System

Abstract

An object of a system according to an embodiment is to enable a user to intuitively understand an optimal style for the user.SOLUTION: A system according to an embodiment includes a mirror, a camera, a generation AI, and a display unit. The mirror reflects the figure of the user. The camera captures a view of the user in the mirror. The generation AI suggests a style based on the hair type, skeleton, personality, and preference of the user captured by the camera. The display unit displays the style proposed by the generation AI on the mirror.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem of making it difficult for users to intuitively understand the style that is best suited to them.

[0005] The system according to the embodiment aims to enable users to intuitively understand the style that best suits them. [Means for solving the problem]

[0006] The system according to the embodiment includes a mirror, a camera, a generation AI, and a display unit. The mirror reflects the user's image. The camera captures the user's image reflected in the mirror. The generation AI suggests a style based on the user's hair type, bone structure, personality, and preferences captured by the camera. The display unit displays the style suggested by the generation AI on the mirror. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to intuitively understand the style that best suits them. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A style suggestion system according to an embodiment of the present invention allows a user to intuitively understand their own appearance in a mirror and to try out multiple styles in real time that suit their hair type, bone structure, personality, and preferences. This allows the user to clearly understand the style that suits them and to find their ideal style by comparing multiple patterns.

[0029] A style suggestion system according to an embodiment includes a mirror, a camera, a generation AI, and a display unit. The mirror reflects a user's image. The camera, for example, captures the user's image reflected in the mirror. The camera can also track the user's movements in real time. The camera can also analyze the user's facial expressions. The generation AI can suggest a style based on the captured user's hair type, bone structure, personality, and preferences. The generation AI can also analyze the user's past styling history to suggest an optimal style. The generation AI can also customize a style based on the user's preferences. The display unit, for example, displays a style suggested by the generation AI on the mirror. The display unit can also update the style in real time according to the user's movements. The display unit can also display multiple styles side by side so the user can compare them. This allows the style suggestion system according to an embodiment to intuitively understand how the user looks in the mirror and try out multiple styles in real time. For example, the user can find the hairstyle that best suits them by trying out styles that suit their hair type and bone structure. Users can also enjoy fashion that is uniquely their own by trying out different styles that suit their individuality and preferences. Furthermore, by comparing multiple styles, users can find their ideal style.

[0030] The display unit can add interactive effects according to the user's movements. For example, when the user moves their hand in front of a mirror, the display unit displays an effect on the screen according to the movement. For example, when the user waves their hand, an effect of fluttering petals is displayed. Furthermore, for example, when the user smiles, the display unit displays an effect of shining stars on the screen. Furthermore, for example, when the user jumps, the display unit displays an effect of spreading ripples on the screen. In this way, adding interactive effects according to the user's movements provides a more intuitive sense of operation.

[0031] The display unit can use augmented reality (AR) technology to display a style the user wants to try in 3D, allowing the user to check it from 360 degrees. For example, the display unit can overlay the style the user wants to try in 3D on the user's image reflected in a mirror. For example, the user can rotate the selected hairstyle 360 ​​degrees to check it. The display unit can also display the style the user wants to try in 3D, allowing the user to check it from different angles. For example, the user can check the style from the front, side, and back perspectives. The display unit can also display the style the user wants to try in 3D, allowing the user to zoom in and out to check the details. For example, the user can zoom in and check the details of the style. This allows the style the user wants to try to be displayed in 3D and checked from 360 degrees.

[0032] The display unit can be linked to a smartphone or tablet, allowing the same functions to be used while away from home. For example, a dedicated app is installed on the smartphone or tablet, and the display unit is linked to the mirror's display system. For example, by launching the app while away from home, the same functions as a mirror at home can be used. The display unit can also be used to try out styles while away from home, for example, using a smartphone or tablet. For example, if a user wants to change their hairstyle while away from home, they can use the app to check the style. The display unit can also be used to compare styles while away from home, for example, using a smartphone or tablet. For example, the user can try out multiple styles while away from home and select the best style. This allows the display unit to be linked to a smartphone or tablet, allowing the same functions to be used while away from home.

[0033] The display unit can be combined with a voice assistant to enable the user to give voice instructions to change or compare styles. The display unit, for example, incorporates a voice assistant in a mirror to enable the user to give voice instructions to change styles. For example, when the user says, "Show me the next hairstyle," the style is changed. The display unit, for example, uses the voice assistant to enable the user to give voice instructions to compare styles. For example, when the user says, "Compare this style with that style," styles are displayed side by side. The display unit, for example, uses the voice assistant to enable the user to check details of styles by voice. For example, when the user says, "Tell me more about this style," detailed information about the style is displayed. In this way, when the display unit is combined with a voice assistant, the user can give voice instructions to change or compare styles.

