System

Abstract

An object of a system according to an embodiment is to quickly diagnose a personal color of a user and propose appropriate fashion and color coordination.SOLUTION: A system includes a reception part, an analysis part, a diagnosis part, a proposal part, and an evaluation part. The receiving unit receives an image of a user. The analysis unit analyzes the image received by the reception unit and analyzes a face color, a hair color, and a pupil color of the user. The diagnosis unit diagnoses the personal color based on the information analyzed by the analysis unit. The proposal unit proposes fashion or color coordination based on the personal color diagnosed by the diagnosis unit. The evaluation unit evaluates the style proposed by the proposal 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 techniques have had the problem of making it difficult to quickly and accurately diagnose a user's personal color and suggest appropriate fashion and color coordination.

[0005] The system according to the embodiment aims to quickly diagnose the user's personal color and suggest appropriate fashion and color coordination. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a diagnosis unit, a suggestion unit, and an evaluation unit. The reception unit receives an image of a user. The analysis unit analyzes the image received by the reception unit and analyzes the user's complexion, hair color, and eye color. The diagnosis unit diagnoses a personal color based on the information analyzed by the analysis unit. The suggestion unit suggests fashion or color coordination based on the personal color diagnosed by the diagnosis unit. The evaluation unit evaluates the style suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly diagnose the user's personal color and suggest appropriate fashion and color coordination. [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 personal color diagnosis system according to an embodiment of the present invention utilizes a generation AI and image recognition technology to quickly diagnose a user's personal color and propose and evaluate fashion and color coordination. In the personal color diagnosis system, a user uploads their own image, and the generation AI uses image recognition technology to analyze the user's complexion, hair color, and eye color to diagnose their personal color. Based on the diagnosis results, fashion and color coordination suggestions are made, and the user's style is further evaluated using a scoring function. For example, in a personal color diagnosis system, a user uploads an image taken with a smartphone or camera. The generation AI then uses image recognition technology to analyze the user's complexion, hair color, and eye color. The generation AI diagnoses the user's personal color based on the information obtained from the image. Based on the diagnosis results, the generation AI proposes clothing and accessories that match the user's personal color. Furthermore, the generation AI scores the clothing and coordination selected by the user and provides feedback. This allows users to easily receive a diagnosis from an image and gain confidence in their everyday styling. This allows the personal color diagnosis system to quickly and accurately diagnose a user's personal color and propose and evaluate appropriate fashion and color coordination. For example, users can upload a photo of their face and the generative AI will provide diagnostic results and suggestions, allowing them to easily find a style that suits them.

[0029] The personal color diagnosis system according to the embodiment includes a reception unit, an analysis unit, a diagnosis unit, a suggestion unit, and an evaluation unit. The reception unit receives a user's image. The user's image may include, but is not limited to, a facial photograph or a full-body photograph. The reception unit may receive, for example, an image taken with a smartphone or a camera. The reception unit may also directly receive images submitted in digital format. The reception unit may also automatically adjust the format and resolution of the image. For example, the reception unit may receive high-resolution images taken with a smartphone. The analysis unit may analyze the image received by the reception unit using a generation AI. The analysis may be performed, for example, by extracting information such as the user's complexion, hair color, and eye color, but is not limited to such an example. For example, the generation AI may extract color information from the image using deep learning technology. The analysis unit may also use pattern recognition technology to analyze the user's complexion, hair color, and eye color. The analysis unit may also use the generation AI to analyze the hue and brightness of the image. For example, the generation AI analyzes the user's complexion based on RGB values ​​and identifies the hue. The diagnosis unit diagnoses the user's personal color based on the information analyzed by the analysis unit. The diagnosis is based on criteria such as, but not limited to, seasonal colors and tones. For example, the diagnosis unit uses the generation AI to diagnose the user's personal color based on the analyzed information. The diagnosis unit can also make a diagnosis based on color samples. The diagnosis unit can also use the generation AI to diagnose the user's personal color by comprehensively assessing the user's complexion, hair color, and eye color. For example, if the user's complexion is warm-toned, the generation AI diagnoses the user's personal color as being spring-type or autumn-type. The suggestion unit suggests fashion and color coordination based on the personal color diagnosed by the diagnosis unit. The suggestion is based on, for example, a method of suggesting clothing and accessories that match the user's personal color, but is not limited to, this example. For example, the suggestion unit uses the generation AI to suggest fashion and color coordination that match the user's personal color. The suggestion unit can also make suggestions based on a style guide.The suggestion unit can also use the generation AI to suggest color combinations that match the user's personal color. For example, the generation AI can suggest bright pastel-colored outfits to a spring-type user. The evaluation unit evaluates the style suggested by the suggestion unit and provides feedback. The evaluation can be, for example, performed by evaluating whether the outfit or outfit selected by the user matches the user's personal color, but is not limited to such an example. For example, the evaluation unit can use the generation AI to evaluate the outfit or outfit selected by the user. The evaluation unit can also perform the evaluation based on the user's feedback. The evaluation unit can also use the generation AI to score the user's style and provide feedback. For example, the generation AI can evaluate whether the outfit selected by the user matches the user's personal color and assign a score. This allows the personal color diagnosis system according to the embodiment to quickly and accurately diagnose the user's personal color and suggest and evaluate appropriate fashion and color coordination. Some or all of the above-described processing by the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input images of the outfit or outfit selected by the user into the generation AI and have the generation AI perform the evaluation.

[0030] The reception unit can receive images taken with a smartphone or a camera. The reception unit, for example, receives images taken with a smartphone. The reception unit can also receive images taken with a camera. For example, the reception unit receives images taken with a smartphone at high resolution. The reception unit can also automatically adjust and receive images taken with a camera. This makes it possible to receive images taken with a smartphone or a camera. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input an image taken with a smartphone to a generation AI and have the generation AI adjust the image.

[0031] The analysis unit can analyze the user's complexion, hair color, and eye color using image recognition technology. The analysis unit extracts color information from an image using, for example, deep learning technology. For example, the analysis unit analyzes the user's complexion using deep learning technology. The analysis unit can also analyze the user's complexion, hair color, and eye color using pattern recognition technology. For example, the analysis unit analyzes the user's hair color using pattern recognition technology. The analysis unit can also analyze the hue and brightness of an image using a generation AI. For example, the analysis unit analyzes the user's eye color using a generation AI. In this way, the user's complexion, hair color, and eye color can be accurately analyzed using image recognition technology. 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 data to a generation AI and cause the generation AI to extract color information.

[0032] The diagnosis unit can diagnose the user's personal color based on the analyzed information. The diagnosis unit performs diagnosis based on criteria such as seasonal colors and tones. For example, the diagnosis unit diagnoses the user's personal color based on seasonal colors. The diagnosis unit can also use a generation AI to diagnose the user's personal color based on the analyzed information. For example, the diagnosis unit uses the generation AI to diagnose the user's personal color by comprehensively assessing the user's complexion, hair color, and eye color. The diagnosis unit can also perform diagnosis based on a color sample. For example, the diagnosis unit diagnoses the user's personal color based on a color sample. This allows the user's personal color to be accurately diagnosed based on the analyzed information. Some or all of the above-described processing in the diagnosis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the diagnosis unit can input the analyzed information into the generation AI and cause the generation AI to perform a personal color diagnosis.

[0033] The suggestion unit can suggest fashion or color coordination based on the diagnosed personal color. The suggestion unit, for example, suggests clothes and accessories that match the user's personal color. For example, the suggestion unit uses a generation AI to suggest fashion and color coordination that match the user's personal color. The suggestion unit can also make suggestions based on a style guide. For example, the suggestion unit can suggest clothes that match the user's personal color based on a style guide. The suggestion unit can also use the generation AI to suggest color combinations that match the user's personal color. For example, the generation AI can suggest bright pastel colored clothes to a spring-type user. This makes it possible to suggest appropriate fashion and color coordination based on the diagnosed personal color. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the diagnosed personal color into the generation AI and cause the generation AI to suggest fashion and color coordination.

