system

Abstract

The system according to the embodiment aims to propose an ideal hairstyle based on the user's hair type and length, thereby bridging the gap between the image and the finished product. [Solution] A system according to an embodiment includes a collection unit, a suggestion unit, a display unit, and a child support unit. The collection unit collects information on a user's hair type and hair length. The suggestion unit suggests hairstyles based on the information collected by the collection unit. The display unit displays the hairstyles suggested by the suggestion unit as 3D models. The child support unit suggests hairstyles based on information on a child's hair type and hair length entered by a parent.

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] With conventional technology, it is difficult to suggest an ideal hairstyle based on the user's hair type and length, which can lead to a gap between the image and the finished product.

[0005] The system according to the embodiment aims to propose an ideal hairstyle based on the user's hair type and length, thereby bridging the gap between the image and the finished product. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a suggestion unit, a display unit, and a child support unit. The collection unit collects information on a user's hair type and hair length. The suggestion unit suggests a hairstyle based on the information collected by the collection unit. The display unit displays the hairstyle suggested by the suggestion unit as a 3D model. The child support unit suggests a hairstyle based on information on a child's hair type and hair length input by a parent. [Effects of the Invention]

[0007] The system according to the embodiment can propose an ideal hairstyle based on the user's hair type and length, thereby bridging the gap between the image and the finished product. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​haircut ordering system according to an embodiment of the present invention collects information about a user's hair type and length, proposes an optimal hairstyle, and displays it as a 3D model. This system allows users to input their own hair type and length, and the AI ​​then proposes the optimal hairstyle. The user can then check the resulting look on the 3D model. This system also supports children's haircuts. Parents input their child's hair type and length, and the AI ​​then proposes the optimal hairstyle. For example, a user inputs their own hair type and length into the system, including hair thickness, stiffness, whether or not it is curly, and its length. Based on this information, the AI ​​then proposes the optimal hairstyle. The user can review the proposed hairstyle and make any necessary modifications. Furthermore, the system generates a 3D model to bridge the gap between the user's image and the actual result. The user can review this 3D model and preview the final result. For example, the user can check the final result with a shorter or permed hair style on the 3D model. This system also supports children's haircuts. Parents input their child's hair type and length, and the system proposes the optimal hairstyle. This allows parents to order their children's haircuts with peace of mind. In this way, the AI ​​haircut ordering system is an innovative idea that smooths communication with hairdressers and helps realize the ideal hairstyle. Users simply input their hair type and length, and the AI ​​will suggest the optimal hairstyle and allow them to check the finished result on a 3D model. Furthermore, since it also supports children's haircuts, parents can order with peace of mind. This allows the AI ​​haircut ordering system to collect information on the user's hair type and length, suggest the optimal hairstyle, and display it as a 3D model.

[0029] An AI haircut ordering system according to an embodiment includes a collection unit, a suggestion unit, a display unit, and a child support unit. The collection unit collects information about a user's hair type and hair length. The information about the user's hair type and hair length includes, but is not limited to, hair thickness, hardness, whether or not the hair is curly, and length. The collection unit collects information about hair thickness, hardness, whether or not the hair is curly, and length input by the user. The collection unit can also estimate the user's emotions and adjust the timing of collecting the hair type and hair length information based on the estimated user emotions. For example, if the user is relaxed, the timing of collecting the hair type and hair length information can be delayed to allow the user to input the information calmly. The suggestion unit uses the information from the collection unit to suggest an optimal hairstyle using AI. The AI ​​uses, for example, techniques such as machine learning and deep learning to suggest an optimal hairstyle for the user. The suggestion unit can also estimate the user's emotions and adjust the hairstyle suggestion method based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can suggest hairstyles with detailed descriptions. The display unit generates a 3D model based on the information from the suggestion unit and displays it to the user. The 3D model is generated based on, for example, the software used and the display resolution. The display unit can also estimate the user's emotions and adjust the display method of the 3D model based on the estimated user's emotions. For example, if the user is relaxed, a detailed 3D model is displayed so the user can carefully review it. The child support unit suggests an optimal hairstyle based on information about the child's hair type and hair length entered by the parent. The child support unit can also estimate the parent's emotions and adjust the timing of collecting information about the child's hair type and hair length based on the estimated parent's emotions. For example, if the parent is relaxed, the timing of collecting information about the child's hair type and hair length can be delayed to allow the parent to enter the information calmly. In this way, the AI ​​haircut ordering system according to the embodiment can collect information about the user's hair type and hair length, suggest an optimal hairstyle, and display it as a 3D model.

[0030] The collection unit can collect information such as hair thickness, hardness, whether or not curls are present, and length input by the user. The collection unit collects, for example, information such as hair thickness, hardness, whether or not curls are present, and length input by the user. For example, the collection unit clarifies a specific measurement method and standard for hair thickness based on the hair thickness input by the user. Hair thickness includes, for example, thin, normal, and thick. The collection unit also clarifies a specific measurement method and standard for hair hardness based on the hair hardness input by the user. Hair hardness includes, for example, soft, normal, and hard. Furthermore, the collection unit clarifies a specific standard for whether or not curls are present based on the hair hardness input by the user. Whether or not curls are present includes, for example, straight hair, light curls, and strong curls. In this way, by collecting detailed hair information input by the user, more accurate hairstyle suggestions can be made.

[0031] The suggestion unit can use AI to suggest hairstyles based on information from the collection unit. For example, the suggestion unit uses AI to suggest hairstyles based on information from the collection unit. The AI ​​uses technologies such as machine learning and deep learning to suggest the optimal hairstyle for the user. For example, the AI ​​suggests the optimal hairstyle based on information about the user's hair type and hair length. The suggestion unit can also estimate the user's emotions and adjust the method of suggesting hairstyles based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can suggest hairstyles with detailed descriptions. This makes it possible to use AI to suggest the optimal hairstyle for the user.

[0032] The display unit can generate a 3D model based on the information from the suggestion unit and display it to the user. The display unit generates a 3D model based on, for example, the information from the suggestion unit and displays it to the user. The 3D model is generated based on, for example, the software used and the display resolution. For example, the display unit can use specific software to generate the 3D model. The display unit can also adjust how the 3D model is displayed. For example, the display unit adjusts the display resolution and viewpoint based on the user's device information. This allows the user to check the finished product in advance by generating a 3D model and displaying it to the user.

