System

Abstract

An object of a system according to an embodiment is to propose optimal fashion coordination in consideration of information such as a user's preference, body type, season, and event.SOLUTION: A system includes a user information collection unit, a coordination generation unit, and a display unit. The user information collection unit collects information such as user's preference, body type, season, and event. The coordinate generation part generates optimal fashion coordinates on the basis of the information collected by the user information collection part. The display unit displays the coordination generated by the coordination generation unit to 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] Conventional technologies do not adequately propose optimal fashion coordination that takes into consideration information such as a user's preferences, body type, season, and events, and there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal fashion coordination taking into consideration information such as the user's preferences, body type, season, and event. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information collection unit, a coordinate generation unit, and a display unit. The user information collection unit collects information such as a user's preferences, body type, season, and events. The coordinate generation unit generates an optimal fashion coordinate based on the information collected by the user information collection unit. The display unit displays the coordinate generated by the coordinate generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal fashion coordination by taking into consideration information such as the user's preferences, body type, season, and event. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 fashion coordination suggestion system according to an embodiment of the present invention is a system that uses a generation AI to suggest fashion coordination to a user. This system provides information such as the user's preferences, body type, season, and event as input to the generation AI, which then suggests optimal fashion coordination based on that information. This allows the fashion coordination suggestion system to easily allow users to find a style that suits them.

[0029] A fashion coordination suggestion system according to an embodiment includes a user information collection unit, a coordination generation unit, and a display unit. The user information collection unit collects information such as a user's preferences, body type, season, and events. For example, a user inputs information such as "I like casual styles," "I'm 160 cm tall," and "I'm going to a summer beach party." The coordination generation unit generates an optimal fashion coordination based on the information collected by the user information collection unit. For example, the generation AI proposes a coordination such as "a white T-shirt, denim shorts, sandals, and sunglasses." The generation AI references a fashion database and generates a coordination taking into account the latest trends and styles. The display unit displays the coordination generated by the coordination generation unit to the user. For example, the user can select one that suits their preferences from multiple coordinations proposed by the generation AI. This allows the fashion coordination suggestion system according to an embodiment to propose an optimal fashion coordination based on information such as a user's preferences, body type, season, and events.

[0030] The user information collection unit automatically collects the user's past fashion history and purchase history and provides it to the generation AI, thereby enabling more accurate coordination suggestions. For example, the user information collection unit automatically obtains the user's history of past purchases from an online shop and provides it to the generation AI. For example, the generation AI suggests coordinations that suit the user based on the brands and types of items the user has purchased in the past. This allows more accurate coordination suggestions to be made based on the user's past fashion history and purchase history.

[0031] The user information collection unit can use the user's real-time location information to suggest outfits that take into account local weather and event information. The user information collection unit, for example, obtains location information from the user's smartphone and collects local weather information in real time. For example, it suggests waterproof items for rainy days. The user information collection unit also collects local event information and provides it to the generation AI. For example, when attending a local festival, it suggests outfits that are suitable for the festival. This makes it possible to suggest outfits that take into account local weather and event information based on the user's real-time location information.

[0032] The user information collection unit can refer to the fashion styles of the user's friends and family and make group coordination suggestions. The user information collection unit, for example, collects the fashion styles of the user's friends and family from social media and provides this to the generation AI. For example, the unit can suggest group coordination based on the brands and styles that friends often wear. The user information collection unit also refers to the fashion styles of the user's family and suggests coordination for the whole family. For example, the whole family can enjoy coordinating outfits with the same theme. This allows the unit to refer to the fashion styles of the user's friends and family and make group coordination suggestions.

[0033] The user information collection unit can upload photos of clothes that the user owns and suggest new coordinations based on those photos. For example, the user information collection unit uploads photos of clothes that the user owns, and the generation AI analyzes the images to identify the items. For example, it can suggest pants and accessories that go well with a jacket that the user owns. The user information collection unit can also suggest combinations with existing items based on photos of clothes that the user owns. For example, it can suggest skirts and shoes that go well with a shirt that the user owns. This makes it possible to suggest new coordinations based on photos of clothes that the user owns.

[0034] The coordination generation unit can reflect the user's past ratings and feedback and continuously improve the coordination. For example, the coordination generation unit collects data on coordinations that users have previously rated, and the generation AI proposes new coordinations based on that information. For example, it prioritizes suggestions of styles that users have given high ratings to. The coordination generation unit also improves the method by which the generation AI proposes coordinations based on user feedback. For example, it excludes styles that the user does not like. This allows the user's past ratings and feedback to be reflected and the coordination to be continuously improved.