[0034] The evaluation unit can evaluate the clothing or coordination selected by the user and provide feedback. For example, the evaluation unit evaluates whether the clothing or coordination selected by the user matches a personal color. For example, the evaluation unit uses a generation AI to evaluate the clothing or coordination selected by the user. The evaluation unit can also perform evaluations based on user feedback. For example, the evaluation unit evaluates the clothing or coordination based on user feedback. The evaluation unit can also use a generation AI to score the user's style and provide feedback. For example, the generation AI can evaluate whether the clothing selected by the user matches a personal color and assign a score. This allows the user's styling to be improved by evaluating the clothing or coordination selected by the user and providing feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input images of the clothing or coordination selected by the user into the generation AI and have the generation AI perform the evaluation.

[0035] The reception unit can analyze the user's past image upload history and select an appropriate reception method. For example, the reception unit can analyze time periods during which the user frequently uploaded in the past and prompt the user to upload during those time periods. The reception unit can also prioritize reception of devices that the user has used in the past. Furthermore, the reception unit can also suggest the optimal image format based on the user's past upload history. This allows the optimal reception method to be selected by analyzing the user's past image upload 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.

[0036] The reception unit can perform filtering based on the user's current environment when receiving an image. For example, if the user uploads an image taken in a dark environment, the reception unit can apply a filter that corrects lighting. Furthermore, if the user uploads an image taken in an environment with a cluttered background, the reception unit can also apply a filter that blurs the background. Furthermore, if the user uploads an image taken under natural light, the reception unit can also apply a filter that preserves natural color tones. This allows for filtering based on the user's current environment, making it possible to receive more appropriate images. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's environmental data into the generation AI and cause the generation AI to apply filtering.

[0037] When accepting an image, the acceptance unit can select an appropriate acceptance means according to the user's input method. For example, if the user instructs "upload a face photo" by voice, the acceptance unit can prioritize the voice input and accept the image. Furthermore, if the user inputs "upload an image" by text, the acceptance unit can also prioritize the text input and accept the image. Furthermore, if the user directly uploads an image, the acceptance unit can also prioritize the image input and accept the image. This allows the system to be easily used by users by selecting the optimal acceptance means according to the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's input data into a generation AI and have the generation AI select the optimal acceptance means.

[0038] When receiving images, the reception unit can prioritize receiving highly relevant images by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving images related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving images related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving images related to the area around the user's home. In this way, highly relevant images can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant images.

[0039] When receiving an image, the reception unit can analyze the user's social media activity and receive related images. For example, the reception unit can preferentially receive images posted by the user on social media. The reception unit can also analyze the user's social media activity and receive related images. Furthermore, the reception unit can also receive related images by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related images can be preferentially received. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to select related images.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving an image. The reception unit can, for example, suggest an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially select a specific reception method based on the user's past feedback. Furthermore, the reception unit can also customize the reception method by reflecting the user's past feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's feedback data into the generation AI and have the generation AI customize the reception method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the image. For example, in the case of an important image, the analysis unit performs a detailed analysis and provides detailed analysis results. The analysis unit can also perform a standard analysis for an ordinary image and provide necessary information. Furthermore, in the case of an image with low importance, the analysis unit can perform a simplified analysis and provide basic information. In this way, by adjusting the level of detail of the analysis based on the importance of the image, necessary information can be appropriately provided. 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 importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the image category. For example, in the case of a face photo, the analysis unit can apply a face recognition algorithm to perform the analysis. In addition, in the case of a landscape photo, the analysis unit can also apply a landscape analysis algorithm to perform the analysis. Furthermore, in the case of a product photo, the analysis unit can also apply a product recognition algorithm to perform the analysis. This improves the analysis accuracy by applying an appropriate analysis algorithm depending on the image category. 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 category data into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve accuracy by adjusting the analysis algorithm based on the user's past analysis results. The analysis unit can also improve analysis accuracy by learning specific patterns from the user's past analysis results. Furthermore, the analysis unit can adjust the level of detail of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved 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.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time of image submission. For example, the analysis unit can prioritize analysis of the most recent image and provide results quickly. The analysis unit can also postpone analysis of images that were submitted earlier. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time of submission. This allows the most recent image to be analyzed preferentially by determining the analysis priority based on the time of image submission. 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 submission time data into the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the images. For example, the analysis unit can prioritize analysis of highly relevant images and quickly provide results. The analysis unit can also postpone analysis of less relevant images. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the images. In this way, by adjusting the order of analysis based on the relevance of the images, highly relevant images can be analyzed preferentially. 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 have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Alternatively, if the user has general knowledge, the analysis unit can provide analysis results that use less technical terminology. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terminology in the analysis 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 using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the way the analysis results are presented.

[0047] During diagnosis, the diagnostic unit can predict a current diagnosis by referring to past diagnostic data. The diagnostic unit predicts a current diagnostic result, for example, based on the user's past diagnostic data. The diagnostic unit can also learn specific patterns from the past diagnostic data and predict a current diagnostic result. Furthermore, the diagnostic unit can improve the accuracy of the diagnosis by referring to the past diagnostic data. In this way, by referring to the past diagnostic data, the current diagnostic result is predicted and the accuracy of the diagnosis is improved. Some or all of the above-mentioned processing in the diagnostic unit may be performed, for example, using AI or without AI. For example, the diagnostic unit can input past diagnostic data into a generation AI and cause the generation AI to predict a current diagnostic result.

[0048] The diagnosis unit can apply different diagnostic methods to each image category during diagnosis. For example, in the case of a face photo, the diagnosis unit can apply a facial color diagnosis method to make a diagnosis. Furthermore, in the case of a hair color photo, the diagnosis unit can also apply a hair color diagnosis method to make a diagnosis. Furthermore, in the case of an eye color photo, the diagnosis unit can also apply an eye color diagnosis method to make a diagnosis. In this way, by applying an appropriate diagnostic method to each image category, the accuracy of the diagnosis is improved. Some or all of the above-mentioned processing in the diagnosis unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnosis unit can input image category data into the generation AI and cause the generation AI to apply an appropriate diagnostic method.

[0049] The diagnostic unit can perform a diagnosis while taking into account the user's attribute information. The diagnostic unit can provide an appropriate diagnostic result, for example, by taking into account the user's age. The diagnostic unit can also provide an appropriate diagnostic result by taking into account the user's gender. Furthermore, the diagnostic unit can provide an appropriate diagnostic result by taking into account the user's lifestyle. This allows for more appropriate diagnostic results to be provided by taking into account the user's attribute information. Some or all of the above-described processing in the diagnostic unit can be performed, for example, using AI, or can be performed without using AI. For example, the diagnostic unit can input the user's attribute information data into the generation AI and cause the generation AI to provide a diagnostic result.

[0050] During diagnosis, the diagnosis unit can analyze changes in the diagnosis based on the time of image submission. For example, the diagnosis unit can prioritize diagnosing and analyzing changes in recently submitted images. The diagnosis unit can also postpone diagnosing and analyzing changes in older submitted images. Furthermore, the diagnosis unit can dynamically analyze changes in the diagnosis based on the time of submission. This makes it possible to understand changes in the user's condition by analyzing changes in the diagnosis based on the time of image submission. Some or all of the above-mentioned processing in the diagnosis unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnosis unit can input image submission time data into the generation AI and cause the generation AI to analyze changes in the diagnosis.

