system

Abstract

The system according to this embodiment aims to accurately convey the user's desired hairstyle and color. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives the user's request. The analysis unit analyzes the request received by the reception unit. The generation unit generates an image based on the request analyzed by the analysis unit. The provision unit provides the image generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot 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 an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to accurately convey to a beautician the hairstyle and color desired by a user.

[0005] The system according to the embodiment aims to accurately convey the hairstyle and color desired by a user.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives the desires of a user. The analysis unit analyzes the desires received by the reception unit. The generation unit generates an image based on the desires analyzed by the analysis unit. The provision unit provides the image generated by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment can accurately communicate the user's desired hairstyle and color. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The generation AI system according to an embodiment of the present invention is a system that supports users in accurately communicating their desired hairstyle and color to a hairdresser when using a hair salon. This generation AI system works by having the user input their desired hairstyle and color in natural language, and the generation AI analyzing that input to generate images of the hairstyle and color based on the user's wishes. The generated images can be reviewed by the user and shown to the hairdresser. This ensures that the user's wishes are accurately conveyed to the hairdresser, resulting in a highly satisfying service. For example, a user might input specific wishes such as "shoulder length, bangs swept to the side, and a light brown color." This input is sent to the generation AI. Next, the generation AI analyzes the input wishes and generates images of the hairstyle and color based on the user's wishes. The generated images can be reviewed by the user, and if they do not match their wishes, they can input their wishes again, and the generation AI can generate a new image. Finally, the user presents the generated image to the hairdresser, who can then perform the hairstyle and color treatment based on the presented image, ensuring that the user's wishes are accurately conveyed to the hairdresser, resulting in a highly satisfying service. This allows the AI ​​generation system to accurately convey the user's wishes to the hairdresser.

[0029] The generation AI system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives the user's requests. User requests include, for example, hairstyles, colors, and styles, but are not limited to such examples. The reception unit can, for example, receive requests entered by the user in natural language. The analysis unit analyzes the requests received by the reception unit. Analysis is performed by, for example, text analysis, image analysis, and speech analysis, but is not limited to such examples. The analysis unit can, for example, analyze the user's requests using natural language processing technology. The generation unit generates images based on the requests analyzed by the analysis unit. Image generation is performed by, for example, a generation AI, but is not limited to such examples. The generation unit can, for example, use a generation AI to generate images of hairstyles and colors based on the user's requests. The provision unit provides the images generated by the generation unit. Provision is performed by, for example, displaying the images on the user's smartphone or tablet, but is not limited to such examples. The provision unit can, for example, display the generated images on the user's smartphone or tablet so that the user can confirm them. As a result, the generation AI system according to this embodiment can accurately convey the user's wishes to the hairdresser.

[0030] The generation unit can generate images using a generative AI. For example, the generation unit generates images using a generative AI. The generative AI includes, but is not limited to, GANs (Generative Opposite Networks) and VAEs (Variational Autoencoders). For example, the generation unit can generate images of hairstyles and colors based on the user's preferences using a GAN. The generation unit can also generate images of hairstyles and colors based on the user's preferences using a VAE. This improves the accuracy of image generation by using a generative AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the generation unit can generate images using a generative AI model that takes the user's preferences as input and outputs images of hairstyles and colors.

[0031] The generation unit can generate images of hairstyles or colors based on the user's preferences using a generative AI. For example, the generation unit can generate images of hairstyles and colors based on the user's preferences using a generative AI. The generative AI includes, but is not limited to, GANs (Generative Opposite Networks) and VAEs (Variational Autoencoders). For example, the generation unit can generate images of hairstyles and colors based on the user's preferences using a GAN. Alternatively, the generation unit can generate images of hairstyles and colors based on the user's preferences using a VAE. This ensures that the user's preferences are accurately reflected in the generated images. Some or all of the above-described processes in the generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the generation unit can generate images using a generative AI model that takes the user's preferences as input and outputs images of hairstyles and colors.

[0032] The provider can display the generated image on the user's smartphone or tablet. For example, the provider can display the generated image on the user's smartphone or tablet. Smartphones and tablets include, but are not limited to, operating systems such as iOS and Android. For example, the provider can display the generated image on an iOS device. The provider can also display the generated image on an Android device. This allows the user to confirm the generated image. Some or all of the processing described above in the provider may be performed using, for example, AI, or without AI. For example, the provider can display the generated image using an application for displaying images on the user's smartphone or tablet.

[0033] The service provider can support the process of users reviewing generated images and requesting corrections. For example, the service provider can support the process of users reviewing generated images and requesting corrections. The process of requesting corrections includes, but is not limited to, feedback forms and chat functions. For example, the service provider can request corrections to generated images using a feedback form. Alternatively, the service provider can request corrections to generated images using a chat function. This allows users to correct images as they wish. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can support image correction using an AI model that takes user feedback as input and outputs a corrected image.

[0034] The generation unit can continuously improve the generating AI by utilizing historical data or user feedback. For example, the generation unit can continuously improve the generating AI by utilizing historical data or user feedback. Historical data includes, but is not limited to, past images or user feedback. The generation unit can retrain the generating AI using past images, for example. The generation unit can also adjust the parameters of the generating AI using user feedback, for example. This allows the generating AI to be continuously improved and its accuracy to increase. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can continuously improve the generating AI using an AI model that takes historical data or user feedback as input and improves the generating AI.