[0034] Generative AI can refer to background information and topic models to understand the context. For example, when summarizing, generative AI automatically collects relevant background information and refers to it to understand the context. For example, it collects related news articles and academic papers. Generative AI also uses topic models, for example, to understand the context when summarizing. For example, it extracts related keywords and phrases based on the topic model. Generative AI also references relevant background information and topic models when summarizing, and builds a system to understand the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summarization by referring to background information and topic models to understand the context.

[0035] Generative AI can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, generative AI analyzes the logical structure of an answer and generates a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. Generative AI can also analyze the development of arguments in an answer and build a system that generates a logical summary. For example, it can generate a summary based on the importance and relevance of the arguments. Generative AI can also analyze the logical structure of an answer and the development of the arguments to develop an algorithm for generating a logical summary. For example, it can evaluate the logical consistency and the importance of the arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of the arguments.

[0036] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0037] The style suggestion system can also monitor the user's health and make style suggestions based on that health. For example, it can analyze the user's skin condition using a camera and suggest skin care and makeup based on the skin's health. It can also monitor the user's physical condition using sensors and suggest fashion and hairstyles based on that physical condition. It can also track the user's exercise volume and suggest refreshing styles after exercise. This makes it possible to suggest styles based on the user's health, allowing for more personalized services.

[0038] The style suggestion system can also make style suggestions based on the user's lifestyle. For example, it can suggest styles that match the user's occupation or hobbies. For example, it can suggest formal styles suitable for business situations or casual styles suitable for outdoor activities. It can also suggest styles that match specific events or seasons based on the user's schedule. It can also analyze the user's past style history and suggest styles that correspond to changes in lifestyle. This makes it possible to suggest styles based on the user's lifestyle, allowing for the provision of more practical services.

[0039] The style suggestion system can also make style suggestions based on the user's social relationships. For example, it can suggest styles that correspond to the user's relationships with friends and family. For example, it can suggest a casual style suitable for family get-togethers or a glamorous style suitable for a party with friends. It can also suggest styles suitable for business situations based on the user's relationships at work. It can also analyze the user's social media activity and suggest styles that match the latest trends. This makes it possible to suggest styles based on the user's social relationships, thereby providing a more practical service.

[0040] The style suggestion system can also make style suggestions based on the user's cultural background. For example, it can suggest styles that are appropriate for the culture of the user's country or region. For example, it can suggest styles that are suitable for traditional clothing or cultural events. It can also suggest appropriate styles based on the user's religion or beliefs. For example, it can suggest styles that are suitable for specific religious events. It can also customize styles based on the user's language and cultural preferences. This makes it possible to suggest styles based on the user's cultural background, allowing for more personalized services.

[0041] The style suggestion system can also make style suggestions based on the user's environment. For example, it can suggest styles that suit the climate and weather in the user's area. For example, it can suggest clothes made of warm materials to a user living in a cold region, and clothes made of cool materials to a user living in a hot region. It can also suggest styles based on the culture and trends in the user's area. For example, it can suggest styles that match local fashion events and trends. It can also customize styles based on the user's environment. This makes it possible to suggest styles based on the user's environment, thereby providing a more practical service.

[0042] The style suggestion system can also make style suggestions based on the user's hobbies and interests. For example, if the user likes sports, styles suitable for sports can be suggested. For example, sportswear and active styles can be suggested. If the user likes music, styles suitable for music events can be suggested. For example, styles suitable for concerts and festivals can be suggested. Furthermore, if the user is interested in art and design, it is possible to suggest creative styles. For example, styles incorporating unique designs and artistic elements can be suggested. This makes it possible to suggest styles based on the user's hobbies and interests, allowing for the provision of more personalized services.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The mirror reflects the user's image, allowing the user to see their own image. Step 2: The camera captures the user's reflection in the mirror. The camera can also track the user's movements in real time and analyze facial expressions. Step 3: The Generative AI suggests a style based on the user's captured hair type, bone structure, personality, and preferences. In addition, the Generative AI can analyze the user's past styling history to suggest the most suitable style and customize the style based on the user's preferences. Step 4: The display unit displays the style suggested by the generative AI on the mirror. Furthermore, the display unit can update the style in real time according to the user's movements and can also display multiple styles side by side for the user to compare.

