System

Abstract

An object of a system according to an embodiment is to propose optimal clothes according to daily temperature and weather.SOLUTION: A system includes a weather data collection unit, a clothing data management unit, a clothing proposal unit, and a display unit. The weather data collection unit collects and analyzes weather data. The clothing data management unit manages clothing data of the user based on the weather data collected by the weather data collection unit. The clothing proposal unit proposes clothing optimal for conditions of the day on the basis of the clothing data managed by the clothing data management unit. The display unit displays the clothing proposed by the clothing proposal unit on the device of the user.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] With conventional technology, selecting the most appropriate clothing for each day's temperature and weather was a hassle and difficult to do efficiently.

[0005] The system according to the embodiment aims to suggest optimal clothing depending on the daily temperature and weather. [Means for solving the problem]

[0006] The system according to the embodiment includes a weather data collection unit, a clothing data management unit, a clothing suggestion unit, and a display unit. The weather data collection unit collects and analyzes weather data. The clothing data management unit manages clothing data held by the user based on the weather data collected by the weather data collection unit. The clothing suggestion unit suggests clothing that is optimal for the conditions of the day based on the clothing data managed by the clothing data management unit. The display unit displays the clothing suggested by the clothing suggestion unit on the user's device. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable clothing depending on the daily temperature and weather. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

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

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. 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."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

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

[0024] 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.

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

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

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

[0028] (Example 1) The clothing suggestion system according to the embodiment of the present invention uses a generation AI to select the most suitable clothes from among the clothes a user owns depending on the daily temperature and weather. This allows the clothing suggestion system to select the most suitable clothes for the user depending on the daily temperature and weather.

[0029] An outfit suggestion system according to an embodiment includes a weather data collection unit, an outfit data management unit, an outfit suggestion unit, and a display unit. The weather data collection unit collects and analyzes weather data. For example, the weather data collection unit collects data such as temperature, humidity, precipitation probability, and wind speed. The weather data collection unit also determines the weather conditions of the day based on the weather data. The outfit data management unit manages the user's outfit data based on the weather data collected by the weather data collection unit. For example, the outfit data management unit manages data such as the type, material, color, size, and seasonality of each outfit. The outfit suggestion unit suggests outfits optimal for the day's conditions based on the outfit data managed by the outfit data management unit. For example, the outfit suggestion unit suggests a warm coat or a windproof jacket on a cold, windy day. The display unit displays the outfits suggested by the outfit suggestion unit on the user's device. For example, the display unit displays images and detailed information of the suggested outfits via a smartphone app. This allows the outfit suggestion system according to an embodiment to suggest optimal outfits based on the user's outfit data and weather data.

[0030] The weather data collection unit can analyze past weather patterns and predict future weather. For example, the weather data collection unit collects weather data from the past 10 years and analyzes seasonal weather patterns. This allows the unit to understand weather trends for specific seasons and months and use this information to predict future weather. The weather data collection unit also uses machine learning algorithms to predict future weather based on the weather data. For example, it inputs data such as temperature, humidity, and precipitation probability to predict the weather for the next week. The weather data collection unit also analyzes past weather patterns to understand the frequency and trends of abnormal weather. This allows the unit to predict periods when abnormal weather is likely to occur and notify the user in advance. This allows for more accurate clothing recommendations by predicting future weather.

[0031] The weather data collection unit can acquire the user's location information in real time and analyze detailed weather data for each region. For example, the weather data collection unit uses the GPS function of the user's smartphone to acquire location information in real time and collect weather data for that region. This allows for providing detailed weather information based on the user's current location. The weather data collection unit also collects data from surrounding weather stations based on the user's location information and performs detailed weather analysis for each region. For example, it takes into account different weather conditions in urban and suburban areas. The weather data collection unit also analyzes the user's movement history and collects weather data for frequently visited locations in advance. This allows for providing weather information for the user's destination in advance. This allows for providing detailed weather information based on the user's current location.

[0032] The weather data collection unit can collect health-related data such as pollen information and ultraviolet light information in addition to weather data and use it for analysis. For example, the weather data collection unit collects pollen information in addition to weather data and suggests allergy countermeasures to the user. For example, on days with high pollen counts, the unit recommends the use of a mask or anti-pollen products. The weather data collection unit also collects ultraviolet light information and suggests the use of sunscreen to the user. For example, on days with strong ultraviolet light, the unit recommends the use of sunscreen cream and a hat. The weather data collection unit also supports the user's health management based on health-related data. For example, on days with high humidity, the unit recommends staying hydrated as a countermeasure against heatstroke. This makes it possible to suggest more appropriate clothing based on health-related data.

[0033] The weather data collection unit integrates data from different weather data sources to perform more accurate weather analysis. For example, the weather data collection unit integrates data from different weather data sources (e.g., the Japan Meteorological Agency, private weather services, satellite data) to improve the accuracy of weather analysis, thereby providing more accurate weather forecasts. The weather data collection unit also collects weather data from different data sources in real time and performs integrated analysis. For example, it combines and analyzes data from multiple weather observation stations. The weather data collection unit also uses a data integration algorithm to centralize data from different weather data sources to improve the accuracy of analysis. For example, it complements overlapping or missing data. As a result, by integrating data from different data sources, more accurate weather forecasts are possible.

[0034] The clothing data management unit collects data on the user's body type and physical condition, and can make more personalized clothing suggestions. The clothing data management unit, for example, collects the user's body type data (height, weight, body fat percentage, etc.) and suggests optimal clothing based on that data. For example, it suggests clothes with a size and silhouette that suits the user's body type. The clothing data management unit also collects the user's physical condition data (for example, sleep state, stress level, allergy information) and makes clothing suggestions based on that data. For example, it suggests relaxing clothing on days when the user is not feeling well. The clothing data management unit also makes personalized clothing suggestions according to the season and weather based on the user's body type and physical condition data. For example, it suggests clothing that will help maintain body temperature on cold days. This makes it possible to make more appropriate clothing suggestions based on the user's body type and physical condition.

