System

Abstract

An object of a system according to an embodiment is to enable a user to specifically imagine a desired hairstyle.SOLUTION: A system includes a face photograph input unit, a hairstyle input unit, and a generation unit. The face photograph input unit inputs a face photograph of a user. A hairstyle input part inputs a desired hairstyle by a sentence or an image. The generation unit generates an image in which the hairstyle is changed based on the information input by the face photograph input unit and the hairstyle input unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult for users to visualize the hairstyle they desire.

[0005] The system according to the embodiment aims to enable a user to specifically imagine a desired hairstyle. [Means for solving the problem]

[0006] The system according to the embodiment includes a face photo input unit, a hairstyle input unit, and a generation unit. The face photo input unit inputs a face photo of a user. The hairstyle input unit inputs a desired hairstyle in the form of text or an image. The generation unit generates an image with a changed hairstyle based on the information input by the face photo input unit and the hairstyle input unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable the user to specifically imagine the hairstyle they want. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The hairstyle simulation system according to the embodiment of the present invention is a system that automatically reads a user's facial photograph and uses a generation AI to generate an image that reflects the user's desired hairstyle. This allows the hairstyle simulation system to simulate the user's desired hairstyle in advance based on the user's facial photograph.

[0029] A hairstyle simulation system according to an embodiment includes a face photo input unit, a hairstyle input unit, and a generation unit. The face photo input unit inputs a face photo of a user. For example, a face photo taken with a smartphone or a digital camera is uploaded. The face photo input unit can also accept image files in JPEG or PNG format. The hairstyle input unit inputs a desired hairstyle using text or an image. For example, a specific instruction such as "I want a short haircut" or "I want long, curly hair" is input. The hairstyle input unit can also upload a reference image of the desired hairstyle. The generation unit generates an image with a changed hairstyle based on the information input by the face photo input unit and the hairstyle input unit. For example, the generation AI generates an image with a changed hairstyle using a text generation AI (e.g., LLM). The generation AI can also generate an image with a changed hairstyle using a multimodal generation AI. The generation AI also generates an image with the desired hairstyle applied to the user's face photo. This allows the hairstyle simulation system to generate an image with a changed hairstyle based on the user's face photo and the desired hairstyle.

[0030] The facial photo input unit can automatically adjust the angle of the face and facial expression to enable optimal hairstyle simulation. For example, when a facial photo is uploaded, the facial photo input unit automatically adjusts the angle of the face and converts it into a photo from the front. For example, a photo taken from an angle is corrected to a frontal view. The facial photo input unit also automatically converts the facial expression in the facial photo to a neutral state. For example, it changes a smiling or surprised expression to a neutral expression. The facial photo input unit also synthesizes multiple photos to generate an optimal facial photo. For example, it combines photos with different angles and expressions to create a neutral frontal facial photo. This automatically adjusts the facial angle and facial expression, enabling more accurate hairstyle simulation.

[0031] The face photo input unit analyzes skin tone and facial contours at the same time as the face photo is input, and can suggest not only hairstyles but also makeup. For example, when the face photo input unit uploads a face photo, the system analyzes skin tone and suggests optimal makeup shades. For example, it displays foundation and lip colors that match the skin tone. The face photo input unit also analyzes facial contours and suggests makeup styles that suit the face shape. For example, it suggests sharp eyeliner for round faces and soft blush for long faces. The face photo input unit also suggests total coordination of hairstyle and makeup based on skin tone and facial contours. For example, it displays eye shadow and blush colors that match the hairstyle. In this way, total coordination of hairstyle and makeup is possible by analyzing skin tone and facial contours.

[0032] The facial photo input unit uses 3D scanning technology to input a three-dimensional facial photo, enabling more realistic hairstyle simulations. The facial photo input unit 3D scans the user's face using, for example, a smartphone camera and generates a three-dimensional facial model. For example, it scans each part of the face in detail to create a 3D model. The facial photo input unit also uses 3D scanning technology to analyze the facial contours and features in detail and reflect this in the hairstyle simulation. For example, it proposes a hairstyle that takes into account the contours and bone structure of the face. The facial photo input unit also realistically reproduces the volume and texture of the hairstyle based on the 3D scan data. For example, it displays the length and curl of the hair in three dimensions. As a result, using 3D scanning technology enables more realistic hairstyle simulations.

