System

Abstract

An object of a system according to an embodiment is to propose optimal coordination based on a fashion sense of a user.SOLUTION: A system according to an embodiment includes a clothing data upload unit, a fashion sense analysis unit, and a coordination generation unit. The clothing data upload unit uploads clothing data of a user. The fashion sense analysis unit analyzes the clothing data uploaded by the clothing data upload unit. A coordination generation part generates coordination on the basis of the result analyzed by the fashion sense analysis part.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 techniques have had the problem of making it difficult to suggest optimal outfits based on a user's individual fashion sense.

[0005] The system according to the embodiment aims to propose optimal coordination based on the user's fashion sense. [Means for solving the problem]

[0006] The system according to the embodiment includes a clothing data upload unit, a fashion sense analysis unit, and a coordinate creation unit. The clothing data upload unit uploads clothing data of a user. The fashion sense analysis unit analyzes the clothing data uploaded by the clothing data upload unit. The coordinate creation unit creates a coordinate based on the analysis results of the fashion sense analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable outfit based on the fashion sense of the user. [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 suggestion system according to an embodiment of the present invention allows users to upload their own clothing data, and a generation AI analyzes the data to generate outfits tailored to the user's preferences. This allows the fashion suggestion system to suggest optimal outfits to the user, and if the user likes the outfits, they can purchase them.

[0029] A fashion suggestion system according to an embodiment includes a clothing data upload unit, a fashion sense analysis unit, and a coordinate generation unit. The clothing data upload unit uploads a user's clothing data. For example, the user takes photos of shirts, pants, skirts, jackets, etc., and uploads them to an app. The fashion sense analysis unit analyzes the uploaded clothing data to understand the user's fashion sense. For example, the analysis unit analyzes the user's preferred colors, frequently chosen brands, and styles. The generation AI analyzes the user's preferences based on the uploaded clothing data. The coordinate generation unit generates a coordinate that is optimal for the user based on the analysis results. For example, the generation AI considers the user's preferred colors and styles and suggests a coordinate that combines a shirt, pants, accessories, etc. The generation AI generates a coordinate that matches the user's preferences, making it likely that the user will like it. As a result, the fashion suggestion system according to an embodiment suggests a coordinate that is optimal for the user, and if the user likes the outfit, the user can purchase the outfit.

[0030] When a user uploads a photo of their clothes, the clothing data upload unit allows the generation AI to automatically remove the background and extract the detailed features of the clothes. For example, when a user uploads a photo of their clothes, the clothing data upload unit automatically removes the background using image processing technology. For example, it removes furniture, walls, etc. in the background of the photo taken by the user, and extracts detailed features such as the outline, color, and texture of the clothes. This allows the detailed features of the clothes uploaded by the user to be accurately extracted.

[0031] The clothing data uploading unit allows users to input not only clothing data but also other physical data such as their body type, height, and weight, enabling more accurate analysis. For example, when a user uploads a photo of clothing, the clothing data uploading unit also allows users to input physical data such as their height, weight, and body type. For example, if a user inputs their height and weight, the generation AI can analyze the fit of the clothing based on that data. This enables more accurate analysis that takes into account the user's physical data.

[0032] The fashion sense analysis unit can also refer to the user's past fashion history and social media posts to understand more detailed preferences. For example, when the generation AI analyzes clothing data, the fashion sense analysis unit can refer to the user's past fashion history to understand more detailed preferences. For example, it can analyze data on clothes the user has purchased in the past and photos of clothes worn by the user. This allows it to understand the user's preferences in more detail.

[0033] The fashion sense analysis unit can feed back the analysis results to the user and generate a report that allows the user to understand their own fashion sense. For example, the fashion sense analysis unit uses a generation AI to analyze clothing data and feed back the results to the user. For example, it can generate a report of the user's preferred colors, styles, brands, and other trends and provide it to the user. This allows the user to understand their own fashion sense.

[0034] The fashion sense analysis unit can also consider the fashion preferences of the user's friends and family to make group coordination suggestions. For example, the generation AI can consider the fashion preferences of the user's friends and family to make group coordination suggestions. For example, it can suggest coordination that harmonizes with the entire group based on the styles and colors preferred by the user's friends and family. This makes it possible to suggest coordination that harmonizes with the entire group.