[0035] The coordination generation unit can make suggestions that prioritize comfort, taking into account the user's health condition and activity level. For example, the coordination generation unit has the generation AI suggest coordination that prioritizes comfort based on the user's health condition and activity level. For example, for a user who exercises a lot, items made from materials that are easy to move in are suggested. The coordination generation unit also takes into account the user's health condition, and the generation AI suggests appropriate items. For example, if the user has an allergy, items made from materials that are allergy-friendly are suggested. This makes it possible to suggest coordination that prioritizes comfort, taking into account the user's health condition and activity level.

[0036] The coordinate generation unit can incorporate fashion styles from different cultures and regions to propose new styles to the user. For example, the coordinate generation unit collects fashion styles from different cultures and regions from a database, and the generation AI proposes new styles based on that information. For example, it proposes coordinates that incorporate African prints and Japanese clothing. The coordinate generation unit also proposes coordinates that combine styles from different cultures and regions to suit the user's preferences and body type. For example, it proposes new styles that incorporate colors and designs that the user likes. This makes it possible to propose new styles to the user by incorporating fashion styles from different cultures and regions.

[0037] The coordination generation unit can propose special coordinations that match seasonal trends and events. For example, the coordination generation unit collects seasonal trends from a database, and the generation AI proposes special coordinations based on that information. For example, in spring, it proposes coordinations that incorporate floral items. The coordination generation unit also proposes special coordinations that match events. For example, it proposes an coordination based on red and green for a Christmas party. This makes it possible to propose special coordinations that match seasonal trends and events.

[0038] The display unit can display the suggested outfits as a 3D model, allowing the user to check them according to their own body type. For example, the display unit can display the suggested outfits as a 3D model, allowing the user to check them according to their own body type. For example, when the user inputs their height and weight, a 3D model is generated based on that information. The display unit can also allow the user to check the outfits from different angles. For example, it can provide views from the front, back, and side. This allows the suggested outfits to be displayed as a 3D model, allowing the user to check them according to their own body type.

[0039] The display unit displays ratings and comments from other users on the proposed coordination, allowing the user to use them as a reference for making a selection. The display unit, for example, displays ratings and comments from other users on the proposed coordination, allowing the user to use them as a reference for making a selection. For example, it displays a list of rating scores and comments from other users. The display unit also provides an index showing the reliability of the ratings and comments. For example, it displays profile information and past rating history of the evaluator. This allows the user to display ratings and comments from other users on the proposed coordination, allowing the user to use them as a reference for making a selection.

[0040] The display unit displays the suggested coordination against different backgrounds and situations, making it easier for the user to imagine the actual usage scenario. For example, the display unit displays the suggested coordination against different backgrounds and situations, making it easier for the user to imagine the actual usage scenario. For example, the display unit allows the user to select backgrounds such as the beach, office, or party. The display unit also allows the user to check coordinations according to the scenario. For example, the display unit compares coordinations for casual and formal scenarios. This allows the suggested coordination to be displayed against different backgrounds and situations, making it easier for the user to imagine the actual usage scenario.

[0041] The display unit can display suggested outfits in different price ranges and brands, providing options that fit the user's budget. For example, the display unit can display suggested outfits in different price ranges, allowing the user to make selections that fit their budget. For example, items of the same style can be displayed in high and low price ranges. The display unit can also display items from different brands for comparison. For example, luxury brands and affordable brands can be displayed side by side. This allows suggested outfits to be displayed in different price ranges and brands, providing options that fit the user's budget.

[0042] The coordinate generation unit can encourage repeat purchases by referring to the user's past purchase history for items in the suggested coordination. The coordinate generation unit, for example, can encourage repeat purchases of items in the suggested coordination by referring to the user's past purchase history. For example, based on a brand or item that the user has previously purchased, the coordinate generation unit can suggest new items from the same brand. The coordinate generation unit also presents benefits of repeat purchases based on the user's purchase history. For example, it can offer discounts or special offers for repeat purchases. This makes it possible to encourage repeat purchases by referring to the user's past purchase history for items in the suggested coordination.

[0043] The display unit displays reviews and ratings by other users for the items in the proposed coordination, allowing the user to use them as a reference for purchasing. The display unit, for example, displays reviews and ratings by other users for the items in the proposed coordination, allowing the user to use them as a reference for purchasing. For example, it displays a list of rating scores and comments by other users. The display unit also provides an index indicating the reliability of the reviews and ratings. For example, it displays profile information and past rating history of the evaluator. This allows the user to display reviews and ratings by other users for the items in the proposed coordination, allowing the user to use them as a reference for purchasing.