[0035] The reception desk can analyze the user's past preference history and select the most suitable reception method. For example, the reception desk can analyze the user's past preference history and select the most suitable reception method. Past preference history includes, but is not limited to, past requests and history databases. For example, the reception desk can prioritize receiving similar requests based on the hairstyle or color the user has requested in the past. The reception desk can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest reception methods to be used during specific time periods based on the user's past preference history. This allows the reception desk to select the most suitable reception method based on the user's past preference history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can select the most suitable reception method using an AI model that takes past preference history as input.

[0036] The reception desk can filter the user's requests based on their current hair condition and lifestyle. For example, the reception desk can filter the user's requests based on their current hair condition and lifestyle. Current hair condition includes, but is not limited to, hair length and texture. Lifestyle includes, but is not limited to, work and hobbies. For example, the reception desk can suggest appropriate hairstyles and colors based on the user's current hair length and texture. The reception desk can also suggest practical hairstyles and colors based on the user's lifestyle. Furthermore, the reception desk can suggest hairstyles and colors that minimize damage based on the user's hair health. This allows the reception desk to suggest appropriate preferences based on the user's current hair condition and lifestyle. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can use an AI model that takes data on the user's hair condition and lifestyle as input and suggests appropriate preferences to perform filtering.

[0037] The reception desk can prioritize requests based on the user's geographical location information when a request is received. For example, the reception desk prioritizes requests based on the user's geographical location information when a request is received. Geographical location information includes, but is not limited to, GPS data and address information. For example, the reception desk can prioritize providing information on hair salons close to the user's current location. The reception desk can also prioritize requests for hairstyles and colors desired by the user in a specific area. Furthermore, the reception desk can suggest the most suitable hair salon candidates based on the user's geographical location information. This allows the reception desk to receive the most suitable requests based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can receive requests using an AI model that takes the user's geographical location information as input and prioritizes receiving requests based on their relevance.

[0038] The reception desk can analyze the user's social media activity when a request is received and accept relevant requests. For example, the reception desk can analyze the user's social media activity when a request is received and accept relevant requests. Social media activity includes, but is not limited to, posts and the number of likes. For example, the reception desk can accept requests based on images of hairstyles and colors that the user has shared on social media. The reception desk can also suggest trendy hairstyles and colors based on the user's social media posts. Furthermore, the reception desk can accept requests based on, for example, the hairstyles and colors of the user's social media followers and friends. This allows the reception desk to accept relevant requests based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can accept requests using an AI model that takes the user's social media activity as input and accepts relevant requests.

[0039] The analysis unit can adjust the level of detail of the analysis according to the importance of the desired item during the analysis. For example, the analysis unit can adjust the level of detail of the analysis according to the importance of the desired item during the analysis. The importance of the desired item includes, but is not limited to, user priority or request frequency. For example, the analysis unit can perform a detailed analysis on the desired item that the user considers particularly important. The analysis unit can also perform a concise analysis on the desired item that the user considers less important. Furthermore, the analysis unit can set analysis priorities according to the importance of the user's desired item. This allows the level of detail of the analysis to be adjusted according to the importance of the desired item. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that takes the importance of the user's desired item as input and adjusts the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the desired category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the desired category during analysis. Desired categories include, for example, hairstyle, color, and style, but are not limited to such examples. Analysis algorithms include, for example, clustering and regression analysis, but are not limited to such examples. For example, the analysis unit can apply a hairstyle-specific analysis algorithm to a hairstyle request. Also, for example, the analysis unit can apply a color-specific analysis algorithm to a color request. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the user's desired category, for example. This allows the optimal analysis algorithm to be applied according to the desired category. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can perform analysis using an AI model that takes the user's desired category as input and applies the optimal analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on the desired submission time during the analysis. For example, the analysis unit can determine the priority of analysis based on the desired submission time during the analysis. The desired submission time includes, but is not limited to, the submission date and time. For example, the analysis unit can prioritize analysis if the user submits their request urgently. Also, for example, the analysis unit can perform analysis with the normal priority if the user submits their request with ample time. Furthermore, the analysis unit can adjust the analysis schedule according to the user's desired submission time. This allows the analysis priority to be determined according to the desired submission time. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can determine the priority of analysis using an AI model that takes the user's desired submission time as input.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the desired items during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the desired items during analysis. The relevance of desired items includes, but is not limited to, similarity of content or related topics. For example, if a user's desired items are related to other desired items, the analysis unit can prioritize the analysis of the most relevant desired items. The analysis unit can also perform analysis in the normal order if, for example, the user's desired items are independent. Furthermore, the analysis unit can optimize the order of analysis according to the relevance of the user's desired items. This allows the order of analysis to be adjusted according to the relevance of the desired items. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can adjust the order of analysis using an AI model that takes the relevance of the user's desired items as input and adjusts the order of analysis.

[0043] The generation unit can adjust the image generation accuracy according to the desired level of detail during generation. For example, the generation unit can adjust the image generation accuracy according to the desired level of detail during generation. The desired level of detail includes, but is not limited to, specific request details or detailed specifications. For example, if the user inputs detailed requests, the generation unit can generate a high-precision image. Also, if the user inputs concise requests, the generation unit can generate a simple image. Furthermore, the generation unit can adjust the image generation accuracy according to the user's desired level of detail. This allows the image generation accuracy to be adjusted according to the desired level of detail. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can adjust the image generation accuracy using an AI model that takes the user's desired level of detail as input and adjusts the image generation accuracy.

