System

Abstract

To provide a system according to an embodiment, the system intending to analyze face information of a customer and propose an optimum hairstyle and makeup.SOLUTION: A system according to an embodiment includes: an acquisition part; a face recognition part; a determination part; and a proposal part. The acquisition part acquires face information of a customer. The face recognition part recognizes a shape and characteristics of the face of the customer on the basis of the face information acquired by the acquisition part. The determination part determines a hairstyle and makeup suitable for the customer on the basis of the recognition results by the face recognition part. The proposal part proposes the determination result by the determination part to the customer.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately analyze facial information to suggest optimal hairstyles and makeup for customers, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze facial information of a customer and propose optimal hairstyles and makeup. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a face recognition unit, a determination unit, and a suggestion unit. The acquisition unit acquires face information of a customer. The face recognition unit recognizes the shape and features of the customer's face based on the face information acquired by the acquisition unit. The determination unit determines a hairstyle and makeup suitable for the customer based on the recognition result by the face recognition unit. The suggestion unit suggests the determination result by the determination unit to the customer. Effect of the Invention

[0007] The system according to the embodiment can analyze facial information of a customer and suggest optimal hairstyles and makeup. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A customization simulation system according to an embodiment of the present invention is a system that acquires face information of a customer, recognizes the shape and features of the customer's face using face recognition technology, and proposes a hairstyle and makeup that suits the customer based on the recognition result. This system acquires face information of a customer, recognizes the shape and features of the face, and makes a proposal. The proposal is made using AR display, and a simulation is performed on a display device such as a tablet or a smart mirror. Furthermore, simulation under different lighting conditions is also possible. The customer's evaluation of the proposal is estimated based on the customer's facial expression, and a positive evaluation or a negative evaluation is determined. Based on the estimation result, a hairstyle and makeup that suits the customer are adjusted. For example, the face information of a customer is acquired based on the output from multiple cameras. The shape and features of the customer's face can be captured in detail using a camera from the front and a camera from the side. Next, the shape and features of the customer's face are recognized based on the acquired face information. Using face recognition technology, the customer's facial contour and features such as the eyes, nose, and mouth are analyzed. For example, it is possible to recognize whether the customer's face is round or long. Based on the recognition result, a hairstyle and makeup that suits the customer is determined. For example, for a customer with a round face, a hairstyle and makeup that will have a slimming effect can be suggested. The suggestions are made using AR display. A simulation of a hairstyle and makeup that suits the customer is displayed on a display device such as a tablet or smart mirror. For example, if the customer has a tablet, their face is displayed on the tablet screen, and the suggested hairstyle and makeup are reflected in real time. In addition, simulations under different lighting conditions are also possible. For example, it is possible to simulate how the hairstyle and makeup will look under different lighting conditions, such as natural light in the daytime and indoor lighting at night. The customer's evaluation of the suggestions is estimated based on the customer's facial expression. For example, if the customer is smiling, it is judged to be a positive evaluation, and if the customer has a dissatisfied expression, it is judged to be a negative evaluation. Based on the estimation result, the hairstyle and makeup that suits the customer are adjusted.For example, if there are many negative reviews, the system can improve customer satisfaction by suggesting a different hairstyle or makeup. This allows the customization simulation system to suggest the optimal hairstyle and makeup based on the customer's facial information.

[0029] The customization simulation system according to the embodiment includes an acquisition unit, a face recognition unit, a determination unit, and a suggestion unit. The acquisition unit acquires face information of a customer. The face information of the customer includes, for example, the contour of the face, and the positions and shapes of the eyes, nose, and mouth, but is not limited to such examples. The acquisition unit acquires face information of the customer using, for example, multiple cameras. For example, a camera from the front and a camera from the side can be used to capture the shape and features of the customer's face in detail. The face recognition unit recognizes the shape and features of the customer's face based on the face information acquired by the acquisition unit. The face recognition unit analyzes the contour of the customer's face and features such as the eyes, nose, and mouth using, for example, face recognition technology. For example, it can recognize whether the customer's face is round or long. The determination unit determines a hairstyle and makeup that suits the customer based on the recognition result by the face recognition unit. For example, the determination unit can suggest a hairstyle and makeup that has the effect of tightening the face to a customer with a round face. The suggestion unit suggests the determination result by the determination unit to the customer. The suggestion unit uses, for example, AR display to simulate and display hairstyles and makeup that suit the customer on a display device such as a tablet or smart mirror. For example, if the customer has a tablet, the customer's face is displayed on the tablet screen, and the suggested hairstyle and makeup are reflected in real time. This allows the customization simulation system to suggest the optimal hairstyle and makeup based on the customer's facial information.

[0030] The acquisition unit acquires the customer's facial information. The customer's facial information includes, for example, the contour of the face, the position and shape of the eyes, nose, and mouth, but is not limited to such examples. The acquisition unit acquires the customer's facial information using, for example, multiple cameras. Specifically, the shape and features of the customer's face can be captured in detail using a camera from the front and a camera from the side. This allows the acquisition unit to accurately acquire three-dimensional information of the customer's face. Furthermore, the acquisition unit can acquire more accurate facial information by adjusting the resolution and frame rate of the camera. For example, by using a high-resolution camera, fine facial features and skin texture can be captured in detail. Furthermore, the acquisition unit can use an appropriate lighting device and background screen to minimize the influence of lighting conditions and background. This allows the acquisition unit to acquire high-quality facial information in a stable environment. Furthermore, the acquisition unit can use a face detection algorithm to automate the process of acquiring facial information. For example, the camera can automatically detect the customer's face and acquire facial information at an optimal angle and distance. This allows the acquisition unit to efficiently and accurately acquire customer's facial information and improve the performance of the entire system.

[0031] The face recognition unit recognizes the shape and features of the customer's face based on the face information acquired by the acquisition unit. The face recognition unit, for example, uses face recognition technology to analyze the customer's facial contour and features such as the eyes, nose, and mouth. Specifically, the face recognition unit can utilize image analysis technology using deep learning to recognize each part of the customer's face with high accuracy. For example, the face recognition unit can recognize whether the customer's face is round or long. In addition, the face recognition unit can analyze detailed features such as the size and position of the eyes, the shape of the nose, and the width of the mouth to evaluate the overall balance of the customer's face. Furthermore, the face recognition unit can use past data and statistical information to more accurately recognize the features of the customer's face. For example, the recognition accuracy for customers with specific facial features can be improved based on a large amount of face information acquired in the past. As a result, the face recognition unit can recognize the shape and features of the customer's face with high accuracy and provide information necessary for the processing of the determination unit, which is the next step.

[0032] The determination unit determines a hairstyle and makeup that suit the customer based on the recognition result by the face recognition unit. For example, the determination unit can suggest hairstyles and makeup that have the effect of tightening the face to a customer with a round face. Specifically, the determination unit selects an optimal hairstyle and makeup pattern based on the shape and features of the face provided by the face recognition unit. For example, a layered cut that makes the contours of the face look sharp and eye makeup that emphasizes the eyes can be suggested to a customer with a round face. In addition, a voluminous hairstyle that makes the length of the face look balanced and makeup using blush can be suggested to a customer with a long face. Furthermore, the determination unit can customize according to the customer's preferences and lifestyle. For example, if a customer usually prefers natural makeup, makeup with a natural finish can be suggested. In this way, the determination unit can determine the optimal hairstyle and makeup according to the facial features and individual needs of the customer and provide it to the suggestion unit.

