System

Abstract

An object of a system according to an embodiment is to propose an optimal fashion based on a body shape and facial features of a user.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a collation unit, and a provision unit. The reception unit receives an image from a user. The analysis unit analyzes the image received by the reception unit and extracts a body shape and facial features. The collation unit collates the feature extracted by the analysis unit with the database of the fashion model. The providing unit provides advice based on the fashion calculated by the collating unit.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 technology has had the problem of making it difficult to suggest the most suitable fashion for each individual user.

[0005] The system according to the embodiment aims to propose optimal fashion based on the user's body shape and facial features. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a matching unit, and a provision unit. The reception unit receives an image from a user. The analysis unit analyzes the image received by the reception unit and extracts body shape and facial features. The matching unit compares the extracted features with a database of fashion models. The provision unit provides advice based on the fashion calculated by the matching unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable fashion based on the user's body shape and facial features. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

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

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) A fashion advice system according to an embodiment of the present invention analyzes images uploaded by users and suggests fashions that suit them. In this system, users upload their own images, and AI analyzes the images to extract body shape and facial features. The AI ​​then compares the extracted features with a database of fashion models to calculate the optimal fashion. Finally, the AI ​​provides specific advice based on the calculated fashion. For example, when a user uploads a full-body image of themselves, the AI ​​analyzes the image to extract features such as height, weight, facial shape, and bone structure. The AI ​​then compares these features with a database of fashion models to calculate the optimal fashion style. For example, the AI ​​suggests clothing styles and colors that suit a particular body type, as well as how to select accessories. Finally, the AI ​​provides these suggestions as advice to the user. This allows the fashion advice system to easily find fashions that suit them and eliminates the need for hesitation in fashion choices. Furthermore, the advice provided by the AI ​​is based on the latest fashion trends, allowing users to always enjoy stylish outfits. This allows the fashion advice system to easily find fashions that suit them and eliminates the need for hesitation in fashion choices. Furthermore, the advice provided by the AI ​​is based on the latest fashion trends, allowing users to always enjoy stylish outfits.

[0029] A fashion advice system according to an embodiment includes a receiving unit, an analysis unit, a matching unit, and a providing unit. The receiving unit receives images from a user. Images from the user may be in formats such as, but not limited to, JPEG, PNG, and GIF. The receiving unit stores the images uploaded by the user on a server. The receiving unit may also check the image format and convert the image into an appropriate format. For example, the receiving unit may convert a JPEG image into PNG. The analysis unit analyzes the images received by the receiving unit and extracts body shape and facial features. Body shape features include, but are not limited to, height, weight, and body fat percentage. Facial features include, but are not limited to, face shape, eye size, and nose shape. The analysis unit extracts the user's body shape and facial features using, for example, image analysis technology. The analysis unit may also use AI to analyze the body shape and facial features in detail. For example, the analysis unit may use deep learning technology to extract the user's body shape and facial features with high accuracy. The matching unit performs matching with a fashion model database based on the features extracted by the analysis unit. The fashion model database includes, for example, information such as the model's name, height, weight, and fashion style, but is not limited to these examples. The matching unit matches the most suitable fashion model based on, for example, the extracted features. The matching unit can also use AI to match with the fashion model database. For example, the matching unit uses a machine learning algorithm to match the user's features with fashion model data. The providing unit provides advice based on the fashion calculated by the matching unit. The advice can include, for example, clothing styles and colors that suit a specific body type, how to select accessories, etc., but is not limited to these examples. The providing unit provides the advice in, for example, text format. The providing unit can also provide advice using images or videos. For example, the providing unit can provide specific advice to the user using images or videos of fashion models. In this way, the fashion advice system according to the embodiment can analyze the user's images and provide optimal fashion advice.

[0030] The analysis unit can extract height, weight, facial shape, and skeletal features. The analysis unit, for example, measures height. A method for measuring height includes, for example, using image analysis technology to estimate height from a full-body image of the user. The analysis unit can also measure weight. A method for measuring weight includes, for example, performing analysis based on weight information entered by the user. The analysis unit can also analyze facial shape. A method for analyzing facial shape includes, for example, extracting facial contours and classifying their shapes. The analysis unit can also extract skeletal features. A method for extracting skeletal features includes, for example, analyzing the position and shape of the skeleton. This allows the analysis unit to analyze the user's body shape and facial features in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input a full-body image of the user into the generation AI and cause the generation AI to extract height, weight, facial shape, and skeletal features.

[0031] The matching unit can compare the extracted features with a fashion model database. For example, the matching unit compares the extracted features with the fashion model database. The fashion model database includes, for example, information such as the model's name, height, weight, and fashion style, but is not limited to these examples. For example, the matching unit matches the optimal fashion model based on the extracted features. The matching unit can also use AI to compare with the fashion model database. For example, the matching unit uses a machine learning algorithm to compare the user's features with fashion model data. This allows the matching unit to match the optimal fashion model based on the user's features. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input the user's feature data into the generation AI and cause the generation AI to perform comparison with the fashion model database.

[0032] The providing unit can suggest clothing styles and colors, and how to select accessories that suit a specific body type. The providing unit, for example, suggests clothing styles that suit a specific body type. Clothing styles include, but are not limited to, casual, formal, and sporty. The providing unit can also suggest colors that suit a specific body type. Color selection criteria include, but are not limited to, warm colors, cool colors, and monotone. The providing unit can also suggest how to select accessories. Accessories include, but are not limited to, necklaces, earrings, and bracelets. This allows the providing unit to suggest the optimal fashion style for the user. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user characteristic data into a generating AI and cause the generating AI to suggest the optimal fashion style.

[0033] The providing unit can provide advice based on fashion trends. The providing unit provides advice based on, for example, the latest fashion trends. Fashion trends include, but are not limited to, seasonal trends and trends of specific brands. The providing unit can provide specific advice to the user based on, for example, the latest fashion trends. The providing unit can also use AI to analyze fashion trends and provide advice. For example, the providing unit can analyze the latest fashion trends using a machine learning algorithm and provide optimal advice to the user. This allows the providing unit to always provide advice based on the latest fashion trends. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the latest fashion trend data to a generation AI and cause the generation AI to generate advice.