[0044] The generation unit can apply different generation algorithms depending on the desired category during generation. For example, the generation unit can apply different generation algorithms depending on the desired category during generation. Desired categories include, for example, hairstyle, color, style, etc., but are not limited to such examples. Generation algorithms include, for example, GAN (Generative Opposite Network) and VAE (Variational Autoencoder), but are not limited to such examples. For example, the generation unit can apply a generation algorithm specifically for hairstyles to a desired hairstyle. Also, for example, the generation unit can apply a generation algorithm specifically for colors to a desired color. Furthermore, the generation unit can select the optimal generation algorithm depending on the user's desired category, for example. This allows the optimal generation algorithm to be applied according to the desired category. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can generate images using an AI model that takes the user's desired category as input and applies the optimal generation algorithm.

[0045] The generation unit can determine the priority of generation according to the desired submission time during generation. For example, the generation unit can determine the priority of generation according to the desired submission time during generation. The desired submission time includes, but is not limited to, the submission date and time. For example, the generation unit can prioritize image generation if the user submits their request urgently. Also, for example, the generation unit can generate images with normal priority if the user submits their request with ample time. Furthermore, the generation unit can adjust the generation schedule according to the user's desired submission time. This allows the generation priority to be determined according to the desired submission time. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can determine the priority of generation using an AI model that takes the user's desired submission time as input and determines the generation priority.

[0046] The generation unit can adjust the order of generation based on the relevance of the desired items during generation. For example, the generation unit can adjust the order of generation based on the relevance of the desired items during generation. The relevance of desired items includes, but is not limited to, similarity of content or related topics. For example, if a user's desired items are related to other desired items, the generation unit can prioritize generating images for the most relevant desired items. The generation unit can also generate images in the normal order if, for example, the user's desired items are independent. Furthermore, the generation unit can optimize the order of generation according to the relevance of the user's desired items. This allows the order of generation to be adjusted according to the relevance of the desired items. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can adjust the order of generation using an AI model that takes the relevance of the user's desired items as input and adjusts the order of generation.

[0047] The service provider can select the optimal display method based on the user's past operation history at the time of service provision. For example, the service provider selects the optimal display method based on the user's past operation history at the time of service provision. Past operation history includes, but is not limited to, click history and operation logs. The service provider can, for example, prioritize providing display methods that the user has preferred to use in the past. The service provider can also, for example, suggest the optimal display method based on the user's past operation history. Furthermore, the service provider can, for example, provide a customized display method based on the user's operation history. This allows the service provider to select the optimal display method based on the user's past operation history. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can select a display method using an AI model that takes the user's past operation history as input and selects the optimal display method.

[0048] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, the service provider can select the optimal display method at the time of delivery, taking into account the user's device information. Device information includes, but is not limited to, the type of device and the OS version. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Also, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Furthermore, if the user is using a desktop, the service provider can provide a high-resolution display method. This allows the service provider to select the optimal display method based on the user's device information. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can select a display method using an AI model that takes the user's device information as input and selects the optimal display method.

[0049] The service provider can select the optimal display method based on the user's geographical location information at the time of service provision. For example, the service provider can select the optimal display method based on the user's geographical location information at the time of service provision. Geographical location information includes, but is not limited to, GPS data and address information. For example, the service provider can prioritize providing information on hair salons close to the user's current location. The service provider can also prioritize providing information on hairstyles and colors desired by the user in a specific area. Furthermore, the service provider can suggest optimal hair salon candidates based on the user's geographical location information. This allows the service provider to select the optimal display method based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can select a display method using an AI model that takes the user's geographical location information as input and selects the optimal display method.

[0050] The service provider can analyze the user's social media activity and provide relevant images at the time of delivery. For example, the service provider can analyze the user's social media activity and provide relevant images at the time of delivery. Social media activity includes, but is not limited to, posts and the number of likes. For example, the service provider can provide relevant images based on images of hairstyles and colors shared by the user on social media. The service provider can also suggest trendy hairstyles and colors based on the user's social media posts. Furthermore, the service provider can provide relevant images based on the hairstyles and colors of the user's social media followers and friends. This allows the service provider to provide relevant images based on the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can provide images using an AI model that takes the user's social media activity as input and provides relevant images.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The reception desk can analyze a user's past salon visit history and suggest the most suitable stylist. For example, if a user was satisfied with a particular stylist in the past, that stylist can be prioritized for suggestion. Similarly, if a user preferred a specific style in the past, a stylist specializing in that style can be suggested. Furthermore, based on the user's past feedback, the system can evaluate the skills and responsiveness of stylists and select the most suitable one. This allows for the suggestion of the best stylist based on the user's past usage history.

[0053] The generation unit can suggest the optimal hairstyle and color based on the user's face shape and skin tone. For example, it can analyze the user's face shape and suggest a hairstyle that suits it, such as a short haircut for a round face or long hair for a long face. It can also analyze the user's skin tone and suggest a color that suits it. Furthermore, it can generate variations of hairstyles and colors based on the user's facial features, providing multiple options. This allows the system to suggest the optimal hairstyle and color based on the user's face shape and skin tone.

[0054] The generation unit can suggest practical hairstyles and colors based on the user's lifestyle. For example, if the user frequently plays sports, it can suggest a hairstyle that requires little maintenance. If the user prioritizes use in business settings, it can suggest formal hairstyles and colors. Furthermore, if the user has plans to attend a specific event, it can suggest hairstyles and colors suitable for that event. This allows for the suggestion of practical hairstyles and colors based on the user's lifestyle.

