System

Abstract

An object of a system according to an embodiment is to identify a type of a user's face and propose an optimal hairstyle based on the type.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a collation unit, and a provision unit. The reception unit receives an image from a user. The analysis unit analyzes the image received by the reception unit and specifies a type of the face. The collation unit collates the hairstyle based on the type of the face specified by the analysis unit. The providing unit provides the user with the hairstyle collated by the collating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately identify face types and match hairstyles to suggest optimal hairstyles for users, and there is room for improvement.

[0005] The system according to the embodiment aims to identify the face type of the user and to suggest an optimal hairstyle based on the face type. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a matching unit, and a providing unit. The receiving unit receives an image from a user. The analyzing unit analyzes the image received by the receiving unit and identifies a face type. The matching unit matches a hairstyle based on the face type identified by the analyzing unit. The providing unit provides the user with the hairstyle matched by the matching unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify the user's face type and suggest the most suitable hairstyle based on the type. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A hairstyle advice system according to an embodiment of the present invention analyzes an image provided by a user and suggests a hairstyle that best suits that person. When a user inputs an image into the system, AI performs a detailed analysis of the user's face type and suggests a hairstyle that suits that person. For example, the hairstyle advice system analyzes an image provided by the user to identify the face shape and features. Next, the AI ​​compares the identified face type with hairstyle models from around Japan to calculate the optimal hairstyle. For example, a user with a round face may be suggested a hairstyle that will slim the face. Finally, the hairstyle advice system provides the user with an image and description of the suggested hairstyle. This allows the user to easily find the hairstyle that best suits them and avoids mistakes at the salon. This allows the hairstyle advice system to provide the user with highly accurate hairstyle advice. For example, the user can refer to the advice provided by the system to select a hairstyle that suits them. Furthermore, because the AI ​​analyzes the user's face type in detail and compares it with a vast number of hairstyle models, highly accurate advice is provided.

[0029] A hairstyle advice system according to an embodiment includes a receiving unit, an analyzing unit, a comparing unit, and a providing unit. The receiving unit receives an image from a user. The image from the user may be in, for example, a JPEG format, a PNG format, or a resolution, but is not limited to these examples. The receiving unit, for example, uploads the image provided by the user to the system. The receiving unit may also check the image format and resolution and convert it into an appropriate format. The analyzing unit uses AI to analyze the image received by the receiving unit and identify a face type. Examples of face types include, but are not limited to, round face, oval face, and square face. The analyzing unit may, for example, analyze facial shape and features in detail to identify a face type. The analyzing unit may also use AI to analyze features such as facial contours, eye position, and nose shape. The comparing unit compares the face type identified by the analyzing unit with hairstyle models from around Japan. Examples of hairstyle models include, but are not limited to, short hair, long hair, and perm. For example, the matching unit searches a database for a vast number of hairstyle models based on the identified face type and calculates the hairstyle that best suits the person. The matching unit can also use AI to select the optimal hairstyle from the hairstyle models. The providing unit provides the user with the hairstyle matched by the matching unit. The providing unit can also provide the user with, for example, an image and description of the matched hairstyle. The providing unit can also provide the user with advice to support them in requesting a haircut at a beauty salon. For example, the providing unit can display an image and description of a hairstyle suggested by the system, so that the user can refer to the image and description to request a haircut at a beauty salon. This allows the hairstyle advice system according to the embodiment to provide the user with highly accurate hairstyle advice. For example, the user can refer to the advice provided by the system to select a hairstyle that suits them. Furthermore, because the AI ​​analyzes the face type in detail and matches it with a vast number of hairstyle models, highly accurate advice is provided.

[0030] The analysis unit can analyze the shape and features of a face to identify the face type. The analysis unit can, for example, analyze the contours of a face to identify the face type. For example, if the contours of a face are round, the analysis unit can identify the face as round. The analysis unit can also analyze the position of the eyes to identify the face type. For example, if the eyes are positioned toward the center, the analysis unit can identify the face as oval. The analysis unit can also analyze the shape of the nose to identify the face type. For example, if the nose is high, the analysis unit can identify the face as square. This allows for a more accurate face type to be identified by analyzing the shape and features of the face in detail. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input facial contour data to the generation AI and cause the generation AI to identify the face type.

[0031] The matching unit can match the identified face type with hairstyle models from all over Japan. For example, the matching unit searches a database of a huge number of hairstyle models based on the identified face type and calculates the hairstyle that best suits that person. For example, the matching unit can suggest a hairstyle that has a slimming effect to a user with a round face. The matching unit can also suggest a hairstyle that balances the face to a user with an oval face. The matching unit can also suggest a hairstyle that softens the corners of the face to a user with a square face. This makes it possible to suggest the optimal hairstyle by matching with hairstyle models from all over Japan. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the identified face type data into a generation AI and cause the generation AI to calculate the optimal hairstyle.

[0032] The providing unit can provide an image or description of the matched hairstyle to the user. The providing unit, for example, provides an image of the matched hairstyle to the user. For example, the providing unit can display an image of the hairstyle suggested by the system, so that the user can refer to it when requesting a haircut at a hair salon. The providing unit can also provide a description of the matched hairstyle to the user. For example, the providing unit can explain the characteristics of the hairstyle and the cutting procedure. The providing unit can also provide the user with advice to support the user in requesting a haircut at a hair salon. For example, the providing unit can display an image or description of the hairstyle suggested by the system, so that the user can refer to it when requesting a haircut at a hair salon. In this way, by providing an image or description of the hairstyle, the user can have a concrete image. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input image data of the matched hairstyle to a generation AI and cause the generation AI to generate an image.