[0044] The display unit can compare items for the suggested coordination from different online shops, allowing the user to purchase them at the optimal price and conditions. For example, the display unit can compare items for the suggested coordination from different online shops, allowing the user to purchase them at the optimal price and conditions. For example, the display unit can display the same item from multiple shops and compare prices and shipping costs. The display unit can also display ratings and reviews of the online shops. For example, the display unit can provide indicators of the shop's reliability and delivery speed. This allows the user to compare items for the suggested coordination from different online shops, allowing the user to purchase them at the optimal price and conditions.

[0045] The display unit can link the items of the suggested coordination with a rental service to provide an option other than purchase. For example, the display unit can link the items of the suggested coordination with a rental service to allow the user to choose an option other than purchase. For example, the display unit can allow a specific item to be rented for a certain period of time. The display unit can also display ratings and reviews of the rental service. For example, the display unit can provide an indicator of the reliability and fees of the rental service. This allows the items of the suggested coordination to be linked with a rental service to provide an option other than purchase.

[0046] The display unit can collect user feedback on the saved coordination and reflect it in the next suggestion. For example, the display unit collects user feedback on the saved coordination and the generation AI reflects it in the next suggestion. For example, the display unit preferentially suggests coordination styles that the user has given high ratings to. The display unit also improves the method by which the generation AI suggests coordination based on the user feedback. For example, it excludes styles that the user does not like. In this way, the display unit collects user feedback on the saved coordination and reflects it in the next suggestion.

[0047] The display unit can link the saved outfits with the user's calendar or schedule and automatically suggest them for a specific day. For example, the display unit links the saved outfits with the user's calendar or schedule and automatically suggests them for a specific day. For example, the display unit suggests outfits to match the date of an event set by the user. The display unit also suggests outfits based on the user's schedule. For example, the display unit suggests a business casual style for the user's work days and a relaxed style for their days off. In this way, the saved outfits are linked with the user's calendar or schedule and automatically suggested for a specific day.

[0048] The display unit can synchronize the saved outfits across different devices and platforms, allowing them to be accessed anywhere. For example, the display unit can synchronize the saved outfits across different devices and platforms, allowing the user to access them anywhere. For example, the same outfit can be viewed on a smartphone, tablet, and PC. The display unit can also save outfits using cloud storage, allowing them to be accessed offline. For example, the saved outfits can be viewed even when there is no internet connection. This allows the saved outfits to be synchronized across different devices and platforms, allowing them to be accessed anywhere.

[0049] The display unit can provide a function for sharing saved outfits with other users and collaboratively creating outfits. The display unit, for example, provides a function for sharing saved outfits with other users and collaboratively creating outfits. For example, creating outfits together with friends and family and exchanging opinions. The display unit also provides a function for collaborative editing in real time. For example, multiple users can simultaneously edit outfits and add comments. This provides a function for sharing saved outfits with other users and collaboratively creating outfits.

[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 user information collection unit can collect information about the user's lifestyle and hobbies and provide it to the generation AI. For example, if the user likes outdoor activities, it can suggest outfits suitable for outdoor activities. If the user frequently attends music festivals, it can suggest styles suitable for festivals. Furthermore, if the user plays a specific sport, it can suggest activewear suitable for that sport. This makes it possible to suggest outfits based on the user's lifestyle and hobbies.

[0052] The user information collection unit can collect the user's health data and provide it to the generation AI. For example, if the user uses a fitness tracker, the data can be used to understand the user's health condition and suggest appropriate outfits. If the user has allergies, the unit can suggest items made from hypoallergenic materials. Furthermore, if the user has specific health goals, the unit can suggest styles that match those goals. This allows the unit to suggest outfits based on the user's health data.

[0053] The user information collection unit can collect information about the user's occupation and work environment and provide it to the generation AI. For example, if the user works in an office, it can suggest a business casual outfit. If the user works remotely, it can suggest a relaxed style. Furthermore, if the user works in a specific industry, it can suggest a style appropriate for that industry. This allows it to suggest outfits based on the user's occupation and work environment.

[0054] The user information collection unit can collect information about the user's travel plans and provide it to the generation AI. For example, if the user is planning an overseas trip, it will suggest outfits suited to the climate and culture of that region. If the user is planning a business trip, it will suggest styles suitable for business situations. Furthermore, if the user is planning an outdoor trip, it will suggest items suitable for outdoor activities. This makes it possible to suggest outfits based on the user's travel plans.

[0055] The user information collection unit can collect information about the user's pet and provide it to the generation AI. For example, if the user has a dog, it will suggest outfits suitable for walking the dog. If the user has a cat, it will suggest items made from materials that are less likely to attract cat hair. Furthermore, if the user is traveling with a pet, it will suggest styles that are pet-friendly. This allows it to suggest outfits based on information about the user's pet.