[0034] The reception unit can analyze the user's past image upload history and select a reception method. The reception unit, for example, analyzes the user's past image upload history. The image upload history includes, for example, past upload dates and times, types of uploaded images, etc., but is not limited to these examples. The reception unit, for example, analyzes time periods during which the user frequently uploaded images in the past and prioritizes reception during those time periods. The reception unit can also prioritize upload methods (camera, gallery, etc.) that the user has used in the past. Furthermore, the reception unit can also suggest reception methods tailored to specific events or seasons based on the user's past upload history. This allows the reception unit to provide the optimal reception method based on the user's past history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past upload history data into the generation AI and have the generation AI select the optimal reception method.

[0035] When receiving images, the reception unit can filter the images based on the user's fashion trends and areas of interest. The reception unit, for example, analyzes the user's fashion trends. Fashion trends include, but are not limited to, past purchase history and social media activity. The reception unit, for example, receives only relevant images based on the user's current fashion style. The reception unit can also preferentially receive images of brands and designers in which the user is interested. Furthermore, the reception unit can analyze the user's past fashion trends and filter and receive related images. This allows the reception unit to receive only relevant images based on the user's interests. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's fashion trend data into the generation AI and have the generation AI filter related images.

[0036] When receiving an image, the reception unit can select a reception means according to the user's input method. The reception unit, for example, analyzes the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the reception unit receives the image using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also receive related images using text analysis technology. Furthermore, if the user selects image input, the reception unit can also provide the optimal reception means using image recognition technology. This allows the reception unit to provide the optimal reception means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to a generation AI and cause the generation AI to select the optimal reception means.

[0037] When receiving images, the reception unit can prioritize receiving highly relevant images based on the user's geographical location information. The reception unit, for example, acquires the user's geographical location information. Methods for acquiring geographical location information include, but are not limited to, GPS data, location information from an IP address, etc. For example, when the user is in a specific area, the reception unit can prioritize receiving fashion images related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving images related to fashion styles at the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving images related to everyday fashion styles. This allows the reception unit to prioritize receiving highly relevant images based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to select highly relevant images.

[0038] When receiving an image, the reception unit can receive related images based on the user's social media activity. The reception unit, for example, analyzes the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the reception unit preferentially receives images shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive related fashion images. Furthermore, the reception unit can receive related images based on the activity of the user's friends on social media. This allows the reception unit to receive related images based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to select related images.

[0039] The reception unit can customize the reception method based on the user's past feedback when receiving an image. The reception unit, for example, analyzes the user's past feedback. Past feedback includes, but is not limited to, the user's ratings, comments, and usage history. For example, the reception unit preferentially suggests reception methods that the user has previously rated highly. The reception unit can also customize the reception method to avoid reception methods that the user has previously dissatisfied with. Furthermore, the reception unit can also suggest an optimal reception method based on the user's past feedback. This allows the reception unit to provide the optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to select the optimal reception method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of body shape and facial features. For example, if body shape features are important, the analysis unit performs a detailed body shape analysis. Body shape features include, but are not limited to, height, weight, and body fat percentage. Furthermore, if facial features are important, the analysis unit can perform a detailed facial analysis. Facial features include, but are not limited to, face shape, eye size, and nose shape. Furthermore, if both body shape and facial features are important, the analysis unit can perform a balanced analysis. This allows the analysis unit to adjust the level of detail of the analysis depending on the importance of body shape and facial features. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's body shape and facial feature data into the generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the body shape and face category. For example, if the body shape is slim, the analysis unit applies an analysis algorithm specialized for slim body shapes. Body shape categories include, but are not limited to, slim, athletic, and plus size. Furthermore, if the face is round, the analysis unit can apply an analysis algorithm specialized for round faces. Face categories include, but are not limited to, round, oval, and square. Furthermore, if the body shape and facial features are complex, the analysis unit can apply a complex analysis algorithm. This allows the analysis unit to apply the optimal analysis algorithm depending on the body shape and face category. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's body shape and face category data into the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. Past analysis results include, but are not limited to, past analysis data and analysis result evaluations. The analysis unit can also extract specific trends from the user's past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can analyze the user's past analysis results and optimize the analysis algorithm. This allows the analysis unit to improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the analysis priority based on the time when the image was taken. The analysis unit, for example, acquires the time when the image was taken. Methods for acquiring the time when the image was taken include, but are not limited to, metadata on the date and time of the image being taken, a timestamp, etc. The analysis unit, for example, prioritizes analyzing the most recent image. The analysis unit can also prioritize analyzing images taken during a specific event or season. Furthermore, the analysis unit can prioritize analyzing images taken within a period specified by the user. This allows the analysis unit to determine the analysis priority based on the time when the image was taken. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image taking time data to the generation AI and have the generation AI determine the analysis priority.

[0044] During analysis, the analysis unit can adjust the analysis order based on the relevance of the images. The analysis unit, for example, evaluates the relevance of the images. Criteria for evaluating the relevance include, but are not limited to, images of the same theme or images of the same event. The analysis unit, for example, prioritizes analysis of highly relevant images specified by the user. The analysis unit can also prioritize analysis of highly relevant images based on the user's past analysis results. Furthermore, the analysis unit can prioritize analysis of highly relevant images based on the user's fields of interest. This allows the analysis unit to adjust the analysis order based on the relevance of the images. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image relevance data to the generation AI and cause the generation AI to adjust the analysis order.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. Evaluation criteria for the level of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has technical expertise, the analysis unit can provide the analysis results using detailed technical terminology. Furthermore, if the user is a beginner, the analysis unit can provide the analysis results in simple language. Furthermore, the analysis unit can select appropriate technical terminology according to the user's level of expertise and provide the analysis results. This allows the analysis unit to use appropriate technical terminology according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0046] The matching unit can improve the accuracy of matching based on the correlation between body shape and facial features during matching. The matching unit, for example, analyzes the correlation between body shape and facial features. Methods for evaluating the correlation include, but are not limited to, correlation coefficient and covariance. The matching unit can improve the accuracy of matching based on the correlation between body shape and facial features. The matching unit can also perform matching while taking into account the balance between body shape and facial features. Furthermore, the matching unit can suggest an optimal fashion style based on the correlation between body shape and facial features. This allows the matching unit to improve the accuracy of matching by taking into account the correlation between body shape and facial features. Some or all of the above-mentioned processing in the matching unit may be performed using, or without, AI. For example, the matching unit can input body shape and facial feature data into a generation AI and cause the generation AI to analyze the correlation.