[0033] The providing unit can provide the user with advice to support the user in requesting a haircut at a hair salon. The providing unit, for example, advises the user on the procedure for haircutting at a hair salon. For example, the providing unit explains the procedure for haircutting of a hairstyle suggested by the system. The providing unit can also advise the user on points to note when haircutting at a hair salon. For example, the providing unit explains points to note when haircutting at a hair salon. The providing unit can also advise the user on how to request a haircut at a hair salon. For example, the providing unit explains how to request a hairstyle suggested by the system. This supports the request for a haircut at a hair salon, allowing the user to select an appropriate hairstyle. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on haircut procedures into a generating AI and cause the generating AI to generate advice.

[0034] The reception unit can analyze the user's past image submission history and select the optimal reception method. For example, the reception unit can analyze the time period in which the user previously submitted images and encourage them to submit images during the same time period. The reception unit can also analyze the device the user previously used and prioritize reception on the same device. The reception unit can also analyze the frequency with which the user previously submitted images and suggest the optimal reception frequency. In this way, by analyzing the past image submission history, the optimal reception method for the user can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past image submission history data into a generation AI and have the generation AI select the optimal reception method.

[0035] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving images. For example, when the user inputs their current situation, the reception unit performs filtering according to the situation. The reception unit can also register the user's areas of interest in advance and receive only images related to those areas. The reception unit can also preferentially receive related images based on the user's current location information. This allows for filtering according to the user's situation and areas of interest, thereby receiving more appropriate images. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's current situation data to the generation AI and have the generation AI perform filtering.

[0036] When accepting an image, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user selects voice input, the acceptance unit accepts the image using voice recognition technology. Furthermore, if the user selects text input, the acceptance unit can also accept the image using text analysis technology. Furthermore, if the user selects image input, the acceptance unit can also accept the image using image analysis technology. This allows for smooth acceptance by selecting an acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using AI, for example, or may be performed without using AI. For example, the acceptance unit can input voice data to a generation AI and have the generation AI perform voice recognition.

[0037] When receiving images, the reception unit can prioritize receiving highly relevant images by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving images related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving images related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving images related to the area around the user's home. In this way, highly relevant images can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant images.

[0038] The reception unit can analyze the user's social media activity and receive related images when receiving images. For example, the reception unit preferentially receives images posted by the user on social media. The reception unit can also analyze the user's social media activity and receive related images. The reception unit can also receive related images by referring to the activity of the user's friends on social media. In this way, related images can be efficiently received by analyzing social media activity. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to select related images.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving an image. The reception unit can, for example, suggest an optimal reception method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and improve the reception procedure. The reception unit can also customize the reception interface by referring to the user's past feedback. In this way, the optimal reception method can be provided to the user by reflecting the past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input past feedback data into a generation AI and have the generation AI customize the reception method.

[0040] When analyzing a face, the analysis unit can adjust the level of detail of the analysis based on important facial features. For example, when the facial contours are clear, the analysis unit performs a detailed analysis to identify the face type. Furthermore, when the facial features are unclear, the analysis unit can perform a simplified analysis to identify the face type. Furthermore, when there are many facial features, the analysis unit can perform a detailed analysis to identify the face type. This enables more accurate analysis by adjusting the level of detail of the analysis based on important facial features. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input facial feature data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] When analyzing a face, the analysis unit can apply different analysis algorithms depending on the face category. For example, in the case of a round face, the analysis unit can apply an analysis algorithm specifically for round faces. In addition, in the case of an oval face, the analysis unit can also apply an analysis algorithm specifically for oval faces. In addition, in the case of a square face, the analysis unit can also apply an analysis algorithm specifically for square faces. In this way, by applying an analysis algorithm according to the face category, more appropriate analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input face category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0042] When analyzing a face, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the analysis algorithm. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. In this way, by referring to the past analysis results, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] When analyzing a face, the analysis unit can determine the analysis priority based on the time of image submission. The analysis unit can determine the analysis priority based on, for example, the time period when the image was submitted. The analysis unit can also determine the analysis priority based on the date when the image was submitted. The analysis unit can also determine the analysis priority based on the frequency with which the image was submitted. This enables efficient analysis by determining the analysis priority based on the time when the image was submitted. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input submission time data into the generation AI and have the generation AI determine the analysis priority.

[0044] When analyzing faces, the analysis unit can adjust the order of analysis based on the relevance of the faces. For example, if the relevance of the faces is high, the analysis unit performs the analysis as a priority. Furthermore, if the relevance of the faces is low, the analysis unit can also perform the analysis later. Furthermore, if the relevance of the faces is medium, the analysis unit can also perform the analysis in order. In this way, by adjusting the order of analysis based on the relevance of the faces, efficient analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input face relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0045] When analyzing a face, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide the analysis result using detailed technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide the analysis result in simple language. Alternatively, the analysis unit can provide the analysis result using appropriate technical terminology according to the user's level of expertise. This allows for the provision of analysis results that are easy to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0046] The matching unit can improve the accuracy of matching by taking into account the interrelationships between hairstyles during matching. For example, the matching unit takes into account the interrelationships between hairstyles and prioritizes matching of related hairstyles. The matching unit can also analyze the interrelationships between hairstyles and improve the accuracy of matching. The matching unit can also suggest an optimal hairstyle based on the interrelationships between hairstyles. In this way, the accuracy of matching is improved by taking the interrelationships between hairstyles into consideration. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input hairstyle interrelationship data into the generation AI and cause the generation AI to improve the accuracy of matching.