[0035] The fashion sense analysis unit can provide the user with fashion advice and trend information based on the analysis results. For example, the generation AI can provide the user with fashion advice based on the analysis results. For example, it can suggest what items to add based on the user's preferred style. This makes it possible to provide the user with useful fashion information.

[0036] The coordination generation unit can propose the best coordination for a specific situation, taking into consideration the user's schedule and event information. For example, if the user is attending a wedding, the generation AI can propose a formal coordination that is appropriate for the situation. This makes it possible to propose the best coordination for a specific situation.

[0037] The coordinate generation unit can allow the user to provide real-time feedback on the generated coordinates, and regenerate the coordinates based on that feedback. The coordinate generation unit, for example, builds a system in which the user can provide real-time feedback on the coordinates generated by the generation AI. For example, if the user inputs feedback such as "I like this coordinate, but I would like to change the colors," the generation AI regenerates the coordinates based on that feedback. This allows the coordinates to be regenerated based on the user's feedback.

[0038] The coordination generation unit can also simultaneously suggest coordinations that correspond to different seasons and weather conditions. For example, when the generation AI generates a coordination, the coordination generation unit simultaneously suggests coordinations that correspond to different seasons and weather conditions. For example, the unit simultaneously suggests coordinations that the user should wear in summer and coordinations that the user should wear in winter. This makes it possible to suggest coordinations that correspond to different seasons and weather conditions.

[0039] The coordinate generation unit can add a function to share the generated coordinates with the user's friends and family and receive feedback from others. The coordinate generation unit adds, for example, a function to share the coordinates generated by the generation AI with the user's friends and family. For example, the user sends the suggested coordinates to friends and family and receives feedback. This allows the user to receive feedback from others.

[0040] The coordinate generation unit can add a function that allows the user to simulate trying on the proposed coordinates and check how they will look when actually worn. The coordinate generation unit can add a function that allows the user to simulate trying on the coordinates proposed by the generation AI, for example. For example, the user can virtually try on the proposed clothes and check how they will look when actually worn. This allows the user to check how they will look when actually worn.

[0041] The coordinate generation unit can provide a function that allows the user to customize the proposed coordinate and fine-tune it to suit their own preferences. The coordinate generation unit provides a function that allows the user to customize the coordinate proposed by the generation AI, for example. For example, the user can change the color or style of the proposed coordinate. This allows the user to fine-tune the coordinate to suit their own preferences.

[0042] The coordination generation unit can add a function that allows the user to compare the proposed coordination with other users and refer to popular coordinations. For example, the coordination generation unit adds a function that allows the user to compare the coordination proposed by the generation AI with other users. For example, the user can compare the proposed coordination with other users' coordinations and refer to popular coordinations. This allows the user to refer to other users' coordinations.

[0043] The coordination generation unit can provide a function that allows the user to select options in different price ranges for the proposed coordination. The coordination generation unit provides a function that allows the user to select options in different price ranges for the coordination proposed by the generation AI, for example. For example, the user selects an option that suits their budget from the proposed coordination. This allows the user to select options in different price ranges.

[0044] The feedback processing unit allows the generation AI to automatically extract the key points of feedback when a user inputs feedback after a purchase and reflect them in the next suggestion. For example, when a user inputs feedback after a purchase, the generation AI automatically extracts the key points of the feedback. For example, if a user inputs feedback such as "I really liked this shirt, but the pants size was a little big," the generation AI extracts those key points. This allows the user's feedback to be reflected in the next suggestion.

[0045] When a user inputs feedback after a purchase, the feedback processing unit allows the generation AI to compare it with past feedback and analyze changes in the user's preferences. For example, when a user inputs feedback after a purchase, the feedback processing unit allows the generation AI to compare it with past feedback. For example, if a user inputs feedback such as "I really liked this shirt, but the pants size was a little big," the generation AI compares it with past feedback and analyzes changes in preferences. This makes it possible to analyze changes in the user's preferences.

[0046] The feedback processing unit can add a function to compare the feedback a user enters after a purchase with that of other users and extract common problems and areas for improvement. For example, when a user enters feedback after a purchase, the feedback processing unit can add a function to compare the feedback with that of other users. For example, if a user enters feedback such as "I really liked this shirt, but the pants size was a little big," the generation AI will compare it with the feedback of other users and extract common problems and areas for improvement. This makes it possible to extract common problems and areas for improvement.