[0047] The matching unit can perform matching based on attribute information of the fashion model during matching. The matching unit, for example, acquires attribute information of the fashion model. Attribute information includes, for example, height, weight, style, brand, etc., but is not limited to these examples. The matching unit performs matching based on attribute information such as the fashion model's height and weight. The matching unit can also perform matching based on attribute information such as the fashion model's facial shape and bone structure. Furthermore, the matching unit can perform matching based on attribute information such as the fashion model's style and brand preferences. This allows the matching unit to perform matching taking into account the attribute information of the fashion model. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input attribute information data of the fashion model to a generation AI and have the generation AI perform matching.

[0048] During matching, the matching unit can weight the matching based on the update frequency of the fashion model database. The matching unit, for example, evaluates the update frequency of the fashion model database. Evaluation criteria for the update frequency include, but are not limited to, daily updates, weekly updates, and monthly updates. The matching unit, for example, prioritizes matching the latest fashion model data. The matching unit can also perform matching by placing emphasis on fashion model data that is updated regularly. Furthermore, the matching unit can weight specific fashion model data based on the user's interests. This allows the matching unit to weight the matching based on the update frequency of the fashion model database. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input update frequency data of the fashion model database to the generation AI and cause the generation AI to perform matching weighting.

[0049] The matching unit can perform matching based on the geographical distribution of fashion models during matching. The matching unit, for example, acquires the geographical distribution of fashion models. Methods for acquiring the geographical distribution include, but are not limited to, the number of models by region and popular models by region. For example, when a user is in a specific region, the matching unit prioritizes matching data of fashion models related to that region. Furthermore, when a user is traveling, the matching unit can prioritize matching data of fashion models from the user's travel destination. Furthermore, when a user is at home, the matching unit can prioritize matching data of everyday fashion models. This allows the matching unit to perform matching taking into account the geographical distribution of fashion models. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input geographical distribution data of fashion models to a generation AI and have the generation AI perform matching.

[0050] The matching unit can improve the accuracy of matching by referring to literature related to the fashion model during matching. The matching unit, for example, refers to literature related to the fashion model. Related literature includes, for example, fashion magazine articles, academic papers, etc., but is not limited to these examples. The matching unit improves the accuracy of matching, for example, based on literature related to the fashion model. The matching unit can also perform matching by referring to literature related to the style and trends of the fashion model. Furthermore, the matching unit can improve the accuracy of matching based on literature related to the history and background of the fashion model. In this way, the matching unit can improve the accuracy of matching by referring to literature related to the fashion model. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input literature data related to the fashion model into the generation AI and cause the generation AI to improve the accuracy of matching.

[0051] The matching unit can perform matching based on the market value of the fashion model during matching. The matching unit, for example, evaluates the market value of the fashion model. Criteria for evaluating market value include, but are not limited to, the model's contract amount and brand evaluation. For example, the matching unit prioritizes matching data of fashion models with high market value. The matching unit can also weight the matching based on the market value of the fashion model. Furthermore, the matching unit can perform matching by prioritizing data of fashion models with high market value based on the user's interests. This allows the matching unit to perform matching while taking into account the market value of the fashion model. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input market value data of the fashion model into the generation AI and have the generation AI perform matching.

[0052] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of fashion. The providing unit, for example, evaluates the importance of fashion. Criteria for evaluating the importance include, for example, the user's level of interest and the influence of trends, but are not limited to these examples. The providing unit, for example, provides detailed advice on important fashion items. The providing unit can also provide concise advice on everyday fashion items. Furthermore, the providing unit can also provide detailed advice on fashion items related to a specific event or season. This allows the providing unit to adjust the level of detail of the advice based on the importance of fashion. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input fashion importance data to a generating AI and cause the generating AI to adjust the level of detail of the advice.

[0053] When providing advice, the providing unit can apply different advice algorithms depending on the fashion category. The providing unit, for example, classifies fashion categories. Fashion categories include, but are not limited to, casual, formal, and sporty. For example, when providing advice on casual fashion, the providing unit applies an algorithm specialized for casual fashion. Furthermore, when providing advice on formal fashion, the providing unit can apply an algorithm specialized for formal fashion. Furthermore, when providing advice on sports fashion, the providing unit can apply an algorithm specialized for sports fashion. This allows the providing unit to apply an optimal advice algorithm depending on the fashion category. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input fashion category data to the generating AI and cause the generating AI to apply the optimal advice algorithm.

[0054] When providing advice, the providing unit can improve the accuracy of the advice based on the user's past advice results. The providing unit, for example, analyzes the user's past advice results. Past advice results include, but are not limited to, evaluations of past advice and user feedback. The providing unit, for example, corrects current advice based on the user's past advice results. The providing unit can also extract specific trends from the user's past advice results and reflect them in current advice. Furthermore, the providing unit can analyze the user's past advice results and optimize the advice algorithm. This allows the providing unit to improve the accuracy of advice by referring to the user's past advice results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0055] When providing advice, the providing unit can determine the priority of advice based on the submission time of the fashion. The providing unit, for example, acquires the submission time of the fashion. Methods for acquiring the submission time include, but are not limited to, metadata of the submission date and time, a timestamp, etc. The providing unit, for example, prioritizes providing advice regarding the latest fashion items. The providing unit can also prioritize providing advice regarding fashion items related to a specific event or season. Furthermore, the providing unit can prioritize providing advice regarding fashion items within a period specified by the user. This allows the providing unit to determine the priority of advice based on the submission time of the fashion. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input fashion submission time data to a generation AI and cause the generation AI to determine the priority of advice.

[0056] When providing advice, the providing unit can adjust the order of advice based on fashion relevance. The providing unit, for example, evaluates the fashion relevance. Criteria for evaluating relevance include, but are not limited to, fashions with the same theme or fashions related to the same event. For example, the providing unit can prioritize providing advice related to highly relevant fashion items specified by the user. The providing unit can also prioritize providing advice related to highly relevant fashion items based on the user's past advice results. Furthermore, the providing unit can prioritize providing advice related to highly relevant fashion items based on the user's field of interest. This allows the providing unit to adjust the order of advice based on fashion relevance. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input fashion relevance data to a generating AI and cause the generating AI to adjust the order of advice.