[0047] The matching unit can perform matching while taking into account attribute information of the hairstyle model. For example, the matching unit performs matching while taking into account the age of the hairstyle model. The matching unit can also perform matching while taking into account the gender of the hairstyle model. The matching unit can also perform matching while taking into account the hair type of the hairstyle model. This makes it possible to propose a more appropriate hairstyle by taking into account the attribute information of the model. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input attribute information data of the model to the generation AI and cause the generation AI to perform matching.

[0048] The matching unit can weight the matching based on the popularity of the hairstyle when matching. For example, the matching unit prioritizes matching of hairstyles with high popularity. The matching unit can also prioritize matching of hairstyles with low popularity. The matching unit can also match hairstyles with medium popularity in order. In this way, by weighting the matching based on popularity, it is possible to propose hairstyles that reflect the latest trends. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit inputs popularity data to the generation AI and causes the generation AI to perform the matching weighting.

[0049] The matching unit can perform matching while taking into account the geographical distribution of hairstyles. For example, the matching unit can consider the geographical distribution of hairstyles and suggest the optimal hairstyle for each region. The matching unit can also analyze the geographical distribution of hairstyles to improve the accuracy of matching. The matching unit can also perform matching that reflects regional trends based on the geographical distribution of hairstyles. This makes it possible to suggest hairstyles that reflect regional trends by taking the geographical distribution into consideration. Some or all of the above-mentioned processing in the matching unit can be performed using, for example, AI, or can be performed without using AI. For example, the matching unit can input geographical distribution data into a generation AI and have the generation AI perform matching.

[0050] The matching unit can improve the accuracy of matching by referring to literature related to hairstyles during matching. For example, the matching unit refers to literature related to hairstyles and performs matching that reflects the latest trends. The matching unit can also analyze literature related to hairstyles to improve the accuracy of matching. The matching unit can also suggest an optimal hairstyle based on literature related to hairstyles. This makes it possible to perform matching that reflects the latest trends by referring to related literature. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of matching.

[0051] The matching unit can perform matching while taking into account the market value of the hairstyle. For example, the matching unit prioritizes matching of hairstyles with high market value. The matching unit can also perform matching after hairstyles with low market value. The matching unit can also match hairstyles with medium market value in order. This makes it possible to propose hairstyles with higher value by taking market value into consideration. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input market value data into the generation AI and have the generation AI perform matching.

[0052] The providing unit can adjust the level of detail to be provided based on the importance of the hairstyle when providing the information. For example, the providing unit provides a detailed description for a hairstyle with high importance. The providing unit can also provide a simplified description for a hairstyle with low importance. The providing unit can also provide a description with an appropriate level of detail for a hairstyle with medium importance. In this way, by adjusting the level of detail to be provided based on the importance of the hairstyle, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input hairstyle importance data to the generating AI and cause the generating AI to adjust the level of detail to be provided.

[0053] The providing unit can apply different providing algorithms depending on the hairstyle category when providing information. For example, the providing unit can apply a casual providing algorithm to a casual hairstyle. Furthermore, the providing unit can also apply a formal providing algorithm to a formal hairstyle. Furthermore, the providing unit can also apply a providing algorithm specialized for trends to a trendy hairstyle. In this way, by applying a providing algorithm according to the hairstyle category, more appropriate information can be provided. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input hairstyle category data to the generation AI and cause the generation AI to apply the providing algorithm.

[0054] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the information. The providing unit can improve the accuracy of the current provision, for example, based on the user's past provision results. The providing unit can also analyze the user's past provision results and improve the provision algorithm. The providing unit can also adjust the level of detail of the provision by referring to the user's past provision results. This improves the accuracy of the provision by referring to the past provision results. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0055] The providing unit can determine the priority of provision based on the time of submission of the hairstyle at the time of provision. The providing unit can determine the priority of provision based on, for example, the time period when the hairstyle was submitted. The providing unit can also determine the priority of provision based on the date when the hairstyle was submitted. The providing unit can also determine the priority of provision based on the frequency with which the hairstyle was submitted. This enables efficient provision by determining the priority of provision based on the time of submission. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input submission time data into the generation AI and cause the generation AI to determine the priority of provision.

[0056] The providing unit can adjust the order of providing information based on the relevance of the hairstyle when providing the information. For example, if the relevance of the hairstyle is high, the providing unit provides the information preferentially. Furthermore, if the relevance of the hairstyle is low, the providing unit can also provide the information later. Furthermore, if the relevance of the hairstyle is medium, the providing unit can also provide the information in order. In this way, by adjusting the order of providing information based on the relevance, more appropriate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the hairstyle relevance data to the generating AI and cause the generating AI to adjust the order of providing the information.

[0057] The providing unit can adjust the use of technical terminology in the provided information depending on the user's level of expertise when providing the information. For example, if the user has technical expertise, the providing unit can provide the information using detailed technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can provide the information in simple language. Furthermore, the providing unit can provide the information using appropriate technical terminology depending on the user's level of expertise. This allows for the provision of information that is easy to understand by adjusting the use of technical terminology depending on the user's level of expertise. Some or all of the above-described processing by the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's level of expertise data into the generating AI and cause the generating AI to use technical terminology.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can analyze the user's past hairstyle history and suggest the most suitable hairstyle. For example, the reception unit can analyze the trends in hairstyles selected by the user in the past and suggest similar styles. The reception unit can also take into account hairstyles that the user has avoided in the past and make suggestions that exclude them. Furthermore, the reception unit can make suggestions according to the season or event based on the user's past hairstyle history. This makes it possible to make more personalized hairstyle suggestions by utilizing the user's past history.