[0033] The suggestion unit proposes the judgment result by the judgment unit to the customer. The suggestion unit, for example, uses AR display to simulate and display hairstyles and makeup that suit the customer on a display device such as a tablet or a smart mirror. Specifically, the suggestion unit reflects the hairstyle and makeup information provided by the judgment unit on the customer's face in real time. For example, if the customer has a tablet, the customer's face is displayed on the tablet screen, and the proposed hairstyle and makeup are reflected in real time. This allows the customer to visually confirm the style that suits him or her. In addition, when a smart mirror is used, the proposed style is displayed superimposed on the customer's face reflected in the mirror, allowing the customer to experience the simulation in a more natural way. Furthermore, the suggestion unit can provide multiple options so that the customer can try different styles. For example, the customer can try different combinations of hairstyles and makeup and choose the style that he or she likes the most. This allows the suggestion unit to make visual and interactive suggestions to the customer and help him or her choose the best style.

[0034] The suggestion unit can display the determination result by the determination unit in augmented reality (AR). The suggestion unit, for example, uses AR display to simulate and display hairstyles and makeup that suit the customer. For example, if the customer has a tablet, the customer's face is displayed on the tablet screen, and the suggested hairstyle and makeup are reflected in real time. This makes it possible to visually show the proposed content to the customer through AR display.

[0035] The suggestion unit can perform a simulation using augmented reality (AR) display on a display device such as a tablet or a smart mirror. The suggestion unit, for example, simulates and displays hairstyles and makeup that suit the customer on a display device such as a tablet or a smart mirror. For example, if the customer has a tablet, the customer's face is displayed on the tablet screen, and the suggested hairstyle and makeup are reflected in real time. This makes it possible to provide a simulation in real time to the customer using the tablet or smart mirror.

[0036] The suggestion unit can execute a simulation under different lighting conditions. The suggestion unit can simulate how the hairstyle and makeup look under different lighting conditions, such as natural light in the daytime and indoor lighting at night. This allows for more realistic suggestions by simulating how the hairstyle and makeup look under different lighting conditions.

[0037] The suggestion unit may include an estimation unit that estimates the customer's evaluation of the suggestion made by the suggestion unit. The suggestion unit may include an estimation unit that estimates the customer's evaluation of the suggestion based on, for example, the customer's facial expression. For example, if the customer is smiling, the evaluation is determined to be positive, and if the customer has a dissatisfied expression, the evaluation is determined to be negative. This makes it possible to improve the content of the suggestion by estimating the customer's evaluation.

[0038] The determination unit can adjust the hairstyle and makeup to suit the customer based on the estimation result by the estimation unit. For example, if there are many negative evaluations, the determination unit can improve customer satisfaction by suggesting a different hairstyle and makeup. In this way, customer satisfaction can be improved by adjusting the content of the proposal based on the customer's evaluation.

[0039] The acquisition unit can acquire the output from multiple cameras as face information. For example, a front camera and a side camera can be used to capture the shape and features of a customer's face in detail. By using multiple cameras, more detailed face information can be acquired.

[0040] The acquisition unit can analyze the customer's past facial information acquisition history and select the optimal acquisition method. For example, the optimal acquisition method is selected based on the customer's preferred angle and lighting conditions in the past. Also, the acquisition unit can select the method that captures the most natural facial expression from the customer's past facial information acquisition history. Furthermore, the acquisition unit can select the optimal acquisition method by referring to the device and environment that the customer used in the past. As a result, the acquisition accuracy is improved by selecting the optimal acquisition method based on the past history.

[0041] When acquiring face information, the acquisition unit can perform filtering based on the customer's current living situation and areas of interest. For example, if the customer is at work, facial expressions suitable for a business scene can be acquired with priority. Also, if the customer is engaged in a hobby activity, facial expressions related to the activity can be acquired with priority. Furthermore, facial expressions in a home environment where the customer is relaxing can be acquired with priority. In this way, by filtering face information based on the customer's living situation and areas of interest, more relevant information can be acquired.

[0042] When acquiring face information, the acquisition unit can prioritize acquiring highly relevant information by taking into consideration the geographical location information of the customer. For example, if the customer is in an urban area, information on hairstyles and makeup suitable for urban areas can be acquired preferentially. Also, if the customer is in the suburbs, information suitable for natural styles can be acquired preferentially. Furthermore, if the customer is participating in a specific event, information related to the event can be acquired preferentially. In this way, by taking into consideration the geographical location information, information suitable for the region can be acquired.

[0043] When acquiring face information, the acquisition unit can analyze the social media activity of the customer and acquire related information. For example, optimal face information can be acquired based on photos shared by the customer on social media. Also, related face information can be acquired based on trends the customer follows on social media. Furthermore, related face information can be acquired based on posts that the customer "liked" on social media. In this way, information based on the customer's interests can be acquired by analyzing social media activity.

[0044] During facial recognition, the facial recognition unit can optimize the recognition algorithm by referring to the customer's past facial recognition data. For example, an optimal recognition algorithm is selected based on the customer's past facial recognition data. Also, an algorithm that emphasizes specific features can be applied from the customer's past facial recognition data. Furthermore, an algorithm that analyzes the customer's past facial recognition data and improves recognition accuracy can be applied. This makes it possible to optimize the recognition algorithm by referring to past data.

[0045] During face recognition, the face recognition unit can perform detailed analysis of the customer's facial features and integrate face information from different angles. For example, the face recognition unit can integrate information from the front and side of the customer's face to perform detailed face recognition. It can also integrate information from the upper and lower parts of the customer's face to improve recognition accuracy. It can also integrate different facial expressions of the customer to perform comprehensive face recognition. This improves recognition accuracy by integrating information from different angles.

[0046] The face recognition unit can perform face recognition while taking into account the geographical distribution of customers. For example, if the customer is in an urban area, a face recognition algorithm suitable for urban areas can be applied. Also, if the customer is in a suburban area, a face recognition algorithm suitable for natural environments can be applied. Furthermore, if the customer is in a specific area, face recognition can be performed while taking into account the characteristics of that area. In this way, by taking into account the geographical distribution, recognition suitable for the area is possible.

[0047] The face recognition unit can improve the accuracy of face recognition by referring to related literature of the customer. Also, the recognition algorithm can be optimized based on research data on the shape of the customer's face. Furthermore, the latest research results on the features of the customer's face can be incorporated to improve the recognition accuracy. As a result, the recognition accuracy is improved by referring to related literature.

[0048] At the time of judgment, the judgment unit can judge the most suitable style by referring to the customer's past style history. Also, the judgment unit can judge the most suitable makeup based on the customer's past makeup history. Furthermore, the judgment unit can analyze the customer's past style history and judge the style that suits the customer best. In this way, the judgment unit can judge the most suitable style by referring to the past style history.

[0049] During the determination, the determination unit can apply different style algorithms based on the facial features of the customer. For example, the determination unit can apply an optimal hairstyle algorithm based on the shape of the customer's face. Also, the determination unit can apply an optimal makeup algorithm based on the facial features of the customer. Furthermore, the determination unit can apply different style algorithms based on the contours of the customer's face. This allows for more appropriate suggestions by applying the optimal style algorithm based on the facial features.