[0047] The feedback processing unit can provide a function whereby, when a user inputs feedback after a purchase, the generation AI automatically summarizes the feedback and provides it to manufacturers and brands. The feedback processing unit can provide a function whereby, when a user inputs feedback after a purchase, the generation AI automatically summarizes the feedback. For example, if a user inputs feedback such as "I really liked this shirt, but the pants size was a little big," the generation AI summarizes the main points and provides them to manufacturers and brands. This allows feedback to be summarized and provided to manufacturers and brands.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The fashion suggestion system can further include a lifestyle data acquisition unit that acquires the user's lifestyle data. For example, by having the user input information such as their daily activities, hobbies, and work, the generation AI can use that data to suggest outfits that suit the user's lifestyle. For example, a user who likes the outdoors can be suggested a casual style that is easy to move in, while a user who often works in business settings can be suggested a formal style. This allows the system to provide outfits that match the user's lifestyle.

[0050] The fashion suggestion system can also be equipped with a health data acquisition unit that acquires the user's health data. For example, data can be acquired from a fitness tracker or smartwatch that the user uses daily, and the generation AI can use that data to suggest outfits suited to the user's health condition. For example, if the user exercises frequently, it can suggest clothes made of breathable materials and designed to be easy to move in. Conversely, if the user does a lot of desk work, it can suggest clothes made of materials and designed to be relaxing. This allows the system to provide outfits tailored to the user's health condition.

[0051] The fashion suggestion system can further include a purchase history acquisition unit that acquires the user's purchasing history. For example, data on the clothes and accessories the user has purchased in the past can be acquired, and the generation AI can analyze the user's purchasing trends based on that data. For example, if the user has a preference for a particular brand or style, the system can suggest outfits that take that tendency into account. This allows the system to provide outfits based on the user's purchasing trends.

[0052] The fashion suggestion system can further include a travel plan acquisition unit that acquires the user's travel plans. For example, if the user inputs their travel destination and travel period, the generation AI can use that data to suggest outfits suited to the climate and culture of the destination. For example, if the user is traveling to a beach resort, light, cool clothing can be suggested, and conversely, if the user is traveling to a cold region, clothing that provides protection against the cold can be suggested. This allows the system to provide outfits that match the user's travel plans.

[0053] The fashion suggestion system can further include an entertainment data acquisition unit that acquires data on the user's favorite music and movies. For example, by inputting the user's favorite music genres and movie styles, the generation AI can suggest outfits that suit the user's entertainment preferences based on that data. For example, a casual and edgy style can be suggested to a user who likes rock music, and an elegant and sophisticated style can be suggested to a user who likes classical music. This makes it possible to provide outfits that match the user's entertainment preferences.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The clothing data uploading unit uploads the user's clothing data. For example, it takes photos of the user's shirts, pants, skirts, jackets, etc. and uploads them to the app. Step 2: The fashion sense analysis unit analyzes the uploaded clothing data to understand the user's fashion sense, such as the user's preferred color trends, favorite brands, and styles. Step 3: The coordinate generator generates the optimal coordinate for the user based on the analysis results. For example, it proposes coordinates that combine shirts, pants, accessories, etc., taking into account the user's preferred colors and styles.

[0056] (Example 2) The fashion suggestion system according to an embodiment of the present invention allows users to upload their own clothing data, and a generation AI analyzes the data to generate outfits tailored to the user's preferences. This allows the fashion suggestion system to suggest optimal outfits to the user, and if the user likes the outfits, they can purchase them.

[0057] A fashion suggestion system according to an embodiment includes a clothing data upload unit, a fashion sense analysis unit, and a coordinate generation unit. The clothing data upload unit uploads a user's clothing data. For example, the user takes photos of shirts, pants, skirts, jackets, etc., and uploads them to an app. The fashion sense analysis unit analyzes the uploaded clothing data to understand the user's fashion sense. For example, the analysis unit analyzes the user's preferred colors, frequently chosen brands, and styles. The generation AI analyzes the user's preferences based on the uploaded clothing data. The coordinate generation unit generates a coordinate that is optimal for the user based on the analysis results. For example, the generation AI considers the user's preferred colors and styles and suggests a coordinate that combines a shirt, pants, accessories, etc. The generation AI generates a coordinate that matches the user's preferences, making it likely that the user will like it. As a result, the fashion suggestion system according to an embodiment suggests a coordinate that is optimal for the user, and if the user likes the outfit, the user can purchase the outfit.