[0060] The analysis unit can analyze the color and tone of the user's skin and identify a hairstyle based on the analysis. For example, if the user has light skin, the analysis unit can suggest a light-colored hairstyle. Also, if the user has warm skin, the analysis unit can suggest a warm-colored hairstyle. Furthermore, the analysis unit can suggest a hair color based on the user's skin color and tone. This makes it possible to suggest a hairstyle that matches the user's skin color and tone.

[0061] The matching unit can suggest hairstyles based on the user's lifestyle. For example, if the user has an active lifestyle, the matching unit can suggest a low-maintenance hairstyle. Also, if the user prioritizes business use, the matching unit can suggest a formal hairstyle. Furthermore, if the user is engaged in a creative profession, the matching unit can suggest a unique hairstyle. This makes it possible to suggest hairstyles that match the user's lifestyle.

[0062] The providing unit can customize the content of the hairstyle suggestions based on the user's past feedback. For example, the providing unit can prioritize suggestions of hairstyles that the user has liked in the past. The providing unit can also make suggestions that exclude hairstyles that the user has avoided in the past. Furthermore, the providing unit can analyze the user's past feedback and improve the content of the suggestions. This makes it possible to make more personalized hairstyle suggestions by utilizing the user's past feedback.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit receives an image from a user. The image from the user may be in, but is not limited to, a JPEG format, a PNG format, or a resolution. The reception unit, for example, uploads the image provided by the user to the system. The reception unit may also check the image format and resolution and convert it to an appropriate format. Step 2: The analysis unit uses AI to analyze the image received by the reception unit and identify the face type. Examples of face types include, but are not limited to, round face, oval face, and square face. The analysis unit, for example, analyzes the shape and features of the face in detail to identify the face type. The analysis unit can also use AI to analyze features such as the facial contour, eye position, and nose shape. Step 3: The matching unit matches the face type identified by the analysis unit with hairstyle models from around Japan. Hairstyle models include, but are not limited to, short, long, and permed styles. For example, the matching unit searches a database of a vast number of hairstyle models based on the identified face type and calculates the hairstyle that best suits the person. The matching unit can also use AI to select the optimal hairstyle from the hairstyle models. Step 4: The providing unit provides the user with the hairstyle matched by the matching unit. The providing unit, for example, provides the user with an image and description of the matched hairstyle. The providing unit can also provide the user with advice to support them in requesting a haircut at a hair salon. For example, the providing unit displays an image and description of the hairstyle suggested by the system, so that the user can refer to them when requesting a haircut at a hair salon.

[0065] (Example 2) A hairstyle advice system according to an embodiment of the present invention analyzes an image provided by a user and suggests a hairstyle that best suits that person. When a user inputs an image into the system, AI performs a detailed analysis of the user's face type and suggests a hairstyle that suits that person. For example, the hairstyle advice system analyzes an image provided by the user to identify the face shape and features. Next, the AI ​​compares the identified face type with hairstyle models from around Japan to calculate the optimal hairstyle. For example, a user with a round face may be suggested a hairstyle that will slim the face. Finally, the hairstyle advice system provides the user with an image and description of the suggested hairstyle. This allows the user to easily find the hairstyle that best suits them and avoids mistakes at the salon. This allows the hairstyle advice system to provide the user with highly accurate hairstyle advice. For example, the user can refer to the advice provided by the system to select a hairstyle that suits them. Furthermore, because the AI ​​analyzes the user's face type in detail and compares it with a vast number of hairstyle models, highly accurate advice is provided.

[0066] A hairstyle advice system according to an embodiment includes a receiving unit, an analyzing unit, a comparing unit, and a providing unit. The receiving unit receives an image from a user. The image from the user may be in, for example, a JPEG format, a PNG format, or a resolution, but is not limited to these examples. The receiving unit, for example, uploads the image provided by the user to the system. The receiving unit may also check the image format and resolution and convert it into an appropriate format. The analyzing unit uses AI to analyze the image received by the receiving unit and identify a face type. Examples of face types include, but are not limited to, round face, oval face, and square face. The analyzing unit may, for example, analyze facial shape and features in detail to identify a face type. The analyzing unit may also use AI to analyze features such as facial contours, eye position, and nose shape. The comparing unit compares the face type identified by the analyzing unit with hairstyle models from around Japan. Examples of hairstyle models include, but are not limited to, short hair, long hair, and perm. For example, the matching unit searches a database for a vast number of hairstyle models based on the identified face type and calculates the hairstyle that best suits the person. The matching unit can also use AI to select the optimal hairstyle from the hairstyle models. The providing unit provides the user with the hairstyle matched by the matching unit. The providing unit can also provide the user with, for example, an image and description of the matched hairstyle. The providing unit can also provide the user with advice to support them in requesting a haircut at a beauty salon. For example, the providing unit can display an image and description of a hairstyle suggested by the system, so that the user can refer to the image and description to request a haircut at a beauty salon. This allows the hairstyle advice system according to the embodiment to provide the user with highly accurate hairstyle advice. For example, the user can refer to the advice provided by the system to select a hairstyle that suits them. Furthermore, because the AI ​​analyzes the face type in detail and matches it with a vast number of hairstyle models, highly accurate advice is provided.

[0067] The analysis unit can analyze the shape and features of a face to identify the face type. The analysis unit can, for example, analyze the contours of a face to identify the face type. For example, if the contours of a face are round, the analysis unit can identify the face as round. The analysis unit can also analyze the position of the eyes to identify the face type. For example, if the eyes are positioned toward the center, the analysis unit can identify the face as oval. The analysis unit can also analyze the shape of the nose to identify the face type. For example, if the nose is high, the analysis unit can identify the face as square. This allows for a more accurate face type to be identified by analyzing the shape and features of the face in detail. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input facial contour data to the generation AI and cause the generation AI to identify the face type.

