System

Abstract

A system is provided.SOLUTION: A system comprising: means for providing an interface for a user to input schedule information; means for transmitting the schedule information to a server; means for acquiring weather information based on the schedule information; means for collecting latest fashion trend information based on the schedule information and the weather information; means for acquiring a past fashion history of the user; means for proposing an optimal fashion to the user based on the weather information, the fashion trend information, and the past fashion history of the user; and means for displaying the proposed fashion.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, it is extremely important for users to choose appropriate fashion based on their schedule. However, understanding the weather and the latest fashion trends and choosing appropriate fashion based on that takes time and effort, which is a challenge for many users. The present invention aims to solve this problem by providing a system that allows users to easily receive fashion suggestions based on their schedule, weather, and trends. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means: A system including: means for providing an interface for a user to input schedule information; means for transmitting the schedule information to a server; means for acquiring weather information based on the schedule information; means for collecting the latest fashion trend information based on the schedule information and the weather information; means for acquiring the user's past fashion history; means for suggesting optimal fashion to the user based on the weather information, fashion trend information, and the user's past fashion history; and means for displaying the suggested fashion. This allows the user to receive optimal fashion suggestions that take into account the weather and trends based on the schedule information they input, saving them time and effort.

[0006] "User" refers to a person who uses the fashion suggestion system and enters schedule information and other information.

[0007] "Interface" refers to the functional part that provides a screen or form for the user to input information.

[0008] "Schedule information" refers to information such as date, location, and activity details entered by the user.

[0009] "Server" refers to a central computer system that receives and processes information sent from user terminals.

[0010] "Weather Information" refers to information regarding weather conditions at a specified date, time and location.

[0011] "API" stands for Application Programming Interface and refers to a means of communicating with external services and systems.

[0012] "Fashion trend information" refers to information about currently popular fashion styles and items.

[0013] "Scraping" refers to the technique of automatically collecting information from a website.

[0014] "Past fashion history" refers to information about the fashion choices and suggestions the user has made in the past.

[0015] An "algorithm" refers to a computational procedure for solving a particular problem.

[0016] "Fashion suggestions" refers to suggestions for optimal clothing generated based on the user's schedule information, weather information, trend information, and past history.

[0017] "User terminal" refers to a device (smartphone, tablet, PC, etc.) that a user uses to access the system. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention relates to a system that proposes optimal fashion based on schedule information specified by the user, combining weather and the latest fashion trend information. The system is composed of a user terminal, a server, and related databases and APIs.

[0040] System Configuration

[0041] User terminal: A device (smartphone, tablet, PC, etc.) that a user uses to input and display information.

[0042] Server: The central system that processes information and generates proposals.

[0043] Database: A data storage for storing a user's past fashion history and other related data.

[0044] API: An interface with external services to obtain weather and trend information.

[0045] System Operation

[0046] 1. The user terminal provides a form for inputting "when, where, and what to do."

[0047] 2. The user enters schedule information into the form. For example, "Tomorrow at 12:00 in Tokyo, lunch with a friend at a cafe."

[0048] 3. The user device sends the entered information to the server in JSON format.

[0049] 4. The server sends a request to the weather information API based on the received schedule information and retrieves weather information for the specified date, time, and location. For example, the weather information for tomorrow in Tokyo is retrieved as sunny with a temperature of 20 degrees.

[0050] 5. The server collects the latest fashion trend information. This is done by scraping trend information from fashion websites. For example, it obtains information that "casual styles are popular in spring."

[0051] 6. The server retrieves the user's past fashion history from the database. For example, it checks the styles the user has chosen in the past and their ratings.

[0052] 7. The server then runs an algorithm based on this information to generate optimal fashion recommendations. For example, it might select a "light jacket" based on the weather (sunny, 20 degrees) and suggest a "white casual shirt, denim pants, and sneakers" that reflects the current trend (casual style).

[0053] 8. The server sends the generated proposal in JSON format to the user device.

[0054] 9. The user device displays the received fashion suggestions. The user can review the suggestions and make a selection.

[0055] Specific examples

[0056] scenario

[0057] Take the example of a user choosing an outfit to wear to lunch at a cafe with a friend tomorrow.

[0058] 1. The user device provides an application form for inputting "Tomorrow at 12 o'clock, Tokyo, lunch at a cafe with a friend."

[0059] 2. The user enters "Tomorrow 12:00, Tokyo, lunch at a cafe with a friend."

[0060] 3. The user device sends this information to the server.

[0061] 4. The server uses the weather API to obtain "Tomorrow's weather in Tokyo: sunny, temperature 20 degrees."

[0062] 5. The server scrapes a famous fashion site to obtain trend information such as "casual wear is popular in spring."

[0063] 6. The server references past fashion history from the database and extracts information that "the user prefers casual style."

[0064] 7. The server processes the above information with an algorithm and suggests "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0065] 8. The server sends the proposal to the user terminal.

[0066] 9. The user device displays suggestions, showing the user "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0067] 10. The user reviews the suggestions and selects an outfit.

[0068] As described above, the present invention is capable of easily and quickly suggesting appropriate fashions based on the user's schedule information.

[0069] The processing flow will be explained below.

[0070] Step 1:

[0071] The user terminal provides a form for inputting "when, where, and what to do."

[0072] Step 2:

[0073] The user enters event information (date, time, location, and event details) into the form. For example, "Tomorrow at 12 o'clock in Tokyo, lunch at a cafe with a friend."

[0074] Step 3:

[0075] The user terminal sends the entered schedule information to the server in JSON format.

[0076] Step 4:

[0077] The server analyzes the received schedule information and sends a request to the weather forecast API to obtain weather information for the specified date, time, and location. For example, it obtains "sunny, with a temperature of 20 degrees" as the weather for tomorrow in Tokyo.

[0078] Step 5:

[0079] The server accesses a fashion information website and obtains the latest fashion trend information by scraping or via API. For example, it obtains information such as "Casual wear is popular this spring."

[0080] Step 6:

[0081] The server retrieves the user's past fashion history from the database, for example, checking the history of casual styles chosen by the user in the past.

[0082] Step 7:

[0083] The server runs an algorithm to generate optimal fashion suggestions based on weather information, fashion trend information, and the user's past fashion history. For example, if the weather is sunny and the temperature is 20 degrees, the server will select a "light jacket" and incorporate trendy casual wear, suggesting a "white casual shirt, denim pants, a light spring coat, and sneakers."

[0084] Step 8:

[0085] The server sends the generated fashion suggestions to the user's device in JSON format.

[0086] Step 9:

[0087] The user terminal displays the received fashion suggestions to the user, who can view the suggested items and styles.

[0088] Step 10:

[0089] The user checks the suggested fashion styles and selects one.

[0090] Example 1

[0091] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0092] In modern society, users face the challenge of having to gather a great deal of information in order to select the perfect fashion for themselves. Gathering this information takes a great deal of time and effort, especially when weather and the latest fashion trends must be taken into account. It is also not easy to record a user's past fashion choices and reflect them in their next selection. There is a need for a system that can solve these problems and allow users to easily select the perfect fashion.

[0093] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0094] In this invention, the server includes means for providing an interface for a user to input schedule information, means for transmitting the schedule information to the server, means for acquiring weather information based on the schedule information, means for collecting the latest fashion trend information based on the schedule information and the weather information, means for acquiring the user's past fashion history, means for suggesting optimal fashion to the user based on the weather information, fashion trend information, and the user's past fashion history, and means for displaying the suggested fashions. This allows the user to quickly and easily receive optimal fashion suggestions based on their schedule information.

[0095] A "user terminal" is an electronic device that allows a user to input and display information, and specifically includes a smartphone, tablet, or PC.

[0096] The "server" is a central system that processes information and generates proposals. It receives and analyzes data from user devices, accesses necessary external resources to obtain information, and generates optimal proposals.

[0097] An "interface" is something that provides a means for a user to input information in a manner that is easy for the user to operate, and specifically refers to user interface components such as forms, buttons, and text boxes.

[0098] "Schedule information" refers to specific information about a user's future plans and actions, such as date, time, location, activity details, and accompanying persons.

[0099] "Weather information" refers to weather data relating to a specific date, time and location, including, for example, temperature, weather, and probability of precipitation.

[0100] "Fashion trend information" refers to information about the latest fashion trends and fashions, and is obtained from fashion sites and the like.

[0101] "Fashion history" is information about fashions selected by the user in the past, and includes data such as selection history and ratings.

[0102] An "algorithm" is a set of calculations used to generate optimal fashion based on the information, and is used to synthesize and analyze data to create recommendations.

[0103] A "suggestion" is the specific content of the optimal fashion presented to the user, and includes specific combinations of clothing and accessories.

[0104] The present invention relates to a system that combines weather information and the latest fashion trend information to suggest optimal fashion based on schedule information specified by a user. This system is composed of a user terminal, a server, and related databases and APIs. Specific embodiments are described below.

[0105] System Configuration

[0106] 1. User Device

[0107] A user inputs schedule information using an electronic device such as a smartphone, tablet, or PC. The user terminal provides an interface for inputting schedule information.

[0108] 2. Server

[0109] The server is a central system that processes information and generates proposals. It receives data from user devices, accesses necessary external resources to obtain information, and generates optimal proposals.

[0110] 3. Database

[0111] The database is a data storage for storing a user's past fashion history and other related data.

[0112] 4. API

[0113] It is an interface with external services to obtain weather information and fashion trend information. Specifically, it uses an application programming interface such as the OpenWeatherMap API to obtain weather information.

[0114] System Operation

[0115] 1. The user device displays a form for entering schedule information.

[0116] For example, a smartphone app displays a form asking the user, "What time, where and what will you be doing tomorrow?"

[0117] 2. The user enters the appointment information

[0118] The user inputs specific schedule information such as "tomorrow at 12 o'clock in Tokyo, lunch at a cafe with a friend."

[0119] 3. The user device sends the input information to the server

[0120] The user device converts the input information into JSON format and sends it to the server. For example, the following data is generated: { "date": "tomorrow", "time": "12 o'clock", "location": "Tokyo", "activity": "lunch at a cafe", "company": "friends"}.

[0121] 4. The server retrieves weather information

[0122] The server sends a request to a weather information API (e.g., OpenWeatherMap API) to obtain weather information for the specified date, time, and location. For example, it obtains data such as "sunny, temperature 20 degrees."

[0123] 5. The server collects the latest fashion trends.

[0124] The server scrapes fashion sites to obtain trend information such as "casual styles are popular in spring."

[0125] 6. The server retrieves the user's past fashion history

[0126] The server accesses the database to retrieve the user's past fashion history, for example, extracting information such as "evaluation of the casual styles chosen by the user in the past."

[0127] 7. The server processes the information algorithmically and generates fashion suggestions.

[0128] The server runs an algorithm based on weather information, trend information, and past fashion history to generate optimal fashion suggestions. Specifically, it creates a suggestion of "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0129] 8. The server sends the generated proposal to the user device.

[0130] The suggestions are then converted back to JSON format and sent to the user's device. For example, the data is { "outfit": ["white casual shirt", "denim pants", "light spring coat", "sneakers"]}.

[0131] 9. The user device displays the suggestions.

[0132] The user device analyzes the received data and displays it in an easy-to-read format. The user can then check the suggested fashion. For example, a smartphone app might display "Tomorrow's fashion suggestions: a white casual shirt, denim pants, a light spring coat, and sneakers."

[0133] Prompt Sentence Examples

[0134] Below is an example of a prompt that can be input to a generative AI model:

[0135] "I'm planning to have lunch with a friend at a cafe in Tokyo tomorrow at noon. The weather is sunny and the temperature is 20 degrees. Casual spring styles are in fashion. What kind of outfit would be appropriate? Please give me some specific suggestions."

[0136] In this way, the present system can quickly suggest optimal fashion based on the user's schedule information.

[0137] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0138] Step 1:

[0139] The user terminal displays a schedule information input form.

[0140] Specifically, when the application is launched, the user terminal displays an interface that provides input fields such as "date," "time," "location," "activity," and "companion."

[0141] Step 2:

[0142] The user enters the appointment information.

[0143] The user inputs schedule information into the form, such as "Tomorrow at 12 noon in Tokyo, lunch with a friend at a cafe." This input information is collected through the interface.

[0144] Step 3:

[0145] The user terminal converts the input information into JSON format and sends it to the server.

[0146] It receives the schedule information entered by the user as input, processes the data to convert it into JSON format, and generates the JSON object { "date": "tomorrow", "time": "12 o'clock", "location": "Tokyo", "activity": "lunch at a cafe", "company": "friend"} as output, and sends it to the server.

[0147] Step 4:

[0148] The server retrieves weather information.

[0149] The server parses the received JSON data and sends the input "date" and "location" information as a request to the weather information API. Specifically, it uses the OpenWeatherMap API to obtain weather information for the specified date, time, and location. The output is weather information such as "sunny, temperature 20 degrees."

[0150] Step 5:

[0151] The server collects the latest fashion trend information.

[0152] The server scrapes trend information from fashion websites and collects external fashion information as input. Specifically, it extracts the necessary trend data using regular expressions and data filtering. The output is trend information such as "casual styles are popular in spring."

[0153] Step 6:

[0154] The server acquires the user's past fashion history.

[0155] The server accesses the database and retrieves past fashion history based on the user ID as input. Specifically, it retrieves the necessary information from the database using SQL queries. The output is historical data such as "evaluation of the user's past casual style choices."

[0156] Step 7:

[0157] The server processes the information algorithmically and generates fashion suggestions.

[0158] The server inputs weather information, trend information, and past fashion history into the algorithm. Specific data calculations use machine learning models and rule-based systems to determine the optimal fashion. The output generates suggestions such as "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0159] Step 8:

[0160] The server transmits the generated proposal to the user terminal.

[0161] The server converts the generated fashion suggestions into JSON format and sends this data to the user's device as output. Specifically, it sends the following data: { "outfit": ["white casual shirt", "denim pants", "light spring coat", "sneakers"]}.

[0162] Step 9:

[0163] The user terminal displays the proposal.

[0164] The user device parses the received JSON data and displays the suggested information received as input. Specifically, the interface displays "Tomorrow's fashion suggestions: white casual shirt, denim pants, light spring coat, sneakers."

[0165] Step 10:

[0166] The user reviews the suggestions and selects an outfit.

[0167] The user checks the displayed suggestions and selects and prepares the actual outfit based on the information provided as input. Specifically, the user takes out the clothes from the closet based on the suggestions and prepares to wear them.

[0168] (Application example 1)

[0169] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0170] In recent years, the amount of information available about fashion has rapidly increased, making it difficult for users to select the best outfit for their schedule and environment. Furthermore, there is a lack of ways for users to check the best outfits based on their own fashion style and trend information in real time. This makes it difficult to quickly choose the right fashion outfit, which can be time-consuming. Furthermore, when visually checking fashion suggestions, users are unable to try them on or visualize them, making it difficult to make a satisfactory decision.

[0171] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0172] In this invention, the server includes means for providing an interface for a user to input schedule information, means for transmitting the schedule information to the server, means for acquiring weather information based on the schedule information, means for collecting the latest fashion trend information based on the schedule information and the weather information, means for acquiring the user's past fashion history, means for suggesting optimal fashion to the user based on the weather information, fashion trend information, and the user's past fashion history, means for displaying the suggested fashion, means for providing the user with speech input and converting the schedule information into text using voice recognition, and means for displaying the optimal fashion superimposed on the user's field of view using augmented reality technology. This allows the user to quickly receive optimal fashion suggestions based on their schedule and environment and visually confirm them, enabling them to make quick and satisfying fashion selections.

[0173] A "user" is someone who uses this system to input schedule information and receive fashion suggestions.

[0174] The "server" is a central processing device that receives information sent by users, collects weather information and fashion trend information, and uses algorithms to generate optimal fashion suggestions.

[0175] An "interface" is a device or software that provides a screen for a user to input schedule information or a means for voice input.

[0176] "Schedule information" is specific schedule information such as "when, where, and what to do" input by the user.

[0177] "Weather information" is meteorological data for a specified date, time and location, including temperature, weather, humidity, etc.

[0178] "Fashion trend information" is information about currently popular fashion styles and items.

[0179] "Fashion history" is a record of the fashion styles chosen by the user in the past and their evaluations.

[0180] "Speech recognition" is a technology that analyzes the voice spoken by a user and converts it into text data.

[0181] "Augmented reality technology" is a technology that displays virtual information overlaid on real-world images.

[0182] The "optimal fashion suggestion" is an outfit suggestion generated based on the user's schedule information, weather information, fashion trend information, and past fashion history.

[0183] To implement this invention, it is necessary to use a user terminal and server, a voice recognition API, a weather information acquisition API, a fashion trend information collection means, a database, a machine learning algorithm, and augmented reality technology.

[0184] System Configuration

[0185] User terminal: A device such as smart glasses or a smartphone that allows the user to input schedule information and check suggested fashion.

[0186] Server: A central processing unit that processes information and generates fashion suggestions.

[0187] Database: A data storage for saving a user's past fashion history and related data.

[0188] API: An interface for obtaining weather information, voice recognition, and fashion trend information from external services.

[0189] Augmented reality technology: A technology that provides users with visual fashion suggestions.

[0190] System Operation

[0191] 1. The user device provides an interface that accepts voice input from the user. This interface has the function of converting voice into text data using the Google Speech-to-Text API.

[0192] 2. Voice input example: You input schedule information such as "Tomorrow at 12 o'clock in Tokyo, lunch with a friend at a cafe." The voice recognition API converts this into text.

[0193] 3. The user device sends the schedule information converted into text using voice recognition in JSON format to the server.

[0194] 4. Based on the received schedule information, the server sends a request to the OpenWeatherMap API to obtain weather information and retrieves weather data for the specified date, time, and location.

[0195] 5. The server uses a web scraping tool (e.g., BeautifulSoup) to collect the latest fashion trend information from external fashion sites.

[0196] 6. The server queries the user's past fashion history from the database and retrieves that information.

[0197] 7. The server generates optimal fashion suggestions using a machine learning algorithm (e.g., Scikit-learn) based on weather information, fashion trend information, and the user's past fashion history.

[0198] 8. The server sends the generated fashion suggestions in JSON format to the user device.

[0199] 9. The user device uses augmented reality technology such as Google ARCore to display fashion suggestions superimposed on the user's field of view, allowing the user to virtually try on the items.

[0200] Specific examples

[0201] For example, a user might say:

[0202] "Tomorrow at 12 noon, Tokyo, lunch with a friend at a cafe."

[0203] The server obtains and generates the following information based on the received schedule information:

[0204] Weather: Sunny, 20 degrees

[0205] Latest Fashion Trends: Spring Casual Style

[0206] Past fashion history: Prefers casual style

[0207] Based on this information, the server generates a fashion suggestion of "a white casual shirt, denim pants, a light spring coat, and sneakers," which is then displayed on the user's device using augmented reality technology.

[0208] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0209] Step 1:

[0210] The user terminal provides an interface for the user to input his / her schedule information by voice.

[0211] Input: User's voice input. Example: "Tomorrow 12:00, Tokyo, lunch with a friend at a cafe."

[0212] Output: Audio data

[0213] Step 2:

[0214] The user device sends the voice data to the Google Speech-to-Text API, which converts the voice into text data.

[0215] Input: Audio data

[0216] Output: Text data. Example: "Tomorrow 12:00, Tokyo, lunch at a cafe with a friend."

[0217] Step 3:

[0218] The user terminal transmits the schedule information converted into text data in JSON format to the server.

[0219] Input: Text data

[0220] Output: Event information in JSON format

[0221] Step 4:

[0222] Based on the received schedule information, the server sends a request to the OpenWeatherMap API to obtain weather information for the specified date, time, and location.

[0223] Input: Event information in JSON format

[0224] Output: Weather information. Example: Sunny, temperature 20 degrees.

[0225] Step 5:

[0226] The server uses a web scraping tool (e.g., BeautifulSoup) to collect the latest fashion trend information from external fashion sites.

[0227] Input: Scraping script execution command in the server

[0228] Output: Fashion trend information. Example: Casual style is popular in spring.

[0229] Step 6:

[0230] The server refers to the user's past fashion history from the database and acquires that information.

[0231] Input: Identification information such as user ID or username

[0232] Output: Past fashion history data. Example: Prefer casual style

[0233] Step 7:

[0234] The server generates optimal fashion suggestions using machine learning algorithms (e.g., Scikit-learn) based on weather information, fashion trend information, and the user's past fashion history.

[0235] Input: Weather information, fashion trend information, past fashion history

[0236] Output: Optimal fashion suggestions. Example: White casual shirt, denim pants, light spring coat, sneakers

[0237] Step 8:

[0238] The server sends the generated fashion suggestions in JSON format to the user's device.