[0033] The facial photo input unit can simultaneously input facial photos of family and friends to perform hairstyle simulations for multiple people. For example, the facial photo input unit allows a user to simultaneously upload facial photos of family and friends to perform hairstyle simulations for multiple people. For example, it simulates hairstyles for all family members at once. The facial photo input unit also suggests hairstyle coordination for the entire group based on facial photos of multiple people. For example, it suggests hairstyles that suit everyone based on family photos or photos of a group of friends. The facial photo input unit also analyzes facial photos of family and friends to individually suggest the best hairstyle for each person. For example, it suggests a short haircut for the mother and long hair for the children. This makes it possible to simulate hairstyles for multiple people by simultaneously inputting facial photos of family and friends.

[0034] The hairstyle input unit can refer to the user's past hairstyle history and suggest the most suitable hairstyle. For example, the hairstyle input unit stores the user's past hairstyle history in a database and refers to it when inputting a desired hairstyle. For example, suggestions are made based on images and styles of hairstyles tried in the past. The hairstyle input unit also analyzes the past hairstyle history and suggests a hairstyle that suits the user best. For example, hairstyles that were popular in the past are displayed preferentially. The hairstyle input unit also suggests hairstyles that reflect current trends and fashions based on the user's hairstyle history. For example, it suggests a combination of a past hairstyle and the latest style. In this way, the most suitable hairstyle can be suggested to the user by referring to the past hairstyle history.

[0035] The hairstyle input unit can suggest an optimal hairstyle by taking into consideration the user's lifestyle and occupation. The hairstyle input unit, for example, inputs information about the user's lifestyle and occupation and suggests an optimal hairstyle based on that information. For example, it suggests a hairstyle suitable for business situations or a hairstyle that suits an active lifestyle. The hairstyle input unit also considers the user's occupation and daily activities and suggests a hairstyle that is easy to maintain. For example, it suggests a hairstyle that is easy to maintain for someone with a busy job. The hairstyle input unit also considers the user's schedule and hobbies in order to suggest a hairstyle that suits the user's lifestyle and occupation. For example, it suggests a hairstyle that is easy to move in for someone who plays sports. In this way, a more suitable hairstyle can be suggested by taking into consideration the user's lifestyle and occupation.

[0036] The hairstyle input unit can consider the user's hair type and hair health condition to suggest the most suitable hairstyle. The hairstyle input unit, for example, inputs the user's hair type and hair health condition and suggests the most suitable hairstyle based on that. For example, it suggests a voluminous hairstyle for someone with thin hair. The hairstyle input unit also analyzes the hair type and hair health condition to suggest a hairstyle that minimizes damage. For example, it suggests a style that reduces damage for someone whose hair is prone to damage. The hairstyle input unit also considers the user's hair type and hair health condition to suggest a style that is easy to maintain. For example, it suggests a style that has a moisturizing effect for someone whose hair is prone to dryness. In this way, the most suitable hairstyle can be suggested by considering the hair type and hair health condition.

[0037] The generation unit can analyze the user's facial features in detail and generate the optimal hairstyle. For example, the generation AI analyzes the user's facial features in detail and generates the optimal hairstyle. For example, it suggests a hairstyle that suits the shape and contours of the face. The generation unit also detects facial feature points and generates a hairstyle based on them. For example, it suggests a hairstyle that matches the width and height of the face. The generation unit also realistically reproduces the volume and texture of the hairstyle based on the user's facial features. For example, it displays the length and curl of the hair in detail. This allows the optimal hairstyle to be generated by analyzing the facial features in detail.

[0038] The generation unit can automatically analyze the balance between the user's facial photo and hairstyle and generate the optimal hairstyle. For example, the generation unit uses a generation AI to automatically analyze the balance of the hairstyle based on the user's facial photo and generate the optimal hairstyle. For example, it displays a hairstyle that suits the shape and contours of the face. The generation unit also detects facial feature points to analyze the balance between the facial photo and hairstyle and generates a hairstyle based on that. For example, it suggests a hairstyle that matches the width and height of the face. The generation unit also analyzes the balance between the user's facial photo and desired hairstyle and builds a system that generates the optimal hairstyle. For example, it displays a hairstyle that suits the proportions of the face. This makes it possible to generate the optimal hairstyle by analyzing the balance between the facial photo and hairstyle.