[0058] When a user uploads a photo of their clothes, the clothing data upload unit allows the generation AI to automatically remove the background and extract the detailed features of the clothes. For example, when a user uploads a photo of their clothes, the clothing data upload unit automatically removes the background using image processing technology. For example, it removes furniture, walls, etc. in the background of the photo taken by the user, and extracts detailed features such as the outline, color, and texture of the clothes. This allows the detailed features of the clothes uploaded by the user to be accurately extracted.

[0059] The clothing data uploading unit allows users to input not only clothing data but also other physical data such as their body type, height, and weight, enabling more accurate analysis. For example, when a user uploads a photo of clothing, the clothing data uploading unit also allows users to input physical data such as their height, weight, and body type. For example, if a user inputs their height and weight, the generation AI can analyze the fit of the clothing based on that data. This enables more accurate analysis that takes into account the user's physical data.

[0060] The clothing data upload unit uses the emotion estimation function to analyze the emotions expressed when a user uploads a photo of clothing, and can provide an interface for eliciting positive emotions. For example, when a user uploads a photo of clothing, the clothing data upload unit uses the emotion estimation function to analyze the user's facial expressions and voice and estimate their emotions. For example, if the user is smiling while taking a photo, a positive emotion is detected. This allows the user to enter data with a positive emotion.

[0061] The fashion sense analysis unit can also refer to the user's past fashion history and social media posts to understand more detailed preferences. For example, when the generation AI analyzes clothing data, the fashion sense analysis unit can refer to the user's past fashion history to understand more detailed preferences. For example, it can analyze data on clothes the user has purchased in the past and photos of clothes worn by the user. This allows it to understand the user's preferences in more detail.

[0062] The fashion sense analysis unit can feed back the analysis results to the user and generate a report that allows the user to understand their own fashion sense. For example, the fashion sense analysis unit uses a generation AI to analyze clothing data and feed back the results to the user. For example, it can generate a report of the user's preferred colors, styles, brands, and other trends and provide it to the user. This allows the user to understand their own fashion sense.

[0063] The fashion sense analysis unit uses the emotion estimation function to analyze the user's feelings toward clothes they have worn in the past, and can understand their preferences based on those emotions. For example, the generation AI uses the emotion estimation function to analyze the user's feelings toward clothes they have worn in the past. For example, it analyzes the user's facial expressions and comments when wearing a particular piece of clothing to understand their emotions. This makes it possible to understand the user's preferences based on their emotions.

[0064] The fashion sense analysis unit can also consider the fashion preferences of the user's friends and family to make group coordination suggestions. For example, the generation AI can consider the fashion preferences of the user's friends and family to make group coordination suggestions. For example, it can suggest coordination that harmonizes with the entire group based on the styles and colors preferred by the user's friends and family. This makes it possible to suggest coordination that harmonizes with the entire group.

[0065] The fashion sense analysis unit can provide the user with fashion advice and trend information based on the analysis results. For example, the generation AI can provide the user with fashion advice based on the analysis results. For example, it can suggest what items to add based on the user's preferred style. This makes it possible to provide the user with useful fashion information.

[0066] The fashion sense analysis unit can use the emotion estimation function to display in real time the user's emotions toward clothes worn in the past, allowing the user to reconfirm their own preferences. For example, the generation AI in the fashion sense analysis unit uses the emotion estimation function to display in real time the user's emotions toward clothes worn in the past. For example, the generation AI can display the user's emotions when wearing specific clothes and reconfirm their preferences based on those emotions. This allows the user to reconfirm their own preferences.

[0067] The coordination generation unit can propose the best coordination for a specific situation, taking into consideration the user's schedule and event information. For example, if the user is attending a wedding, the generation AI can propose a formal coordination that is appropriate for the situation. This makes it possible to propose the best coordination for a specific situation.