[0045] (Example 2) A style suggestion system according to an embodiment of the present invention allows a user to intuitively understand their own appearance in a mirror and to try out multiple styles in real time that suit their hair type, bone structure, personality, and preferences. This allows the user to clearly understand the style that suits them and to find their ideal style by comparing multiple patterns.

[0046] A style suggestion system according to an embodiment includes a mirror, a camera, a generation AI, and a display unit. The mirror reflects a user's image. The camera, for example, captures the user's image reflected in the mirror. The camera can also track the user's movements in real time. The camera can also analyze the user's facial expressions. The generation AI can suggest a style based on the captured user's hair type, bone structure, personality, and preferences. The generation AI can also analyze the user's past styling history to suggest an optimal style. The generation AI can also customize a style based on the user's preferences. The display unit, for example, displays a style suggested by the generation AI on the mirror. The display unit can also update the style in real time according to the user's movements. The display unit can also display multiple styles side by side so the user can compare them. This allows the style suggestion system according to an embodiment to intuitively understand how the user looks in the mirror and try out multiple styles in real time. For example, the user can find the hairstyle that best suits them by trying out styles that suit their hair type and bone structure. Users can also enjoy fashion that is uniquely their own by trying out different styles that suit their individuality and preferences. Furthermore, by comparing multiple styles, users can find their ideal style.

[0047] The display unit can add interactive effects according to the user's movements. For example, when the user moves their hand in front of a mirror, the display unit displays an effect on the screen according to the movement. For example, when the user waves their hand, an effect of fluttering petals is displayed. Furthermore, for example, when the user smiles, the display unit displays an effect of shining stars on the screen. Furthermore, for example, when the user jumps, the display unit displays an effect of spreading ripples on the screen. In this way, adding interactive effects according to the user's movements provides a more intuitive sense of operation.

[0048] The display unit can use augmented reality (AR) technology to display a style the user wants to try in 3D, allowing the user to check it from 360 degrees. For example, the display unit can overlay the style the user wants to try in 3D on the user's image reflected in a mirror. For example, the user can rotate the selected hairstyle 360 ​​degrees to check it. The display unit can also display the style the user wants to try in 3D, allowing the user to check it from different angles. For example, the user can check the style from the front, side, and back perspectives. The display unit can also display the style the user wants to try in 3D, allowing the user to zoom in and out to check the details. For example, the user can zoom in and check the details of the style. This allows the style the user wants to try to be displayed in 3D and checked from 360 degrees.

[0049] The display unit can use the emotion estimation function to analyze the user's facial expression when looking in the mirror and provide feedback in real time to elicit positive emotions. The display unit, for example, uses a camera to analyze the user's facial expression when looking in the mirror, and displays a compliment if a positive expression is detected. For example, it displays "What a lovely smile!". The display unit also analyzes the user's facial expression when looking in the mirror with a camera and displays encouraging words if a negative expression is detected. For example, it displays "Do your best!". The display unit also analyzes the user's facial expression when looking in the mirror with a camera and displays advice if a neutral expression is detected. For example, it displays "Smile a little more." In this way, the display unit analyzes the user's facial expression when looking in the mirror and provides feedback in real time to elicit positive emotions.

[0050] The display unit can be linked to a smartphone or tablet, allowing the same functions to be used while away from home. For example, a dedicated app is installed on the smartphone or tablet, and the display unit is linked to the mirror's display system. For example, by launching the app while away from home, the same functions as a mirror at home can be used. The display unit can also be used to try out styles while away from home, for example, using a smartphone or tablet. For example, if a user wants to change their hairstyle while away from home, they can use the app to check the style. The display unit can also be used to compare styles while away from home, for example, using a smartphone or tablet. For example, the user can try out multiple styles while away from home and select the best style. This allows the display unit to be linked to a smartphone or tablet, allowing the same functions to be used while away from home.

[0051] The display unit can be combined with a voice assistant to enable the user to give voice instructions to change or compare styles. The display unit, for example, incorporates a voice assistant in a mirror to enable the user to give voice instructions to change styles. For example, when the user says, "Show me the next hairstyle," the style is changed. The display unit, for example, uses the voice assistant to enable the user to give voice instructions to compare styles. For example, when the user says, "Compare this style with that style," styles are displayed side by side. The display unit, for example, uses the voice assistant to enable the user to check details of styles by voice. For example, when the user says, "Tell me more about this style," detailed information about the style is displayed. In this way, when the display unit is combined with a voice assistant, the user can give voice instructions to change or compare styles.