[0035] The clothing data management unit can analyze the user's past clothing history to understand preferences and trends. For example, the clothing data management unit records the history of clothing worn by the user in the past and analyzes that data. For example, it can understand the user's tendency to prefer clothing of a particular color or style. The clothing data management unit also analyzes the user's preferences and trends based on the user's past clothing history and reflects this in the next clothing suggestion. For example, it can prioritize suggestions based on the user's favorite brands and designs. The clothing data management unit also analyzes the user's past clothing history to understand clothing trends by season or event. For example, it can make clothing suggestions tailored to specific events. This makes it possible to make more appropriate clothing suggestions based on the user's preferences and trends.

[0036] The clothing data management unit collects the user's lifestyle and activity data and can make more appropriate clothing suggestions. The clothing data management unit, for example, collects the user's lifestyle data (e.g., occupation, hobbies, daily activities) and makes clothing suggestions based on that data. For example, it suggests business casual clothing on days when the user works in the office. The clothing data management unit also collects the user's activity data (e.g., amount of exercise, distance traveled, calories burned) and makes clothing suggestions based on that data. For example, it suggests clothing that is easy to move in on days when the user exercises a lot. The clothing data management unit also suggests appropriate clothing according to the season and weather based on the user's lifestyle and activity data. For example, it suggests waterproof clothing on days when the user will be doing outdoor activities. This makes it possible to make more appropriate clothing suggestions based on the user's lifestyle and activities.

[0037] The clothing data management unit can share clothing data between different users and reflect trends and popular clothing in suggestions. The clothing data management unit, for example, shares clothing data between different users and analyzes trends and popular clothing. For example, it reflects clothing preferred by users of the same age or in the same area in suggestions. The clothing data management unit also suggests popular clothing styles and brands based on clothing data between users. For example, it prioritizes suggestions of clothing that are popular in a particular season. The clothing data management unit also shares clothing data between different users and builds a system that reflects trends and popular clothing in real time. For example, it reflects the latest fashion trends in suggestions. This makes it possible to make more attractive clothing suggestions by reflecting trends and popular clothing in suggestions.

[0038] The clothing suggestion unit can suggest appropriate clothing by taking into account the user's schedule and plans. For example, the clothing suggestion unit collects the user's schedule data (e.g., schedules from a calendar app) and makes clothing suggestions based on that data. For example, formal clothing is suggested for the day of a business meeting. The clothing suggestion unit also builds a system that suggests appropriate clothing based on the user's plans. For example, sportswear is suggested for days when exercise is planned. The clothing suggestion unit also suggests clothing suitable for specific events or activities based on the user's schedule data. For example, casual and stylish clothing is suggested for dates. This makes it possible to make more appropriate clothing suggestions based on the user's schedule and plans.

[0039] The clothing suggestion unit can improve the accuracy of suggestions by reflecting the user's past feedback. For example, the clothing suggestion unit collects feedback provided by the user in the past and improves the accuracy of clothing suggestions based on that data. For example, it prioritizes suggesting clothing that the user rated as "appropriate." The clothing suggestion unit also analyzes the user's feedback data and identifies areas for improvement in suggestions. For example, it avoids characteristics of clothing that the user rated as "inappropriate." The clothing suggestion unit also understands the user's preferences and tendencies based on past feedback and reflects them in the next clothing suggestion. For example, it suggests clothing in colors and styles that the user prefers. This allows the accuracy of suggestions to be improved based on the user's past feedback.

[0040] The clothing suggestion unit reflects the opinions of the user's friends and family, allowing it to make more multifaceted suggestions. For example, the clothing suggestion unit collects feedback from the user's friends and family and makes clothing suggestions based on that data. For example, it prioritizes suggesting outfits that friends have rated as "suitable." The clothing suggestion unit also builds a system that adjusts the suggestions based on the opinions of the user's friends and family. For example, it avoids outfits that family members have rated as "inappropriate." The clothing suggestion unit also understands the user's preferences and tendencies based on feedback from friends and family, and reflects these in the next clothing suggestion. For example, it suggests outfits that friends have rated as "stylish." This allows for more multifaceted clothing suggestions by reflecting the opinions of the user's friends and family.

[0041] The clothing suggestion unit can make special clothing suggestions according to different seasons or events. The clothing suggestion unit, for example, builds a system that makes special clothing suggestions for each season. For example, it suggests clothes made of cool materials in the summer and clothes made of warm materials in the winter. The clothing suggestion unit also makes clothing suggestions according to specific events (e.g., weddings, parties, sporting events). For example, it suggests dresses or suits for formal events. The clothing suggestion unit also collects the user's schedule data and adjusts the suggestions based on that data in order to make special clothing suggestions according to seasons or events. For example, it suggests gorgeous clothing for a Christmas party. This makes it possible to make special clothing suggestions according to seasons and events.

[0042] The display unit can display the proposed outfit in 3D on the user's avatar, allowing the user to visually confirm it. The display unit, for example, builds a system that displays the proposed outfit in 3D on the user's avatar. For example, the display unit allows the user to rotate their avatar to check the whole outfit. The display unit also dresses the 3D-displayed avatar in the proposed outfit and displays detailed information about the outfit (material, brand, size, etc.). For example, the user can click on the outfit details to check them. The display unit also displays the proposed outfit in 3D, allowing the user to visually check how it will look when actually worn. For example, the display unit provides a function that allows the user to check the outfit from different angles. This allows the user to visually check the proposed outfit.

[0043] The display unit can display detailed information about the suggested outfit (such as material, brand, and place of purchase) to allow the user to obtain more information. The display unit, for example, builds a system that displays detailed information about the suggested outfit (such as material, brand, and place of purchase). For example, the user can click on the outfit details to check them. The display unit also displays information about the material and brand of the suggested outfit to allow the user to understand the quality and characteristics of the outfit. For example, it displays the characteristics of the material and the history of the brand. The display unit also displays information about the place of purchase and price of the suggested outfit to help the user when purchasing the outfit. For example, it provides a link to an online shop. This allows the user to obtain detailed information about the suggested outfit.