[0033] The child correspondence unit allows the suggestion unit to suggest a hairstyle based on information about the child's hair type and hair length input by the parent. The child correspondence unit allows the suggestion unit to suggest a hairstyle based on, for example, information about the child's hair type and hair length input by the parent. The suggestion unit suggests an optimal hairstyle based on, for example, information about the child's hair type and hair length input by the parent. The child correspondence unit can also estimate the parent's emotions and adjust the timing of collecting information about the child's hair type and hair length based on the estimated parent's emotions. For example, if the parent is relaxed, the timing of collecting information about the child's hair type and hair length can be delayed so that the parent can input the information calmly. This allows the optimal hairstyle to be suggested based on the information about the child's hair type and hair length input by the parent.

[0034] The collection unit can analyze the user's past hairstyle history and select an information collection method. The collection unit, for example, analyzes the user's past hairstyle history and selects an information collection method. For example, the collection unit suggests a method for collecting information on hair type and hair length based on hairstyles selected by the user in the past. The collection unit can also select an information collection method based on the most frequently selected style from the user's past hairstyle history. Furthermore, the collection unit can analyze the user's past hairstyle history and suggest a method for efficiently collecting information on hair type and hair length. This enables efficient information collection based on the past hairstyle history.

[0035] The collection unit can filter the information on hair type and hair length based on the user's current living situation and the season when collecting the information on hair type and hair length. For example, when collecting information on hair type and hair length, the collection unit filters the information based on the user's current living situation and the season. For example, if the collection unit collects information on hair type and hair length in the summer, the collection unit filters the information to suggest hairstyles appropriate for the season. In addition, if the user leads a busy lifestyle, the collection unit can filter the information to suggest hairstyles that are easy to maintain. Furthermore, if the user plans to attend a specific event, the collection unit can filter the information to suggest hairstyles appropriate for the event. This makes it possible to suggest hairstyles appropriate for the user's living situation and the season.

[0036] When collecting information on hair type and hair length, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting information on hair type and hair length, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, if the user lives in a humid area, the collection unit can prioritize collecting information on hairstyles that are resistant to humidity. Furthermore, if the user lives in a cold area, the collection unit can prioritize collecting information on hairstyles that are suitable for cold weather. Furthermore, if the user lives in an urban area, the collection unit can prioritize collecting information on hairstyles that are suitable for urban life. This makes it possible to collect appropriate hairstyle information based on the user's geographical location information.

[0037] The collection unit can analyze the user's social media activities and collect related information when collecting information on hair type and hair length. For example, when collecting information on hair type and hair length, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can collect information on hairstyles that the user frequently shares on social media. The collection unit can also analyze hairstylists that the user follows and hairstyle trends and collect related information. Furthermore, the collection unit can also collect information on hairstyles that the user has many likes and comments on on social media. This makes it possible to collect information on related hairstyles based on the user's social media activities.

[0038] The suggestion unit can adjust the level of detail of the suggestion based on the importance of hair type and hair length when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of hair type and hair length when making a suggestion. For example, if the hair type is fine, the suggestion unit suggests a hairstyle that includes a detailed care method. Also, if the hair length is short, the suggestion unit can suggest a hairstyle that requires less styling effort. Furthermore, if the hair type is hard, the suggestion unit can suggest a hairstyle that includes a styling method to make the hair look soft. This makes it possible to adjust the level of detail of the suggestion appropriately depending on the importance of hair type and hair length.

[0039] The suggestion unit can apply different suggestion algorithms depending on the user's face shape and skin color when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the user's face shape and skin color when making a suggestion. For example, if the user has a round face, the suggestion unit can suggest a hairstyle that makes the face look slimmer. Furthermore, if the user has a light skin color, the suggestion unit can also suggest a light-colored hairstyle. Furthermore, if the user has a long face, the suggestion unit can also suggest a hairstyle that makes the face look shorter. This makes it possible to suggest an appropriate hairstyle depending on the user's face shape and skin color.

[0040] The suggestion unit can determine the priority of suggestions based on the user's past hairstyle history when making suggestions. For example, the suggestion unit determines the priority of suggestions based on the user's past hairstyle history when making suggestions. For example, the suggestion unit preferentially suggests the most frequently selected hairstyle based on hairstyles selected by the user in the past. The suggestion unit can also preferentially suggest the style that has generated the highest satisfaction from the user's past hairstyle history. Furthermore, the suggestion unit can analyze the user's past hairstyle history and preferentially suggest the most suitable style. This makes it possible to determine the appropriate priority of suggestions based on the user's past hairstyle history.

[0041] The suggestion unit can adjust the order of suggestions based on the user's lifestyle when making suggestions. For example, the suggestion unit can adjust the order of suggestions based on the user's lifestyle when making suggestions. For example, if the user leads a busy lifestyle, the suggestion unit can prioritize suggesting hairstyles that are easy to maintain. Also, if the user enjoys outdoor activities, the suggestion unit can prioritize suggesting hairstyles that are suitable for an active lifestyle. Furthermore, if the user leads a sociable lifestyle, the suggestion unit can prioritize suggesting stylish hairstyles. This makes it possible to suggest hairstyles appropriate for the user's lifestyle.

[0042] When displaying a 3D model, the display unit can select the optimal display method by referring to the user's past hairstyle history. For example, when displaying a 3D model, the display unit selects the optimal display method by referring to the user's past hairstyle history. For example, the display unit displays the most frequently selected hairstyle in the 3D model based on hairstyles selected by the user in the past. The display unit can also display the most satisfying style from the user's past hairstyle history in the 3D model. Furthermore, the display unit can analyze the user's past hairstyle history and display the most suitable style in the 3D model. This makes it possible to display an appropriate 3D model based on the user's past hairstyle history.

