system

Abstract

The system according to this embodiment aims to suggest optimal fashion and color coordination based on the user's personal color. [Solution] The system according to the embodiment comprises an acquisition unit, an extraction unit, a diagnosis unit, and a suggestion unit. The acquisition unit acquires an image of the user. The extraction unit extracts the user's characteristics from the image acquired by the acquisition unit. The diagnosis unit diagnoses the user's personal color based on the user's characteristics extracted by the extraction unit. The suggestion unit proposes at least one fashion item or color coordination based on the personal color diagnosed by the diagnosis unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, suggestions for fashion and color coordination based on the user's personal color have not been sufficiently made, and there is room for improvement.

[0005] The system according to an embodiment aims to propose an optimal fashion and color coordination based on the user's personal color.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an extraction unit, a diagnosis unit, and a suggestion unit. The acquisition unit acquires an image of the user. The extraction unit extracts the user's characteristics from the image acquired by the acquisition unit. The diagnosis unit diagnoses the user's personal color based on the user's characteristics extracted by the extraction unit. The suggestion unit proposes at least one fashion item or color coordination based on the personal color diagnosed by the diagnosis unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest optimal fashion and color coordination based on the user's personal color. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 2 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The fashion suggestion system according to an embodiment of the present invention is a system that proposes fashion and color coordination tailored to an individual's personal color by utilizing generative AI and image recognition technology. This fashion suggestion system acquires an image of the user and extracts the user's characteristics from the image. Next, it diagnoses the personal color based on the extracted characteristics and proposes fashion and color coordination based on the diagnosis result. It also has a function to evaluate the user's fashion and color coordination based on the acquired image. For example, the user uploads an image to the system. This image is acquired by the acquisition unit, and the user's characteristics are extracted using image recognition technology. For example, skin color, hair color, eye color, etc., are extracted as characteristics. Next, based on the extracted characteristics, the generative AI diagnoses the user's personal color. The diagnosis result is classified into one of four personal color types: spring, summer, autumn, and winter. Based on the diagnosis result, the suggestion unit proposes the most suitable fashion and color coordination for the user. For example, it proposes bright and warm-colored fashion to a spring-type user, and cool and vivid-colored fashion to a winter-type user. This allows the user to bring out their best features. Furthermore, the evaluation unit evaluates the user's current fashion and color coordination based on the acquired image. The evaluation results show how well the fashion chosen by the user matches their personal color. For example, a high evaluation score indicates that the chosen fashion is a very good match for the user's personal color, while a low evaluation score indicates that there is room for improvement. This system allows users to easily find the optimal fashion and color coordination based on their personal color, and further improve their fashion sense. In this way, the fashion suggestion system can suggest the optimal fashion and color coordination based on the user's personal color, thereby improving the user's fashion sense.

[0029] The fashion suggestion system according to this embodiment comprises an acquisition unit, an extraction unit, a diagnosis unit, and a suggestion unit. The acquisition unit acquires images of the user. User images include, but are not limited to, facial photographs, full-body photographs, and specific poses. The acquisition unit takes images of the user using, for example, a smartphone camera. The acquisition unit can also upload images that the user already possesses. Furthermore, the acquisition unit has a function to automatically adjust the resolution and quality of the images. For example, the acquisition unit adjusts the brightness and contrast of the images to acquire them in an optimal state. The extraction unit extracts user features from the images acquired by the acquisition unit. Features include, but are not limited to, facial shape, skin color, hair color, and eye color. The extraction unit analyzes the facial shape of the user using, for example, image recognition technology. The extraction unit can also analyze skin color to identify the user's personal color. Furthermore, the extraction unit analyzes hair color and eye color to understand the user's features in detail. The diagnosis unit diagnoses the personal color based on the user features extracted by the extraction unit. Personal colors are classified into four types, for example, spring, summer, autumn, and winter, but are not limited to these examples. The diagnostic unit diagnoses the user's personal color, for example, using a generative AI. The generative AI takes the user's characteristics as input and outputs the personal color. The generative AI diagnoses the personal color based on the user's skin color, hair color, and eye color, for example. The suggestion unit proposes fashion or color coordination based on the personal color diagnosed by the diagnostic unit. Suggestions include, for example, clothing, accessories, and shoes, but are not limited to these examples. For example, the suggestion unit suggests bright and warm-colored fashion to a spring-type user. The suggestion unit can also suggest cool and vibrant-colored fashion to a winter-type user. Furthermore, the suggestion unit makes customized suggestions based on the user's preferences and lifestyle. Thus, the fashion suggestion system according to this embodiment can acquire an image of the user and propose fashion and color coordination based on their personal color.

[0030] The acquisition unit acquires images of the user. These images may include, but are not limited to, facial photos, full-body photos, or specific poses. The acquisition unit can, for example, take images of the user using a smartphone camera. It can also upload images that the user already possesses. Furthermore, the acquisition unit has a function to automatically adjust the resolution and quality of images. For example, it adjusts the brightness and contrast of the image to acquire it in the optimal state. The acquisition unit can also display guidelines and frames when the user is taking an image, instructing them on the optimal shooting angle and pose. This allows the user to easily acquire high-quality images. The acquisition unit also has a function to take multiple images in succession and automatically select the most suitable image from among them. For example, if a user takes multiple photos from different angles and with different expressions, the acquisition unit selects the best image considering the balance of the face direction and expression. Furthermore, the acquisition unit has a function to automatically recognize the background of an image and remove unwanted backgrounds. This allows for clearer extraction of the user's features. The acquisition unit also takes image privacy into consideration; acquired images are encrypted and stored securely. The user can review the acquired images and delete or retake them as needed. This allows the acquisition unit to efficiently and accurately acquire the user's images and provide data suitable for the next processing step.

[0031] The extraction unit extracts user features from images acquired by the acquisition unit. These features include, but are not limited to, facial shape, skin color, hair color, and eye color. For example, the extraction unit analyzes the user's facial shape using image recognition technology. It can also analyze skin color to identify the user's personal color. Furthermore, it analyzes hair and eye color to gain a detailed understanding of the user's features. The extraction unit employs a deep learning-based image analysis algorithm to achieve highly accurate feature extraction. For example, facial shape analysis uses an algorithm that analyzes the contours of the face and the positional relationships of each part to accurately identify the user's facial shape. Skin color analysis uses color space conversion technology to convert the user's skin color from RGB values ​​to a specific color space to identify their personal color. Additionally, hair and eye color analysis analyzes color attributes such as hue, saturation, and brightness to gain a detailed understanding of the user's features. The extraction unit integrates these features to grasp the user's overall appearance. Furthermore, the extraction unit can also analyze dynamic features such as the user's facial expressions and posture. This allows for a comprehensive understanding of the user's diverse characteristics and provides data suitable for the next diagnostic step.

[0032] The diagnostic unit diagnoses the user's personal color based on the user's characteristics extracted by the extraction unit. Personal colors are classified into four types, such as spring, summer, autumn, and winter, but are not limited to these examples. The diagnostic unit diagnoses the user's personal color using, for example, generative AI. The generative AI takes the user's characteristics as input and outputs the personal color. The generative AI diagnoses the personal color based on, for example, the user's skin color, hair color, and eye color. The generative AI is pre-trained on a large dataset, achieving highly accurate diagnoses. For example, based on the user's skin color, the generative AI analyzes the balance of hue, saturation, and brightness to identify the most suitable personal color. It also combines information on hair color and eye color to evaluate the user's overall color tone and diagnose the optimal personal color. Based on the generative AI's diagnosis results, the diagnostic unit can provide the user with a detailed personal color diagnosis report. This report includes the user's personal color type, examples of suitable colors, and examples of colors to avoid. Furthermore, the diagnostic unit can customize the personal color diagnosis results based on the user's preferences and lifestyle. For example, if a user prefers a particular color, the diagnostic results will take that color into consideration. This allows the diagnostic unit to provide the user with a highly accurate and personalized personal color diagnosis.