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

[0057] Step 1: The user information collection unit collects information about the user's preferences, body type, season, events, etc. For example, the user inputs information such as "I like casual styles," "I'm 160 cm tall," and "I'm going to a summer beach party." Step 2: The coordination generation unit generates the optimal fashion coordination based on the information collected by the user information collection unit. For example, the generation AI might suggest a coordination such as "a white T-shirt, denim shorts, sandals, and sunglasses." The generation AI references a fashion database and generates coordination taking into account the latest trends and styles. Step 3: The display unit displays the outfits generated by the outfit generation unit to the user. For example, the user can select an outfit that suits their taste from among the outfits proposed by the AI.

[0058] (Example 2) The fashion coordination suggestion system according to an embodiment of the present invention is a system that uses a generation AI to suggest fashion coordination to a user. This system provides information such as the user's preferences, body type, season, and event as input to the generation AI, which then suggests optimal fashion coordination based on that information. This allows the fashion coordination suggestion system to easily allow users to find a style that suits them.

[0059] A fashion coordination suggestion system according to an embodiment includes a user information collection unit, a coordination generation unit, and a display unit. The user information collection unit collects information such as a user's preferences, body type, season, and events. For example, a user inputs information such as "I like casual styles," "I'm 160 cm tall," and "I'm going to a summer beach party." The coordination generation unit generates an optimal fashion coordination based on the information collected by the user information collection unit. For example, the generation AI proposes a coordination such as "a white T-shirt, denim shorts, sandals, and sunglasses." The generation AI references a fashion database and generates a coordination taking into account the latest trends and styles. The display unit displays the coordination generated by the coordination generation unit to the user. For example, the user can select one that suits their preferences from multiple coordinations proposed by the generation AI. This allows the fashion coordination suggestion system according to an embodiment to propose an optimal fashion coordination based on information such as a user's preferences, body type, season, and events.

[0060] The user information collection unit automatically collects the user's past fashion history and purchase history and provides it to the generation AI, thereby enabling more accurate coordination suggestions. For example, the user information collection unit automatically obtains the user's history of past purchases from an online shop and provides it to the generation AI. For example, the generation AI suggests coordinations that suit the user based on the brands and types of items the user has purchased in the past. This allows more accurate coordination suggestions to be made based on the user's past fashion history and purchase history.

[0061] The user information collection unit can use the user's real-time location information to suggest outfits that take into account local weather and event information. The user information collection unit, for example, obtains location information from the user's smartphone and collects local weather information in real time. For example, it suggests waterproof items for rainy days. The user information collection unit also collects local event information and provides it to the generation AI. For example, when attending a local festival, it suggests outfits that are suitable for the festival. This makes it possible to suggest outfits that take into account local weather and event information based on the user's real-time location information.

[0062] The user information collection unit can use the emotion estimation function to analyze the emotion a user has when entering information and collect information in the form of questions that elicit positive emotions. The user information collection unit, for example, analyzes facial expressions and tone of voice when a user enters information to estimate the emotion. For example, if the user is entering information with a smile, it provides positive feedback. Furthermore, if the user is feeling stressed, the user information collection unit lowers the difficulty of the questions. For example, it starts with simple questions and gradually collects more detailed information. This makes it possible to analyze the emotion a user has when entering information and collect information in the form of questions that elicit positive emotions.

[0063] The user information collection unit can refer to the fashion styles of the user's friends and family and make group coordination suggestions. The user information collection unit, for example, collects the fashion styles of the user's friends and family from social media and provides this to the generation AI. For example, the unit can suggest group coordination based on the brands and styles that friends often wear. The user information collection unit also refers to the fashion styles of the user's family and suggests coordination for the whole family. For example, the whole family can enjoy coordinating outfits with the same theme. This allows the unit to refer to the fashion styles of the user's friends and family and make group coordination suggestions.

[0064] The user information collection unit can upload photos of clothes that the user owns and suggest new coordinations based on those photos. For example, the user information collection unit uploads photos of clothes that the user owns, and the generation AI analyzes the images to identify the items. For example, it can suggest pants and accessories that go well with a jacket that the user owns. The user information collection unit can also suggest combinations with existing items based on photos of clothes that the user owns. For example, it can suggest skirts and shoes that go well with a shirt that the user owns. This makes it possible to suggest new coordinations based on photos of clothes that the user owns.