[0239] Input: Data for optimal fashion suggestions

[0240] Output: Fashion suggestion data in JSON format

[0241] Step 9:

[0242] The user's device uses augmented reality technology such as Google ARCore to display fashion suggestions superimposed on the user's field of vision.

[0243] Input: Fashion proposal data in JSON format

[0244] Output: Fashion suggestions displayed in augmented reality

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

[0246] This invention relates to a system that suggests optimal fashion based on the schedule information and emotions specified by the user, combining weather and the latest fashion trend information. This system is composed of a user terminal, a server, an emotion engine, and related databases and APIs.

[0247] System Configuration

[0248] User device: The device (smartphone, tablet, PC, etc.) on which the user enters information and on which suggestions are displayed.

[0249] Server: The central system that processes information and generates proposals.

[0250] Emotion engine: A function for recognizing and analyzing the user's emotional state.

[0251] Database: A data storage for storing a user's past fashion history and other related data.

[0252] API: An interface with external services to obtain weather information and fashion trend information.

[0253] System Operation

[0254] 1. The user terminal provides a form for inputting "when, where, and what to do."

[0255] 2. The user enters schedule information into the form (e.g., tomorrow 12:00, Tokyo, lunch at a cafe with a friend).

[0256] 3. The user device sends the entered schedule information to the server in JSON format.

[0257] 4. The server analyzes the received schedule information and sends a request to the weather forecast API to obtain weather information for the specified date, time, and location. For example, it obtains "sunny, with a temperature of 20 degrees" as the weather for Tokyo tomorrow.

[0258] 5. The server accesses a fashion information website and obtains the latest fashion trend information via scraping or API. For example, it obtains information that "casual wear is popular in spring."

[0259] 6. The server retrieves the user's past fashion history from the database. For example, it checks the user's past casual style choices.

[0260] 7. The emotion engine recognizes the user's emotional state by analyzing the user's facial expressions, voice, or input text. For example, if the user expresses the emotion "I want to relax today," the emotion engine identifies that emotional state.

[0261] 8. The server runs an algorithm to generate optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state as recognized by the emotion engine. For example, if the weather is sunny and the temperature is 20 degrees, it will suggest a "white casual shirt, denim pants, a light spring coat, and sneakers," which are ideal for a relaxed look.

[0262] 9. The server sends the generated fashion suggestions in JSON format to the user device.

[0263] 10. The user terminal displays the received fashion suggestions to the user, who can view the suggested items and styles.

[0264] 11. The user reviews the suggested fashion styles and selects one.

[0265] Specific examples

[0266] scenario

[0267] Consider an example where a user is choosing an outfit to wear to lunch at a cafe with a friend tomorrow and also receives suggestions that match their mood that day.

[0268] 1. The user device provides an application form for inputting "Tomorrow at 12 o'clock, Tokyo, lunch at a cafe with a friend."

[0269] 2. The user enters "Tomorrow 12:00, Tokyo, lunch at a cafe with a friend."

[0270] 3. The user device sends this information to the server.

[0271] 4. The server uses the weather API to obtain "Tomorrow's weather in Tokyo: sunny, temperature 20 degrees."

[0272] 5. The server scrapes fashion sites to obtain information on "spring casual wear trends."

[0273] 6. The server references past fashion history from the database and extracts information that "the user prefers casual style."

[0274] 7. The emotion engine recognizes the user's emotional state, "I feel like relaxing," from their facial expressions and voice.

[0275] 8. The server runs an algorithm based on weather, trends, past history, and sentiment to suggest "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0276] 9. The server sends the proposal to the user terminal.

[0277] 10. The user device displays suggestions, suggesting "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0278] 11. The user reviews the suggestions and selects an outfit.

[0279] In this way, the present invention can provide more personalized fashion suggestions based on the user's schedule information and emotional state.

[0280] The processing flow will be explained below.

[0281] Step 1:

[0282] The user terminal provides a form for inputting "when, where, and what to do."

[0283] Step 2:

[0284] The user inputs schedule information into the form (for example, tomorrow at 12 o'clock in Tokyo, lunch with a friend at a cafe).

[0285] Step 3:

[0286] The user terminal sends the entered schedule information to the server in JSON format.

[0287] Step 4:

[0288] The server analyzes the received schedule information and sends a request to the weather forecast API to obtain weather information for the specified date, time, and location. For example, it obtains "sunny, with a temperature of 20 degrees" as the weather for tomorrow in Tokyo.

[0289] Step 5:

[0290] The server accesses a fashion information website and obtains the latest fashion trend information via scraping or API. For example, it obtains information that "casual wear is popular in spring."

[0291] Step 6:

[0292] The server retrieves the user's past fashion history from the database, for example, checking the history of casual styles chosen by the user in the past.

[0293] Step 7:

[0294] The emotion engine recognizes the user's emotional state by analyzing their facial expressions, voice, or input text. For example, if a user expresses the emotion "I want to relax today," the engine identifies that emotional state.

[0295] Step 8:

[0296] The server runs an algorithm to generate optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state as recognized by the emotion engine. For example, if the weather is sunny and the temperature is 20 degrees, the server will suggest a "white casual shirt, denim pants, a light spring coat, and sneakers," which are ideal for a relaxed look.

[0297] Step 9:

[0298] The server sends the generated fashion suggestions to the user's device in JSON format.

[0299] Step 10:

[0300] The user terminal displays the received fashion suggestions to the user, who can view the suggested items and styles.

[0301] Step 11:

[0302] The user checks the suggested fashion styles and selects one.

[0303] Example 2

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

[0305] Conventional fashion suggestion systems can provide users with information on their schedules, weather, and the latest fashion trends, but they are unable to suggest optimal fashions that take into account the user's emotional state. As a result, they have been unable to provide sufficient support for users in choosing fashion that matches their mood and emotions on that day. Furthermore, there are few systems that use past fashion history to make personalized suggestions, and the suggestions they provide are general.

[0306] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0307] In this invention, the server includes means for providing an interface for a user to input schedule information, means for transmitting the schedule information to the server, means for acquiring weather information based on the schedule information, means for collecting the latest fashion trend information based on the schedule information and weather information, means for acquiring the user's past fashion history, means for recognizing the user's emotional state, means for suggesting optimal fashion to the user based on the weather information, fashion trend information, the user's past fashion history and the user's emotional state, and means for displaying the suggested fashion, thereby enabling more personalized optimal fashion suggestions that take into account the user's schedule information and emotional state.

[0308] "User terminal" refers to a device (such as a smartphone, tablet, or PC) on which a user inputs information and displays suggestions.

[0309] "Server" refers to the central system that processes information and generates suggestions.

[0310] "Interface" refers to a means for providing a user interface (UI) for a user to input schedule information.

[0311] "Schedule information" refers to information such as "when, where, and what to do" input by the user.

[0312] "Weather information" refers to information about weather conditions at a specified date, time and location, and is obtained through an external API.

[0313] "Fashion Trend Information" means information regarding current fashions and styles that is obtained from external sources.

[0314] "Database" refers to data storage for storing a user's past fashion history and other related data.

[0315] "Emotion engine" refers to a system for recognizing and analyzing a user's emotional state.

[0316] "Fashion suggestions" refers to suggestions about optimal fashion that are generated based on weather information, fashion trend information, the user's past fashion history, and emotional state.

[0317] "API" refers to an application programming interface for interfacing with external services and data sources.

[0318] "JSON format" refers to a format in which data is structured using JavaScript Object Notation, and is used for data communication between servers and user terminals.

[0319] The "HTTPS protocol" refers to a protocol for securely sending and receiving data over the Internet.

[0320] The present invention relates to a system that suggests optimal fashion based on the schedule information and emotions specified by the user. This system is composed of a user terminal, a server, an emotion engine, and related databases and APIs.

[0321] Hardware and software used

[0322] User device: A device such as a smartphone, tablet, or PC that allows a user to input information and view suggestions. A dedicated application or web interface is required.

[0323] Server: A central system for processing information and generating proposals, which may be a cloud-based server or a dedicated physical server.

[0324] Emotion engine: Software for recognizing and analyzing the user's emotional state, including facial expression recognition API and emotion analysis API.

[0325] Database: This is the data storage for storing the user's past fashion history and related data, and an SQL database or NoSQL database is used.

[0326] API: An interface for obtaining external weather information and fashion trend information, including weather forecast APIs and fashion information APIs.

[0327] Specific examples of program processing

[0328] In the present invention, a user inputs schedule information such as "tomorrow at 12 o'clock in Tokyo, lunch with a friend at a cafe." The user terminal converts this information into JSON format and sends it to the server.

[0329] The server analyzes the received schedule information and sends a request to an external weather forecast API to obtain weather information for the specified date, time, and location. For example, it obtains "sunny, 20 degrees Celsius" as the weather for tomorrow in Tokyo.

[0330] Next, the server accesses a fashion information website to obtain the latest fashion trend information. This information can be obtained via an API or by using web scraping technology. For example, it can obtain information such as "casual wear is popular this spring."

[0331] Next, the server retrieves the user's past fashion history from the database, for example, by referring to information such as "the user has preferred casual styles in the past."

[0332] The emotion engine analyzes facial expressions, voice, and input text to recognize the user's emotional state. For example, if the user indicates that they want to relax today, it will identify that information.

[0333] Finally, the server generates fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the emotional state recognized by the emotion engine, such as "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0334] The server sends the generated fashion suggestions in JSON format to the user's device, which then displays them to the user. The user can view the suggested items and styles and select appropriate outfits. This process enables more personalized and optimal fashion suggestions that take into account the user's schedule information and emotional state.

[0335] Prompt Sentence Examples

[0336] The following is an example of a prompt that may be entered into the system:

[0337] "I'm planning to meet a friend for lunch at a cafe in Tokyo tomorrow at 12 o'clock. What's the weather like and what kind of clothing would you recommend?"

[0338] "I feel like relaxing. What kind of outfit would you recommend for tomorrow?"

[0339] Based on these prompts, the system can provide appropriate fashion suggestions to the user.

[0340] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0341] Step 1:

[0342] User device provides input of schedule information

[0343] The user device displays a user interface (UI) and provides a form where the user can enter "when, where, and what to do." Input fields include date, time, location, and activity details. For example, in a smartphone app, users enter information by tapping text fields.

[0344] Input: None

[0345] Output: A form for the user to fill out

[0346] Step 2:

[0347] The user enters the appointment information

[0348] The user enters information into the provided form, for example, specific schedule information such as "tomorrow at 12 o'clock in Tokyo, lunch with a friend at a cafe," which allows the system to understand the user's plans.

[0349] Input: Event information entered by the user into the form

[0350] Output: Entered schedule information

[0351] Step 3:

[0352] The user device sends the input information to the server.

[0353] The user device converts the entered schedule information into JSON format and sends an HTTPS request to the server. For example, it generates the following JSON data:

[0354] json

[0355] {

[0356] "date": "tomorrow",

[0357] "time": "12 o'clock",

[0358] "location": "Tokyo",

[0359] "activity": "Lunch at a cafe with a friend"

[0360] }

[0361] Input: Information entered by the user into a form

[0362] Output: JSON formatted event information sent to the server

[0363] Step 4:

[0364] The server retrieves weather information

[0365] Based on the schedule information received by the server, a request is sent to an external weather forecast API (e.g., OpenWeatherMap) to obtain weather information for the specified date, time, and location. The server receives weather information for the specified date, time, and location (e.g., "Tomorrow's weather in Tokyo: sunny, temperature 20 degrees") in JSON format.

[0366] Input: Event information in JSON format

[0367] Output: JSON data of retrieved weather information

[0368] Step 5:

[0369] The server obtains fashion trend information

[0370] The server accesses a fashion information website to obtain the latest fashion trend information. This information is obtained via API or collected using web scraping technology. For example, the server obtains trend information such as "casual wear is popular in spring."

[0371] Input: None (request for trend information collection)

[0372] Output: Data of acquired fashion trend information

[0373] Step 6:

[0374] The server acquires the user's past fashion history.

[0375] The server searches the user's past fashion history from an internal database and obtains the user's preferred style and past selection information. For example, it uses an SQL query to perform the process of "obtaining past fashion selections based on the user ID."

[0376] Input: User ID

[0377] Output: User's past fashion history data

[0378] Step 7:

[0379] Emotion engine recognizes user emotions

[0380] The emotion engine analyzes facial expressions and voice to recognize the user's emotional state. It uses APIs to identify emotions, such as "I want to relax." If the user is facing the camera, it uses the facial recognition API.

[0381] Input: User facial and voice data

[0382] Output: Perceived emotional state

[0383] Step 8:

[0384] The server generates fashion suggestions

[0385] The server generates optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the emotional state recognized by the emotion engine. For example, it runs an algorithm that suggests "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0386] Input: Weather information, fashion trend information, user's past fashion history, emotional state

[0387] Output: Data for optimal fashion suggestions

[0388] Step 9:

[0389] The server sends fashion suggestions to the user's device.

[0390] The server converts the generated fashion suggestions into JSON format and sends it to the user's device. For example, the following JSON data is sent to the user's device:

[0391] json

[0392] {

[0393] "outfit": [

[0394] "White casual shirt",

[0395] "denim pants",

[0396] "Lightweight spring coat",

[0397] "sneakers"

[0398] ]

[0399] }

[0400] Input: Data for optimal fashion suggestions

[0401] Output: Fashion suggestions in JSON format sent to the user's device

[0402] Step 10:

[0403] The user's device displays fashion suggestions

[0404] The user's device analyzes the received fashion suggestions and visually displays them to the user. The app UI presents the user with a "white casual shirt, denim pants, a light spring coat, and sneakers."

[0405] Input: JSON data of fashion proposals

[0406] Output: Displayed fashion suggestions

[0407] Step 11:

[0408] The user checks and selects a fashion style

[0409] The user checks the displayed fashion suggestions and taps the item they want to choose from the options, which then feeds the selected information back into the system.

[0410] Input: Displayed fashion suggestions

[0411] Output: Data of fashion items selected by the user

[0412] (Application example 2)

[0413] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0414] While conventional fashion suggestion systems can make suggestions that take into account a user's schedule and weather information, it is difficult to make personalized suggestions that combine the user's emotional state and the latest fashion trend information. Furthermore, they do not provide an interface for users to easily purchase suggested fashion items, making it difficult to improve user satisfaction.

[0415] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing an interface for a user to input schedule information, means for transmitting the schedule information to the server, means for acquiring weather information based on the schedule information, means for collecting the latest fashion trend information based on the schedule information and the weather information, means for acquiring the user's past fashion history, means for recognizing and analyzing the user's emotional state, means for suggesting optimal fashion to the user based on the weather information, fashion trend information, the user's past fashion history, and the user's emotional state, and means for displaying the suggested fashion and providing an interface that makes it possible to purchase the suggested fashion in a virtual store. This enables personalized fashion suggestions that comprehensively consider the user's schedule information, weather information, fashion trend information, past fashion history, and emotional state, and allows the suggested items to be easily purchased in the virtual store.

[0416] The "interface for the user to input schedule information" refers to a user-operated device that the user uses to input information about future schedules (time, location, and activity details).

[0417] The "means for transmitting the schedule information to the server" is a mechanism for transmitting the schedule information input by the user to the server via a network.

[0418] "Means for obtaining weather information" refers to means for obtaining weather information for a specified date, time and location from an external database or API.

[0419] The "means for collecting the latest fashion trend information" is a function for collecting information on currently popular styles and fashion items from external sources.

[0420] The "means for acquiring the user's past fashion history" is a method for acquiring data relating to fashion styles and items selected by the user in the past from data storage.

[0421] The "means for recognizing and analyzing the user's emotional state" refers to a system that includes an algorithm that reads emotions from the user's facial expressions, voice, text input, etc., and analyzes that state.

[0422] The "means for suggesting optimal fashion" is a function that generates and presents the optimal fashion style to the user based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state.

[0423] The "means for providing an interface that enables purchases in a virtual store" is a user interface that allows the user to check the suggested fashion items in a virtual shopping space and carry out the purchasing procedure.

[0424] This invention is a system that suggests optimal fashion based on the user's schedule information and emotions, combined with weather and the latest fashion trend information. This system consists of the following main components:

[0425] User terminal: A device (smartphone, tablet, PC, etc.) on which a user inputs information and displays suggestions.

[0426] Server: The central system that processes information and generates proposals.

[0427] Emotion engine: A function for recognizing and analyzing the user's emotional state.

[0428] Database: A data storage for storing a user's past fashion history and other related data.

[0429] API: An interface with external services to obtain weather information and fashion trend information.

[0430] System Operation

[0431] 1. The user terminal provides an interface for the user to input schedule information (e.g., "Lunch with a friend at a cafe in Tokyo tomorrow at 12 o'clock"). The interface includes a form for inputting the date, time, location, and activity details.

[0432] 2. The user enters the appointment information into the form, and the information is sent to the server in JSON format.

[0433] 3. The server sends a request to the weather API based on the received schedule information to obtain weather information for the specified date, time, and location. For example, it obtains information such as "Tomorrow's weather in Tokyo will be sunny with a temperature of 20 degrees."

[0434] 4. The server also collects the latest fashion trend information from external fashion sources, either via API or scraping. For example, it obtains information that "casual wear is popular this spring."

[0435] 5. The server retrieves the user's past fashion history from the database, allowing the user to refer to the styles and items they have chosen in the past.

[0436] 6. The emotion engine recognizes and analyzes the user's emotional state from facial expressions, voice, text input, etc. For example, it identifies an emotional state such as "I feel like relaxing today."

[0437] 7. The server generates optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state recognized by the emotion engine. The suggestions are specific advice such as "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0438] 8. The server sends the generated fashion suggestions in JSON format to the user device, which displays them.

[0439] Hardware and Software

[0440] Hardware:

[0441] Smartphone: Used by users to enter information and view suggested fashions.

[0442] software:

[0443] Flask: A web framework for server-side processing.

[0444] Weather Information API: Used to obtain weather information.

[0445] Fashion trend information API and scraping technology: Used to collect fashion trend information.

[0446] Emotion recognition engine: Used to analyze the user's emotional state.

[0447] Specific examples

[0448] For example, if a user inputs a schedule such as "I'm going to have lunch with a friend at a cafe in Tokyo tomorrow at 12 o'clock" and selects "I feel like relaxing" as the emotional state, the system will act based on the following prompt:

[0449] The user has plans to meet a friend for lunch at a cafe in Tokyo tomorrow at 12 noon. He feels like relaxing. The suggested outfit is a white casual shirt, denim pants, a light spring coat, and sneakers.

[0450] This makes it possible to make personalized fashion suggestions that comprehensively take into account the user's schedule information, weather information, fashion trend information, past history, and emotional state, and to easily purchase the suggested items in a virtual store.

[0451] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0452] Step 1:

[0453] The user device provides an interface for the user to input schedule information. For example, the user inputs schedule information such as "Lunch at a cafe with a friend in Tokyo tomorrow at 12 o'clock." The device receives the date, time, location, and activity details as input, and sends this information in JSON format to the server.

[0454] Step 2:

[0455] The server receives the schedule information sent by the user. Based on the received schedule information, it sends a request to the weather information API. For example, it sends a request to obtain weather information for the specified time "tomorrow at 12:00 in Tokyo." It receives the schedule information as input, obtains weather information based on that schedule information, and outputs the weather information.

[0456] Step 3:

[0457] The server analyzes the weather information obtained from the weather information API. For example, it obtains information such as "Tomorrow's weather in Tokyo will be sunny with a temperature of 20 degrees." It analyzes the response from the weather information API and outputs it as weather information.

[0458] Step 4:

[0459] The server collects the latest fashion trend information using fashion API or scraping technology. For example, it obtains information such as "casual wear is popular in spring." It sends a request to an external fashion information source as input and outputs the latest fashion trend information.

[0460] Step 5:

[0461] The server retrieves the user's past fashion history from the database. For example, it references the user's past casual style choices. It receives the user ID as input and outputs the user's past fashion history.

[0462] Step 6:

[0463] The emotion engine recognizes and analyzes the user's emotional state from their facial expressions, voice, text input, etc. For example, it identifies the emotional state "I feel like relaxing today." It receives the user's emotional data as input and outputs the emotional state.

[0464] Step 7:

[0465] The server generates optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state recognized by the emotion engine. For example, it suggests "a white casual shirt, denim pants, a light spring coat, and sneakers." It receives the output data of all previous steps as input and outputs fashion suggestions.

[0466] Step 8:

[0467] The server sends the generated fashion suggestions in JSON format to the user device. The user device displays the received fashion suggestions to the user. It receives fashion suggestions as input and displays them. It provides an interface for the user to review the suggestions and purchase them in a virtual store.