[0033] The suggestion unit proposes fashion or color coordination based on the personal color diagnosed by the diagnostic unit. Suggestions include, but are not limited to, clothing, accessories, and shoes. For example, the suggestion unit may suggest bright and warm-colored fashion to a spring-type user. It may also suggest cool and vibrant-colored fashion to a winter-type user. Furthermore, the suggestion unit provides customized suggestions based on the user's preferences and lifestyle. The suggestion unit uses generative AI to select the most suitable fashion items for the user's personal color. The generative AI takes the user's personal color type as input and outputs items with the most suitable colors and designs. For example, it may suggest bright pink or pastel-colored clothing to a spring-type user, and deep blue or black clothing to a winter-type user. The suggestion unit can provide more personalized suggestions by considering the user's past purchase history and preferred style. Furthermore, the suggestion unit also provides fashion suggestions according to the season and event. For example, it may suggest a light and casual style for a summer beach party, and an elegant and chic style for a formal winter event. The suggestion department can also provide images and videos of outfit examples to visually represent the suggested content to the user. This allows users to concretely understand what the suggested fashion items would look like when actually worn. The suggestion department can collect user feedback and continuously improve the accuracy and satisfaction level of the suggestions. In this way, the suggestion department can provide users with optimal fashion suggestions and increase user satisfaction.

[0034] The acquisition unit includes an evaluation unit that evaluates the user's fashion or color coordination based on the acquired image. The evaluation unit, for example, evaluates how well the user's fashion matches their personal color. The evaluation unit performs evaluations based on, for example, the degree of color matching and the degree of fashion trend suitability. For example, the evaluation unit assigns a high evaluation score if the fashion chosen by the user matches their personal color very well. The evaluation unit can also assign a low evaluation score if the fashion chosen by the user does not match their personal color, indicating that there is room for improvement. This allows the user to know the degree of suitability of their fashion and color coordination and receive appropriate advice. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or without AI. For example, the evaluation unit can input the user's fashion image into a generating AI and have the generating AI evaluate the degree of fashion suitability.

[0035] The acquisition unit analyzes the user's past image acquisition history and selects the optimal acquisition method. For example, the acquisition unit prioritizes suggesting image acquisition methods that the user has previously preferred (selfies, photos taken by others, etc.). The acquisition unit can also analyze the time of day and location of images previously taken by the user and suggest the optimal acquisition timing. Furthermore, the acquisition unit can suggest the optimal camera settings based on the resolution and quality of images previously taken by the user. This allows the system to provide the optimal image acquisition method based on the user's past history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past image data into a generating AI and have the generating AI select the optimal acquisition method.

[0036] The image acquisition unit performs filtering based on the user's current environment and circumstances when acquiring images. For example, if the user is outdoors, the acquisition unit uses natural light to acquire the optimal image. If the user is indoors, the acquisition unit can also adjust the camera settings according to the lighting conditions. Furthermore, if the user is moving, the acquisition unit can increase the shutter speed to prevent blurring. This allows the acquisition of the optimal image according to the user's environment and circumstances. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's environment data into a generating AI and have the generating AI perform optimal filtering.

[0037] The image acquisition unit prioritizes acquiring highly relevant images by considering the user's geographical location information when acquiring images. For example, if the user is in a tourist destination, the acquisition unit prioritizes acquiring images that capture the characteristics of that location. Similarly, if the user is at home, the acquisition unit can prioritize acquiring images that capture the characteristics of the room. Furthermore, if the user is at an event venue, the acquisition unit can prioritize acquiring images that capture the atmosphere of that event. This allows for the acquisition of optimal images based on the user's geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location data into a generating AI and have the generating AI acquire highly relevant images.

[0038] The acquisition unit analyzes the user's social media activity when acquiring images and retrieves relevant images. For example, the acquisition unit analyzes the style of images the user has shared on social media and retrieves images of a similar style. The acquisition unit can also retrieve relevant images by referencing images from accounts the user follows on social media. Furthermore, the acquisition unit can analyze the characteristics of images the user has "liked" on social media and retrieve images with similar characteristics. This allows for the acquisition of optimal images based on the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media data into a generating AI and have the generating AI perform the acquisition of relevant images.

[0039] The extraction unit adjusts the accuracy of feature extraction based on the image quality and resolution. For example, in high-resolution images, the extraction unit extracts features in detail. In low-resolution images, the extraction unit can extract and interpolate broad features. Furthermore, if the image quality is low, the extraction unit can remove noise before extracting features. This allows for optimal feature extraction according to the image quality and resolution. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input image data into a generating AI and have the generating AI perform the adjustment of feature extraction accuracy.

[0040] The extraction unit improves the accuracy of feature extraction by referring to the user's past fashion history. For example, the extraction unit extracts similar features from the current image based on the features of fashion items the user has selected in the past. The extraction unit can also analyze the user's past fashion history to identify preferred styles and extract features from them. Furthermore, the extraction unit can extract similar features from the current image based on the features of fashion items the user has evaluated in the past. This allows for optimal feature extraction based on the user's past fashion history. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the user's past fashion data into a generating AI and have the generating AI perform improvements to the accuracy of feature extraction.

[0041] The extraction unit prioritizes the extraction of highly relevant features, taking into account the user's geographical location information during feature extraction. For example, if the user is in a tourist destination, the extraction unit can extract features from images that capture the characteristics of that location. Similarly, if the user is at home, the extraction unit can extract features from images that capture the characteristics of the interior of the room. Furthermore, if the user is at an event venue, the extraction unit can extract features from images that capture the atmosphere of the event. This allows for the extraction of optimal features based on the user's geographical location information. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the user's geographical location data into a generating AI and have the generating AI perform the extraction of highly relevant features.

[0042] The extraction unit analyzes the user's social media activity during feature extraction and extracts relevant features. For example, the extraction unit analyzes the style of images the user has shared on social media and extracts features of similar styles. The extraction unit can also extract relevant features by referencing images from accounts the user follows on social media. Furthermore, the extraction unit can analyze the features of images the user has "liked" on social media and extract similar features. This allows for the extraction of optimal features based on the user's social media activity. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the user's social media data into a generating AI and have the generating AI perform the extraction of relevant features.

[0043] The diagnostic unit improves the accuracy of the diagnosis by referring to the user's past diagnostic results during the diagnosis. For example, the diagnostic unit adjusts the current diagnosis result based on the user's past diagnostic results. The diagnostic unit can also analyze the user's past diagnostic results to identify and diagnose their preferred personal color type. Furthermore, the diagnostic unit can adjust the current diagnosis result based on the user's past evaluations. This allows the diagnostic unit to provide an optimal personal color diagnosis based on the user's past diagnostic results. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's past diagnostic data into a generating AI and have the generating AI perform the task of improving the accuracy of the diagnosis.

[0044] The diagnostic unit adjusts the diagnostic results during the diagnosis, taking into account the user's current fashion tendencies. For example, the diagnostic unit adjusts the diagnostic results based on the fashion style the user currently prefers. The diagnostic unit can also analyze the user's current fashion tendencies and suggest the optimal personal color type. Furthermore, the diagnostic unit can adjust the diagnostic results based on the characteristics of the fashion items the user is currently wearing. This allows the diagnostic unit to provide an optimal personal color diagnosis based on the user's current fashion tendencies. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's fashion data into a generating AI and have the generating AI perform the adjustment of the diagnostic results.