[0039] The generation unit can generate the optimal hairstyle by taking into account the user's hair type and hair health. For example, the generation AI analyzes the user's hair type and hair health and generates the optimal hairstyle based on that. For example, it suggests a hairstyle with volume for someone with thin hair. The generation unit also analyzes hair type and hair health and generates a hairstyle that minimizes damage. For example, it suggests a style that reduces damage for someone whose hair is prone to damage. The generation unit also takes into account the user's hair type and hair health and generates a style that is easy to maintain. For example, it suggests a style that has a moisturizing effect for someone whose hair is prone to dryness. In this way, the optimal hairstyle can be generated by taking hair type and hair health into account.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The hairstyle simulation system can also analyze the user's hair growth pattern and predict future hairstyles. For example, it can predict hair length and volume several months from now based on past hairstyle history and hair growth rate, and suggest hairstyles based on that. It can also suggest a future hairstyle maintenance schedule by analyzing hair growth patterns. For example, it can display the timing of regular haircuts and treatments. Furthermore, it can simulate future hairstyle changes based on hair growth patterns and visually present them to the user. This allows users to check their future hairstyle in advance and change their hairstyle in a planned manner.

[0042] The hairstyle simulation system can also analyze the health of the user's scalp and suggest the optimal hairstyle. For example, it can analyze the scalp's dryness and oil balance and suggest a hairstyle based on that. Analyzing the scalp's health can also suggest hairstyles that don't put stress on the scalp. For example, it can suggest scalp-friendly styles for people with sensitive scalps. It can also provide scalp care advice based on the scalp's health. For example, it can recommend the use of shampoos and treatments to prevent scalp dryness. This allows it to suggest a more suitable hairstyle by taking the scalp's health into consideration.

[0043] The hairstyle simulation system can also analyze the user's fashion style and suggest a total coordination of hairstyle and fashion. For example, it can analyze the user's everyday clothing and accessory style and suggest a hairstyle that matches them. By analyzing the fashion style, it can also suggest a hairstyle that suits a specific event or season. For example, it can suggest a hairstyle that is suitable for a wedding or party. It can also suggest a combination of hairstyle and accessories based on the fashion style. For example, it can display a hairstyle that matches specific earrings or necklace. This makes it possible to create a more consistent total coordination by taking the fashion style into consideration.

[0044] The hairstyle simulation system can also analyze the user's hair damage level and suggest the most suitable hairstyle. For example, if the hair is damaged, it will suggest a style that hides the damage. By analyzing the hair damage level, it can also suggest a hairstyle that minimizes damage. For example, for someone whose hair is prone to damage, it will suggest a style that reduces damage. It can also suggest hairstyle maintenance methods based on the hair damage level. For example, it will display treatments and care methods to repair damage. In this way, it can suggest a more suitable hairstyle by taking the hair damage level into consideration.

[0045] The hairstyle simulation system can also analyze the thickness and density of the user's hair to suggest the most suitable hairstyle. For example, for someone with thin hair, it can suggest a style that adds volume. By analyzing hair thickness and density, it can also suggest hairstyles that make the most of the hair's characteristics. For example, for someone with thick hair, it can suggest a light style that doesn't feel heavy. It can also suggest hairstyle maintenance methods based on hair thickness and density. For example, for someone with thin hair, it can display care methods to maintain volume. In this way, it can suggest a more suitable hairstyle by taking hair thickness and density into consideration.