[0043] The display unit can adjust the display resolution and viewpoint based on the user's device information when displaying a 3D model. For example, when displaying a 3D model, the display unit adjusts the display resolution and viewpoint based on the user's device information. For example, when the user is using a smartphone, the display unit displays the 3D model at a resolution that matches the screen size. Also, when the user is using a tablet, the display unit can display the 3D model at a resolution optimized for a large screen. Furthermore, when the user is using a desktop PC, the display unit can display a detailed 3D model at a high resolution. This allows the 3D model to be displayed at the optimal resolution and viewpoint based on the user's device information.

[0044] The display unit can select the optimal display method by taking into account the user's geographical location information when displaying a 3D model. For example, when displaying a 3D model, the display unit selects the optimal display method by taking into account the user's geographical location information. For example, if the user lives in a humid region, the display unit can display a 3D model of a hairstyle that is resistant to humidity. Furthermore, if the user lives in a cold region, the display unit can display a 3D model of a hairstyle that is suitable for cold weather. Furthermore, if the user lives in an urban area, the display unit can display a 3D model of a hairstyle that is suitable for urban life. This makes it possible to display an appropriate 3D model based on the user's geographical location information.

[0045] The display unit can analyze the user's social media activity and display related hairstyles when displaying a 3D model. For example, the display unit can analyze the user's social media activity and display related hairstyles when displaying a 3D model. For example, the display unit can display 3D models of hairstyles that the user frequently shares on social media. The display unit can also analyze trends in hairdressers and hairstyles that the user follows and display related 3D models. Furthermore, the display unit can display 3D models of hairstyles that the user has many likes and comments on on social media. This makes it possible to display 3D models of related hairstyles based on the user's social media activity.

[0046] The child correspondence unit can analyze the child's past hairstyle history and select the optimal information collection method. The child correspondence unit, for example, analyzes the child's past hairstyle history and selects the optimal information collection method. For example, the child correspondence unit suggests a method for collecting information about hair type and hair length based on hairstyles chosen by the child in the past. The child correspondence unit can also select an information collection method based on the most frequently chosen style from the child's past hairstyle history. Furthermore, the child correspondence unit can analyze the child's past hairstyle history and suggest a method for efficiently collecting information about hair type and hair length. This enables efficient information collection based on the past hairstyle history.

[0047] The child corresponding unit can filter information about a child's hair type and hair length based on the parent's current living situation and the season when collecting the information. For example, when collecting information about a child's hair type and hair length, the child corresponding unit filters the information based on the parent's current living situation and the season. For example, if a parent collects information about a child's hair type and hair length in the summer, the child corresponding unit filters the information to suggest a hairstyle appropriate for the season. In addition, if a parent leads a busy life, the child corresponding unit can filter the information to suggest a hairstyle that is easy to maintain. Furthermore, if a parent plans to attend a specific event, the child corresponding unit can filter the information to suggest a hairstyle appropriate for the event. This makes it possible to suggest an appropriate hairstyle according to the parent's living situation and the season.

[0048] When collecting information on a child's hair type and hair length, the child corresponding unit can prioritize collecting highly relevant information by taking into account the parent's geographical location information. For example, when collecting information on a child's hair type and hair length, the child corresponding unit prioritizes collecting highly relevant information by taking into account the parent's geographical location information. For example, if a parent lives in a humid area, the child corresponding unit can prioritize collecting information on hairstyles that are resistant to humidity. Also, if a parent lives in a cold area, the child corresponding unit can prioritize collecting information on hairstyles that are suitable for cold weather. Furthermore, if a parent lives in an urban area, the child corresponding unit can prioritize collecting information on hairstyles that are suitable for urban life. This makes it possible to collect appropriate hairstyle information based on the parent's geographical location information.

[0049] The child correspondence unit can analyze the social media activities of the parent and collect related information when collecting information on the child's hair type and hair length. For example, the child correspondence unit can analyze the social media activities of the parent and collect related information when collecting information on the child's hair type and hair length. For example, the child correspondence unit can collect information on the child's hairstyle that the parent frequently shares on social media. The child correspondence unit can also analyze the hairdressers and hairstyle trends that the parent follows and collect related information. Furthermore, the child correspondence unit can also collect information on the child's hairstyle that the parent has received the most likes and comments on on social media. This makes it possible to collect related hairstyle information based on the parent's social media activity.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The collection unit can also take the user's health condition into consideration when collecting information about the user's hair type and hair length. For example, if the user has allergies, the collection unit can collect that information and suggest a hairstyle that takes the allergy into consideration. Also, if the user is taking a specific medication, the collection unit can collect information about the hair type and hair length taking into account the effects of the medication. Furthermore, if the user is pregnant, the collection unit can collect information to suggest a hairstyle suitable for pregnancy. This makes it possible to suggest an appropriate hairstyle according to the user's health condition.

[0052] The display unit can also take into account the remaining battery level of the user's device when generating a 3D model based on the information from the suggestion unit and displaying it to the user. For example, the display unit can display a simplified 3D model when the battery level of the user's device is low. Alternatively, the display unit can display a detailed 3D model when the battery level of the user's device is sufficient. Furthermore, the display unit can adjust the display time according to the remaining battery level of the user's device. This makes it possible to display an appropriate 3D model according to the remaining battery level of the user's device.

[0053] The collection unit may also take into account the user's family structure when collecting information about the user's hair type and hair length. For example, if the user has children, the collection unit may also collect information about the children's hair type and hair length to suggest hairstyles suitable for the whole family. If the user lives with elderly parents, the collection unit may also collect information to suggest hairstyles suitable for elderly people. Furthermore, if the user has pets, the collection unit may also collect information about the hair type and hair length taking into account the influence of pet hair. This makes it possible to suggest hairstyles appropriate for the user's family structure.

[0054] The display unit can also take the user's visual preferences into consideration when generating a 3D model based on the information from the suggestion unit and displaying it to the user. For example, if the user prefers bright colors, the display unit can display a bright-colored 3D model. If the user prefers simple designs, the display unit can display a simple 3D model. If the user prefers detailed designs, the display unit can display a detailed 3D model. This makes it possible to display an appropriate 3D model according to the user's visual preferences.

