system

Abstract

The system according to the embodiment aims to provide an optimal drying method according to the condition of the scalp and hair. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, and an adjustment unit. The collection unit detects the condition of the scalp or the length of the hair. The analysis unit analyzes the information collected by the collection unit and determines the drying method. The adjustment unit adjusts the air volume and temperature of the dryer based on the drying method determined by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology is unable to automatically provide the optimal drying method according to the condition of the scalp and hair, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal drying method according to the condition of the scalp and hair. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and an adjustment unit. The collection unit detects the condition of the scalp or the length of the hair. The analysis unit analyzes the information collected by the collection unit and determines the drying method. The adjustment unit adjusts the air volume and temperature of the dryer based on the drying method determined by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal drying method according to the condition of the scalp and hair. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A hair dryer system according to an embodiment of the present invention performs professional-level hair drying according to the condition of the scalp and the length of the hair. The hair dryer system detects the condition of the scalp and the length of the hair, and AI analyzes the optimal drying method and automatically adjusts the air volume and temperature of the dryer. This mechanism allows users to easily achieve professional-level hair drying at home, just like a hair dryer. For example, the hair dryer system uses sensors to collect information such as the humidity and temperature of the user's scalp, as well as the length and density of the hair. The AI ​​then analyzes the collected information and determines the optimal drying method. For example, if the scalp is dry, the air volume is reduced and the temperature is set low. On the other hand, if the hair is long, the air volume is increased and the temperature is set high. Furthermore, the air volume and temperature of the hair dryer are automatically adjusted based on the drying method determined by the AI. This allows users to achieve optimal drying without any special operation. This allows the hair dryer system to provide optimal drying according to the condition of the user's scalp and hair. For example, it can dry hair efficiently while minimizing hair damage. Furthermore, drying according to the scalp condition helps maintain scalp health. This allows the user to maintain beautiful hair while providing comfortable hair care.

[0029] A hair dryer system according to an embodiment includes a collection unit, an analysis unit, and an adjustment unit. The collection unit detects the condition of the scalp or the length of hair. The collection unit collects information such as scalp humidity and temperature, hair length, and density using, for example, a humidity sensor, a temperature sensor, and a distance sensor. The collection unit can also automatically detect information when a user simply holds the hair dryer close to the scalp. For example, the collection unit detects scalp humidity and temperature in real time and measures hair length and density. The analysis unit analyzes the information collected by the collection unit and determines the optimal drying method. The analysis unit analyzes the information using, for example, a machine learning algorithm. For example, the analysis unit determines the optimal drying method based on information such as scalp humidity and temperature, hair length, and density. Furthermore, the analysis unit can also improve analysis accuracy using a deep learning algorithm. The adjustment unit adjusts the airflow and temperature of the hair dryer based on the drying method determined by the analysis unit. For example, the adjustment unit automatically adjusts the airflow and temperature. For example, the adjustment unit may increase or decrease the airflow rate or the temperature depending on the condition of the scalp or the length of the hair. This allows the hair dryer system according to the embodiment to provide optimal drying depending on the condition of the user's scalp or hair. For example, the adjustment unit may automatically adjust the airflow rate or temperature to achieve optimal drying without the user having to perform any special operation.

[0030] The collection unit includes a humidity sensor, a temperature sensor, and a distance sensor. The humidity sensor measures relative humidity and detects scalp humidity. For example, the humidity sensor provides information for adjusting the drying method when the scalp humidity is high. The temperature sensor includes types such as thermistors and thermocouples and measures scalp temperature. For example, the temperature sensor provides information for adjusting the airflow and temperature when the scalp temperature is high. The distance sensor includes types such as ultrasonic sensors and laser sensors and measures hair length and density. For example, the distance sensor provides information for increasing the airflow when the hair is long. This allows for more accurate detection of scalp and hair conditions by using various sensors. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit inputs data from the humidity sensor, temperature sensor, and distance sensor into a generation AI, which analyzes the data to detect the scalp and hair conditions.

[0031] The analysis unit analyzes information using a machine learning algorithm. Machine learning algorithms include, for example, decision trees and support vector machines, and analyze the information provided by the collection unit. For example, the analysis unit uses a decision tree algorithm to analyze information such as scalp humidity and temperature, hair length and density, and determine the optimal drying method. The analysis unit can also use a support vector machine to classify the collected data and determine the drying method. Furthermore, the analysis unit can also use a machine learning algorithm to improve the accuracy of the analysis based on past data. For example, the analysis unit learns from past data and analyzes the user's hair care patterns to provide a more accurate drying method. Thus, the use of a machine learning algorithm improves the accuracy of the analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the data provided by the collection unit into a generation AI, which then analyzes the data and determines the optimal drying method.

[0032] The adjustment unit can automatically adjust the air volume and temperature. The adjustment unit automatically adjusts the air volume and temperature based on, for example, feedback from a sensor. For example, the adjustment unit reduces the air volume and sets a low temperature when the scalp humidity is high. The adjustment unit can also increase the air volume and set a high temperature when the hair is long. Furthermore, the adjustment unit can automatically adjust the air volume and temperature to achieve optimal drying without the user having to perform any special operations. For example, the adjustment unit automatically adjusts the air volume and temperature when the user simply holds the hair dryer. This allows optimal drying to be achieved through automatic adjustment without the user having to perform any special operations. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data from a sensor into a generation AI, which analyzes the data and automatically adjusts the air volume and temperature.

[0033] The collection unit can automatically detect information when a user simply brings the hair dryer close to the scalp. The collection unit can automatically detect information when a user simply brings the hair dryer close to the scalp, for example, by adjusting the sensitivity and detection range of the sensor. For example, the collection unit can detect the humidity and temperature of the scalp in real time and measure the length and density of hair. The collection unit can also automatically collect information when a user simply brings the hair dryer close to the scalp. This allows the user to easily collect information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data from the sensor into a generation AI, which can analyze the data and detect information.

[0034] The adjustment unit can weaken or strengthen the airflow and lower or raise the temperature depending on the condition of the scalp or the length of the hair. For example, when the scalp humidity is high, the adjustment unit weakens the airflow and sets the temperature low. For example, when the scalp humidity is low, the adjustment unit strengthens the airflow and sets the temperature high. The adjustment unit can also strengthen the airflow and set the temperature high when the hair is long. For example, when the hair is short, the adjustment unit weakens the airflow and sets the temperature low. This makes it possible to provide optimal drying according to the condition of the scalp and hair. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data from a sensor into the generation AI, which analyzes the data and adjusts the airflow and temperature.