[0046] The hairstyle simulation system can also analyze the user's hair pigmentation and suggest the most suitable hairstyle. For example, for someone with strong hair pigmentation, it can suggest a style that makes the most of the pigment. Also, by analyzing hair pigmentation, it can suggest a hairstyle that makes the most of the hair color. For example, for someone with light hair pigmentation, it can suggest a style with a light color. Furthermore, it can suggest hairstyle maintenance methods based on hair pigmentation. For example, it can display care methods to maintain the pigment. In this way, it can suggest a more suitable hairstyle by taking hair pigmentation into consideration.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The face photo input unit inputs a face photo of the user. For example, a face photo taken with a smartphone or digital camera is uploaded. The face photo input unit can also accept image files in JPEG or PNG format. Step 2: In the hairstyle input section, you can enter your desired hairstyle using text or an image. For example, you can enter specific instructions such as "I want a short haircut" or "I want long hair with curls." You can also upload a reference image of your desired hairstyle. Step 3: The generation unit generates an image with a changed hairstyle based on the information input by the face photo input unit and the hairstyle input unit. For example, the generation AI generates an image with a changed hairstyle using a text generation AI (e.g., LLM). The generation AI can also generate an image with a changed hairstyle using a multimodal generation AI. The generation AI also generates an image with the desired hairstyle applied to the user's face photo.

[0049] (Example 2) The hairstyle simulation system according to the embodiment of the present invention is a system that automatically reads a user's facial photograph and uses a generation AI to generate an image that reflects the user's desired hairstyle. This allows the hairstyle simulation system to simulate the user's desired hairstyle in advance based on the user's facial photograph.

[0050] A hairstyle simulation system according to an embodiment includes a face photo input unit, a hairstyle input unit, and a generation unit. The face photo input unit inputs a face photo of a user. For example, a face photo taken with a smartphone or a digital camera is uploaded. The face photo input unit can also accept image files in JPEG or PNG format. The hairstyle input unit inputs a desired hairstyle using text or an image. For example, a specific instruction such as "I want a short haircut" or "I want long, curly hair" is input. The hairstyle input unit can also upload a reference image of the desired hairstyle. The generation unit generates an image with a changed hairstyle based on the information input by the face photo input unit and the hairstyle input unit. For example, the generation AI generates an image with a changed hairstyle using a text generation AI (e.g., LLM). The generation AI can also generate an image with a changed hairstyle using a multimodal generation AI. The generation AI also generates an image with the desired hairstyle applied to the user's face photo. This allows the hairstyle simulation system to generate an image with a changed hairstyle based on the user's face photo and the desired hairstyle.

[0051] The facial photo input unit can automatically adjust the angle of the face and facial expression to enable optimal hairstyle simulation. For example, when a facial photo is uploaded, the facial photo input unit automatically adjusts the angle of the face and converts it into a photo from the front. For example, a photo taken from an angle is corrected to a frontal view. The facial photo input unit also automatically converts the facial expression in the facial photo to a neutral state. For example, it changes a smiling or surprised expression to a neutral expression. The facial photo input unit also synthesizes multiple photos to generate an optimal facial photo. For example, it combines photos with different angles and expressions to create a neutral frontal facial photo. This automatically adjusts the facial angle and facial expression, enabling more accurate hairstyle simulation.

[0052] The face photo input unit analyzes skin tone and facial contours at the same time as the face photo is input, and can suggest not only hairstyles but also makeup. For example, when the face photo input unit uploads a face photo, the system analyzes skin tone and suggests optimal makeup shades. For example, it displays foundation and lip colors that match the skin tone. The face photo input unit also analyzes facial contours and suggests makeup styles that suit the face shape. For example, it suggests sharp eyeliner for round faces and soft blush for long faces. The face photo input unit also suggests total coordination of hairstyle and makeup based on skin tone and facial contours. For example, it displays eye shadow and blush colors that match the hairstyle. In this way, total coordination of hairstyle and makeup is possible by analyzing skin tone and facial contours.

[0053] The facial photo input unit can use the emotion estimation function to analyze the emotions of a user when uploading a photo and provide feedback to elicit positive emotions. For example, when uploading a facial photo, the facial photo input unit analyzes the user's facial expression and calculates an emotion score. For example, if the smile score is high, the system provides feedback such as "What a lovely smile." The facial photo input unit also uses the emotion estimation function to display a positive message if the user has negative emotions. For example, it displays "Please relax while taking the photo." The facial photo input unit also provides advice to elicit positive emotions based on the user's emotion score. For example, it displays specific instructions such as "Please smile a little more." This allows for better photography by analyzing the user's emotions and providing feedback to elicit positive emotions.