[0468] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0469] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0470] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0471] [Second embodiment]

[0472] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0473] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0474] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0475] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0476] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0477] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0478] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0479] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0480] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0481] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0482] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0483] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0484] The present invention relates to a system that proposes optimal fashion based on schedule information specified by the user, combining weather and the latest fashion trend information. The system is composed of a user terminal, a server, and related databases and APIs.

[0485] System Configuration

[0486] User terminal: A device (smartphone, tablet, PC, etc.) that a user uses to input and display information.

[0487] Server: The central system that processes information and generates proposals.

[0488] Database: A data storage for storing a user's past fashion history and other related data.

[0489] API: An interface with external services to obtain weather and trend information.

[0490] System Operation

[0491] 1. The user terminal provides a form for inputting "when, where, and what to do."

[0492] 2. The user enters schedule information into the form. For example, "Tomorrow at 12:00 in Tokyo, lunch with a friend at a cafe."

[0493] 3. The user device sends the entered information to the server in JSON format.

[0494] 4. The server sends a request to the weather information API based on the received schedule information and retrieves weather information for the specified date, time, and location. For example, the weather information for tomorrow in Tokyo is retrieved as sunny with a temperature of 20 degrees.

[0495] 5. The server collects the latest fashion trend information. This is done by scraping trend information from fashion websites. For example, it obtains information that "casual styles are popular in spring."

[0496] 6. The server retrieves the user's past fashion history from the database. For example, it checks the styles the user has chosen in the past and their ratings.

[0497] 7. The server then runs an algorithm based on this information to generate optimal fashion recommendations. For example, it might select a "light jacket" based on the weather (sunny, 20 degrees) and suggest a "white casual shirt, denim pants, and sneakers" that reflects the current trend (casual style).

[0498] 8. The server sends the generated proposal in JSON format to the user device.

[0499] 9. The user device displays the received fashion suggestions. The user can review the suggestions and make a selection.

[0500] Specific examples

[0501] scenario

[0502] Take the example of a user choosing an outfit to wear to lunch at a cafe with a friend tomorrow.

[0503] 1. The user device provides an application form for inputting "Tomorrow at 12 o'clock, Tokyo, lunch at a cafe with a friend."

[0504] 2. The user enters "Tomorrow 12:00, Tokyo, lunch at a cafe with a friend."

[0505] 3. The user device sends this information to the server.

[0506] 4. The server uses the weather API to obtain "Tomorrow's weather in Tokyo: sunny, temperature 20 degrees."

[0507] 5. The server scrapes a famous fashion site to obtain trend information such as "casual wear is popular in spring."

[0508] 6. The server references past fashion history from the database and extracts information that "the user prefers casual style."

[0509] 7. The server processes the above information with an algorithm and suggests "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0510] 8. The server sends the proposal to the user terminal.

[0511] 9. The user device displays suggestions, showing the user "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0512] 10. The user reviews the suggestions and selects an outfit.

[0513] As described above, the present invention is capable of easily and quickly suggesting appropriate fashions based on the user's schedule information.

[0514] The processing flow will be explained below.

[0515] Step 1:

[0516] The user terminal provides a form for inputting "when, where, and what to do."

[0517] Step 2:

[0518] The user enters event information (date, time, location, and event details) into the form. For example, "Tomorrow at 12 o'clock in Tokyo, lunch at a cafe with a friend."

[0519] Step 3:

[0520] The user terminal sends the entered schedule information to the server in JSON format.

[0521] Step 4:

[0522] The server analyzes the received schedule information and sends a request to the weather forecast API to obtain weather information for the specified date, time, and location. For example, it obtains "sunny, with a temperature of 20 degrees" as the weather for tomorrow in Tokyo.

[0523] Step 5:

[0524] The server accesses a fashion information website and obtains the latest fashion trend information by scraping or via API. For example, it obtains information such as "Casual wear is popular this spring."

[0525] Step 6:

[0526] The server retrieves the user's past fashion history from the database, for example, checking the history of casual styles chosen by the user in the past.

[0527] Step 7:

[0528] The server runs an algorithm to generate optimal fashion suggestions based on weather information, fashion trend information, and the user's past fashion history. For example, if the weather is sunny and the temperature is 20 degrees, the server will select a "light jacket" and incorporate trendy casual wear, suggesting a "white casual shirt, denim pants, a light spring coat, and sneakers."

[0529] Step 8:

[0530] The server sends the generated fashion suggestions to the user's device in JSON format.

[0531] Step 9:

[0532] The user terminal displays the received fashion suggestions to the user, who can view the suggested items and styles.

[0533] Step 10:

[0534] The user checks the suggested fashion styles and selects one.

[0535] Example 1

[0536] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0537] In modern society, users face the challenge of having to gather a great deal of information in order to select the perfect fashion for themselves. Gathering this information takes a great deal of time and effort, especially when weather and the latest fashion trends must be taken into account. It is also not easy to record a user's past fashion choices and reflect them in their next selection. There is a need for a system that can solve these problems and allow users to easily select the perfect fashion.

[0538] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0539] In this invention, the server includes means for providing an interface for a user to input schedule information, means for transmitting the schedule information to the server, means for acquiring weather information based on the schedule information, means for collecting the latest fashion trend information based on the schedule information and the weather information, means for acquiring the user's past fashion history, means for suggesting optimal fashion to the user based on the weather information, fashion trend information, and the user's past fashion history, and means for displaying the suggested fashions. This allows the user to quickly and easily receive optimal fashion suggestions based on their schedule information.

[0540] A "user terminal" is an electronic device that allows a user to input and display information, and specifically includes a smartphone, tablet, or PC.

[0541] The "server" is a central system that processes information and generates proposals. It receives and analyzes data from user devices, accesses necessary external resources to obtain information, and generates optimal proposals.

[0542] An "interface" is something that provides a means for a user to input information in a manner that is easy for the user to operate, and specifically refers to user interface components such as forms, buttons, and text boxes.

[0543] "Schedule information" refers to specific information about a user's future plans and actions, such as date, time, location, activity details, and accompanying persons.

[0544] "Weather information" refers to weather data relating to a specific date, time and location, including, for example, temperature, weather, and probability of precipitation.

[0545] "Fashion trend information" refers to information about the latest fashion trends and fashions, and is obtained from fashion sites and the like.

[0546] "Fashion history" is information about fashions selected by the user in the past, and includes data such as selection history and ratings.

[0547] An "algorithm" is a set of calculations used to generate optimal fashion based on the information, and is used to synthesize and analyze data to create recommendations.

[0548] A "suggestion" is the specific content of the optimal fashion presented to the user, and includes specific combinations of clothing and accessories.

[0549] The present invention relates to a system that combines weather information and the latest fashion trend information to suggest optimal fashion based on schedule information specified by a user. This system is composed of a user terminal, a server, and related databases and APIs. Specific embodiments are described below.

[0550] System Configuration

[0551] 1. User Device

[0552] A user inputs schedule information using an electronic device such as a smartphone, tablet, or PC. The user terminal provides an interface for inputting schedule information.

[0553] 2. Server

[0554] The server is a central system that processes information and generates proposals. It receives data from user devices, accesses necessary external resources to obtain information, and generates optimal proposals.

[0555] 3. Database

[0556] The database is a data storage for storing a user's past fashion history and other related data.

[0557] 4. API

[0558] It is an interface with external services to obtain weather information and fashion trend information. Specifically, it uses an application programming interface such as the OpenWeatherMap API to obtain weather information.

[0559] System Operation

[0560] 1. The user device displays a form for entering schedule information.

[0561] For example, a smartphone app displays a form asking the user, "What time, where and what will you be doing tomorrow?"

[0562] 2. The user enters the appointment information

[0563] The user inputs specific schedule information such as "tomorrow at 12 o'clock in Tokyo, lunch at a cafe with a friend."

[0564] 3. The user device sends the input information to the server

[0565] The user device converts the input information into JSON format and sends it to the server. For example, the following data is generated: { "date": "tomorrow", "time": "12 o'clock", "location": "Tokyo", "activity": "lunch at a cafe", "company": "friends"}.

[0566] 4. The server retrieves weather information

[0567] The server sends a request to a weather information API (e.g., OpenWeatherMap API) to obtain weather information for the specified date, time, and location. For example, it obtains data such as "sunny, temperature 20 degrees."

[0568] 5. The server collects the latest fashion trends.

[0569] The server scrapes fashion sites to obtain trend information such as "casual styles are popular in spring."

[0570] 6. The server retrieves the user's past fashion history

[0571] The server accesses the database to retrieve the user's past fashion history, for example, extracting information such as "evaluation of the casual styles chosen by the user in the past."

[0572] 7. The server processes the information algorithmically and generates fashion suggestions.

[0573] The server runs an algorithm based on weather information, trend information, and past fashion history to generate optimal fashion suggestions. Specifically, it creates a suggestion of "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0574] 8. The server sends the generated proposal to the user device.

[0575] The suggestions are then converted back to JSON format and sent to the user's device. For example, the data is { "outfit": ["white casual shirt", "denim pants", "light spring coat", "sneakers"]}.

[0576] 9. The user device displays the suggestions.

[0577] The user device analyzes the received data and displays it in an easy-to-read format. The user can then check the suggested fashion. For example, a smartphone app might display "Tomorrow's fashion suggestions: a white casual shirt, denim pants, a light spring coat, and sneakers."

[0578] Prompt Sentence Examples

[0579] Below is an example of a prompt that can be input to a generative AI model:

[0580] "I'm planning to have lunch with a friend at a cafe in Tokyo tomorrow at noon. The weather is sunny and the temperature is 20 degrees. Casual spring styles are in fashion. What kind of outfit would be appropriate? Please give me some specific suggestions."

[0581] In this way, the present system can quickly suggest optimal fashion based on the user's schedule information.

[0582] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0583] Step 1:

[0584] The user terminal displays a schedule information input form.

[0585] Specifically, when the application is launched, the user terminal displays an interface that provides input fields such as "date," "time," "location," "activity," and "companion."

[0586] Step 2:

[0587] The user enters the appointment information.

[0588] The user inputs schedule information into the form, such as "Tomorrow at 12 noon in Tokyo, lunch with a friend at a cafe." This input information is collected through the interface.

[0589] Step 3:

[0590] The user terminal converts the input information into JSON format and sends it to the server.

[0591] It receives the schedule information entered by the user as input, processes the data to convert it into JSON format, and generates the JSON object { "date": "tomorrow", "time": "12 o'clock", "location": "Tokyo", "activity": "lunch at a cafe", "company": "friend"} as output, and sends it to the server.

[0592] Step 4:

[0593] The server retrieves weather information.

[0594] The server parses the received JSON data and sends the input "date" and "location" information as a request to the weather information API. Specifically, it uses the OpenWeatherMap API to obtain weather information for the specified date, time, and location. The output is weather information such as "sunny, temperature 20 degrees."

[0595] Step 5:

[0596] The server collects the latest fashion trend information.

[0597] The server scrapes trend information from fashion websites and collects external fashion information as input. Specifically, it extracts the necessary trend data using regular expressions and data filtering. The output is trend information such as "casual styles are popular in spring."

[0598] Step 6:

[0599] The server acquires the user's past fashion history.

[0600] The server accesses the database and retrieves past fashion history based on the user ID as input. Specifically, it retrieves the necessary information from the database using SQL queries. The output is historical data such as "evaluation of the user's past casual style choices."

[0601] Step 7:

[0602] The server processes the information algorithmically and generates fashion suggestions.

[0603] The server inputs weather information, trend information, and past fashion history into the algorithm. Specific data calculations use machine learning models and rule-based systems to determine the optimal fashion. The output generates suggestions such as "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0604] Step 8:

[0605] The server transmits the generated proposal to the user terminal.

[0606] The server converts the generated fashion suggestions into JSON format and sends this data to the user's device as output. Specifically, it sends the following data: { "outfit": ["white casual shirt", "denim pants", "light spring coat", "sneakers"]}.

[0607] Step 9:

[0608] The user terminal displays the proposal.

[0609] The user device parses the received JSON data and displays the suggested information received as input. Specifically, the interface displays "Tomorrow's fashion suggestions: white casual shirt, denim pants, light spring coat, sneakers."

[0610] Step 10:

[0611] The user reviews the suggestions and selects an outfit.

[0612] The user checks the displayed suggestions and selects and prepares the actual outfit based on the information provided as input. Specifically, the user takes out the clothes from the closet based on the suggestions and prepares to wear them.

[0613] (Application example 1)

[0614] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0615] In recent years, the amount of information available about fashion has rapidly increased, making it difficult for users to select the best outfit for their schedule and environment. Furthermore, there is a lack of ways for users to check the best outfits based on their own fashion style and trend information in real time. This makes it difficult to quickly choose the right fashion outfit, which can be time-consuming. Furthermore, when visually checking fashion suggestions, users are unable to try them on or visualize them, making it difficult to make a satisfactory decision.

[0616] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0617] In this invention, the server includes means for providing an interface for a user to input schedule information, means for transmitting the schedule information to the server, means for acquiring weather information based on the schedule information, means for collecting the latest fashion trend information based on the schedule information and the weather information, means for acquiring the user's past fashion history, means for suggesting optimal fashion to the user based on the weather information, fashion trend information, and the user's past fashion history, means for displaying the suggested fashion, means for providing the user with speech input and converting the schedule information into text using voice recognition, and means for displaying the optimal fashion superimposed on the user's field of view using augmented reality technology. This allows the user to quickly receive optimal fashion suggestions based on their schedule and environment and visually confirm them, enabling them to make quick and satisfying fashion selections.

[0618] A "user" is someone who uses this system to input schedule information and receive fashion suggestions.

[0619] The "server" is a central processing device that receives information sent by users, collects weather information and fashion trend information, and uses algorithms to generate optimal fashion suggestions.

[0620] An "interface" is a device or software that provides a screen for a user to input schedule information or a means for voice input.

[0621] "Schedule information" is specific schedule information such as "when, where, and what to do" input by the user.

[0622] "Weather information" is meteorological data for a specified date, time and location, including temperature, weather, humidity, etc.

[0623] "Fashion trend information" is information about currently popular fashion styles and items.

[0624] "Fashion history" is a record of the fashion styles chosen by the user in the past and their evaluations.

[0625] "Speech recognition" is a technology that analyzes the voice spoken by a user and converts it into text data.

[0626] "Augmented reality technology" is a technology that displays virtual information overlaid on real-world images.

[0627] The "optimal fashion suggestion" is an outfit suggestion generated based on the user's schedule information, weather information, fashion trend information, and past fashion history.

[0628] To implement this invention, it is necessary to use a user terminal and server, a voice recognition API, a weather information acquisition API, a fashion trend information collection means, a database, a machine learning algorithm, and augmented reality technology.

[0629] System Configuration

[0630] User terminal: A device such as smart glasses or a smartphone that allows the user to input schedule information and check suggested fashion.

[0631] Server: A central processing unit that processes information and generates fashion suggestions.

[0632] Database: A data storage for saving a user's past fashion history and related data.

[0633] API: An interface for obtaining weather information, voice recognition, and fashion trend information from external services.

[0634] Augmented reality technology: A technology that provides users with visual fashion suggestions.

[0635] System Operation

[0636] 1. The user device provides an interface that accepts voice input from the user. This interface has the function of converting voice into text data using the Google Speech-to-Text API.

[0637] 2. Voice input example: You input schedule information such as "Tomorrow at 12 o'clock in Tokyo, lunch with a friend at a cafe." The voice recognition API converts this into text.

[0638] 3. The user device sends the schedule information converted into text using voice recognition in JSON format to the server.

[0639] 4. Based on the received schedule information, the server sends a request to the OpenWeatherMap API to obtain weather information and retrieves weather data for the specified date, time, and location.

[0640] 5. The server uses a web scraping tool (e.g., BeautifulSoup) to collect the latest fashion trend information from external fashion sites.

[0641] 6. The server queries the user's past fashion history from the database and retrieves that information.

[0642] 7. The server generates optimal fashion suggestions using a machine learning algorithm (e.g., Scikit-learn) based on weather information, fashion trend information, and the user's past fashion history.

[0643] 8. The server sends the generated fashion suggestions in JSON format to the user device.

[0644] 9. The user device uses augmented reality technology such as Google ARCore to display fashion suggestions superimposed on the user's field of view, allowing the user to virtually try on the items.

[0645] Specific examples

[0646] For example, a user might say:

[0647] "Tomorrow at 12 noon, Tokyo, lunch with a friend at a cafe."

[0648] The server obtains and generates the following information based on the received schedule information:

[0649] Weather: Sunny, 20 degrees

[0650] Latest Fashion Trends: Spring Casual Style

[0651] Past fashion history: Prefers casual style

[0652] Based on this information, the server generates a fashion suggestion of "a white casual shirt, denim pants, a light spring coat, and sneakers," which is then displayed on the user's device using augmented reality technology.

[0653] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0654] Step 1:

[0655] The user terminal provides an interface for the user to input his / her schedule information by voice.

[0656] Input: User's voice input. Example: "Tomorrow 12:00, Tokyo, lunch with a friend at a cafe."

[0657] Output: Audio data

[0658] Step 2:

[0659] The user device sends the voice data to the Google Speech-to-Text API, which converts the voice into text data.

[0660] Input: Audio data

[0661] Output: Text data. Example: "Tomorrow 12:00, Tokyo, lunch at a cafe with a friend."

[0662] Step 3:

[0663] The user terminal transmits the schedule information converted into text data in JSON format to the server.

[0664] Input: Text data

[0665] Output: Event information in JSON format

[0666] Step 4:

[0667] Based on the received schedule information, the server sends a request to the OpenWeatherMap API to obtain weather information for the specified date, time, and location.

[0668] Input: Event information in JSON format

[0669] Output: Weather information. Example: Sunny, temperature 20 degrees.

[0670] Step 5:

[0671] The server uses a web scraping tool (e.g., BeautifulSoup) to collect the latest fashion trend information from external fashion sites.

[0672] Input: Scraping script execution command in the server

[0673] Output: Fashion trend information. Example: Casual style is popular in spring.

[0674] Step 6:

[0675] The server refers to the user's past fashion history from the database and acquires that information.

[0676] Input: Identification information such as user ID or username

[0677] Output: Past fashion history data. Example: Prefer casual style

[0678] Step 7:

[0679] The server generates optimal fashion suggestions using machine learning algorithms (e.g., Scikit-learn) based on weather information, fashion trend information, and the user's past fashion history.

[0680] Input: Weather information, fashion trend information, past fashion history

[0681] Output: Optimal fashion suggestions. Example: White casual shirt, denim pants, light spring coat, sneakers

[0682] Step 8:

[0683] The server sends the generated fashion suggestions in JSON format to the user's device.

[0684] Input: Data for optimal fashion suggestions

[0685] Output: Fashion suggestion data in JSON format

[0686] Step 9:

[0687] The user's device uses augmented reality technology such as Google ARCore to display fashion suggestions superimposed on the user's field of vision.

[0688] Input: Fashion proposal data in JSON format

[0689] Output: Fashion suggestions displayed in augmented reality

[0690] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0691] This invention relates to a system that suggests optimal fashion based on the schedule information and emotions specified by the user, combining weather and the latest fashion trend information. This system is composed of a user terminal, a server, an emotion engine, and related databases and APIs.

[0692] System Configuration

[0693] User device: The device (smartphone, tablet, PC, etc.) on which the user enters information and on which suggestions are displayed.

[0694] Server: The central system that processes information and generates proposals.

[0695] Emotion engine: A function for recognizing and analyzing the user's emotional state.

[0696] Database: A data storage for storing a user's past fashion history and other related data.

[0697] API: An interface with external services to obtain weather information and fashion trend information.

[0698] System Operation

[0699] 1. The user terminal provides a form for inputting "when, where, and what to do."

[0700] 2. The user enters schedule information into the form (e.g., tomorrow 12:00, Tokyo, lunch at a cafe with a friend).

[0701] 3. The user device sends the entered schedule information to the server in JSON format.

[0702] 4. The server analyzes the received schedule information and sends a request to the weather forecast API to obtain weather information for the specified date, time, and location. For example, it obtains "sunny, with a temperature of 20 degrees" as the weather for Tokyo tomorrow.

[0703] 5. The server accesses a fashion information website and obtains the latest fashion trend information via scraping or API. For example, it obtains information that "casual wear is popular in spring."

[0704] 6. The server retrieves the user's past fashion history from the database. For example, it checks the user's past casual style choices.

[0705] 7. The emotion engine recognizes the user's emotional state by analyzing the user's facial expressions, voice, or input text. For example, if the user expresses the emotion "I want to relax today," the emotion engine identifies that emotional state.

[0706] 8. The server runs an algorithm to generate optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state as recognized by the emotion engine. For example, if the weather is sunny and the temperature is 20 degrees, it will suggest a "white casual shirt, denim pants, a light spring coat, and sneakers," which are ideal for a relaxed look.