[0035] The analysis unit can determine the drying method based on information on scalp humidity or temperature, and hair length or density. The analysis unit determines the optimal drying method based on information such as scalp humidity and temperature, and hair length and density. For example, the analysis unit can reduce the airflow and set the temperature low when scalp humidity is high. The analysis unit can also increase the airflow and set the temperature high when hair is long. Furthermore, the analysis unit can comprehensively analyze information such as scalp humidity and temperature, and hair length and density to provide the optimal drying method. This makes it possible to provide the optimal drying method based on a variety of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data provided by the collection unit into the generation AI, which then analyzes the data and determines the optimal drying method.

[0036] The adjustment unit can automatically adjust the air volume and temperature to achieve optimal drying without the user having to perform any special operations. The adjustment unit automatically adjusts the air volume and temperature based on, for example, feedback from a sensor. For example, the adjustment unit can reduce the air volume and set a low temperature when the scalp is humid. The adjustment unit can also increase the air volume and set a high temperature when the hair is long. Furthermore, the adjustment unit can automatically adjust the air volume and temperature to achieve optimal drying without the user having to perform any special operations. For example, the adjustment unit automatically adjusts the air volume and temperature when the user simply holds the hair dryer. This allows the user to easily achieve optimal drying. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data from a sensor into a generation AI, which analyzes the data and automatically adjusts the air volume and temperature.

[0037] The collection unit can include sensors that detect not only scalp humidity and temperature, but also scalp oil content and pH value. For example, the collection unit can include a sensor that detects scalp oil content to provide a drying method that takes the oil balance into consideration. For example, if the scalp is highly oily, the collection unit can reduce the airflow and set a low temperature. The collection unit can also include a sensor that detects the scalp's pH value to provide a drying method based on the pH value. For example, if the scalp's pH value is high, the collection unit can reduce the airflow and set a low temperature. Furthermore, the collection unit can comprehensively detect the scalp's humidity, temperature, oil content, and pH value to provide the optimal drying method. This allows for more detailed detection of the scalp condition. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data on scalp oil content and pH value into the generation AI, which can then analyze the data and provide the optimal drying method.

[0038] The collection unit can include a sensor that detects not only hair length but also hair thickness and damage level. For example, the collection unit can include a sensor that detects hair thickness and provide a drying method appropriate to the thickness. For example, if the hair is thick, the collection unit increases the airflow and sets a higher temperature. The collection unit can also include a sensor that detects the hair damage level and provide a drying method that minimizes damage. For example, if the hair damage level is high, the collection unit decreases the airflow and sets a lower temperature. Furthermore, the collection unit can comprehensively detect the hair length, thickness, and damage level and provide the optimal drying method. This allows for more detailed detection of the hair condition. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data on hair thickness and damage level into the generation AI, which can then analyze the data and provide the optimal drying method.

[0039] The collection unit can be added with a function to provide feedback of the detected information to the user in real time. The collection unit, for example, displays the detected scalp humidity and temperature to the user in real time. For example, the collection unit displays the scalp humidity and temperature using a display. The collection unit can also display the detected hair length and thickness to the user in real time. For example, the collection unit notifies the user of the hair length and thickness using an audio notification. Furthermore, the collection unit can suggest the optimal drying method to the user in real time based on the detected information. For example, the collection unit suggests the optimal drying method to the user using a display or an audio notification. This allows the user to check the information in real time. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the detected information to a generation AI, which analyzes the data and provides feedback in real time.

[0040] The collection unit may be added with a function to transmit detected information to the cloud and share it with other devices. For example, the collection unit transmits detected scalp humidity and temperature to the cloud and shares it with other devices. For example, the collection unit encrypts the data and transmits it to the cloud. The collection unit can also transmit detected hair length and thickness to the cloud and share it with other devices. For example, the collection unit transmits data using Bluetooth (registered trademark) or Wi-Fi. Furthermore, the collection unit can transmit the detected information to the cloud and work with other devices to provide an optimal drying method. For example, the collection unit adjusts the drying method in cooperation with other devices based on the data transmitted to the cloud. This allows the information to be transmitted to the cloud and shared with other devices. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input the detected information to a generation AI, which analyzes the data and transmits it to the cloud to share it with other devices.

[0041] The collection unit can be added with a function to automatically record the user's hair care history based on the detected information. The collection unit automatically records the user's hair care history based on, for example, the detected scalp humidity and temperature. For example, the collection unit records the date, the products used, the treatment details, etc. The collection unit can also automatically record the user's hair care history based on the detected hair length and thickness. For example, the collection unit can provide an optimal drying method by referring to past data. Furthermore, the collection unit can automatically record the user's hair care history based on the detected information and provide an optimal drying method by referring to past data. This allows the user's hair care history to be automatically recorded. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the detected information into a generation AI, which can analyze the data and record the hair care history.

[0042] The collection unit can add a function to suggest optimal hair care products to a user based on the detected information. The collection unit can suggest optimal hair care products based on, for example, the detected scalp humidity and temperature. For example, the collection unit can suggest products such as shampoo, conditioner, and treatment. The collection unit can also suggest optimal hair care products based on the detected hair length and thickness. For example, the collection unit can suggest products according to hair length and thickness. Furthermore, the collection unit can suggest optimal hair care products by referring to the user's hair care history based on the detected information. For example, the collection unit can suggest optimal products based on past usage history. This makes it possible to suggest optimal hair care products to a user. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the detected information to a generation AI, which can analyze the data and suggest optimal hair care products.

[0043] The analysis unit can improve analysis accuracy by using a deep learning algorithm in addition to a machine learning algorithm. The analysis unit can improve analysis accuracy by, for example, combining a machine learning algorithm and a deep learning algorithm. For example, the analysis unit can use a deep learning algorithm to analyze the optimal drying method based on scalp condition and hair length. The analysis unit can also use a machine learning algorithm and a deep learning algorithm together to improve analysis accuracy based on past data. Thus, the use of a deep learning algorithm improves analysis accuracy. Deep learning algorithms include, for example, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data provided by the collection unit into a generation AI, which then analyzes the data and determines the optimal drying method.

[0044] The analysis unit can improve analysis accuracy by referring to past data and learning the user's hair care patterns. The analysis unit, for example, refers to past data and learns the user's hair care patterns. For example, the analysis unit analyzes the user's hair care patterns based on past usage history and past analysis results, and provides an optimal drying method. The analysis unit can also improve analysis accuracy by referring to past data and learning the user's hair care patterns. In this way, by referring to past data, analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data into a generation AI, which analyzes the data and provides an optimal drying method.