[0054] The facial photo input unit uses 3D scanning technology to input a three-dimensional facial photo, enabling more realistic hairstyle simulations. The facial photo input unit 3D scans the user's face using, for example, a smartphone camera and generates a three-dimensional facial model. For example, it scans each part of the face in detail to create a 3D model. The facial photo input unit also uses 3D scanning technology to analyze the facial contours and features in detail and reflect this in the hairstyle simulation. For example, it proposes a hairstyle that takes into account the contours and bone structure of the face. The facial photo input unit also realistically reproduces the volume and texture of the hairstyle based on the 3D scan data. For example, it displays the length and curl of the hair in three dimensions. As a result, using 3D scanning technology enables more realistic hairstyle simulations.

[0055] The facial photo input unit can simultaneously input facial photos of family and friends to perform hairstyle simulations for multiple people. For example, the facial photo input unit allows a user to simultaneously upload facial photos of family and friends to perform hairstyle simulations for multiple people. For example, it simulates hairstyles for all family members at once. The facial photo input unit also suggests hairstyle coordination for the entire group based on facial photos of multiple people. For example, it suggests hairstyles that suit everyone based on family photos or photos of a group of friends. The facial photo input unit also analyzes facial photos of family and friends to individually suggest the best hairstyle for each person. For example, it suggests a short haircut for the mother and long hair for the children. This makes it possible to simulate hairstyles for multiple people by simultaneously inputting facial photos of family and friends.

[0056] The facial photo input unit uses an emotion estimation function to display the user's emotion in real time when uploading a photo, encouraging the user to take a photo at the optimal timing. For example, when uploading a facial photo, the facial photo input unit analyzes the user's emotion in real time and displays an emotion score. For example, if the smile score is high, the system displays "Now's your chance to take a photo." The facial photo input unit also uses the emotion estimation function to detect the moment when the user is most relaxed and encourages the user to take a photo at that moment. For example, it displays "Take a deep breath and relax." The facial photo input unit also advises the user on the optimal timing for taking a photo based on the user's emotion score. For example, it displays specific instructions such as "Smile a little more." In this way, by displaying the user's emotion in real time, it becomes possible to take a photo at the optimal timing.

[0057] The hairstyle input unit can refer to the user's past hairstyle history and suggest the most suitable hairstyle. For example, the hairstyle input unit stores the user's past hairstyle history in a database and refers to it when inputting a desired hairstyle. For example, suggestions are made based on images and styles of hairstyles tried in the past. The hairstyle input unit also analyzes the past hairstyle history and suggests a hairstyle that suits the user best. For example, hairstyles that were popular in the past are displayed preferentially. The hairstyle input unit also suggests hairstyles that reflect current trends and fashions based on the user's hairstyle history. For example, it suggests a combination of a past hairstyle and the latest style. In this way, the most suitable hairstyle can be suggested to the user by referring to the past hairstyle history.

[0058] The hairstyle input unit can suggest an optimal hairstyle by taking into consideration the user's lifestyle and occupation. The hairstyle input unit, for example, inputs information about the user's lifestyle and occupation and suggests an optimal hairstyle based on that information. For example, it suggests a hairstyle suitable for business situations or a hairstyle that suits an active lifestyle. The hairstyle input unit also considers the user's occupation and daily activities and suggests a hairstyle that is easy to maintain. For example, it suggests a hairstyle that is easy to maintain for someone with a busy job. The hairstyle input unit also considers the user's schedule and hobbies in order to suggest a hairstyle that suits the user's lifestyle and occupation. For example, it suggests a hairstyle that is easy to move in for someone who plays sports. In this way, a more suitable hairstyle can be suggested by taking into consideration the user's lifestyle and occupation.

[0059] The hairstyle input unit uses the emotion estimation function to analyze the emotion of the user when inputting the desired hairstyle and can suggest a hairstyle that elicits positive emotions. For example, when the desired hairstyle is input, the hairstyle input unit analyzes the user's emotion and suggests a hairstyle that elicits positive emotions. For example, hairstyles with a high emotion score are preferentially displayed. Furthermore, the hairstyle input unit uses the emotion estimation function to display a positive message and suggest a hairstyle when the user has negative emotions. For example, it displays "This hairstyle is perfect for you." Furthermore, the hairstyle input unit suggests a hairstyle that elicits positive emotions based on the user's emotion score. For example, it displays a specific message such as "This hairstyle will enhance your charm." This makes it possible to analyze the user's emotion and suggest a hairstyle that elicits positive emotions.