[0055] The service provider can offer a function that allows users to share generated images on social media. For example, users can share images of hairstyles and colors they have generated on social media platforms such as Instagram® and Facebook®. It can also collect feedback from friends and followers on the shared images and use that feedback to make new suggestions. Furthermore, through sharing on social media, it can understand trends and popular styles among other users and incorporate them into its suggestions. This allows users to share generated images on social media and receive new suggestions based on feedback.

[0056] The service provider can offer a function to print user-generated images. For example, users can print images of hairstyles and colors they have generated using their home printers. Furthermore, for use in hair salons, images can be printed in high resolution. Additionally, a 2D code (e.g., QR code®) can be added to the printed image, allowing hairdressers to scan the code to access detailed information. This enables users to print their generated images and supports their use in hair salons.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk receives the user's preferences. These preferences may include, but are not limited to, hairstyle, color, and style. The reception desk can, for example, accept preferences entered by the user in natural language. Step 2: The analysis unit analyzes the requests received by the reception unit. The analysis is performed using methods such as text analysis, image analysis, and speech analysis, but is not limited to these examples. The analysis unit can, for example, use natural language processing technology to analyze the user's requests. Step 3: The generation unit generates images based on the preferences analyzed by the analysis unit. Image generation is performed using, for example, a generation AI, but is not limited to such examples. For example, the generation unit can use a generation AI to generate images of hairstyles and colors based on the user's preferences. Step 4: The providing unit provides the image generated by the generating unit. The provision is carried out, for example, by displaying the image on the user's smartphone or tablet, but is not limited to such an example. The providing unit can, for example, display the generated image on the user's smartphone or tablet so that the user can confirm it.

[0059] (Example of form 2) The generation AI system according to an embodiment of the present invention is a system that supports users in accurately communicating their desired hairstyle and color to a hairdresser when using a hair salon. This generation AI system works by having the user input their desired hairstyle and color in natural language, and the generation AI analyzing that input to generate images of the hairstyle and color based on the user's wishes. The generated images can be reviewed by the user and shown to the hairdresser. This ensures that the user's wishes are accurately conveyed to the hairdresser, resulting in a highly satisfying service. For example, a user might input specific wishes such as "shoulder length, bangs swept to the side, and a light brown color." This input is sent to the generation AI. Next, the generation AI analyzes the input wishes and generates images of the hairstyle and color based on the user's wishes. The generated images can be reviewed by the user, and if they do not match their wishes, they can input their wishes again, and the generation AI can generate a new image. Finally, the user presents the generated image to the hairdresser, who can then perform the hairstyle and color treatment based on the presented image, ensuring that the user's wishes are accurately conveyed to the hairdresser, resulting in a highly satisfying service. This allows the AI ​​generation system to accurately convey the user's wishes to the hairdresser.

[0060] The generation AI system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives the user's requests. User requests include, for example, hairstyles, colors, and styles, but are not limited to such examples. The reception unit can, for example, receive requests entered by the user in natural language. The analysis unit analyzes the requests received by the reception unit. Analysis is performed by, for example, text analysis, image analysis, and speech analysis, but is not limited to such examples. The analysis unit can, for example, analyze the user's requests using natural language processing technology. The generation unit generates images based on the requests analyzed by the analysis unit. Image generation is performed by, for example, a generation AI, but is not limited to such examples. The generation unit can, for example, use a generation AI to generate images of hairstyles and colors based on the user's requests. The provision unit provides the images generated by the generation unit. Provision is performed by, for example, displaying the images on the user's smartphone or tablet, but is not limited to such examples. The provision unit can, for example, display the generated images on the user's smartphone or tablet so that the user can confirm them. As a result, the generation AI system according to this embodiment can accurately convey the user's wishes to the hairdresser.

[0061] The generation unit can generate images using a generative AI. For example, the generation unit generates images using a generative AI. The generative AI includes, but is not limited to, GANs (Generative Opposite Networks) and VAEs (Variational Autoencoders). For example, the generation unit can generate images of hairstyles and colors based on the user's preferences using a GAN. The generation unit can also generate images of hairstyles and colors based on the user's preferences using a VAE. This improves the accuracy of image generation by using a generative AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the generation unit can generate images using a generative AI model that takes the user's preferences as input and outputs images of hairstyles and colors.

[0062] The generation unit can generate images of hairstyles or colors based on the user's preferences using a generative AI. For example, the generation unit can generate images of hairstyles and colors based on the user's preferences using a generative AI. The generative AI includes, but is not limited to, GANs (Generative Opposite Networks) and VAEs (Variational Autoencoders). For example, the generation unit can generate images of hairstyles and colors based on the user's preferences using a GAN. Alternatively, the generation unit can generate images of hairstyles and colors based on the user's preferences using a VAE. This ensures that the user's preferences are accurately reflected in the generated images. Some or all of the above-described processes in the generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the generation unit can generate images using a generative AI model that takes the user's preferences as input and outputs images of hairstyles and colors.

[0063] The provider can display the generated image on the user's smartphone or tablet. For example, the provider can display the generated image on the user's smartphone or tablet. Smartphones and tablets include, but are not limited to, operating systems such as iOS and Android. For example, the provider can display the generated image on an iOS device. The provider can also display the generated image on an Android device. This allows the user to confirm the generated image. Some or all of the processing described above in the provider may be performed using, for example, AI, or without AI. For example, the provider can display the generated image using an application for displaying images on the user's smartphone or tablet.

[0064] The service provider can support the process of users reviewing generated images and requesting corrections. For example, the service provider can support the process of users reviewing generated images and requesting corrections. The process of requesting corrections includes, but is not limited to, feedback forms and chat functions. For example, the service provider can request corrections to generated images using a feedback form. Alternatively, the service provider can request corrections to generated images using a chat function. This allows users to correct images as they wish. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can support image correction using an AI model that takes user feedback as input and outputs a corrected image.