[0707] 9. The server sends the generated fashion suggestions in JSON format to the user device.

[0708] 10. The user terminal displays the received fashion suggestions to the user, who can view the suggested items and styles.

[0709] 11. The user reviews the suggested fashion styles and selects one.

[0710] Specific examples

[0711] scenario

[0712] Consider an example where a user is choosing an outfit to wear to lunch at a cafe with a friend tomorrow and also receives suggestions that match their mood that day.

[0713] 1. The user device provides an application form for inputting "Tomorrow at 12 o'clock, Tokyo, lunch at a cafe with a friend."

[0714] 2. The user enters "Tomorrow 12:00, Tokyo, lunch at a cafe with a friend."

[0715] 3. The user device sends this information to the server.

[0716] 4. The server uses the weather API to obtain "Tomorrow's weather in Tokyo: sunny, temperature 20 degrees."

[0717] 5. The server scrapes fashion sites to obtain information on "spring casual wear trends."

[0718] 6. The server references past fashion history from the database and extracts information that "the user prefers casual style."

[0719] 7. The emotion engine recognizes the user's emotional state, "I feel like relaxing," from their facial expressions and voice.

[0720] 8. The server runs an algorithm based on weather, trends, past history, and sentiment to suggest "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0721] 9. The server sends the proposal to the user terminal.

[0722] 10. The user device displays suggestions, suggesting "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0723] 11. The user reviews the suggestions and selects an outfit.

[0724] In this way, the present invention can provide more personalized fashion suggestions based on the user's schedule information and emotional state.

[0725] The processing flow will be explained below.

[0726] Step 1:

[0727] The user terminal provides a form for inputting "when, where, and what to do."

[0728] Step 2:

[0729] The user inputs schedule information into the form (for example, tomorrow at 12 o'clock in Tokyo, lunch with a friend at a cafe).

[0730] Step 3:

[0731] The user terminal sends the entered schedule information to the server in JSON format.

[0732] Step 4:

[0733] The server analyzes the received schedule information and sends a request to the weather forecast API to obtain weather information for the specified date, time, and location. For example, it obtains "sunny, with a temperature of 20 degrees" as the weather for tomorrow in Tokyo.

[0734] Step 5:

[0735] The server accesses a fashion information website and obtains the latest fashion trend information via scraping or API. For example, it obtains information that "casual wear is popular in spring."

[0736] Step 6:

[0737] The server retrieves the user's past fashion history from the database, for example, checking the history of casual styles chosen by the user in the past.

[0738] Step 7:

[0739] The emotion engine recognizes the user's emotional state by analyzing their facial expressions, voice, or input text. For example, if a user expresses the emotion "I want to relax today," the engine identifies that emotional state.

[0740] Step 8:

[0741] The server runs an algorithm to generate optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state as recognized by the emotion engine. For example, if the weather is sunny and the temperature is 20 degrees, the server will suggest a "white casual shirt, denim pants, a light spring coat, and sneakers," which are ideal for a relaxed look.

[0742] Step 9:

[0743] The server sends the generated fashion suggestions to the user's device in JSON format.

[0744] Step 10:

[0745] The user terminal displays the received fashion suggestions to the user, who can view the suggested items and styles.

[0746] Step 11:

[0747] The user checks the suggested fashion styles and selects one.

[0748] Example 2

[0749] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0750] Conventional fashion suggestion systems can provide users with information on their schedules, weather, and the latest fashion trends, but they are unable to suggest optimal fashions that take into account the user's emotional state. As a result, they have been unable to provide sufficient support for users in choosing fashion that matches their mood and emotions on that day. Furthermore, there are few systems that use past fashion history to make personalized suggestions, and the suggestions they provide are general.

[0751] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0752] In this invention, the server includes means for providing an interface for a user to input schedule information, means for transmitting the schedule information to the server, means for acquiring weather information based on the schedule information, means for collecting the latest fashion trend information based on the schedule information and weather information, means for acquiring the user's past fashion history, means for recognizing the user's emotional state, means for suggesting optimal fashion to the user based on the weather information, fashion trend information, the user's past fashion history and the user's emotional state, and means for displaying the suggested fashion, thereby enabling more personalized optimal fashion suggestions that take into account the user's schedule information and emotional state.

[0753] "User terminal" refers to a device (such as a smartphone, tablet, or PC) on which a user inputs information and displays suggestions.

[0754] "Server" refers to the central system that processes information and generates suggestions.

[0755] "Interface" refers to a means for providing a user interface (UI) for a user to input schedule information.

[0756] "Schedule information" refers to information such as "when, where, and what to do" input by the user.

[0757] "Weather information" refers to information about weather conditions at a specified date, time and location, and is obtained through an external API.

[0758] "Fashion Trend Information" means information regarding current fashions and styles that is obtained from external sources.

[0759] "Database" refers to data storage for storing a user's past fashion history and other related data.

[0760] "Emotion engine" refers to a system for recognizing and analyzing a user's emotional state.

[0761] "Fashion suggestions" refers to suggestions about optimal fashion that are generated based on weather information, fashion trend information, the user's past fashion history, and emotional state.

[0762] "API" refers to an application programming interface for interfacing with external services and data sources.

[0763] "JSON format" refers to a format in which data is structured using JavaScript Object Notation, and is used for data communication between servers and user terminals.

[0764] The "HTTPS protocol" refers to a protocol for securely sending and receiving data over the Internet.

[0765] The present invention relates to a system that suggests optimal fashion based on the schedule information and emotions specified by the user. This system is composed of a user terminal, a server, an emotion engine, and related databases and APIs.

[0766] Hardware and software used

[0767] User device: A device such as a smartphone, tablet, or PC that allows a user to input information and view suggestions. A dedicated application or web interface is required.

[0768] Server: A central system for processing information and generating proposals, which may be a cloud-based server or a dedicated physical server.

[0769] Emotion engine: Software for recognizing and analyzing the user's emotional state, including facial expression recognition API and emotion analysis API.

[0770] Database: This is the data storage for storing the user's past fashion history and related data, and an SQL database or NoSQL database is used.

[0771] API: An interface for obtaining external weather information and fashion trend information, including weather forecast APIs and fashion information APIs.

[0772] Specific examples of program processing

[0773] In the present invention, a user inputs schedule information such as "tomorrow at 12 o'clock in Tokyo, lunch with a friend at a cafe." The user terminal converts this information into JSON format and sends it to the server.

[0774] The server analyzes the received schedule information and sends a request to an external weather forecast API to obtain weather information for the specified date, time, and location. For example, it obtains "sunny, 20 degrees Celsius" as the weather for tomorrow in Tokyo.

[0775] Next, the server accesses a fashion information website to obtain the latest fashion trend information. This information can be obtained via an API or by using web scraping technology. For example, it can obtain information such as "casual wear is popular this spring."

[0776] Next, the server retrieves the user's past fashion history from the database, for example, by referring to information such as "the user has preferred casual styles in the past."

[0777] The emotion engine analyzes facial expressions, voice, and input text to recognize the user's emotional state. For example, if the user indicates that they want to relax today, it will identify that information.

[0778] Finally, the server generates fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the emotional state recognized by the emotion engine, such as "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0779] The server sends the generated fashion suggestions in JSON format to the user's device, which then displays them to the user. The user can view the suggested items and styles and select appropriate outfits. This process enables more personalized and optimal fashion suggestions that take into account the user's schedule information and emotional state.

[0780] Prompt Sentence Examples

[0781] The following is an example of a prompt that may be entered into the system:

[0782] "I'm planning to meet a friend for lunch at a cafe in Tokyo tomorrow at 12 o'clock. What's the weather like and what kind of clothing would you recommend?"

[0783] "I feel like relaxing. What kind of outfit would you recommend for tomorrow?"

[0784] Based on these prompts, the system can provide appropriate fashion suggestions to the user.

[0785] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0786] Step 1:

[0787] User device provides input of schedule information

[0788] The user device displays a user interface (UI) and provides a form where the user can enter "when, where, and what to do." Input fields include date, time, location, and activity details. For example, in a smartphone app, users enter information by tapping text fields.

[0789] Input: None

[0790] Output: A form for the user to fill out

[0791] Step 2:

[0792] The user enters the appointment information

[0793] The user enters information into the provided form, for example, specific schedule information such as "tomorrow at 12 o'clock in Tokyo, lunch with a friend at a cafe," which allows the system to understand the user's plans.

[0794] Input: Event information entered by the user into the form

[0795] Output: Entered schedule information

[0796] Step 3:

[0797] The user device sends the input information to the server.

[0798] The user device converts the entered schedule information into JSON format and sends an HTTPS request to the server. For example, it generates the following JSON data:

[0799] json

[0800] {

[0801] "date": "tomorrow",

[0802] "time": "12 o'clock",

[0803] "location": "Tokyo",

[0804] "activity": "Lunch at a cafe with a friend"

[0805] }

[0806] Input: Information entered by the user into a form

[0807] Output: JSON formatted event information sent to the server

[0808] Step 4:

[0809] The server retrieves weather information

[0810] Based on the schedule information received by the server, a request is sent to an external weather forecast API (e.g., OpenWeatherMap) to obtain weather information for the specified date, time, and location. The server receives weather information for the specified date, time, and location (e.g., "Tomorrow's weather in Tokyo: sunny, temperature 20 degrees") in JSON format.

[0811] Input: Event information in JSON format

[0812] Output: JSON data of retrieved weather information

[0813] Step 5:

[0814] The server obtains fashion trend information

[0815] The server accesses a fashion information website to obtain the latest fashion trend information. This information is obtained via API or collected using web scraping technology. For example, the server obtains trend information such as "casual wear is popular in spring."

[0816] Input: None (request for trend information collection)

[0817] Output: Data of acquired fashion trend information

[0818] Step 6:

[0819] The server acquires the user's past fashion history.

[0820] The server searches the user's past fashion history from an internal database and obtains the user's preferred style and past selection information. For example, it uses an SQL query to perform the process of "obtaining past fashion selections based on the user ID."

[0821] Input: User ID

[0822] Output: User's past fashion history data

[0823] Step 7:

[0824] Emotion engine recognizes user emotions

[0825] The emotion engine analyzes facial expressions and voice to recognize the user's emotional state. It uses APIs to identify emotions, such as "I want to relax." If the user is facing the camera, it uses the facial recognition API.

[0826] Input: User facial and voice data

[0827] Output: Perceived emotional state

[0828] Step 8:

[0829] The server generates fashion suggestions

[0830] The server generates optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the emotional state recognized by the emotion engine. For example, it runs an algorithm that suggests "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0831] Input: Weather information, fashion trend information, user's past fashion history, emotional state

[0832] Output: Data for optimal fashion suggestions

[0833] Step 9:

[0834] The server sends fashion suggestions to the user's device.

[0835] The server converts the generated fashion suggestions into JSON format and sends it to the user's device. For example, the following JSON data is sent to the user's device:

[0836] json

[0837] {

[0838] "outfit": [

[0839] "White casual shirt",

[0840] "denim pants",

[0841] "Lightweight spring coat",

[0842] "sneakers"

[0843] ]

[0844] }

[0845] Input: Data for optimal fashion suggestions

[0846] Output: Fashion suggestions in JSON format sent to the user's device

[0847] Step 10:

[0848] The user's device displays fashion suggestions

[0849] The user's device analyzes the received fashion suggestions and visually displays them to the user. The app UI presents the user with a "white casual shirt, denim pants, a light spring coat, and sneakers."

[0850] Input: JSON data of fashion proposals

[0851] Output: Displayed fashion suggestions

[0852] Step 11:

[0853] The user checks and selects a fashion style

[0854] The user checks the displayed fashion suggestions and taps the item they want to choose from the options, which then feeds the selected information back into the system.

[0855] Input: Displayed fashion suggestions

[0856] Output: Data of fashion items selected by the user

[0857] (Application example 2)

[0858] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0859] While conventional fashion suggestion systems can make suggestions that take into account a user's schedule and weather information, it is difficult to make personalized suggestions that combine the user's emotional state and the latest fashion trend information. Furthermore, they do not provide an interface for users to easily purchase suggested fashion items, making it difficult to improve user satisfaction.

[0860] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing an interface for a user to input schedule information, means for transmitting the schedule information to the server, means for acquiring weather information based on the schedule information, means for collecting the latest fashion trend information based on the schedule information and the weather information, means for acquiring the user's past fashion history, means for recognizing and analyzing the user's emotional state, means for suggesting optimal fashion to the user based on the weather information, fashion trend information, the user's past fashion history, and the user's emotional state, and means for displaying the suggested fashion and providing an interface that makes it possible to purchase the suggested fashion in a virtual store. This enables personalized fashion suggestions that comprehensively consider the user's schedule information, weather information, fashion trend information, past fashion history, and emotional state, and allows the suggested items to be easily purchased in the virtual store.

[0861] The "interface for the user to input schedule information" refers to a user-operated device that the user uses to input information about future schedules (time, location, and activity details).

[0862] The "means for transmitting the schedule information to the server" is a mechanism for transmitting the schedule information input by the user to the server via a network.

[0863] "Means for obtaining weather information" refers to means for obtaining weather information for a specified date, time and location from an external database or API.

[0864] The "means for collecting the latest fashion trend information" is a function for collecting information on currently popular styles and fashion items from external sources.

[0865] The "means for acquiring the user's past fashion history" is a method for acquiring data relating to fashion styles and items selected by the user in the past from data storage.

[0866] The "means for recognizing and analyzing the user's emotional state" refers to a system that includes an algorithm that reads emotions from the user's facial expressions, voice, text input, etc., and analyzes that state.

[0867] The "means for suggesting optimal fashion" is a function that generates and presents the optimal fashion style to the user based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state.

[0868] The "means for providing an interface that enables purchases in a virtual store" is a user interface that allows the user to check the suggested fashion items in a virtual shopping space and carry out the purchasing procedure.

[0869] This invention is a system that suggests optimal fashion based on the user's schedule information and emotions, combined with weather and the latest fashion trend information. This system consists of the following main components:

[0870] User terminal: A device (smartphone, tablet, PC, etc.) on which a user inputs information and displays suggestions.

[0871] Server: The central system that processes information and generates proposals.

[0872] Emotion engine: A function for recognizing and analyzing the user's emotional state.

[0873] Database: A data storage for storing a user's past fashion history and other related data.

[0874] API: An interface with external services to obtain weather information and fashion trend information.

[0875] System Operation

[0876] 1. The user terminal provides an interface for the user to input schedule information (e.g., "Lunch with a friend at a cafe in Tokyo tomorrow at 12 o'clock"). The interface includes a form for inputting the date, time, location, and activity details.

[0877] 2. The user enters the appointment information into the form, and the information is sent to the server in JSON format.

[0878] 3. The server sends a request to the weather API based on the received schedule information to obtain weather information for the specified date, time, and location. For example, it obtains information such as "Tomorrow's weather in Tokyo will be sunny with a temperature of 20 degrees."

[0879] 4. The server also collects the latest fashion trend information from external fashion sources, either via API or scraping. For example, it obtains information that "casual wear is popular this spring."

[0880] 5. The server retrieves the user's past fashion history from the database, allowing the user to refer to the styles and items they have chosen in the past.

[0881] 6. The emotion engine recognizes and analyzes the user's emotional state from facial expressions, voice, text input, etc. For example, it identifies an emotional state such as "I feel like relaxing today."

[0882] 7. The server generates optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state recognized by the emotion engine. The suggestions are specific advice such as "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0883] 8. The server sends the generated fashion suggestions in JSON format to the user device, which displays them.

[0884] Hardware and Software

[0885] Hardware:

[0886] Smartphone: Used by users to enter information and view suggested fashions.

[0887] software:

[0888] Flask: A web framework for server-side processing.

[0889] Weather Information API: Used to obtain weather information.

[0890] Fashion trend information API and scraping technology: Used to collect fashion trend information.

[0891] Emotion recognition engine: Used to analyze the user's emotional state.

[0892] Specific examples

[0893] For example, if a user inputs a schedule such as "I'm going to have lunch with a friend at a cafe in Tokyo tomorrow at 12 o'clock" and selects "I feel like relaxing" as the emotional state, the system will act based on the following prompt:

[0894] The user has plans to meet a friend for lunch at a cafe in Tokyo tomorrow at 12 noon. He feels like relaxing. The suggested outfit is a white casual shirt, denim pants, a light spring coat, and sneakers.

[0895] This makes it possible to make personalized fashion suggestions that comprehensively take into account the user's schedule information, weather information, fashion trend information, past history, and emotional state, and to easily purchase the suggested items in a virtual store.

[0896] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0897] Step 1:

[0898] The user device provides an interface for the user to input schedule information. For example, the user inputs schedule information such as "Lunch at a cafe with a friend in Tokyo tomorrow at 12 o'clock." The device receives the date, time, location, and activity details as input, and sends this information in JSON format to the server.

[0899] Step 2:

[0900] The server receives the schedule information sent by the user. Based on the received schedule information, it sends a request to the weather information API. For example, it sends a request to obtain weather information for the specified time "tomorrow at 12:00 in Tokyo." It receives the schedule information as input, obtains weather information based on that schedule information, and outputs the weather information.

[0901] Step 3:

[0902] The server analyzes the weather information obtained from the weather information API. For example, it obtains information such as "Tomorrow's weather in Tokyo will be sunny with a temperature of 20 degrees." It analyzes the response from the weather information API and outputs it as weather information.

[0903] Step 4:

[0904] The server collects the latest fashion trend information using fashion API or scraping technology. For example, it obtains information such as "casual wear is popular in spring." It sends a request to an external fashion information source as input and outputs the latest fashion trend information.

[0905] Step 5:

[0906] The server retrieves the user's past fashion history from the database. For example, it references the user's past casual style choices. It receives the user ID as input and outputs the user's past fashion history.

[0907] Step 6:

[0908] The emotion engine recognizes and analyzes the user's emotional state from their facial expressions, voice, text input, etc. For example, it identifies the emotional state "I feel like relaxing today." It receives the user's emotional data as input and outputs the emotional state.

[0909] Step 7:

[0910] The server generates optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state recognized by the emotion engine. For example, it suggests "a white casual shirt, denim pants, a light spring coat, and sneakers." It receives the output data of all previous steps as input and outputs fashion suggestions.

[0911] Step 8:

[0912] The server sends the generated fashion suggestions in JSON format to the user device. The user device displays the received fashion suggestions to the user. It receives fashion suggestions as input and displays them. It provides an interface for the user to review the suggestions and purchase them in a virtual store.

[0913] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0914] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0915] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0916] [Third embodiment]

[0917] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0918] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0919] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0920] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0921] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0922] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0923] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0924] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0925] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0926] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0927] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0928] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0929] The present invention relates to a system that proposes optimal fashion based on schedule information specified by the user, combining weather and the latest fashion trend information. The system is composed of a user terminal, a server, and related databases and APIs.

[0930] System Configuration

[0931] User terminal: A device (smartphone, tablet, PC, etc.) that a user uses to input and display information.

[0932] Server: The central system that processes information and generates proposals.

[0933] Database: A data storage for storing a user's past fashion history and other related data.

[0934] API: An interface with external services to obtain weather and trend information.

[0935] System Operation

[0936] 1. The user terminal provides a form for inputting "when, where, and what to do."

[0937] 2. The user enters schedule information into the form. For example, "Tomorrow at 12:00 in Tokyo, lunch with a friend at a cafe."

[0938] 3. The user device sends the entered information to the server in JSON format.

[0939] 4. The server sends a request to the weather information API based on the received schedule information and retrieves weather information for the specified date, time, and location. For example, the weather information for tomorrow in Tokyo is retrieved as sunny with a temperature of 20 degrees.

[0940] 5. The server collects the latest fashion trend information. This is done by scraping trend information from fashion websites. For example, it obtains information that "casual styles are popular in spring."

[0941] 6. The server retrieves the user's past fashion history from the database. For example, it checks the styles the user has chosen in the past and their ratings.

[0942] 7. The server then runs an algorithm based on this information to generate optimal fashion recommendations. For example, it might select a "light jacket" based on the weather (sunny, 20 degrees) and suggest a "white casual shirt, denim pants, and sneakers" that reflects the current trend (casual style).

[0943] 8. The server sends the generated proposal in JSON format to the user device.

[0944] 9. The user device displays the received fashion suggestions. The user can review the suggestions and make a selection.

[0945] Specific examples

[0946] scenario

[0947] Take the example of a user choosing an outfit to wear to lunch at a cafe with a friend tomorrow.

[0948] 1. The user device provides an application form for inputting "Tomorrow at 12 o'clock, Tokyo, lunch at a cafe with a friend."

[0949] 2. The user enters "Tomorrow 12:00, Tokyo, lunch at a cafe with a friend."