[0060] The hairstyle input unit can consider the user's hair type and hair health condition to suggest the most suitable hairstyle. The hairstyle input unit, for example, inputs the user's hair type and hair health condition and suggests the most suitable hairstyle based on that. For example, it suggests a voluminous hairstyle for someone with thin hair. The hairstyle input unit also analyzes the hair type and hair health condition to suggest a hairstyle that minimizes damage. For example, it suggests a style that reduces damage for someone whose hair is prone to damage. The hairstyle input unit also considers the user's hair type and hair health condition to suggest a style that is easy to maintain. For example, it suggests a style that has a moisturizing effect for someone whose hair is prone to dryness. In this way, the most suitable hairstyle can be suggested by considering the hair type and hair health condition.

[0061] The hairstyle input unit uses the emotion estimation function to display the user's emotion in real time when inputting the desired hairstyle, and can suggest the most suitable hairstyle. For example, when the desired hairstyle is input, the hairstyle input unit analyzes the user's emotion in real time and displays an emotion score. For example, hairstyles with a high emotion score are preferentially displayed. The hairstyle input unit also uses the emotion estimation function to suggest the hairstyle that the user feels most positive about. For example, it displays "This hairstyle is perfect for you." The hairstyle input unit also builds a system that suggests the most suitable hairstyle based on the user's emotion score. For example, hairstyles with a high emotion score are preferentially displayed. In this way, the most suitable hairstyle can be suggested by displaying the user's emotion in real time.

[0062] The generation unit can analyze the user's facial features in detail and generate the optimal hairstyle. For example, the generation AI analyzes the user's facial features in detail and generates the optimal hairstyle. For example, it suggests a hairstyle that suits the shape and contours of the face. The generation unit also detects facial feature points and generates a hairstyle based on them. For example, it suggests a hairstyle that matches the width and height of the face. The generation unit also realistically reproduces the volume and texture of the hairstyle based on the user's facial features. For example, it displays the length and curl of the hair in detail. This allows the optimal hairstyle to be generated by analyzing the facial features in detail.

[0063] The generation unit uses the emotion estimation function to analyze the emotions of the user when generating the desired hairstyle, and can generate a hairstyle that elicits positive emotions. For example, when the generation AI analyzes a hairstyle, the generation unit analyzes the user's emotions in real time and generates a hairstyle that elicits positive emotions. For example, hairstyles with high emotion scores are preferentially displayed. The generation unit also uses the emotion estimation function to display a positive message and generate a hairstyle when the user has negative emotions. For example, it displays "This hairstyle is perfect for you." The generation unit also generates a hairstyle that elicits positive emotions based on the user's emotion score. For example, it displays a specific message such as "This hairstyle will enhance your charm." This makes it possible to analyze the user's emotions and generate a hairstyle that elicits positive emotions.

[0064] The generation unit can automatically analyze the balance between the user's facial photo and hairstyle and generate the optimal hairstyle. For example, the generation unit uses a generation AI to automatically analyze the balance of the hairstyle based on the user's facial photo and generate the optimal hairstyle. For example, it displays a hairstyle that suits the shape and contours of the face. The generation unit also detects facial feature points to analyze the balance between the facial photo and hairstyle and generates a hairstyle based on that. For example, it suggests a hairstyle that matches the width and height of the face. The generation unit also analyzes the balance between the user's facial photo and desired hairstyle and builds a system that generates the optimal hairstyle. For example, it displays a hairstyle that suits the proportions of the face. This makes it possible to generate the optimal hairstyle by analyzing the balance between the facial photo and hairstyle.