[0045] The diagnostic unit adjusts the diagnostic results during the diagnosis, taking into account the user's geographical location information. For example, if the user lives in a cold region, the diagnostic unit may suggest a personal color type with warm tones. If the user lives in a warm region, the diagnostic unit may also suggest a personal color type with cool tones. Furthermore, if the user lives in an urban area, the diagnostic unit may also suggest a personal color type with modern tones. This allows for the provision of an optimal personal color diagnosis based on the user's geographical location information. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's geographical location data into a generating AI and have the generating AI perform the adjustment of the diagnostic results.

[0046] The diagnostic unit analyzes the user's social media activity during the diagnostic process and provides relevant diagnostic results. For example, the diagnostic unit analyzes the style of images the user has shared on social media and suggests a personal color type with a similar style. The diagnostic unit can also suggest a relevant personal color type by referring to images from accounts the user follows on social media. Furthermore, the diagnostic unit can analyze the characteristics of images the user has "liked" on social media and suggest a personal color type with similar characteristics. This allows for the provision of an optimal personal color diagnosis based on the user's social media activity. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant diagnostic results.

[0047] The suggestion unit makes optimal suggestions by referring to the user's past fashion history. For example, the suggestion unit makes current suggestions based on the characteristics of fashion items the user has previously selected. The suggestion unit can also analyze the user's past fashion history to identify and suggest preferred styles. Furthermore, the suggestion unit can make current suggestions based on the characteristics of fashion items the user has previously rated. This allows the suggestion unit to provide optimal suggestions based on the user's past fashion history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past fashion data into a generating AI and have the generating AI perform the task of providing optimal suggestions.

[0048] The suggestion unit customizes the suggested content by considering the user's current fashion trends. For example, the suggestion unit customizes the suggested content based on the fashion style the user currently prefers. The suggestion unit can also analyze the user's current fashion trends and make optimal suggestions. Furthermore, the suggestion unit can customize the suggested content based on the characteristics of the fashion items the user is currently wearing. This allows the suggestion unit to provide optimal suggestions based on the user's current fashion trends. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's fashion data into a generating AI and have the generating AI perform the customization of the suggested content.

[0049] The suggestion unit makes optimal suggestions by considering the user's geographical location information. For example, if the user lives in a cold region, the suggestion unit may suggest fashion in warm colors. If the user lives in a warm region, the suggestion unit may also suggest fashion in cool colors. Furthermore, if the user lives in an urban area, the suggestion unit may also suggest fashion in a modern style. This allows the system to provide optimal suggestions based on the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing optimal suggestions.

[0050] The suggestion unit analyzes the user's social media activity and provides relevant suggestions. For example, it can analyze the style of images the user has shared on social media and suggest fashion in a similar style. It can also suggest relevant fashion by referencing images from accounts the user follows on social media. Furthermore, it can analyze the characteristics of images the user has "liked" on social media and suggest fashion with similar characteristics. This allows the suggestion unit to provide optimal suggestions based on the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant suggestions.

[0051] The evaluation unit improves the accuracy of its evaluations by referring to the user's past fashion history. For example, the evaluation unit performs its current evaluation based on the characteristics of fashion items the user has previously selected. The evaluation unit can also analyze the user's past fashion history to identify and evaluate their preferred style. Furthermore, the evaluation unit can perform its current evaluation based on the characteristics of fashion items the user has previously evaluated. This allows the evaluation unit to provide the most optimal evaluation based on the user's past fashion history. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's past fashion data into a generating AI and have the generating AI perform the task of improving the accuracy of the evaluations.

[0052] The evaluation unit adjusts the evaluation results during the evaluation process, taking into account the user's current fashion trends. For example, the evaluation unit adjusts the evaluation results based on the fashion style the user currently prefers. The evaluation unit can also analyze the user's current fashion trends and provide the optimal evaluation. Furthermore, the evaluation unit can adjust the evaluation results based on the characteristics of the fashion items the user is currently wearing. This allows the evaluation unit to provide the optimal evaluation based on the user's current fashion trends. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's fashion data into a generating AI and have the generating AI perform the adjustment of the evaluation results.

[0053] The evaluation unit adjusts the evaluation results while considering the user's geographical location information. For example, if the user lives in a cold region, the evaluation unit will rate warm-colored fashion highly. Similarly, if the user lives in a warm region, the evaluation unit may rate cool-colored fashion highly. Furthermore, if the user lives in an urban area, the evaluation unit may rate modern-style fashion highly. This allows the evaluation unit to provide the most appropriate evaluation based on the user's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's geographical location data into a generating AI and have the generating AI perform the adjustment of the evaluation results.

[0054] The evaluation unit analyzes the user's social media activity during the evaluation process and provides relevant evaluation results. For example, the evaluation unit analyzes the style of images the user has shared on social media and highly rates similar styles of fashion. The evaluation unit can also refer to images from accounts the user follows on social media and highly rate relevant fashion. Furthermore, the evaluation unit can analyze the characteristics of images the user has "liked" on social media and highly rate fashion with similar characteristics. This allows the evaluation unit to provide an optimal evaluation based on the user's social media activity. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant evaluation results.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The acquisition unit can analyze the user's past image acquisition history and select the optimal acquisition method. For example, it can prioritize suggesting image acquisition methods that the user has preferred in the past (selfies, photos taken by others, etc.). The acquisition unit can also analyze the time of day and location of images previously taken by the user and suggest the optimal acquisition timing. Furthermore, the acquisition unit can suggest the optimal camera settings based on the resolution and quality of images previously taken by the user. This allows the acquisition unit to provide the optimal image acquisition method based on the user's past history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past image data into a generating AI and have the generating AI select the optimal acquisition method.

[0057] The image acquisition unit can prioritize acquiring highly relevant images by considering the user's geographical location information when acquiring images. For example, if the user is in a tourist area, it can prioritize acquiring images that capture the characteristics of that place. Similarly, if the user is at home, it can prioritize acquiring images that capture the characteristics of the room. Furthermore, if the user is at an event venue, it can prioritize acquiring images that capture the atmosphere of that event. This allows for the acquisition of optimal images based on the user's geographical location information. Some or all of the above processing in the image acquisition unit may be performed using AI, for example, or without AI. For example, the image acquisition unit can input the user's geographical location data into a generating AI and have the generating AI acquire highly relevant images.

[0058] The extraction unit can improve the accuracy of feature extraction by referring to the user's past fashion history. For example, it can extract similar features from the current image based on the features of fashion items the user has selected in the past. The extraction unit can also analyze the user's past fashion history to identify preferred styles and extract features from them. Furthermore, the extraction unit can extract similar features from the current image based on the features of fashion items the user has evaluated in the past. This allows for optimal feature extraction based on the user's past fashion history. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the user's past fashion data into a generating AI and have the generating AI perform improvements to the accuracy of feature extraction.

[0059] The diagnostic unit can improve the accuracy of the diagnosis by referring to the user's past diagnostic results during the diagnosis. For example, it can adjust the current diagnostic result based on the user's past diagnostic results. The diagnostic unit can also analyze the user's past diagnostic results to identify and diagnose their preferred personal color type. Furthermore, the diagnostic unit can adjust the current diagnostic result based on the user's past evaluations. This allows the diagnostic unit to provide an optimal personal color diagnosis based on the user's past diagnostic results. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's past diagnostic data into a generating AI and have the generating AI perform the task of improving the accuracy of the diagnosis.

[0060] The suggestion unit can customize its suggestions by considering the user's current fashion trends. For example, it can customize suggestions based on the user's currently preferred fashion style. It can also analyze the user's current fashion trends and provide optimal suggestions. Furthermore, it can customize suggestions based on the characteristics of the fashion items the user is currently wearing. This allows it to provide optimal suggestions based on the user's current fashion trends. Some or all of the above processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's fashion data into a generating AI and have the generating AI customize the suggestions.