[0051] During the diagnosis, the diagnostic unit can analyze the diagnosis by referring to related market data. The diagnostic unit can provide diagnostic results that reflect the latest trends based on, for example, the related market data. The diagnostic unit can also suggest products that match the user's personal color based on the market data. Furthermore, the diagnostic unit can improve the accuracy of the diagnostic results by referring to market data. In this way, by referring to the related market data, diagnostic results that reflect the latest trends can be provided. Some or all of the above-mentioned processing in the diagnostic unit can be performed, for example, using AI, or can be performed without using AI. For example, the diagnostic unit can input related market data into a generation AI and have the generation AI perform a diagnostic analysis.

[0052] The diagnostic unit can analyze the diagnosis taking into account the technical maturity level during the diagnosis. For example, when the technical maturity level is high, the diagnostic unit can provide detailed diagnostic results. Furthermore, when the technical maturity level is low, the diagnostic unit can provide concise and to-the-point diagnostic results. Furthermore, the diagnostic unit can adjust the way the diagnostic results are presented based on the technical maturity level. This allows for providing diagnostic results appropriate for the user by taking the technical maturity level into consideration. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input technical maturity data into a generation AI and have the generation AI perform diagnostic analysis.

[0053] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the fashion or color. For example, the suggestion unit provides detailed suggestions for important fashions or colors. The suggestion unit can also provide standard suggestions for general fashions or colors. Furthermore, the suggestion unit can also provide simplified suggestions for less important fashions or colors. By adjusting the level of detail of the proposal based on the importance of the fashion or color, it is possible to provide optimal suggestions for the user. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input fashion and color importance data into a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0054] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the fashion or color category. For example, in the case of casual fashion, the suggestion unit can apply a suggestion algorithm for casual fashion. Furthermore, in the case of formal fashion, the suggestion unit can also apply a suggestion algorithm for formal fashion. Furthermore, in the case of sports fashion, the suggestion unit can apply a suggestion algorithm for sports. This improves the accuracy of suggestions by applying an appropriate suggestion algorithm depending on the fashion or color category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input fashion and color category data into the generation AI and cause the generation AI to apply an appropriate suggestion algorithm.

[0055] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit can improve accuracy by adjusting the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also learn specific patterns from the user's past suggestion results and improve the suggestion accuracy. Furthermore, the suggestion unit can adjust the level of detail of the suggestion by referring to the user's past suggestion results. In this way, the suggestion accuracy is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0056] When making suggestions, the suggestion unit can determine the priority of suggestions based on the submission date of the fashions and colors. For example, the suggestion unit can prioritize the latest fashions and colors and provide results quickly. The suggestion unit can also postpone the proposal of fashions and colors that were submitted earlier. Furthermore, the suggestion unit can dynamically adjust the priority of suggestions based on the submission date. This allows the latest suggestions to be provided preferentially by determining the priority of suggestions based on the submission date of the fashions and colors. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the submission date of fashions and colors into the generation AI and have the generation AI determine the priority of suggestions.

[0057] The suggestion unit can adjust the order of suggestions based on the relevance of fashions and colors when making suggestions. For example, the suggestion unit can prioritize suggesting highly relevant fashions and colors and quickly provide results. The suggestion unit can also postpone suggesting less relevant fashions and colors. Furthermore, the suggestion unit can dynamically adjust the order of suggestions based on the relevance of fashions and colors. In this way, by adjusting the order of suggestions based on the relevance of fashions and colors, highly relevant suggestions can be provided preferentially. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input fashion and color relevance data into a generation AI and cause the generation AI to adjust the order of suggestions.

[0058] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can provide a proposal result that uses a lot of technical terminology. Alternatively, if the user has general knowledge, the suggestion unit can provide a proposal result that uses less technical terminology. Furthermore, the suggestion unit can adjust the way the proposal result is expressed according to the user's level of expertise. This allows the suggestion to be provided in a way that is easy for the user to understand by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the way the proposal result is expressed.

[0059] During evaluation, the evaluation unit can analyze the user's past style and select the optimal evaluation method. The evaluation unit selects the optimal evaluation method based on, for example, the user's past style. The evaluation unit can also learn specific patterns from the user's past style and select an evaluation method. Furthermore, the evaluation unit can analyze the user's past style and adjust the level of detail of the evaluation. In this way, the optimal evaluation method can be selected by analyzing the user's past style. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's past style data into the generation AI and have the generation AI select the evaluation method.

[0060] The evaluation unit can customize the evaluation means based on the user's current living situation at the time of evaluation. The evaluation unit, for example, provides an appropriate evaluation means taking into account the user's current living situation. The evaluation unit can also adjust the level of detail of the evaluation based on the user's living situation. Furthermore, the evaluation unit can customize the evaluation means to reflect the user's living situation. This allows the evaluation optimal for the user to be provided by customizing the evaluation means based on the user's current living situation. Some or all of the above-described processing in the evaluation unit may be performed, for example, using AI or without AI. For example, the evaluation unit can input the user's living situation data into the generation AI and cause the generation AI to customize the evaluation means.

[0061] The evaluation unit can improve the evaluation method by reflecting user feedback during evaluation. The evaluation unit improves the evaluation method based on user feedback, for example. The evaluation unit can also learn specific improvements from user feedback and adjust the evaluation method. Furthermore, the evaluation unit can adjust the level of detail of the evaluation by reflecting user feedback. In this way, the evaluation method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input user feedback data into a generation AI and have the generation AI improve the evaluation method.

[0062] During evaluation, the evaluation unit can select the optimal evaluation method by taking into account the user's geographical location information. For example, if the user is in a specific area, the evaluation unit selects an evaluation method related to that area. Furthermore, if the user is traveling, the evaluation unit can select an evaluation method related to the travel destination. Furthermore, if the user is at home, the evaluation unit can select an evaluation method related to the area around the user's home. In this way, the optimal evaluation method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the evaluation method.

[0063] During evaluation, the evaluation unit can analyze the user's social media activity and suggest evaluation means. The evaluation unit can suggest evaluation means based on, for example, the content posted by the user on social media. The evaluation unit can also analyze the user's social media activity and suggest related evaluation means. Furthermore, the evaluation unit can also suggest evaluation means with reference to the activity of the user's friends on social media. In this way, the optimal evaluation means can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the user's social media data into the generation AI and have the generation AI execute the suggestion of evaluation means.

[0064] The evaluation unit can customize the evaluation method by reflecting the user's past feedback during evaluation. The evaluation unit can, for example, suggest an optimal evaluation method based on the user's past feedback. The evaluation unit can also preferentially select a specific evaluation method from the user's past feedback. Furthermore, the evaluation unit can customize the evaluation method by reflecting the user's past feedback. In this way, the optimal evaluation method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI or without using AI. For example, the evaluation unit can input the user's feedback data into the generation AI and cause the generation AI to customize the evaluation method.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] When accepting a user's images, the acceptance unit can analyze the user's past upload history and select the optimal acceptance method. For example, the acceptance unit can analyze the time periods during which the user frequently uploaded in the past and prompt the user to accept images during those time periods. The acceptance unit can also prioritize accepting images from devices that the user has used in the past. Furthermore, the acceptance unit can also suggest the optimal image format based on the user's past upload history. In this way, the optimal acceptance method can be selected by analyzing the user's past image upload history. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's past upload history data into the generation AI and have the generation AI select the optimal acceptance method.

[0067] When analyzing a user's complexion, hair color, and eye color using image recognition technology, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve accuracy by adjusting the analysis algorithm based on the user's past analysis results. The analysis unit can also improve analysis accuracy by learning specific patterns from the user's past analysis results. Furthermore, the analysis unit can adjust the level of detail of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved 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.