[0068] The coordinate generation unit can allow the user to provide real-time feedback on the generated coordinates, and regenerate the coordinates based on that feedback. The coordinate generation unit, for example, builds a system in which the user can provide real-time feedback on the coordinates generated by the generation AI. For example, if the user inputs feedback such as "I like this coordinate, but I would like to change the colors," the generation AI regenerates the coordinates based on that feedback. This allows the coordinates to be regenerated based on the user's feedback.

[0069] The coordination generation unit uses the emotion estimation function to analyze the user's emotions regarding the proposed coordination and can preferentially suggest coordination that elicits positive emotions. For example, the generation AI in the coordination generation unit uses the emotion estimation function to analyze the user's emotions regarding the proposed coordination. For example, if the user smiles at the proposed coordination, it is determined that the coordination elicits positive emotions. This makes it possible to suggest coordination that elicits positive emotions from the user.

[0070] The coordination generation unit can also simultaneously suggest coordinations that correspond to different seasons and weather conditions. For example, when the generation AI generates a coordination, the coordination generation unit simultaneously suggests coordinations that correspond to different seasons and weather conditions. For example, the unit simultaneously suggests coordinations that the user should wear in summer and coordinations that the user should wear in winter. This makes it possible to suggest coordinations that correspond to different seasons and weather conditions.

[0071] The coordinate generation unit can add a function to share the generated coordinates with the user's friends and family and receive feedback from others. The coordinate generation unit adds, for example, a function to share the coordinates generated by the generation AI with the user's friends and family. For example, the user sends the suggested coordinates to friends and family and receives feedback. This allows the user to receive feedback from others.

[0072] The coordinate generation unit can use the emotion estimation function to display the user's emotions regarding the proposed coordination in real time, allowing the user to select a coordination based on their own emotions. For example, the generation AI of the coordinate generation unit uses the emotion estimation function to display the user's emotions regarding the proposed coordination in real time. For example, if the user smiles in response to the proposed coordination, the emotion is displayed in real time. This allows the user to select a coordination based on their own emotions.

[0073] The coordinate generation unit can add a function that allows the user to simulate trying on the proposed coordinates and check how they will look when actually worn. The coordinate generation unit can add a function that allows the user to simulate trying on the coordinates proposed by the generation AI, for example. For example, the user can virtually try on the proposed clothes and check how they will look when actually worn. This allows the user to check how they will look when actually worn.

[0074] The coordinate generation unit can provide a function that allows the user to customize the proposed coordinate and fine-tune it to suit their own preferences. The coordinate generation unit provides a function that allows the user to customize the coordinate proposed by the generation AI, for example. For example, the user can change the color or style of the proposed coordinate. This allows the user to fine-tune the coordinate to suit their own preferences.

[0075] The coordination generation unit can use the emotion estimation function to analyze the user's emotions regarding the proposed coordination and display a purchase promotion message to elicit positive emotions. For example, the coordination generation unit uses the emotion estimation function of the generation AI to analyze the user's emotions regarding the proposed coordination. For example, if the user smiles in response to the proposed coordination, a purchase promotion message is displayed based on that emotion. This can elicit positive emotions from the user and encourage purchases.

[0076] The coordination generation unit can add a function that allows the user to compare the proposed coordination with other users and refer to popular coordinations. For example, the coordination generation unit adds a function that allows the user to compare the coordination proposed by the generation AI with other users. For example, the user can compare the proposed coordination with other users' coordinations and refer to popular coordinations. This allows the user to refer to other users' coordinations.

[0077] The coordination generation unit can provide a function that allows the user to select options in different price ranges for the proposed coordination. The coordination generation unit provides a function that allows the user to select options in different price ranges for the coordination proposed by the generation AI, for example. For example, the user selects an option that suits their budget from the proposed coordination. This allows the user to select options in different price ranges.

[0078] The coordination generation unit can use the emotion estimation function to display the user's emotion regarding the proposed coordination in real time, allowing the user to make a purchase decision based on their own emotion. For example, the generation AI of the coordination generation unit uses the emotion estimation function to display the user's emotion regarding the proposed coordination in real time. For example, if the user smiles in response to the proposed coordination, the emotion is displayed in real time. This allows the user to make a purchase decision based on their own emotion.