[0061] The evaluation unit can analyze the user's social media activity during the evaluation process and provide relevant evaluation results. For example, it can analyze the style of images the user has shared on social media and highly rate similar styles of fashion. The evaluation unit can also refer to images from accounts the user follows on social media and highly rate related fashion. Furthermore, the evaluation unit can analyze the characteristics of images the user has "liked" on social media and highly rate fashion with similar characteristics. This allows for the provision of optimal evaluations based on the user's social media activity. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's social media data into a generating AI and have the generating AI provide relevant evaluation results.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The acquisition unit acquires the user's image. The user's image may include, but is not limited to, a face photo, a full-body photo, or a specific pose. The acquisition unit may, for example, take a picture of the user using a smartphone camera. The acquisition unit can also upload images that the user already has. Furthermore, the acquisition unit has a function to automatically adjust the resolution and quality of the image. For example, the acquisition unit adjusts the brightness and contrast of the image to acquire the image in an optimal state. Step 2: The extraction unit extracts user features from the image acquired by the acquisition unit. These features include, but are not limited to, facial shape, skin color, hair color, and eye color. For example, the extraction unit may analyze the user's facial shape using image recognition technology. The extraction unit can also analyze skin color to identify the user's personal color. Furthermore, the extraction unit may analyze hair color and eye color to gain a detailed understanding of the user's features. Step 3: The diagnostic unit diagnoses the user's personal color based on the user's characteristics extracted by the extraction unit. Personal colors are classified into four types, for example, spring, summer, autumn, and winter, but are not limited to these examples. The diagnostic unit diagnoses the user's personal color using, for example, a generative AI. The generative AI takes the user's characteristics as input and outputs the personal color. The generative AI diagnoses the personal color based on, for example, the user's skin color, hair color, and eye color. Step 4: The suggestion department proposes fashion or color coordination based on the personal color diagnosed by the diagnostic department. Suggestions may include, but are not limited to, clothing, accessories, and shoes. For example, the suggestion department might suggest bright and warm-colored fashion to a spring-type user. It might also suggest cool and vibrant-colored fashion to a winter-type user. Furthermore, the suggestion department provides customized suggestions based on the user's preferences and lifestyle.

[0064] (Example of form 2) The fashion suggestion system according to an embodiment of the present invention is a system that proposes fashion and color coordination tailored to an individual's personal color by utilizing generative AI and image recognition technology. This fashion suggestion system acquires an image of the user and extracts the user's characteristics from the image. Next, it diagnoses the personal color based on the extracted characteristics and proposes fashion and color coordination based on the diagnosis result. It also has a function to evaluate the user's fashion and color coordination based on the acquired image. For example, the user uploads an image to the system. This image is acquired by the acquisition unit, and the user's characteristics are extracted using image recognition technology. For example, skin color, hair color, eye color, etc., are extracted as characteristics. Next, based on the extracted characteristics, the generative AI diagnoses the user's personal color. The diagnosis result is classified into one of four personal color types: spring, summer, autumn, and winter. Based on the diagnosis result, the suggestion unit proposes the most suitable fashion and color coordination for the user. For example, it proposes bright and warm-colored fashion to a spring-type user, and cool and vivid-colored fashion to a winter-type user. This allows the user to bring out their best features. Furthermore, the evaluation unit evaluates the user's current fashion and color coordination based on the acquired image. The evaluation results show how well the fashion chosen by the user matches their personal color. For example, a high evaluation score indicates that the chosen fashion is a very good match for the user's personal color, while a low evaluation score indicates that there is room for improvement. This system allows users to easily find the optimal fashion and color coordination based on their personal color, and further improve their fashion sense. In this way, the fashion suggestion system can suggest the optimal fashion and color coordination based on the user's personal color, thereby improving the user's fashion sense.

[0065] The fashion suggestion system according to this embodiment comprises an acquisition unit, an extraction unit, a diagnosis unit, and a suggestion unit. The acquisition unit acquires images of the user. User images include, but are not limited to, facial photographs, full-body photographs, and specific poses. The acquisition unit takes images of the user using, for example, a smartphone camera. The acquisition unit can also upload images that the user already possesses. Furthermore, the acquisition unit has a function to automatically adjust the resolution and quality of the images. For example, the acquisition unit adjusts the brightness and contrast of the images to acquire them in an optimal state. The extraction unit extracts user features from the images acquired by the acquisition unit. Features include, but are not limited to, facial shape, skin color, hair color, and eye color. The extraction unit analyzes the facial shape of the user using, for example, image recognition technology. The extraction unit can also analyze skin color to identify the user's personal color. Furthermore, the extraction unit analyzes hair color and eye color to understand the user's features in detail. The diagnosis unit diagnoses the personal color based on the user features extracted by the extraction unit. Personal colors are classified into four types, for example, spring, summer, autumn, and winter, but are not limited to these examples. The diagnostic unit diagnoses the user's personal color, for example, using a generative AI. The generative AI takes the user's characteristics as input and outputs the personal color. The generative AI diagnoses the personal color based on the user's skin color, hair color, and eye color, for example. The suggestion unit proposes fashion or color coordination based on the personal color diagnosed by the diagnostic unit. Suggestions include, for example, clothing, accessories, and shoes, but are not limited to these examples. For example, the suggestion unit suggests bright and warm-colored fashion to a spring-type user. The suggestion unit can also suggest cool and vibrant-colored fashion to a winter-type user. Furthermore, the suggestion unit makes customized suggestions based on the user's preferences and lifestyle. Thus, the fashion suggestion system according to this embodiment can acquire an image of the user and propose fashion and color coordination based on their personal color.

[0066] The acquisition unit acquires images of the user. These images may include, but are not limited to, facial photos, full-body photos, or specific poses. The acquisition unit can, for example, take images of the user using a smartphone camera. It can also upload images that the user already possesses. Furthermore, the acquisition unit has a function to automatically adjust the resolution and quality of images. For example, it adjusts the brightness and contrast of the image to acquire it in the optimal state. The acquisition unit can also display guidelines and frames when the user is taking an image, instructing them on the optimal shooting angle and pose. This allows the user to easily acquire high-quality images. The acquisition unit also has a function to take multiple images in succession and automatically select the most suitable image from among them. For example, if a user takes multiple photos from different angles and with different expressions, the acquisition unit selects the best image considering the balance of the face direction and expression. Furthermore, the acquisition unit has a function to automatically recognize the background of an image and remove unwanted backgrounds. This allows for clearer extraction of the user's features. The acquisition unit also takes image privacy into consideration; acquired images are encrypted and stored securely. The user can review the acquired images and delete or retake them as needed. This allows the acquisition unit to efficiently and accurately acquire the user's images and provide data suitable for the next processing step.

[0067] The extraction unit extracts user features from images acquired by the acquisition unit. These features include, but are not limited to, facial shape, skin color, hair color, and eye color. For example, the extraction unit analyzes the user's facial shape using image recognition technology. It can also analyze skin color to identify the user's personal color. Furthermore, it analyzes hair and eye color to gain a detailed understanding of the user's features. The extraction unit employs a deep learning-based image analysis algorithm to achieve highly accurate feature extraction. For example, facial shape analysis uses an algorithm that analyzes the contours of the face and the positional relationships of each part to accurately identify the user's facial shape. Skin color analysis uses color space conversion technology to convert the user's skin color from RGB values ​​to a specific color space to identify their personal color. Additionally, hair and eye color analysis analyzes color attributes such as hue, saturation, and brightness to gain a detailed understanding of the user's features. The extraction unit integrates these features to grasp the user's overall appearance. Furthermore, the extraction unit can also analyze dynamic features such as the user's facial expressions and posture. This allows for a comprehensive understanding of the user's diverse characteristics and provides data suitable for the next diagnostic step.