[0055] The collection unit can also take the user's diet into consideration when collecting information about the user's hair type and hair length. For example, if the user is a vegetarian, the collection unit can collect that information and suggest a hairstyle suitable for vegetarians. Also, if the user is consuming a specific nutrient, the collection unit can collect information about the hair type and hair length taking into account the influence of the nutrient. Furthermore, if the user is on a diet, the collection unit can collect information to suggest a hairstyle suitable for the diet. This makes it possible to suggest an appropriate hairstyle according to the user's diet.

[0056] The display unit can also take the user's auditory preferences into consideration when generating a 3D model based on information from the suggestion unit and displaying it to the user. For example, if the user wants to view the 3D model while listening to music, the display unit can display the 3D model while playing music. The display unit can also display the 3D model without audio if the user wants to view the 3D model in a quiet environment. Furthermore, if the user requests a specific audio guide, the display unit can display the 3D model while providing the audio guide. This makes it possible to display an appropriate 3D model according to the user's auditory preferences.

[0057] The collection unit can also take the user's exercise habits into consideration when collecting information about the user's hair type and hair length. For example, if the user exercises regularly, the collection unit can collect that information and suggest a hairstyle suitable for exercise. If the user plays a specific sport, the collection unit can also collect information to suggest a hairstyle suitable for that sport. Furthermore, if the user does not exercise, the collection unit can collect information to suggest a hairstyle suitable for everyday life. This makes it possible to suggest an appropriate hairstyle based on the user's exercise habits.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects information about the user's hair type and length. Specifically, it collects information such as hair thickness, hardness, whether or not it is curly, and its length. The collection unit also estimates the user's emotions and can adjust the timing of information collection if the user is relaxed. Step 2: The suggestion unit uses AI to suggest the most suitable hairstyle based on the information from the collection unit. The AI ​​uses techniques such as machine learning and deep learning to suggest the most suitable hairstyle for the user. The suggestion unit also estimates the user's emotions and suggests hairstyles with detailed descriptions if the user is relaxed. Step 3: The display unit generates a 3D model based on the information from the suggestion unit and displays it to the user. The 3D model is generated based on the software used and the display resolution. The display unit also estimates the user's emotions and displays a detailed 3D model if the user is relaxed. Step 4: The child support unit uses the parent's input information about the child's hair type and length to suggest the optimal hairstyle. The child support unit also estimates the parent's emotions and can adjust the timing of information collection if the parent is relaxed.

[0060] (Example 2) The AI ​​haircut ordering system according to an embodiment of the present invention collects information about a user's hair type and length, proposes an optimal hairstyle, and displays it as a 3D model. This system allows users to input their own hair type and length, and the AI ​​then proposes the optimal hairstyle. The user can then check the resulting look on the 3D model. This system also supports children's haircuts. Parents input their child's hair type and length, and the AI ​​then proposes the optimal hairstyle. For example, a user inputs their own hair type and length into the system, including hair thickness, stiffness, whether or not it is curly, and its length. Based on this information, the AI ​​then proposes the optimal hairstyle. The user can review the proposed hairstyle and make any necessary modifications. Furthermore, the system generates a 3D model to bridge the gap between the user's image and the actual result. The user can review this 3D model and preview the final result. For example, the user can check the final result with a shorter or permed hair style on the 3D model. This system also supports children's haircuts. Parents input their child's hair type and length, and the system proposes the optimal hairstyle. This allows parents to order their children's haircuts with peace of mind. In this way, the AI ​​haircut ordering system is an innovative idea that smooths communication with hairdressers and helps realize the ideal hairstyle. Users simply input their hair type and length, and the AI ​​will suggest the optimal hairstyle and allow them to check the finished result on a 3D model. Furthermore, since it also supports children's haircuts, parents can order with peace of mind. This allows the AI ​​haircut ordering system to collect information on the user's hair type and length, suggest the optimal hairstyle, and display it as a 3D model.

[0061] An AI haircut ordering system according to an embodiment includes a collection unit, a suggestion unit, a display unit, and a child support unit. The collection unit collects information about a user's hair type and hair length. The information about the user's hair type and hair length includes, but is not limited to, hair thickness, hardness, whether or not the hair is curly, and length. The collection unit collects information about hair thickness, hardness, whether or not the hair is curly, and length input by the user. The collection unit can also estimate the user's emotions and adjust the timing of collecting the hair type and hair length information based on the estimated user emotions. For example, if the user is relaxed, the timing of collecting the hair type and hair length information can be delayed to allow the user to input the information calmly. The suggestion unit uses the information from the collection unit to suggest an optimal hairstyle using AI. The AI ​​uses, for example, techniques such as machine learning and deep learning to suggest an optimal hairstyle for the user. The suggestion unit can also estimate the user's emotions and adjust the hairstyle suggestion method based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can suggest hairstyles with detailed descriptions. The display unit generates a 3D model based on the information from the suggestion unit and displays it to the user. The 3D model is generated based on, for example, the software used and the display resolution. The display unit can also estimate the user's emotions and adjust the display method of the 3D model based on the estimated user's emotions. For example, if the user is relaxed, a detailed 3D model is displayed so the user can carefully review it. The child support unit suggests an optimal hairstyle based on information about the child's hair type and hair length entered by the parent. The child support unit can also estimate the parent's emotions and adjust the timing of collecting information about the child's hair type and hair length based on the estimated parent's emotions. For example, if the parent is relaxed, the timing of collecting information about the child's hair type and hair length can be delayed to allow the parent to enter the information calmly. In this way, the AI ​​haircut ordering system according to the embodiment can collect information about the user's hair type and hair length, suggest an optimal hairstyle, and display it as a 3D model.

[0062] The collection unit can collect information such as hair thickness, hardness, whether or not curls are present, and length input by the user. The collection unit collects, for example, information such as hair thickness, hardness, whether or not curls are present, and length input by the user. For example, the collection unit clarifies a specific measurement method and standard for hair thickness based on the hair thickness input by the user. Hair thickness includes, for example, thin, normal, and thick. The collection unit also clarifies a specific measurement method and standard for hair hardness based on the hair hardness input by the user. Hair hardness includes, for example, soft, normal, and hard. Furthermore, the collection unit clarifies a specific standard for whether or not curls are present based on the hair hardness input by the user. Whether or not curls are present includes, for example, straight hair, light curls, and strong curls. In this way, by collecting detailed hair information input by the user, more accurate hairstyle suggestions can be made.