[0065] The user information collection unit uses the emotion estimation function to analyze the emotion of the user when entering information in real time and adjust the input content, thereby collecting more accurate information. The user information collection unit, for example, uses the emotion estimation function to analyze the emotion of the user when entering information in real time. For example, if the user is feeling stressed, the difficulty of the questions is reduced. Furthermore, the user information collection unit collects detailed information when the user is relaxed. For example, when the user is relaxed, the unit asks detailed questions about the user's favorite style and colors. In this way, the emotion of the user when entering information can be analyzed in real time and the input content can be adjusted, thereby collecting more accurate information.

[0066] The coordination generation unit can reflect the user's past ratings and feedback and continuously improve the coordination. For example, the coordination generation unit collects data on coordinations that users have previously rated, and the generation AI proposes new coordinations based on that information. For example, it prioritizes suggestions of styles that users have given high ratings to. The coordination generation unit also improves the method by which the generation AI proposes coordinations based on user feedback. For example, it excludes styles that the user does not like. This allows the user's past ratings and feedback to be reflected and the coordination to be continuously improved.

[0067] The coordination generation unit can make suggestions that prioritize comfort, taking into account the user's health condition and activity level. For example, the coordination generation unit has the generation AI suggest coordination that prioritizes comfort based on the user's health condition and activity level. For example, for a user who exercises a lot, items made from materials that are easy to move in are suggested. The coordination generation unit also takes into account the user's health condition, and the generation AI suggests appropriate items. For example, if the user has an allergy, items made from materials that are allergy-friendly are suggested. This makes it possible to suggest coordination that prioritizes comfort, taking into account the user's health condition and activity level.

[0068] The coordination generation unit can use the emotion estimation function to analyze the user's current emotional state and suggest coordination that matches that emotion. For example, when the generation AI summarizes, the coordination generation unit uses the emotion estimation function to capture the emotional nuances of the answer. For example, it generates a summary based on an emotion score. The summary generation unit also uses the emotion estimation function to build a system in which the generation AI reflects the emotional elements of the answer in the evaluation. For example, it performs the evaluation based on the emotion score. The summary generation unit also uses the emotion estimation function to develop an algorithm for the generation AI to generate a summary that captures the emotional nuances of the answer. For example, it generates a summary based on the emotion score and reflects that in the evaluation. In this way, by generating a summary that captures the emotional nuances, emotional elements can also be reflected in the evaluation.

[0069] The coordinate generation unit can incorporate fashion styles from different cultures and regions to propose new styles to the user. For example, the coordinate generation unit collects fashion styles from different cultures and regions from a database, and the generation AI proposes new styles based on that information. For example, it proposes coordinates that incorporate African prints and Japanese clothing. The coordinate generation unit also proposes coordinates that combine styles from different cultures and regions to suit the user's preferences and body type. For example, it proposes new styles that incorporate colors and designs that the user likes. This makes it possible to propose new styles to the user by incorporating fashion styles from different cultures and regions.

[0070] The coordination generation unit can propose special coordinations that match seasonal trends and events. For example, the coordination generation unit collects seasonal trends from a database, and the generation AI proposes special coordinations based on that information. For example, in spring, it proposes coordinations that incorporate floral items. The coordination generation unit also proposes special coordinations that match events. For example, it proposes an coordination based on red and green for a Christmas party. This makes it possible to propose special coordinations that match seasonal trends and events.

[0071] The coordinate generation unit can use the emotion estimation function to propose coordinates based on themes (e.g., relaxed, energetic) based on the user's emotions. The coordinate generation unit, for example, uses the emotion estimation function to propose coordinates based on themes based on the user's emotions. For example, a relaxed style is proposed for a user who feels like relaxing. On the other hand, bright colors and items that are easy to move in are proposed for a user who is feeling energetic. In this way, coordinates based on themes can be proposed based on the user's emotions.

[0072] The display unit can display the suggested outfits as a 3D model, allowing the user to check them according to their own body type. For example, the display unit can display the suggested outfits as a 3D model, allowing the user to check them according to their own body type. For example, when the user inputs their height and weight, a 3D model is generated based on that information. The display unit can also allow the user to check the outfits from different angles. For example, it can provide views from the front, back, and side. This allows the suggested outfits to be displayed as a 3D model, allowing the user to check them according to their own body type.

[0073] The display unit displays ratings and comments from other users on the proposed coordination, allowing the user to use them as a reference for making a selection. The display unit, for example, displays ratings and comments from other users on the proposed coordination, allowing the user to use them as a reference for making a selection. For example, it displays a list of rating scores and comments from other users. The display unit also provides an index showing the reliability of the ratings and comments. For example, it displays profile information and past rating history of the evaluator. This allows the user to display ratings and comments from other users on the proposed coordination, allowing the user to use them as a reference for making a selection.