[0068] The diagnostic unit diagnoses the user's personal color based on the user's characteristics extracted by the extraction unit. Personal colors are classified into four types, such as spring, summer, autumn, and winter, but are not limited to these examples. The diagnostic unit diagnoses the user's personal color using, for example, generative AI. The generative AI takes the user's characteristics as input and outputs the personal color. The generative AI diagnoses the personal color based on, for example, the user's skin color, hair color, and eye color. The generative AI is pre-trained on a large dataset, achieving highly accurate diagnoses. For example, based on the user's skin color, the generative AI analyzes the balance of hue, saturation, and brightness to identify the most suitable personal color. It also combines information on hair color and eye color to evaluate the user's overall color tone and diagnose the optimal personal color. Based on the generative AI's diagnosis results, the diagnostic unit can provide the user with a detailed personal color diagnosis report. This report includes the user's personal color type, examples of suitable colors, and examples of colors to avoid. Furthermore, the diagnostic unit can customize the personal color diagnosis results based on the user's preferences and lifestyle. For example, if a user prefers a particular color, the diagnostic results will take that color into consideration. This allows the diagnostic unit to provide the user with a highly accurate and personalized personal color diagnosis.

[0069] The suggestion unit proposes fashion or color coordination based on the personal color diagnosed by the diagnostic unit. Suggestions include, but are not limited to, clothing, accessories, and shoes. For example, the suggestion unit may suggest bright and warm-colored fashion to a spring-type user. It may also suggest cool and vibrant-colored fashion to a winter-type user. Furthermore, the suggestion unit provides customized suggestions based on the user's preferences and lifestyle. The suggestion unit uses generative AI to select the most suitable fashion items for the user's personal color. The generative AI takes the user's personal color type as input and outputs items with the most suitable colors and designs. For example, it may suggest bright pink or pastel-colored clothing to a spring-type user, and deep blue or black clothing to a winter-type user. The suggestion unit can provide more personalized suggestions by considering the user's past purchase history and preferred style. Furthermore, the suggestion unit also provides fashion suggestions according to the season and event. For example, it may suggest a light and casual style for a summer beach party, and an elegant and chic style for a formal winter event. The suggestion department can also provide images and videos of outfit examples to visually represent the suggested content to the user. This allows users to concretely understand what the suggested fashion items would look like when actually worn. The suggestion department can collect user feedback and continuously improve the accuracy and satisfaction level of the suggestions. In this way, the suggestion department can provide users with optimal fashion suggestions and increase user satisfaction.

[0070] The acquisition unit includes an evaluation unit that evaluates the user's fashion or color coordination based on the acquired image. The evaluation unit, for example, evaluates how well the user's fashion matches their personal color. The evaluation unit performs evaluations based on, for example, the degree of color matching and the degree of fashion trend suitability. For example, the evaluation unit assigns a high evaluation score if the fashion chosen by the user matches their personal color very well. The evaluation unit can also assign a low evaluation score if the fashion chosen by the user does not match their personal color, indicating that there is room for improvement. This allows the user to know the degree of suitability of their fashion and color coordination and receive appropriate advice. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or without AI. For example, the evaluation unit can input the user's fashion image into a generating AI and have the generating AI evaluate the degree of fashion suitability.

[0071] The acquisition unit estimates the user's emotions and adjusts the timing of image acquisition based on the estimated emotions. For example, if the user is relaxed, the acquisition unit may acquire images with intervals of several seconds to capture natural expressions. If the user is tense, the acquisition unit may wait until the user relaxes before acquiring images. Furthermore, if the user is excited, the acquisition unit may acquire images continuously to capture that moment. This allows for image acquisition at the optimal timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0072] The acquisition unit analyzes the user's past image acquisition history and selects the optimal acquisition method. For example, the acquisition unit prioritizes suggesting image acquisition methods that the user has previously preferred (selfies, photos taken by others, etc.). The acquisition unit can also analyze the time of day and location of images previously taken by the user and suggest the optimal acquisition timing. Furthermore, the acquisition unit can suggest the optimal camera settings based on the resolution and quality of images previously taken by the user. This allows the system to provide the optimal image acquisition method based on the user's past history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past image data into a generating AI and have the generating AI select the optimal acquisition method.

[0073] The image acquisition unit performs filtering based on the user's current environment and circumstances when acquiring images. For example, if the user is outdoors, the acquisition unit uses natural light to acquire the optimal image. If the user is indoors, the acquisition unit can also adjust the camera settings according to the lighting conditions. Furthermore, if the user is moving, the acquisition unit can increase the shutter speed to prevent blurring. This allows the acquisition of the optimal image according to the user's environment and circumstances. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's environment data into a generating AI and have the generating AI perform optimal filtering.

[0074] The acquisition unit estimates the user's emotions and determines the priority of images to acquire based on the estimated emotions. For example, if the user is relaxed, the acquisition unit prioritizes acquiring natural facial expressions. If the user is tense, the acquisition unit can also acquire multiple images to capture moments of relaxation. Furthermore, if the user is excited, the acquisition unit can acquire images in sequence to capture those moments. This allows for the acquisition of optimal images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0075] The image acquisition unit prioritizes acquiring highly relevant images by considering the user's geographical location information when acquiring images. For example, if the user is in a tourist destination, the acquisition unit prioritizes acquiring images that capture the characteristics of that location. Similarly, if the user is at home, the acquisition unit can prioritize acquiring images that capture the characteristics of the room. Furthermore, if the user is at an event venue, the acquisition unit can prioritize acquiring images that capture the atmosphere of that event. This allows for the acquisition of optimal images based on the user's geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location data into a generating AI and have the generating AI acquire highly relevant images.

[0076] The acquisition unit analyzes the user's social media activity when acquiring images and retrieves relevant images. For example, the acquisition unit analyzes the style of images the user has shared on social media and retrieves images of a similar style. The acquisition unit can also retrieve relevant images by referencing images from accounts the user follows on social media. Furthermore, the acquisition unit can analyze the characteristics of images the user has "liked" on social media and retrieve images with similar characteristics. This allows for the acquisition of optimal images based on the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media data into a generating AI and have the generating AI perform the acquisition of relevant images.

[0077] The extraction unit estimates the user's emotions and adjusts the feature extraction method based on the estimated user emotions. For example, if the user is relaxed, the extraction unit will extract features by emphasizing natural facial expressions and posture. If the user is tense, the extraction unit can also capture moments of relaxation and extract features. Furthermore, if the user is excited, the extraction unit can also extract features by emphasizing facial expressions and posture at that moment. This allows for the provision of an optimal feature extraction method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The extraction unit adjusts the accuracy of feature extraction based on the image quality and resolution. For example, in high-resolution images, the extraction unit extracts features in detail. In low-resolution images, the extraction unit can extract and interpolate broad features. Furthermore, if the image quality is low, the extraction unit can remove noise before extracting features. This allows for optimal feature extraction according to the image quality and resolution. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input image data into a generating AI and have the generating AI perform the adjustment of feature extraction accuracy.

[0079] The extraction unit improves the accuracy of feature extraction by referring to the user's past fashion history. For example, the extraction unit extracts similar features from the current image based on the features of fashion items the user has selected in the past. The extraction unit can also analyze the user's past fashion history to identify preferred styles and extract features from them. Furthermore, the extraction unit can extract similar features from the current image based on the features of fashion items the user has evaluated in the past. This allows for optimal feature extraction based on the user's past fashion history. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the user's past fashion data into a generating AI and have the generating AI perform improvements to the accuracy of feature extraction.