[0065] The generation unit can continuously improve the generating AI by utilizing historical data or user feedback. For example, the generation unit can continuously improve the generating AI by utilizing historical data or user feedback. Historical data includes, but is not limited to, past images or user feedback. The generation unit can retrain the generating AI using past images, for example. The generation unit can also adjust the parameters of the generating AI using user feedback, for example. This allows the generating AI to be continuously improved and its accuracy to increase. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can continuously improve the generating AI using an AI model that takes historical data or user feedback as input and improves the generating AI.

[0066] The reception desk can estimate the user's emotions and adjust the desired reception timing based on the estimated emotions. For example, the reception desk can estimate the user's emotions and adjust the desired reception timing based on the estimated emotions. User emotions include, but are not limited to, joy, sadness, and anger. The reception desk can estimate the user's emotions using, for example, an emotion analysis algorithm. Furthermore, the reception desk can accept requests at the normal reception timing if, for example, the user is relaxed. In addition, the reception desk can prioritize requests to quickly process them if, for example, the user is in a hurry. Furthermore, the reception desk can accept requests while providing careful explanations if, for example, the user is feeling anxious. This allows the reception timing to be adjusted according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can use an AI model that takes user emotion data as input and adjusts the desired reception timing accordingly.

[0067] The reception desk can analyze the user's past preference history and select the most suitable reception method. For example, the reception desk can analyze the user's past preference history and select the most suitable reception method. Past preference history includes, but is not limited to, past requests and history databases. For example, the reception desk can prioritize receiving similar requests based on the hairstyle or color the user has requested in the past. The reception desk can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest reception methods to be used during specific time periods based on the user's past preference history. This allows the reception desk to select the most suitable reception method based on the user's past preference history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can select the most suitable reception method using an AI model that takes past preference history as input.

[0068] The reception desk can filter the user's requests based on their current hair condition and lifestyle. For example, the reception desk can filter the user's requests based on their current hair condition and lifestyle. Current hair condition includes, but is not limited to, hair length and texture. Lifestyle includes, but is not limited to, work and hobbies. For example, the reception desk can suggest appropriate hairstyles and colors based on the user's current hair length and texture. The reception desk can also suggest practical hairstyles and colors based on the user's lifestyle. Furthermore, the reception desk can suggest hairstyles and colors that minimize damage based on the user's hair health. This allows the reception desk to suggest appropriate preferences based on the user's current hair condition and lifestyle. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can use an AI model that takes data on the user's hair condition and lifestyle as input and suggests appropriate preferences to perform filtering.

[0069] The reception unit can estimate the user's emotions and determine the priority of requests to be received based on the estimated emotions. For example, the reception unit can estimate the user's emotions and determine the priority of requests to be received based on the estimated emotions. User emotions include, but are not limited to, joy, sadness, and anger. The reception unit can estimate the user's emotions using, for example, an emotion analysis algorithm. The reception unit can also, for example, prioritize requests that help the user relax if they are nervous. Furthermore, the reception unit can also, for example, prioritize detailed requests if the user is having fun. The reception unit can also, for example, set a priority for requests to be received quickly if the user is in a hurry. This allows the priority of requests to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception desk can use an AI model that takes user emotional data as input and determines the priority of preferences.

[0070] The reception desk can prioritize requests based on the user's geographical location information when a request is received. For example, the reception desk prioritizes requests based on the user's geographical location information when a request is received. Geographical location information includes, but is not limited to, GPS data and address information. For example, the reception desk can prioritize providing information on hair salons close to the user's current location. The reception desk can also prioritize requests for hairstyles and colors desired by the user in a specific area. Furthermore, the reception desk can suggest the most suitable hair salon candidates based on the user's geographical location information. This allows the reception desk to receive the most suitable requests based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can receive requests using an AI model that takes the user's geographical location information as input and prioritizes receiving requests based on their relevance.

[0071] The reception desk can analyze the user's social media activity when a request is received and accept relevant requests. For example, the reception desk can analyze the user's social media activity when a request is received and accept relevant requests. Social media activity includes, but is not limited to, posts and the number of likes. For example, the reception desk can accept requests based on images of hairstyles and colors that the user has shared on social media. The reception desk can also suggest trendy hairstyles and colors based on the user's social media posts. Furthermore, the reception desk can accept requests based on, for example, the hairstyles and colors of the user's social media followers and friends. This allows the reception desk to accept relevant requests based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can accept requests using an AI model that takes the user's social media activity as input and accepts relevant requests.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. User emotions include, but are not limited to, joy, sadness, and anger. The analysis unit can estimate the user's emotions using, for example, an emotion analysis algorithm. Furthermore, the analysis unit can provide detailed analysis results if, for example, the user is relaxed. It can also provide concise analysis results if, for example, the user is in a hurry. Furthermore, the analysis unit can provide reassuring analysis results if, for example, the user is feeling anxious. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can take user emotion data as input and adjust the way the analysis is represented using an AI model that adjusts the method of representation for the analysis.