[0068] The matching unit can match the identified face type with hairstyle models from all over Japan. For example, the matching unit searches a database of a huge number of hairstyle models based on the identified face type and calculates the hairstyle that best suits that person. For example, the matching unit can suggest a hairstyle that has a slimming effect to a user with a round face. The matching unit can also suggest a hairstyle that balances the face to a user with an oval face. The matching unit can also suggest a hairstyle that softens the corners of the face to a user with a square face. This makes it possible to suggest the optimal hairstyle by matching with hairstyle models from all over Japan. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the identified face type data into a generation AI and cause the generation AI to calculate the optimal hairstyle.

[0069] The providing unit can provide an image or description of the matched hairstyle to the user. The providing unit, for example, provides an image of the matched hairstyle to the user. For example, the providing unit can display an image of the hairstyle suggested by the system, so that the user can refer to it when requesting a haircut at a hair salon. The providing unit can also provide a description of the matched hairstyle to the user. For example, the providing unit can explain the characteristics of the hairstyle and the cutting procedure. The providing unit can also provide the user with advice to support the user in requesting a haircut at a hair salon. For example, the providing unit can display an image or description of the hairstyle suggested by the system, so that the user can refer to it when requesting a haircut at a hair salon. In this way, by providing an image or description of the hairstyle, the user can have a concrete image. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input image data of the matched hairstyle to a generation AI and cause the generation AI to generate an image.

[0070] The providing unit can provide the user with advice to support the user in requesting a haircut at a hair salon. The providing unit, for example, advises the user on the procedure for haircutting at a hair salon. For example, the providing unit explains the procedure for haircutting of a hairstyle suggested by the system. The providing unit can also advise the user on points to note when haircutting at a hair salon. For example, the providing unit explains points to note when haircutting at a hair salon. The providing unit can also advise the user on how to request a haircut at a hair salon. For example, the providing unit explains how to request a hairstyle suggested by the system. This supports the request for a haircut at a hair salon, allowing the user to select an appropriate hairstyle. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on haircut procedures into a generating AI and cause the generating AI to generate advice.

[0071] The reception unit can estimate the user's emotions and adjust the timing of image reception based on the estimated user emotions. For example, if the user is relaxed, the reception unit can immediately receive images, providing a smooth experience. Furthermore, if the user is nervous, the reception unit can slightly delay image reception to give the user time to calm down. Furthermore, if the user is in a hurry, the reception unit can quickly receive images to minimize waiting time. By adjusting the timing of image reception according to the user's emotions, images can be received at the optimal timing for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0072] The reception unit can analyze the user's past image submission history and select the optimal reception method. For example, the reception unit can analyze the time period in which the user previously submitted images and encourage them to submit images during the same time period. The reception unit can also analyze the device the user previously used and prioritize reception on the same device. The reception unit can also analyze the frequency with which the user previously submitted images and suggest the optimal reception frequency. In this way, by analyzing the past image submission history, the optimal reception method for the user can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past image submission history data into a generation AI and have the generation AI select the optimal reception method.

[0073] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving images. For example, when the user inputs their current situation, the reception unit performs filtering according to the situation. The reception unit can also register the user's areas of interest in advance and receive only images related to those areas. The reception unit can also preferentially receive related images based on the user's current location information. This allows for filtering according to the user's situation and areas of interest, thereby receiving more appropriate images. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's current situation data to the generation AI and have the generation AI perform filtering.

[0074] When accepting an image, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user selects voice input, the acceptance unit accepts the image using voice recognition technology. Furthermore, if the user selects text input, the acceptance unit can also accept the image using text analysis technology. Furthermore, if the user selects image input, the acceptance unit can also accept the image using image analysis technology. This allows for smooth acceptance by selecting an acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using AI, for example, or may be performed without using AI. For example, the acceptance unit can input voice data to a generation AI and have the generation AI perform voice recognition.

[0075] The reception unit can estimate the user's emotions and determine the priority of images to be received based on the estimated user emotions. For example, when the user is excited, the reception unit can prioritize receiving important images. Furthermore, when the user is relaxed, the reception unit can also prioritize receiving images in order. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving images with high urgency. By determining the priority of images according to the user's emotions, important images can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0076] When receiving images, the reception unit can prioritize receiving highly relevant images by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving images related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving images related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving images related to the area around the user's home. In this way, highly relevant images can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant images.

[0077] The reception unit can analyze the user's social media activity and receive related images when receiving images. For example, the reception unit preferentially receives images posted by the user on social media. The reception unit can also analyze the user's social media activity and receive related images. The reception unit can also receive related images by referring to the activity of the user's friends on social media. In this way, related images can be efficiently received by analyzing social media activity. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to select related images.

[0078] The reception unit can customize the reception method by reflecting the user's past feedback when receiving an image. The reception unit can, for example, suggest an optimal reception method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and improve the reception procedure. The reception unit can also customize the reception interface by referring to the user's past feedback. In this way, the optimal reception method can be provided to the user by reflecting the past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input past feedback data into a generation AI and have the generation AI customize the reception method.