[0057] When providing advice, the providing unit can adjust the use of technical terminology in the advice depending on the user's level of expertise. The providing unit, for example, evaluates the user's level of expertise. Evaluation criteria for the level of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has technical expertise, the providing unit can provide advice using detailed technical terminology. Also, if the user is a beginner, the providing unit can provide advice in simple language. Furthermore, the providing unit can select appropriate technical terminology depending on the user's level of expertise and provide the advice. This allows the providing unit to use appropriate technical terminology depending on the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The analysis unit can take into account the user's past fashion history when analyzing the user's body shape and facial features. For example, the analysis unit can suggest styles that suit the user's current body shape and facial features based on fashion styles that the user has liked in the past. The analysis unit can also consider and exclude fashion styles that the user has avoided in the past. Furthermore, the analysis unit can analyze changes in trends from the user's past fashion history and suggest styles based on the latest trends. This allows the analysis unit to utilize the user's past fashion history to provide more personalized fashion advice.

[0060] When matching the user's features with the fashion model database, the matching unit can adjust the matching criteria based on the user's lifestyle. For example, if the user has an active lifestyle, sporty fashion models can be matched preferentially. Also, if the user places importance on fashion for business situations, formal fashion models can be matched preferentially. Furthermore, if the user prefers a casual lifestyle, casual fashion models can be matched preferentially. This allows the matching unit to match the optimal fashion model according to the user's lifestyle.

[0061] When providing advice to a user, the providing unit can adjust the content of the advice based on the user's current activity status. For example, if the user is out, advice on fashion to wear while out can be provided. Also, if the user is at home, advice on relaxing fashion can be provided. Furthermore, if the user plans to attend a specific event, advice on fashion suitable for the event can be provided. This allows the providing unit to provide optimal advice according to the user's current activity status.

[0062] The reception unit can analyze the user's past image upload history and customize the image reception method based on the user's preferences. For example, if the user has previously uploaded images during a specific time period, the reception unit can prioritize reception during that time period. Also, if the user has previously uploaded images in a specific format, the reception unit can prioritize reception of images in that format. Furthermore, if the user has previously uploaded images during a specific event or season, the reception unit can suggest a reception method suited to that event or season. This allows the reception unit to provide the optimal reception method based on the user's past history.

[0063] When matching the user's features with the fashion model database, the matching unit can adjust the matching criteria based on the user's occupation. For example, if the user is a business person, it can prioritize matching fashion models suitable for business scenes. Also, if the user is engaged in a creative profession, it can prioritize matching fashion models with unique personalities. Furthermore, if the user is a student, it can also prioritize matching casual fashion models. This allows the matching unit to match the optimal fashion model according to the user's occupation.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit receives an image from a user. The image from the user may be in a format such as, but not limited to, JPEG, PNG, or GIF. The reception unit may store the image uploaded by the user on a server, check the image format, and convert it to an appropriate format. For example, an image in JPEG format may be converted to PNG format. Step 2: The analysis unit analyzes the image received by the reception unit and extracts body shape and facial features. Body shape features include, for example, height, weight, and body fat percentage, while facial features include face shape, eye size, and nose shape. The analysis unit uses image analysis technology, AI, and deep learning technology to extract the user's body shape and facial features with high accuracy. Step 3: The matching unit compares the features extracted by the analysis unit with a database of fashion models. The fashion model database includes information such as the model's name, height, weight, and fashion style. The matching unit matches the most suitable fashion model based on the extracted features and uses AI and machine learning algorithms to match the user's features with the fashion model data. Step 4: The provider provides advice based on the fashion calculated by the collation unit. The advice may include clothing styles and colors that suit a particular body type, how to choose accessories, etc. The provider provides advice in text format, images, or videos, and provides specific advice using images or videos of fashion models.

[0066] (Example 2) A fashion advice system according to an embodiment of the present invention analyzes images uploaded by users and suggests fashions that suit them. In this system, users upload their own images, and AI analyzes the images to extract body shape and facial features. The AI ​​then compares the extracted features with a database of fashion models to calculate the optimal fashion. Finally, the AI ​​provides specific advice based on the calculated fashion. For example, when a user uploads a full-body image of themselves, the AI ​​analyzes the image to extract features such as height, weight, facial shape, and bone structure. The AI ​​then compares these features with a database of fashion models to calculate the optimal fashion style. For example, the AI ​​suggests clothing styles and colors that suit a particular body type, as well as how to select accessories. Finally, the AI ​​provides these suggestions as advice to the user. This allows the fashion advice system to easily find fashions that suit them and eliminates the need for hesitation in fashion choices. Furthermore, the advice provided by the AI ​​is based on the latest fashion trends, allowing users to always enjoy stylish outfits. This allows the fashion advice system to easily find fashions that suit them and eliminates the need for hesitation in fashion choices. Furthermore, the advice provided by the AI ​​is based on the latest fashion trends, allowing users to always enjoy stylish outfits.

[0067] A fashion advice system according to an embodiment includes a receiving unit, an analysis unit, a matching unit, and a providing unit. The receiving unit receives images from a user. Images from the user may be in formats such as, but not limited to, JPEG, PNG, and GIF. The receiving unit stores the images uploaded by the user on a server. The receiving unit may also check the image format and convert the image into an appropriate format. For example, the receiving unit may convert a JPEG image into PNG. The analysis unit analyzes the images received by the receiving unit and extracts body shape and facial features. Body shape features include, but are not limited to, height, weight, and body fat percentage. Facial features include, but are not limited to, face shape, eye size, and nose shape. The analysis unit extracts the user's body shape and facial features using, for example, image analysis technology. The analysis unit may also use AI to analyze the body shape and facial features in detail. For example, the analysis unit may use deep learning technology to extract the user's body shape and facial features with high accuracy. The matching unit performs matching with a fashion model database based on the features extracted by the analysis unit. The fashion model database includes, for example, information such as the model's name, height, weight, and fashion style, but is not limited to these examples. The matching unit matches the most suitable fashion model based on, for example, the extracted features. The matching unit can also use AI to match with the fashion model database. For example, the matching unit uses a machine learning algorithm to match the user's features with fashion model data. The providing unit provides advice based on the fashion calculated by the matching unit. The advice can include, for example, clothing styles and colors that suit a specific body type, how to select accessories, etc., but is not limited to these examples. The providing unit provides the advice in, for example, text format. The providing unit can also provide advice using images or videos. For example, the providing unit can provide specific advice to the user using images or videos of fashion models. In this way, the fashion advice system according to the embodiment can analyze the user's images and provide optimal fashion advice.