[0045] The analysis unit can be added with a function for determining the optimal drying method depending on different seasons and climatic conditions. The analysis unit determines the optimal drying method depending on, for example, the season. For example, the analysis unit reduces the airflow rate and sets the temperature higher in winter. The analysis unit can also determine the optimal drying method depending on climatic conditions. For example, the analysis unit increases the airflow rate and sets the temperature lower when humidity is high. The analysis unit can also provide the optimal drying method taking into account the season and climatic conditions. This makes it possible to provide the optimal drying method depending on the season and climatic conditions. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on seasons and climatic conditions into the generation AI, which can analyze the data and determine the optimal drying method.

[0046] The analysis unit can add a function to cooperate with other beauty devices and provide comprehensive beauty advice. The analysis unit, for example, cooperates with other beauty devices to provide comprehensive beauty advice. For example, the analysis unit provides an optimal drying method based on information from a skin care device or a hair care device. The analysis unit can also cooperate with other beauty devices to provide comprehensive beauty advice to a user. For example, the analysis unit provides comprehensive beauty advice based on information from other beauty devices. In this way, comprehensive beauty advice can be provided by cooperation with other beauty devices. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from other beauty devices into a generation AI, which analyzes the data and provides comprehensive beauty advice.

[0047] The analysis unit can add a function to adjust the analysis results by taking into account the user's lifestyle habits and dietary information. The analysis unit provides an optimal drying method by taking into account the user's lifestyle habits, for example. For example, the analysis unit adjusts the drying method by taking into account the user's sleep patterns and exercise habits. The analysis unit can also provide an optimal drying method by taking into account the user's dietary information. For example, the analysis unit adjusts the drying method by taking into account the user's nutritional balance and meal frequency. Furthermore, the analysis unit can adjust the analysis results based on the user's lifestyle habits and dietary information. This makes it possible to provide more appropriate analysis results by taking into account the user's lifestyle habits and dietary information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the user's lifestyle habits and dietary information into the generation AI, which can analyze the data and provide an optimal drying method.

[0048] The analysis unit can add a function to customize the analysis method according to changes in the user's hairstyle. The analysis unit, for example, detects changes in the user's hairstyle and customizes the analysis method. For example, the analysis unit provides an optimal drying method according to changes in the user's hairstyle. The analysis unit can also customize the analysis method taking into account changes in the user's hairstyle. For example, the analysis unit detects changes in hairstyle such as cutting, coloring, or perming, and adjusts the analysis method. This allows the analysis method to be customized according to changes in the user's hairstyle. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on changes in the user's hairstyle into the generation AI, which can analyze the data and provide an optimal drying method.

[0049] The adjustment unit can be added with a function to adjust not only the air volume and temperature, but also the air direction and pattern. The adjustment unit, for example, adjusts the air direction to provide an optimal drying method. For example, the adjustment unit adjusts the angle of the air to direct the air toward specific parts of the scalp or hair. The adjustment unit can also adjust the air pattern to provide an optimal drying method. For example, the adjustment unit changes the air strength pattern to efficiently dry the hair. Furthermore, the adjustment unit can comprehensively adjust the air volume, temperature, and air direction and pattern to provide an optimal drying method. In this way, by adjusting the air direction and pattern, a more optimal drying method can be provided. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data from a sensor into the generation AI, which analyzes the data and adjusts the air direction and pattern.

[0050] The adjustment unit can refer to the user's past usage history and automatically select optimal settings. The adjustment unit, for example, refers to the user's past usage history and automatically sets optimal airflow and temperature. For example, the adjustment unit automatically selects settings preferred by the user based on the past usage history. The adjustment unit can also provide an optimal drying method based on the user's past usage history. For example, the adjustment unit refers to the past usage history and automatically sets optimal airflow direction and pattern. In this way, optimal settings can be automatically selected by referring to the user's past usage history. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data of the past usage history into a generation AI, which analyzes the data and automatically selects optimal settings.

[0051] The adjustment unit can be added with a function to provide multiple drying modes according to different hair types and scalp conditions. The adjustment unit provides the optimal drying mode according to, for example, hair type. For example, the adjustment unit provides a drying mode according to hair types such as straight hair, curly hair, and thin hair. The adjustment unit can also provide the optimal drying mode according to scalp condition. For example, the adjustment unit provides a drying mode according to scalp humidity and temperature. Furthermore, the adjustment unit can provide multiple drying modes by comprehensively considering hair type and scalp condition. This makes it possible to provide drying modes according to different hair types and scalp conditions. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, AI, for example. For example, the adjustment unit can input data on hair type and scalp condition into the generation AI, which can analyze the data and provide the optimal drying mode.

[0052] The adjustment unit can add a function to cooperate with other smart devices to provide a comprehensive beauty environment. The adjustment unit, for example, cooperates with other smart devices to provide a comprehensive beauty environment. For example, the adjustment unit provides an optimal drying method based on information from a smart mirror or a smart speaker. The adjustment unit can also cooperate with other smart devices to provide a comprehensive beauty environment to the user. For example, the adjustment unit provides a comprehensive beauty environment based on information from other smart devices. In this way, by cooperating with other smart devices, a comprehensive beauty environment can be provided. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data from other smart devices into a generation AI, which analyzes the data to provide a comprehensive beauty environment.

[0053] The adjustment unit can add a function to customize the method of adjusting the air volume and temperature by reflecting user feedback. The adjustment unit customizes the method of adjusting the air volume and temperature based on, for example, user feedback. For example, the adjustment unit changes the method of adjusting the air volume and temperature based on survey results and post-use evaluations. The adjustment unit can also provide an optimal drying method by reflecting user feedback. For example, the adjustment unit customizes the direction and pattern of the air based on user feedback. This makes it possible to customize the method of adjusting the air volume and temperature by reflecting user feedback. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input user feedback data into a generation AI, which analyzes the data and customizes the method of adjusting the air volume and temperature.

[0054] The adjustment unit can add a function to adjust the air volume and temperature taking into account the user's usage of the hair care product. The adjustment unit, for example, provides the optimal air volume and temperature taking into account the user's usage of the hair care product. For example, the adjustment unit adjusts the air volume and temperature based on the frequency and amount of use. The adjustment unit can also provide the optimal drying method based on the user's usage of the hair care product. For example, the adjustment unit adjusts the air direction and pattern taking into account the type and amount of product used. This makes it possible to provide the optimal air volume and temperature by taking into account the user's usage of the hair care product. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on the usage of the hair care product into the generation AI, which can analyze the data and provide the optimal air volume and temperature.