[0079] The analysis unit can estimate the user's emotions and adjust the face type analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis to identify the face type. Furthermore, if the user is nervous, the analysis unit can perform a simplified analysis to identify the face type. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis to identify the face type. This enables more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0080] When analyzing a face, the analysis unit can adjust the level of detail of the analysis based on important facial features. For example, when the facial contours are clear, the analysis unit performs a detailed analysis to identify the face type. Furthermore, when the facial features are unclear, the analysis unit can perform a simplified analysis to identify the face type. Furthermore, when there are many facial features, the analysis unit can perform a detailed analysis to identify the face type. This enables more accurate analysis by adjusting the level of detail of the analysis based on important facial features. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input facial feature data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0081] When analyzing a face, the analysis unit can apply different analysis algorithms depending on the face category. For example, in the case of a round face, the analysis unit can apply an analysis algorithm specifically for round faces. In addition, in the case of an oval face, the analysis unit can also apply an analysis algorithm specifically for oval faces. In addition, in the case of a square face, the analysis unit can also apply an analysis algorithm specifically for square faces. In this way, by applying an analysis algorithm according to the face category, more appropriate analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input face category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0082] When analyzing a face, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the analysis algorithm. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. In this way, by referring to the past analysis results, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and adjust the length of the analysis. Furthermore, if the user is nervous, the analysis unit can perform a simplified analysis and adjust the length of the analysis. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis and adjust the length of the analysis. This allows for more appropriate analysis by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0084] When analyzing a face, the analysis unit can determine the analysis priority based on the time of image submission. The analysis unit can determine the analysis priority based on, for example, the time period when the image was submitted. The analysis unit can also determine the analysis priority based on the date when the image was submitted. The analysis unit can also determine the analysis priority based on the frequency with which the image was submitted. This enables efficient analysis by determining the analysis priority based on the time when the image was submitted. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input submission time data into the generation AI and have the generation AI determine the analysis priority.

[0085] When analyzing faces, the analysis unit can adjust the order of analysis based on the relevance of the faces. For example, if the relevance of the faces is high, the analysis unit performs the analysis as a priority. Furthermore, if the relevance of the faces is low, the analysis unit can also perform the analysis later. Furthermore, if the relevance of the faces is medium, the analysis unit can also perform the analysis in order. In this way, by adjusting the order of analysis based on the relevance of the faces, efficient analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input face relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0086] When analyzing a face, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide the analysis result using detailed technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide the analysis result in simple language. Alternatively, the analysis unit can provide the analysis result using appropriate technical terminology according to the user's level of expertise. This allows for the provision of analysis results that are easy to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0087] The matching unit can estimate the user's emotions and adjust the hairstyle matching criteria based on the estimated user's emotions. For example, if the user is relaxed, the matching unit can match a hairstyle using detailed matching criteria. If the user is nervous, the matching unit can also match a hairstyle using simplified matching criteria. If the user is in a hurry, the matching unit can also match a hairstyle using quick matching criteria. This allows the matching criteria to be adjusted according to the user's emotions, thereby suggesting a more appropriate hairstyle. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the matching unit can be performed using AI, for example, or without AI. For example, the matching unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0088] The matching unit can improve the accuracy of matching by taking into account the interrelationships between hairstyles during matching. For example, the matching unit takes into account the interrelationships between hairstyles and prioritizes matching of related hairstyles. The matching unit can also analyze the interrelationships between hairstyles and improve the accuracy of matching. The matching unit can also suggest an optimal hairstyle based on the interrelationships between hairstyles. In this way, the accuracy of matching is improved by taking the interrelationships between hairstyles into consideration. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input hairstyle interrelationship data into the generation AI and cause the generation AI to improve the accuracy of matching.

[0089] The matching unit can perform matching while taking into account attribute information of the hairstyle model. For example, the matching unit performs matching while taking into account the age of the hairstyle model. The matching unit can also perform matching while taking into account the gender of the hairstyle model. The matching unit can also perform matching while taking into account the hair type of the hairstyle model. This makes it possible to propose a more appropriate hairstyle by taking into account the attribute information of the model. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input attribute information data of the model to the generation AI and cause the generation AI to perform matching.

[0090] The matching unit can weight the matching based on the popularity of the hairstyle when matching. For example, the matching unit prioritizes matching of hairstyles with high popularity. The matching unit can also prioritize matching of hairstyles with low popularity. The matching unit can also match hairstyles with medium popularity in order. In this way, by weighting the matching based on popularity, it is possible to propose hairstyles that reflect the latest trends. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit inputs popularity data to the generation AI and causes the generation AI to perform the matching weighting.

[0091] The matching unit can estimate the user's emotions and adjust the display order of the matching results based on the estimated user emotions. For example, if the user is relaxed, the matching unit can display detailed matching results in order. Furthermore, if the user is nervous, the matching unit can also prioritize displaying simplified matching results. Furthermore, if the user is in a hurry, the matching unit can display important matching results first. By adjusting the display order according to the user's emotions, more appropriate matching results can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the matching unit can be performed using, for example, AI, or without AI. For example, the matching unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0092] The matching unit can perform matching while taking into account the geographical distribution of hairstyles. For example, the matching unit can consider the geographical distribution of hairstyles and suggest the optimal hairstyle for each region. The matching unit can also analyze the geographical distribution of hairstyles to improve the accuracy of matching. The matching unit can also perform matching that reflects regional trends based on the geographical distribution of hairstyles. This makes it possible to suggest hairstyles that reflect regional trends by taking the geographical distribution into consideration. Some or all of the above-mentioned processing in the matching unit can be performed using, for example, AI, or can be performed without using AI. For example, the matching unit can input geographical distribution data into a generation AI and have the generation AI perform matching.