[0052] The display unit can use the emotion estimation function to monitor the emotion of the user when looking in the mirror in real time and provide feedback according to the emotion. For example, the display unit captures the user's facial expression when looking in the mirror with a camera and analyzes the emotion in real time using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on changes in facial expression and provides feedback. The display unit also records the user's voice when looking in the mirror and estimates the emotion in real time using voice analysis technology. For example, the display unit analyzes the tone and speed of the voice, calculates an emotion score, and provides feedback. The display unit also collects biometric data (heart rate and electrodermal activity) from the user when looking in the mirror with a sensor and analyzes the emotion in real time using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on fluctuations in heart rate and provides feedback. In this way, the user's emotions can be monitored in real time and appropriate feedback can be provided, thereby improving learning effectiveness.

[0053] Generative AI can refer to background information and topic models to understand the context. For example, when summarizing, generative AI automatically collects relevant background information and refers to it to understand the context. For example, it collects related news articles and academic papers. Generative AI also uses topic models, for example, to understand the context when summarizing. For example, it extracts related keywords and phrases based on the topic model. Generative AI also references relevant background information and topic models when summarizing, and builds a system to understand the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summarization by referring to background information and topic models to understand the context.

[0054] Generative AI can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, generative AI analyzes the logical structure of an answer and generates a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. Generative AI can also analyze the development of arguments in an answer and build a system that generates a logical summary. For example, it can generate a summary based on the importance and relevance of the arguments. Generative AI can also analyze the logical structure of an answer and the development of the arguments to develop an algorithm for generating a logical summary. For example, it can evaluate the logical consistency and the importance of the arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of the arguments.

[0055] The generative AI uses the emotion estimation function to generate summaries that capture the emotional nuances of answers, allowing the emotional elements to be reflected in the evaluation. For example, when summarizing, the generative AI uses the emotion estimation function to capture the emotional nuances of answers. For example, it generates a summary based on an emotion score. The generative AI also uses the emotion estimation function to build a system that reflects the emotional elements of answers in the evaluation. For example, it performs the evaluation based on the emotion score. The generative AI also uses the emotion estimation function to develop an algorithm for generating summaries that capture the emotional nuances of answers. For example, it generates a summary based on the emotion score and reflects it in the evaluation. In this way, by generating a summary that captures the emotional nuances, the emotional elements can also be reflected in the evaluation.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The style suggestion system can also monitor the user's health and make style suggestions based on that health. For example, it can analyze the user's skin condition using a camera and suggest skin care and makeup based on the skin's health. It can also monitor the user's physical condition using sensors and suggest fashion and hairstyles based on that physical condition. It can also track the user's exercise volume and suggest refreshing styles after exercise. This makes it possible to suggest styles based on the user's health, allowing for more personalized services.

[0058] The style suggestion system can also make style suggestions based on the user's lifestyle. For example, it can suggest styles that match the user's occupation or hobbies. For example, it can suggest formal styles suitable for business situations or casual styles suitable for outdoor activities. It can also suggest styles that match specific events or seasons based on the user's schedule. It can also analyze the user's past style history and suggest styles that correspond to changes in lifestyle. This makes it possible to suggest styles based on the user's lifestyle, allowing for the provision of more practical services.

[0059] The style suggestion system can also estimate the user's emotions and suggest styles based on those emotions. For example, if the user is feeling stressed, a relaxing style can be suggested. For example, clothes made of soft materials and colors with a relaxing effect can be suggested. If the user is happy, a bright and colorful style can be suggested. For example, clothes with vivid colors and designs can be suggested. Furthermore, if the user is tired, a comfortable and refreshing style can be suggested. This makes it possible to suggest styles based on the user's emotions, allowing for a more personalized service.

[0060] The style suggestion system can also make style suggestions based on the user's social relationships. For example, it can suggest styles that correspond to the user's relationships with friends and family. For example, it can suggest a casual style suitable for family get-togethers or a glamorous style suitable for a party with friends. It can also suggest styles suitable for business situations based on the user's relationships at work. It can also analyze the user's social media activity and suggest styles that match the latest trends. This makes it possible to suggest styles based on the user's social relationships, thereby providing a more practical service.

[0061] The style suggestion system can also estimate the user's emotions and provide feedback based on those emotions. For example, if the user expresses positive emotions when looking in the mirror, the system can display compliments and encouraging words, such as "Nice style!". If the user expresses negative emotions, the system can also display encouraging words and advice, such as "Try this style too." Furthermore, if the user expresses neutral emotions, the system can display style suggestions and advice. This makes it possible to provide feedback based on the user's emotions and provide a more personalized service.