[0068] When diagnosing a user's personal color based on the analyzed information, the diagnosis unit can take the user's attribute information into consideration. For example, the diagnosis unit can provide an appropriate diagnosis result by taking the user's age into consideration. The diagnosis unit can also provide an appropriate diagnosis result by taking the user's gender into consideration. Furthermore, the diagnosis unit can provide an appropriate diagnosis result by taking the user's lifestyle into consideration. In this way, by taking the user's attribute information into consideration, a more appropriate diagnosis result can be provided. Some or all of the above-described processing in the diagnosis unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnosis unit can input the user's attribute information data into a generation AI and have the generation AI provide a diagnosis result.

[0069] When proposing fashion or color coordination based on the diagnosed personal color, the suggestion unit can improve the accuracy of the suggestions by referring to the user's past suggestion results. For example, the suggestion unit can improve accuracy by adjusting the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also learn specific patterns from the user's past suggestion results and improve the suggestion accuracy. Furthermore, the suggestion unit can adjust the level of detail of the suggestions by referring to the user's past suggestion results. In this way, the suggestion unit can improve the accuracy of the suggestions by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0070] When evaluating the clothing or coordination selected by the user and providing feedback, the evaluation unit can analyze the user's past styles to select the optimal evaluation method. For example, the evaluation unit selects the optimal evaluation method based on the user's past styles. The evaluation unit can also learn specific patterns from the user's past styles and select an evaluation method. Furthermore, the evaluation unit can analyze the user's past styles to adjust the level of detail of the evaluation. In this way, the optimal evaluation method can be selected by analyzing the user's past styles. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's past style data into the generation AI and have the generation AI select the evaluation method.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit receives an image of the user. Examples of the user's image include, but are not limited to, a face photo and a full-body photo. The reception unit receives, for example, an image taken with a smartphone or a camera. The reception unit can also directly receive images submitted in digital format. Furthermore, the reception unit has a function to automatically adjust the format and resolution of the image. For example, the reception unit receives an image taken with a smartphone in high resolution. Step 2: The analysis unit uses the generation AI to analyze the image received by the reception unit. The analysis is performed, for example, by extracting information such as the user's complexion, hair color, and eye color, but is not limited to such an example. For example, the generation AI extracts color information from the image using deep learning technology. The analysis unit can also analyze the user's complexion, hair color, and eye color using pattern recognition technology. The analysis unit can also analyze the hue and brightness of the image using the generation AI. For example, the generation AI analyzes the complexion based on RGB values ​​and identifies the hue. Step 3: The diagnosis unit diagnoses the user's personal color based on the information analyzed by the analysis unit. The diagnosis is performed based on criteria such as seasonal colors and tones, but is not limited to these examples. For example, the diagnosis unit uses a generation AI to diagnose the user's personal color based on the analyzed information. The diagnosis unit can also make a diagnosis based on a color sample. The diagnosis unit can also use the generation AI to diagnose the personal color by comprehensively judging the user's complexion, hair color, and eye color. For example, if the user's complexion is warm-toned, the generation AI will diagnose the user's personal color as being of a spring or autumn type. Step 4: The suggestion unit suggests fashion and color coordination based on the personal color diagnosed by the diagnosis unit. The suggestion is made, for example, by suggesting clothes and accessories that match the user's personal color, but is not limited to this example. For example, the suggestion unit uses a generation AI to suggest fashion and color coordination that matches the user's personal color. The suggestion unit can also make suggestions based on a style guide. The suggestion unit can also use the generation AI to suggest color combinations that match the user's personal color. For example, the generation AI can suggest bright pastel colored clothes to a spring-type user. Step 5: The evaluation unit evaluates the style proposed by the suggestion unit and provides feedback. The evaluation is performed, for example, by evaluating whether the clothes and coordination selected by the user match the personal color, but is not limited to this example. For example, the evaluation unit uses a generation AI to evaluate the clothes and coordination selected by the user. The evaluation unit can also perform the evaluation based on the user's feedback. The evaluation unit can also use a generation AI to score the user's style and provide feedback. For example, the generation AI can evaluate whether the clothes selected by the user match the personal color and assign a score.

[0073] (Example 2) A personal color diagnosis system according to an embodiment of the present invention utilizes a generation AI and image recognition technology to quickly diagnose a user's personal color and propose and evaluate fashion and color coordination. In the personal color diagnosis system, a user uploads their own image, and the generation AI uses image recognition technology to analyze the user's complexion, hair color, and eye color to diagnose their personal color. Based on the diagnosis results, fashion and color coordination suggestions are made, and the user's style is further evaluated using a scoring function. For example, in a personal color diagnosis system, a user uploads an image taken with a smartphone or camera. The generation AI then uses image recognition technology to analyze the user's complexion, hair color, and eye color. The generation AI diagnoses the user's personal color based on the information obtained from the image. Based on the diagnosis results, the generation AI proposes clothing and accessories that match the user's personal color. Furthermore, the generation AI scores the clothing and coordination selected by the user and provides feedback. This allows users to easily receive a diagnosis from an image and gain confidence in their everyday styling. This allows the personal color diagnosis system to quickly and accurately diagnose a user's personal color and propose and evaluate appropriate fashion and color coordination. For example, users can upload a photo of their face and the generative AI will provide diagnostic results and suggestions, allowing them to easily find a style that suits them.

[0074] The personal color diagnosis system according to the embodiment includes a reception unit, an analysis unit, a diagnosis unit, a suggestion unit, and an evaluation unit. The reception unit receives a user's image. The user's image may include, but is not limited to, a facial photograph or a full-body photograph. The reception unit may receive, for example, an image taken with a smartphone or a camera. The reception unit may also directly receive images submitted in digital format. The reception unit may also automatically adjust the format and resolution of the image. For example, the reception unit may receive high-resolution images taken with a smartphone. The analysis unit may analyze the image received by the reception unit using a generation AI. The analysis may be performed, for example, by extracting information such as the user's complexion, hair color, and eye color, but is not limited to such an example. For example, the generation AI may extract color information from the image using deep learning technology. The analysis unit may also use pattern recognition technology to analyze the user's complexion, hair color, and eye color. The analysis unit may also use the generation AI to analyze the hue and brightness of the image. For example, the generation AI analyzes the user's complexion based on RGB values ​​and identifies the hue. The diagnosis unit diagnoses the user's personal color based on the information analyzed by the analysis unit. The diagnosis is based on criteria such as, but not limited to, seasonal colors and tones. For example, the diagnosis unit uses the generation AI to diagnose the user's personal color based on the analyzed information. The diagnosis unit can also make a diagnosis based on color samples. The diagnosis unit can also use the generation AI to diagnose the user's personal color by comprehensively assessing the user's complexion, hair color, and eye color. For example, if the user's complexion is warm-toned, the generation AI diagnoses the user's personal color as being spring-type or autumn-type. The suggestion unit suggests fashion and color coordination based on the personal color diagnosed by the diagnosis unit. The suggestion is based on, for example, a method of suggesting clothing and accessories that match the user's personal color, but is not limited to, this example. For example, the suggestion unit uses the generation AI to suggest fashion and color coordination that match the user's personal color. The suggestion unit can also make suggestions based on a style guide.The suggestion unit can also use the generation AI to suggest color combinations that match the user's personal color. For example, the generation AI can suggest bright pastel-colored outfits to a spring-type user. The evaluation unit evaluates the style suggested by the suggestion unit and provides feedback. The evaluation can be, for example, performed by evaluating whether the outfit or outfit selected by the user matches the user's personal color, but is not limited to such an example. For example, the evaluation unit can use the generation AI to evaluate the outfit or outfit selected by the user. The evaluation unit can also perform the evaluation based on the user's feedback. The evaluation unit can also use the generation AI to score the user's style and provide feedback. For example, the generation AI can evaluate whether the outfit selected by the user matches the user's personal color and assign a score. This allows the personal color diagnosis system according to the embodiment to quickly and accurately diagnose the user's personal color and suggest and evaluate appropriate fashion and color coordination. Some or all of the above-described processing by the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input images of the outfit or outfit selected by the user into the generation AI and have the generation AI perform the evaluation.