[0080] The extraction unit estimates the user's emotions and determines the priority of features to extract based on the estimated user emotions. For example, if the user is relaxed, the extraction unit prioritizes extracting features that capture natural facial expressions and postures. If the user is tense, the extraction unit can also capture moments of relaxation and extract features. Furthermore, if the user is excited, the extraction unit can prioritize extracting features that capture facial expressions and postures at that moment. This allows for the extraction of optimal features according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI, or not. For example, the extraction unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The extraction unit prioritizes the extraction of highly relevant features, taking into account the user's geographical location information during feature extraction. For example, if the user is in a tourist destination, the extraction unit can extract features from images that capture the characteristics of that location. Similarly, if the user is at home, the extraction unit can extract features from images that capture the characteristics of the interior of the room. Furthermore, if the user is at an event venue, the extraction unit can extract features from images that capture the atmosphere of the event. This allows for the extraction of optimal features based on the user's geographical location information. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the user's geographical location data into a generating AI and have the generating AI perform the extraction of highly relevant features.

[0082] The extraction unit analyzes the user's social media activity during feature extraction and extracts relevant features. For example, the extraction unit analyzes the style of images the user has shared on social media and extracts features of similar styles. The extraction unit can also extract relevant features by referencing images from accounts the user follows on social media. Furthermore, the extraction unit can analyze the features of images the user has "liked" on social media and extract similar features. This allows for the extraction of optimal features based on the user's social media activity. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the user's social media data into a generating AI and have the generating AI perform the extraction of relevant features.

[0083] The diagnostic unit estimates the user's emotions and adjusts the personal color diagnosis method based on the estimated emotions. For example, if the user is relaxed, the diagnostic unit performs a detailed diagnosis and suggests multiple personal color types. If the user is tense, the diagnostic unit can also perform a concise diagnosis and suggest a main personal color type. Furthermore, if the user is excited, the diagnostic unit can provide a diagnosis result with visually stimulating effects. This allows for the provision of an optimal personal color diagnosis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or not using AI. For example, the diagnostic unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0084] The diagnostic unit improves the accuracy of the diagnosis by referring to the user's past diagnostic results during the diagnosis. For example, the diagnostic unit adjusts the current diagnosis result based on the user's past diagnostic results. The diagnostic unit can also analyze the user's past diagnostic results to identify and diagnose their preferred personal color type. Furthermore, the diagnostic unit can adjust the current diagnosis result based on the user's past evaluations. This allows the diagnostic unit to provide an optimal personal color diagnosis based on the user's past diagnostic results. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's past diagnostic data into a generating AI and have the generating AI perform the task of improving the accuracy of the diagnosis.

[0085] The diagnostic unit adjusts the diagnostic results during the diagnosis, taking into account the user's current fashion tendencies. For example, the diagnostic unit adjusts the diagnostic results based on the fashion style the user currently prefers. The diagnostic unit can also analyze the user's current fashion tendencies and suggest the optimal personal color type. Furthermore, the diagnostic unit can adjust the diagnostic results based on the characteristics of the fashion items the user is currently wearing. This allows the diagnostic unit to provide an optimal personal color diagnosis based on the user's current fashion tendencies. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's fashion data into a generating AI and have the generating AI perform the adjustment of the diagnostic results.

[0086] The diagnostic unit estimates the user's emotions and adjusts the display method of the diagnostic results based on the estimated emotions. For example, if the user is relaxed, the diagnostic unit displays detailed diagnostic results. If the user is tense, the diagnostic unit can also display concise diagnostic results. Furthermore, if the user is excited, the diagnostic unit can display diagnostic results with visually stimulating effects. This allows for the provision of the optimal display method of diagnostic results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or not using AI. For example, the diagnostic unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0087] The diagnostic unit adjusts the diagnostic results during the diagnosis, taking into account the user's geographical location information. For example, if the user lives in a cold region, the diagnostic unit may suggest a personal color type with warm tones. If the user lives in a warm region, the diagnostic unit may also suggest a personal color type with cool tones. Furthermore, if the user lives in an urban area, the diagnostic unit may also suggest a personal color type with modern tones. This allows for the provision of an optimal personal color diagnosis based on the user's geographical location information. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's geographical location data into a generating AI and have the generating AI perform the adjustment of the diagnostic results.

[0088] The diagnostic unit analyzes the user's social media activity during the diagnostic process and provides relevant diagnostic results. For example, the diagnostic unit analyzes the style of images the user has shared on social media and suggests a personal color type with a similar style. The diagnostic unit can also suggest a relevant personal color type by referring to images from accounts the user follows on social media. Furthermore, the diagnostic unit can analyze the characteristics of images the user has "liked" on social media and suggest a personal color type with similar characteristics. This allows for the provision of an optimal personal color diagnosis based on the user's social media activity. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant diagnostic results.

[0089] The suggestion unit estimates the user's emotions and adjusts the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit provides detailed suggestions and multiple options. If the user is tense, it can also provide concise and easy-to-understand suggestions. Furthermore, if the user is excited, it can present suggestions with visually stimulating effects. This allows the suggestion unit to provide the most appropriate way to present suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0090] The suggestion unit makes optimal suggestions by referring to the user's past fashion history. For example, the suggestion unit makes current suggestions based on the characteristics of fashion items the user has previously selected. The suggestion unit can also analyze the user's past fashion history to identify and suggest preferred styles. Furthermore, the suggestion unit can make current suggestions based on the characteristics of fashion items the user has previously rated. This allows the suggestion unit to provide optimal suggestions based on the user's past fashion history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past fashion data into a generating AI and have the generating AI perform the task of providing optimal suggestions.

[0091] The suggestion unit customizes the suggested content by considering the user's current fashion trends. For example, the suggestion unit customizes the suggested content based on the fashion style the user currently prefers. The suggestion unit can also analyze the user's current fashion trends and make optimal suggestions. Furthermore, the suggestion unit can customize the suggested content based on the characteristics of the fashion items the user is currently wearing. This allows the suggestion unit to provide optimal suggestions based on the user's current fashion trends. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's fashion data into a generating AI and have the generating AI perform the customization of the suggested content.

[0092] The suggestion unit estimates the user's emotions and determines the priority of suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit may prioritize detailed suggestions. If the user is tense, the suggestion unit may also prioritize concise suggestions. Furthermore, if the user is excited, the suggestion unit may prioritize visually stimulating suggestions. This allows for the provision of optimal suggestion priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The suggestion unit makes optimal suggestions by considering the user's geographical location information. For example, if the user lives in a cold region, the suggestion unit may suggest fashion in warm colors. If the user lives in a warm region, the suggestion unit may also suggest fashion in cool colors. Furthermore, if the user lives in an urban area, the suggestion unit may also suggest fashion in a modern style. This allows the system to provide optimal suggestions based on the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing optimal suggestions.

[0094] The suggestion unit analyzes the user's social media activity and provides relevant suggestions. For example, it can analyze the style of images the user has shared on social media and suggest fashion in a similar style. It can also suggest relevant fashion by referencing images from accounts the user follows on social media. Furthermore, it can analyze the characteristics of images the user has "liked" on social media and suggest fashion with similar characteristics. This allows the suggestion unit to provide optimal suggestions based on the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant suggestions.

[0095] The evaluation unit estimates the user's emotions and adjusts the evaluation method based on the estimated emotions. For example, if the user is relaxed, the evaluation unit performs a detailed evaluation and provides multiple evaluation criteria. If the user is tense, the evaluation unit can also perform a concise and easy-to-understand evaluation. Furthermore, if the user is excited, the evaluation unit can perform an evaluation with visually stimulating effects. This allows for the provision of the optimal evaluation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The evaluation unit improves the accuracy of its evaluations by referring to the user's past fashion history. For example, the evaluation unit performs its current evaluation based on the characteristics of fashion items the user has previously selected. The evaluation unit can also analyze the user's past fashion history to identify and evaluate their preferred style. Furthermore, the evaluation unit can perform its current evaluation based on the characteristics of fashion items the user has previously evaluated. This allows the evaluation unit to provide the most optimal evaluation based on the user's past fashion history. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's past fashion data into a generating AI and have the generating AI perform the task of improving the accuracy of the evaluations.