[0063] The suggestion unit can use AI to suggest hairstyles based on information from the collection unit. For example, the suggestion unit uses AI to suggest hairstyles based on information from the collection unit. The AI ​​uses technologies such as machine learning and deep learning to suggest the optimal hairstyle for the user. For example, the AI ​​suggests the optimal hairstyle based on information about the user's hair type and hair length. The suggestion unit can also estimate the user's emotions and adjust the method of suggesting hairstyles based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can suggest hairstyles with detailed descriptions. This makes it possible to use AI to suggest the optimal hairstyle for the user.

[0064] The display unit can generate a 3D model based on the information from the suggestion unit and display it to the user. The display unit generates a 3D model based on, for example, the information from the suggestion unit and displays it to the user. The 3D model is generated based on, for example, the software used and the display resolution. For example, the display unit can use specific software to generate the 3D model. The display unit can also adjust how the 3D model is displayed. For example, the display unit adjusts the display resolution and viewpoint based on the user's device information. This allows the user to check the finished product in advance by generating a 3D model and displaying it to the user.

[0065] The child correspondence unit allows the suggestion unit to suggest a hairstyle based on information about the child's hair type and hair length input by the parent. The child correspondence unit allows the suggestion unit to suggest a hairstyle based on, for example, information about the child's hair type and hair length input by the parent. The suggestion unit suggests an optimal hairstyle based on, for example, information about the child's hair type and hair length input by the parent. The child correspondence unit can also estimate the parent's emotions and adjust the timing of collecting information about the child's hair type and hair length based on the estimated parent's emotions. For example, if the parent is relaxed, the timing of collecting information about the child's hair type and hair length can be delayed so that the parent can input the information calmly. This allows the optimal hairstyle to be suggested based on the information about the child's hair type and hair length input by the parent.

[0066] The collection unit can estimate the user's emotions and adjust the timing of collecting information on hair type and hair length based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of collecting information on hair type and hair length based on the estimated user's emotions. For example, if the user is relaxed, the collection unit delays the timing of collecting information on hair type and hair length, allowing the user to input information calmly. Furthermore, if the user is in a hurry, the collection unit can quickly collect information on hair type and hair length, simplifying the input procedure. Furthermore, if the user is feeling stressed, the collection unit can adjust the timing of collecting information on hair type and hair length, providing an environment in which the user can relax. In this way, by adjusting the timing of information collection according to the user's emotions, the user can input information in a relaxed state.

[0067] The collection unit can analyze the user's past hairstyle history and select an information collection method. The collection unit, for example, analyzes the user's past hairstyle history and selects an information collection method. For example, the collection unit suggests a method for collecting information on hair type and hair length based on hairstyles selected by the user in the past. The collection unit can also select an information collection method based on the most frequently selected style from the user's past hairstyle history. Furthermore, the collection unit can analyze the user's past hairstyle history and suggest a method for efficiently collecting information on hair type and hair length. This enables efficient information collection based on the past hairstyle history.

[0068] The collection unit can filter the information on hair type and hair length based on the user's current living situation and the season when collecting the information on hair type and hair length. For example, when collecting information on hair type and hair length, the collection unit filters the information based on the user's current living situation and the season. For example, if the collection unit collects information on hair type and hair length in the summer, the collection unit filters the information to suggest hairstyles appropriate for the season. In addition, if the user leads a busy lifestyle, the collection unit can filter the information to suggest hairstyles that are easy to maintain. Furthermore, if the user plans to attend a specific event, the collection unit can filter the information to suggest hairstyles appropriate for the event. This makes it possible to suggest hairstyles appropriate for the user's living situation and the season.

[0069] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions and determines the priority of information to be collected based on the estimated user's emotions. For example, when the user is relaxed, the collection unit prioritizes collecting detailed information about hair type and hair length. Also, when the user is in a hurry, the collection unit can prioritize collecting basic information about hair type and hair length. Furthermore, when the user is feeling stressed, the collection unit can reduce the amount of information to be collected so that the user does not feel burdened. In this way, by determining the priority of information according to the user's emotions, efficient information collection is possible.

[0070] When collecting information on hair type and hair length, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting information on hair type and hair length, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, if the user lives in a humid area, the collection unit can prioritize collecting information on hairstyles that are resistant to humidity. Furthermore, if the user lives in a cold area, the collection unit can prioritize collecting information on hairstyles that are suitable for cold weather. Furthermore, if the user lives in an urban area, the collection unit can prioritize collecting information on hairstyles that are suitable for urban life. This makes it possible to collect appropriate hairstyle information based on the user's geographical location information.

[0071] The collection unit can analyze the user's social media activities and collect related information when collecting information on hair type and hair length. For example, when collecting information on hair type and hair length, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can collect information on hairstyles that the user frequently shares on social media. The collection unit can also analyze hairstylists that the user follows and hairstyle trends and collect related information. Furthermore, the collection unit can also collect information on hairstyles that the user has many likes and comments on on social media. This makes it possible to collect information on related hairstyles based on the user's social media activities.

[0072] The suggestion unit can estimate the user's emotion and adjust the hairstyle suggestion method based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the hairstyle suggestion method based on the estimated user's emotion. For example, when the user is relaxed, the suggestion unit can suggest a hairstyle that includes a detailed explanation. When the user is in a hurry, the suggestion unit can also suggest a hairstyle that is concise and to the point. Furthermore, when the user is stressed, the suggestion unit can also suggest a hairstyle that is visually relaxing. In this way, by adjusting the suggestion method according to the user's emotion, more appropriate hairstyles can be suggested.

[0073] The suggestion unit can adjust the level of detail of the suggestion based on the importance of hair type and hair length when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of hair type and hair length when making a suggestion. For example, if the hair type is fine, the suggestion unit suggests a hairstyle that includes a detailed care method. Also, if the hair length is short, the suggestion unit can suggest a hairstyle that requires less styling effort. Furthermore, if the hair type is hard, the suggestion unit can suggest a hairstyle that includes a styling method to make the hair look soft. This makes it possible to adjust the level of detail of the suggestion appropriately depending on the importance of hair type and hair length.