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

[0056] The hair dryer system may further include a voice assistant unit. The voice assistant unit can recognize a user's voice commands and adjust the settings of the hair dryer. For example, if the user says, "Increase the airflow," the voice assistant unit recognizes the command and sends an instruction to the adjustment unit to increase the airflow. Alternatively, if the user says, "Decrease the temperature," the voice assistant unit can send an instruction to the adjustment unit to decrease the temperature. Furthermore, the voice assistant unit can learn the user's preferences and past usage history and suggest optimal settings. This allows the user to operate the hair dryer using only their voice without using their hands, improving convenience.

[0057] The hair dryer system can further include an environmental sensor unit. The environmental sensor unit detects the temperature and humidity in the room and provides that information to the analysis unit. For example, if the humidity in the room is high, the analysis unit instructs the adjustment unit to increase the airflow and set the temperature low. Also, if the room temperature is low, the analysis unit can decrease the airflow and set the temperature high. Furthermore, the environmental sensor unit can detect the air quality in the room and recommend a hair dryer with an air purification function if necessary. This allows the user to perform optimal drying according to the indoor environment.

[0058] The hair dryer system can also adjust the drying method taking into account the user's usage of hair care products. For example, if the user uses a specific shampoo or treatment, the analysis unit can adjust the airflow and temperature according to the ingredients in the shampoo or treatment. Also, if the user uses hair oil, the analysis unit can reduce the airflow and set a lower temperature. Furthermore, if the user uses a hair mask, the analysis unit can adjust the airflow and temperature to maximize the effectiveness of the product. This allows the system to provide the optimal drying method according to the user's usage of hair care products.

[0059] The hair dryer system can also customize the drying method according to changes in the user's hairstyle. For example, if the user cuts or colores their hair, the analysis unit can adjust the air volume and temperature accordingly. Also, if the user gets a perm, the analysis unit can reduce the air volume and set a lower temperature. Furthermore, if the user wears hair extensions, the analysis unit can adjust the air volume and temperature to minimize damage to the extensions. This allows the system to provide the optimal drying method according to changes in the user's hairstyle.

[0060] The hair dryer system can further adjust the drying method taking into account the user's lifestyle habits and dietary information. For example, the analysis unit can adjust the air volume and temperature taking into account the user's sleep patterns and exercise habits. It can also provide a drying method that is appropriate for the user's nutritional balance and meal frequency taking into account the user's dietary information. Furthermore, the analysis results can be adjusted based on the user's lifestyle habits and dietary information to provide a more appropriate drying method. This makes it possible to provide an optimal drying method that is appropriate for the user's lifestyle habits and dietary information.

[0061] The hair dryer system can also customize the drying method based on the user's hair care history. For example, the analysis unit can provide the optimal air volume and temperature based on past usage history and treatment details. It can also refer to the user's hair care history and suggest the optimal drying method based on past data. Furthermore, the air direction and pattern can be adjusted based on the user's hair care history to provide more effective drying. This makes it possible to provide the optimal drying method according to the user's hair care history.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The collection unit detects the scalp condition or hair length. The collection unit uses humidity, temperature, and distance sensors to collect information such as scalp humidity and temperature, hair length, and density. It can also automatically detect information when the user simply brings the hair dryer close to the scalp. Step 2: The analysis unit analyzes the information collected by the collection unit and determines the optimal drying method. The analysis unit analyzes the information using machine learning algorithms and deep learning algorithms, and determines the optimal drying method based on information such as scalp humidity and temperature, hair length and density. Step 3: The adjustment unit adjusts the airflow and temperature of the dryer based on the drying method determined by the analysis unit. The adjustment unit weakens or strengthens the airflow and lowers or raises the temperature depending on the condition of the scalp and the length of the hair.

[0064] (Example 2) A hair dryer system according to an embodiment of the present invention performs professional-level hair drying according to the condition of the scalp and the length of the hair. The hair dryer system detects the condition of the scalp and the length of the hair, and AI analyzes the optimal drying method and automatically adjusts the air volume and temperature of the dryer. This mechanism allows users to easily achieve professional-level hair drying at home, just like a hair dryer. For example, the hair dryer system uses sensors to collect information such as the humidity and temperature of the user's scalp, as well as the length and density of the hair. The AI ​​then analyzes the collected information and determines the optimal drying method. For example, if the scalp is dry, the air volume is reduced and the temperature is set low. On the other hand, if the hair is long, the air volume is increased and the temperature is set high. Furthermore, the air volume and temperature of the hair dryer are automatically adjusted based on the drying method determined by the AI. This allows users to achieve optimal drying without any special operation. This allows the hair dryer system to provide optimal drying according to the condition of the user's scalp and hair. For example, it can dry hair efficiently while minimizing hair damage. Furthermore, drying according to the scalp condition helps maintain scalp health. This allows the user to maintain beautiful hair while providing comfortable hair care.

[0065] A hair dryer system according to an embodiment includes a collection unit, an analysis unit, and an adjustment unit. The collection unit detects the condition of the scalp or the length of hair. The collection unit collects information such as scalp humidity and temperature, hair length, and density using, for example, a humidity sensor, a temperature sensor, and a distance sensor. The collection unit can also automatically detect information when a user simply holds the hair dryer close to the scalp. For example, the collection unit detects scalp humidity and temperature in real time and measures hair length and density. The analysis unit analyzes the information collected by the collection unit and determines the optimal drying method. The analysis unit analyzes the information using, for example, a machine learning algorithm. For example, the analysis unit determines the optimal drying method based on information such as scalp humidity and temperature, hair length, and density. Furthermore, the analysis unit can also improve analysis accuracy using a deep learning algorithm. The adjustment unit adjusts the airflow and temperature of the hair dryer based on the drying method determined by the analysis unit. For example, the adjustment unit automatically adjusts the airflow and temperature. For example, the adjustment unit may increase or decrease the airflow rate or the temperature depending on the condition of the scalp or the length of the hair. This allows the hair dryer system according to the embodiment to provide optimal drying depending on the condition of the user's scalp or hair. For example, the adjustment unit may automatically adjust the airflow rate or temperature to achieve optimal drying without the user having to perform any special operation.

[0066] The collection unit includes a humidity sensor, a temperature sensor, and a distance sensor. The humidity sensor measures relative humidity and detects scalp humidity. For example, the humidity sensor provides information for adjusting the drying method when the scalp humidity is high. The temperature sensor includes types such as thermistors and thermocouples and measures scalp temperature. For example, the temperature sensor provides information for adjusting the airflow and temperature when the scalp temperature is high. The distance sensor includes types such as ultrasonic sensors and laser sensors and measures hair length and density. For example, the distance sensor provides information for increasing the airflow when the hair is long. This allows for more accurate detection of scalp and hair conditions by using various sensors. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit inputs data from the humidity sensor, temperature sensor, and distance sensor into a generation AI, which analyzes the data to detect the scalp and hair conditions.