[0050] At the time of judgment, the judgment unit can determine the priority of judgment based on the time of submission of the customer. For example, if the customer submits early in the morning, the optimal style at that time can be judged as a priority. Also, if the customer submits during the day, the optimal style at that time can be judged as a priority. Furthermore, if the customer submits at night, the optimal style at that time can be judged as a priority. In this way, by determining the priority based on the time of submission, it becomes possible to make timely proposals.

[0051] The determination unit can adjust the order of determination based on the relevance of the customer when making the determination. For example, if the customer is participating in a specific event, the determination unit can determine with priority a style related to the event. Also, if the customer is engaged in a specific occupation, the determination unit can determine with priority a style related to the occupation. Furthermore, if the customer has a specific hobby, the determination unit can determine with priority a style related to the hobby. This allows for more appropriate suggestions to be made by adjusting the order based on the relevance.

[0052] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the style when making a suggestion. For example, the suggestion unit can make a detailed suggestion for a style related to an important event. Also, for an everyday style, the suggestion unit can make a concise suggestion. Furthermore, for a style suitable for a specific scene, the suggestion unit can make a detailed suggestion tailored to the scene. This allows for more appropriate suggestions by adjusting the level of detail based on the importance.

[0053] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the style category. For example, in the case of a hairstyle, the suggestion unit can apply a suggestion algorithm based on the hair type and face shape. In addition, in the case of makeup, the suggestion unit can apply a suggestion algorithm based on the skin color and face features. Furthermore, in the case of a fashion style, the suggestion unit can apply a suggestion algorithm based on the body type and preferences. In this way, by applying an algorithm according to the category, more appropriate suggestions can be made.

[0054] At the time of proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the style. For example, if a customer submits early in the morning, the most suitable style at that time can be given priority in the proposal. Also, if a customer submits during the day, the most suitable style at that time can be given priority in the proposal. Furthermore, if a customer submits at night, the most suitable style at that time can be given priority in the proposal. In this way, by determining the priority based on the time of submission, timely proposals are possible.

[0055] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the styles. For example, if the customer is participating in a specific event, the suggestion unit can give priority to suggesting styles related to the event. Also, if the customer is engaged in a specific occupation, the suggestion unit can give priority to suggesting styles related to the occupation. Furthermore, if the customer has a specific hobby, the suggestion unit can give priority to suggesting styles related to the hobby. This allows for more appropriate suggestions by adjusting the order based on relevance.

[0056] The estimation unit can optimize the estimation algorithm by referring to the past evaluation data of the customer at the time of estimation. Also, an algorithm that emphasizes a specific feature can be applied from the past evaluation data of the customer. Furthermore, an algorithm that analyzes the past evaluation data of the customer and improves the estimation accuracy can be applied. In this way, the estimation algorithm can be optimized by referring to the past evaluation data.

[0057] During estimation, the estimation unit can analyze subtle changes in the customer's facial expression to improve the accuracy of the evaluation. For example, the estimation unit can analyze subtle changes in the customer's smile to estimate a positive evaluation. Also, the estimation unit can analyze subtle changes in the customer's brow wrinkles to estimate a negative evaluation. Furthermore, the estimation unit can analyze subtle changes in the customer's eye movements to improve the accuracy of the evaluation. In this way, the accuracy of the evaluation is improved by analyzing subtle changes in facial expression.

[0058] During estimation, the estimation unit can perform evaluation taking into account the geographical distribution of customers. For example, if the customer is in an urban area, evaluation criteria suitable for urban areas can be applied. Also, if the customer is in a suburban area, evaluation criteria suitable for a natural environment can be applied. Furthermore, if the customer is in a specific area, evaluation can be performed taking into account the characteristics of that area. In this way, by taking geographical distribution into account, evaluation suitable for the area can be made.

[0059] The estimation unit can improve the accuracy of the evaluation by referring to related literature of the customer at the time of estimation. The estimation unit can improve the evaluation accuracy by referring to related literature on the facial features of the customer, for example. Also, the evaluation algorithm can be optimized based on research data on the shape of the customer's face. Furthermore, the latest research results on the facial features of the customer can be incorporated to improve the evaluation accuracy. As a result, the accuracy of the evaluation is improved by referring to related literature.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] When acquiring the customer's facial information, the acquisition unit can analyze the customer's skin condition. For example, it can measure the dryness and oil content of the skin and suggest the optimal makeup based on this. It can also analyze the skin tone, blemishes, and wrinkles and suggest skin care products based on this. Furthermore, it can provide appropriate makeup and skin care advice taking into account the skin condition according to the season and weather. This makes it possible to make more personalized suggestions based on the customer's skin condition.

[0062] The face recognition unit can take into account the age and gender of the customer when recognizing the shape and features of the customer's face. For example, it can suggest trendy hairstyles and makeup to younger customers, and more subdued styles to middle-aged and older customers. It can also suggest men's makeup and hairstyles to male customers, and women's styles to female customers. It can also suggest skin care products according to age and gender. This allows for more appropriate suggestions based on the customer's age and gender.

[0063] The determination unit can determine the hairstyle and makeup based on the lifestyle of the customer. For example, for a customer who frequently plays sports, it can suggest sweat-resistant makeup and a hairstyle that is easy to move in. Also, for a customer who often wears business attire, it can suggest a formal and clean style. Furthermore, for a customer who likes outdoor activities, it can suggest a natural style and makeup that takes into account UV protection. This makes it possible to make more practical suggestions based on the customer's lifestyle.

[0064] The suggestion unit can suggest hairstyles and makeup based on the cultural background of the customer. For example, the cultural identity of the customer can be respected by suggesting a style based on a particular culture or tradition. The suggestion unit can also suggest a style that matches a particular event or festival. Furthermore, the suggestion unit can provide the customer with new style options by suggesting a style that incorporates trends from different cultural spheres. This allows for more personalized suggestions based on the cultural background of the customer.

[0065] The suggestion unit can suggest hairstyles and makeup based on the health condition of the customer. For example, allergy-friendly makeup products can be suggested to a customer with allergies. Hypoallergenic skin care products and makeup can be suggested to a customer with sensitive skin. Furthermore, appropriate products and styles can be suggested according to specific health conditions (e.g., pregnancy, treatment, etc.). This allows for safer and more appropriate suggestions based on the customer's health condition.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The acquisition unit acquires facial information of a customer. The facial information of a customer includes, for example, the contours of the face, and the positions and shapes of the eyes, nose, and mouth. The acquisition unit acquires the facial information of the customer using multiple cameras. For example, a camera from the front and a camera from the side can be used to capture the shape and features of the customer's face in detail. Step 2: The face recognition unit recognizes the shape and features of the customer's face based on the face information acquired by the acquisition unit. The face recognition unit uses face recognition technology to analyze the customer's facial contours and features such as the eyes, nose, and mouth. For example, it can recognize whether the customer's face is round or long. Step 3: The determination unit determines a hairstyle and makeup that suits the customer based on the recognition result by the face recognition unit. For example, the determination unit can suggest a hairstyle and makeup that has the effect of tightening the face to a customer with a round face. Step 4: The suggestion unit suggests the result of the judgment by the judgment unit to the customer. The suggestion unit, for example, uses AR display to simulate and display hairstyles and makeup that suit the customer on a display device such as a tablet or a smart mirror. For example, if the customer has a tablet, the customer's face is displayed on the tablet screen, and the suggested hairstyle and makeup are reflected in real time.