[0075] The reception unit can receive images taken with a smartphone or a camera. The reception unit, for example, receives images taken with a smartphone. The reception unit can also receive images taken with a camera. For example, the reception unit receives images taken with a smartphone at high resolution. The reception unit can also automatically adjust and receive images taken with a camera. This makes it possible to receive images taken with a smartphone or a camera. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input an image taken with a smartphone to a generation AI and have the generation AI adjust the image.

[0076] The analysis unit can analyze the user's complexion, hair color, and eye color using image recognition technology. The analysis unit extracts color information from an image using, for example, deep learning technology. For example, the analysis unit analyzes the user's complexion using deep learning technology. The analysis unit can also analyze the user's complexion, hair color, and eye color using pattern recognition technology. For example, the analysis unit analyzes the user's hair color using pattern recognition technology. The analysis unit can also analyze the hue and brightness of an image using a generation AI. For example, the analysis unit analyzes the user's eye color using a generation AI. In this way, the user's complexion, hair color, and eye color can be accurately analyzed using image recognition technology. 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 data to a generation AI and cause the generation AI to extract color information.

[0077] The diagnosis unit can diagnose the user's personal color based on the analyzed information. The diagnosis unit performs diagnosis based on criteria such as seasonal colors and tones. For example, the diagnosis unit diagnoses the user's personal color based on seasonal colors. The diagnosis unit can also use a generation AI to diagnose the user's personal color based on the analyzed information. For example, the diagnosis unit uses the generation AI to diagnose the user's personal color by comprehensively assessing the user's complexion, hair color, and eye color. The diagnosis unit can also perform diagnosis based on a color sample. For example, the diagnosis unit diagnoses the user's personal color based on a color sample. This allows the user's personal color to be accurately diagnosed based on the analyzed information. Some or all of the above-described processing in the diagnosis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the diagnosis unit can input the analyzed information into the generation AI and cause the generation AI to perform a personal color diagnosis.

[0078] The suggestion unit can suggest fashion or color coordination based on the diagnosed personal color. The suggestion unit, for example, suggests clothes and accessories that match the user's personal color. For example, the suggestion unit uses a generation AI to suggest fashion and color coordination that match the user's personal color. The suggestion unit can also make suggestions based on a style guide. For example, the suggestion unit can suggest clothes that match the user's personal color based on a style guide. The suggestion unit can also use the generation AI to suggest color combinations that match the user's personal color. For example, the generation AI can suggest bright pastel colored clothes to a spring-type user. This makes it possible to suggest appropriate fashion and color coordination based on the diagnosed personal color. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the diagnosed personal color into the generation AI and cause the generation AI to suggest fashion and color coordination.

[0079] The evaluation unit can evaluate the clothing or coordination selected by the user and provide feedback. For example, the evaluation unit evaluates whether the clothing or coordination selected by the user matches a personal color. For example, the evaluation unit uses a generation AI to evaluate the clothing or coordination selected by the user. The evaluation unit can also perform evaluations based on user feedback. For example, the evaluation unit evaluates the clothing or coordination based on user feedback. The evaluation unit can also use a generation AI to score the user's style and provide feedback. For example, the generation AI can evaluate whether the clothing selected by the user matches a personal color and assign a score. This allows the user's styling to be improved by evaluating the clothing or coordination selected by the user and providing feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input images of the clothing or coordination selected by the user into the generation AI and have the generation AI perform the evaluation.

[0080] The reception unit can estimate the user's emotions and adjust the timing of image reception based on the estimated user emotions. For example, if the user is relaxed, the reception unit can immediately receive images, providing a smooth experience. Furthermore, if the user is feeling stressed, the reception unit can slightly delay image reception to give the user time to calm down. Furthermore, if the user is in a hurry, the reception unit can quickly receive images and immediately proceed to analysis. This allows the image reception timing to be adjusted according to the user's emotions, thereby allowing the image to be received at the optimal timing for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0081] The reception unit can analyze the user's past image upload history and select an appropriate reception method. For example, the reception unit can analyze time periods during which the user frequently uploaded in the past and prompt the user to upload during those time periods. The reception unit can also prioritize reception of devices that the user has used in the past. Furthermore, the reception unit can also suggest the optimal image format based on the user's past upload history. This allows the optimal reception method to be selected by analyzing the user's past image upload 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.

[0082] The reception unit can perform filtering based on the user's current environment when receiving an image. For example, if the user uploads an image taken in a dark environment, the reception unit can apply a filter that corrects lighting. Furthermore, if the user uploads an image taken in an environment with a cluttered background, the reception unit can also apply a filter that blurs the background. Furthermore, if the user uploads an image taken under natural light, the reception unit can also apply a filter that preserves natural color tones. This allows for filtering based on the user's current environment, making it possible to receive more appropriate images. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's environmental data into the generation AI and cause the generation AI to apply filtering.

[0083] When accepting an image, the acceptance unit can select an appropriate acceptance means according to the user's input method. For example, if the user instructs "upload a face photo" by voice, the acceptance unit can prioritize the voice input and accept the image. Furthermore, if the user inputs "upload an image" by text, the acceptance unit can also prioritize the text input and accept the image. Furthermore, if the user directly uploads an image, the acceptance unit can also prioritize the image input and accept the image. This allows the system to be easily used by users by selecting the optimal acceptance means according to the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's input data into a generation AI and have the generation AI select the optimal acceptance means.

[0084] The reception unit can estimate the user's emotions and determine the priority of images to be received based on the estimated user emotions. For example, if the user is relaxed, the reception unit can set the priority of images to be received to normal. Furthermore, if the user is stressed, the reception unit can set the priority of images to be received to high and process them quickly. Furthermore, if the user is in a hurry, the reception unit can set the priority of images to be received to the highest priority and immediately proceed to analysis. This allows the reception of images that are optimal for the user to be prioritized by determining the priority of images to be received according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0085] When receiving images, the reception unit can prioritize receiving highly relevant images by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving images related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving images related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving images related to the area around the user's home. In this way, highly relevant images can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant images.

[0086] When receiving an image, the reception unit can analyze the user's social media activity and receive related images. For example, the reception unit can preferentially receive images posted by the user on social media. The reception unit can also analyze the user's social media activity and receive related images. Furthermore, the reception unit can also receive related images by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related images can be preferentially received. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to select related images.

[0087] The reception unit can customize the reception method by reflecting the user's past feedback when receiving an image. The reception unit can, for example, suggest an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially select a specific reception method based on the user's past feedback. Furthermore, the reception unit can also customize the reception method by reflecting the user's past feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's feedback data into the generation AI and have the generation AI customize the reception method.

[0088] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise and to-the-point analysis results when the user is stressed. Furthermore, the analysis unit can quickly provide analysis results when the user is in a hurry. By adjusting the way the analysis is presented based on the user's emotions, analysis results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the image. For example, in the case of an important image, the analysis unit performs a detailed analysis and provides detailed analysis results. The analysis unit can also perform a standard analysis for an ordinary image and provide necessary information. Furthermore, in the case of an image with low importance, the analysis unit can perform a simplified analysis and provide basic information. In this way, by adjusting the level of detail of the analysis based on the importance of the image, necessary information can be appropriately provided. 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 importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0090] During analysis, the analysis unit can apply different analysis algorithms depending on the image category. For example, in the case of a face photo, the analysis unit can apply a face recognition algorithm to perform the analysis. In addition, in the case of a landscape photo, the analysis unit can also apply a landscape analysis algorithm to perform the analysis. Furthermore, in the case of a product photo, the analysis unit can also apply a product recognition algorithm to perform the analysis. This improves the analysis accuracy by applying an appropriate analysis algorithm depending on the image category. 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 category data into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0091] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve accuracy by adjusting the analysis algorithm based on the user's past analysis results. The analysis unit can also improve analysis accuracy by learning specific patterns from the user's past analysis results. Furthermore, the analysis unit can adjust the level of detail of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved 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.