[0067] The analysis unit analyzes information using a machine learning algorithm. Machine learning algorithms include, for example, decision trees and support vector machines, and analyze the information provided by the collection unit. For example, the analysis unit uses a decision tree algorithm to analyze information such as scalp humidity and temperature, hair length and density, and determine the optimal drying method. The analysis unit can also use a support vector machine to classify the collected data and determine the drying method. Furthermore, the analysis unit can also use a machine learning algorithm to improve the accuracy of the analysis based on past data. For example, the analysis unit learns from past data and analyzes the user's hair care patterns to provide a more accurate drying method. Thus, the use of a machine learning algorithm improves the accuracy of the analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the data provided by the collection unit into a generation AI, which then analyzes the data and determines the optimal drying method.

[0068] The adjustment unit can automatically adjust the air volume and temperature. The adjustment unit automatically adjusts the air volume and temperature based on, for example, feedback from a sensor. For example, the adjustment unit reduces the air volume and sets a low temperature when the scalp humidity is high. The adjustment unit can also increase the air volume and set a high temperature when the hair is long. Furthermore, the adjustment unit can automatically adjust the air volume and temperature to achieve optimal drying without the user having to perform any special operations. For example, the adjustment unit automatically adjusts the air volume and temperature when the user simply holds the hair dryer. This allows optimal drying to be achieved through automatic adjustment without the user having to perform any special operations. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data from a sensor into a generation AI, which analyzes the data and automatically adjusts the air volume and temperature.

[0069] The collection unit can automatically detect information when a user simply brings the hair dryer close to the scalp. The collection unit can automatically detect information when a user simply brings the hair dryer close to the scalp, for example, by adjusting the sensitivity and detection range of the sensor. For example, the collection unit can detect the humidity and temperature of the scalp in real time and measure the length and density of hair. The collection unit can also automatically collect information when a user simply brings the hair dryer close to the scalp. This allows the user to easily collect information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data from the sensor into a generation AI, which can analyze the data and detect information.

[0070] The adjustment unit can weaken or strengthen the airflow and lower or raise the temperature depending on the condition of the scalp or the length of the hair. For example, when the scalp humidity is high, the adjustment unit weakens the airflow and sets the temperature low. For example, when the scalp humidity is low, the adjustment unit strengthens the airflow and sets the temperature high. The adjustment unit can also strengthen the airflow and set the temperature high when the hair is long. For example, when the hair is short, the adjustment unit weakens the airflow and sets the temperature low. This makes it possible to provide optimal drying according to the condition of the scalp and hair. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data from a sensor into the generation AI, which analyzes the data and adjusts the airflow and temperature.

[0071] The analysis unit can determine the drying method based on information on scalp humidity or temperature, and hair length or density. The analysis unit determines the optimal drying method based on information such as scalp humidity and temperature, and hair length and density. For example, the analysis unit can reduce the airflow and set the temperature low when scalp humidity is high. The analysis unit can also increase the airflow and set the temperature high when hair is long. Furthermore, the analysis unit can comprehensively analyze information such as scalp humidity and temperature, and hair length and density to provide the optimal drying method. This makes it possible to provide the optimal drying method based on a variety of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data provided by the collection unit into the generation AI, which then analyzes the data and determines the optimal drying method.

[0072] The adjustment unit can automatically adjust the air volume and temperature to achieve optimal drying without the user having to perform any special operations. The adjustment unit automatically adjusts the air volume and temperature based on, for example, feedback from a sensor. For example, the adjustment unit can reduce the air volume and set a low temperature when the scalp is humid. The adjustment unit can also increase the air volume and set a high temperature when the hair is long. Furthermore, the adjustment unit can automatically adjust the air volume and temperature to achieve optimal drying without the user having to perform any special operations. For example, the adjustment unit automatically adjusts the air volume and temperature when the user simply holds the hair dryer. This allows the user to easily achieve optimal drying. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data from a sensor into a generation AI, which analyzes the data and automatically adjusts the air volume and temperature.

[0073] The collection unit can estimate the user's emotions and adjust the timing of collecting scalp and hair information based on the estimated user emotions. The collection unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, if the user is relaxed, the collection unit can delay the collection timing to collect more detailed information. Furthermore, if the user is in a hurry, the collection unit can also accelerate the collection timing to quickly collect information. Furthermore, if the user is feeling stressed, the collection unit can adjust the collection timing to reduce the user's burden. This allows the timing of information collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into a generation AI, which analyzes the data to estimate the emotion and adjust the collection timing.

[0074] The collection unit can include sensors that detect not only scalp humidity and temperature, but also scalp oil content and pH value. For example, the collection unit can include a sensor that detects scalp oil content to provide a drying method that takes the oil balance into consideration. For example, if the scalp is highly oily, the collection unit can reduce the airflow and set a low temperature. The collection unit can also include a sensor that detects the scalp's pH value to provide a drying method based on the pH value. For example, if the scalp's pH value is high, the collection unit can reduce the airflow and set a low temperature. Furthermore, the collection unit can comprehensively detect the scalp's humidity, temperature, oil content, and pH value to provide the optimal drying method. This allows for more detailed detection of the scalp condition. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data on scalp oil content and pH value into the generation AI, which can then analyze the data and provide the optimal drying method.

[0075] The collection unit can include a sensor that detects not only hair length but also hair thickness and damage level. For example, the collection unit can include a sensor that detects hair thickness and provide a drying method appropriate to the thickness. For example, if the hair is thick, the collection unit increases the airflow and sets a higher temperature. The collection unit can also include a sensor that detects the hair damage level and provide a drying method that minimizes damage. For example, if the hair damage level is high, the collection unit decreases the airflow and sets a lower temperature. Furthermore, the collection unit can comprehensively detect the hair length, thickness, and damage level and provide the optimal drying method. This allows for more detailed detection of the hair condition. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data on hair thickness and damage level into the generation AI, which can then analyze the data and provide the optimal drying method.

[0076] The collection unit can be added with a function to provide feedback of the detected information to the user in real time. The collection unit, for example, displays the detected scalp humidity and temperature to the user in real time. For example, the collection unit displays the scalp humidity and temperature using a display. The collection unit can also display the detected hair length and thickness to the user in real time. For example, the collection unit notifies the user of the hair length and thickness using an audio notification. Furthermore, the collection unit can suggest the optimal drying method to the user in real time based on the detected information. For example, the collection unit suggests the optimal drying method to the user using a display or an audio notification. This allows the user to check the information in real time. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the detected information to a generation AI, which analyzes the data and provides feedback in real time.