[0093] The matching unit can improve the accuracy of matching by referring to literature related to hairstyles during matching. For example, the matching unit refers to literature related to hairstyles and performs matching that reflects the latest trends. The matching unit can also analyze literature related to hairstyles to improve the accuracy of matching. The matching unit can also suggest an optimal hairstyle based on literature related to hairstyles. This makes it possible to perform matching that reflects the latest trends by referring to related literature. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of matching.

[0094] The matching unit can perform matching while taking into account the market value of the hairstyle. For example, the matching unit prioritizes matching of hairstyles with high market value. The matching unit can also perform matching after hairstyles with low market value. The matching unit can also match hairstyles with medium market value in order. This makes it possible to propose hairstyles with higher value by taking market value into consideration. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input market value data into the generation AI and have the generation AI perform matching.

[0095] The providing unit can estimate the user's emotions and adjust the hairstyle provision method based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can select a provision method that includes detailed explanations. Furthermore, if the user is nervous, the providing unit can select a provision method that includes simplified explanations. Furthermore, if the user is in a hurry, the providing unit can select a quick provision method. This allows for a more appropriate hairstyle to be provided by adjusting the provision method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0096] The providing unit can adjust the level of detail to be provided based on the importance of the hairstyle when providing the information. For example, the providing unit provides a detailed description for a hairstyle with high importance. The providing unit can also provide a simplified description for a hairstyle with low importance. The providing unit can also provide a description with an appropriate level of detail for a hairstyle with medium importance. In this way, by adjusting the level of detail to be provided based on the importance of the hairstyle, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input hairstyle importance data to the generating AI and cause the generating AI to adjust the level of detail to be provided.

[0097] The providing unit can apply different providing algorithms depending on the hairstyle category when providing information. For example, the providing unit can apply a casual providing algorithm to a casual hairstyle. Furthermore, the providing unit can also apply a formal providing algorithm to a formal hairstyle. Furthermore, the providing unit can also apply a providing algorithm specialized for trends to a trendy hairstyle. In this way, by applying a providing algorithm according to the hairstyle category, more appropriate information can be provided. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input hairstyle category data to the generation AI and cause the generation AI to apply the providing algorithm.

[0098] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the information. The providing unit can improve the accuracy of the current provision, for example, based on the user's past provision results. The providing unit can also analyze the user's past provision results and improve the provision algorithm. The providing unit can also adjust the level of detail of the provision by referring to the user's past provision results. This improves the accuracy of the provision by referring to the past provision results. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0099] The providing unit can estimate the user's emotions and adjust the length of the information provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide longer information with detailed explanations. Furthermore, if the user is nervous, the providing unit can provide shorter information with simplified explanations. Furthermore, if the user is in a hurry, the providing unit can provide information quickly. By adjusting the length of the information provided according to the user's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0100] The providing unit can determine the priority of provision based on the time of submission of the hairstyle at the time of provision. The providing unit can determine the priority of provision based on, for example, the time period when the hairstyle was submitted. The providing unit can also determine the priority of provision based on the date when the hairstyle was submitted. The providing unit can also determine the priority of provision based on the frequency with which the hairstyle was submitted. This enables efficient provision by determining the priority of provision based on the time of submission. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input submission time data into the generation AI and cause the generation AI to determine the priority of provision.

[0101] The providing unit can adjust the order of providing information based on the relevance of the hairstyle when providing the information. For example, if the relevance of the hairstyle is high, the providing unit provides the information preferentially. Furthermore, if the relevance of the hairstyle is low, the providing unit can also provide the information later. Furthermore, if the relevance of the hairstyle is medium, the providing unit can also provide the information in order. In this way, by adjusting the order of providing information based on the relevance, more appropriate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the hairstyle relevance data to the generating AI and cause the generating AI to adjust the order of providing the information.

[0102] The providing unit can adjust the use of technical terminology in the provided information depending on the user's level of expertise when providing the information. For example, if the user has technical expertise, the providing unit can provide the information using detailed technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can provide the information in simple language. Furthermore, the providing unit can provide the information using appropriate technical terminology depending on the user's level of expertise. This allows for the provision of information that is easy to understand by adjusting the use of technical terminology depending on the user's level of expertise. Some or all of the above-described processing by the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's level of expertise data into the generating AI and cause the generating AI to use technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, matching unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives an image from a user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and identifies a face type using AI. The matching unit is realized by the identification processing unit 290 of the data processing device 12 and matches the identified face type with a hairstyle model. The provision unit is realized by the control unit 46A of the smart device 14 and provides the user with an image and description of the suggested hairstyle. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, matching unit, and providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives an image from a user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and identifies a face type using AI. The matching unit is realized by the identification processing unit 290 of the data processing device 12 and matches the identified face type with a hairstyle model. The providing unit is realized by the control unit 46A of the smart glasses 214 and provides an image and description of a suggested hairstyle to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, matching unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives an image from a user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and identifies a face type using AI. The matching unit is realized by the identification processing unit 290 of the data processing device 12 and matches the identified face type with a hairstyle model. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides the user with an image and description of the suggested hairstyle. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, matching unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives an image from a user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and identifies a face type using AI. The matching unit is realized by the identification processing unit 290 of the data processing device 12 and matches the identified face type with a hairstyle model. The provision unit is realized by the control unit 46A of the robot 414 and provides an image and description of a suggested hairstyle to the user.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The reception unit can analyze the user's past hairstyle history and suggest the most suitable hairstyle. For example, the reception unit can analyze the trends in hairstyles selected by the user in the past and suggest similar styles. The reception unit can also take into account hairstyles that the user has avoided in the past and make suggestions that exclude them. Furthermore, the reception unit can make suggestions according to the season or event based on the user's past hairstyle history. This makes it possible to make more personalized hairstyle suggestions by utilizing the user's past history.