[0074] The suggestion unit can apply different suggestion algorithms depending on the user's face shape and skin color when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the user's face shape and skin color when making a suggestion. For example, if the user has a round face, the suggestion unit can suggest a hairstyle that makes the face look slimmer. Furthermore, if the user has a light skin color, the suggestion unit can also suggest a light-colored hairstyle. Furthermore, if the user has a long face, the suggestion unit can also suggest a hairstyle that makes the face look shorter. This makes it possible to suggest an appropriate hairstyle depending on the user's face shape and skin color.

[0075] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated user's emotions. The suggestion unit, for example, estimates the user's emotions and adjusts the length of the suggestion based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can make a longer suggestion including detailed explanations. Also, if the user is in a hurry, the suggestion unit can make a short suggestion that is concise and to the point. Furthermore, if the user is feeling stressed, the suggestion unit can make a short suggestion that is visually relaxing. In this way, by adjusting the length of the suggestion according to the user's emotions, it is possible to suggest a more appropriate hairstyle.

[0076] The suggestion unit can determine the priority of suggestions based on the user's past hairstyle history when making suggestions. For example, the suggestion unit determines the priority of suggestions based on the user's past hairstyle history when making suggestions. For example, the suggestion unit preferentially suggests the most frequently selected hairstyle based on hairstyles selected by the user in the past. The suggestion unit can also preferentially suggest the style that has generated the highest satisfaction from the user's past hairstyle history. Furthermore, the suggestion unit can analyze the user's past hairstyle history and preferentially suggest the most suitable style. This makes it possible to determine the appropriate priority of suggestions based on the user's past hairstyle history.

[0077] The suggestion unit can adjust the order of suggestions based on the user's lifestyle when making suggestions. For example, the suggestion unit can adjust the order of suggestions based on the user's lifestyle when making suggestions. For example, if the user leads a busy lifestyle, the suggestion unit can prioritize suggesting hairstyles that are easy to maintain. Also, if the user enjoys outdoor activities, the suggestion unit can prioritize suggesting hairstyles that are suitable for an active lifestyle. Furthermore, if the user leads a sociable lifestyle, the suggestion unit can prioritize suggesting stylish hairstyles. This makes it possible to suggest hairstyles appropriate for the user's lifestyle.

[0078] The display unit can estimate the user's emotions and adjust the display method of the 3D model based on the estimated user's emotions. For example, the display unit can estimate the user's emotions and adjust the display method of the 3D model based on the estimated user's emotions. For example, when the user is relaxed, the display unit can display a detailed 3D model so that the user can carefully review it. When the user is in a hurry, the display unit can also display a concise 3D model that focuses on the main points. Furthermore, when the user is feeling stressed, the display unit can display a 3D model that is visually relaxing. This allows for more appropriate display by adjusting the display method of the 3D model according to the user's emotions.

[0079] When displaying a 3D model, the display unit can select the optimal display method by referring to the user's past hairstyle history. For example, when displaying a 3D model, the display unit selects the optimal display method by referring to the user's past hairstyle history. For example, the display unit displays the most frequently selected hairstyle in the 3D model based on hairstyles selected by the user in the past. The display unit can also display the most satisfying style from the user's past hairstyle history in the 3D model. Furthermore, the display unit can analyze the user's past hairstyle history and display the most suitable style in the 3D model. This makes it possible to display an appropriate 3D model based on the user's past hairstyle history.

[0080] The display unit can adjust the display resolution and viewpoint based on the user's device information when displaying a 3D model. For example, when displaying a 3D model, the display unit adjusts the display resolution and viewpoint based on the user's device information. For example, when the user is using a smartphone, the display unit displays the 3D model at a resolution that matches the screen size. Also, when the user is using a tablet, the display unit can display the 3D model at a resolution optimized for a large screen. Furthermore, when the user is using a desktop PC, the display unit can display a detailed 3D model at a high resolution. This allows the 3D model to be displayed at the optimal resolution and viewpoint based on the user's device information.

[0081] The display unit can estimate the user's emotions and adjust the display order of the 3D models based on the estimated user's emotions. For example, the display unit can estimate the user's emotions and adjust the display order of the 3D models based on the estimated user's emotions. For example, if the user is relaxed, the display unit can first display a detailed 3D model so that the user can carefully review it. If the user is in a hurry, the display unit can first display a concise and to-the-point 3D model. Furthermore, if the user is feeling stressed, the display unit can first display a 3D model that is visually relaxing. This allows for more appropriate display by adjusting the display order of the 3D models according to the user's emotions.

[0082] The display unit can select the optimal display method by taking into account the user's geographical location information when displaying a 3D model. For example, when displaying a 3D model, the display unit selects the optimal display method by taking into account the user's geographical location information. For example, if the user lives in a humid region, the display unit can display a 3D model of a hairstyle that is resistant to humidity. Furthermore, if the user lives in a cold region, the display unit can display a 3D model of a hairstyle that is suitable for cold weather. Furthermore, if the user lives in an urban area, the display unit can display a 3D model of a hairstyle that is suitable for urban life. This makes it possible to display an appropriate 3D model based on the user's geographical location information.

[0083] The display unit can analyze the user's social media activity and display related hairstyles when displaying a 3D model. For example, the display unit can analyze the user's social media activity and display related hairstyles when displaying a 3D model. For example, the display unit can display 3D models of hairstyles that the user frequently shares on social media. The display unit can also analyze trends in hairdressers and hairstyles that the user follows and display related 3D models. Furthermore, the display unit can display 3D models of hairstyles that the user has many likes and comments on on social media. This makes it possible to display 3D models of related hairstyles based on the user's social media activity.