[0044] The display unit can display the proposed outfit against different backgrounds and situations, allowing the user to check how it will look in various situations. The display unit, for example, builds a system that displays the proposed outfit against different backgrounds and situations. For example, it allows the user to select backgrounds such as an office, a cafe, or outdoors. The display unit also displays the proposed outfit in different situations, allowing the user to check how it will look. For example, it provides a function for switching between daytime and nighttime situations. The display unit also displays the proposed outfit against different backgrounds and situations, allowing the user to visually check how it will look when actually worn. For example, it displays situations such as an event or a party. This allows the user to visually check the proposed outfit in various situations.

[0045] The display unit can share the suggested outfits with the user's friends and family and collect their opinions. For example, the display unit adds a function to share the suggested outfits with the user's friends and family. For example, it can make it possible to share images of the outfits via social networking sites or messaging apps. The display unit also adds a function to collect opinions from friends and family and makes outfit suggestions based on that data. For example, it can prioritize suggesting outfits that friends have rated as "looking good." The display unit also builds a system that shares the suggested outfits and collects feedback from friends and family in real time. For example, it can provide an online voting function. This allows the user to share the suggested outfits with friends and family and collect their opinions.

[0046] The clothing suggestion unit can collect user feedback and improve the accuracy of suggestions based on that feedback. The clothing suggestion unit, for example, builds a system that collects detailed comments and ratings from users and analyzes that data. For example, it allows users to input specific comments such as "This outfit was comfortable." The clothing suggestion unit also identifies areas for improvement in suggestions based on the user's comments and ratings. For example, it improves size suggestions based on ratings such as "This outfit didn't fit." The clothing suggestion unit also analyzes the detailed comments and ratings and reflects them in the next clothing suggestion. For example, it prioritizes suggesting favorite colors based on a user's comment such as "I like this color." This allows the accuracy of suggestions to be improved based on user feedback.

[0047] The clothing suggestion unit can analyze the user's behavioral data (whether or not the clothing was actually worn) to improve the accuracy of suggestions. The clothing suggestion unit, for example, records whether the user actually wore the suggested clothing and builds a system to analyze that data. For example, it automatically records the history of clothing worn by the user. The clothing suggestion unit also improves the accuracy of suggestions based on the user's behavioral data. For example, it analyzes the characteristics of clothing actually worn by the user and reflects this in the next suggestion. The clothing suggestion unit also understands the user's preferences and tendencies based on data on whether the clothing was actually worn and reflects this in the next clothing suggestion. For example, it prioritizes suggesting clothing styles that the user wears frequently. This makes it possible to improve the accuracy of suggestions based on the user's behavioral data.

[0048] The clothing suggestion unit can compare feedback between different users and identify common problems and areas for improvement. The clothing suggestion unit, for example, builds a system that compares feedback between different users and identifies common problems and areas for improvement. For example, if multiple users evaluate an item as "the size didn't fit," the problem is improved. The clothing suggestion unit also identifies common problems based on feedback between users and reflects them in the next suggestion. For example, if multiple users evaluate an item as "I didn't like the color," the unit avoids suggesting that color. The clothing suggestion unit also analyzes feedback between different users and identifies common areas for improvement. For example, if multiple users evaluate the material as "comfortable," the unit preferentially suggests that material. In this way, by comparing feedback between different users, common problems and areas for improvement can be identified and the accuracy of suggestions can be improved.

[0049] The clothing suggestion unit can integrate feedback from different devices and platforms and perform a comprehensive evaluation. The clothing suggestion unit, for example, integrates feedback from different devices (smartphones, tablets, PCs, etc.) and builds a system for performing a comprehensive evaluation. For example, it centrally manages feedback from all devices. The clothing suggestion unit also integrates feedback from different platforms (SNS, email, in-app feedback, etc.) and performs a comprehensive evaluation. For example, it centrally manages feedback from all platforms. The clothing suggestion unit also collects feedback from different devices and platforms in real time and builds a system for performing a comprehensive evaluation. For example, it analyzes all feedback in real time. This makes it possible to integrate feedback from different devices and platforms to perform a comprehensive evaluation and improve the accuracy of suggestions.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The clothing suggestion system can further collect lifestyle data on the user to improve the accuracy of suggestions. For example, it can collect data on the user's occupation, hobbies, and daily activities, and make clothing suggestions based on that data. For days when the user works in the office, it can suggest business casual clothing, and for days when the user engages in outdoor activities, it can suggest waterproof clothing. It can also collect activity data such as the user's exercise volume, distance traveled, and calories burned, and make clothing suggestions based on that data. For days when the user is actively exercising, it can suggest clothing that is easy to move in. This makes it possible to make more appropriate clothing suggestions based on the user's lifestyle and activities.

[0052] The clothing suggestion system can also analyze a user's past clothing history to understand their preferences and trends. For example, it can record the history of clothing worn by the user in the past and analyze that data. It can understand the user's preference for specific colors and styles of clothing and reflect this in the next clothing suggestion. It can prioritize suggestions based on the user's favorite brands and designs and suggest clothing suited to specific events. This makes it possible to make more appropriate clothing suggestions based on the user's preferences and trends.

[0053] The clothing suggestion system can also share clothing data between different users and reflect trends and popular clothing in the suggestions. For example, it can reflect the clothing preferred by users of the same age or region in the suggestions, suggesting popular clothing styles and brands. It can prioritize suggestions of clothing that are popular in a particular season and reflect the latest fashion trends in the suggestions. In this way, by reflecting trends and popular clothing in the suggestions, it becomes possible to make more attractive clothing suggestions.

[0054] The clothing suggestion system can also take into account the user's schedule and plans to suggest appropriate clothing. For example, it can collect the user's schedule data (events from a calendar app) and make clothing suggestions based on that data. For example, it can suggest formal attire for a business meeting, and sportswear for a day when exercise is planned. It can suggest clothing suitable for a specific event or activity, and casual yet stylish clothing for a date. This allows it to suggest more appropriate clothing based on the user's schedule and plans.