[0062] The style suggestion system can also make style suggestions based on the user's cultural background. For example, it can suggest styles that are appropriate for the culture of the user's country or region. For example, it can suggest styles that are suitable for traditional clothing or cultural events. It can also suggest appropriate styles based on the user's religion or beliefs. For example, it can suggest styles that are suitable for specific religious events. It can also customize styles based on the user's language and cultural preferences. This makes it possible to suggest styles based on the user's cultural background, allowing for more personalized services.

[0063] The style suggestion system can also estimate the user's emotions and suggest music based on those emotions. For example, if the user wants to relax, music with a relaxing effect can be suggested, such as classical music or nature sounds. If the user wants to cheer up, energetic music can be suggested, such as pop or rock. Furthermore, if the user wants to concentrate, music that improves concentration can be suggested, such as instrumental or ambient music. This makes it possible to suggest music based on the user's emotions, allowing for a more personalized service.

[0064] The style suggestion system can also make style suggestions based on the user's environment. For example, it can suggest styles that suit the climate and weather in the user's area. For example, it can suggest clothes made of warm materials to a user living in a cold region, and clothes made of cool materials to a user living in a hot region. It can also suggest styles based on the culture and trends in the user's area. For example, it can suggest styles that match local fashion events and trends. It can also customize styles based on the user's environment. This makes it possible to suggest styles based on the user's environment, thereby providing a more practical service.

[0065] The style suggestion system can also estimate the user's emotions and suggest makeup based on the emotions. For example, if the user expresses positive emotions, the system can suggest glamorous makeup, such as bright lipstick and eyeshadow. If the user expresses negative emotions, the system can also suggest relaxing makeup, such as makeup with natural colors. Furthermore, if the user expresses neutral emotions, the system can suggest makeup that can be used daily, such as simple and easy-to-use makeup. This makes it possible to suggest makeup based on the user's emotions, thereby providing a more personalized service.

[0066] The style suggestion system can also make style suggestions based on the user's hobbies and interests. For example, if the user likes sports, styles suitable for sports can be suggested. For example, sportswear and active styles can be suggested. If the user likes music, styles suitable for music events can be suggested. For example, styles suitable for concerts and festivals can be suggested. Furthermore, if the user is interested in art and design, it is possible to suggest creative styles. For example, styles incorporating unique designs and artistic elements can be suggested. This makes it possible to suggest styles based on the user's hobbies and interests, allowing for the provision of more personalized services.

[0067] The processing flow of the second embodiment will be briefly explained below.

[0068] Step 1: The mirror reflects the user's image, allowing the user to see their own image. Step 2: The camera captures the user's reflection in the mirror. The camera can also track the user's movements in real time and analyze facial expressions. Step 3: The Generative AI suggests a style based on the user's captured hair type, bone structure, personality, and preferences. In addition, the Generative AI can analyze the user's past styling history to suggest the most suitable style and customize the style based on the user's preferences. Step 4: The display unit displays the style suggested by the generative AI on the mirror. Furthermore, the display unit can update the style in real time according to the user's movements and can also display multiple styles side by side for the user to compare.

[0069] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0070] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0071] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0072] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0073] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0074] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0075] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0076] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0077] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0078] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0079] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0080] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0081] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0082] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0083] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0087] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0088] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0103] 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.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0110] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0119] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0120] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0121] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0122] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0123] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0124] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0125] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0126] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0127] 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.

[0128] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0129] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0130] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0131] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0132] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0133] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0134] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0135] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0136] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A mirror that reflects the user's image, a camera that captures the user's image reflected in the mirror; A generation AI that suggests styles based on the user's hair type, bone structure, personality, and preferences captured by the camera; a display unit that displays the style proposed by the generation AI on the mirror. A system characterized by:

2. The display unit Using augmented reality (AR) technology, the style the user wants to try is displayed in 3D, allowing them to check it from a 360-degree angle.

2. The system of claim 1.

3. The display unit Link with your smartphone or tablet to enjoy the same functionality on the go 2. The system of claim 1.

4. The generated AI is In addition to the user's hair type and bone structure, the system also analyzes skin tone and texture to provide more detailed style suggestions.

2. The system of claim 1.

5. The generated AI is Analyzing the user's social media posts and following accounts to provide style suggestions based on the latest trends and preferences 2. The system of claim 1.

6. The generated AI is Try on styles and view them in different lighting conditions and backgrounds to simulate how they will look in real-world environments 2. The system of claim 1.

7. The generated AI is It provides different perspectives for the styles being compared, allowing for 360-degree comparisons.

2. The system of claim 1.

8. The display unit Analyze the user's facial expressions when looking in the mirror and provide real-time feedback to elicit positive emotions.

2. The system of claim 1.

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