[0084] The child response unit can estimate the parent's emotions and adjust the timing of collecting information about the child's hair type and hair length based on the estimated parent's emotions. The child response unit can, for example, estimate the parent's emotions and adjust the timing of collecting information about the child's hair type and hair length based on the estimated parent's emotions. For example, if the parent is relaxed, the child response unit can delay the timing of collecting information about the child's hair type and hair length, allowing the parent to enter information calmly. Furthermore, if the parent is in a hurry, the child response unit can quickly collect information about the child's hair type and hair length, simplifying the input procedure. Furthermore, if the parent is stressed, the child response unit can adjust the timing of collecting information about the child's hair type and hair length, providing an environment in which the parent can relax. Adjusting the timing of information collection according to the parent's emotions allows the parent to enter information in a relaxed state.

[0085] The child correspondence unit can analyze the child's past hairstyle history and select the optimal information collection method. The child correspondence unit, for example, analyzes the child's past hairstyle history and selects the optimal information collection method. For example, the child correspondence unit suggests a method for collecting information about hair type and hair length based on hairstyles chosen by the child in the past. The child correspondence unit can also select an information collection method based on the most frequently chosen style from the child's past hairstyle history. Furthermore, the child correspondence unit can analyze the child's past hairstyle history and suggest a method for efficiently collecting information about hair type and hair length. This enables efficient information collection based on the past hairstyle history.

[0086] The child corresponding unit can filter information about a child's hair type and hair length based on the parent's current living situation and the season when collecting the information. For example, when collecting information about a child's hair type and hair length, the child corresponding unit filters the information based on the parent's current living situation and the season. For example, if a parent collects information about a child's hair type and hair length in the summer, the child corresponding unit filters the information to suggest a hairstyle appropriate for the season. In addition, if a parent leads a busy life, the child corresponding unit can filter the information to suggest a hairstyle that is easy to maintain. Furthermore, if a parent plans to attend a specific event, the child corresponding unit can filter the information to suggest a hairstyle appropriate for the event. This makes it possible to suggest an appropriate hairstyle according to the parent's living situation and the season.

[0087] The child correspondence unit can estimate the parent's emotions and determine the priority of information to be collected based on the estimated parent's emotions. The child correspondence unit can, for example, estimate the parent's emotions and determine the priority of information to be collected based on the estimated parent's emotions. For example, if the parent is relaxed, the child correspondence unit can prioritize collecting detailed information about the child's hair type and hair length. Also, if the parent is in a hurry, the child correspondence unit can prioritize collecting basic information about the child's hair type and hair length. Furthermore, if the parent is stressed, the child correspondence unit can reduce the amount of information to be collected so that the parent does not feel burdened. In this way, by determining the priority of information according to the parent's emotions, efficient information collection is possible.

[0088] When collecting information on a child's hair type and hair length, the child corresponding unit can prioritize collecting highly relevant information by taking into account the parent's geographical location information. For example, when collecting information on a child's hair type and hair length, the child corresponding unit prioritizes collecting highly relevant information by taking into account the parent's geographical location information. For example, if a parent lives in a humid area, the child corresponding unit can prioritize collecting information on hairstyles that are resistant to humidity. Also, if a parent lives in a cold area, the child corresponding unit can prioritize collecting information on hairstyles that are suitable for cold weather. Furthermore, if a parent lives in an urban area, the child corresponding unit can prioritize collecting information on hairstyles that are suitable for urban life. This makes it possible to collect appropriate hairstyle information based on the parent's geographical location information.

[0089] The child correspondence unit can analyze the social media activities of the parent and collect related information when collecting information on the child's hair type and hair length. For example, the child correspondence unit can analyze the social media activities of the parent and collect related information when collecting information on the child's hair type and hair length. For example, the child correspondence unit can collect information on the child's hairstyle that the parent frequently shares on social media. The child correspondence unit can also analyze the hairdressers and hairstyle trends that the parent follows and collect related information. Furthermore, the child correspondence unit can also collect information on the child's hairstyle that the parent has received the most likes and comments on on social media. This makes it possible to collect related hairstyle information based on the parent's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, suggestion unit, display unit, and child support 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 is realized by the control unit 46A of the smart device 14 and collects information such as hair thickness, hardness, whether or not it is curly, and length input by the user. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses AI to suggest an optimal hairstyle. The display unit is realized, for example, by the control unit 46A of the smart device 14 and generates a 3D model and displays it to the user. The child support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal hairstyle based on information about the child's hair type and hair length input by the parent. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, suggestion unit, display unit, and child support 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 is realized by the control unit 46A of the smart glasses 214 and collects information such as hair thickness, hardness, whether or not it is curly, and length input by the user. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses AI to suggest an optimal hairstyle. The display unit is realized, for example, by the control unit 46A of the smart glasses 214 and generates a 3D model and displays it to the user. The child support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal hairstyle based on information about the child's hair type and hair length input by the parent. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, suggestion unit, display unit, and child support 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 is realized by the control unit 46A of the headset-type terminal 314 and collects information such as hair thickness, hardness, whether or not it is curly, and length input by the user. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses AI to suggest an optimal hairstyle. The display unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and generates a 3D model and displays it to the user. The child support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal hairstyle based on information about the child's hair type and hair length input by the parent. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, suggestion unit, display unit, and child support 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 is realized by the control unit 46A of the robot 414 and collects information such as hair thickness, hardness, whether or not it is curly, and length input by the user. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses AI to suggest an optimal hairstyle. The display unit is realized, for example, by the control unit 46A of the robot 414 and generates a 3D model and displays it to the user. The child support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal hairstyle based on information about the child's hair type and hair length input by the parent.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The collection unit can also take the user's health condition into consideration when collecting information about the user's hair type and hair length. For example, if the user has allergies, the collection unit can collect that information and suggest a hairstyle that takes the allergy into consideration. Also, if the user is taking a specific medication, the collection unit can collect information about the hair type and hair length taking into account the effects of the medication. Furthermore, if the user is pregnant, the collection unit can collect information to suggest a hairstyle suitable for pregnancy. This makes it possible to suggest an appropriate hairstyle according to the user's health condition.

[0092] The suggestion unit can also take the user's hobbies and interests into consideration when estimating the user's emotions and suggesting hairstyles based on the estimated user's emotions. For example, if the user likes sports, the suggestion unit can suggest hairstyles suitable for sports. If the user likes music, the suggestion unit can also suggest hairstyles suitable for music events. Furthermore, if the user likes traveling, the suggestion unit can also suggest hairstyles that are easy to maintain while traveling. This makes it possible to suggest appropriate hairstyles according to the user's hobbies and interests.