[0068] (Example 2) A customization simulation system according to an embodiment of the present invention is a system that acquires face information of a customer, recognizes the shape and features of the customer's face using face recognition technology, and proposes a hairstyle and makeup that suits the customer based on the recognition result. This system acquires face information of a customer, recognizes the shape and features of the face, and makes a proposal. The proposal is made using AR display, and a simulation is performed on a display device such as a tablet or a smart mirror. Furthermore, simulation under different lighting conditions is also possible. The customer's evaluation of the proposal is estimated based on the customer's facial expression, and a positive evaluation or a negative evaluation is determined. Based on the estimation result, a hairstyle and makeup that suits the customer are adjusted. For example, the face information of a customer is acquired based on the output from multiple cameras. The shape and features of the customer's face can be captured in detail using a camera from the front and a camera from the side. Next, the shape and features of the customer's face are recognized based on the acquired face information. Using face recognition technology, the customer's facial contour and features such as the eyes, nose, and mouth are analyzed. For example, it is possible to recognize whether the customer's face is round or long. Based on the recognition result, a hairstyle and makeup that suits the customer is determined. For example, for a customer with a round face, a hairstyle and makeup that will have a slimming effect can be suggested. The suggestions are made using AR display. A simulation of a hairstyle and makeup that suits the customer is displayed on a display device such as a tablet or smart mirror. For example, if the customer has a tablet, their face is displayed on the tablet screen, and the suggested hairstyle and makeup are reflected in real time. In addition, simulations under different lighting conditions are also possible. For example, it is possible to simulate how the hairstyle and makeup will look under different lighting conditions, such as natural light in the daytime and indoor lighting at night. The customer's evaluation of the suggestions is estimated based on the customer's facial expression. For example, if the customer is smiling, it is judged to be a positive evaluation, and if the customer has a dissatisfied expression, it is judged to be a negative evaluation. Based on the estimation result, the hairstyle and makeup that suits the customer are adjusted.For example, if there are many negative reviews, the system can improve customer satisfaction by suggesting a different hairstyle or makeup. This allows the customization simulation system to suggest the optimal hairstyle and makeup based on the customer's facial information.

[0069] The customization simulation system according to the embodiment includes an acquisition unit, a face recognition unit, a determination unit, and a suggestion unit. The acquisition unit acquires face information of a customer. The face information of the customer includes, for example, the contour of the face, and the positions and shapes of the eyes, nose, and mouth, but is not limited to such examples. The acquisition unit acquires face information of the customer using, for example, multiple cameras. For example, a camera from the front and a camera from the side can be used to capture the shape and features of the customer's face in detail. The face recognition unit recognizes the shape and features of the customer's face based on the face information acquired by the acquisition unit. The face recognition unit analyzes the contour of the customer's face and features such as the eyes, nose, and mouth using, for example, face recognition technology. For example, it can recognize whether the customer's face is round or long. The determination unit determines a hairstyle and makeup that suits the customer based on the recognition result by the face recognition unit. For example, the determination unit can suggest a hairstyle and makeup that has the effect of tightening the face to a customer with a round face. The suggestion unit suggests the determination result by the determination unit to the customer. The suggestion unit uses, for example, AR display to simulate and display hairstyles and makeup that suit the customer on a display device such as a tablet or smart mirror. For example, if the customer has a tablet, the customer's face is displayed on the tablet screen, and the suggested hairstyle and makeup are reflected in real time. This allows the customization simulation system to suggest the optimal hairstyle and makeup based on the customer's facial information.

[0070] The acquisition unit acquires the customer's facial information. The customer's facial information includes, for example, the contour of the face, the position and shape of the eyes, nose, and mouth, but is not limited to such examples. The acquisition unit acquires the customer's facial information using, for example, multiple cameras. Specifically, the shape and features of the customer's face can be captured in detail using a camera from the front and a camera from the side. This allows the acquisition unit to accurately acquire three-dimensional information of the customer's face. Furthermore, the acquisition unit can acquire more accurate facial information by adjusting the resolution and frame rate of the camera. For example, by using a high-resolution camera, fine facial features and skin texture can be captured in detail. Furthermore, the acquisition unit can use an appropriate lighting device and background screen to minimize the influence of lighting conditions and background. This allows the acquisition unit to acquire high-quality facial information in a stable environment. Furthermore, the acquisition unit can use a face detection algorithm to automate the process of acquiring facial information. For example, the camera can automatically detect the customer's face and acquire facial information at an optimal angle and distance. This allows the acquisition unit to efficiently and accurately acquire customer's facial information and improve the performance of the entire system.

[0071] The face recognition unit recognizes the shape and features of the customer's face based on the face information acquired by the acquisition unit. The face recognition unit, for example, uses face recognition technology to analyze the customer's facial contour and features such as the eyes, nose, and mouth. Specifically, the face recognition unit can utilize image analysis technology using deep learning to recognize each part of the customer's face with high accuracy. For example, the face recognition unit can recognize whether the customer's face is round or long. In addition, the face recognition unit can analyze detailed features such as the size and position of the eyes, the shape of the nose, and the width of the mouth to evaluate the overall balance of the customer's face. Furthermore, the face recognition unit can use past data and statistical information to more accurately recognize the features of the customer's face. For example, the recognition accuracy for customers with specific facial features can be improved based on a large amount of face information acquired in the past. As a result, the face recognition unit can recognize the shape and features of the customer's face with high accuracy and provide information necessary for the processing of the determination unit, which is the next step.

[0072] The determination unit determines a hairstyle and makeup that suit the customer based on the recognition result by the face recognition unit. For example, the determination unit can suggest hairstyles and makeup that have the effect of tightening the face to a customer with a round face. Specifically, the determination unit selects an optimal hairstyle and makeup pattern based on the shape and features of the face provided by the face recognition unit. For example, a layered cut that makes the contours of the face look sharp and eye makeup that emphasizes the eyes can be suggested to a customer with a round face. In addition, a voluminous hairstyle that makes the length of the face look balanced and makeup using blush can be suggested to a customer with a long face. Furthermore, the determination unit can customize according to the customer's preferences and lifestyle. For example, if a customer usually prefers natural makeup, makeup with a natural finish can be suggested. In this way, the determination unit can determine the optimal hairstyle and makeup according to the facial features and individual needs of the customer and provide it to the suggestion unit.

[0073] The suggestion unit proposes the judgment result by the judgment unit to the customer. The suggestion unit, for example, uses AR display to simulate and display hairstyles and makeup that suit the customer on a display device such as a tablet or a smart mirror. Specifically, the suggestion unit reflects the hairstyle and makeup information provided by the judgment unit on the customer's face in real time. For example, if the customer has a tablet, the customer's face is displayed on the tablet screen, and the proposed hairstyle and makeup are reflected in real time. This allows the customer to visually confirm the style that suits him or her. In addition, when a smart mirror is used, the proposed style is displayed superimposed on the customer's face reflected in the mirror, allowing the customer to experience the simulation in a more natural way. Furthermore, the suggestion unit can provide multiple options so that the customer can try different styles. For example, the customer can try different combinations of hairstyles and makeup and choose the style that he or she likes the most. This allows the suggestion unit to make visual and interactive suggestions to the customer and help him or her choose the best style.

[0074] The suggestion unit can display the determination result by the determination unit in augmented reality (AR). The suggestion unit, for example, uses AR display to simulate and display hairstyles and makeup that suit the customer. For example, if the customer has a tablet, the customer's face is displayed on the tablet screen, and the suggested hairstyle and makeup are reflected in real time. This makes it possible to visually show the proposed content to the customer through AR display.