[0092] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide a longer analysis result. Furthermore, if the user is stressed, the analysis unit can provide a concise and short analysis result. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis and provide a shorter analysis result. This allows the analysis length to be adjusted according to the user's emotions, thereby providing the optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0093] During analysis, the analysis unit can determine the analysis priority based on the time of image submission. For example, the analysis unit can prioritize analysis of the most recent image and provide results quickly. The analysis unit can also postpone analysis of images that were submitted earlier. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time of submission. This allows the most recent image to be analyzed preferentially by determining the analysis priority based on the time of image submission. 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 submission time data into the generation AI and have the generation AI determine the analysis priority.

[0094] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the images. For example, the analysis unit can prioritize analysis of highly relevant images and quickly provide results. The analysis unit can also postpone analysis of less relevant images. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the images. In this way, by adjusting the order of analysis based on the relevance of the images, highly relevant images can be analyzed preferentially. 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 have the generation AI adjust the order of analysis.

[0095] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Alternatively, if the user has general knowledge, the analysis unit can provide analysis results that use less technical terminology. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terminology in the analysis 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 using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the way the analysis results are presented.

[0096] The diagnosis unit can estimate the user's emotions and adjust the display method of the diagnosis based on the estimated user emotions. For example, the diagnosis unit can provide a detailed diagnosis result when the user is relaxed. The diagnosis unit can also provide a concise and to-the-point diagnosis result when the user is stressed. Furthermore, the diagnosis unit can also provide a quick diagnosis result when the user is in a hurry. By adjusting the display method of the diagnosis according to the user's emotions, it is possible to provide a diagnosis result that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the diagnosis unit can be performed using, for example, an AI, or without an AI. For example, the diagnosis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0097] During diagnosis, the diagnostic unit can predict a current diagnosis by referring to past diagnostic data. The diagnostic unit predicts a current diagnostic result, for example, based on the user's past diagnostic data. The diagnostic unit can also learn specific patterns from the past diagnostic data and predict a current diagnostic result. Furthermore, the diagnostic unit can improve the accuracy of the diagnosis by referring to the past diagnostic data. In this way, by referring to the past diagnostic data, the current diagnostic result is predicted and the accuracy of the diagnosis is improved. Some or all of the above-mentioned processing in the diagnostic unit may be performed, for example, using AI or without AI. For example, the diagnostic unit can input past diagnostic data into a generation AI and cause the generation AI to predict a current diagnostic result.

[0098] The diagnosis unit can apply different diagnostic methods to each image category during diagnosis. For example, in the case of a face photo, the diagnosis unit can apply a facial color diagnosis method to make a diagnosis. Furthermore, in the case of a hair color photo, the diagnosis unit can also apply a hair color diagnosis method to make a diagnosis. Furthermore, in the case of an eye color photo, the diagnosis unit can also apply an eye color diagnosis method to make a diagnosis. In this way, by applying an appropriate diagnostic method to each image category, the accuracy of the diagnosis is improved. Some or all of the above-mentioned processing in the diagnosis unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnosis unit can input image category data into the generation AI and cause the generation AI to apply an appropriate diagnostic method.

[0099] The diagnostic unit can perform a diagnosis while taking into account the user's attribute information. The diagnostic unit can provide an appropriate diagnostic result, for example, by taking into account the user's age. The diagnostic unit can also provide an appropriate diagnostic result by taking into account the user's gender. Furthermore, the diagnostic unit can provide an appropriate diagnostic result by taking into account the user's lifestyle. This allows for more appropriate diagnostic results to be provided by taking into account the user's attribute information. Some or all of the above-described processing in the diagnostic unit can be performed, for example, using AI, or can be performed without using AI. For example, the diagnostic unit can input the user's attribute information data into the generation AI and cause the generation AI to provide a diagnostic result.

[0100] The diagnostic unit can estimate the user's emotions and adjust the importance of the diagnosis based on the estimated user emotions. For example, if the user is relaxed, the diagnostic unit performs a diagnosis with a normal importance level. Furthermore, if the user is feeling stressed, the diagnostic unit can set the importance level high and provide a diagnostic result quickly. Furthermore, if the user is in a hurry, the diagnostic unit can set the importance level as the highest priority and provide a diagnostic result immediately. This allows the optimal diagnostic result to be provided by adjusting the importance of the diagnosis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the diagnostic unit can be performed using, for example, an AI. For example, the diagnostic unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0101] During diagnosis, the diagnosis unit can analyze changes in the diagnosis based on the time of image submission. For example, the diagnosis unit can prioritize diagnosing and analyzing changes in recently submitted images. The diagnosis unit can also postpone diagnosing and analyzing changes in older submitted images. Furthermore, the diagnosis unit can dynamically analyze changes in the diagnosis based on the time of submission. This makes it possible to understand changes in the user's condition by analyzing changes in the diagnosis based on the time of image submission. Some or all of the above-mentioned processing in the diagnosis unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnosis unit can input image submission time data into the generation AI and cause the generation AI to analyze changes in the diagnosis.

[0102] During the diagnosis, the diagnostic unit can analyze the diagnosis by referring to related market data. The diagnostic unit can provide diagnostic results that reflect the latest trends based on, for example, the related market data. The diagnostic unit can also suggest products that match the user's personal color based on the market data. Furthermore, the diagnostic unit can improve the accuracy of the diagnostic results by referring to market data. In this way, by referring to the related market data, diagnostic results that reflect the latest trends can be provided. Some or all of the above-mentioned processing in the diagnostic unit can be performed, for example, using AI, or can be performed without using AI. For example, the diagnostic unit can input related market data into a generation AI and have the generation AI perform a diagnostic analysis.

[0103] The diagnostic unit can analyze the diagnosis taking into account the technical maturity level during the diagnosis. For example, when the technical maturity level is high, the diagnostic unit can provide detailed diagnostic results. Furthermore, when the technical maturity level is low, the diagnostic unit can provide concise and to-the-point diagnostic results. Furthermore, the diagnostic unit can adjust the way the diagnostic results are presented based on the technical maturity level. This allows for providing diagnostic results appropriate for the user by taking the technical maturity level into consideration. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input technical maturity data into a generation AI and have the generation AI perform diagnostic analysis.

[0104] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. The suggestion unit can also provide concise and to-the-point suggestions when the user is stressed. Furthermore, the suggestion unit can also provide quick suggestions when the user is in a hurry. By adjusting the way the suggestions are expressed according to the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0105] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the fashion or color. For example, the suggestion unit provides detailed suggestions for important fashions or colors. The suggestion unit can also provide standard suggestions for general fashions or colors. Furthermore, the suggestion unit can also provide simplified suggestions for less important fashions or colors. By adjusting the level of detail of the proposal based on the importance of the fashion or color, it is possible to provide optimal suggestions for the user. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input fashion and color importance data into a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0106] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the fashion or color category. For example, in the case of casual fashion, the suggestion unit can apply a suggestion algorithm for casual fashion. Furthermore, in the case of formal fashion, the suggestion unit can also apply a suggestion algorithm for formal fashion. Furthermore, in the case of sports fashion, the suggestion unit can apply a suggestion algorithm for sports. This improves the accuracy of suggestions by applying an appropriate suggestion algorithm depending on the fashion or color category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input fashion and color category data into the generation AI and cause the generation AI to apply an appropriate suggestion algorithm.