[0055] The clothing suggestion system can further collect user feedback and improve the accuracy of suggestions based on that feedback. For example, it can collect feedback provided by users in the past and improve the accuracy of clothing suggestions based on that data. It can prioritize suggesting outfits that users have rated as "appropriate" and analyze the feedback data to identify areas for improvement in the suggestions. It can avoid the characteristics of outfits that users have rated as "inappropriate" and reflect these in the next outfit suggestions. This allows the accuracy of suggestions to be improved based on users' past feedback.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The weather data collection unit collects and analyzes weather data. For example, the weather data collection unit collects data such as temperature, humidity, probability of precipitation, and wind speed to understand the weather conditions of the day. Step 2: The clothing data management unit manages the user's clothing data based on the weather data collected by the weather data collection unit, such as the type, material, color, size, and seasonality of each piece of clothing. Step 3: The clothing suggestion unit suggests the best clothing for the day's conditions based on the clothing data managed by the clothing data management unit. For example, on a cold, windy day, it suggests a warm coat or windproof jacket. Step 4: The display unit displays the outfits suggested by the outfit suggestion unit on the user's device, for example, through a smartphone app, displaying images and detailed information about the outfits.

[0058] (Example 2) The clothing suggestion system according to the embodiment of the present invention uses a generation AI to select the most suitable clothes from among the clothes a user owns depending on the daily temperature and weather. This allows the clothing suggestion system to select the most suitable clothes for the user depending on the daily temperature and weather.

[0059] An outfit suggestion system according to an embodiment includes a weather data collection unit, an outfit data management unit, an outfit suggestion unit, and a display unit. The weather data collection unit collects and analyzes weather data. For example, the weather data collection unit collects data such as temperature, humidity, precipitation probability, and wind speed. The weather data collection unit also determines the weather conditions of the day based on the weather data. The outfit data management unit manages the user's outfit data based on the weather data collected by the weather data collection unit. For example, the outfit data management unit manages data such as the type, material, color, size, and seasonality of each outfit. The outfit suggestion unit suggests outfits optimal for the day's conditions based on the outfit data managed by the outfit data management unit. For example, the outfit suggestion unit suggests a warm coat or a windproof jacket on a cold, windy day. The display unit displays the outfits suggested by the outfit suggestion unit on the user's device. For example, the display unit displays images and detailed information of the suggested outfits via a smartphone app. This allows the outfit suggestion system according to an embodiment to suggest optimal outfits based on the user's outfit data and weather data.

[0060] The weather data collection unit can analyze past weather patterns and predict future weather. For example, the weather data collection unit collects weather data from the past 10 years and analyzes seasonal weather patterns. This allows the unit to understand weather trends for specific seasons and months and use this information to predict future weather. The weather data collection unit also uses machine learning algorithms to predict future weather based on the weather data. For example, it inputs data such as temperature, humidity, and precipitation probability to predict the weather for the next week. The weather data collection unit also analyzes past weather patterns to understand the frequency and trends of abnormal weather. This allows the unit to predict periods when abnormal weather is likely to occur and notify the user in advance. This allows for more accurate clothing recommendations by predicting future weather.

[0061] The weather data collection unit can acquire the user's location information in real time and analyze detailed weather data for each region. For example, the weather data collection unit uses the GPS function of the user's smartphone to acquire location information in real time and collect weather data for that region. This allows for providing detailed weather information based on the user's current location. The weather data collection unit also collects data from surrounding weather stations based on the user's location information and performs detailed weather analysis for each region. For example, it takes into account different weather conditions in urban and suburban areas. The weather data collection unit also analyzes the user's movement history and collects weather data for frequently visited locations in advance. This allows for providing weather information for the user's destination in advance. This allows for providing detailed weather information based on the user's current location.

[0062] The weather data collection unit can use the emotion estimation function to analyze what emotions a user felt under what weather conditions in the past and evaluate weather data based on those emotions. For example, the weather data collection unit records what emotions a user felt under what weather conditions in the past and analyzes the data. For example, if a user tends to feel negative emotions on rainy days, the weather data collection unit evaluates the weather data based on that information. The weather data collection unit also uses the emotion estimation function to analyze in real time what emotions a user feels under specific weather conditions. For example, for a user who feels positive emotions on a sunny day, a sunny weather forecast can be highlighted and displayed. The weather data collection unit also customizes the evaluation criteria for the weather data based on the user's emotion data. For example, it can prioritize weather conditions that the user prefers and avoid weather conditions that evoke negative emotions. This allows for more personalized clothing suggestions by evaluating weather data based on the user's emotions.

[0063] The weather data collection unit can collect health-related data such as pollen information and ultraviolet light information in addition to weather data and use it for analysis. For example, the weather data collection unit collects pollen information in addition to weather data and suggests allergy countermeasures to the user. For example, on days with high pollen counts, the unit recommends the use of a mask or anti-pollen products. The weather data collection unit also collects ultraviolet light information and suggests the use of sunscreen to the user. For example, on days with strong ultraviolet light, the unit recommends the use of sunscreen cream and a hat. The weather data collection unit also supports the user's health management based on health-related data. For example, on days with high humidity, the unit recommends staying hydrated as a countermeasure against heatstroke. This makes it possible to suggest more appropriate clothing based on health-related data.

[0064] The weather data collection unit integrates data from different weather data sources to perform more accurate weather analysis. For example, the weather data collection unit integrates data from different weather data sources (e.g., the Japan Meteorological Agency, private weather services, satellite data) to improve the accuracy of weather analysis, thereby providing more accurate weather forecasts. The weather data collection unit also collects weather data from different data sources in real time and performs integrated analysis. For example, it combines and analyzes data from multiple weather observation stations. The weather data collection unit also uses a data integration algorithm to centralize data from different weather data sources to improve the accuracy of analysis. For example, it complements overlapping or missing data. As a result, by integrating data from different data sources, more accurate weather forecasts are possible.