[0074] The display unit can use the emotion estimation function to analyze the emotion of the user when selecting an outfit, and prioritize displaying the outfit that elicits the most positive response. The display unit, for example, uses the emotion estimation function to analyze the emotion of the user when selecting an outfit, and prioritize displaying the outfit that elicits the most positive response. For example, the display unit prioritizes displaying outfits selected by the user while smiling. The display unit also adjusts the display order of outfits based on the user's emotion. For example, the display unit displays outfits for which the user shows positive emotion at the top. This allows the display of outfits that elicit the most positive response to be analyzed, and prioritize displaying the outfits that elicit the most positive response.

[0075] The display unit displays the suggested coordination against different backgrounds and situations, making it easier for the user to imagine the actual usage scenario. For example, the display unit displays the suggested coordination against different backgrounds and situations, making it easier for the user to imagine the actual usage scenario. For example, the display unit allows the user to select backgrounds such as the beach, office, or party. The display unit also allows the user to check coordinations according to the scenario. For example, the display unit compares coordinations for casual and formal scenarios. This allows the suggested coordination to be displayed against different backgrounds and situations, making it easier for the user to imagine the actual usage scenario.

[0076] The display unit can display suggested outfits in different price ranges and brands, providing options that fit the user's budget. For example, the display unit can display suggested outfits in different price ranges, allowing the user to make selections that fit their budget. For example, items of the same style can be displayed in high and low price ranges. The display unit can also display items from different brands for comparison. For example, luxury brands and affordable brands can be displayed side by side. This allows suggested outfits to be displayed in different price ranges and brands, providing options that fit the user's budget.

[0077] The display unit can use the emotion estimation function to analyze the emotion of the user when selecting an outfit in real time and dynamically adjust the options. The display unit, for example, uses the emotion estimation function to analyze the emotion of the user when selecting an outfit in real time and dynamically adjust the options. For example, if the user shows positive emotion, the display unit suggests an additional similar style. The display unit also changes the options depending on the user's emotion. For example, if the user shows negative emotion, the display unit suggests a different style. In this way, the emotion of the user when selecting an outfit can be analyzed in real time and the options can be dynamically adjusted.

[0078] The coordinate generation unit can encourage repeat purchases by referring to the user's past purchase history for items in the suggested coordination. The coordinate generation unit, for example, can encourage repeat purchases of items in the suggested coordination by referring to the user's past purchase history. For example, based on a brand or item that the user has previously purchased, the coordinate generation unit can suggest new items from the same brand. The coordinate generation unit also presents benefits of repeat purchases based on the user's purchase history. For example, it can offer discounts or special offers for repeat purchases. This makes it possible to encourage repeat purchases by referring to the user's past purchase history for items in the suggested coordination.

[0079] The display unit displays reviews and ratings by other users for the items in the proposed coordination, allowing the user to use them as a reference for purchasing. The display unit, for example, displays reviews and ratings by other users for the items in the proposed coordination, allowing the user to use them as a reference for purchasing. For example, it displays a list of rating scores and comments by other users. The display unit also provides an index indicating the reliability of the reviews and ratings. For example, it displays profile information and past rating history of the evaluator. This allows the user to display reviews and ratings by other users for the items in the proposed coordination, allowing the user to use them as a reference for purchasing.

[0080] The display unit can use the emotion estimation function to analyze the emotion of the user when performing the purchase checkout and provide a purchase process that elicits positive emotions. The display unit, for example, uses the emotion estimation function to analyze the emotion of the user when performing the purchase checkout and provide a purchase process that elicits positive emotions. For example, if the user is feeling stressed, the display unit provides a simple purchase checkout. Furthermore, if the user is relaxed, the display unit provides detailed purchase options. For example, when the user is relaxed, additional items or services are suggested. In this way, the display unit can analyze the emotion of the user when performing the purchase checkout and provide a purchase process that elicits positive emotions.

[0081] The display unit can compare items for the suggested coordination from different online shops, allowing the user to purchase them at the optimal price and conditions. For example, the display unit can compare items for the suggested coordination from different online shops, allowing the user to purchase them at the optimal price and conditions. For example, the display unit can display the same item from multiple shops and compare prices and shipping costs. The display unit can also display ratings and reviews of the online shops. For example, the display unit can provide indicators of the shop's reliability and delivery speed. This allows the user to compare items for the suggested coordination from different online shops, allowing the user to purchase them at the optimal price and conditions.

[0082] The display unit can link the items of the suggested coordination with a rental service to provide an option other than purchase. For example, the display unit can link the items of the suggested coordination with a rental service to allow the user to choose an option other than purchase. For example, the display unit can allow a specific item to be rented for a certain period of time. The display unit can also display ratings and reviews of the rental service. For example, the display unit can provide an indicator of the reliability and fees of the rental service. This allows the items of the suggested coordination to be linked with a rental service to provide an option other than purchase.