[0097] The evaluation unit adjusts the evaluation results during the evaluation process, taking into account the user's current fashion trends. For example, the evaluation unit adjusts the evaluation results based on the fashion style the user currently prefers. The evaluation unit can also analyze the user's current fashion trends and provide the optimal evaluation. Furthermore, the evaluation unit can adjust the evaluation results based on the characteristics of the fashion items the user is currently wearing. This allows the evaluation unit to provide the optimal evaluation based on the user's current fashion trends. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's fashion data into a generating AI and have the generating AI perform the adjustment of the evaluation results.

[0098] The evaluation unit estimates the user's emotions and adjusts the display method of the evaluation results based on the estimated user emotions. For example, if the user is relaxed, the evaluation unit displays detailed evaluation results. If the user is tense, the evaluation unit can also display concise evaluation results. Furthermore, if the user is excited, the evaluation unit can display evaluation results with visually stimulating effects. This allows for the provision of an optimal display method of evaluation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The evaluation unit adjusts the evaluation results while considering the user's geographical location information. For example, if the user lives in a cold region, the evaluation unit will rate warm-colored fashion highly. Similarly, if the user lives in a warm region, the evaluation unit may rate cool-colored fashion highly. Furthermore, if the user lives in an urban area, the evaluation unit may rate modern-style fashion highly. This allows the evaluation unit to provide the most appropriate evaluation based on the user's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's geographical location data into a generating AI and have the generating AI perform the adjustment of the evaluation results.

[0100] The evaluation unit analyzes the user's social media activity during the evaluation process and provides relevant evaluation results. For example, the evaluation unit analyzes the style of images the user has shared on social media and highly rates similar styles of fashion. The evaluation unit can also refer to images from accounts the user follows on social media and highly rate relevant fashion. Furthermore, the evaluation unit can analyze the characteristics of images the user has "liked" on social media and highly rate fashion with similar characteristics. This allows the evaluation unit to provide an optimal evaluation based on the user's social media activity. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant evaluation results.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions and multiple options. If the user is tense, it can present concise and easy-to-understand suggestions. Furthermore, if the user is excited, it can present suggestions with visually stimulating effects. This allows the suggestion unit to provide the most appropriate presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0103] The acquisition unit can analyze the user's past image acquisition history and select the optimal acquisition method. For example, it can prioritize suggesting image acquisition methods that the user has preferred in the past (selfies, photos taken by others, etc.). The acquisition unit can also analyze the time of day and location of images previously taken by the user and suggest the optimal acquisition timing. Furthermore, the acquisition unit can suggest the optimal camera settings based on the resolution and quality of images previously taken by the user. This allows the acquisition unit to provide the optimal image acquisition method based on the user's past history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past image data into a generating AI and have the generating AI select the optimal acquisition method.

[0104] The evaluation unit can estimate the user's emotions and adjust the evaluation method based on the estimated emotions. For example, if the user is relaxed, it can perform a detailed evaluation and provide multiple evaluation criteria. If the user is tense, it can perform a concise and easy-to-understand evaluation. Furthermore, if the user is excited, it can perform an evaluation with visually stimulating effects. This allows for the provision of the optimal evaluation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0105] The diagnostic unit can estimate the user's emotions and adjust the personal color diagnosis method based on the estimated emotions. For example, if the user is relaxed, it can perform a detailed diagnosis and suggest multiple personal color types. If the user is tense, it can perform a concise diagnosis and suggest a main personal color type. Furthermore, if the user is excited, it can provide a diagnosis result with visually stimulating effects. This allows for the provision of an optimal personal color diagnosis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or not using AI. For example, the diagnostic unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0106] The extraction unit can estimate the user's emotions and adjust the feature extraction method based on the estimated user emotions. For example, if the user is relaxed, features can be extracted with emphasis on natural facial expressions and posture. If the user is tense, features can be extracted by capturing moments of relaxation. Furthermore, if the user is excited, features can be extracted with emphasis on facial expressions and posture at that moment. This allows for the provision of an optimal feature extraction method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI, or not using AI. For example, the extraction unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0107] The image acquisition unit can prioritize acquiring highly relevant images by considering the user's geographical location information when acquiring images. For example, if the user is in a tourist area, it can prioritize acquiring images that capture the characteristics of that place. Similarly, if the user is at home, it can prioritize acquiring images that capture the characteristics of the room. Furthermore, if the user is at an event venue, it can prioritize acquiring images that capture the atmosphere of that event. This allows for the acquisition of optimal images based on the user's geographical location information. Some or all of the above processing in the image acquisition unit may be performed using AI, for example, or without AI. For example, the image acquisition unit can input the user's geographical location data into a generating AI and have the generating AI acquire highly relevant images.

[0108] The extraction unit can improve the accuracy of feature extraction by referring to the user's past fashion history. For example, it can extract similar features from the current image based on the features of fashion items the user has selected in the past. The extraction unit can also analyze the user's past fashion history to identify preferred styles and extract features from them. Furthermore, the extraction unit can extract similar features from the current image based on the features of fashion items the user has evaluated in the past. This allows for optimal feature extraction based on the user's past fashion history. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the user's past fashion data into a generating AI and have the generating AI perform improvements to the accuracy of feature extraction.

[0109] The diagnostic unit can improve the accuracy of the diagnosis by referring to the user's past diagnostic results during the diagnosis. For example, it can adjust the current diagnostic result based on the user's past diagnostic results. The diagnostic unit can also analyze the user's past diagnostic results to identify and diagnose their preferred personal color type. Furthermore, the diagnostic unit can adjust the current diagnostic result based on the user's past evaluations. This allows the diagnostic unit to provide an optimal personal color diagnosis based on the user's past diagnostic results. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's past diagnostic data into a generating AI and have the generating AI perform the task of improving the accuracy of the diagnosis.

[0110] The suggestion unit can customize its suggestions by considering the user's current fashion trends. For example, it can customize suggestions based on the user's currently preferred fashion style. It can also analyze the user's current fashion trends and provide optimal suggestions. Furthermore, it can customize suggestions based on the characteristics of the fashion items the user is currently wearing. This allows it to provide optimal suggestions based on the user's current fashion trends. Some or all of the above processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's fashion data into a generating AI and have the generating AI customize the suggestions.

[0111] The evaluation unit can analyze the user's social media activity during the evaluation process and provide relevant evaluation results. For example, it can analyze the style of images the user has shared on social media and highly rate similar styles of fashion. The evaluation unit can also refer to images from accounts the user follows on social media and highly rate related fashion. Furthermore, the evaluation unit can analyze the characteristics of images the user has "liked" on social media and highly rate fashion with similar characteristics. This allows for the provision of optimal evaluations based on the user's social media activity. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's social media data into a generating AI and have the generating AI provide relevant evaluation results.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The acquisition unit acquires the user's image. The user's image may include, but is not limited to, a face photo, a full-body photo, or a specific pose. The acquisition unit may, for example, take a picture of the user using a smartphone camera. The acquisition unit can also upload images that the user already has. Furthermore, the acquisition unit has a function to automatically adjust the resolution and quality of the image. For example, the acquisition unit adjusts the brightness and contrast of the image to acquire the image in an optimal state. Step 2: The extraction unit extracts user features from the image acquired by the acquisition unit. These features include, but are not limited to, facial shape, skin color, hair color, and eye color. For example, the extraction unit may analyze the user's facial shape using image recognition technology. The extraction unit can also analyze skin color to identify the user's personal color. Furthermore, the extraction unit may analyze hair color and eye color to gain a detailed understanding of the user's features. Step 3: The diagnostic unit diagnoses the user's personal color based on the user's characteristics extracted by the extraction unit. Personal colors are classified into four types, for example, spring, summer, autumn, and winter, but are not limited to these examples. The diagnostic unit diagnoses the user's personal color using, for example, a generative AI. The generative AI takes the user's characteristics as input and outputs the personal color. The generative AI diagnoses the personal color based on, for example, the user's skin color, hair color, and eye color. Step 4: The suggestion department proposes fashion or color coordination based on the personal color diagnosed by the diagnostic department. Suggestions may include, but are not limited to, clothing, accessories, and shoes. For example, the suggestion department might suggest bright and warm-colored fashion to a spring-type user. It might also suggest cool and vibrant-colored fashion to a winter-type user. Furthermore, the suggestion department provides customized suggestions based on the user's preferences and lifestyle.