[0079] The feedback processing unit allows the generation AI to automatically extract the key points of feedback when a user inputs feedback after a purchase and reflect them in the next suggestion. For example, when a user inputs feedback after a purchase, the generation AI automatically extracts the key points of the feedback. For example, if a user inputs feedback such as "I really liked this shirt, but the pants size was a little big," the generation AI extracts those key points. This allows the user's feedback to be reflected in the next suggestion.

[0080] When a user inputs feedback after a purchase, the feedback processing unit allows the generation AI to compare it with past feedback and analyze changes in the user's preferences. For example, when a user inputs feedback after a purchase, the feedback processing unit allows the generation AI to compare it with past feedback. For example, if a user inputs feedback such as "I really liked this shirt, but the pants size was a little big," the generation AI compares it with past feedback and analyzes changes in preferences. This makes it possible to analyze changes in the user's preferences.

[0081] The feedback processing unit can use the emotion estimation function to analyze the emotions a user feels when entering feedback after a purchase, and provide an interface for eliciting positive emotions. For example, the feedback processing unit uses the emotion estimation function of the generation AI to analyze the emotions a user feels when entering feedback after a purchase. For example, the feedback processing unit analyzes the facial expressions and voice of the user when entering feedback to understand the emotions. This allows the user to enter feedback with positive emotions.

[0082] The feedback processing unit can add a function to compare the feedback a user enters after a purchase with that of other users and extract common problems and areas for improvement. For example, when a user enters feedback after a purchase, the feedback processing unit can add a function to compare the feedback with that of other users. For example, if a user enters feedback such as "I really liked this shirt, but the pants size was a little big," the generation AI will compare it with the feedback of other users and extract common problems and areas for improvement. This makes it possible to extract common problems and areas for improvement.

[0083] The feedback processing unit can provide a function whereby, when a user inputs feedback after a purchase, the generation AI automatically summarizes the feedback and provides it to manufacturers and brands. The feedback processing unit can provide a function whereby, when a user inputs feedback after a purchase, the generation AI automatically summarizes the feedback. For example, if a user inputs feedback such as "I really liked this shirt, but the pants size was a little big," the generation AI summarizes the main points and provides them to manufacturers and brands. This allows feedback to be summarized and provided to manufacturers and brands.

[0084] The feedback processing unit can use the emotion estimation function to display the emotion of the user when entering feedback after a purchase in real time, allowing the user to provide feedback based on their own emotion. For example, the feedback processing unit uses the emotion estimation function of the generation AI to display the emotion of the user when entering feedback after a purchase in real time. For example, the feedback processing unit analyzes the facial expression and voice when the user enters feedback and displays the emotion in real time. This allows the user to provide feedback based on their own emotion.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The fashion suggestion system can further include a lifestyle data acquisition unit that acquires the user's lifestyle data. For example, by having the user input information such as their daily activities, hobbies, and work, the generation AI can use that data to suggest outfits that suit the user's lifestyle. For example, a user who likes the outdoors can be suggested a casual style that is easy to move in, while a user who often works in business settings can be suggested a formal style. This allows the system to provide outfits that match the user's lifestyle.

[0087] The fashion suggestion system can also be equipped with a health data acquisition unit that acquires the user's health data. For example, data can be acquired from a fitness tracker or smartwatch that the user uses daily, and the generation AI can use that data to suggest outfits suited to the user's health condition. For example, if the user exercises frequently, it can suggest clothes made of breathable materials and designed to be easy to move in. Conversely, if the user does a lot of desk work, it can suggest clothes made of materials and designed to be relaxing. This allows the system to provide outfits tailored to the user's health condition.

[0088] The fashion suggestion system can further include a purchase history acquisition unit that acquires the user's purchasing history. For example, data on the clothes and accessories the user has purchased in the past can be acquired, and the generation AI can analyze the user's purchasing trends based on that data. For example, if the user has a preference for a particular brand or style, the system can suggest outfits that take that tendency into account. This allows the system to provide outfits based on the user's purchasing trends.

[0089] The fashion suggestion system can further include a travel plan acquisition unit that acquires the user's travel plans. For example, if the user inputs their travel destination and travel period, the generation AI can use that data to suggest outfits suited to the climate and culture of the destination. For example, if the user is traveling to a beach resort, light, cool clothing can be suggested, and conversely, if the user is traveling to a cold region, clothing that provides protection against the cold can be suggested. This allows the system to provide outfits that match the user's travel plans.