[0075] The suggestion unit can perform a simulation using augmented reality (AR) display on a display device such as a tablet or a smart mirror. The suggestion unit, for example, simulates and displays hairstyles and makeup that suit the customer on a display device such as a tablet or a smart mirror. For example, if the customer has a tablet, the customer's face is displayed on the tablet screen, and the suggested hairstyle and makeup are reflected in real time. This makes it possible to provide a simulation in real time to the customer using the tablet or smart mirror.

[0076] The suggestion unit can execute a simulation under different lighting conditions. The suggestion unit can simulate how the hairstyle and makeup look under different lighting conditions, such as natural light in the daytime and indoor lighting at night. This allows for more realistic suggestions by simulating how the hairstyle and makeup look under different lighting conditions.

[0077] The suggestion unit may include an estimation unit that estimates the customer's evaluation of the suggestion made by the suggestion unit. The suggestion unit may include an estimation unit that estimates the customer's evaluation of the suggestion based on, for example, the customer's facial expression. For example, if the customer is smiling, the evaluation is determined to be positive, and if the customer has a dissatisfied expression, the evaluation is determined to be negative. This makes it possible to improve the content of the suggestion by estimating the customer's evaluation.

[0078] The estimation unit can estimate whether the customer's evaluation is positive or negative based on the customer's smiling or dissatisfied facial expression. The estimation unit estimates the evaluation based on the customer's facial expression, for example. For example, if the customer is smiling, it is determined to be a positive evaluation, and if the customer has a dissatisfied facial expression, it is determined to be a negative evaluation. In this way, more accurate feedback can be obtained by estimating the evaluation based on the customer's facial expression. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0079] The determination unit can adjust the hairstyle and makeup to suit the customer based on the estimation result by the estimation unit. For example, if there are many negative evaluations, the determination unit can improve customer satisfaction by suggesting a different hairstyle and makeup. In this way, customer satisfaction can be improved by adjusting the content of the proposal based on the customer's evaluation.

[0080] The acquisition unit can acquire the output from multiple cameras as face information. For example, a front camera and a side camera can be used to capture the shape and features of a customer's face in detail. By using multiple cameras, more detailed face information can be acquired.

[0081] The acquisition unit can estimate the customer's emotions and adjust the timing of acquiring the facial information based on the estimated customer's emotions. For example, if the customer is relaxed, the timing of acquiring the facial information is delayed to capture a natural expression. Also, if the customer is nervous, the facial information can be acquired early to capture the expression before the customer relaxes. Furthermore, the facial information can be acquired the moment the customer smiles, and analysis can be performed based on the positive expression. In this way, by adjusting the timing of acquiring the facial information based on the customer's emotions, a more natural expression can be captured. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] The acquisition unit can analyze the customer's past facial information acquisition history and select the optimal acquisition method. For example, the optimal acquisition method is selected based on the customer's preferred angle and lighting conditions in the past. Also, the acquisition unit can select the method that captures the most natural facial expression from the customer's past facial information acquisition history. Furthermore, the acquisition unit can select the optimal acquisition method by referring to the device and environment that the customer used in the past. As a result, the acquisition accuracy is improved by selecting the optimal acquisition method based on the past history.

[0083] When acquiring face information, the acquisition unit can perform filtering based on the customer's current living situation and areas of interest. For example, if the customer is at work, facial expressions suitable for a business scene can be acquired with priority. Also, if the customer is engaged in a hobby activity, facial expressions related to the activity can be acquired with priority. Furthermore, facial expressions in a home environment where the customer is relaxing can be acquired with priority. In this way, by filtering face information based on the customer's living situation and areas of interest, more relevant information can be acquired.

[0084] The acquisition unit can estimate the emotion of the customer and determine the priority of the facial information to be acquired based on the estimated emotion of the customer. For example, if the customer shows a smile, that facial expression is acquired as the top priority. Also, if the customer shows a dissatisfied expression, that facial expression can be acquired as the next priority. Furthermore, if the customer has a blank expression, that facial expression can be acquired last. In this way, by determining the priority of the facial information based on the emotion of the customer, important facial expressions can be acquired preferentially. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0085] When acquiring face information, the acquisition unit can prioritize acquiring highly relevant information by taking into consideration the geographical location information of the customer. For example, if the customer is in an urban area, information on hairstyles and makeup suitable for urban areas can be acquired preferentially. Also, if the customer is in the suburbs, information suitable for natural styles can be acquired preferentially. Furthermore, if the customer is participating in a specific event, information related to the event can be acquired preferentially. In this way, by taking into consideration the geographical location information, information suitable for the region can be acquired.

[0086] When acquiring face information, the acquisition unit can analyze the social media activity of the customer and acquire related information. For example, optimal face information can be acquired based on photos shared by the customer on social media. Also, related face information can be acquired based on trends the customer follows on social media. Furthermore, related face information can be acquired based on posts that the customer "liked" on social media. In this way, information based on the customer's interests can be acquired by analyzing social media activity.

[0087] The face recognition unit can estimate the customer's emotions and adjust the accuracy of face recognition based on the estimated customer's emotions. For example, when the customer is relaxed, detailed face recognition is performed to improve accuracy. Also, when the customer is nervous, simple face recognition can be performed to quickly obtain results. Furthermore, when the customer smiles, the accuracy can be adjusted based on the facial expression. As a result, the recognition accuracy is improved by adjusting the accuracy of face recognition based on the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] During facial recognition, the facial recognition unit can optimize the recognition algorithm by referring to the customer's past facial recognition data. For example, an optimal recognition algorithm is selected based on the customer's past facial recognition data. Also, an algorithm that emphasizes specific features can be applied from the customer's past facial recognition data. Furthermore, an algorithm that analyzes the customer's past facial recognition data and improves recognition accuracy can be applied. This makes it possible to optimize the recognition algorithm by referring to past data.

[0089] During face recognition, the face recognition unit can perform detailed analysis of the customer's facial features and integrate face information from different angles. For example, the face recognition unit can integrate information from the front and side of the customer's face to perform detailed face recognition. It can also integrate information from the upper and lower parts of the customer's face to improve recognition accuracy. It can also integrate different facial expressions of the customer to perform comprehensive face recognition. This improves recognition accuracy by integrating information from different angles.

[0090] The face recognition unit can estimate the customer's emotions and adjust the order in which the face recognition results are displayed based on the estimated customer's emotions. For example, if the customer is relaxed, detailed face recognition results can be displayed first. Also, if the customer is nervous, simple face recognition results can be displayed first. Furthermore, if the customer smiles, the order in which the results are displayed can be adjusted based on the customer's facial expression. This allows more appropriate information to be provided by adjusting the display order of the results based on the customer's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0091] The face recognition unit can perform face recognition while taking into account the geographical distribution of customers. For example, if the customer is in an urban area, a face recognition algorithm suitable for urban areas can be applied. Also, if the customer is in a suburban area, a face recognition algorithm suitable for natural environments can be applied. Furthermore, if the customer is in a specific area, face recognition can be performed while taking into account the characteristics of that area. In this way, by taking into account the geographical distribution, recognition suitable for the area is possible.