[0068] The analysis unit can extract height, weight, facial shape, and skeletal features. The analysis unit, for example, measures height. A method for measuring height includes, for example, using image analysis technology to estimate height from a full-body image of the user. The analysis unit can also measure weight. A method for measuring weight includes, for example, performing analysis based on weight information entered by the user. The analysis unit can also analyze facial shape. A method for analyzing facial shape includes, for example, extracting facial contours and classifying their shapes. The analysis unit can also extract skeletal features. A method for extracting skeletal features includes, for example, analyzing the position and shape of the skeleton. This allows the analysis unit to analyze the user's body shape and facial features in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input a full-body image of the user into the generation AI and cause the generation AI to extract height, weight, facial shape, and skeletal features.

[0069] The matching unit can compare the extracted features with a fashion model database. For example, the matching unit compares the extracted features with the fashion model database. The fashion model database includes, for example, information such as the model's name, height, weight, and fashion style, but is not limited to these examples. For example, the matching unit matches the optimal fashion model based on the extracted features. The matching unit can also use AI to compare with the fashion model database. For example, the matching unit uses a machine learning algorithm to compare the user's features with fashion model data. This allows the matching unit to match the optimal fashion model based on the user's features. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input the user's feature data into the generation AI and cause the generation AI to perform comparison with the fashion model database.

[0070] The providing unit can suggest clothing styles and colors, and how to select accessories that suit a specific body type. The providing unit, for example, suggests clothing styles that suit a specific body type. Clothing styles include, but are not limited to, casual, formal, and sporty. The providing unit can also suggest colors that suit a specific body type. Color selection criteria include, but are not limited to, warm colors, cool colors, and monotone. The providing unit can also suggest how to select accessories. Accessories include, but are not limited to, necklaces, earrings, and bracelets. This allows the providing unit to suggest the optimal fashion style for the user. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user characteristic data into a generating AI and cause the generating AI to suggest the optimal fashion style.

[0071] The providing unit can provide advice based on fashion trends. The providing unit provides advice based on, for example, the latest fashion trends. Fashion trends include, but are not limited to, seasonal trends and trends of specific brands. The providing unit can provide specific advice to the user based on, for example, the latest fashion trends. The providing unit can also use AI to analyze fashion trends and provide advice. For example, the providing unit can analyze the latest fashion trends using a machine learning algorithm and provide optimal advice to the user. This allows the providing unit to always provide advice based on the latest fashion trends. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the latest fashion trend data to a generation AI and cause the generation AI to generate advice.

[0072] The reception unit can estimate the user's emotion and adjust the timing of receiving images based on the estimated user emotion. The reception unit, for example, estimates the user's emotion. Emotion estimation methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the reception unit can immediately receive images to provide a smooth experience. Furthermore, if the user is feeling stressed, the reception unit can slightly delay receiving images and wait until the user calms down. Furthermore, if the user is in a hurry, the reception unit can quickly receive images and immediately start analyzing them. This allows the reception unit to receive images at the optimal timing depending on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0073] The reception unit can analyze the user's past image upload history and select a reception method. The reception unit, for example, analyzes the user's past image upload history. The image upload history includes, for example, past upload dates and times, types of uploaded images, etc., but is not limited to these examples. The reception unit, for example, analyzes time periods during which the user frequently uploaded images in the past and prioritizes reception during those time periods. The reception unit can also prioritize upload methods (camera, gallery, etc.) that the user has used in the past. Furthermore, the reception unit can also suggest reception methods tailored to specific events or seasons based on the user's past upload history. This allows the reception unit to provide the optimal reception method based on the user's past history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past upload history data into the generation AI and have the generation AI select the optimal reception method.

[0074] When receiving images, the reception unit can filter the images based on the user's fashion trends and areas of interest. The reception unit, for example, analyzes the user's fashion trends. Fashion trends include, but are not limited to, past purchase history and social media activity. The reception unit, for example, receives only relevant images based on the user's current fashion style. The reception unit can also preferentially receive images of brands and designers in which the user is interested. Furthermore, the reception unit can analyze the user's past fashion trends and filter and receive related images. This allows the reception unit to receive only relevant images based on the user's interests. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's fashion trend data into the generation AI and have the generation AI filter related images.

[0075] When receiving an image, the reception unit can select a reception means according to the user's input method. The reception unit, for example, analyzes the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the reception unit receives the image using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also receive related images using text analysis technology. Furthermore, if the user selects image input, the reception unit can also provide the optimal reception means using image recognition technology. This allows the reception unit to provide the optimal reception means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to a generation AI and cause the generation AI to select the optimal reception means.

[0076] The reception unit can estimate the user's emotion and determine the priority of images to be received based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion. Emotion estimation methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is excited, the reception unit may preferentially receive the most recent image. Furthermore, if the user is relaxed, the reception unit may also receive past images. Furthermore, if the user is in a hurry, the reception unit may preferentially receive important images. This allows the reception unit to determine the priority of images according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0077] When receiving images, the reception unit can prioritize receiving highly relevant images based on the user's geographical location information. The reception unit, for example, acquires the user's geographical location information. Methods for acquiring geographical location information include, but are not limited to, GPS data, location information from an IP address, etc. For example, when the user is in a specific area, the reception unit can prioritize receiving fashion images related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving images related to fashion styles at the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving images related to everyday fashion styles. This allows the reception unit to prioritize receiving highly relevant images based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to select highly relevant images.

[0078] When receiving an image, the reception unit can receive related images based on the user's social media activity. The reception unit, for example, analyzes the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the reception unit preferentially receives images shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive related fashion images. Furthermore, the reception unit can receive related images based on the activity of the user's friends on social media. This allows the reception unit to receive related images based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to select related images.

[0079] The reception unit can customize the reception method based on the user's past feedback when receiving an image. The reception unit, for example, analyzes the user's past feedback. Past feedback includes, but is not limited to, the user's ratings, comments, and usage history. For example, the reception unit preferentially suggests reception methods that the user has previously rated highly. The reception unit can also customize the reception method to avoid reception methods that the user has previously dissatisfied with. Furthermore, the reception unit can also suggest an optimal reception method based on the user's past feedback. This allows the reception unit to provide the optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to select the optimal reception method.

