engine (e.g., Microsoft Azure Face API, Google Cloud Speech-to-Text) is used to analyze passengers' emotions from their facial expressions and voices. Data processing involves extracting and classifying emotion data. The output is the analyzed emotion data.

[2191] Step 5:

[2192] The server executes an algorithm that generates styling suggestions based on clothing information registered in the database, user input information, sentiment data, and the latest trend information. For data processing, it performs optimization using a machine learning model. The output is the generated styling information.

[2193] Step 6:

[2194] The server executes an algorithm to suggest the optimal ride experience based on the passenger's basic information and interests. It also takes emotional data into consideration to suggest entertainment such as music and movies. For data calculation, it integrates user profiles and real-time data to generate personalized suggestions. The output is a suggested ride experience.

[2195] Step 7:

[2196] The terminal displays generated styling information and ride experience suggestions to the user. The user reviews these suggestions. The input is suggestion data from the server, and the output is displayed on the user's device.

[2197] Step 8:

[2198] The user inputs feedback on the suggested styling and driving experience. This input can be text or a selection, which the device receives. The device then sends this feedback to the server. The output indicates that the feedback has been successfully sent to the server.

[2199] Step 9:

[2200] The server stores the received feedback in a database. The algorithm is updated using the feedback information to improve future suggestions. Data processing involves analyzing the feedback data and introducing a feedback loop into the algorithm. The output is the updated algorithm.

[2201] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[2202] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2203] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[2204] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2205] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[2206] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[2207] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[2208] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[2209] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[2210] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[2211] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[2212] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[2213] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[2214] 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.

[2215] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[2216] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[2217] ...

Claims

1. A means for users to take or upload photos of their clothing, The terminal provides means for sending the photo to the server, The server includes means for analyzing the photograph to identify clothing information, A means of registering the analyzed clothing information in a database, A means for the user to input their current mood, date, and weather information, The terminal provides means for transmitting the information to the server, The server includes means for generating styling based on the database and trend information, means for displaying the styling information generated on the terminal, A means for users to input feedback on styling, The terminal provides means for sending the feedback to the server, The server has means for accumulating the feedback and updating the styling algorithm, A system that includes this.

2. The server includes means for analyzing the photograph using an image recognition algorithm, The system according to claim 1.

3. The server includes means for executing an algorithm to generate optimal styling based on the user's mood, date, and weather information. The system according to claim 1.

Images