[0092] The face recognition unit can improve the accuracy of face recognition by referring to related literature of the customer. Also, the recognition algorithm can be optimized based on research data on the shape of the customer's face. Furthermore, the latest research results on the features of the customer's face can be incorporated to improve the recognition accuracy. As a result, the recognition accuracy is improved by referring to related literature.

[0093] The judgment unit can estimate the customer's emotions and adjust the judgment criteria for hairstyle and makeup based on the estimated customer's emotions. For example, if the customer is relaxed, detailed judgment criteria can be applied. Also, if the customer is nervous, simple judgment criteria can be applied. Furthermore, if the customer smiles, the judgment criteria can be adjusted based on the facial expression. This allows for more appropriate suggestions by adjusting the judgment criteria based on the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] At the time of judgment, the judgment unit can judge the most suitable style by referring to the customer's past style history. Also, the judgment unit can judge the most suitable makeup based on the customer's past makeup history. Furthermore, the judgment unit can analyze the customer's past style history and judge the style that suits the customer best. In this way, the judgment unit can judge the most suitable style by referring to the past style history.

[0095] During the determination, the determination unit can apply different style algorithms based on the facial features of the customer. For example, the determination unit can apply an optimal hairstyle algorithm based on the shape of the customer's face. Also, the determination unit can apply an optimal makeup algorithm based on the facial features of the customer. Furthermore, the determination unit can apply different style algorithms based on the contours of the customer's face. This allows for more appropriate suggestions by applying the optimal style algorithm based on the facial features.

[0096] The judgment unit can estimate the customer's emotions and adjust the display method of the judgment result based on the estimated customer's emotions. For example, if the customer is relaxed, a detailed judgment result can be displayed. Also, if the customer is nervous, a simple judgment result can be displayed. Furthermore, if the customer smiles, the display method of the judgment result can be adjusted based on the customer's facial expression. In this way, more appropriate information can be provided by adjusting the display method based on the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0097] At the time of judgment, the judgment unit can determine the priority of judgment based on the time of submission of the customer. For example, if the customer submits early in the morning, the optimal style at that time can be judged as a priority. Also, if the customer submits during the day, the optimal style at that time can be judged as a priority. Furthermore, if the customer submits at night, the optimal style at that time can be judged as a priority. In this way, by determining the priority based on the time of submission, it becomes possible to make timely proposals.

[0098] The determination unit can adjust the order of determination based on the relevance of the customer when making the determination. For example, if the customer is participating in a specific event, the determination unit can determine with priority a style related to the event. Also, if the customer is engaged in a specific occupation, the determination unit can determine with priority a style related to the occupation. Furthermore, if the customer has a specific hobby, the determination unit can determine with priority a style related to the hobby. This allows for more appropriate suggestions to be made by adjusting the order based on the relevance.

[0099] The suggestion unit can estimate the customer's emotions and adjust the way the proposal is expressed based on the estimated customer's emotions. For example, if the customer is relaxed, a proposal including a detailed explanation can be made. Also, if the customer is nervous, a concise and easy-to-understand proposal can be made. Furthermore, if the customer smiles, the way the proposal is expressed can be adjusted based on the customer's facial expression. This allows for more appropriate proposals by adjusting the way the proposal is expressed based on the customer's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0100] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the style when making a suggestion. For example, the suggestion unit can make a detailed suggestion for a style related to an important event. Also, for an everyday style, the suggestion unit can make a concise suggestion. Furthermore, for a style suitable for a specific scene, the suggestion unit can make a detailed suggestion tailored to the scene. This allows for more appropriate suggestions by adjusting the level of detail based on the importance.

[0101] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the style category. For example, in the case of a hairstyle, the suggestion unit can apply a suggestion algorithm based on the hair type and face shape. In addition, in the case of makeup, the suggestion unit can apply a suggestion algorithm based on the skin color and face features. Furthermore, in the case of a fashion style, the suggestion unit can apply a suggestion algorithm based on the body type and preferences. In this way, by applying an algorithm according to the category, more appropriate suggestions can be made.

[0102] The suggestion unit can estimate the customer's emotions and adjust the length of the proposal based on the estimated customer's emotions. For example, if the customer is relaxed, a detailed proposal can be made. Also, if the customer is in a hurry, a concise proposal can be made. Furthermore, if the customer is excited, the length of the proposal can be adjusted to match the customer's emotions. This allows for more appropriate proposals by adjusting the length of the proposal based on the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0103] At the time of proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the style. For example, if a customer submits early in the morning, the most suitable style at that time can be given priority in the proposal. Also, if a customer submits during the day, the most suitable style at that time can be given priority in the proposal. Furthermore, if a customer submits at night, the most suitable style at that time can be given priority in the proposal. In this way, by determining the priority based on the time of submission, timely proposals are possible.

[0104] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the styles. For example, if the customer is participating in a specific event, the suggestion unit can give priority to suggesting styles related to the event. Also, if the customer is engaged in a specific occupation, the suggestion unit can give priority to suggesting styles related to the occupation. Furthermore, if the customer has a specific hobby, the suggestion unit can give priority to suggesting styles related to the hobby. This allows for more appropriate suggestions by adjusting the order based on relevance.

[0105] The estimation unit can estimate the customer's emotions and adjust the evaluation criteria based on the estimated customer's emotions. For example, if the customer is relaxed, detailed evaluation criteria can be applied. Also, if the customer is nervous, simple evaluation criteria can be applied. Furthermore, if the customer smiles, the evaluation criteria can be adjusted based on the facial expression. This allows for more accurate evaluation by adjusting the evaluation criteria based on the customer's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0106] The estimation unit can optimize the estimation algorithm by referring to the past evaluation data of the customer at the time of estimation. Also, an algorithm that emphasizes a specific feature can be applied from the past evaluation data of the customer. Furthermore, an algorithm that analyzes the past evaluation data of the customer and improves the estimation accuracy can be applied. In this way, the estimation algorithm can be optimized by referring to the past evaluation data.

[0107] During estimation, the estimation unit can analyze subtle changes in the customer's facial expression to improve the accuracy of the evaluation. For example, the estimation unit can analyze subtle changes in the customer's smile to estimate a positive evaluation. Also, the estimation unit can analyze subtle changes in the customer's brow wrinkles to estimate a negative evaluation. Furthermore, the estimation unit can analyze subtle changes in the customer's eye movements to improve the accuracy of the evaluation. In this way, the accuracy of the evaluation is improved by analyzing subtle changes in facial expression.

[0108] The estimation unit can estimate the customer's emotions and adjust the display method of the evaluation based on the estimated customer's emotions. For example, if the customer is relaxed, a detailed evaluation result can be displayed. Also, if the customer is nervous, a simple evaluation result can be displayed. Furthermore, if the customer smiles, the display method of the evaluation result can be adjusted based on the facial expression. This allows for a more appropriate evaluation by adjusting the display method based on the customer's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0109] During estimation, the estimation unit can perform evaluation taking into account the geographical distribution of customers. For example, if the customer is in an urban area, evaluation criteria suitable for urban areas can be applied. Also, if the customer is in a suburban area, evaluation criteria suitable for a natural environment can be applied. Furthermore, if the customer is in a specific area, evaluation can be performed taking into account the characteristics of that area. In this way, by taking geographical distribution into account, evaluation suitable for the area can be made.