[0077] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, if the user is relaxed, the collection unit can prioritize collecting scalp humidity and temperature. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting hair length and thickness. Furthermore, if the user is stressed, the collection unit can prioritize collecting scalp oil and pH value. This allows the information priority to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into a generation AI, which can analyze the data to estimate emotions and determine the priority of information.

[0078] The collection unit may be added with a function to transmit detected information to the cloud and share it with other devices. For example, the collection unit may transmit detected scalp humidity and temperature to the cloud and share it with other devices. For example, the collection unit may encrypt the data and transmit it to the cloud. The collection unit may also transmit detected hair length and thickness to the cloud and share it with other devices. For example, the collection unit may transmit data using Bluetooth or Wi-Fi. Furthermore, the collection unit may transmit detected information to the cloud and work with other devices to provide an optimal drying method. For example, the collection unit may work with other devices to adjust the drying method based on the data transmitted to the cloud. This allows the information to be transmitted to the cloud and shared with other devices. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input detected information to a generation AI, which may analyze the data and transmit it to the cloud to share it with other devices.

[0079] The collection unit can be added with a function to automatically record the user's hair care history based on the detected information. The collection unit automatically records the user's hair care history based on, for example, the detected scalp humidity and temperature. For example, the collection unit records the date, the products used, the treatment details, etc. The collection unit can also automatically record the user's hair care history based on the detected hair length and thickness. For example, the collection unit can provide an optimal drying method by referring to past data. Furthermore, the collection unit can automatically record the user's hair care history based on the detected information and provide an optimal drying method by referring to past data. This allows the user's hair care history to be automatically recorded. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the detected information into a generation AI, which can analyze the data and record the hair care history.

[0080] The collection unit can add a function to suggest optimal hair care products to a user based on the detected information. The collection unit can suggest optimal hair care products based on, for example, the detected scalp humidity and temperature. For example, the collection unit can suggest products such as shampoo, conditioner, and treatment. The collection unit can also suggest optimal hair care products based on the detected hair length and thickness. For example, the collection unit can suggest products according to hair length and thickness. Furthermore, the collection unit can suggest optimal hair care products by referring to the user's hair care history based on the detected information. For example, the collection unit can suggest optimal products based on past usage history. This makes it possible to suggest optimal hair care products to a user. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the detected information to a generation AI, which can analyze the data and suggest optimal hair care products.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, the analysis unit can display detailed analysis results when the user is relaxed. The analysis unit can also display concise analysis results when the user is in a hurry. Furthermore, the analysis unit can display visually easy-to-understand analysis results when the user is stressed. This allows the display method of the analysis results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into a generation AI, which analyzes the data to estimate emotions and adjust the display method of the analysis results.

[0082] The analysis unit can improve analysis accuracy by using a deep learning algorithm in addition to a machine learning algorithm. The analysis unit can improve analysis accuracy by, for example, combining a machine learning algorithm and a deep learning algorithm. For example, the analysis unit can use a deep learning algorithm to analyze the optimal drying method based on scalp condition and hair length. The analysis unit can also use a machine learning algorithm and a deep learning algorithm together to improve analysis accuracy based on past data. Thus, the use of a deep learning algorithm improves analysis accuracy. Deep learning algorithms include, for example, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data provided by the collection unit into a generation AI, which then analyzes the data and determines the optimal drying method.

[0083] The analysis unit can improve analysis accuracy by referring to past data and learning the user's hair care patterns. The analysis unit, for example, refers to past data and learns the user's hair care patterns. For example, the analysis unit analyzes the user's hair care patterns based on past usage history and past analysis results, and provides an optimal drying method. The analysis unit can also improve analysis accuracy by referring to past data and learning the user's hair care patterns. In this way, by referring to past data, analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data into a generation AI, which analyzes the data and provides an optimal drying method.

[0084] The analysis unit can be added with a function for determining the optimal drying method depending on different seasons and climatic conditions. The analysis unit determines the optimal drying method depending on, for example, the season. For example, the analysis unit reduces the airflow rate and sets the temperature higher in winter. The analysis unit can also determine the optimal drying method depending on climatic conditions. For example, the analysis unit increases the airflow rate and sets the temperature lower when humidity is high. The analysis unit can also provide the optimal drying method taking into account the season and climatic conditions. This makes it possible to provide the optimal drying method depending on the season and climatic conditions. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on seasons and climatic conditions into the generation AI, which can analyze the data and determine the optimal drying method.

[0085] The analysis unit can estimate the user's emotions and prioritize analysis results based on the estimated user emotions. The analysis unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, the analysis unit can prioritize displaying detailed analysis results when the user is relaxed. The analysis unit can also prioritize displaying concise analysis results when the user is in a hurry. Furthermore, the analysis unit can prioritize displaying analysis results that are easy to understand visually when the user is stressed. This allows the prioritization of analysis results to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into a generation AI, which analyzes the data to estimate emotions and prioritize the analysis results.

[0086] The analysis unit can add a function to cooperate with other beauty devices and provide comprehensive beauty advice. The analysis unit, for example, cooperates with other beauty devices to provide comprehensive beauty advice. For example, the analysis unit provides an optimal drying method based on information from a skin care device or a hair care device. The analysis unit can also cooperate with other beauty devices to provide comprehensive beauty advice to a user. For example, the analysis unit provides comprehensive beauty advice based on information from other beauty devices. In this way, comprehensive beauty advice can be provided by cooperation with other beauty devices. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from other beauty devices into a generation AI, which analyzes the data and provides comprehensive beauty advice.

[0087] The analysis unit can add a function to adjust the analysis results by taking into account the user's lifestyle habits and dietary information. The analysis unit provides an optimal drying method by taking into account the user's lifestyle habits, for example. For example, the analysis unit adjusts the drying method by taking into account the user's sleep patterns and exercise habits. The analysis unit can also provide an optimal drying method by taking into account the user's dietary information. For example, the analysis unit adjusts the drying method by taking into account the user's nutritional balance and meal frequency. Furthermore, the analysis unit can adjust the analysis results based on the user's lifestyle habits and dietary information. This makes it possible to provide more appropriate analysis results by taking into account the user's lifestyle habits and dietary information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the user's lifestyle habits and dietary information into the generation AI, which can analyze the data and provide an optimal drying method.

[0088] The analysis unit can add a function to customize the analysis method according to changes in the user's hairstyle. The analysis unit, for example, detects changes in the user's hairstyle and customizes the analysis method. For example, the analysis unit provides an optimal drying method according to changes in the user's hairstyle. The analysis unit can also customize the analysis method taking into account changes in the user's hairstyle. For example, the analysis unit detects changes in hairstyle such as cutting, coloring, or perming, and adjusts the analysis method. This allows the analysis method to be customized according to changes in the user's hairstyle. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on changes in the user's hairstyle into the generation AI, which can analyze the data and provide an optimal drying method.