[0065] The generation unit can generate the optimal hairstyle by taking into account the user's hair type and hair health. For example, the generation AI analyzes the user's hair type and hair health and generates the optimal hairstyle based on that. For example, it suggests a hairstyle with volume for someone with thin hair. The generation unit also analyzes hair type and hair health and generates a hairstyle that minimizes damage. For example, it suggests a style that reduces damage for someone whose hair is prone to damage. The generation unit also takes into account the user's hair type and hair health and generates a style that is easy to maintain. For example, it suggests a style that has a moisturizing effect for someone whose hair is prone to dryness. In this way, the optimal hairstyle can be generated by taking hair type and hair health into account.

[0066] The generation unit uses the emotion estimation function to display the user's emotions in real time when generating the desired hairstyle, thereby generating the optimal hairstyle. For example, when the generation AI analyzes a hairstyle, the generation unit analyzes the user's emotions in real time and displays an emotion score. For example, hairstyles with a high emotion score are preferentially displayed. The generation unit also uses the emotion estimation function to generate a hairstyle that the user feels most positively about. For example, it displays "This hairstyle is perfect for you." The generation unit also builds a system that generates the optimal hairstyle based on the user's emotion score. For example, it displays hairstyles with a high emotion score preferentially. In this way, the optimal hairstyle can be generated by displaying the user's emotions in real time.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The hairstyle simulation system can also analyze the user's hair growth pattern and predict future hairstyles. For example, it can predict hair length and volume several months from now based on past hairstyle history and hair growth rate, and suggest hairstyles based on that. It can also suggest a future hairstyle maintenance schedule by analyzing hair growth patterns. For example, it can display the timing of regular haircuts and treatments. Furthermore, it can simulate future hairstyle changes based on hair growth patterns and visually present them to the user. This allows users to check their future hairstyle in advance and change their hairstyle in a planned manner.

[0069] The hairstyle simulation system can also analyze the health of the user's scalp and suggest the optimal hairstyle. For example, it can analyze the scalp's dryness and oil balance and suggest a hairstyle based on that. Analyzing the scalp's health can also suggest hairstyles that don't put stress on the scalp. For example, it can suggest scalp-friendly styles for people with sensitive scalps. It can also provide scalp care advice based on the scalp's health. For example, it can recommend the use of shampoos and treatments to prevent scalp dryness. This allows it to suggest a more suitable hairstyle by taking the scalp's health into consideration.

[0070] The hairstyle simulation system can also analyze the user's fashion style and suggest a total coordination of hairstyle and fashion. For example, it can analyze the user's everyday clothing and accessory style and suggest a hairstyle that matches them. By analyzing the fashion style, it can also suggest a hairstyle that suits a specific event or season. For example, it can suggest a hairstyle that is suitable for a wedding or party. It can also suggest a combination of hairstyle and accessories based on the fashion style. For example, it can display a hairstyle that matches specific earrings or necklace. This makes it possible to create a more consistent total coordination by taking the fashion style into consideration.

[0071] The hairstyle simulation system can further estimate the user's emotions and suggest hairstyle colors based on the estimated emotions. For example, if the user is feeling positive, a bright-colored hairstyle can be suggested. Also, if the user is relaxed, a hairstyle with a natural color can be suggested. Furthermore, using the emotion estimation function, if the user is feeling stressed, it can suggest colors that have a relaxing effect. For example, calming colors such as blue or green can be displayed. This allows for a more satisfying simulation by suggesting hairstyle colors that correspond to the user's emotions.

[0072] The hairstyle simulation system can also estimate the user's emotions and suggest hairstyles based on the estimated emotions. For example, if the user is feeling positive, a glamorous style can be suggested. On the other hand, if the user is relaxed, a simple, subdued style can be suggested. Furthermore, using the emotion estimation function, if the user is feeling stressed, a style with a relaxing effect can be suggested. For example, gentle curls or natural straight hair can be displayed. This allows for a more satisfying simulation by suggesting hairstyles that match the user's emotions.

[0073] The hairstyle simulation system can also estimate the user's emotions and suggest hairstyle lengths based on the estimated emotions. For example, if the user is feeling positive, a longer hairstyle can be suggested. Also, if the user is relaxed, a shorter hairstyle can be suggested. Furthermore, using the emotion estimation function, if the user is feeling stressed, it can suggest a hairstyle that is easy to maintain. For example, medium length or short cut hairstyles can be displayed. This allows for a more satisfying simulation by suggesting hairstyle lengths according to the user's emotions.