[0110] The estimation unit can improve the accuracy of the evaluation by referring to related literature of the customer at the time of estimation. The estimation unit can improve the evaluation accuracy by referring to related literature on the facial features of the customer, for example. Also, the evaluation algorithm can be optimized based on research data on the shape of the customer's face. Furthermore, the latest research results on the facial features of the customer can be incorporated to improve the evaluation accuracy. As a result, the accuracy of the evaluation is improved by referring to related literature.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] When acquiring the customer's facial information, the acquisition unit can analyze the customer's skin condition. For example, it can measure the dryness and oil content of the skin and suggest the optimal makeup based on this. It can also analyze the skin tone, blemishes, and wrinkles and suggest skin care products based on this. Furthermore, it can provide appropriate makeup and skin care advice taking into account the skin condition according to the season and weather. This makes it possible to make more personalized suggestions based on the customer's skin condition.

[0113] The face recognition unit can take into account the age and gender of the customer when recognizing the shape and features of the customer's face. For example, it can suggest trendy hairstyles and makeup to younger customers, and more subdued styles to middle-aged and older customers. It can also suggest men's makeup and hairstyles to male customers, and women's styles to female customers. It can also suggest skin care products according to age and gender. This allows for more appropriate suggestions based on the customer's age and gender.

[0114] The determination unit can determine the hairstyle and makeup based on the lifestyle of the customer. For example, for a customer who frequently plays sports, it can suggest sweat-resistant makeup and a hairstyle that is easy to move in. Also, for a customer who often wears business attire, it can suggest a formal and clean style. Furthermore, for a customer who likes outdoor activities, it can suggest a natural style and makeup that takes into account UV protection. This makes it possible to make more practical suggestions based on the customer's lifestyle.

[0115] The suggestion unit can suggest hairstyles and makeup based on the cultural background of the customer. For example, the cultural identity of the customer can be respected by suggesting a style based on a particular culture or tradition. The suggestion unit can also suggest a style that matches a particular event or festival. Furthermore, the suggestion unit can provide the customer with new style options by suggesting a style that incorporates trends from different cultural spheres. This allows for more personalized suggestions based on the cultural background of the customer.

[0116] The suggestion unit can suggest hairstyles and makeup based on the health condition of the customer. For example, allergy-friendly makeup products can be suggested to a customer with allergies. Hypoallergenic skin care products and makeup can be suggested to a customer with sensitive skin. Furthermore, appropriate products and styles can be suggested according to specific health conditions (e.g., pregnancy, treatment, etc.). This allows for safer and more appropriate suggestions based on the customer's health condition.

[0117] The determination unit can estimate the user's emotion and adjust the color tones of the proposed hairstyle and makeup based on the estimated emotion of the user. For example, if the user is relaxed, a style with calm color tones can be proposed. If the user is excited, a style with bright color tones can be proposed. If the user is sad, a style with bright color tones can be proposed to lift the user's mood. This makes it possible to propose more appropriate color tones based on the user's emotion.

[0118] The suggestion unit can estimate the user's emotion and adjust the tone of the suggested style based on the estimated user's emotion. For example, if the user is relaxed, a natural and simple style can be suggested. If the user is excited, a flashy and eye-catching style can be suggested. If the user is nervous, a style with a calm tone can be suggested to relax the user. This makes it possible to suggest a more appropriate tone based on the user's emotion.

[0119] The acquisition unit can estimate the user's emotions and adjust the method of acquiring face information based on the estimated user's emotions. For example, if the user is relaxed, the timing of acquiring face information can be delayed to capture a natural facial expression. Also, if the user is nervous, face information can be acquired early to capture the facial expression before the user relaxes. Furthermore, face information can be acquired the moment the user smiles, and analysis can be performed based on positive facial expressions. This makes it possible to acquire more natural facial expressions based on the user's emotions.

[0120] The face recognition unit can estimate the user's emotions and adjust the face recognition algorithm based on the estimated user's emotions. For example, if the user is relaxed, detailed face recognition can be performed to improve accuracy. If the user is nervous, simple face recognition can be performed to quickly obtain results. Furthermore, if the user smiles, accuracy can be adjusted based on the facial expression. This enables more appropriate face recognition based on the user's emotions.

[0121] The suggestion unit can estimate the user's emotions and adjust the display method of the suggested content based on the estimated user's emotions. For example, if the user is relaxed, a suggestion including a detailed explanation can be made. If the user is nervous, a concise and easy-to-understand suggestion can be made. Furthermore, if the user smiles, the display method of the suggested content can be adjusted based on the facial expression. This makes it possible to suggest a more appropriate display method based on the user's emotions.

[0122] The process flow of the second embodiment will be briefly described below.

[0123] Step 1: The acquisition unit acquires facial information of a customer. The facial information of a customer includes, for example, the contours of the face, and the positions and shapes of the eyes, nose, and mouth. The acquisition unit acquires the facial information of the customer using multiple cameras. For example, a camera from the front and a camera from the side can be used to capture the shape and features of the customer's face in detail. Step 2: The face recognition unit recognizes the shape and features of the customer's face based on the face information acquired by the acquisition unit. The face recognition unit uses face recognition technology to analyze the customer's facial contours and features such as the eyes, nose, and mouth. For example, it can recognize whether the customer's face is round or long. Step 3: The determination unit determines a hairstyle and makeup that suits the customer based on the recognition result by the face recognition unit. For example, the determination unit can suggest a hairstyle and makeup that has the effect of tightening the face to a customer with a round face. Step 4: The suggestion unit suggests the result of the judgment by the judgment unit to the customer. The suggestion unit, for example, uses AR display to simulate and display hairstyles and makeup that suit the customer on a display device such as a tablet or a smart mirror. For example, if the customer has a tablet, the customer's face is displayed on the tablet screen, and the suggested hairstyle and makeup are reflected in real time.

[0124] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0126] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0127] For example, the acquisition unit acquires face information of the customer via the camera 42 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the face recognition unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the face information from the acquisition unit. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines a hairstyle and makeup that suits the customer based on the recognition result of the face recognition unit. For example, the suggestion unit is realized by the control unit 46A of the smart device 14, and displays a simulation of the suggestion result on a display device such as a tablet or a smart mirror. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 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.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0131] 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.

[0132] 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 the voice according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0134] 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 performed in a secure state.

[0135] Fig. 4 shows an example of 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.

[0136] 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.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0138] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0139] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0140] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0141] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0142] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0143] For example, the acquisition unit acquires face information of the customer via the camera 42 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the face recognition unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the face information from the acquisition unit. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines a hairstyle and makeup that suits the customer based on the recognition result of the face recognition unit. For example, the suggestion unit is realized by the control unit 46A of the smart glasses 214, and displays a simulation of the suggestion result on the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 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.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0147] 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.

[0148] 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 the voice according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0150] 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 performed in a secure state.

[0151] 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.

[0152] 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.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0154] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0155] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0156] 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 voice indicating a user input for 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.

[0157] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0158] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0159] For example, the acquisition unit acquires face information of the customer via the camera 42 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. For example, the face recognition unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the face information from the acquisition unit. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines a hairstyle and makeup that suits the customer based on the recognition result of the face recognition unit. For example, the suggestion unit is realized by the control unit 46A of the headset terminal 314, and displays a simulation of the proposal result on the display of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0163] 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.

[0164] 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 the voice according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0166] 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 performed in a secure state.

[0167] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0168] 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.

[0169] 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.

[0170] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0171] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 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.

[0172] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0173] 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 a voice indicating a user input for 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.