[0080] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. Emotion estimation methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the analysis unit provides detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is in a hurry. Furthermore, the analysis unit can provide visually appealing analysis results when the user is excited. This allows the analysis unit to adjust the presentation method of the analysis according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of body shape and facial features. For example, if body shape features are important, the analysis unit performs a detailed body shape analysis. Body shape features include, but are not limited to, height, weight, and body fat percentage. Furthermore, if facial features are important, the analysis unit can perform a detailed facial analysis. Facial features include, but are not limited to, face shape, eye size, and nose shape. Furthermore, if both body shape and facial features are important, the analysis unit can perform a balanced analysis. This allows the analysis unit to adjust the level of detail of the analysis depending on the importance of body shape and facial features. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's body shape and facial feature data into the generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the body shape and face category. For example, if the body shape is slim, the analysis unit applies an analysis algorithm specialized for slim body shapes. Body shape categories include, but are not limited to, slim, athletic, and plus size. Furthermore, if the face is round, the analysis unit can apply an analysis algorithm specialized for round faces. Face categories include, but are not limited to, round, oval, and square. Furthermore, if the body shape and facial features are complex, the analysis unit can apply a complex analysis algorithm. This allows the analysis unit to apply the optimal analysis algorithm depending on the body shape and face category. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's body shape and face category data into the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. Past analysis results include, but are not limited to, past analysis data and analysis result evaluations. The analysis unit can also extract specific trends from the user's past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can analyze the user's past analysis results and optimize the analysis algorithm. This allows the analysis unit to improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. Emotion estimation methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, when the user is in a hurry, the analysis unit provides analysis results in a short time. The analysis unit can also provide detailed analysis results when the user is relaxed. Furthermore, when the user is excited, the analysis unit can provide visually appealing analysis results. This allows the analysis unit to adjust the length of the analysis according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] During analysis, the analysis unit can determine the analysis priority based on the time when the image was taken. The analysis unit, for example, acquires the time when the image was taken. Methods for acquiring the time when the image was taken include, but are not limited to, metadata on the date and time of the image being taken, a timestamp, etc. The analysis unit, for example, prioritizes analyzing the most recent image. The analysis unit can also prioritize analyzing images taken during a specific event or season. Furthermore, the analysis unit can prioritize analyzing images taken within a period specified by the user. This allows the analysis unit to determine the analysis priority based on the time when the image was taken. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image taking time data to the generation AI and have the generation AI determine the analysis priority.

[0086] During analysis, the analysis unit can adjust the analysis order based on the relevance of the images. The analysis unit, for example, evaluates the relevance of the images. Criteria for evaluating the relevance include, but are not limited to, images of the same theme or images of the same event. The analysis unit, for example, prioritizes analysis of highly relevant images specified by the user. The analysis unit can also prioritize analysis of highly relevant images based on the user's past analysis results. Furthermore, the analysis unit can prioritize analysis of highly relevant images based on the user's fields of interest. This allows the analysis unit to adjust the analysis order based on the relevance of the images. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image relevance data to the generation AI and cause the generation AI to adjust the analysis order.

[0087] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. Evaluation criteria for the level of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has technical expertise, the analysis unit can provide the analysis results using detailed technical terminology. Furthermore, if the user is a beginner, the analysis unit can provide the analysis results in simple language. Furthermore, the analysis unit can select appropriate technical terminology according to the user's level of expertise and provide the analysis results. This allows the analysis unit to use appropriate technical terminology according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0088] The matching unit can estimate the user's emotion and adjust the matching criteria based on the estimated user's emotion. The matching unit, for example, estimates the user's emotion. Emotion estimation methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the matching unit applies detailed matching criteria when the user is relaxed. The matching unit can also apply simple matching criteria when the user is in a hurry. Furthermore, the matching unit can apply visually appealing matching criteria when the user is excited. This allows the matching unit to adjust the matching criteria according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0089] The matching unit can improve the accuracy of matching based on the correlation between body shape and facial features during matching. The matching unit, for example, analyzes the correlation between body shape and facial features. Methods for evaluating the correlation include, but are not limited to, correlation coefficient and covariance. The matching unit can improve the accuracy of matching based on the correlation between body shape and facial features. The matching unit can also perform matching while taking into account the balance between body shape and facial features. Furthermore, the matching unit can suggest an optimal fashion style based on the correlation between body shape and facial features. This allows the matching unit to improve the accuracy of matching by taking into account the correlation between body shape and facial features. Some or all of the above-mentioned processing in the matching unit may be performed using, or without, AI. For example, the matching unit can input body shape and facial feature data into a generation AI and cause the generation AI to analyze the correlation.

[0090] The matching unit can perform matching based on attribute information of the fashion model during matching. The matching unit, for example, acquires attribute information of the fashion model. Attribute information includes, for example, height, weight, style, brand, etc., but is not limited to these examples. The matching unit performs matching based on attribute information such as the fashion model's height and weight. The matching unit can also perform matching based on attribute information such as the fashion model's facial shape and bone structure. Furthermore, the matching unit can perform matching based on attribute information such as the fashion model's style and brand preferences. This allows the matching unit to perform matching taking into account the attribute information of the fashion model. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input attribute information data of the fashion model to a generation AI and have the generation AI perform matching.

[0091] During matching, the matching unit can weight the matching based on the update frequency of the fashion model database. The matching unit, for example, evaluates the update frequency of the fashion model database. Evaluation criteria for the update frequency include, but are not limited to, daily updates, weekly updates, and monthly updates. The matching unit, for example, prioritizes matching the latest fashion model data. The matching unit can also perform matching by placing emphasis on fashion model data that is updated regularly. Furthermore, the matching unit can weight specific fashion model data based on the user's interests. This allows the matching unit to weight the matching based on the update frequency of the fashion model database. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input update frequency data of the fashion model database to the generation AI and cause the generation AI to perform matching weighting.