[0093] The display unit can also take into account the remaining battery level of the user's device when generating a 3D model based on the information from the suggestion unit and displaying it to the user. For example, the display unit can display a simplified 3D model when the battery level of the user's device is low. Alternatively, the display unit can display a detailed 3D model when the battery level of the user's device is sufficient. Furthermore, the display unit can adjust the display time according to the remaining battery level of the user's device. This makes it possible to display an appropriate 3D model according to the remaining battery level of the user's device.

[0094] The collection unit may also take into account the user's family structure when collecting information about the user's hair type and hair length. For example, if the user has children, the collection unit may also collect information about the children's hair type and hair length to suggest hairstyles suitable for the whole family. If the user lives with elderly parents, the collection unit may also collect information to suggest hairstyles suitable for elderly people. Furthermore, if the user has pets, the collection unit may also collect information about the hair type and hair length taking into account the influence of pet hair. This makes it possible to suggest hairstyles appropriate for the user's family structure.

[0095] The suggestion unit can also take the user's occupation into consideration when estimating the user's emotions and proposing a hairstyle based on the estimated user's emotions. For example, if the user is a businessperson, the suggestion unit can suggest a hairstyle suitable for a business setting. Also, if the user is engaged in a creative occupation, the suggestion unit can suggest a unique hairstyle. Furthermore, if the user is a medical professional, the suggestion unit can suggest a clean-looking hairstyle. This makes it possible to suggest an appropriate hairstyle according to the user's occupation.

[0096] The display unit can also take the user's visual preferences into consideration when generating a 3D model based on the information from the suggestion unit and displaying it to the user. For example, if the user prefers bright colors, the display unit can display a bright-colored 3D model. If the user prefers simple designs, the display unit can display a simple 3D model. If the user prefers detailed designs, the display unit can display a detailed 3D model. This makes it possible to display an appropriate 3D model according to the user's visual preferences.

[0097] The collection unit can also take the user's diet into consideration when collecting information about the user's hair type and hair length. For example, if the user is a vegetarian, the collection unit can collect that information and suggest a hairstyle suitable for vegetarians. Also, if the user is consuming a specific nutrient, the collection unit can collect information about the hair type and hair length taking into account the influence of the nutrient. Furthermore, if the user is on a diet, the collection unit can collect information to suggest a hairstyle suitable for the diet. This makes it possible to suggest an appropriate hairstyle according to the user's diet.

[0098] The suggestion unit can also take the user's cultural background into consideration when estimating the user's emotion and suggesting a hairstyle based on the estimated user's emotion. For example, if the user belongs to a specific culture, the suggestion unit can suggest a hairstyle suitable for that culture. Also, if the user has a multicultural background, the suggestion unit can suggest hairstyles suitable for multiple cultures. Furthermore, if the user plans to participate in a specific traditional event, the suggestion unit can suggest a hairstyle suitable for the event. This makes it possible to suggest an appropriate hairstyle according to the user's cultural background.

[0099] The display unit can also take the user's auditory preferences into consideration when generating a 3D model based on information from the suggestion unit and displaying it to the user. For example, if the user wants to view the 3D model while listening to music, the display unit can display the 3D model while playing music. The display unit can also display the 3D model without audio if the user wants to view the 3D model in a quiet environment. Furthermore, if the user requests a specific audio guide, the display unit can display the 3D model while providing the audio guide. This makes it possible to display an appropriate 3D model according to the user's auditory preferences.

[0100] The collection unit can also take the user's exercise habits into consideration when collecting information about the user's hair type and hair length. For example, if the user exercises regularly, the collection unit can collect that information and suggest a hairstyle suitable for exercise. If the user plays a specific sport, the collection unit can also collect information to suggest a hairstyle suitable for that sport. Furthermore, if the user does not exercise, the collection unit can collect information to suggest a hairstyle suitable for everyday life. This makes it possible to suggest an appropriate hairstyle based on the user's exercise habits.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection unit collects information about the user's hair type and length. Specifically, it collects information such as hair thickness, hardness, whether or not it is curly, and its length. The collection unit also estimates the user's emotions and can adjust the timing of information collection if the user is relaxed. Step 2: The suggestion unit uses AI to suggest the most suitable hairstyle based on the information from the collection unit. The AI ​​uses techniques such as machine learning and deep learning to suggest the most suitable hairstyle for the user. The suggestion unit also estimates the user's emotions and suggests hairstyles with detailed descriptions if the user is relaxed. Step 3: The display unit generates a 3D model based on the information from the suggestion unit and displays it to the user. The 3D model is generated based on the software used and the display resolution. The display unit also estimates the user's emotions and displays a detailed 3D model if the user is relaxed. Step 4: The child support unit uses the parent's input information about the child's hair type and length to suggest the optimal hairstyle. The child support unit also estimates the parent's emotions and can adjust the timing of information collection if the parent is relaxed.

[0103] 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.

[0104] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 collection unit that collects information on the user's hair type and hair length; a suggestion unit that suggests a hairstyle based on the information collected by the collection unit; a display unit that displays the hairstyle suggested by the suggestion unit as a 3D model; and a child support unit that suggests hairstyles based on information about the child's hair type and hair length entered by the parent. A system characterized by:

2. The collecting unit Collect information entered by the user such as hair thickness, stiffness, whether it is curly or not, and length 2. The system of claim 1.

3. The proposal unit AI suggests hairstyles based on the information from the collection unit 2. The system of claim 1.

4. The display unit A 3D model is generated based on the information from the suggestion unit and displayed to the user.

2. The system of claim 1.

5. The collecting unit The system estimates the user's emotions and adjusts the timing of collecting information on hair type and length based on the estimated user emotions.

2. The system of claim 1.

6. The collecting unit Analyze the user's past hairstyle history and select the information collection method 2. The system of claim 1.

7. The collecting unit When collecting information on hair type and length, filtering is performed based on the user's current living situation and season.

2. The system of claim 1.

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