[0105] The analysis unit can analyze the color and tone of the user's skin and identify a hairstyle based on the analysis. For example, if the user has light skin, the analysis unit can suggest a light-colored hairstyle. Also, if the user has warm skin, the analysis unit can suggest a warm-colored hairstyle. Furthermore, the analysis unit can suggest a hair color based on the user's skin color and tone. This makes it possible to suggest a hairstyle that matches the user's skin color and tone.

[0106] The matching unit can suggest hairstyles based on the user's lifestyle. For example, if the user has an active lifestyle, the matching unit can suggest a low-maintenance hairstyle. Also, if the user prioritizes business use, the matching unit can suggest a formal hairstyle. Furthermore, if the user is engaged in a creative profession, the matching unit can suggest a unique hairstyle. This makes it possible to suggest hairstyles that match the user's lifestyle.

[0107] The providing unit can estimate the user's emotions and adjust the suggested hairstyle content based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide a suggestion including a detailed explanation. If the user is nervous, the providing unit can also provide a suggestion including a concise explanation. Furthermore, if the user is excited, the providing unit can also suggest a hairstyle based on trends. This makes it possible to adjust the suggested content according to the user's emotions.

[0108] The providing unit can estimate the user's emotions and adjust the order of hairstyle suggestions based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide detailed suggestions in order. Also, if the user is nervous, the providing unit can prioritize brief suggestions. Furthermore, if the user is in a hurry, the providing unit can provide important suggestions first. This makes it possible to adjust the suggestion order according to the user's emotions.

[0109] The reception unit can estimate the user's emotions and adjust the image reception method based on the estimated user's emotions. For example, if the user is relaxed, the reception unit can select a reception method that includes detailed instructions. If the user is nervous, the reception unit can also select a reception method that includes concise instructions. Furthermore, if the user is in a hurry, the reception unit can also select a quick reception method. This makes it possible to adjust the reception method according to the user's emotions.

[0110] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated user's emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a simplified analysis when the user is nervous. Furthermore, the analysis unit can also perform a quick analysis when the user is in a hurry. This makes it possible to adjust the level of detail of the analysis according to the user's emotions.

[0111] The matching unit can estimate the user's emotion and adjust the hairstyle matching criteria based on the estimated user's emotion. For example, when the user is relaxed, the matching unit can match the hairstyle using detailed matching criteria. When the user is nervous, the matching unit can also match the hairstyle using simplified matching criteria. Furthermore, when the user is in a hurry, the matching unit can also match the hairstyle using quick matching criteria. This makes it possible to adjust the matching criteria according to the user's emotion.

[0112] The providing unit can estimate the user's emotions and adjust the hairstyle providing method based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can select a providing method that includes detailed explanations. If the user is nervous, the providing unit can also select a providing method that includes concise explanations. Furthermore, if the user is in a hurry, the providing unit can also select a quick providing method. This makes it possible to adjust the providing method according to the user's emotions.

[0113] The providing unit can customize the content of the hairstyle suggestions based on the user's past feedback. For example, the providing unit can prioritize suggestions of hairstyles that the user has liked in the past. The providing unit can also make suggestions that exclude hairstyles that the user has avoided in the past. Furthermore, the providing unit can analyze the user's past feedback and improve the content of the suggestions. This makes it possible to make more personalized hairstyle suggestions by utilizing the user's past feedback.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The reception unit receives an image from a user. The image from the user may be in, but is not limited to, a JPEG format, a PNG format, or a resolution. The reception unit, for example, uploads the image provided by the user to the system. The reception unit may also check the image format and resolution and convert it to an appropriate format. Step 2: The analysis unit uses AI to analyze the image received by the reception unit and identify the face type. Examples of face types include, but are not limited to, round face, oval face, and square face. The analysis unit, for example, analyzes the shape and features of the face in detail to identify the face type. The analysis unit can also use AI to analyze features such as the facial contour, eye position, and nose shape. Step 3: The matching unit matches the face type identified by the analysis unit with hairstyle models from around Japan. Hairstyle models include, but are not limited to, short, long, and permed styles. For example, the matching unit searches a database of a vast number of hairstyle models based on the identified face type and calculates the hairstyle that best suits the person. The matching unit can also use AI to select the optimal hairstyle from the hairstyle models. Step 4: The providing unit provides the user with the hairstyle matched by the matching unit. The providing unit, for example, provides the user with an image and description of the matched hairstyle. The providing unit can also provide the user with advice to support them in requesting a haircut at a hair salon. For example, the providing unit displays an image and description of the hairstyle suggested by the system, so that the user can refer to them when requesting a haircut at a hair salon.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] [Explanation of symbols]

[0188] 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 reception unit that receives an image from a user; an analysis unit that analyzes the image received by the reception unit and identifies a face type; a matching unit that matches a hairstyle based on the face type identified by the analysis unit; a providing unit that provides the user with the hairstyle matched by the matching unit. A system characterized by:

2. The analysis unit Analyzes facial shape and features to identify face type 2. The system of claim 1.

3. The collation unit Based on the identified face type, it matches with hairstyle models from all over Japan.

2. The system of claim 1.

4. The providing unit Providing the user with an image or description of the matched hairstyle 2. The system of claim 1.

5. The providing unit Providing advice to help users request a haircut at a hair salon 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of image reception based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past image submission history and select the optimal reception method 2. The system of claim 1.

8. The reception unit When receiving images, they are filtered based on the user's current situation and interests.

2. The system of claim 1.

Images