[0065] The weather data collection unit can use the emotion estimation function to predict how a user will feel under specific weather conditions and customize weather data based on the emotion. The weather data collection unit, for example, uses the emotion estimation function to predict how a user will feel under specific weather conditions. For example, for a user who feels negative emotions on a rainy day, the weather data collection unit can suggest rainy day strategies. The weather data collection unit also customizes the display content of weather data based on the user's emotion data. For example, it can highlight and display weather conditions that the user prefers. The weather data collection unit also adjusts the way weather information is provided to the user based on the emotion estimation data. For example, it can use expressions that elicit positive emotions. In this way, customizing weather data based on the user's emotions enables more personalized clothing suggestions.

[0066] The clothing data management unit collects data on the user's body type and physical condition, and can make more personalized clothing suggestions. The clothing data management unit, for example, collects the user's body type data (height, weight, body fat percentage, etc.) and suggests optimal clothing based on that data. For example, it suggests clothes with a size and silhouette that suits the user's body type. The clothing data management unit also collects the user's physical condition data (for example, sleep state, stress level, allergy information) and makes clothing suggestions based on that data. For example, it suggests relaxing clothing on days when the user is not feeling well. The clothing data management unit also makes personalized clothing suggestions according to the season and weather based on the user's body type and physical condition data. For example, it suggests clothing that will help maintain body temperature on cold days. This makes it possible to make more appropriate clothing suggestions based on the user's body type and physical condition.

[0067] The clothing data management unit can analyze the user's past clothing history to understand preferences and trends. For example, the clothing data management unit records the history of clothing worn by the user in the past and analyzes that data. For example, it can understand the user's tendency to prefer clothing of a particular color or style. The clothing data management unit also analyzes the user's preferences and trends based on the user's past clothing history and reflects this in the next clothing suggestion. For example, it can prioritize suggestions based on the user's favorite brands and designs. The clothing data management unit also analyzes the user's past clothing history to understand clothing trends by season or event. For example, it can make clothing suggestions tailored to specific events. This makes it possible to make more appropriate clothing suggestions based on the user's preferences and trends.

[0068] The clothing data management unit can use the emotion estimation function to analyze the emotions felt when a user wears specific clothing and evaluate clothing data based on those emotions. The clothing data management unit, for example, uses the emotion estimation function to analyze the emotions felt when a user wears specific clothing. For example, it prioritizes suggesting clothing that evokes positive emotions for the user. The clothing data management unit also customizes evaluation criteria for clothing data based on the user's emotion data. For example, it prioritizes suggesting clothing styles that the user prefers. The clothing data management unit also records the emotions felt when the user wears specific clothing based on the emotion estimation data and reflects that data in the next clothing suggestion. For example, it suggests clothing that evokes positive emotions. This allows for more personalized clothing suggestions by evaluating clothing data based on the user's emotions.

[0069] The clothing data management unit collects the user's lifestyle and activity data and can make more appropriate clothing suggestions. The clothing data management unit, for example, collects the user's lifestyle data (e.g., occupation, hobbies, daily activities) and makes clothing suggestions based on that data. For example, it suggests business casual clothing on days when the user works in the office. The clothing data management unit also collects the user's activity data (e.g., amount of exercise, distance traveled, calories burned) and makes clothing suggestions based on that data. For example, it suggests clothing that is easy to move in on days when the user exercises a lot. The clothing data management unit also suggests appropriate clothing according to the season and weather based on the user's lifestyle and activity data. For example, it suggests waterproof clothing on days when the user will be doing outdoor activities. This makes it possible to make more appropriate clothing suggestions based on the user's lifestyle and activities.

[0070] The clothing data management unit can share clothing data between different users and reflect trends and popular clothing in suggestions. The clothing data management unit, for example, shares clothing data between different users and analyzes trends and popular clothing. For example, it reflects clothing preferred by users of the same age or in the same area in suggestions. The clothing data management unit also suggests popular clothing styles and brands based on clothing data between users. For example, it prioritizes suggestions of clothing that are popular in a particular season. The clothing data management unit also shares clothing data between different users and builds a system that reflects trends and popular clothing in real time. For example, it reflects the latest fashion trends in suggestions. This makes it possible to make more attractive clothing suggestions by reflecting trends and popular clothing in suggestions.

[0071] The clothing data management unit can use the emotion estimation function to predict the emotion a user will feel when wearing specific clothing, and customize clothing data based on that emotion. The clothing data management unit, for example, uses the emotion estimation function to predict the emotion a user will feel when wearing specific clothing. For example, it preferentially suggests clothing that evokes positive emotions in the user. The clothing data management unit also customizes clothing data based on the user's emotion data. For example, it suggests clothing with a color or design that the user prefers. The clothing data management unit also predicts the emotion a user will feel when wearing specific clothing based on the emotion estimation data, and reflects that data in the next clothing suggestion. For example, it suggests clothing that evokes positive emotions. In this way, customizing clothing data based on the user's emotions enables more personalized clothing suggestions.

[0072] The clothing suggestion unit can suggest appropriate clothing by taking into account the user's schedule and plans. For example, the clothing suggestion unit collects the user's schedule data (e.g., schedules from a calendar app) and makes clothing suggestions based on that data. For example, formal clothing is suggested for the day of a business meeting. The clothing suggestion unit also builds a system that suggests appropriate clothing based on the user's plans. For example, sportswear is suggested for days when exercise is planned. The clothing suggestion unit also suggests clothing suitable for specific events or activities based on the user's schedule data. For example, casual and stylish clothing is suggested for dates. This makes it possible to make more appropriate clothing suggestions based on the user's schedule and plans.

[0073] The clothing suggestion unit can improve the accuracy of suggestions by reflecting the user's past feedback. For example, the clothing suggestion unit collects feedback provided by the user in the past and improves the accuracy of clothing suggestions based on that data. For example, it prioritizes suggesting clothing that the user rated as "appropriate." The clothing suggestion unit also analyzes the user's feedback data and identifies areas for improvement in suggestions. For example, it avoids characteristics of clothing that the user rated as "inappropriate." The clothing suggestion unit also understands the user's preferences and tendencies based on past feedback and reflects them in the next clothing suggestion. For example, it suggests clothing in colors and styles that the user prefers. This allows the accuracy of suggestions to be improved based on the user's past feedback.