[0950] 3. The user device sends this information to the server.

[0951] 4. The server uses the weather API to obtain "Tomorrow's weather in Tokyo: sunny, temperature 20 degrees."

[0952] 5. The server scrapes a famous fashion site to obtain trend information such as "casual wear is popular in spring."

[0953] 6. The server references past fashion history from the database and extracts information that "the user prefers casual style."

[0954] 7. The server processes the above information with an algorithm and suggests "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0955] 8. The server sends the proposal to the user terminal.

[0956] 9. The user device displays suggestions, showing the user "a white casual shirt, denim pants, a light spring coat, and sneakers."

[0957] 10. The user reviews the suggestions and selects an outfit.

[0958] As described above, the present invention is capable of easily and quickly suggesting appropriate fashions based on the user's schedule information.

[0959] The processing flow will be explained below.

[0960] Step 1:

[0961] The user terminal provides a form for inputting "when, where, and what to do."

[0962] Step 2:

[0963] The user enters event information (date, time, location, and event details) into the form. For example, "Tomorrow at 12 o'clock in Tokyo, lunch at a cafe with a friend."

[0964] Step 3:

[0965] The user terminal sends the entered schedule information to the server in JSON format.

[0966] Step 4:

[0967] The server analyzes the received schedule information and sends a request to the weather forecast API to obtain weather information for the specified date, time, and location. For example, it obtains "sunny, with a temperature of 20 degrees" as the weather for tomorrow in Tokyo.

[0968] Step 5:

[0969] The server accesses a fashion information website and obtains the latest fashion trend information by scraping or via API. For example, it obtains information such as "Casual wear is popular this spring."

[0970] Step 6:

[0971] The server retrieves the user's past fashion history from the database, for example, checking the history of casual styles chosen by the user in the past.

[0972] Step 7:

[0973] The server runs an algorithm to generate optimal fashion suggestions based on weather information, fashion trend information, and the user's past fashion history. For example, if the weather is sunny and the temperature is 20 degrees, the server will select a "light jacket" and incorporate trendy casual wear, suggesting a "white casual shirt, denim pants, a light spring coat, and sneakers."

[0974] Step 8:

[0975] The server sends the generated fashion suggestions to the user's device in JSON format.

[0976] Step 9:

[0977] The user terminal displays the received fashion suggestions to the user, who can view the suggested items and styles.

[0978] Step 10:

[0979] The user checks the suggested fashion styles and selects one.

[0980] Example 1

[0981] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0982] In modern society, users face the challenge of having to gather a great deal of information in order to select the perfect fashion for themselves. Gathering this information takes a great deal of time and effort, especially when weather and the latest fashion trends must be taken into account. It is also not easy to record a user's past fashion choices and reflect them in their next selection. There is a need for a system that can solve these problems and allow users to easily select the perfect fashion.

[0983] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0984] In this invention, the server includes means for providing an interface for a user to input schedule information, means for transmitting the schedule information to the server, means for acquiring weather information based on the schedule information, means for collecting the latest fashion trend information based on the schedule information and the weather information, means for acquiring the user's past fashion history, means for suggesting optimal fashion to the user based on the weather information, fashion trend information, and the user's past fashion history, and means for displaying the suggested fashions. This allows the user to quickly and easily receive optimal fashion suggestions based on their schedule information.

[0985] A "user terminal" is an electronic device that allows a user to input and display information, and specifically includes a smartphone, tablet, or PC.

[0986] The "server" is a central system that processes information and generates proposals. It receives and analyzes data from user devices, accesses necessary external resources to obtain information, and generates optimal proposals.

[0987] An "interface" is something that provides a means for a user to input information in a manner that is easy for the user to operate, and specifically refers to user interface components such as forms, buttons, and text boxes.

[0988] "Schedule information" refers to specific information about a user's future plans and actions, such as date, time, location, activity details, and accompanying persons.

[0989] "Weather information" refers to weather data relating to a specific date, time and location, including, for example, temperature, weather, and probability of precipitation.

[0990] "Fashion trend information" refers to information about the latest fashion trends and fashions, and is obtained from fashion sites and the like.

[0991] "Fashion history" is information about fashions selected by the user in the past, and includes data such as selection history and ratings.

[0992] An "algorithm" is a set of calculations used to generate optimal fashion based on the information, and is used to synthesize and analyze data to create recommendations.

[0993] A "suggestion" is the specific content of the optimal fashion presented to the user, and includes specific combinations of clothing and accessories.

[0994] The present invention relates to a system that combines weather information and the latest fashion trend information to suggest optimal fashion based on schedule information specified by a user. This system is composed of a user terminal, a server, and related databases and APIs. Specific embodiments are described below.

[0995] System Configuration

[0996] 1. User Device

[0997] A user inputs schedule information using an electronic device such as a smartphone, tablet, or PC. The user terminal provides an interface for inputting schedule information.

[0998] 2. Server

[0999] The server is a central system that processes information and generates proposals. It receives data from user devices, accesses necessary external resources to obtain information, and generates optimal proposals.

[1000] 3. Database

[1001] The database is a data storage for storing a user's past fashion history and other related data.

[1002] 4. API

[1003] It is an interface with external services to obtain weather information and fashion trend information. Specifically, it uses an application programming interface such as the OpenWeatherMap API to obtain weather information.

[1004] System Operation

[1005] 1. The user device displays a form for entering schedule information.

[1006] For example, a smartphone app displays a form asking the user, "What time, where and what will you be doing tomorrow?"

[1007] 2. The user enters the appointment information

[1008] The user inputs specific schedule information such as "tomorrow at 12 o'clock in Tokyo, lunch at a cafe with a friend."

[1009] 3. The user device sends the input information to the server

[1010] The user device converts the input information into JSON format and sends it to the server. For example, the following data is generated: { "date": "tomorrow", "time": "12 o'clock", "location": "Tokyo", "activity": "lunch at a cafe", "company": "friends"}.

[1011] 4. The server retrieves weather information

[1012] The server sends a request to a weather information API (e.g., OpenWeatherMap API) to obtain weather information for the specified date, time, and location. For example, it obtains data such as "sunny, temperature 20 degrees."

[1013] 5. The server collects the latest fashion trends.

[1014] The server scrapes fashion sites to obtain trend information such as "casual styles are popular in spring."

[1015] 6. The server retrieves the user's past fashion history

[1016] The server accesses the database to retrieve the user's past fashion history, for example, extracting information such as "evaluation of the casual styles chosen by the user in the past."

[1017] 7. The server processes the information algorithmically and generates fashion suggestions.

[1018] The server runs an algorithm based on weather information, trend information, and past fashion history to generate optimal fashion suggestions. Specifically, it creates a suggestion of "a white casual shirt, denim pants, a light spring coat, and sneakers."

[1019] 8. The server sends the generated proposal to the user device.

[1020] The suggestions are then converted back to JSON format and sent to the user's device. For example, the data is { "outfit": ["white casual shirt", "denim pants", "light spring coat", "sneakers"]}.

[1021] 9. The user device displays the suggestions.

[1022] The user device analyzes the received data and displays it in an easy-to-read format. The user can then check the suggested fashion. For example, a smartphone app might display "Tomorrow's fashion suggestions: a white casual shirt, denim pants, a light spring coat, and sneakers."

[1023] Prompt Sentence Examples

[1024] Below is an example of a prompt that can be input to a generative AI model:

[1025] "I'm planning to have lunch with a friend at a cafe in Tokyo tomorrow at noon. The weather is sunny and the temperature is 20 degrees. Casual spring styles are in fashion. What kind of outfit would be appropriate? Please give me some specific suggestions."

[1026] In this way, the present system can quickly suggest optimal fashion based on the user's schedule information.

[1027] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1028] Step 1:

[1029] The user terminal displays a schedule information input form.

[1030] Specifically, when the application is launched, the user terminal displays an interface that provides input fields such as "date," "time," "location," "activity," and "companion."

[1031] Step 2:

[1032] The user enters the appointment information.

[1033] The user inputs schedule information into the form, such as "Tomorrow at 12 noon in Tokyo, lunch with a friend at a cafe." This input information is collected through the interface.

[1034] Step 3:

[1035] The user terminal converts the input information into JSON format and sends it to the server.

[1036] It receives the schedule information entered by the user as input, processes the data to convert it into JSON format, and generates the JSON object { "date": "tomorrow", "time": "12 o'clock", "location": "Tokyo", "activity": "lunch at a cafe", "company": "friend"} as output, and sends it to the server.

[1037] Step 4:

[1038] The server retrieves weather information.

[1039] The server parses the received JSON data and sends the input "date" and "location" information as a request to the weather information API. Specifically, it uses the OpenWeatherMap API to obtain weather information for the specified date, time, and location. The output is weather information such as "sunny, temperature 20 degrees."

[1040] Step 5:

[1041] The server collects the latest fashion trend information.

[1042] The server scrapes trend information from fashion websites and collects external fashion information as input. Specifically, it extracts the necessary trend data using regular expressions and data filtering. The output is trend information such as "casual styles are popular in spring."

[1043] Step 6:

[1044] The server acquires the user's past fashion history.

[1045] The server accesses the database and retrieves past fashion history based on the user ID as input. Specifically, it retrieves the necessary information from the database using SQL queries. The output is historical data such as "evaluation of the user's past casual style choices."

[1046] Step 7:

[1047] The server processes the information algorithmically and generates fashion suggestions.

[1048] The server inputs weather information, trend information, and past fashion history into the algorithm. Specific data calculations use machine learning models and rule-based systems to determine the optimal fashion. The output generates suggestions such as "a white casual shirt, denim pants, a light spring coat, and sneakers."

[1049] Step 8:

[1050] The server transmits the generated proposal to the user terminal.

[1051] The server converts the generated fashion suggestions into JSON format and sends this data to the user's device as output. Specifically, it sends the following data: { "outfit": ["white casual shirt", "denim pants", "light spring coat", "sneakers"]}.

[1052] Step 9:

[1053] The user terminal displays the proposal.

[1054] The user device parses the received JSON data and displays the suggested information received as input. Specifically, the interface displays "Tomorrow's fashion suggestions: white casual shirt, denim pants, light spring coat, sneakers."

[1055] Step 10:

[1056] The user reviews the suggestions and selects an outfit.

[1057] The user checks the displayed suggestions and selects and prepares the actual outfit based on the information provided as input. Specifically, the user takes out the clothes from the closet based on the suggestions and prepares to wear them.

[1058] (Application example 1)

[1059] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1060] In recent years, the amount of information available about fashion has rapidly increased, making it difficult for users to select the best outfit for their schedule and environment. Furthermore, there is a lack of ways for users to check the best outfits based on their own fashion style and trend information in real time. This makes it difficult to quickly choose the right fashion outfit, which can be time-consuming. Furthermore, when visually checking fashion suggestions, users are unable to try them on or visualize them, making it difficult to make a satisfactory decision.

[1061] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1062] In this invention, the server includes means for providing an interface for a user to input schedule information, means for transmitting the schedule information to the server, means for acquiring weather information based on the schedule information, means for collecting the latest fashion trend information based on the schedule information and the weather information, means for acquiring the user's past fashion history, means for suggesting optimal fashion to the user based on the weather information, fashion trend information, and the user's past fashion history, means for displaying the suggested fashion, means for providing the user with speech input and converting the schedule information into text using voice recognition, and means for displaying the optimal fashion superimposed on the user's field of view using augmented reality technology. This allows the user to quickly receive optimal fashion suggestions based on their schedule and environment and visually confirm them, enabling them to make quick and satisfying fashion selections.

[1063] A "user" is someone who uses this system to input schedule information and receive fashion suggestions.

[1064] The "server" is a central processing device that receives information sent by users, collects weather information and fashion trend information, and uses algorithms to generate optimal fashion suggestions.

[1065] An "interface" is a device or software that provides a screen for a user to input schedule information or a means for voice input.

[1066] "Schedule information" is specific schedule information such as "when, where, and what to do" input by the user.

[1067] "Weather information" is meteorological data for a specified date, time and location, including temperature, weather, humidity, etc.

[1068] "Fashion trend information" is information about currently popular fashion styles and items.

[1069] "Fashion history" is a record of the fashion styles chosen by the user in the past and their evaluations.

[1070] "Speech recognition" is a technology that analyzes the voice spoken by a user and converts it into text data.

[1071] "Augmented reality technology" is a technology that displays virtual information overlaid on real-world images.

[1072] The "optimal fashion suggestion" is an outfit suggestion generated based on the user's schedule information, weather information, fashion trend information, and past fashion history.

[1073] To implement this invention, it is necessary to use a user terminal and server, a voice recognition API, a weather information acquisition API, a fashion trend information collection means, a database, a machine learning algorithm, and augmented reality technology.

[1074] System Configuration

[1075] User terminal: A device such as smart glasses or a smartphone that allows the user to input schedule information and check suggested fashion.

[1076] Server: A central processing unit that processes information and generates fashion suggestions.

[1077] Database: A data storage for saving a user's past fashion history and related data.

[1078] API: An interface for obtaining weather information, voice recognition, and fashion trend information from external services.

[1079] Augmented reality technology: A technology that provides users with visual fashion suggestions.

[1080] System Operation

[1081] 1. The user device provides an interface that accepts voice input from the user. This interface has the function of converting voice into text data using the Google Speech-to-Text API.

[1082] 2. Voice input example: You input schedule information such as "Tomorrow at 12 o'clock in Tokyo, lunch with a friend at a cafe." The voice recognition API converts this into text.

[1083] 3. The user device sends the schedule information converted into text using voice recognition in JSON format to the server.

[1084] 4. Based on the received schedule information, the server sends a request to the OpenWeatherMap API to obtain weather information and retrieves weather data for the specified date, time, and location.

[1085] 5. The server uses a web scraping tool (e.g., BeautifulSoup) to collect the latest fashion trend information from external fashion sites.

[1086] 6. The server queries the user's past fashion history from the database and retrieves that information.

[1087] 7. The server generates optimal fashion suggestions using a machine learning algorithm (e.g., Scikit-learn) based on weather information, fashion trend information, and the user's past fashion history.

[1088] 8. The server sends the generated fashion suggestions in JSON format to the user device.

[1089] 9. The user device uses augmented reality technology such as Google ARCore to display fashion suggestions superimposed on the user's field of view, allowing the user to virtually try on the items.

[1090] Specific examples

[1091] For example, a user might say:

[1092] "Tomorrow at 12 noon, Tokyo, lunch with a friend at a cafe."

[1093] The server obtains and generates the following information based on the received schedule information:

[1094] Weather: Sunny, 20 degrees

[1095] Latest Fashion Trends: Spring Casual Style

[1096] Past fashion history: Prefers casual style

[1097] Based on this information, the server generates a fashion suggestion of "a white casual shirt, denim pants, a light spring coat, and sneakers," which is then displayed on the user's device using augmented reality technology.

[1098] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1099] Step 1:

[1100] The user terminal provides an interface for the user to input his / her schedule information by voice.

[1101] Input: User's voice input. Example: "Tomorrow 12:00, Tokyo, lunch with a friend at a cafe."

[1102] Output: Audio data

[1103] Step 2:

[1104] The user device sends the voice data to the Google Speech-to-Text API, which converts the voice into text data.

[1105] Input: Audio data

[1106] Output: Text data. Example: "Tomorrow 12:00, Tokyo, lunch at a cafe with a friend."

[1107] Step 3:

[1108] The user terminal transmits the schedule information converted into text data in JSON format to the server.

[1109] Input: Text data

[1110] Output: Event information in JSON format

[1111] Step 4:

[1112] Based on the received schedule information, the server sends a request to the OpenWeatherMap API to obtain weather information for the specified date, time, and location.

[1113] Input: Event information in JSON format

[1114] Output: Weather information. Example: Sunny, temperature 20 degrees.

[1115] Step 5:

[1116] The server uses a web scraping tool (e.g., BeautifulSoup) to collect the latest fashion trend information from external fashion sites.

[1117] Input: Scraping script execution command in the server

[1118] Output: Fashion trend information. Example: Casual style is popular in spring.

[1119] Step 6:

[1120] The server refers to the user's past fashion history from the database and acquires that information.

[1121] Input: Identification information such as user ID or username

[1122] Output: Past fashion history data. Example: Prefer casual style

[1123] Step 7:

[1124] The server generates optimal fashion suggestions using machine learning algorithms (e.g., Scikit-learn) based on weather information, fashion trend information, and the user's past fashion history.

[1125] Input: Weather information, fashion trend information, past fashion history

[1126] Output: Optimal fashion suggestions. Example: White casual shirt, denim pants, light spring coat, sneakers

[1127] Step 8:

[1128] The server sends the generated fashion suggestions in JSON format to the user's device.

[1129] Input: Data for optimal fashion suggestions

[1130] Output: Fashion suggestion data in JSON format

[1131] Step 9:

[1132] The user's device uses augmented reality technology such as Google ARCore to display fashion suggestions superimposed on the user's field of vision.

[1133] Input: Fashion proposal data in JSON format

[1134] Output: Fashion suggestions displayed in augmented reality

[1135] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1136] This invention relates to a system that suggests optimal fashion based on the schedule information and emotions specified by the user, combining weather and the latest fashion trend information. This system is composed of a user terminal, a server, an emotion engine, and related databases and APIs.

[1137] System Configuration

[1138] User device: The device (smartphone, tablet, PC, etc.) on which the user enters information and on which suggestions are displayed.

[1139] Server: The central system that processes information and generates proposals.

[1140] Emotion engine: A function for recognizing and analyzing the user's emotional state.

[1141] Database: A data storage for storing a user's past fashion history and other related data.

[1142] API: An interface with external services to obtain weather information and fashion trend information.

[1143] System Operation

[1144] 1. The user terminal provides a form for inputting "when, where, and what to do."

[1145] 2. The user enters schedule information into the form (e.g., tomorrow 12:00, Tokyo, lunch at a cafe with a friend).

[1146] 3. The user device sends the entered schedule information to the server in JSON format.

[1147] 4. The server analyzes the received schedule information and sends a request to the weather forecast API to obtain weather information for the specified date, time, and location. For example, it obtains "sunny, with a temperature of 20 degrees" as the weather for Tokyo tomorrow.

[1148] 5. The server accesses a fashion information website and obtains the latest fashion trend information via scraping or API. For example, it obtains information that "casual wear is popular in spring."

[1149] 6. The server retrieves the user's past fashion history from the database. For example, it checks the user's past casual style choices.

[1150] 7. The emotion engine recognizes the user's emotional state by analyzing the user's facial expressions, voice, or input text. For example, if the user expresses the emotion "I want to relax today," the emotion engine identifies that emotional state.

[1151] 8. The server runs an algorithm to generate optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state as recognized by the emotion engine. For example, if the weather is sunny and the temperature is 20 degrees, it will suggest a "white casual shirt, denim pants, a light spring coat, and sneakers," which are ideal for a relaxed look.

[1152] 9. The server sends the generated fashion suggestions in JSON format to the user device.

[1153] 10. The user terminal displays the received fashion suggestions to the user, who can view the suggested items and styles.

[1154] 11. The user reviews the suggested fashion styles and selects one.

[1155] Specific examples

[1156] scenario

[1157] Consider an example where a user is choosing an outfit to wear to lunch at a cafe with a friend tomorrow and also receives suggestions that match their mood that day.

[1158] 1. The user device provides an application form for inputting "Tomorrow at 12 o'clock, Tokyo, lunch at a cafe with a friend."

[1159] 2. The user enters "Tomorrow 12:00, Tokyo, lunch at a cafe with a friend."

[1160] 3. The user device sends this information to the server.

[1161] 4. The server uses the weather API to obtain "Tomorrow's weather in Tokyo: sunny, temperature 20 degrees."

[1162] 5. The server scrapes fashion sites to obtain information on "spring casual wear trends."

[1163] 6. The server references past fashion history from the database and extracts information that "the user prefers casual style."

[1164] 7. The emotion engine recognizes the user's emotional state, "I feel like relaxing," from their facial expressions and voice.

[1165] 8. The server runs an algorithm based on weather, trends, past history, and sentiment to suggest "a white casual shirt, denim pants, a light spring coat, and sneakers."

[1166] 9. The server sends the proposal to the user terminal.

[1167] 10. The user device displays suggestions, suggesting "a white casual shirt, denim pants, a light spring coat, and sneakers."

[1168] 11. The user reviews the suggestions and selects an outfit.

[1169] In this way, the present invention can provide more personalized fashion suggestions based on the user's schedule information and emotional state.

[1170] The processing flow will be explained below.

[1171] Step 1:

[1172] The user terminal provides a form for inputting "when, where, and what to do."

[1173] Step 2:

[1174] The user inputs schedule information into the form (for example, tomorrow at 12 o'clock in Tokyo, lunch with a friend at a cafe).