[0107] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit can improve accuracy by adjusting the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also learn specific patterns from the user's past suggestion results and improve the suggestion accuracy. Furthermore, the suggestion unit can adjust the level of detail of the suggestion by referring to the user's past suggestion results. In this way, the suggestion accuracy is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0108] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions and longer suggestions. Furthermore, if the user is stressed, the suggestion unit can provide concise and short suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide quick suggestions and shorter suggestions. This allows the optimal suggestions to be provided by adjusting the length of the suggestions according to the user's emotions. The 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-described processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0109] When making suggestions, the suggestion unit can determine the priority of suggestions based on the submission date of the fashions and colors. For example, the suggestion unit can prioritize the latest fashions and colors and provide results quickly. The suggestion unit can also postpone the proposal of fashions and colors that were submitted earlier. Furthermore, the suggestion unit can dynamically adjust the priority of suggestions based on the submission date. This allows the latest suggestions to be provided preferentially by determining the priority of suggestions based on the submission date of the fashions and colors. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the submission date of fashions and colors into the generation AI and have the generation AI determine the priority of suggestions.

[0110] The suggestion unit can adjust the order of suggestions based on the relevance of fashions and colors when making suggestions. For example, the suggestion unit can prioritize suggesting highly relevant fashions and colors and quickly provide results. The suggestion unit can also postpone suggesting less relevant fashions and colors. Furthermore, the suggestion unit can dynamically adjust the order of suggestions based on the relevance of fashions and colors. In this way, by adjusting the order of suggestions based on the relevance of fashions and colors, highly relevant suggestions can be provided preferentially. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input fashion and color relevance data into a generation AI and cause the generation AI to adjust the order of suggestions.

[0111] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can provide a proposal result that uses a lot of technical terminology. Alternatively, if the user has general knowledge, the suggestion unit can provide a proposal result that uses less technical terminology. Furthermore, the suggestion unit can adjust the way the proposal result is expressed according to the user's level of expertise. This allows the suggestion to be provided in a way that is easy for the user to understand by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the way the proposal result is expressed.

[0112] The evaluation unit can estimate the user's emotions and adjust the evaluation method based on the estimated user emotions. For example, the evaluation unit can provide a detailed evaluation when the user is relaxed. The evaluation unit can also provide a concise and to-the-point evaluation when the user is stressed. Furthermore, the evaluation unit can also provide a quick evaluation when the user is in a hurry. By adjusting the evaluation method according to the user's emotions, the optimal evaluation can be provided for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the evaluation unit can be performed using, for example, an AI, or without an AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0113] During evaluation, the evaluation unit can analyze the user's past style and select the optimal evaluation method. The evaluation unit selects the optimal evaluation method based on, for example, the user's past style. The evaluation unit can also learn specific patterns from the user's past style and select an evaluation method. Furthermore, the evaluation unit can analyze the user's past style and adjust the level of detail of the evaluation. In this way, the optimal evaluation method can be selected by analyzing the user's past style. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's past style data into the generation AI and have the generation AI select the evaluation method.

[0114] The evaluation unit can customize the evaluation means based on the user's current living situation at the time of evaluation. The evaluation unit, for example, provides an appropriate evaluation means taking into account the user's current living situation. The evaluation unit can also adjust the level of detail of the evaluation based on the user's living situation. Furthermore, the evaluation unit can customize the evaluation means to reflect the user's living situation. This allows the evaluation optimal for the user to be provided by customizing the evaluation means based on the user's current living situation. Some or all of the above-described processing in the evaluation unit may be performed, for example, using AI or without AI. For example, the evaluation unit can input the user's living situation data into the generation AI and cause the generation AI to customize the evaluation means.

[0115] The evaluation unit can improve the evaluation method by reflecting user feedback during evaluation. The evaluation unit improves the evaluation method based on user feedback, for example. The evaluation unit can also learn specific improvements from user feedback and adjust the evaluation method. Furthermore, the evaluation unit can adjust the level of detail of the evaluation by reflecting user feedback. In this way, the evaluation method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input user feedback data into a generation AI and have the generation AI improve the evaluation method.

[0116] The evaluation unit can estimate the user's emotions and determine the priority of the evaluations based on the estimated user emotions. For example, if the user is relaxed, the evaluation unit performs the evaluation with normal priority. Furthermore, if the user is feeling stressed, the evaluation unit can set the priority to high and provide the evaluation results quickly. Furthermore, if the user is in a hurry, the evaluation unit can set the priority to the highest and provide the evaluation results immediately. This allows the evaluations to be prioritized according to the user's emotions, thereby providing the optimal evaluation for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0117] During evaluation, the evaluation unit can select the optimal evaluation method by taking into account the user's geographical location information. For example, if the user is in a specific area, the evaluation unit selects an evaluation method related to that area. Furthermore, if the user is traveling, the evaluation unit can select an evaluation method related to the travel destination. Furthermore, if the user is at home, the evaluation unit can select an evaluation method related to the area around the user's home. In this way, the optimal evaluation method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the evaluation method.

[0118] During evaluation, the evaluation unit can analyze the user's social media activity and suggest evaluation means. The evaluation unit can suggest evaluation means based on, for example, the content posted by the user on social media. The evaluation unit can also analyze the user's social media activity and suggest related evaluation means. Furthermore, the evaluation unit can also suggest evaluation means with reference to the activity of the user's friends on social media. In this way, the optimal evaluation means can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the user's social media data into the generation AI and have the generation AI execute the suggestion of evaluation means.

[0119] The evaluation unit can customize the evaluation method by reflecting the user's past feedback during evaluation. The evaluation unit can, for example, suggest an optimal evaluation method based on the user's past feedback. The evaluation unit can also preferentially select a specific evaluation method from the user's past feedback. Furthermore, the evaluation unit can customize the evaluation method by reflecting the user's past feedback. In this way, the optimal evaluation method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI or without using AI. For example, the evaluation unit can input the user's feedback data into the generation AI and cause the generation AI to customize the evaluation method. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, diagnosis unit, suggestion unit, and evaluation 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 can accept a user's image using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the image using a generative AI. The diagnosis unit is realized by the specific processing unit 290 of the data processing device 12 and diagnoses the personal color based on the analysis results. The suggestion unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and suggests fashion and color coordination based on the diagnosis results. The evaluation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and evaluates the suggested style and provides feedback. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, diagnosis unit, suggestion unit, and evaluation 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 can receive an image of the user using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the image using a generative AI. The diagnosis unit is realized by the specific processing unit 290 of the data processing device 12 and diagnoses the personal color based on the analysis results. The suggestion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and suggests fashion and color coordination based on the diagnosis results. The evaluation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and evaluates the suggested style and provides feedback. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, diagnosis unit, suggestion unit, and evaluation 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 can receive an image of the user using the microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the image using a generative AI. The diagnosis unit is realized by the specific processing unit 290 of the data processing device 12 and diagnoses the personal color based on the analysis results. The suggestion unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12 and suggests fashion and color coordination based on the diagnosis results. The evaluation unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12 and evaluates the proposed style and provides feedback. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, diagnosis unit, suggestion unit, and evaluation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive an image of the user using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the image using a generative AI. The diagnosis unit is realized by the specific processing unit 290 of the data processing device 12 and diagnoses the personal color based on the analysis results. The suggestion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and suggests fashion and color coordination based on the diagnosis results. The evaluation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and evaluates the suggested style and provides feedback.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] When accepting a user's images, the acceptance unit can analyze the user's past upload history and select the optimal acceptance method. For example, the acceptance unit can analyze the time periods during which the user frequently uploaded in the past and prompt the user to accept images during those time periods. The acceptance unit can also prioritize accepting images from devices that the user has used in the past. Furthermore, the acceptance unit can also suggest the optimal image format based on the user's past upload history. In this way, the optimal acceptance method can be selected by analyzing the user's past image upload history. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's past upload history data into the generation AI and have the generation AI select the optimal acceptance method.