[0090] The fashion suggestion system can further include an entertainment data acquisition unit that acquires data on the user's favorite music and movies. For example, by inputting the user's favorite music genres and movie styles, the generation AI can suggest outfits that suit the user's entertainment preferences based on that data. For example, a casual and edgy style can be suggested to a user who likes rock music, and an elegant and sophisticated style can be suggested to a user who likes classical music. This makes it possible to provide outfits that match the user's entertainment preferences.

[0091] The fashion suggestion system can further include an emotion estimation unit that estimates the user's emotions and suggests outfits based on those emotions. For example, the system can analyze the user's facial expressions and voice when uploading a photo to understand the emotion. For example, if the user is smiling while taking a photo, the system can detect a positive emotion and suggest brightly colored and fun-designed clothes based on that emotion. This allows the system to provide outfits based on the user's emotions.

[0092] The fashion suggestion system can further include an emotion feedback unit that estimates the user's emotions and provides feedback based on those emotions. For example, if the user smiles at the suggested outfit, positive feedback can be generated based on that emotion. For example, a message such as "This outfit suits you very well" can be displayed. This allows feedback based on the user's emotions to be provided.

[0093] The fashion suggestion system can further include an emotional purchase promotion unit that estimates the user's emotions and promotes purchases based on those emotions. For example, if the user expresses positive emotions toward the suggested outfit, a message promoting the purchase based on those emotions can be displayed. For example, a message such as "This outfit looks great on you. Buy it now" can be displayed. This allows for promotion of purchases based on the user's emotions.

[0094] The fashion suggestion system can further include an emotion regeneration unit that estimates the user's emotions and regenerates an outfit based on those emotions. For example, if the user expresses negative emotions about the suggested outfit, the outfit can be regenerated based on those emotions. For example, if the user expresses dissatisfaction with the suggested outfit, a new outfit can be proposed based on those emotions. In this way, it is possible to regenerate an outfit based on the user's emotions.

[0095] The fashion suggestion system may further include an emotion / preference understanding unit that estimates the user's emotions and understands the user's preferences based on those emotions. For example, the system may analyze the user's emotions toward clothes worn in the past and understand the user's preferences based on those emotions. For example, if the user shows positive emotions when wearing a particular piece of clothing, the system may understand the user's preferences based on the style and color of the clothing. This allows the system to understand the user's preferences based on the user's emotions.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The clothing data uploading unit uploads the user's clothing data. For example, it takes photos of the user's shirts, pants, skirts, jackets, etc. and uploads them to the app. Step 2: The fashion sense analysis unit analyzes the uploaded clothing data to understand the user's fashion sense, such as the user's preferred color trends, favorite brands, and styles. Step 3: The coordinate generator generates the optimal coordinate for the user based on the analysis results. For example, it proposes coordinates that combine shirts, pants, accessories, etc., taking into account the user's preferred colors and styles.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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]

[0165] 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 clothing data upload unit that uploads user clothing data; a fashion sense analysis unit that analyzes the clothing data uploaded by the clothing data upload unit; a coordinate generation unit that generates a coordinate based on the result of the analysis by the fashion sense analysis unit. A system characterized by:

2. The clothing data upload unit When a user uploads a photo of an outfit, the generative AI automatically removes the background and extracts the outfit's detailed features. The system of claim 1 .

3. The fashion sense analysis unit It also looks at the user's past fashion history and social media posts to understand their preferences in more detail. The system of claim 1 .

4. The coordinate generation unit Taking into account the user's schedule and event information, the app suggests the best outfit for a specific situation. The system of claim 1 .

5. The feedback processing unit When a user enters feedback after a purchase, the generation AI automatically extracts the key points of the feedback and reflects them in the next proposal. The system of claim 1 .

6. The clothing data upload unit Using emotion estimation, we analyze the emotions expressed when users upload photos of their clothes, and provide an interface to elicit positive emotions. The system of claim 1 .

7. The fashion sense analysis unit Using emotion estimation function, analyze the user's feelings towards clothes they have worn in the past and understand their preferences based on those feelings. The system of claim 1 .

8. The coordinate generation unit Using emotion estimation, the system analyzes the user's feelings toward the proposed outfits and prioritizes outfits that evoke positive emotions. The system of claim 1 .

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