[0074] The clothing suggestion unit can use the emotion estimation function to predict how the user will feel about the proposed clothing and adjust the suggestions based on that emotion. For example, the clothing suggestion unit can use the emotion estimation function to predict how the user will feel about the proposed clothing. For example, it can prioritize suggesting clothing that evokes positive emotions in the user. The clothing suggestion unit can also adjust the suggestions based on the user's emotion data. For example, it can avoid clothing that evokes negative emotions in the user. The clothing suggestion unit can also analyze in real time how the user will feel about the proposed clothing based on the emotion estimation data and reflect the results in the next suggestions. For example, it can suggest clothing that evokes positive emotions. This allows for more personalized clothing suggestions by adjusting the suggestions based on the user's emotions.

[0075] The clothing suggestion unit reflects the opinions of the user's friends and family, allowing it to make more multifaceted suggestions. For example, the clothing suggestion unit collects feedback from the user's friends and family and makes clothing suggestions based on that data. For example, it prioritizes suggesting outfits that friends have rated as "suitable." The clothing suggestion unit also builds a system that adjusts the suggestions based on the opinions of the user's friends and family. For example, it avoids outfits that family members have rated as "inappropriate." The clothing suggestion unit also understands the user's preferences and tendencies based on feedback from friends and family, and reflects these in the next clothing suggestion. For example, it suggests outfits that friends have rated as "stylish." This allows for more multifaceted clothing suggestions by reflecting the opinions of the user's friends and family.

[0076] The clothing suggestion unit can make special clothing suggestions according to different seasons or events. The clothing suggestion unit, for example, builds a system that makes special clothing suggestions for each season. For example, it suggests clothes made of cool materials in the summer and clothes made of warm materials in the winter. The clothing suggestion unit also makes clothing suggestions according to specific events (e.g., weddings, parties, sporting events). For example, it suggests dresses or suits for formal events. The clothing suggestion unit also collects the user's schedule data and adjusts the suggestions based on that data in order to make special clothing suggestions according to seasons or events. For example, it suggests gorgeous clothing for a Christmas party. This makes it possible to make special clothing suggestions according to seasons and events.

[0077] The clothing suggestion unit can use the emotion estimation function to predict what emotions a user will feel at a specific event or situation, and customize clothing suggestions based on those emotions. For example, the clothing suggestion unit can use the emotion estimation function to predict what emotions a user will feel at a specific event or situation. For example, it can prioritize suggesting clothing that evokes positive emotions. The clothing suggestion unit can also customize clothing suggestions based on the user's emotion data, depending on the specific event or situation. For example, it can suggest relaxing clothing for an event that makes the user nervous. The clothing suggestion unit can also analyze in real time what emotions a user will feel at a specific event or situation, based on the emotion estimation data, and reflect the results in the next suggestion. For example, it can suggest clothing that evokes positive emotions. This allows for more personalized clothing suggestions by customizing the clothing suggestions based on the user's emotions.

[0078] The display unit can display the proposed outfit in 3D on the user's avatar, allowing the user to visually confirm it. The display unit, for example, builds a system that displays the proposed outfit in 3D on the user's avatar. For example, the display unit allows the user to rotate their avatar to check the whole outfit. The display unit also dresses the 3D-displayed avatar in the proposed outfit and displays detailed information about the outfit (material, brand, size, etc.). For example, the user can click on the outfit details to check them. The display unit also displays the proposed outfit in 3D, allowing the user to visually check how it will look when actually worn. For example, the display unit provides a function that allows the user to check the outfit from different angles. This allows the user to visually check the proposed outfit.

[0079] The display unit can display detailed information about the suggested outfit (such as material, brand, and place of purchase) to allow the user to obtain more information. The display unit, for example, builds a system that displays detailed information about the suggested outfit (such as material, brand, and place of purchase). For example, the user can click on the outfit details to check them. The display unit also displays information about the material and brand of the suggested outfit to allow the user to understand the quality and characteristics of the outfit. For example, it displays the characteristics of the material and the history of the brand. The display unit also displays information about the place of purchase and price of the suggested outfit to help the user when purchasing the outfit. For example, it provides a link to an online shop. This allows the user to obtain detailed information about the suggested outfit.

[0080] The display unit can use the emotion estimation function to analyze the emotion a user feels when viewing suggested clothing in real time, and adjust the display content based on that emotion. The display unit, for example, uses the emotion estimation function to build a system that analyzes the emotion a user feels when viewing suggested clothing in real time. For example, it calculates an emotion score by analyzing the user's facial expressions and voice. The display unit also adjusts the display content of the suggested clothing based on the user's emotion data. For example, it highlights and displays clothing that evokes positive emotions in the user. The display unit also analyzes the emotion a user feels when viewing suggested clothing in real time based on the emotion estimation data, and reflects the results in the next suggestion. For example, it suggests clothing that evokes positive emotions. This allows for more personalized clothing suggestions by adjusting the display content based on the user's emotion.

[0081] The display unit can display the proposed outfit against different backgrounds and situations, allowing the user to check how it will look in various situations. The display unit, for example, builds a system that displays the proposed outfit against different backgrounds and situations. For example, it allows the user to select backgrounds such as an office, a cafe, or outdoors. The display unit also displays the proposed outfit in different situations, allowing the user to check how it will look. For example, it provides a function for switching between daytime and nighttime situations. The display unit also displays the proposed outfit against different backgrounds and situations, allowing the user to visually check how it will look when actually worn. For example, it displays situations such as an event or a party. This allows the user to visually check the proposed outfit in various situations.

[0082] The display unit can share the suggested outfits with the user's friends and family and collect their opinions. For example, the display unit adds a function to share the suggested outfits with the user's friends and family. For example, it can make it possible to share images of the outfits via social networking sites or messaging apps. The display unit also adds a function to collect opinions from friends and family and makes outfit suggestions based on that data. For example, it can prioritize suggesting outfits that friends have rated as "looking good." The display unit also builds a system that shares the suggested outfits and collects feedback from friends and family in real time. For example, it can provide an online voting function. This allows the user to share the suggested outfits with friends and family and collect their opinions.