[0073] The analysis unit can adjust the level of detail of the analysis according to the importance of the desired item during the analysis. For example, the analysis unit can adjust the level of detail of the analysis according to the importance of the desired item during the analysis. The importance of the desired item includes, but is not limited to, user priority or request frequency. For example, the analysis unit can perform a detailed analysis on the desired item that the user considers particularly important. The analysis unit can also perform a concise analysis on the desired item that the user considers less important. Furthermore, the analysis unit can set analysis priorities according to the importance of the user's desired item. This allows the level of detail of the analysis to be adjusted according to the importance of the desired item. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that takes the importance of the user's desired item as input and adjusts the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the desired category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the desired category during analysis. Desired categories include, for example, hairstyle, color, and style, but are not limited to such examples. Analysis algorithms include, for example, clustering and regression analysis, but are not limited to such examples. For example, the analysis unit can apply a hairstyle-specific analysis algorithm to a hairstyle request. Also, for example, the analysis unit can apply a color-specific analysis algorithm to a color request. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the user's desired category, for example. This allows the optimal analysis algorithm to be applied according to the desired category. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can perform analysis using an AI model that takes the user's desired category as input and applies the optimal analysis algorithm.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. User emotions include, but are not limited to, joy, sadness, and anger. The analysis unit can estimate the user's emotions using, for example, an emotion analysis algorithm. Furthermore, the analysis unit can perform a detailed analysis if, for example, the user is relaxed. It can also perform a concise analysis if, for example, the user is in a hurry. Furthermore, the analysis unit can perform a reassuring analysis if, for example, the user is feeling anxious. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can take user emotion data as input and adjust the length of the analysis using an AI model that adjusts the length of the analysis.

[0076] The analysis unit can determine the priority of analysis based on the desired submission time during the analysis. For example, the analysis unit can determine the priority of analysis based on the desired submission time during the analysis. The desired submission time includes, but is not limited to, the submission date and time. For example, the analysis unit can prioritize analysis if the user submits their request urgently. Also, for example, the analysis unit can perform analysis with the normal priority if the user submits their request with ample time. Furthermore, the analysis unit can adjust the analysis schedule according to the user's desired submission time. This allows the analysis priority to be determined according to the desired submission time. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can determine the priority of analysis using an AI model that takes the user's desired submission time as input.

[0077] The analysis unit can adjust the order of analysis based on the relevance of the desired items during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the desired items during analysis. The relevance of desired items includes, but is not limited to, similarity of content or related topics. For example, if a user's desired items are related to other desired items, the analysis unit can prioritize the analysis of the most relevant desired items. The analysis unit can also perform analysis in the normal order if, for example, the user's desired items are independent. Furthermore, the analysis unit can optimize the order of analysis according to the relevance of the user's desired items. This allows the order of analysis to be adjusted according to the relevance of the desired items. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can adjust the order of analysis using an AI model that takes the relevance of the user's desired items as input and adjusts the order of analysis.

[0078] The generation unit can estimate the user's emotions and adjust the representation of the generated images based on the estimated emotions. For example, the generation unit can estimate the user's emotions and adjust the representation of the generated images based on the estimated emotions. User emotions include, but are not limited to, joy, sadness, and anger. The generation unit can estimate the user's emotions using, for example, an emotion analysis algorithm. Furthermore, the generation unit can generate images with soft colors if the user is relaxed. Additionally, the generation unit can generate simple, highly visible images if the user is in a hurry. Furthermore, the generation unit can generate images with vivid colors if the user is excited. This allows the representation of images to be adjusted according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can take user emotion data as input and adjust the image representation using an AI model that adjusts the image representation method.

[0079] The generation unit can adjust the image generation accuracy according to the desired level of detail during generation. For example, the generation unit can adjust the image generation accuracy according to the desired level of detail during generation. The desired level of detail includes, but is not limited to, specific request details or detailed specifications. For example, if the user inputs detailed requests, the generation unit can generate a high-precision image. Also, if the user inputs concise requests, the generation unit can generate a simple image. Furthermore, the generation unit can adjust the image generation accuracy according to the user's desired level of detail. This allows the image generation accuracy to be adjusted according to the desired level of detail. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can adjust the image generation accuracy using an AI model that takes the user's desired level of detail as input and adjusts the image generation accuracy.

[0080] The generation unit can apply different generation algorithms depending on the desired category during generation. For example, the generation unit can apply different generation algorithms depending on the desired category during generation. Desired categories include, for example, hairstyle, color, style, etc., but are not limited to such examples. Generation algorithms include, for example, GAN (Generative Opposite Network) and VAE (Variational Autoencoder), but are not limited to such examples. For example, the generation unit can apply a generation algorithm specifically for hairstyles to a desired hairstyle. Also, for example, the generation unit can apply a generation algorithm specifically for colors to a desired color. Furthermore, the generation unit can select the optimal generation algorithm depending on the user's desired category, for example. This allows the optimal generation algorithm to be applied according to the desired category. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can generate images using an AI model that takes the user's desired category as input and applies the optimal generation algorithm.

[0081] The generation unit can estimate the user's emotions and adjust the length of the generated image based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the length of the generated image based on the estimated user emotions. User emotions include, for example, joy, sadness, anger, etc. The generation unit can estimate the user's emotions using, for example, an emotion analysis algorithm. The generation unit can also generate a detailed image if, for example, the user is relaxed. Furthermore, the generation unit can generate a concise image if, for example, the user is in a hurry. The generation unit can also generate a visually stimulating image if, for example, the user is excited. This allows the length of the image to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, etc., but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can take user emotion data as input and adjust the image length using an AI model that adjusts image length.