[1175] Step 3:

[1176] The user terminal sends the entered schedule information to the server in JSON format.

[1177] Step 4:

[1178] The server analyzes the received schedule information and sends a request to the weather forecast API to obtain weather information for the specified date, time, and location. For example, it obtains "sunny, with a temperature of 20 degrees" as the weather for tomorrow in Tokyo.

[1179] Step 5:

[1180] The server accesses a fashion information website and obtains the latest fashion trend information via scraping or API. For example, it obtains information that "casual wear is popular in spring."

[1181] Step 6:

[1182] The server retrieves the user's past fashion history from the database, for example, checking the history of casual styles chosen by the user in the past.

[1183] Step 7:

[1184] The emotion engine recognizes the user's emotional state by analyzing their facial expressions, voice, or input text. For example, if a user expresses the emotion "I want to relax today," the engine identifies that emotional state.

[1185] Step 8:

[1186] The server runs an algorithm to generate optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state as recognized by the emotion engine. For example, if the weather is sunny and the temperature is 20 degrees, the server will suggest a "white casual shirt, denim pants, a light spring coat, and sneakers," which are ideal for a relaxed look.

[1187] Step 9:

[1188] The server sends the generated fashion suggestions to the user's device in JSON format.

[1189] Step 10:

[1190] The user terminal displays the received fashion suggestions to the user, who can view the suggested items and styles.

[1191] Step 11:

[1192] The user checks the suggested fashion styles and selects one.

[1193] Example 2

[1194] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1195] Conventional fashion suggestion systems can provide users with information on their schedules, weather, and the latest fashion trends, but they are unable to suggest optimal fashions that take into account the user's emotional state. As a result, they have been unable to provide sufficient support for users in choosing fashion that matches their mood and emotions on that day. Furthermore, there are few systems that use past fashion history to make personalized suggestions, and the suggestions they provide are general.

[1196] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1197] In this invention, the server includes means for providing an interface for a user to input schedule information, means for transmitting the schedule information to the server, means for acquiring weather information based on the schedule information, means for collecting the latest fashion trend information based on the schedule information and weather information, means for acquiring the user's past fashion history, means for recognizing the user's emotional state, means for suggesting optimal fashion to the user based on the weather information, fashion trend information, the user's past fashion history and the user's emotional state, and means for displaying the suggested fashion, thereby enabling more personalized optimal fashion suggestions that take into account the user's schedule information and emotional state.

[1198] "User terminal" refers to a device (such as a smartphone, tablet, or PC) on which a user inputs information and displays suggestions.

[1199] "Server" refers to the central system that processes information and generates suggestions.

[1200] "Interface" refers to a means for providing a user interface (UI) for a user to input schedule information.

[1201] "Schedule information" refers to information such as "when, where, and what to do" input by the user.

[1202] "Weather information" refers to information about weather conditions at a specified date, time and location, and is obtained through an external API.

[1203] "Fashion Trend Information" means information regarding current fashions and styles that is obtained from external sources.

[1204] "Database" refers to data storage for storing a user's past fashion history and other related data.

[1205] "Emotion engine" refers to a system for recognizing and analyzing a user's emotional state.

[1206] "Fashion suggestions" refers to suggestions about optimal fashion that are generated based on weather information, fashion trend information, the user's past fashion history, and emotional state.

[1207] "API" refers to an application programming interface for interfacing with external services and data sources.

[1208] "JSON format" refers to a format in which data is structured using JavaScript Object Notation, and is used for data communication between servers and user terminals.

[1209] The "HTTPS protocol" refers to a protocol for securely sending and receiving data over the Internet.

[1210] The present invention relates to a system that suggests optimal fashion based on the schedule information and emotions specified by the user. This system is composed of a user terminal, a server, an emotion engine, and related databases and APIs.

[1211] Hardware and software used

[1212] User device: A device such as a smartphone, tablet, or PC that allows a user to input information and view suggestions. A dedicated application or web interface is required.

[1213] Server: A central system for processing information and generating proposals, which may be a cloud-based server or a dedicated physical server.

[1214] Emotion engine: Software for recognizing and analyzing the user's emotional state, including facial expression recognition API and emotion analysis API.

[1215] Database: This is the data storage for storing the user's past fashion history and related data, and an SQL database or NoSQL database is used.

[1216] API: An interface for obtaining external weather information and fashion trend information, including weather forecast APIs and fashion information APIs.

[1217] Specific examples of program processing

[1218] In the present invention, a user inputs schedule information such as "tomorrow at 12 o'clock in Tokyo, lunch with a friend at a cafe." The user terminal converts this information into JSON format and sends it to the server.

[1219] The server analyzes the received schedule information and sends a request to an external weather forecast API to obtain weather information for the specified date, time, and location. For example, it obtains "sunny, 20 degrees Celsius" as the weather for tomorrow in Tokyo.

[1220] Next, the server accesses a fashion information website to obtain the latest fashion trend information. This information can be obtained via an API or by using web scraping technology. For example, it can obtain information such as "casual wear is popular this spring."

[1221] Next, the server retrieves the user's past fashion history from the database, for example, by referring to information such as "the user has preferred casual styles in the past."

[1222] The emotion engine analyzes facial expressions, voice, and input text to recognize the user's emotional state. For example, if the user indicates that they want to relax today, it will identify that information.

[1223] Finally, the server generates fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the emotional state recognized by the emotion engine, such as "a white casual shirt, denim pants, a light spring coat, and sneakers."

[1224] The server sends the generated fashion suggestions in JSON format to the user's device, which then displays them to the user. The user can view the suggested items and styles and select appropriate outfits. This process enables more personalized and optimal fashion suggestions that take into account the user's schedule information and emotional state.

[1225] Prompt Sentence Examples

[1226] The following is an example of a prompt that may be entered into the system:

[1227] "I'm planning to meet a friend for lunch at a cafe in Tokyo tomorrow at 12 o'clock. What's the weather like and what kind of clothing would you recommend?"

[1228] "I feel like relaxing. What kind of outfit would you recommend for tomorrow?"

[1229] Based on these prompts, the system can provide appropriate fashion suggestions to the user.

[1230] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1231] Step 1:

[1232] User device provides input of schedule information

[1233] The user device displays a user interface (UI) and provides a form where the user can enter "when, where, and what to do." Input fields include date, time, location, and activity details. For example, in a smartphone app, users enter information by tapping text fields.

[1234] Input: None

[1235] Output: A form for the user to fill out

[1236] Step 2:

[1237] The user enters the appointment information

[1238] The user enters information into the provided form, for example, specific schedule information such as "tomorrow at 12 o'clock in Tokyo, lunch with a friend at a cafe," which allows the system to understand the user's plans.

[1239] Input: Event information entered by the user into the form

[1240] Output: Entered schedule information

[1241] Step 3:

[1242] The user device sends the input information to the server.

[1243] The user device converts the entered schedule information into JSON format and sends an HTTPS request to the server. For example, it generates the following JSON data:

[1244] json

[1245] {

[1246] "date": "tomorrow",

[1247] "time": "12 o'clock",

[1248] "location": "Tokyo",

[1249] "activity": "Lunch at a cafe with a friend"

[1250] }

[1251] Input: Information entered by the user into a form

[1252] Output: JSON formatted event information sent to the server

[1253] Step 4:

[1254] The server retrieves weather information

[1255] Based on the schedule information received by the server, a request is sent to an external weather forecast API (e.g., OpenWeatherMap) to obtain weather information for the specified date, time, and location. The server receives weather information for the specified date, time, and location (e.g., "Tomorrow's weather in Tokyo: sunny, temperature 20 degrees") in JSON format.

[1256] Input: Event information in JSON format

[1257] Output: JSON data of retrieved weather information

[1258] Step 5:

[1259] The server obtains fashion trend information

[1260] The server accesses a fashion information website to obtain the latest fashion trend information. This information is obtained via API or collected using web scraping technology. For example, the server obtains trend information such as "casual wear is popular in spring."

[1261] Input: None (request for trend information collection)

[1262] Output: Data of acquired fashion trend information

[1263] Step 6:

[1264] The server acquires the user's past fashion history.

[1265] The server searches the user's past fashion history from an internal database and obtains the user's preferred style and past selection information. For example, it uses an SQL query to perform the process of "obtaining past fashion selections based on the user ID."

[1266] Input: User ID

[1267] Output: User's past fashion history data

[1268] Step 7:

[1269] Emotion engine recognizes user emotions

[1270] The emotion engine analyzes facial expressions and voice to recognize the user's emotional state. It uses APIs to identify emotions, such as "I want to relax." If the user is facing the camera, it uses the facial recognition API.

[1271] Input: User facial and voice data

[1272] Output: Perceived emotional state

[1273] Step 8:

[1274] The server generates fashion suggestions

[1275] The server generates optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the emotional state recognized by the emotion engine. For example, it runs an algorithm that suggests "a white casual shirt, denim pants, a light spring coat, and sneakers."

[1276] Input: Weather information, fashion trend information, user's past fashion history, emotional state

[1277] Output: Data for optimal fashion suggestions

[1278] Step 9:

[1279] The server sends fashion suggestions to the user's device.

[1280] The server converts the generated fashion suggestions into JSON format and sends it to the user's device. For example, the following JSON data is sent to the user's device:

[1281] json

[1282] {

[1283] "outfit": [

[1284] "White casual shirt",

[1285] "denim pants",

[1286] "Lightweight spring coat",

[1287] "sneakers"

[1288] ]

[1289] }

[1290] Input: Data for optimal fashion suggestions

[1291] Output: Fashion suggestions in JSON format sent to the user's device

[1292] Step 10:

[1293] The user's device displays fashion suggestions

[1294] The user's device analyzes the received fashion suggestions and visually displays them to the user. The app UI presents the user with a "white casual shirt, denim pants, a light spring coat, and sneakers."

[1295] Input: JSON data of fashion proposals

[1296] Output: Displayed fashion suggestions

[1297] Step 11:

[1298] The user checks and selects a fashion style

[1299] The user checks the displayed fashion suggestions and taps the item they want to choose from the options, which then feeds the selected information back into the system.

[1300] Input: Displayed fashion suggestions

[1301] Output: Data of fashion items selected by the user

[1302] (Application example 2)

[1303] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1304] While conventional fashion suggestion systems can make suggestions that take into account a user's schedule and weather information, it is difficult to make personalized suggestions that combine the user's emotional state and the latest fashion trend information. Furthermore, they do not provide an interface for users to easily purchase suggested fashion items, making it difficult to improve user satisfaction.

[1305] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing an interface for a user to input schedule information, means for transmitting the schedule information to the server, means for acquiring weather information based on the schedule information, means for collecting the latest fashion trend information based on the schedule information and the weather information, means for acquiring the user's past fashion history, means for recognizing and analyzing the user's emotional state, means for suggesting optimal fashion to the user based on the weather information, fashion trend information, the user's past fashion history, and the user's emotional state, and means for displaying the suggested fashion and providing an interface that makes it possible to purchase the suggested fashion in a virtual store. This enables personalized fashion suggestions that comprehensively consider the user's schedule information, weather information, fashion trend information, past fashion history, and emotional state, and allows the suggested items to be easily purchased in the virtual store.

[1306] The "interface for the user to input schedule information" refers to a user-operated device that the user uses to input information about future schedules (time, location, and activity details).

[1307] The "means for transmitting the schedule information to the server" is a mechanism for transmitting the schedule information input by the user to the server via a network.

[1308] "Means for obtaining weather information" refers to means for obtaining weather information for a specified date, time and location from an external database or API.

[1309] The "means for collecting the latest fashion trend information" is a function for collecting information on currently popular styles and fashion items from external sources.

[1310] The "means for acquiring the user's past fashion history" is a method for acquiring data relating to fashion styles and items selected by the user in the past from data storage.

[1311] The "means for recognizing and analyzing the user's emotional state" refers to a system that includes an algorithm that reads emotions from the user's facial expressions, voice, text input, etc., and analyzes that state.

[1312] The "means for suggesting optimal fashion" is a function that generates and presents the optimal fashion style to the user based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state.

[1313] The "means for providing an interface that enables purchases in a virtual store" is a user interface that allows the user to check the suggested fashion items in a virtual shopping space and carry out the purchasing procedure.

[1314] This invention is a system that suggests optimal fashion based on the user's schedule information and emotions, combined with weather and the latest fashion trend information. This system consists of the following main components:

[1315] User terminal: A device (smartphone, tablet, PC, etc.) on which a user inputs information and displays suggestions.

[1316] Server: The central system that processes information and generates proposals.

[1317] Emotion engine: A function for recognizing and analyzing the user's emotional state.

[1318] Database: A data storage for storing a user's past fashion history and other related data.

[1319] API: An interface with external services to obtain weather information and fashion trend information.

[1320] System Operation

[1321] 1. The user terminal provides an interface for the user to input schedule information (e.g., "Lunch with a friend at a cafe in Tokyo tomorrow at 12 o'clock"). The interface includes a form for inputting the date, time, location, and activity details.

[1322] 2. The user enters the appointment information into the form, and the information is sent to the server in JSON format.

[1323] 3. The server sends a request to the weather API based on the received schedule information to obtain weather information for the specified date, time, and location. For example, it obtains information such as "Tomorrow's weather in Tokyo will be sunny with a temperature of 20 degrees."

[1324] 4. The server also collects the latest fashion trend information from external fashion sources, either via API or scraping. For example, it obtains information that "casual wear is popular this spring."

[1325] 5. The server retrieves the user's past fashion history from the database, allowing the user to refer to the styles and items they have chosen in the past.

[1326] 6. The emotion engine recognizes and analyzes the user's emotional state from facial expressions, voice, text input, etc. For example, it identifies an emotional state such as "I feel like relaxing today."

[1327] 7. The server generates optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state recognized by the emotion engine. The suggestions are specific advice such as "a white casual shirt, denim pants, a light spring coat, and sneakers."

[1328] 8. The server sends the generated fashion suggestions in JSON format to the user device, which displays them.

[1329] Hardware and Software

[1330] Hardware:

[1331] Smartphone: Used by users to enter information and view suggested fashions.

[1332] software:

[1333] Flask: A web framework for server-side processing.

[1334] Weather Information API: Used to obtain weather information.

[1335] Fashion trend information API and scraping technology: Used to collect fashion trend information.

[1336] Emotion recognition engine: Used to analyze the user's emotional state.

[1337] Specific examples

[1338] For example, if a user inputs a schedule such as "I'm going to have lunch with a friend at a cafe in Tokyo tomorrow at 12 o'clock" and selects "I feel like relaxing" as the emotional state, the system will act based on the following prompt:

[1339] The user has plans to meet a friend for lunch at a cafe in Tokyo tomorrow at 12 noon. He feels like relaxing. The suggested outfit is a white casual shirt, denim pants, a light spring coat, and sneakers.

[1340] This makes it possible to make personalized fashion suggestions that comprehensively take into account the user's schedule information, weather information, fashion trend information, past history, and emotional state, and to easily purchase the suggested items in a virtual store.

[1341] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1342] Step 1:

[1343] The user device provides an interface for the user to input schedule information. For example, the user inputs schedule information such as "Lunch at a cafe with a friend in Tokyo tomorrow at 12 o'clock." The device receives the date, time, location, and activity details as input, and sends this information in JSON format to the server.

[1344] Step 2:

[1345] The server receives the schedule information sent by the user. Based on the received schedule information, it sends a request to the weather information API. For example, it sends a request to obtain weather information for the specified time "tomorrow at 12:00 in Tokyo." It receives the schedule information as input, obtains weather information based on that schedule information, and outputs the weather information.

[1346] Step 3:

[1347] The server analyzes the weather information obtained from the weather information API. For example, it obtains information such as "Tomorrow's weather in Tokyo will be sunny with a temperature of 20 degrees." It analyzes the response from the weather information API and outputs it as weather information.

[1348] Step 4:

[1349] The server collects the latest fashion trend information using fashion API or scraping technology. For example, it obtains information such as "casual wear is popular in spring." It sends a request to an external fashion information source as input and outputs the latest fashion trend information.

[1350] Step 5:

[1351] The server retrieves the user's past fashion history from the database. For example, it references the user's past casual style choices. It receives the user ID as input and outputs the user's past fashion history.

[1352] Step 6:

[1353] The emotion engine recognizes and analyzes the user's emotional state from their facial expressions, voice, text input, etc. For example, it identifies the emotional state "I feel like relaxing today." It receives the user's emotional data as input and outputs the emotional state.

[1354] Step 7:

[1355] The server generates optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state recognized by the emotion engine. For example, it suggests "a white casual shirt, denim pants, a light spring coat, and sneakers." It receives the output data of all previous steps as input and outputs fashion suggestions.

[1356] Step 8:

[1357] The server sends the generated fashion suggestions in JSON format to the user device. The user device displays the received fashion suggestions to the user. It receives fashion suggestions as input and displays them. It provides an interface for the user to review the suggestions and purchase them in a virtual store.

[1358] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1359] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1360] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1361] [Fourth embodiment]

[1362] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1363] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1364] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1365] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1366] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1367] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1368] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1369] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1370] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1371] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1372] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1373] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1374] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1375] The present invention relates to a system that proposes optimal fashion based on schedule information specified by the user, combining weather and the latest fashion trend information. The system is composed of a user terminal, a server, and related databases and APIs.

[1376] System Configuration

[1377] User terminal: A device (smartphone, tablet, PC, etc.) that a user uses to input and display information.

[1378] Server: The central system that processes information and generates proposals.

[1379] Database: A data storage for storing a user's past fashion history and other related data.

[1380] API: An interface with external services to obtain weather and trend information.

[1381] System Operation

[1382] 1. The user terminal provides a form for inputting "when, where, and what to do."

[1383] 2. The user enters schedule information into the form. For example, "Tomorrow at 12:00 in Tokyo, lunch with a friend at a cafe."

[1384] 3. The user device sends the entered information to the server in JSON format.

[1385] 4. The server sends a request to the weather information API based on the received schedule information and retrieves weather information for the specified date, time, and location. For example, the weather information for tomorrow in Tokyo is retrieved as sunny with a temperature of 20 degrees.

[1386] 5. The server collects the latest fashion trend information. This is done by scraping trend information from fashion websites. For example, it obtains information that "casual styles are popular in spring."

[1387] 6. The server retrieves the user's past fashion history from the database. For example, it checks the styles the user has chosen in the past and their ratings.

[1388] 7. The server then runs an algorithm based on this information to generate optimal fashion recommendations. For example, it might select a "light jacket" based on the weather (sunny, 20 degrees) and suggest a "white casual shirt, denim pants, and sneakers" that reflects the current trend (casual style).

[1389] 8. The server sends the generated proposal in JSON format to the user device.

[1390] 9. The user device displays the received fashion suggestions. The user can review the suggestions and make a selection.

[1391] Specific examples

[1392] scenario

[1393] Take the example of a user choosing an outfit to wear to lunch at a cafe with a friend tomorrow.

[1394] 1. The user device provides an application form for inputting "Tomorrow at 12 o'clock, Tokyo, lunch at a cafe with a friend."

[1395] 2. The user enters "Tomorrow 12:00, Tokyo, lunch at a cafe with a friend."

[1396] 3. The user device sends this information to the server.

[1397] 4. The server uses the weather API to obtain "Tomorrow's weather in Tokyo: sunny, temperature 20 degrees."

[1398] 5. The server scrapes a famous fashion site to obtain trend information such as "casual wear is popular in spring."

[1399] 6. The server references past fashion history from the database and extracts information that "the user prefers casual style."

[1400] 7. The server processes the above information with an algorithm and suggests "a white casual shirt, denim pants, a light spring coat, and sneakers."

[1401] 8. The server sends the proposal to the user terminal.

[1402] 9. The user device displays suggestions, showing the user "a white casual shirt, denim pants, a light spring coat, and sneakers."

[1403] 10. The user reviews the suggestions and selects an outfit.

[1404] As described above, the present invention is capable of easily and quickly suggesting appropriate fashions based on the user's schedule information.

[1405] The processing flow will be explained below.

[1406] Step 1:

[1407] The user terminal provides a form for inputting "when, where, and what to do."

[1408] Step 2:

[1409] The user enters event information (date, time, location, and event details) into the form. For example, "Tomorrow at 12 o'clock in Tokyo, lunch at a cafe with a friend."

[1410] Step 3:

[1411] The user terminal sends the entered schedule information to the server in JSON format.

[1412] Step 4:

[1413] The server analyzes the received schedule information and sends a request to the weather forecast API to obtain weather information for the specified date, time, and location. For example, it obtains "sunny, with a temperature of 20 degrees" as the weather for tomorrow in Tokyo.