[0083] The display unit can use the emotion estimation function to analyze the user's emotions in real time when going through the purchase process and make suggestions to increase the user's willingness to purchase. For example, the display unit can use the emotion estimation function to analyze the user's emotions in real time when going through the purchase process and make suggestions to increase the user's willingness to purchase. For example, if the user shows positive emotions, the display unit can suggest additional related items. Furthermore, if the user shows negative emotions, the display unit can simplify the purchase process. For example, the display unit can provide an option to make a purchase with one click. This allows the display unit to analyze the user's emotions in real time when going through the purchase process and make suggestions to increase the user's willingness to purchase.

[0084] The display unit can collect user feedback on the saved coordination and reflect it in the next suggestion. For example, the display unit collects user feedback on the saved coordination and the generation AI reflects it in the next suggestion. For example, the display unit preferentially suggests coordination styles that the user has given high ratings to. The display unit also improves the method by which the generation AI suggests coordination based on the user feedback. For example, it excludes styles that the user does not like. In this way, the display unit collects user feedback on the saved coordination and reflects it in the next suggestion.

[0085] The display unit can link the saved outfits with the user's calendar or schedule and automatically suggest them for a specific day. For example, the display unit links the saved outfits with the user's calendar or schedule and automatically suggests them for a specific day. For example, the display unit suggests outfits to match the date of an event set by the user. The display unit also suggests outfits based on the user's schedule. For example, the display unit suggests a business casual style for the user's work days and a relaxed style for their days off. In this way, the saved outfits are linked with the user's calendar or schedule and automatically suggested for a specific day.

[0086] The display unit can use the emotion estimation function to analyze the emotion a user has when saving, and provide a saving method that draws out positive emotions. The display unit, for example, can use the emotion estimation function to analyze the emotion a user has when saving, and provide a saving method that draws out positive emotions. For example, the display unit preferentially displays outfits saved by the user with a smile. The display unit also displays a positive message or animation when saving. For example, it displays a message such as "Great choice!". This makes it possible to analyze the emotion a user has when saving, and provide a saving method that draws out positive emotions.

[0087] The display unit can synchronize the saved outfits across different devices and platforms, allowing them to be accessed anywhere. For example, the display unit can synchronize the saved outfits across different devices and platforms, allowing the user to access them anywhere. For example, the same outfit can be viewed on a smartphone, tablet, and PC. The display unit can also save outfits using cloud storage, allowing them to be accessed offline. For example, the saved outfits can be viewed even when there is no internet connection. This allows the saved outfits to be synchronized across different devices and platforms, allowing them to be accessed anywhere.

[0088] The display unit can provide a function for sharing saved outfits with other users and collaboratively creating outfits. The display unit, for example, provides a function for sharing saved outfits with other users and collaboratively creating outfits. For example, creating outfits together with friends and family and exchanging opinions. The display unit also provides a function for collaborative editing in real time. For example, multiple users can simultaneously edit outfits and add comments. This provides a function for sharing saved outfits with other users and collaboratively creating outfits.

[0089] The display unit can use the emotion estimation function to analyze the emotion a user feels when sharing in real time and make suggestions to increase the willingness to share. The display unit, for example, uses the emotion estimation function to analyze the emotion a user feels when sharing in real time and make suggestions to increase the willingness to share. For example, if a user expresses positive emotion, a message encouraging sharing is displayed. The display unit also provides benefits or rewards when sharing. For example, a discount coupon is provided to users who share. In this way, the display unit can analyze the emotion a user feels when sharing in real time and make suggestions to increase the willingness to share.

[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 user information collection unit can collect information about the user's lifestyle and hobbies and provide it to the generation AI. For example, if the user likes outdoor activities, it can suggest outfits suitable for outdoor activities. If the user frequently attends music festivals, it can suggest styles suitable for festivals. Furthermore, if the user plays a specific sport, it can suggest activewear suitable for that sport. This makes it possible to suggest outfits based on the user's lifestyle and hobbies.

[0092] The user information collection unit can collect the user's health data and provide it to the generation AI. For example, if the user uses a fitness tracker, the data can be used to understand the user's health condition and suggest appropriate outfits. If the user has allergies, the unit can suggest items made from hypoallergenic materials. Furthermore, if the user has specific health goals, the unit can suggest styles that match those goals. This allows the unit to suggest outfits based on the user's health data.

[0093] The user information collection unit can collect information about the user's occupation and work environment and provide it to the generation AI. For example, if the user works in an office, it can suggest a business casual outfit. If the user works remotely, it can suggest a relaxed style. Furthermore, if the user works in a specific industry, it can suggest a style appropriate for that industry. This allows it to suggest outfits based on the user's occupation and work environment.