[0092] The matching unit can estimate the user's emotion and adjust the order in which the matching results are displayed based on the estimated user's emotion. The matching unit, for example, estimates the user's emotion. Emotion estimation methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, when the user is relaxed, the matching unit displays detailed matching results in an orderly manner. Furthermore, when the user is in a hurry, the matching unit can prioritize displaying important matching results. Furthermore, when the user is excited, the matching unit can prioritize displaying visually appealing matching results. This allows the matching unit to adjust the order in which the matching results are displayed according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, an AI. For example, the matching unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0093] The matching unit can perform matching based on the geographical distribution of fashion models during matching. The matching unit, for example, acquires the geographical distribution of fashion models. Methods for acquiring the geographical distribution include, but are not limited to, the number of models by region and popular models by region. For example, when a user is in a specific region, the matching unit prioritizes matching data of fashion models related to that region. Furthermore, when a user is traveling, the matching unit can prioritize matching data of fashion models from the user's travel destination. Furthermore, when a user is at home, the matching unit can prioritize matching data of everyday fashion models. This allows the matching unit to perform matching taking into account the geographical distribution of fashion models. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input geographical distribution data of fashion models to a generation AI and have the generation AI perform matching.

[0094] The matching unit can improve the accuracy of matching by referring to literature related to the fashion model during matching. The matching unit, for example, refers to literature related to the fashion model. Related literature includes, for example, fashion magazine articles, academic papers, etc., but is not limited to these examples. The matching unit improves the accuracy of matching, for example, based on literature related to the fashion model. The matching unit can also perform matching by referring to literature related to the style and trends of the fashion model. Furthermore, the matching unit can improve the accuracy of matching based on literature related to the history and background of the fashion model. In this way, the matching unit can improve the accuracy of matching by referring to literature related to the fashion model. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input literature data related to the fashion model into the generation AI and cause the generation AI to improve the accuracy of matching.

[0095] The matching unit can perform matching based on the market value of the fashion model during matching. The matching unit, for example, evaluates the market value of the fashion model. Criteria for evaluating market value include, but are not limited to, the model's contract amount and brand evaluation. For example, the matching unit prioritizes matching data of fashion models with high market value. The matching unit can also weight the matching based on the market value of the fashion model. Furthermore, the matching unit can perform matching by prioritizing data of fashion models with high market value based on the user's interests. This allows the matching unit to perform matching while taking into account the market value of the fashion model. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input market value data of the fashion model into the generation AI and have the generation AI perform matching.

[0096] The providing unit can estimate the user's emotion and adjust the way the advice is presented based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. Emotion estimation methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the providing unit can provide detailed advice when the user is relaxed. The providing unit can also provide concise advice when the user is in a hurry. Furthermore, the providing unit can provide visually appealing advice when the user is excited. This allows the providing unit to adjust the way the advice is presented depending on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0097] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of fashion. The providing unit, for example, evaluates the importance of fashion. Criteria for evaluating the importance include, for example, the user's level of interest and the influence of trends, but are not limited to these examples. The providing unit, for example, provides detailed advice on important fashion items. The providing unit can also provide concise advice on everyday fashion items. Furthermore, the providing unit can also provide detailed advice on fashion items related to a specific event or season. This allows the providing unit to adjust the level of detail of the advice based on the importance of fashion. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input fashion importance data to a generating AI and cause the generating AI to adjust the level of detail of the advice.

[0098] When providing advice, the providing unit can apply different advice algorithms depending on the fashion category. The providing unit, for example, classifies fashion categories. Fashion categories include, but are not limited to, casual, formal, and sporty. For example, when providing advice on casual fashion, the providing unit applies an algorithm specialized for casual fashion. Furthermore, when providing advice on formal fashion, the providing unit can apply an algorithm specialized for formal fashion. Furthermore, when providing advice on sports fashion, the providing unit can apply an algorithm specialized for sports fashion. This allows the providing unit to apply an optimal advice algorithm depending on the fashion category. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input fashion category data to the generating AI and cause the generating AI to apply the optimal advice algorithm.

[0099] When providing advice, the providing unit can improve the accuracy of the advice based on the user's past advice results. The providing unit, for example, analyzes the user's past advice results. Past advice results include, but are not limited to, evaluations of past advice and user feedback. The providing unit, for example, corrects current advice based on the user's past advice results. The providing unit can also extract specific trends from the user's past advice results and reflect them in current advice. Furthermore, the providing unit can analyze the user's past advice results and optimize the advice algorithm. This allows the providing unit to improve the accuracy of advice by referring to the user's past advice results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0100] The providing unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, when the user is in a hurry, the providing unit provides advice that covers the main points in a short amount of time. The providing unit can also provide detailed advice when the user is relaxed. Furthermore, when the user is excited, the providing unit can provide visually appealing advice. This allows the providing unit to adjust the length of the advice according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0101] When providing advice, the providing unit can determine the priority of advice based on the submission time of the fashion. The providing unit, for example, acquires the submission time of the fashion. Methods for acquiring the submission time include, but are not limited to, metadata of the submission date and time, a timestamp, etc. The providing unit, for example, prioritizes providing advice regarding the latest fashion items. The providing unit can also prioritize providing advice regarding fashion items related to a specific event or season. Furthermore, the providing unit can prioritize providing advice regarding fashion items within a period specified by the user. This allows the providing unit to determine the priority of advice based on the submission time of the fashion. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input fashion submission time data to a generation AI and cause the generation AI to determine the priority of advice.

[0102] When providing advice, the providing unit can adjust the order of advice based on fashion relevance. The providing unit, for example, evaluates the fashion relevance. Criteria for evaluating relevance include, but are not limited to, fashions with the same theme or fashions related to the same event. For example, the providing unit can prioritize providing advice related to highly relevant fashion items specified by the user. The providing unit can also prioritize providing advice related to highly relevant fashion items based on the user's past advice results. Furthermore, the providing unit can prioritize providing advice related to highly relevant fashion items based on the user's field of interest. This allows the providing unit to adjust the order of advice based on fashion relevance. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input fashion relevance data to a generating AI and cause the generating AI to adjust the order of advice.

[0103] When providing advice, the providing unit can adjust the use of technical terminology in the advice depending on the user's level of expertise. The providing unit, for example, evaluates the user's level of expertise. Evaluation criteria for the level of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has technical expertise, the providing unit can provide advice using detailed technical terminology. Also, if the user is a beginner, the providing unit can provide advice in simple language. Furthermore, the providing unit can select appropriate technical terminology depending on the user's level of expertise and provide the advice. This allows the providing unit to use appropriate technical terminology depending on the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, matching unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives an image from a user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the received image and extracts body shape and facial features. The matching unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and compares the extracted features with a database of fashion models. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides advice based on the calculated fashion. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, matching unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives an image from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received image and extracts body shape and facial features. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and compares the extracted features with a database of fashion models. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides advice based on the calculated fashion. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, matching unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives an image from a user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the received image and extracts body shape and facial features. The matching unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and compares the extracted features with a database of fashion models. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides advice based on the calculated fashion. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, matching unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives an image from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received image and extracts body shape and facial features. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and compares the extracted features with a database of fashion models. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides advice based on the calculated fashion.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] When accepting images of a user, the acceptance unit can filter the images based on the user's current mood and physical condition. For example, if the user is tired, the acceptance unit can preferentially accept images of a relaxing fashion style. Also, if the user is in good spirits, the acceptance unit can preferentially accept images of a fashion style that gives a lively impression. Furthermore, if the user plans to participate in a specific event, the acceptance unit can preferentially accept images of a fashion style that is suitable for that event. This allows the acceptance unit to accept the most appropriate images according to the user's mood and physical condition.