[0083] The display unit can use the emotion estimation function to predict the emotion a user will feel when viewing suggested clothing, and customize the display content based on that emotion. For example, the display unit uses the emotion estimation function to build a system that predicts the emotion a user will feel when viewing suggested clothing. For example, the display unit calculates an emotion score by analyzing the user's facial expressions and voice. The display unit also customizes the display content of the suggested clothing based on the user's emotion data. For example, it highlights and displays clothing that evokes positive emotions in the user. The display unit also predicts the emotion a user will feel when viewing suggested clothing based on the emotion estimation data, and reflects the result in the next suggestion. For example, it suggests clothing that evokes positive emotions. This allows the display content to be customized based on the user's emotions, enabling more personalized clothing suggestions.

[0084] The clothing suggestion unit can collect user feedback and improve the accuracy of suggestions based on that feedback. The clothing suggestion unit, for example, builds a system that collects detailed comments and ratings from users and analyzes that data. For example, it allows users to input specific comments such as "This outfit was comfortable." The clothing suggestion unit also identifies areas for improvement in suggestions based on the user's comments and ratings. For example, it improves size suggestions based on ratings such as "This outfit didn't fit." The clothing suggestion unit also analyzes the detailed comments and ratings and reflects them in the next clothing suggestion. For example, it prioritizes suggesting favorite colors based on a user's comment such as "I like this color." This allows the accuracy of suggestions to be improved based on user feedback.

[0085] The clothing suggestion unit can analyze the user's behavioral data (whether or not the clothing was actually worn) to improve the accuracy of suggestions. The clothing suggestion unit, for example, records whether the user actually wore the suggested clothing and builds a system to analyze that data. For example, it automatically records the history of clothing worn by the user. The clothing suggestion unit also improves the accuracy of suggestions based on the user's behavioral data. For example, it analyzes the characteristics of clothing actually worn by the user and reflects this in the next suggestion. The clothing suggestion unit also understands the user's preferences and tendencies based on data on whether the clothing was actually worn and reflects this in the next clothing suggestion. For example, it prioritizes suggesting clothing styles that the user wears frequently. This makes it possible to improve the accuracy of suggestions based on the user's behavioral data.

[0086] The clothing suggestion unit can use the emotion estimation function to analyze the emotion a user has when providing feedback and evaluate the feedback content based on that emotion. The clothing suggestion unit, for example, uses the emotion estimation function to build a system that analyzes the emotion a user has when providing feedback. For example, it calculates an emotion score by analyzing the user's facial expression and voice. The clothing suggestion unit also evaluates the feedback content based on the user's emotion data. For example, it prioritizes evaluation of feedback with positive emotions. The clothing suggestion unit also analyzes the emotion a user has when providing feedback based on the emotion estimation data and reflects the results in the next suggestion. For example, it makes suggestions that elicit positive emotions. This enables more personalized clothing suggestions by evaluating the feedback content based on the user's emotions.

[0087] The clothing suggestion unit can compare feedback between different users and identify common problems and areas for improvement. The clothing suggestion unit, for example, builds a system that compares feedback between different users and identifies common problems and areas for improvement. For example, if multiple users evaluate an item as "the size didn't fit," the problem is improved. The clothing suggestion unit also identifies common problems based on feedback between users and reflects them in the next suggestion. For example, if multiple users evaluate an item as "I didn't like the color," the unit avoids suggesting that color. The clothing suggestion unit also analyzes feedback between different users and identifies common areas for improvement. For example, if multiple users evaluate the material as "comfortable," the unit preferentially suggests that material. In this way, by comparing feedback between different users, common problems and areas for improvement can be identified and the accuracy of suggestions can be improved.

[0088] The clothing suggestion unit can integrate feedback from different devices and platforms and perform a comprehensive evaluation. The clothing suggestion unit, for example, integrates feedback from different devices (smartphones, tablets, PCs, etc.) and builds a system for performing a comprehensive evaluation. For example, it centrally manages feedback from all devices. The clothing suggestion unit also integrates feedback from different platforms (SNS, email, in-app feedback, etc.) and performs a comprehensive evaluation. For example, it centrally manages feedback from all platforms. The clothing suggestion unit also collects feedback from different devices and platforms in real time and builds a system for performing a comprehensive evaluation. For example, it analyzes all feedback in real time. This makes it possible to integrate feedback from different devices and platforms to perform a comprehensive evaluation and improve the accuracy of suggestions.

[0089] The clothing suggestion unit can use the emotion estimation function to predict the emotion a user will have when providing feedback and customize the feedback content based on that emotion. The clothing suggestion unit, for example, uses the emotion estimation function to build a system that predicts the emotion a user will have when providing feedback. For example, it calculates an emotion score by analyzing the user's facial expressions and voice. The clothing suggestion unit also customizes the feedback content based on the user's emotion data. For example, it prioritizes evaluation of feedback in which the user has positive emotions. The clothing suggestion unit also predicts the emotion a user will have when providing feedback based on the emotion estimation data and reflects the result in the next suggestion. For example, it makes suggestions that elicit positive emotions. This allows for more personalized clothing suggestions by customizing the feedback content based on the user's emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The clothing suggestion system can further collect lifestyle data on the user to improve the accuracy of suggestions. For example, it can collect data on the user's occupation, hobbies, and daily activities, and make clothing suggestions based on that data. For days when the user works in the office, it can suggest business casual clothing, and for days when the user engages in outdoor activities, it can suggest waterproof clothing. It can also collect activity data such as the user's exercise volume, distance traveled, and calories burned, and make clothing suggestions based on that data. For days when the user is actively exercising, it can suggest clothing that is easy to move in. This makes it possible to make more appropriate clothing suggestions based on the user's lifestyle and activities.