[0089] The adjustment unit can estimate the user's emotions and change the adjustment method for the air volume and temperature based on the estimated user's emotions. The adjustment unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, if the user is relaxed, the adjustment unit can reduce the air volume and set the temperature low. Furthermore, if the user is in a hurry, the adjustment unit can also increase the air volume and set the temperature high. Furthermore, if the user is feeling stressed, the adjustment unit can adjust the air volume and temperature to reduce the user's burden. This allows the adjustment method for the air volume and temperature to be changed according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the user's facial expression data into the generation AI, which analyzes the data to estimate the emotion and change the adjustment method for the air volume and temperature.

[0090] The adjustment unit can be added with a function to adjust not only the air volume and temperature, but also the air direction and pattern. The adjustment unit, for example, adjusts the air direction to provide an optimal drying method. For example, the adjustment unit adjusts the angle of the air to direct the air toward specific parts of the scalp or hair. The adjustment unit can also adjust the air pattern to provide an optimal drying method. For example, the adjustment unit changes the air strength pattern to efficiently dry the hair. Furthermore, the adjustment unit can comprehensively adjust the air volume, temperature, and air direction and pattern to provide an optimal drying method. In this way, by adjusting the air direction and pattern, a more optimal drying method can be provided. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data from a sensor into the generation AI, which analyzes the data and adjusts the air direction and pattern.

[0091] The adjustment unit can refer to the user's past usage history and automatically select optimal settings. The adjustment unit, for example, refers to the user's past usage history and automatically sets optimal airflow and temperature. For example, the adjustment unit automatically selects settings preferred by the user based on the past usage history. The adjustment unit can also provide an optimal drying method based on the user's past usage history. For example, the adjustment unit refers to the past usage history and automatically sets optimal airflow direction and pattern. In this way, optimal settings can be automatically selected by referring to the user's past usage history. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data of the past usage history into a generation AI, which analyzes the data and automatically selects optimal settings.

[0092] The adjustment unit can be added with a function to provide multiple drying modes according to different hair types and scalp conditions. The adjustment unit provides the optimal drying mode according to, for example, hair type. For example, the adjustment unit provides a drying mode according to hair types such as straight hair, curly hair, and thin hair. The adjustment unit can also provide the optimal drying mode according to scalp condition. For example, the adjustment unit provides a drying mode according to scalp humidity and temperature. Furthermore, the adjustment unit can provide multiple drying modes by comprehensively considering hair type and scalp condition. This makes it possible to provide drying modes according to different hair types and scalp conditions. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, AI, for example. For example, the adjustment unit can input data on hair type and scalp condition into the generation AI, which can analyze the data and provide the optimal drying mode.

[0093] The adjustment unit can estimate the user's emotions and change the frequency of adjusting the air volume and temperature based on the estimated user's emotions. The adjustment unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, the adjustment unit can decrease the frequency of adjusting the air volume and temperature when the user is relaxed. The adjustment unit can also increase the frequency of adjusting the air volume and temperature when the user is in a hurry. Furthermore, the adjustment unit can adjust the frequency of adjusting the air volume and temperature when the user is feeling stressed to reduce the user's burden. This allows the frequency of adjusting the air volume and temperature to be changed according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or without using an AI. For example, the adjustment unit can input the user's facial expression data into the generation AI, which can then analyze the data to infer the emotion and change the frequency of adjusting the air volume and temperature.

[0094] The adjustment unit can add a function to cooperate with other smart devices to provide a comprehensive beauty environment. The adjustment unit, for example, cooperates with other smart devices to provide a comprehensive beauty environment. For example, the adjustment unit provides an optimal drying method based on information from a smart mirror or a smart speaker. The adjustment unit can also cooperate with other smart devices to provide a comprehensive beauty environment to the user. For example, the adjustment unit provides a comprehensive beauty environment based on information from other smart devices. In this way, by cooperating with other smart devices, a comprehensive beauty environment can be provided. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data from other smart devices into a generation AI, which analyzes the data to provide a comprehensive beauty environment.

[0095] The adjustment unit can add a function to customize the method of adjusting the air volume and temperature by reflecting user feedback. The adjustment unit customizes the method of adjusting the air volume and temperature based on, for example, user feedback. For example, the adjustment unit changes the method of adjusting the air volume and temperature based on survey results and post-use evaluations. The adjustment unit can also provide an optimal drying method by reflecting user feedback. For example, the adjustment unit customizes the direction and pattern of the air based on user feedback. This makes it possible to customize the method of adjusting the air volume and temperature by reflecting user feedback. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input user feedback data into a generation AI, which analyzes the data and customizes the method of adjusting the air volume and temperature.

[0096] The adjustment unit can add a function to adjust the air volume and temperature taking into account the user's usage of the hair care product. The adjustment unit, for example, provides the optimal air volume and temperature taking into account the user's usage of the hair care product. For example, the adjustment unit adjusts the air volume and temperature based on the frequency and amount of use. The adjustment unit can also provide the optimal drying method based on the user's usage of the hair care product. For example, the adjustment unit adjusts the air direction and pattern taking into account the type and amount of product used. This makes it possible to provide the optimal air volume and temperature by taking into account the user's usage of the hair care product. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on the usage of the hair care product into the generation AI, which can analyze the data and provide the optimal air volume and temperature. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and adjustment unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit uses sensors in the smart device 14 to collect information such as scalp humidity and temperature, and hair length and density. The analysis unit is realized, for example, by the specific processing unit 290 in the data processing device 12, and analyzes the collected information to determine the optimal drying method. The adjustment unit is realized, for example, by the control unit 46A of the smart device 14, and automatically adjusts the air volume and temperature of the hair dryer based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and adjustment unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit uses sensors in the smart glasses 214 to collect information such as scalp humidity and temperature, and hair length and density. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to determine the optimal drying method. The adjustment unit is realized, for example, by the control unit 46A of the smart glasses 214, and automatically adjusts the air volume and temperature of the dryer based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and adjustment unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit uses a sensor in the headset-type terminal 314 to collect information such as scalp humidity and temperature, and hair length and density. The analysis unit is realized, for example, by the specific processing unit 290 in the data processing device 12, and analyzes the collected information to determine the optimal drying method. The adjustment unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and automatically adjusts the air volume and temperature of the hair dryer based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and adjustment unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit uses sensors in the robot 414 to collect information such as scalp humidity and temperature, and hair length and density. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to determine the optimal drying method. The adjustment unit is realized, for example, by the control unit 46A of the robot 414, and automatically adjusts the air volume and temperature of the dryer based on the analysis results.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The hair dryer system may further include a voice assistant unit. The voice assistant unit can recognize a user's voice commands and adjust the settings of the hair dryer. For example, if the user says, "Increase the airflow," the voice assistant unit recognizes the command and sends an instruction to the adjustment unit to increase the airflow. Alternatively, if the user says, "Decrease the temperature," the voice assistant unit can send an instruction to the adjustment unit to decrease the temperature. Furthermore, the voice assistant unit can learn the user's preferences and past usage history and suggest optimal settings. This allows the user to operate the hair dryer using only their voice without using their hands, improving convenience.