[0106] The analysis unit can take into account the user's past fashion history when analyzing the user's body shape and facial features. For example, the analysis unit can suggest styles that suit the user's current body shape and facial features based on fashion styles that the user has liked in the past. The analysis unit can also consider and exclude fashion styles that the user has avoided in the past. Furthermore, the analysis unit can analyze changes in trends from the user's past fashion history and suggest styles based on the latest trends. This allows the analysis unit to utilize the user's past fashion history to provide more personalized fashion advice.

[0107] When matching the user's features with the fashion model database, the matching unit can adjust the matching criteria based on the user's lifestyle. For example, if the user has an active lifestyle, sporty fashion models can be matched preferentially. Also, if the user places importance on fashion for business situations, formal fashion models can be matched preferentially. Furthermore, if the user prefers a casual lifestyle, casual fashion models can be matched preferentially. This allows the matching unit to match the optimal fashion model according to the user's lifestyle.

[0108] When providing advice to a user, the providing unit can estimate the user's emotions and adjust the tone of the advice based on the estimated emotions. For example, if the user is relaxed, the advice can be provided in a calm tone. If the user is excited, the advice can be provided in an energetic tone. Furthermore, if the user is feeling anxious, the advice can be provided in a tone that gives a sense of security. This allows the providing unit to provide advice in an optimal tone according to the user's emotions.

[0109] When providing advice to a user, the providing unit can adjust the content of the advice based on the user's current activity status. For example, if the user is out, advice on fashion to wear while out can be provided. Also, if the user is at home, advice on relaxing fashion can be provided. Furthermore, if the user plans to attend a specific event, advice on fashion suitable for the event can be provided. This allows the providing unit to provide optimal advice according to the user's current activity status.

[0110] The reception unit can estimate the user's emotions and customize the image reception method based on the estimated emotions. For example, if the user is relaxed, the reception of images can be performed smoothly. If the user is stressed, the reception of images can be delayed a little until the user calms down. Furthermore, if the user is in a hurry, the reception of images can be performed quickly and analysis can be started immediately. In this way, the reception unit can provide the optimal image reception method according to the user's emotions.

[0111] The reception unit can analyze the user's past image upload history and customize the image reception method based on the user's preferences. For example, if the user has previously uploaded images during a specific time period, the reception unit can prioritize reception during that time period. Also, if the user has previously uploaded images in a specific format, the reception unit can prioritize reception of images in that format. Furthermore, if the user has previously uploaded images during a specific event or season, the reception unit can suggest a reception method suited to that event or season. This allows the reception unit to provide the optimal reception method based on the user's past history.

[0112] The analysis unit can estimate the user's emotion and adjust the level of analysis detail based on the estimated emotion. For example, if the user is relaxed, a detailed analysis result can be provided. If the user is in a hurry, a concise analysis result can be provided. Furthermore, if the user is excited, a visually appealing analysis result can be provided. This allows the analysis unit to adjust the level of analysis detail according to the user's emotion.

[0113] When matching the user's features with the fashion model database, the matching unit can adjust the matching criteria based on the user's occupation. For example, if the user is a business person, it can prioritize matching fashion models suitable for business scenes. Also, if the user is engaged in a creative profession, it can prioritize matching fashion models with unique personalities. Furthermore, if the user is a student, it can also prioritize matching casual fashion models. This allows the matching unit to match the optimal fashion model according to the user's occupation.

[0114] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, if the user is excited, advice about the latest fashion items can be provided preferentially. If the user is relaxed, advice about past fashion items can also be provided. Furthermore, if the user is in a hurry, advice about important fashion items can be provided preferentially. This allows the providing unit to determine the priority of advice according to the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit receives an image from a user. The image from the user may be in a format such as, but not limited to, JPEG, PNG, or GIF. The reception unit may store the image uploaded by the user on a server, check the image format, and convert it to an appropriate format. For example, an image in JPEG format may be converted to PNG format. Step 2: The analysis unit analyzes the image received by the reception unit and extracts body shape and facial features. Body shape features include, for example, height, weight, and body fat percentage, while facial features include face shape, eye size, and nose shape. The analysis unit uses image analysis technology, AI, and deep learning technology to extract the user's body shape and facial features with high accuracy. Step 3: The matching unit compares the features extracted by the analysis unit with a database of fashion models. The fashion model database includes information such as the model's name, height, weight, and fashion style. The matching unit matches the most suitable fashion model based on the extracted features and uses AI and machine learning algorithms to match the user's features with the fashion model data. Step 4: The provider provides advice based on the fashion calculated by the collation unit. The advice may include clothing styles and colors that suit a particular body type, how to choose accessories, etc. The provider provides advice in text format, images, or videos, and provides specific advice using images or videos of fashion models.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0147] In the 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

[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 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 reception unit that receives an image from a user; an analysis unit that analyzes the image received by the reception unit and extracts body shape and facial features; a matching unit that matches the features extracted by the analysis unit with a database of fashion models; a providing unit that provides advice based on the fashion calculated by the matching unit. A system characterized by:

2. The analysis unit Extract height, weight, face shape, and skeletal features 2. The system of claim 1.

3. The collation unit Based on the extracted features, the model is matched with a database of fashion models.

2. The system of claim 1.

4. The providing unit Suggestions for clothing styles, colors, and accessories that suit specific body types 2. The system of claim 1.

5. The providing unit Providing advice based on fashion trends 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of image reception based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past image upload history and select the reception method 2. The system of claim 1.

8. The reception unit When receiving images, they are filtered based on the user's fashion trends and areas of interest.

2. The system of claim 1.

Images