[0092] The clothing suggestion system can also analyze a user's past clothing history to understand their preferences and trends. For example, it can record the history of clothing worn by the user in the past and analyze that data. It can understand the user's preference for specific colors and styles of clothing and reflect this in the next clothing suggestion. It can prioritize suggestions based on the user's favorite brands and designs and suggest clothing suited to specific events. This makes it possible to make more appropriate clothing suggestions based on the user's preferences and trends.

[0093] The clothing suggestion system can also share clothing data between different users and reflect trends and popular clothing in the suggestions. For example, it can reflect the clothing preferred by users of the same age or region in the suggestions, suggesting popular clothing styles and brands. It can prioritize suggestions of clothing that are popular in a particular season and reflect the latest fashion trends in the suggestions. In this way, by reflecting trends and popular clothing in the suggestions, it becomes possible to make more attractive clothing suggestions.

[0094] The clothing suggestion system can also take into account the user's schedule and plans to suggest appropriate clothing. For example, it can collect the user's schedule data (events from a calendar app) and make clothing suggestions based on that data. For example, it can suggest formal attire for a business meeting, and sportswear for a day when exercise is planned. It can suggest clothing suitable for a specific event or activity, and casual yet stylish clothing for a date. This allows it to suggest more appropriate clothing based on the user's schedule and plans.

[0095] The clothing suggestion system can further collect user feedback and improve the accuracy of suggestions based on that feedback. For example, it can collect feedback provided by users in the past and improve the accuracy of clothing suggestions based on that data. It can prioritize suggesting outfits that users have rated as "appropriate" and analyze the feedback data to identify areas for improvement in the suggestions. It can avoid the characteristics of outfits that users have rated as "inappropriate" and reflect these in the next outfit suggestions. This allows the accuracy of suggestions to be improved based on users' past feedback.

[0096] The clothing suggestion system can further use an emotion estimation function to analyze the emotions felt by the user when wearing a particular outfit and evaluate the clothing data based on those emotions. For example, the system can prioritize suggesting outfits that evoke positive emotions in the user and customize the evaluation criteria for the clothing data based on the emotion data. It can prioritize suggesting clothing styles preferred by the user and reflect this in the next outfit suggestion based on the emotion estimation data. This allows for more personalized outfit suggestions by evaluating the clothing data based on the user's emotions.

[0097] The clothing suggestion system can further use emotion estimation functionality to predict how the user will feel about the suggested outfits and adjust the suggestions based on those emotions. For example, it can prioritize suggesting outfits that evoke positive emotions in the user and adjust the suggestions based on emotion data. It can also avoid outfits that evoke negative emotions in the user and reflect this in the next suggestions based on emotion estimation data. This allows for more personalized outfit suggestions by adjusting suggestions based on the user's emotions.

[0098] The clothing suggestion system can further use emotion estimation functions to predict how a user will feel in a particular event or situation, and customize clothing suggestions based on those emotions. For example, it can prioritize suggesting clothing that evokes positive emotions in the user, and customize clothing suggestions for specific events or situations based on emotion data. For events that tend to make the user nervous, it can suggest relaxing clothing, and reflect this in the next suggestion based on emotion estimation data. In this way, customizing clothing suggestions based on the user's emotions enables more personalized clothing suggestions.

[0099] The clothing suggestion system can also use an emotion estimation function to analyze the emotions expressed when a user provides feedback and evaluate the feedback content based on those emotions. For example, it can analyze the user's facial expressions and voice to calculate an emotion score and prioritize feedback with positive emotions. The emotion estimation data can be reflected in the next suggestion, making suggestions that elicit positive emotions. This allows for more personalized clothing suggestions by evaluating the feedback content based on the user's emotions.

[0100] The clothing suggestion system can also use emotion estimation to predict the emotions a user will have when providing feedback and customize the feedback content based on those emotions. For example, it can analyze the user's facial expressions and voice to calculate an emotion score and prioritize feedback with positive emotions. The emotion estimation data can be reflected in the next suggestion, making suggestions that elicit positive emotions. This allows for more personalized clothing suggestions by customizing the feedback content based on the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The weather data collection unit collects and analyzes weather data. For example, the weather data collection unit collects data such as temperature, humidity, probability of precipitation, and wind speed to understand the weather conditions of the day. Step 2: The clothing data management unit manages the user's clothing data based on the weather data collected by the weather data collection unit, such as the type, material, color, size, and seasonality of each piece of clothing. Step 3: The clothing suggestion unit suggests the best clothing for the day's conditions based on the clothing data managed by the clothing data management unit. For example, on a cold, windy day, it suggests a warm coat or windproof jacket. Step 4: The display unit displays the outfits suggested by the outfit suggestion unit on the user's device, for example, through a smartphone app, displaying images and detailed information about the outfits.

[0103] 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.

[0104] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] 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.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] 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.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] 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.

[0153] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0154] 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.

[0155] 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).

[0156] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0164] The hardware resource that executes the specific process 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 process may be a single processor.

[0165] 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.

[0166] 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.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] 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.

[0169] 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. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a meteorological data collection unit that collects and analyzes meteorological data; a clothing data management unit that manages clothing data owned by a user based on the weather data collected by the weather data collection unit; a clothing suggestion unit that suggests clothing that is most suitable for the conditions of the day based on the clothing data managed by the clothing data management unit; a display unit that displays the outfits suggested by the outfit suggestion unit on the user's device. A system characterized by:

2. The meteorological data collection unit Analyzing past weather patterns and predicting future weather 2. The system of claim 1.

3. The meteorological data collection unit Obtaining the user's location information in real time and analyzing detailed weather data for each region 2. The system of claim 1.

4. The meteorological data collection unit Analyzing what emotions the user felt under what weather conditions in the past, and evaluating weather data based on those emotions 2. The system of claim 1.

5. The meteorological data collection unit In addition to the weather data, health-related data such as pollen and ultraviolet information will also be collected and used for analysis.

2. The system of claim 1.

6. The meteorological data collection unit Integrate data from different weather data sources to perform more accurate weather analysis 2. The system of claim 1.

Images