[0074] The hairstyle simulation system can also estimate the user's emotions and suggest hairstyle textures based on the estimated emotions. For example, if the user is feeling positive, a voluminous texture can be suggested. Also, if the user is relaxed, a natural texture can be suggested. Furthermore, using the emotion estimation function, it is possible to suggest textures that are easy to maintain if the user is feeling stressed. For example, straight or lightly wavy hairstyles can be displayed. This allows for a more satisfying simulation by suggesting hairstyle textures that correspond to the user's emotions.

[0075] The hairstyle simulation system can also analyze the user's hair damage level and suggest the most suitable hairstyle. For example, if the hair is damaged, it will suggest a style that hides the damage. By analyzing the hair damage level, it can also suggest a hairstyle that minimizes damage. For example, for someone whose hair is prone to damage, it will suggest a style that reduces damage. It can also suggest hairstyle maintenance methods based on the hair damage level. For example, it will display treatments and care methods to repair damage. In this way, it can suggest a more suitable hairstyle by taking the hair damage level into consideration.

[0076] The hairstyle simulation system can also analyze the thickness and density of the user's hair to suggest the most suitable hairstyle. For example, for someone with thin hair, it can suggest a style that adds volume. By analyzing hair thickness and density, it can also suggest hairstyles that make the most of the hair's characteristics. For example, for someone with thick hair, it can suggest a light style that doesn't feel heavy. It can also suggest hairstyle maintenance methods based on hair thickness and density. For example, for someone with thin hair, it can display care methods to maintain volume. In this way, it can suggest a more suitable hairstyle by taking hair thickness and density into consideration.

[0077] The hairstyle simulation system can also analyze the user's hair pigmentation and suggest the most suitable hairstyle. For example, for someone with strong hair pigmentation, it can suggest a style that makes the most of the pigment. Also, by analyzing hair pigmentation, it can suggest a hairstyle that makes the most of the hair color. For example, for someone with light hair pigmentation, it can suggest a style with a light color. Furthermore, it can suggest hairstyle maintenance methods based on hair pigmentation. For example, it can display care methods to maintain the pigment. In this way, it can suggest a more suitable hairstyle by taking hair pigmentation into consideration.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The face photo input unit inputs a face photo of the user. For example, a face photo taken with a smartphone or digital camera is uploaded. The face photo input unit can also accept image files in JPEG or PNG format. Step 2: In the hairstyle input section, you can enter your desired hairstyle using text or an image. For example, you can enter specific instructions such as "I want a short haircut" or "I want long hair with curls." You can also upload a reference image of your desired hairstyle. Step 3: The generation unit generates an image with a changed hairstyle based on the information input by the face photo input unit and the hairstyle input unit. For example, the generation AI generates an image with a changed hairstyle using a text generation AI (e.g., LLM). The generation AI can also generate an image with a changed hairstyle using a multimodal generation AI. The generation AI also generates an image with the desired hairstyle applied to the user's face photo.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0093] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0146] 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]

[0147] 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 face photo input unit for inputting a face photo of a user; a hairstyle input section for inputting a desired hairstyle in text or an image; a generation unit that generates an image with a changed hairstyle based on the information input by the face photo input unit and the hairstyle input unit. A system characterized by:

2. The face photo input unit Automatically adjusts facial angle and facial expression to simulate optimal hairstyles 2. The system of claim 1.

3. The face photo input unit When a face photo is input, the system analyzes the skin tone and facial contours, and suggests not only hairstyles but also makeup.

2. The system of claim 1.

4. The face photo input unit Analyze the emotions users feel when uploading photos and provide feedback to elicit positive emotions 2. The system of claim 1.

5. The face photo input unit 3D scanning technology is used to input facial photos in three dimensions, enabling more realistic hairstyle simulations.

2. The system of claim 1.

6. The face photo input unit Simultaneously input photos of family and friends to simulate hairstyles for multiple people.

2. The system of claim 1.

7. The face photo input unit Displays users' emotions in real time as they upload photos, encouraging them to take photos at the optimal moment 2. The system of claim 1.

8. The hairstyle input unit Refers to the user's past hairstyle history and suggests the best hairstyle 2. The system of claim 1.

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