[0082] The generation unit can determine the priority of generation according to the desired submission time during generation. For example, the generation unit can determine the priority of generation according to the desired submission time during generation. The desired submission time includes, but is not limited to, the submission date and time. For example, the generation unit can prioritize image generation if the user submits their request urgently. Also, for example, the generation unit can generate images with normal priority if the user submits their request with ample time. Furthermore, the generation unit can adjust the generation schedule according to the user's desired submission time. This allows the generation priority to be determined according to the desired submission time. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can determine the priority of generation using an AI model that takes the user's desired submission time as input and determines the generation priority.

[0083] The generation unit can adjust the order of generation based on the relevance of the desired items during generation. For example, the generation unit can adjust the order of generation based on the relevance of the desired items during generation. The relevance of desired items includes, but is not limited to, similarity of content or related topics. For example, if a user's desired items are related to other desired items, the generation unit can prioritize generating images for the most relevant desired items. The generation unit can also generate images in the normal order if, for example, the user's desired items are independent. Furthermore, the generation unit can optimize the order of generation according to the relevance of the user's desired items. This allows the order of generation to be adjusted according to the relevance of the desired items. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can adjust the order of generation using an AI model that takes the relevance of the user's desired items as input and adjusts the order of generation.

[0084] The service provider can estimate the user's emotions and adjust the display method of the images based on the estimated emotions. For example, the service provider can estimate the user's emotions and adjust the display method of the images based on the estimated emotions. User emotions include, but are not limited to, joy, sadness, and anger. The service provider can estimate the user's emotions using, for example, an emotion analysis algorithm. The service provider can also provide, for example, a display method with soft colors when the user is relaxed. Furthermore, the service provider can also provide, for example, a display method with simple and highly visible colors when the user is in a hurry. Furthermore, the service provider can also provide, for example, a display method with vivid colors when the user is excited. This allows the display method of images to be adjusted according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can use user emotion data as input and adjust the image display method using an AI model that adjusts the image display method.

[0085] The service provider can select the optimal display method based on the user's past operation history at the time of service provision. For example, the service provider selects the optimal display method based on the user's past operation history at the time of service provision. Past operation history includes, but is not limited to, click history and operation logs. The service provider can, for example, prioritize providing display methods that the user has preferred to use in the past. The service provider can also, for example, suggest the optimal display method based on the user's past operation history. Furthermore, the service provider can, for example, provide a customized display method based on the user's operation history. This allows the service provider to select the optimal display method based on the user's past operation history. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can select a display method using an AI model that takes the user's past operation history as input and selects the optimal display method.

[0086] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, the service provider can select the optimal display method at the time of delivery, taking into account the user's device information. Device information includes, but is not limited to, the type of device and the OS version. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Also, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Furthermore, if the user is using a desktop, the service provider can provide a high-resolution display method. This allows the service provider to select the optimal display method based on the user's device information. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can select a display method using an AI model that takes the user's device information as input and selects the optimal display method.

[0087] The service provider can estimate the user's emotions and adjust the operation procedures for the images it provides based on the estimated emotions. For example, the service provider can estimate the user's emotions and adjust the operation procedures for the images it provides based on the estimated emotions. User emotions include, but are not limited to, joy, sadness, and anger. The service provider can estimate the user's emotions using, for example, an emotion analysis algorithm. The service provider can also provide, for example, detailed operation procedures if the user is relaxed. Furthermore, the service provider can provide, for example, concise operation procedures if the user is in a hurry. The service provider can also provide, for example, reassuring operation procedures if the user is feeling anxious. This allows the operation procedures for images to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can use user emotion data as input and adjust the operating procedures using an AI model that adjusts the operating procedures.

[0088] The service provider can select the optimal display method based on the user's geographical location information at the time of service provision. For example, the service provider can select the optimal display method based on the user's geographical location information at the time of service provision. Geographical location information includes, but is not limited to, GPS data and address information. For example, the service provider can prioritize providing information on hair salons close to the user's current location. The service provider can also prioritize providing information on hairstyles and colors desired by the user in a specific area. Furthermore, the service provider can suggest optimal hair salon candidates based on the user's geographical location information. This allows the service provider to select the optimal display method based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can select a display method using an AI model that takes the user's geographical location information as input and selects the optimal display method.

[0089] The service provider can analyze the user's social media activity and provide relevant images at the time of delivery. For example, the service provider can analyze the user's social media activity and provide relevant images at the time of delivery. Social media activity includes, but is not limited to, posts and the number of likes. For example, the service provider can provide relevant images based on images of hairstyles and colors shared by the user on social media. The service provider can also suggest trendy hairstyles and colors based on the user's social media posts. Furthermore, the service provider can provide relevant images based on the hairstyles and colors of the user's social media followers and friends. This allows the service provider to provide relevant images based on the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can provide images using an AI model that takes the user's social media activity as input and provides relevant images.

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

[0091] The reception desk can analyze a user's past salon visit history and suggest the most suitable stylist. For example, if a user was satisfied with a particular stylist in the past, that stylist can be prioritized for suggestion. Similarly, if a user preferred a specific style in the past, a stylist specializing in that style can be suggested. Furthermore, based on the user's past feedback, the system can evaluate the skills and responsiveness of stylists and select the most suitable one. This allows for the suggestion of the best stylist based on the user's past usage history.

[0092] The generation unit can suggest the optimal hairstyle and color based on the user's face shape and skin tone. For example, it can analyze the user's face shape and suggest a hairstyle that suits it, such as a short haircut for a round face or long hair for a long face. It can also analyze the user's skin tone and suggest a color that suits it. Furthermore, it can generate variations of hairstyles and colors based on the user's facial features, providing multiple options. This allows the system to suggest the optimal hairstyle and color based on the user's face shape and skin tone.