[0122] When analyzing a user's complexion, hair color, and eye color using image recognition technology, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve accuracy by adjusting the analysis algorithm based on the user's past analysis results. The analysis unit can also improve analysis accuracy by learning specific patterns from the user's past analysis results. Furthermore, the analysis unit can adjust the level of detail of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved 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.

[0123] When diagnosing a user's personal color based on the analyzed information, the diagnosis unit can take the user's attribute information into consideration. For example, the diagnosis unit can provide an appropriate diagnosis result by taking the user's age into consideration. The diagnosis unit can also provide an appropriate diagnosis result by taking the user's gender into consideration. Furthermore, the diagnosis unit can provide an appropriate diagnosis result by taking the user's lifestyle into consideration. In this way, by taking the user's attribute information into consideration, a more appropriate diagnosis result can be provided. Some or all of the above-described processing in the diagnosis unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnosis unit can input the user's attribute information data into a generation AI and have the generation AI provide a diagnosis result.

[0124] When proposing fashion or color coordination based on the diagnosed personal color, the suggestion unit can improve the accuracy of the suggestions by referring to the user's past suggestion results. For example, the suggestion unit can improve accuracy by adjusting the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also learn specific patterns from the user's past suggestion results and improve the suggestion accuracy. Furthermore, the suggestion unit can adjust the level of detail of the suggestions by referring to the user's past suggestion results. In this way, the suggestion unit can improve the accuracy of the suggestions by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0125] When evaluating the clothing or coordination selected by the user and providing feedback, the evaluation unit can analyze the user's past styles to select the optimal evaluation method. For example, the evaluation unit selects the optimal evaluation method based on the user's past styles. The evaluation unit can also learn specific patterns from the user's past styles and select an evaluation method. Furthermore, the evaluation unit can analyze the user's past styles to adjust the level of detail of the evaluation. In this way, the optimal evaluation method can be selected by analyzing the user's past styles. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's past style data into the generation AI and have the generation AI select the evaluation method.

[0126] The reception unit can estimate the user's emotions and adjust the timing of image reception based on the estimated user emotions. For example, if the user is relaxed, the reception unit can immediately receive images, providing a smooth experience. Furthermore, if the user is feeling stressed, the reception unit can slightly delay image reception to give the user time to calm down. Furthermore, if the user is in a hurry, the reception unit can quickly receive images and immediately proceed to analysis. This allows the image reception timing to be adjusted according to the user's emotions, allowing the image to be received at the optimal timing for the user. Emotion estimation is achieved using an emotion estimation function, such as 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-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0127] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can also provide concise and to-the-point analysis results. If the user is in a hurry, the analysis unit can also provide analysis results quickly. By adjusting the way the analysis is presented based on the user's emotions, analysis results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0128] The diagnosis unit can estimate the user's emotions and adjust the display method of the diagnosis based on the estimated user emotions. For example, if the user is relaxed, the diagnosis unit can provide a detailed diagnosis result. Furthermore, if the user is stressed, the diagnosis unit can provide a concise and to-the-point diagnosis result. Furthermore, if the user is in a hurry, the diagnosis unit can quickly provide a diagnosis result. By adjusting the display method of the diagnosis according to the user's emotions, it is possible to provide a diagnosis result that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the diagnosis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the diagnosis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0129] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, the suggestion unit can also provide concise and to-the-point suggestions. If the user is in a hurry, the suggestion unit can also provide quick suggestions. This allows the suggestion unit to adjust the way the suggestions are expressed based on the user's emotions, making the suggestions easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotions.

[0130] The evaluation unit can estimate the user's emotions and adjust the evaluation method based on the estimated user emotions. For example, if the user is relaxed, the evaluation unit can provide a detailed evaluation. If the user is stressed, the evaluation unit can also provide a concise and to-the-point evaluation. If the user is in a hurry, the evaluation unit can also provide a quick evaluation. This allows the evaluation method to be adjusted according to the user's emotions, thereby providing an optimal evaluation for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception unit receives an image of the user. Examples of the user's image include, but are not limited to, a face photo and a full-body photo. The reception unit receives, for example, an image taken with a smartphone or a camera. The reception unit can also directly receive images submitted in digital format. Furthermore, the reception unit has a function to automatically adjust the format and resolution of the image. For example, the reception unit receives an image taken with a smartphone in high resolution. Step 2: The analysis unit uses the generation AI to analyze the image received by the reception unit. The analysis is performed, for example, by extracting information such as the user's complexion, hair color, and eye color, but is not limited to such an example. For example, the generation AI extracts color information from the image using deep learning technology. The analysis unit can also analyze the user's complexion, hair color, and eye color using pattern recognition technology. The analysis unit can also analyze the hue and brightness of the image using the generation AI. For example, the generation AI analyzes the complexion based on RGB values ​​and identifies the hue. Step 3: The diagnosis unit diagnoses the user's personal color based on the information analyzed by the analysis unit. The diagnosis is performed based on criteria such as seasonal colors and tones, but is not limited to these examples. For example, the diagnosis unit uses a generation AI to diagnose the user's personal color based on the analyzed information. The diagnosis unit can also make a diagnosis based on a color sample. The diagnosis unit can also use the generation AI to diagnose the personal color by comprehensively judging the user's complexion, hair color, and eye color. For example, if the user's complexion is warm-toned, the generation AI will diagnose the user's personal color as being of a spring or autumn type. Step 4: The suggestion unit suggests fashion and color coordination based on the personal color diagnosed by the diagnosis unit. The suggestion is made, for example, by suggesting clothes and accessories that match the user's personal color, but is not limited to this example. For example, the suggestion unit uses a generation AI to suggest fashion and color coordination that matches the user's personal color. The suggestion unit can also make suggestions based on a style guide. The suggestion unit can also use the generation AI to suggest color combinations that match the user's personal color. For example, the generation AI can suggest bright pastel colored clothes to a spring-type user. Step 5: The evaluation unit evaluates the style proposed by the suggestion unit and provides feedback. The evaluation is performed, for example, by evaluating whether the clothes and coordination selected by the user match the personal color, but is not limited to this example. For example, the evaluation unit uses a generation AI to evaluate the clothes and coordination selected by the user. The evaluation unit can also perform the evaluation based on the user's feedback. The evaluation unit can also use a generation AI to score the user's style and provide feedback. For example, the generation AI can evaluate whether the clothes selected by the user match the personal color and assign a score.

[0133] 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.

[0134] 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.

[0135] 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.

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

[0138] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

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

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[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 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.

[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 (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).

[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] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 of a user; an analysis unit that analyzes the image received by the reception unit and analyzes the user's facial color, hair color, and eye color; a diagnosis unit that diagnoses personal color based on the information analyzed by the analysis unit; a suggestion unit that suggests fashion or color coordination based on the personal color diagnosed by the diagnosis unit; an evaluation unit that evaluates the style proposed by the suggestion unit. A system characterized by:

2. The reception unit Accepts images taken with a smartphone or camera 2. The system of claim 1.

3. The analysis unit Analyzes the user's complexion, hair color, and eye color using image recognition technology 2. The system of claim 1.

4. The diagnostic unit Diagnose the user's personal color based on the analyzed information 2. The system of claim 1.

5. The proposal unit Propose fashion or color coordination based on diagnosed personal color 2. The system of claim 1.

6. The evaluation unit Rate the user's outfit or outfit selection and provide feedback 2. The system of claim 1.

7. 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.

8. The reception unit Analyze the user's past image upload history and select the appropriate reception method.

2. The system of claim 1.

Images