[1414] Step 5:

[1415] The server accesses a fashion information website and obtains the latest fashion trend information by scraping or via API. For example, it obtains information such as "Casual wear is popular this spring."

[1416] Step 6:

[1417] The server retrieves the user's past fashion history from the database, for example, checking the history of casual styles chosen by the user in the past.

[1418] Step 7:

[1419] The server runs an algorithm to generate optimal fashion suggestions based on weather information, fashion trend information, and the user's past fashion history. For example, if the weather is sunny and the temperature is 20 degrees, the server will select a "light jacket" and incorporate trendy casual wear, suggesting a "white casual shirt, denim pants, a light spring coat, and sneakers."

[1420] Step 8:

[1421] The server sends the generated fashion suggestions to the user's device in JSON format.

[1422] Step 9:

[1423] The user terminal displays the received fashion suggestions to the user, who can view the suggested items and styles.

[1424] Step 10:

[1425] The user checks the suggested fashion styles and selects one.

[1426] Example 1

[1427] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1428] In modern society, users face the challenge of having to gather a great deal of information in order to select the perfect fashion for themselves. Gathering this information takes a great deal of time and effort, especially when weather and the latest fashion trends must be taken into account. It is also not easy to record a user's past fashion choices and reflect them in their next selection. There is a need for a system that can solve these problems and allow users to easily select the perfect fashion.

[1429] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1430] In this invention, the server includes means for providing an interface for a user to input schedule information, means for transmitting the schedule information to the server, means for acquiring weather information based on the schedule information, means for collecting the latest fashion trend information based on the schedule information and the weather information, means for acquiring the user's past fashion history, means for suggesting optimal fashion to the user based on the weather information, fashion trend information, and the user's past fashion history, and means for displaying the suggested fashions. This allows the user to quickly and easily receive optimal fashion suggestions based on their schedule information.

[1431] A "user terminal" is an electronic device that allows a user to input and display information, and specifically includes a smartphone, tablet, or PC.

[1432] The "server" is a central system that processes information and generates proposals. It receives and analyzes data from user devices, accesses necessary external resources to obtain information, and generates optimal proposals.

[1433] An "interface" is something that provides a means for a user to input information in a manner that is easy for the user to operate, and specifically refers to user interface components such as forms, buttons, and text boxes.

[1434] "Schedule information" refers to specific information about a user's future plans and actions, such as date, time, location, activity details, and accompanying persons.

[1435] "Weather information" refers to weather data relating to a specific date, time and location, including, for example, temperature, weather, and probability of precipitation.

[1436] "Fashion trend information" refers to information about the latest fashion trends and fashions, and is obtained from fashion sites and the like.

[1437] "Fashion history" is information about fashions selected by the user in the past, and includes data such as selection history and ratings.

[1438] An "algorithm" is a set of calculations used to generate optimal fashion based on the information, and is used to synthesize and analyze data to create recommendations.

[1439] A "suggestion" is the specific content of the optimal fashion presented to the user, and includes specific combinations of clothing and accessories.

[1440] The present invention relates to a system that combines weather information and the latest fashion trend information to suggest optimal fashion based on schedule information specified by a user. This system is composed of a user terminal, a server, and related databases and APIs. Specific embodiments are described below.

[1441] System Configuration

[1442] 1. User Device

[1443] A user inputs schedule information using an electronic device such as a smartphone, tablet, or PC. The user terminal provides an interface for inputting schedule information.

[1444] 2. Server

[1445] The server is a central system that processes information and generates proposals. It receives data from user devices, accesses necessary external resources to obtain information, and generates optimal proposals.

[1446] 3. Database

[1447] The database is a data storage for storing a user's past fashion history and other related data.

[1448] 4. API

[1449] It is an interface with external services to obtain weather information and fashion trend information. Specifically, it uses an application programming interface such as the OpenWeatherMap API to obtain weather information.

[1450] System Operation

[1451] 1. The user device displays a form for entering schedule information.

[1452] For example, a smartphone app displays a form asking the user, "What time, where and what will you be doing tomorrow?"

[1453] 2. The user enters the appointment information

[1454] The user inputs specific schedule information such as "tomorrow at 12 o'clock in Tokyo, lunch at a cafe with a friend."

[1455] 3. The user device sends the input information to the server

[1456] The user device converts the input information into JSON format and sends it to the server. For example, the following data is generated: { "date": "tomorrow", "time": "12 o'clock", "location": "Tokyo", "activity": "lunch at a cafe", "company": "friends"}.

[1457] 4. The server retrieves weather information

[1458] The server sends a request to a weather information API (e.g., OpenWeatherMap API) to obtain weather information for the specified date, time, and location. For example, it obtains data such as "sunny, temperature 20 degrees."

[1459] 5. The server collects the latest fashion trends.

[1460] The server scrapes fashion sites to obtain trend information such as "casual styles are popular in spring."

[1461] 6. The server retrieves the user's past fashion history

[1462] The server accesses the database to retrieve the user's past fashion history, for example, extracting information such as "evaluation of the casual styles chosen by the user in the past."

[1463] 7. The server processes the information algorithmically and generates fashion suggestions.

[1464] The server runs an algorithm based on weather information, trend information, and past fashion history to generate optimal fashion suggestions. Specifically, it creates a suggestion of "a white casual shirt, denim pants, a light spring coat, and sneakers."

[1465] 8. The server sends the generated proposal to the user device.

[1466] The suggestions are then converted back to JSON format and sent to the user's device. For example, the data is { "outfit": ["white casual shirt", "denim pants", "light spring coat", "sneakers"]}.

[1467] 9. The user device displays the suggestions.

[1468] The user device analyzes the received data and displays it in an easy-to-read format. The user can then check the suggested fashion. For example, a smartphone app might display "Tomorrow's fashion suggestions: a white casual shirt, denim pants, a light spring coat, and sneakers."

[1469] Prompt Sentence Examples

[1470] Below is an example of a prompt that can be input to a generative AI model:

[1471] "I'm planning to have lunch with a friend at a cafe in Tokyo tomorrow at noon. The weather is sunny and the temperature is 20 degrees. Casual spring styles are in fashion. What kind of outfit would be appropriate? Please give me some specific suggestions."

[1472] In this way, the present system can quickly suggest optimal fashion based on the user's schedule information.

[1473] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1474] Step 1:

[1475] The user terminal displays a schedule information input form.

[1476] Specifically, when the application is launched, the user terminal displays an interface that provides input fields such as "date," "time," "location," "activity," and "companion."

[1477] Step 2:

[1478] The user enters the appointment information.

[1479] The user inputs schedule information into the form, such as "Tomorrow at 12 noon in Tokyo, lunch with a friend at a cafe." This input information is collected through the interface.

[1480] Step 3:

[1481] The user terminal converts the input information into JSON format and sends it to the server.

[1482] It receives the schedule information entered by the user as input, processes the data to convert it into JSON format, and generates the JSON object { "date": "tomorrow", "time": "12 o'clock", "location": "Tokyo", "activity": "lunch at a cafe", "company": "friend"} as output, and sends it to the server.

[1483] Step 4:

[1484] The server retrieves weather information.

[1485] The server parses the received JSON data and sends the input "date" and "location" information as a request to the weather information API. Specifically, it uses the OpenWeatherMap API to obtain weather information for the specified date, time, and location. The output is weather information such as "sunny, temperature 20 degrees."

[1486] Step 5:

[1487] The server collects the latest fashion trend information.

[1488] The server scrapes trend information from fashion websites and collects external fashion information as input. Specifically, it extracts the necessary trend data using regular expressions and data filtering. The output is trend information such as "casual styles are popular in spring."

[1489] Step 6:

[1490] The server acquires the user's past fashion history.

[1491] The server accesses the database and retrieves past fashion history based on the user ID as input. Specifically, it retrieves the necessary information from the database using SQL queries. The output is historical data such as "evaluation of the user's past casual style choices."

[1492] Step 7:

[1493] The server processes the information algorithmically and generates fashion suggestions.

[1494] The server inputs weather information, trend information, and past fashion history into the algorithm. Specific data calculations use machine learning models and rule-based systems to determine the optimal fashion. The output generates suggestions such as "a white casual shirt, denim pants, a light spring coat, and sneakers."

[1495] Step 8:

[1496] The server transmits the generated proposal to the user terminal.

[1497] The server converts the generated fashion suggestions into JSON format and sends this data to the user's device as output. Specifically, it sends the following data: { "outfit": ["white casual shirt", "denim pants", "light spring coat", "sneakers"]}.

[1498] Step 9:

[1499] The user terminal displays the proposal.

[1500] The user device parses the received JSON data and displays the suggested information received as input. Specifically, the interface displays "Tomorrow's fashion suggestions: white casual shirt, denim pants, light spring coat, sneakers."

[1501] Step 10:

[1502] The user reviews the suggestions and selects an outfit.

[1503] The user checks the displayed suggestions and selects and prepares the actual outfit based on the information provided as input. Specifically, the user takes out the clothes from the closet based on the suggestions and prepares to wear them.

[1504] (Application example 1)

[1505] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1506] In recent years, the amount of information available about fashion has rapidly increased, making it difficult for users to select the best outfit for their schedule and environment. Furthermore, there is a lack of ways for users to check the best outfits based on their own fashion style and trend information in real time. This makes it difficult to quickly choose the right fashion outfit, which can be time-consuming. Furthermore, when visually checking fashion suggestions, users are unable to try them on or visualize them, making it difficult to make a satisfactory decision.

[1507] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1508] In this invention, the server includes means for providing an interface for a user to input schedule information, means for transmitting the schedule information to the server, means for acquiring weather information based on the schedule information, means for collecting the latest fashion trend information based on the schedule information and the weather information, means for acquiring the user's past fashion history, means for suggesting optimal fashion to the user based on the weather information, fashion trend information, and the user's past fashion history, means for displaying the suggested fashion, means for providing the user with speech input and converting the schedule information into text using voice recognition, and means for displaying the optimal fashion superimposed on the user's field of view using augmented reality technology. This allows the user to quickly receive optimal fashion suggestions based on their schedule and environment and visually confirm them, enabling them to make quick and satisfying fashion selections.

[1509] A "user" is someone who uses this system to input schedule information and receive fashion suggestions.

[1510] The "server" is a central processing device that receives information sent by users, collects weather information and fashion trend information, and uses algorithms to generate optimal fashion suggestions.

[1511] An "interface" is a device or software that provides a screen for a user to input schedule information or a means for voice input.

[1512] "Schedule information" is specific schedule information such as "when, where, and what to do" input by the user.

[1513] "Weather information" is meteorological data for a specified date, time and location, including temperature, weather, humidity, etc.

[1514] "Fashion trend information" is information about currently popular fashion styles and items.

[1515] "Fashion history" is a record of the fashion styles chosen by the user in the past and their evaluations.

[1516] "Speech recognition" is a technology that analyzes the voice spoken by a user and converts it into text data.

[1517] "Augmented reality technology" is a technology that displays virtual information overlaid on real-world images.

[1518] The "optimal fashion suggestion" is an outfit suggestion generated based on the user's schedule information, weather information, fashion trend information, and past fashion history.

[1519] To implement this invention, it is necessary to use a user terminal and server, a voice recognition API, a weather information acquisition API, a fashion trend information collection means, a database, a machine learning algorithm, and augmented reality technology.

[1520] System Configuration

[1521] User terminal: A device such as smart glasses or a smartphone that allows the user to input schedule information and check suggested fashion.

[1522] Server: A central processing unit that processes information and generates fashion suggestions.

[1523] Database: A data storage for saving a user's past fashion history and related data.

[1524] API: An interface for obtaining weather information, voice recognition, and fashion trend information from external services.

[1525] Augmented reality technology: A technology that provides users with visual fashion suggestions.

[1526] System Operation

[1527] 1. The user device provides an interface that accepts voice input from the user. This interface has the function of converting voice into text data using the Google Speech-to-Text API.

[1528] 2. Voice input example: You input schedule information such as "Tomorrow at 12 o'clock in Tokyo, lunch with a friend at a cafe." The voice recognition API converts this into text.

[1529] 3. The user device sends the schedule information converted into text using voice recognition in JSON format to the server.

[1530] 4. Based on the received schedule information, the server sends a request to the OpenWeatherMap API to obtain weather information and retrieves weather data for the specified date, time, and location.

[1531] 5. The server uses a web scraping tool (e.g., BeautifulSoup) to collect the latest fashion trend information from external fashion sites.

[1532] 6. The server queries the user's past fashion history from the database and retrieves that information.

[1533] 7. The server generates optimal fashion suggestions using a machine learning algorithm (e.g., Scikit-learn) based on weather information, fashion trend information, and the user's past fashion history.

[1534] 8. The server sends the generated fashion suggestions in JSON format to the user device.

[1535] 9. The user device uses augmented reality technology such as Google ARCore to display fashion suggestions superimposed on the user's field of view, allowing the user to virtually try on the items.

[1536] Specific examples

[1537] For example, a user might say:

[1538] "Tomorrow at 12 noon, Tokyo, lunch with a friend at a cafe."

[1539] The server obtains and generates the following information based on the received schedule information:

[1540] Weather: Sunny, 20 degrees

[1541] Latest Fashion Trends: Spring Casual Style

[1542] Past fashion history: Prefers casual style

[1543] Based on this information, the server generates a fashion suggestion of "a white casual shirt, denim pants, a light spring coat, and sneakers," which is then displayed on the user's device using augmented reality technology.

[1544] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1545] Step 1:

[1546] The user terminal provides an interface for the user to input his / her schedule information by voice.

[1547] Input: User's voice input. Example: "Tomorrow 12:00, Tokyo, lunch with a friend at a cafe."

[1548] Output: Audio data

[1549] Step 2:

[1550] The user device sends the voice data to the Google Speech-to-Text API, which converts the voice into text data.

[1551] Input: Audio data

[1552] Output: Text data. Example: "Tomorrow 12:00, Tokyo, lunch at a cafe with a friend."

[1553] Step 3:

[1554] The user terminal transmits the schedule information converted into text data in JSON format to the server.

[1555] Input: Text data

[1556] Output: Event information in JSON format

[1557] Step 4:

[1558] Based on the received schedule information, the server sends a request to the OpenWeatherMap API to obtain weather information for the specified date, time, and location.

[1559] Input: Event information in JSON format

[1560] Output: Weather information. Example: Sunny, temperature 20 degrees.

[1561] Step 5:

[1562] The server uses a web scraping tool (e.g., BeautifulSoup) to collect the latest fashion trend information from external fashion sites.

[1563] Input: Scraping script execution command in the server

[1564] Output: Fashion trend information. Example: Casual style is popular in spring.

[1565] Step 6:

[1566] The server refers to the user's past fashion history from the database and acquires that information.

[1567] Input: Identification information such as user ID or username

[1568] Output: Past fashion history data. Example: Prefer casual style

[1569] Step 7:

[1570] The server generates optimal fashion suggestions using machine learning algorithms (e.g., Scikit-learn) based on weather information, fashion trend information, and the user's past fashion history.

[1571] Input: Weather information, fashion trend information, past fashion history

[1572] Output: Optimal fashion suggestions. Example: White casual shirt, denim pants, light spring coat, sneakers

[1573] Step 8:

[1574] The server sends the generated fashion suggestions in JSON format to the user's device.

[1575] Input: Data for optimal fashion suggestions

[1576] Output: Fashion suggestion data in JSON format

[1577] Step 9:

[1578] The user's device uses augmented reality technology such as Google ARCore to display fashion suggestions superimposed on the user's field of vision.

[1579] Input: Fashion proposal data in JSON format

[1580] Output: Fashion suggestions displayed in augmented reality

[1581] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1582] This invention relates to a system that suggests optimal fashion based on the schedule information and emotions specified by the user, combining weather and the latest fashion trend information. This system is composed of a user terminal, a server, an emotion engine, and related databases and APIs.

[1583] System Configuration

[1584] User device: The device (smartphone, tablet, PC, etc.) on which the user enters information and on which suggestions are displayed.

[1585] Server: The central system that processes information and generates proposals.

[1586] Emotion engine: A function for recognizing and analyzing the user's emotional state.

[1587] Database: A data storage for storing a user's past fashion history and other related data.

[1588] API: An interface with external services to obtain weather information and fashion trend information.

[1589] System Operation

[1590] 1. The user terminal provides a form for inputting "when, where, and what to do."

[1591] 2. The user enters schedule information into the form (e.g., tomorrow 12:00, Tokyo, lunch at a cafe with a friend).

[1592] 3. The user device sends the entered schedule information to the server in JSON format.

[1593] 4. The server analyzes the received schedule information and sends a request to the weather forecast API to obtain weather information for the specified date, time, and location. For example, it obtains "sunny, with a temperature of 20 degrees" as the weather for Tokyo tomorrow.

[1594] 5. The server accesses a fashion information website and obtains the latest fashion trend information via scraping or API. For example, it obtains information that "casual wear is popular in spring."

[1595] 6. The server retrieves the user's past fashion history from the database. For example, it checks the user's past casual style choices.

[1596] 7. The emotion engine recognizes the user's emotional state by analyzing the user's facial expressions, voice, or input text. For example, if the user expresses the emotion "I want to relax today," the emotion engine identifies that emotional state.

[1597] 8. The server runs an algorithm to generate optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state as recognized by the emotion engine. For example, if the weather is sunny and the temperature is 20 degrees, it will suggest a "white casual shirt, denim pants, a light spring coat, and sneakers," which are ideal for a relaxed look.

[1598] 9. The server sends the generated fashion suggestions in JSON format to the user device.

[1599] 10. The user terminal displays the received fashion suggestions to the user, who can view the suggested items and styles.

[1600] 11. The user reviews the suggested fashion styles and selects one.

[1601] Specific examples

[1602] scenario

[1603] Consider an example where a user is choosing an outfit to wear to lunch at a cafe with a friend tomorrow and also receives suggestions that match their mood that day.

[1604] 1. The user device provides an application form for inputting "Tomorrow at 12 o'clock, Tokyo, lunch at a cafe with a friend."

[1605] 2. The user enters "Tomorrow 12:00, Tokyo, lunch at a cafe with a friend."

[1606] 3. The user device sends this information to the server.

[1607] 4. The server uses the weather API to obtain "Tomorrow's weather in Tokyo: sunny, temperature 20 degrees."

[1608] 5. The server scrapes fashion sites to obtain information on "spring casual wear trends."

[1609] 6. The server references past fashion history from the database and extracts information that "the user prefers casual style."

[1610] 7. The emotion engine recognizes the user's emotional state, "I feel like relaxing," from their facial expressions and voice.

[1611] 8. The server runs an algorithm based on weather, trends, past history, and sentiment to suggest "a white casual shirt, denim pants, a light spring coat, and sneakers."

[1612] 9. The server sends the proposal to the user terminal.

[1613] 10. The user device displays suggestions, suggesting "a white casual shirt, denim pants, a light spring coat, and sneakers."

[1614] 11. The user reviews the suggestions and selects an outfit.

[1615] In this way, the present invention can provide more personalized fashion suggestions based on the user's schedule information and emotional state.

[1616] The processing flow will be explained below.

[1617] Step 1:

[1618] The user terminal provides a form for inputting "when, where, and what to do."

[1619] Step 2:

[1620] The user inputs schedule information into the form (for example, tomorrow at 12 o'clock in Tokyo, lunch with a friend at a cafe).

[1621] Step 3:

[1622] The user terminal sends the entered schedule information to the server in JSON format.

[1623] Step 4:

[1624] The server analyzes the received schedule information and sends a request to the weather forecast API to obtain weather information for the specified date, time, and location. For example, it obtains "sunny, with a temperature of 20 degrees" as the weather for tomorrow in Tokyo.

[1625] Step 5:

[1626] The server accesses a fashion information website and obtains the latest fashion trend information via scraping or API. For example, it obtains information that "casual wear is popular in spring."

[1627] Step 6:

[1628] The server retrieves the user's past fashion history from the database, for example, checking the history of casual styles chosen by the user in the past.

[1629] Step 7:

[1630] The emotion engine recognizes the user's emotional state by analyzing their facial expressions, voice, or input text. For example, if a user expresses the emotion "I want to relax today," the engine identifies that emotional state.

[1631] Step 8:

[1632] The server runs an algorithm to generate optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state as recognized by the emotion engine. For example, if the weather is sunny and the temperature is 20 degrees, the server will suggest a "white casual shirt, denim pants, a light spring coat, and sneakers," which are ideal for a relaxed look.

[1633] Step 9:

[1634] The server sends the generated fashion suggestions to the user's device in JSON format.

[1635] Step 10:

[1636] The user terminal displays the received fashion suggestions to the user, who can view the suggested items and styles.

[1637] Step 11:

[1638] The user checks the suggested fashion styles and selects one.