[0174] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0175] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0176] For example, the acquisition unit acquires face information of the customer via the camera 42 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the face recognition unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the face information from the acquisition unit. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines a hairstyle and makeup that suits the customer based on the recognition result of the face recognition unit. For example, the suggestion unit is realized by the control unit 46A of the robot 414, and displays a simulation of the proposal result on the display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0177] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0178] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0179] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0180] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0181] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0182] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0184] 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.

[0185] 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 Universal Serial Bus (USB) 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.

[0186] In addition, 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 upon request from the data processing device 12.

[0187] 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.

[0188] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0189] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0190] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0191] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0192] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0193] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0194] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0195] (Appendix 1) An acquisition unit for acquiring face information of a customer; a face recognition unit that recognizes the shape and features of the face of the customer based on the face information acquired by the acquisition unit; a determination unit that determines a hairstyle and makeup suitable for the customer based on a recognition result by the face recognition unit; a proposal unit that proposes the judgment result by the judgment unit to the customer. A system characterized by: (Appendix 2) The suggestion unit, The determination result by the determination unit is displayed in augmented reality (AR). 2. The system of claim 1, (Appendix 3) The suggestion unit, Simulation with Augmented Reality (AR) display on a tablet or smart mirror display device 2. The system of claim 1, (Appendix 4) The suggestion unit, Run simulations under different lighting conditions 2. The system of claim 1, (Appendix 5) The suggestion unit, The device includes an estimation unit that estimates a customer's evaluation of the proposal made by the suggestion unit. 2. The system of claim 1, (Appendix 6) The estimation unit is Infer whether a customer's review is positive or negative based on the customer's smile or dissatisfied facial expression 6. The system of claim 5, (Appendix 7) The determination unit is Adjusting a hairstyle and makeup to suit the customer based on the estimation result by the estimation unit 2. The system of claim 1, (Appendix 8) The acquisition unit is The face information is obtained from outputs from a plurality of cameras. 2. The system of claim 1, (Appendix 9) The acquisition unit is Estimating customer emotions and adjusting the timing of acquiring facial information based on the estimated customer emotions 2. The system of claim 1, (Appendix 10) The acquisition unit is Analyze the customer's past facial information acquisition history and select the optimal acquisition method 2. The system of claim 1, (Appendix 11) The acquisition unit is When capturing facial information, it is filtered based on the customer's current living situation and areas of interest. 2. The system of claim 1, (Appendix 12) The acquisition unit is Estimating customer emotions and prioritizing the facial information to be acquired based on the estimated customer emotions 2. The system of claim 1, (Appendix 13) The acquisition unit is When acquiring face information, the system takes into account the customer's geographic location information to prioritize acquiring relevant information. 2. The system of claim 1, (Appendix 14) The acquisition unit is Upon capturing face information, analyze the customer's social media activity to capture relevant information 2. The system of claim 1, (Appendix 15) The face recognition unit is Estimate customer sentiment and adjust facial recognition accuracy based on estimated customer sentiment 2. The system of claim 1, (Appendix 16) The face recognition unit is When performing facial recognition, the recognition algorithm is optimized by referencing the customer's past facial recognition data. 2. The system of claim 1, (Appendix 17) The face recognition unit is During facial recognition, the system performs detailed analysis of the customer's facial features and integrates facial information from different angles. 2. The system of claim 1, (Appendix 18) The face recognition unit is Estimate customer sentiment and adjust the order in which facial recognition results are displayed based on the estimated customer sentiment 2. The system of claim 1, (Appendix 19) The face recognition unit is When performing face recognition, the geographical distribution of customers is taken into account. 2. The system of claim 1, (Appendix 20) The face recognition unit is When recognizing faces, refer to the relevant literature of the customer to improve the accuracy of recognition 2. The system of claim 1, (Appendix 21) The determination unit is To estimate a customer's emotion and adjust a criterion for hairstyle and makeup based on the estimated customer's emotion. 2. The system of claim 1, (Appendix 22) The determination unit is When deciding on a style, the system refers to the customer's past style history to determine the most suitable style. 2. The system of claim 1, (Appendix 23) The determination unit is At the time of validation, apply different style algorithms based on the customer's facial features 2. The system of claim 1, (Appendix 24) The determination unit is Infer customer sentiment and adjust how results are displayed based on the inferred customer sentiment 2. The system of claim 1, (Appendix 25) The determination unit is At the time of adjudication, prioritize adjudications based on the timing of customer submissions 2. The system of claim 1, (Appendix 26) The determination unit is At adjudication time, adjust the order of adjudications based on customer relevance 2. The system of claim 1, (Appendix 27) The suggestion unit, Infer customer sentiment and tailor the wording of offers based on the inferred customer sentiment 2. The system of claim 1, (Appendix 28) The suggestion unit, When making suggestions, adjust the detail of the suggestions based on the importance of the style 2. The system of claim 1, (Appendix 29) The suggestion unit, When making suggestions, apply different suggestion algorithms depending on the style category 2. The system of claim 1, (Appendix 30) The suggestion unit, Estimate customer sentiment and adjust the length of the offer based on the estimated customer sentiment 2. The system of claim 1, (Appendix 31) The suggestion unit, When submitting a style, we prioritize the suggestions based on when they were submitted. 2. The system of claim 1, (Appendix 32) The suggestion unit, When making suggestions, adjust the order of suggestions based on style relevance 2. The system of claim 1, (Appendix 33) The estimation unit is Estimate customer sentiment and adjust rating scale based on estimated customer sentiment 6. The system of claim 5, (Appendix 34) The estimation unit is When making a prediction, the prediction algorithm is optimized by referencing past customer evaluation data. 6. The system of claim 5, (Appendix 35) The estimation unit is When making an estimate, subtle changes in the customer's facial expression are analyzed to improve the accuracy of the evaluation. 6. The system of claim 5, (Appendix 36) The estimation unit is Infer customer sentiment and adjust how ratings are displayed based on the inferred customer sentiment 6. The system of claim 5, (Appendix 37) The estimation unit is When making estimates, the geographical distribution of customers is taken into account. 6. The system of claim 5, (Appendix 38) The estimation unit is When making an estimate, we refer to relevant literature from the customer to improve the accuracy of the evaluation. 6. The system of claim 5, [Explanation of symbols]

[0196] 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. An acquisition unit for acquiring face information of a customer; a face recognition unit that recognizes the shape and features of the face of the customer based on the face information acquired by the acquisition unit; a determination unit that determines a hairstyle and makeup suitable for the customer based on a recognition result by the face recognition unit; a proposal unit that proposes the judgment result by the judgment unit to the customer. A system characterized by:

2. The suggestion unit, The determination result by the determination unit is displayed in augmented reality.

2. The system of claim 1 .

3. The suggestion unit, Simulation of an augmented reality display on a tablet or smart mirror display device 2. The system of claim 1 .

4. The suggestion unit, Run simulations under different lighting conditions 2. The system of claim 1 .

5. The suggestion unit, An estimation unit that estimates an evaluation of the customer with respect to the proposal 2. The system of claim 1 .

6. The estimation unit is Inferring whether the customer's evaluation is positive or negative based on the customer's smile or dissatisfied expression 6. The system of claim 5.

7. The determination unit is Adjusting a hairstyle and makeup to suit the customer based on the estimation result by the estimation unit 6. The system of claim 5.

8. The acquisition unit is The face information is obtained from outputs from a plurality of cameras.

2. The system of claim 1 .

Images