[0094] The user information collection unit can collect information about the user's travel plans and provide it to the generation AI. For example, if the user is planning an overseas trip, it will suggest outfits suited to the climate and culture of that region. If the user is planning a business trip, it will suggest styles suitable for business situations. Furthermore, if the user is planning an outdoor trip, it will suggest items suitable for outdoor activities. This makes it possible to suggest outfits based on the user's travel plans.

[0095] The user information collection unit can collect information about the user's pet and provide it to the generation AI. For example, if the user has a dog, it will suggest outfits suitable for walking the dog. If the user has a cat, it will suggest items made from materials that are less likely to attract cat hair. Furthermore, if the user is traveling with a pet, it will suggest styles that are pet-friendly. This allows it to suggest outfits based on information about the user's pet.

[0096] The coordinate generation unit estimates the user's emotions and, based on the estimated emotions, can suggest a relaxed style if the user feels like relaxing. For example, sweatpants and a cardigan made of soft material are suggested to a user who feels like relaxing. Also, if the user feels stressed, a coordinate that emphasizes comfort is suggested. Furthermore, if the user feels like refreshing, items in bright colors and light materials are suggested. In this way, a relaxed style can be suggested based on the user's emotions.

[0097] The coordinate generation unit estimates the user's emotions and, based on the estimated emotions, can suggest styles that are easy to move in when the user is feeling energetic. For example, sportswear or activewear can be suggested to a user who is feeling energetic. Furthermore, when the user is showing positive emotions, items with bright colors or bold designs can be suggested. Furthermore, when the user is feeling adventurous, outdoor styles can be suggested. In this way, energetic styles can be suggested based on the user's emotions.

[0098] The coordinate generation unit estimates the user's emotions and, based on the estimated emotions, can suggest a style that will lift the user's spirits when the user is feeling down. For example, items with bright colors and fun designs can be suggested to a user who is feeling down. Also, a style that will help the user relax can be suggested when the user is tired. Furthermore, items with warm materials and designs can be suggested when the user is feeling lonely. In this way, a style that will lift the user's spirits can be suggested based on the user's emotions.

[0099] The coordination generation unit can estimate the user's emotions and, based on the estimated emotions, suggest coordination that matches the emotions the user feels on a special day. For example, if the user is celebrating their birthday, a glamorous style is suggested. Also, if the user is celebrating their wedding anniversary, a romantic style is suggested. Furthermore, if the user is attending an important event, a style appropriate for the event is suggested. In this way, it is possible to suggest coordination that matches the emotions the user feels on a special day.

[0100] The coordinate generation unit estimates the user's emotions and, based on the estimated emotions, can suggest a challenging style if the user feels like taking on a new challenge. For example, if the user feels like taking on a new challenge, it can suggest styles that incorporate trends and items with unique designs. If the user is feeling adventurous, it can suggest coordination that incorporates fashion styles from different cultures or regions. If the user feels like gaining confidence, it can suggest an elegant style. In this way, it is possible to suggest a challenging style based on the user's emotions.

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

[0102] Step 1: The user information collection unit collects information about the user's preferences, body type, season, events, etc. For example, the user inputs information such as "I like casual styles," "I'm 160 cm tall," and "I'm going to a summer beach party." Step 2: The coordination generation unit generates the optimal fashion coordination based on the information collected by the user information collection unit. For example, the generation AI might suggest a coordination such as "a white T-shirt, denim shorts, sandals, and sunglasses." The generation AI references a fashion database and generates coordination taking into account the latest trends and styles. Step 3: The display unit displays the outfits generated by the outfit generation unit to the user. For example, the user can select an outfit that suits their taste from among the outfits proposed by the AI.

[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 a 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

[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 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and 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 user information collection unit that collects information such as user preferences, body type, seasons, events, etc.; a coordinate generation unit that generates an optimal fashion coordinate based on the information collected by the user information collection unit; a display unit that displays the coordinates generated by the coordinate generation unit to a user; A system characterized by:

2. The user information collection unit Using the user's real-time location information, the system suggests outfits that take into account local weather and event information.

2. The system of claim 1.

3. The coordinate generation unit Reflecting past user ratings and feedback to continuously improve 2. The system of claim 1.

4. The display unit The proposed outfits are displayed as 3D models, allowing users to check how they fit their body type.

2. The system of claim 1.

5. The user information collection unit Using emotion estimation, the system analyzes the emotions users feel when they input information and collects information in the form of questions that elicit positive emotions.

2. The system of claim 1.

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