[0114] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0117] For example, the acquisition unit can acquire an image of the user using the camera 42 of the smart device 14. The extraction unit is implemented by the specific processing unit 290 of the data processing device 12 and extracts the user's features from the acquired image. The diagnosis unit is implemented by the specific processing unit 290 of the data processing device 12 and diagnoses the user's personal color based on the extracted features. The suggestion unit is implemented by the control unit 46A of the smart device 14 and suggests fashion and color coordination based on the diagnosis results. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12 and evaluates the user's fashion and color coordination based on the acquired image. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] For example, the acquisition unit can acquire an image of the user using the camera 42 of the smart glasses 214. The extraction unit is implemented by the identification processing unit 290 of the data processing device 12 and extracts the user's features from the acquired image. The diagnosis unit is implemented by the identification processing unit 290 of the data processing device 12 and diagnoses the user's personal color based on the extracted features. The suggestion unit is implemented by the control unit 46A of the smart glasses 214 and suggests fashion and color coordination based on the diagnosis results. The evaluation unit is implemented by the identification processing unit 290 of the data processing device 12 and evaluates the user's fashion and color coordination based on the acquired image. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] For example, the acquisition unit can acquire the user's image using the camera 42 of the headset terminal 314. The extraction unit is implemented by the specific processing unit 290 of the data processing device 12 and extracts the user's features from the acquired image. The diagnosis unit is implemented by the specific processing unit 290 of the data processing device 12 and diagnoses the personal color based on the extracted features. The suggestion unit is implemented by the control unit 46A of the headset terminal 314 and suggests fashion and color coordination based on the diagnosis results. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12 and evaluates the user's fashion and color coordination based on the acquired image. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 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.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] For example, the acquisition unit can acquire images of the user using the camera 42 of the robot 414. The extraction unit is implemented by the specific processing unit 290 of the data processing device 12 and extracts the user's features from the acquired images. The diagnosis unit is implemented by the specific processing unit 290 of the data processing device 12 and diagnoses the user's personal color based on the extracted features. The suggestion unit is implemented by the control unit 46A of the robot 414 and suggests fashion and color coordination based on the diagnosis results. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12 and evaluates the user's fashion and color coordination based on the acquired images. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0167] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) An acquisition unit that acquires the user's image, An extraction unit extracts the user's features from the image acquired by the acquisition unit, A diagnostic unit that diagnoses personal color based on the user's characteristics extracted by the extraction unit, The system includes a suggestion unit that proposes at least one fashion item or color coordination based on the personal color diagnosed by the diagnostic unit. A system characterized by the following features. (Note 2) The system includes an evaluation unit that evaluates at least one of the user's fashion items or color coordination based on the acquired image. The system described in Appendix 1, characterized by the features described herein. (Note 3) The acquisition unit is, By estimating the user's emotions, the timing of image acquisition is adjusted based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The acquisition unit is, Analyze the user's past image acquisition history and select the appropriate acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The acquisition unit is, When acquiring images, filtering is performed based on the user's current environment or situation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, It estimates the user's emotions and determines the priority of images to retrieve based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, When acquiring images, the system prioritizes retrieving highly relevant images by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When acquiring images, the system analyzes the user's social media activity and retrieves relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 9) The extraction unit is We estimate the user's emotions and adjust the feature extraction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The extraction unit is During feature extraction, the extraction accuracy is adjusted based on the image quality and resolution. The system described in Appendix 1, characterized by the features described herein. (Note 11) The extraction unit is When extracting features, we improve the accuracy of the extraction by referring to the user's past fashion history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The extraction unit is It estimates the user's emotions and determines the priority of features to extract based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The extraction unit is During feature extraction, the system prioritizes the extraction of highly relevant features by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The extraction unit is During feature extraction, the user's social media activity is analyzed to extract relevant features. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned diagnostic unit, The system estimates the user's emotions and adjusts the personal color analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned diagnostic unit, During diagnosis, the system improves the accuracy of the diagnosis by referring to the user's past diagnostic results. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned diagnostic unit, During the diagnosis, the results are adjusted to take into account the user's current fashion trends. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned diagnostic unit, It estimates the user's emotions and adjusts how the diagnostic results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned diagnostic unit, During diagnosis, the diagnostic results are adjusted to take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned diagnostic unit, During the diagnostic process, the system analyzes the user's social media activity and provides relevant diagnostic results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making suggestions, the system refers to the user's past fashion history to provide the most suitable recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making suggestions, customize the suggestions to take into account the user's current fashion trends. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, we take the user's geographical location into consideration to provide the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making suggestions, we analyze the user's social media activity and provide relevant recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 27) The evaluation unit described above, It estimates the user's emotions and adjusts the evaluation method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The evaluation unit described above, During the evaluation process, we refer to the user's past fashion history to improve the accuracy of the evaluation. The system described in Appendix 2, characterized by the features described herein. (Note 29) The evaluation unit described above, During the evaluation process, the evaluation results will be adjusted to take into account the user's current fashion trends. The system described in Appendix 2, characterized by the features described herein. (Note 30) The evaluation unit described above, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The evaluation unit described above, During the evaluation process, the evaluation results will be adjusted to take into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 32) The evaluation unit described above, During the evaluation process, we analyze the user's social media activity and provide relevant evaluation results. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires the user's image, An extraction unit extracts the user's features from the image acquired by the acquisition unit, A diagnostic unit that diagnoses personal color based on the user's characteristics extracted by the extraction unit, The system includes a suggestion unit that proposes at least one fashion item or color coordination based on the personal color diagnosed by the diagnostic unit. A system characterized by the following features.

2. The system includes an evaluation unit that evaluates at least one of the user's fashion items or color coordination based on the acquired image. The system according to feature 1.

3. The acquisition unit is, By estimating the user's emotions, the timing of image acquisition is adjusted based on the estimated user emotions. The system according to feature 1.

4. The acquisition unit is, The system analyzes the user's past image acquisition history and selects an appropriate acquisition method. The system according to feature 1.

5. The acquisition unit is, When acquiring images, filtering is performed based on the user's current environment or situation. The system according to feature 1.

6. The acquisition unit is, The system estimates the user's emotions and determines the priority of images to acquire based on the estimated user emotions. The system according to feature 1.

7. The acquisition unit is, When acquiring images, the system prioritizes acquiring images that are highly relevant, taking into account the user's geographical location information. The system according to feature 1.

8. The acquisition unit is, When acquiring images, the system analyzes the user's social media activity and retrieves relevant images. The system according to feature 1.

9. The extraction unit is The user's emotions are estimated, and the feature extraction method is adjusted based on the estimated user emotions. The system according to feature 1.

Images