[1639] Example 2

[1640] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1641] Conventional fashion suggestion systems can provide users with information on their schedules, weather, and the latest fashion trends, but they are unable to suggest optimal fashions that take into account the user's emotional state. As a result, they have been unable to provide sufficient support for users in choosing fashion that matches their mood and emotions on that day. Furthermore, there are few systems that use past fashion history to make personalized suggestions, and the suggestions they provide are general.

[1642] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1643] In this invention, the server includes means for providing an interface for a user to input schedule information, means for transmitting the schedule information to the server, means for acquiring weather information based on the schedule information, means for collecting the latest fashion trend information based on the schedule information and weather information, means for acquiring the user's past fashion history, means for recognizing the user's emotional state, means for suggesting optimal fashion to the user based on the weather information, fashion trend information, the user's past fashion history and the user's emotional state, and means for displaying the suggested fashion, thereby enabling more personalized optimal fashion suggestions that take into account the user's schedule information and emotional state.

[1644] "User terminal" refers to a device (such as a smartphone, tablet, or PC) on which a user inputs information and displays suggestions.

[1645] "Server" refers to the central system that processes information and generates suggestions.

[1646] "Interface" refers to a means for providing a user interface (UI) for a user to input schedule information.

[1647] "Schedule information" refers to information such as "when, where, and what to do" input by the user.

[1648] "Weather information" refers to information about weather conditions at a specified date, time and location, and is obtained through an external API.

[1649] "Fashion Trend Information" means information regarding current fashions and styles that is obtained from external sources.

[1650] "Database" refers to data storage for storing a user's past fashion history and other related data.

[1651] "Emotion engine" refers to a system for recognizing and analyzing a user's emotional state.

[1652] "Fashion suggestions" refers to suggestions about optimal fashion that are generated based on weather information, fashion trend information, the user's past fashion history, and emotional state.

[1653] "API" refers to an application programming interface for interfacing with external services and data sources.

[1654] "JSON format" refers to a format in which data is structured using JavaScript Object Notation, and is used for data communication between servers and user terminals.

[1655] The "HTTPS protocol" refers to a protocol for securely sending and receiving data over the Internet.

[1656] The present invention relates to a system that suggests optimal fashion based on the schedule information and emotions specified by the user. This system is composed of a user terminal, a server, an emotion engine, and related databases and APIs.

[1657] Hardware and software used

[1658] User device: A device such as a smartphone, tablet, or PC that allows a user to input information and view suggestions. A dedicated application or web interface is required.

[1659] Server: A central system for processing information and generating proposals, which may be a cloud-based server or a dedicated physical server.

[1660] Emotion engine: Software for recognizing and analyzing the user's emotional state, including facial expression recognition API and emotion analysis API.

[1661] Database: This is the data storage for storing the user's past fashion history and related data, and an SQL database or NoSQL database is used.

[1662] API: An interface for obtaining external weather information and fashion trend information, including weather forecast APIs and fashion information APIs.

[1663] Specific examples of program processing

[1664] In the present invention, a user inputs schedule information such as "tomorrow at 12 o'clock in Tokyo, lunch with a friend at a cafe." The user terminal converts this information into JSON format and sends it to the server.

[1665] The server analyzes the received schedule information and sends a request to an external weather forecast API to obtain weather information for the specified date, time, and location. For example, it obtains "sunny, 20 degrees Celsius" as the weather for tomorrow in Tokyo.

[1666] Next, the server accesses a fashion information website to obtain the latest fashion trend information. This information can be obtained via an API or by using web scraping technology. For example, it can obtain information such as "casual wear is popular this spring."

[1667] Next, the server retrieves the user's past fashion history from the database, for example, by referring to information such as "the user has preferred casual styles in the past."

[1668] The emotion engine analyzes facial expressions, voice, and input text to recognize the user's emotional state. For example, if the user indicates that they want to relax today, it will identify that information.

[1669] Finally, the server generates fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the emotional state recognized by the emotion engine, such as "a white casual shirt, denim pants, a light spring coat, and sneakers."

[1670] The server sends the generated fashion suggestions in JSON format to the user's device, which then displays them to the user. The user can view the suggested items and styles and select appropriate outfits. This process enables more personalized and optimal fashion suggestions that take into account the user's schedule information and emotional state.

[1671] Prompt Sentence Examples

[1672] The following is an example of a prompt that may be entered into the system:

[1673] "I'm planning to meet a friend for lunch at a cafe in Tokyo tomorrow at 12 o'clock. What's the weather like and what kind of clothing would you recommend?"

[1674] "I feel like relaxing. What kind of outfit would you recommend for tomorrow?"

[1675] Based on these prompts, the system can provide appropriate fashion suggestions to the user.

[1676] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1677] Step 1:

[1678] User device provides input of schedule information

[1679] The user device displays a user interface (UI) and provides a form where the user can enter "when, where, and what to do." Input fields include date, time, location, and activity details. For example, in a smartphone app, users enter information by tapping text fields.

[1680] Input: None

[1681] Output: A form for the user to fill out

[1682] Step 2:

[1683] The user enters the appointment information

[1684] The user enters information into the provided form, for example, specific schedule information such as "tomorrow at 12 o'clock in Tokyo, lunch with a friend at a cafe," which allows the system to understand the user's plans.

[1685] Input: Event information entered by the user into the form

[1686] Output: Entered schedule information

[1687] Step 3:

[1688] The user device sends the input information to the server.

[1689] The user device converts the entered schedule information into JSON format and sends an HTTPS request to the server. For example, it generates the following JSON data:

[1690] json

[1691] {

[1692] "date": "tomorrow",

[1693] "time": "12 o'clock",

[1694] "location": "Tokyo",

[1695] "activity": "Lunch at a cafe with a friend"

[1696] }

[1697] Input: Information entered by the user into a form

[1698] Output: JSON formatted event information sent to the server

[1699] Step 4:

[1700] The server retrieves weather information

[1701] Based on the schedule information received by the server, a request is sent to an external weather forecast API (e.g., OpenWeatherMap) to obtain weather information for the specified date, time, and location. The server receives weather information for the specified date, time, and location (e.g., "Tomorrow's weather in Tokyo: sunny, temperature 20 degrees") in JSON format.

[1702] Input: Event information in JSON format

[1703] Output: JSON data of retrieved weather information

[1704] Step 5:

[1705] The server obtains fashion trend information

[1706] The server accesses a fashion information website to obtain the latest fashion trend information. This information is obtained via API or collected using web scraping technology. For example, the server obtains trend information such as "casual wear is popular in spring."

[1707] Input: None (request for trend information collection)

[1708] Output: Data of acquired fashion trend information

[1709] Step 6:

[1710] The server acquires the user's past fashion history.

[1711] The server searches the user's past fashion history from an internal database and obtains the user's preferred style and past selection information. For example, it uses an SQL query to perform the process of "obtaining past fashion selections based on the user ID."

[1712] Input: User ID

[1713] Output: User's past fashion history data

[1714] Step 7:

[1715] Emotion engine recognizes user emotions

[1716] The emotion engine analyzes facial expressions and voice to recognize the user's emotional state. It uses APIs to identify emotions, such as "I want to relax." If the user is facing the camera, it uses the facial recognition API.

[1717] Input: User facial and voice data

[1718] Output: Perceived emotional state

[1719] Step 8:

[1720] The server generates fashion suggestions

[1721] The server generates optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the emotional state recognized by the emotion engine. For example, it runs an algorithm that suggests "a white casual shirt, denim pants, a light spring coat, and sneakers."

[1722] Input: Weather information, fashion trend information, user's past fashion history, emotional state

[1723] Output: Data for optimal fashion suggestions

[1724] Step 9:

[1725] The server sends fashion suggestions to the user's device.

[1726] The server converts the generated fashion suggestions into JSON format and sends it to the user's device. For example, the following JSON data is sent to the user's device:

[1727] json

[1728] {

[1729] "outfit": [

[1730] "White casual shirt",

[1731] "denim pants",

[1732] "Lightweight spring coat",

[1733] "sneakers"

[1734] ]

[1735] }

[1736] Input: Data for optimal fashion suggestions

[1737] Output: Fashion suggestions in JSON format sent to the user's device

[1738] Step 10:

[1739] The user's device displays fashion suggestions

[1740] The user's device analyzes the received fashion suggestions and visually displays them to the user. The app UI presents the user with a "white casual shirt, denim pants, a light spring coat, and sneakers."

[1741] Input: JSON data of fashion proposals

[1742] Output: Displayed fashion suggestions

[1743] Step 11:

[1744] The user checks and selects a fashion style

[1745] The user checks the displayed fashion suggestions and taps the item they want to choose from the options, which then feeds the selected information back into the system.

[1746] Input: Displayed fashion suggestions

[1747] Output: Data of fashion items selected by the user

[1748] (Application example 2)

[1749] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1750] While conventional fashion suggestion systems can make suggestions that take into account a user's schedule and weather information, it is difficult to make personalized suggestions that combine the user's emotional state and the latest fashion trend information. Furthermore, they do not provide an interface for users to easily purchase suggested fashion items, making it difficult to improve user satisfaction.

[1751] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing an interface for a user to input schedule information, means for transmitting the schedule information to the server, means for acquiring weather information based on the schedule information, means for collecting the latest fashion trend information based on the schedule information and the weather information, means for acquiring the user's past fashion history, means for recognizing and analyzing the user's emotional state, means for suggesting optimal fashion to the user based on the weather information, fashion trend information, the user's past fashion history, and the user's emotional state, and means for displaying the suggested fashion and providing an interface that makes it possible to purchase the suggested fashion in a virtual store. This enables personalized fashion suggestions that comprehensively consider the user's schedule information, weather information, fashion trend information, past fashion history, and emotional state, and allows the suggested items to be easily purchased in the virtual store.

[1752] The "interface for the user to input schedule information" refers to a user-operated device that the user uses to input information about future schedules (time, location, and activity details).

[1753] The "means for transmitting the schedule information to the server" is a mechanism for transmitting the schedule information input by the user to the server via a network.

[1754] "Means for obtaining weather information" refers to means for obtaining weather information for a specified date, time and location from an external database or API.

[1755] The "means for collecting the latest fashion trend information" is a function for collecting information on currently popular styles and fashion items from external sources.

[1756] The "means for acquiring the user's past fashion history" is a method for acquiring data relating to fashion styles and items selected by the user in the past from data storage.

[1757] The "means for recognizing and analyzing the user's emotional state" refers to a system that includes an algorithm that reads emotions from the user's facial expressions, voice, text input, etc., and analyzes that state.

[1758] The "means for suggesting optimal fashion" is a function that generates and presents the optimal fashion style to the user based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state.

[1759] The "means for providing an interface that enables purchases in a virtual store" is a user interface that allows the user to check the suggested fashion items in a virtual shopping space and carry out the purchasing procedure.

[1760] This invention is a system that suggests optimal fashion based on the user's schedule information and emotions, combined with weather and the latest fashion trend information. This system consists of the following main components:

[1761] User terminal: A device (smartphone, tablet, PC, etc.) on which a user inputs information and displays suggestions.

[1762] Server: The central system that processes information and generates proposals.

[1763] Emotion engine: A function for recognizing and analyzing the user's emotional state.

[1764] Database: A data storage for storing a user's past fashion history and other related data.

[1765] API: An interface with external services to obtain weather information and fashion trend information.

[1766] System Operation

[1767] 1. The user terminal provides an interface for the user to input schedule information (e.g., "Lunch with a friend at a cafe in Tokyo tomorrow at 12 o'clock"). The interface includes a form for inputting the date, time, location, and activity details.

[1768] 2. The user enters the appointment information into the form, and the information is sent to the server in JSON format.

[1769] 3. The server sends a request to the weather API based on the received schedule information to obtain weather information for the specified date, time, and location. For example, it obtains information such as "Tomorrow's weather in Tokyo will be sunny with a temperature of 20 degrees."

[1770] 4. The server also collects the latest fashion trend information from external fashion sources, either via API or scraping. For example, it obtains information that "casual wear is popular this spring."

[1771] 5. The server retrieves the user's past fashion history from the database, allowing the user to refer to the styles and items they have chosen in the past.

[1772] 6. The emotion engine recognizes and analyzes the user's emotional state from facial expressions, voice, text input, etc. For example, it identifies an emotional state such as "I feel like relaxing today."

[1773] 7. The server generates optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state recognized by the emotion engine. The suggestions are specific advice such as "a white casual shirt, denim pants, a light spring coat, and sneakers."

[1774] 8. The server sends the generated fashion suggestions in JSON format to the user device, which displays them.

[1775] Hardware and Software

[1776] Hardware:

[1777] Smartphone: Used by users to enter information and view suggested fashions.

[1778] software:

[1779] Flask: A web framework for server-side processing.

[1780] Weather Information API: Used to obtain weather information.

[1781] Fashion trend information API and scraping technology: Used to collect fashion trend information.

[1782] Emotion recognition engine: Used to analyze the user's emotional state.

[1783] Specific examples

[1784] For example, if a user inputs a schedule such as "I'm going to have lunch with a friend at a cafe in Tokyo tomorrow at 12 o'clock" and selects "I feel like relaxing" as the emotional state, the system will act based on the following prompt:

[1785] The user has plans to meet a friend for lunch at a cafe in Tokyo tomorrow at 12 noon. He feels like relaxing. The suggested outfit is a white casual shirt, denim pants, a light spring coat, and sneakers.

[1786] This makes it possible to make personalized fashion suggestions that comprehensively take into account the user's schedule information, weather information, fashion trend information, past history, and emotional state, and to easily purchase the suggested items in a virtual store.

[1787] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1788] Step 1:

[1789] The user device provides an interface for the user to input schedule information. For example, the user inputs schedule information such as "Lunch at a cafe with a friend in Tokyo tomorrow at 12 o'clock." The device receives the date, time, location, and activity details as input, and sends this information in JSON format to the server.

[1790] Step 2:

[1791] The server receives the schedule information sent by the user. Based on the received schedule information, it sends a request to the weather information API. For example, it sends a request to obtain weather information for the specified time "tomorrow at 12:00 in Tokyo." It receives the schedule information as input, obtains weather information based on that schedule information, and outputs the weather information.

[1792] Step 3:

[1793] The server analyzes the weather information obtained from the weather information API. For example, it obtains information such as "Tomorrow's weather in Tokyo will be sunny with a temperature of 20 degrees." It analyzes the response from the weather information API and outputs it as weather information.

[1794] Step 4:

[1795] The server collects the latest fashion trend information using fashion API or scraping technology. For example, it obtains information such as "casual wear is popular in spring." It sends a request to an external fashion information source as input and outputs the latest fashion trend information.

[1796] Step 5:

[1797] The server retrieves the user's past fashion history from the database. For example, it references the user's past casual style choices. It receives the user ID as input and outputs the user's past fashion history.

[1798] Step 6:

[1799] The emotion engine recognizes and analyzes the user's emotional state from their facial expressions, voice, text input, etc. For example, it identifies the emotional state "I feel like relaxing today." It receives the user's emotional data as input and outputs the emotional state.

[1800] Step 7:

[1801] The server generates optimal fashion suggestions based on weather information, fashion trend information, the user's past fashion history, and the user's emotional state recognized by the emotion engine. For example, it suggests "a white casual shirt, denim pants, a light spring coat, and sneakers." It receives the output data of all previous steps as input and outputs fashion suggestions.

[1802] Step 8:

[1803] The server sends the generated fashion suggestions in JSON format to the user device. The user device displays the received fashion suggestions to the user. It receives fashion suggestions as input and displays them. It provides an interface for the user to review the suggestions and purchase them in a virtual store.

[1804] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1805] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1806] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1807] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1808] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1809] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1810] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1811] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1812] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1813] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1814] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1815] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1816] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1817] 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.

[1818] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1819] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1820] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1821] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1822] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1823] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1824] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1825] The following is further disclosed regarding the above embodiment.

[1826] (Claim 1)

[1827] means for providing an interface for a user to input schedule information;

[1828] means for transmitting the schedule information to a server;

[1829] means for acquiring weather information based on the schedule information;

[1830] means for collecting the latest fashion trend information based on the schedule information and the weather information;

[1831] A means for acquiring a user's past fashion history;

[1832] means for suggesting optimal fashion to a user based on the weather information, fashion trend information, and the user's past fashion history;

[1833] means for displaying said suggested fashion;

[1834] A system including:

[1835] (Claim 2)

[1836] 2. The system according to claim 1, wherein the means for acquiring weather information acquires the weather information through an API.

[1837] (Claim 3)

[1838] 2. The system according to claim 1, wherein the means for collecting fashion trend information scrapes trend information from external fashion sites.

[1839] "Example 1"

[1840] (Claim 1)

[1841] means for providing an interface for a user to input schedule information;

[1842] means for transmitting the schedule information to an information processing device;

[1843] means for acquiring weather information based on the schedule information;

[1844] means for collecting the latest fashion trend information based on the schedule information and the weather information;

[1845] A means for acquiring a user's past fashion history;

[1846] a means for suggesting optimal fashion to a user based on the weather information, fashion trend information, and the user's past fashion history;

[1847] means for displaying said suggested fashion;

[1848] A system including:

[1849] (Claim 2)

[1850] 2. The system according to claim 1, wherein the means for obtaining weather information obtains the weather information through an application programming interface.

[1851] (Claim 3)

[1852] 2. The system according to claim 1, wherein the means for collecting fashion trend information scrapes trend information from an external fashion information providing site.

[1853] "Application Example 1"

[1854] (Claim 1)

[1855] means for providing an interface for a user to input schedule information;

[1856] means for transmitting the schedule information to a server;

[1857] means for acquiring weather information based on the schedule information;

[1858] means for collecting the latest fashion trend information based on the schedule information and the weather information;

[1859] A means for acquiring a user's past fashion history;

[1860] means for suggesting optimal fashion to a user based on the weather information, fashion trend information, and the user's past fashion history;

[1861] means for displaying said suggested fashion;

[1862] a means for providing speech input to a user and converting the schedule information into text using speech recognition;

[1863] A means for displaying the most suitable fashion overlaid on the user's field of vision using augmented reality technology;

[1864] A system including:

[1865] (Claim 2)

[1866] 2. The system according to claim 1, wherein the means for acquiring weather information acquires the weather information through an API.

[1867] (Claim 3)

[1868] 2. The system according to claim 1, wherein the means for collecting fashion trend information scrapes trend information from external fashion sites.

[1869] "Example 2: Combining Emotion Engines"

[1870] (Claim 1)

[1871] means for providing an interface for a user to input schedule information;

[1872] means for transmitting the schedule information to a server;

[1873] means for acquiring weather information based on the schedule information;

[1874] means for collecting the latest fashion trend information based on the schedule information and the weather information;

[1875] A means for acquiring a user's past fashion history;

[1876] means for recognizing the emotional state of a user;

[1877] means for suggesting optimal fashion to a user based on the weather information, fashion trend information, the user's past fashion history, and the user's emotional state;

[1878] means for displaying said suggested fashion;

[1879] A system including:

[1880] (Claim 2)

[1881] 2. The system according to claim 1, wherein the means for acquiring weather information acquires the weather information through an API.

[1882] (Claim 3)

[1883] 2. The system of claim 1, wherein the means for collecting fashion trend information obtains trend information from an external source.

[1884] "Application example 2 when combining emotion engines"

[1885] (Claim 1)

[1886] means for providing an interface for a user to input schedule information;

[1887] means for transmitting the schedule information to a server;

[1888] means for acquiring weather information based on the schedule information;

[1889] means for collecting the latest fashion trend information based on the schedule information and the weather information;

[1890] A means for acquiring a user's past fashion history;

[1891] means for recognizing and analyzing the emotional state of a user;

[1892] means for suggesting optimal fashion to a user based on the weather information, fashion trend information, the user's past fashion history, and the user's emotional state;

[1893] means for providing an interface for displaying said suggested fashions and making them available for purchase in a virtual store;

[1894] A system including:

[1895] (Claim 2)

[1896] 2. The system according to claim 1, wherein the means for acquiring weather information acquires the weather information through a data acquisition interface.

[1897] (Claim 3)

[1898] 2. The system of claim 1, wherein the means for collecting fashion trend information collects trend information from an external fashion information source. [Explanation of symbols]

[1899] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for providing an interface for a user to input schedule information; means for transmitting the schedule information to a server; means for acquiring weather information based on the schedule information; means for collecting the latest fashion trend information based on the schedule information and the weather information; A means for acquiring a user's past fashion history; means for suggesting optimal fashion to a user based on the weather information, fashion trend information, and the user's past fashion history; means for displaying said suggested fashion; A system including:

2. 2. The system according to claim 1, wherein the means for acquiring weather information acquires the weather information through an API.

3. 2. The system according to claim 1, wherein the means for collecting fashion trend information scrapes trend information from external fashion sites.

Images