[0099] The hair dryer system can further include an environmental sensor unit. The environmental sensor unit detects the temperature and humidity in the room and provides that information to the analysis unit. For example, if the humidity in the room is high, the analysis unit instructs the adjustment unit to increase the airflow and set the temperature low. Also, if the room temperature is low, the analysis unit can decrease the airflow and set the temperature high. Furthermore, the environmental sensor unit can detect the air quality in the room and recommend a hair dryer with an air purification function if necessary. This allows the user to perform optimal drying according to the indoor environment.

[0100] The hair dryer system can further estimate the user's emotions and adjust the drying method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can reduce the airflow and set the temperature low. If the user is in a hurry, the analysis unit can increase the airflow and set the temperature high. Furthermore, if the user is feeling stressed, the analysis unit can adjust the airflow and temperature to reduce the user's stress. This makes it possible to provide the optimal drying method according to the user's emotions.

[0101] The hair dryer system can also adjust the drying method taking into account the user's usage of hair care products. For example, if the user uses a specific shampoo or treatment, the analysis unit can adjust the airflow and temperature according to the ingredients in the shampoo or treatment. Also, if the user uses hair oil, the analysis unit can reduce the airflow and set a lower temperature. Furthermore, if the user uses a hair mask, the analysis unit can adjust the airflow and temperature to maximize the effectiveness of the product. This allows the system to provide the optimal drying method according to the user's usage of hair care products.

[0102] The hair dryer system can further estimate the user's emotions and adjust the drying time based on the estimated emotions. For example, if the user is relaxed, the analysis unit can extend the drying time to dry slowly. If the user is in a hurry, the analysis unit can shorten the drying time to dry quickly. Furthermore, if the user is stressed, the analysis unit can adjust the drying time to reduce the user's burden. This makes it possible to provide an optimal drying time according to the user's emotions.

[0103] The hair dryer system can also customize the drying method according to changes in the user's hairstyle. For example, if the user cuts or colores their hair, the analysis unit can adjust the air volume and temperature accordingly. Also, if the user gets a perm, the analysis unit can reduce the air volume and set a lower temperature. Furthermore, if the user wears hair extensions, the analysis unit can adjust the air volume and temperature to minimize damage to the extensions. This allows the system to provide the optimal drying method according to changes in the user's hairstyle.

[0104] The hair dryer system can further estimate the user's emotions and select a drying mode based on the estimated emotions. For example, if the user is relaxed, the analysis unit can select massage mode, reducing the airflow and setting the temperature low. If the user is in a hurry, the analysis unit can select quick mode, increasing the airflow and setting the temperature high. Furthermore, if the user is stressed, the analysis unit can select relaxation mode, adjusting the airflow and temperature to reduce the user's stress. This allows the system to provide the optimal drying mode according to the user's emotions.

[0105] The hair dryer system can further adjust the drying method taking into account the user's lifestyle habits and dietary information. For example, the analysis unit can adjust the air volume and temperature taking into account the user's sleep patterns and exercise habits. It can also provide a drying method that is appropriate for the user's nutritional balance and meal frequency taking into account the user's dietary information. Furthermore, the analysis results can be adjusted based on the user's lifestyle habits and dietary information to provide a more appropriate drying method. This makes it possible to provide an optimal drying method that is appropriate for the user's lifestyle habits and dietary information.

[0106] The hair dryer system can further estimate the user's emotions and suggest a drying method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can suggest a drying method that has a relaxing effect. If the user is in a hurry, the analysis unit can suggest a method that allows for quick drying. Furthermore, if the user is stressed, the analysis unit can suggest a drying method that has a stress-reducing effect. This makes it possible to suggest the optimal drying method according to the user's emotions.

[0107] The hair dryer system can also customize the drying method based on the user's hair care history. For example, the analysis unit can provide the optimal air volume and temperature based on past usage history and treatment details. It can also refer to the user's hair care history and suggest the optimal drying method based on past data. Furthermore, the air direction and pattern can be adjusted based on the user's hair care history to provide more effective drying. This makes it possible to provide the optimal drying method according to the user's hair care history.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The collection unit detects the scalp condition or hair length. The collection unit uses humidity, temperature, and distance sensors to collect information such as scalp humidity and temperature, hair length, and density. It can also automatically detect information when the user simply brings the hair dryer close to the scalp. Step 2: The analysis unit analyzes the information collected by the collection unit and determines the optimal drying method. The analysis unit analyzes the information using machine learning algorithms and deep learning algorithms, and determines the optimal drying method based on information such as scalp humidity and temperature, hair length and density. Step 3: The adjustment unit adjusts the airflow and temperature of the dryer based on the drying method determined by the analysis unit. The adjustment unit weakens or strengthens the airflow and lowers or raises the temperature depending on the condition of the scalp and the length of the hair.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] 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.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] 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.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] 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.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] 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.

[0155] 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.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0170] 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.

[0171] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0172] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0173] 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.

[0174] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0175] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0179] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0181] [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collecting unit for detecting the condition of the scalp or the length of the hair; an analysis unit that analyzes the information collected by the collection unit and determines a drying method; an adjustment unit that adjusts the air volume and temperature of the dryer based on the drying method determined by the analysis unit. A system characterized by:

2. The collecting unit Includes humidity sensor, temperature sensor, and distance sensor 2. The system of claim 1.

3. The analysis unit Analyze information using machine learning algorithms 2. The system of claim 1.

4. The adjustment unit Automatically adjusts airflow and temperature 2. The system of claim 1.

5. The collecting unit The user simply holds the hair dryer close to the scalp and the information is automatically detected.

2. The system of claim 1.

6. The adjustment unit Adjust the airflow and temperature depending on the condition of your scalp or the length of your hair.

2. The system of claim 1.

7. The analysis unit Decide on the drying method based on scalp humidity or temperature, hair length or density.

2. The system of claim 1.

8. The adjustment unit Automatically adjusts air volume and temperature for optimal drying without requiring any special user operation.

2. The system of claim 1.

Images