[0093] The generation unit can suggest practical hairstyles and colors based on the user's lifestyle. For example, if the user frequently plays sports, it can suggest a hairstyle that requires little maintenance. If the user prioritizes use in business settings, it can suggest formal hairstyles and colors. Furthermore, if the user has plans to attend a specific event, it can suggest hairstyles and colors suitable for that event. This allows for the suggestion of practical hairstyles and colors based on the user's lifestyle.

[0094] The service provider can offer a function that allows users to share generated images on social media. For example, users can share images of hairstyles and colors they have generated on social media platforms such as Instagram and Facebook. It can also collect feedback from friends and followers on the shared images and use that feedback to make new suggestions. Furthermore, through sharing on social media, it can understand trends and popular styles among other users and incorporate them into its suggestions. This allows users to share generated images on social media and receive new suggestions based on feedback.

[0095] The service provider can offer a function to print user-generated images. For example, users can print images of hairstyles and colors they have generated using their home printers. Furthermore, for use in hair salons, images can be printed in high resolution. Additionally, a QR code can be added to the printed image, allowing hairdressers to scan the code to access detailed information. This enables users to print their generated images and supports their use in hair salons.

[0096] The reception desk can estimate the user's emotions and adjust the preferred reception method based on those estimates. For example, if the user is relaxed, the request can be processed using the standard reception method. If the user is nervous, the request can be processed with more detailed explanations. Furthermore, if the user is in a hurry, priority reception can be provided to process the request quickly. This allows the reception method to be adjusted according to the user's emotions.

[0097] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on those emotions. For example, if the user is excited, a detailed analysis can be prioritized. If the user is feeling anxious, analysis results that provide reassurance can be prioritized. Furthermore, if the user is in a hurry, a priority can be set for rapid analysis. In this way, the priority of analysis can be determined according to the user's emotions.

[0098] The generation unit can estimate the user's emotions and adjust the style of the generated images based on those emotions. For example, if the user is relaxed, it can generate images with soft colors. If the user is excited, it can generate images with vibrant colors. Furthermore, if the user is sad, it can generate images with calm colors. This allows the image style to be adjusted according to the user's emotions.

[0099] The display unit can estimate the user's emotions and adjust the timing of image display based on those emotions. For example, if the user is relaxed, the image can be displayed at the normal timing. If the user is in a hurry, the image can be displayed quickly. Furthermore, if the user is feeling anxious, the image can be displayed at a time that provides reassurance. In this way, the timing of image display can be adjusted according to the user's emotions.

[0100] The delivery unit can estimate the user's emotions and adjust the resolution of the images provided based on those emotions. For example, if the user is relaxed, a high-resolution image can be provided. If the user is in a hurry, a low-resolution image can be provided quickly. Furthermore, if the user is excited, a visually stimulating high-resolution image can be provided. This allows the image resolution to be adjusted according to the user's emotions.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The reception desk receives the user's preferences. These preferences may include, but are not limited to, hairstyle, color, and style. The reception desk can, for example, accept preferences entered by the user in natural language. Step 2: The analysis unit analyzes the requests received by the reception unit. The analysis is performed using methods such as text analysis, image analysis, and speech analysis, but is not limited to these examples. The analysis unit can, for example, use natural language processing technology to analyze the user's requests. Step 3: The generation unit generates images based on the preferences analyzed by the analysis unit. Image generation is performed using, for example, a generation AI, but is not limited to such examples. For example, the generation unit can use a generation AI to generate images of hairstyles and colors based on the user's preferences. Step 4: The providing unit provides the image generated by the generating unit. The provision is carried out, for example, by displaying the image on the user's smartphone or tablet, but is not limited to such an example. The providing unit can, for example, display the generated image on the user's smartphone or tablet so that the user can confirm it.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives the user's request. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's request. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an image based on the analyzed request. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated image to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives the user's request. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's request. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates an image based on the analyzed request. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214 and provides the generated image to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives the user's request. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's request. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an image based on the analyzed request. The provision unit is implemented by the display 343 of the headset terminal 314 and provides the generated image to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0140] As shown in Figure 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.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives the user's request. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's request. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates an image based on the analyzed request. The provision unit is implemented, for example, by the speaker 240 of the robot 414 and provides the generated image to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0156] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] (Note 1) A reception desk that takes user requests, An analysis unit analyzes the requests received by the reception unit, A generation unit that generates an image based on the desired outcome analyzed by the aforementioned analysis unit, The system includes a providing unit that provides the image generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Generate images using a generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The AI ​​generates images of hairstyles or colors based on the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, The generated image is displayed on the user's smartphone or tablet. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Supports the process of users reviewing generated images and requesting corrections. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is We continuously improve our generating AI by leveraging past data or user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the desired timing for receiving the request based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past request history and select the most suitable application method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When a user submits a request, filtering is performed based on their current hair condition or lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving requests, the system prioritizes requests that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a request, the system analyzes the user's social media activity and accepts relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, adjust the level of detail according to the desired importance. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the desired category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined according to the desired submission date. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on desired relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the way images are represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the image generation accuracy is adjusted according to the desired level of detail. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different generation algorithms are applied depending on the desired category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the generated images based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the generation priority is determined according to the desired submission date. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the generation order is adjusted based on the desired relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how images are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the optimal display method is selected based on the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts the image interaction instructions based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal display method is selected based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant images. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that takes user requests, An analysis unit analyzes the requests received by the reception unit, A generation unit that generates an image based on the desired outcome analyzed by the aforementioned analysis unit, The system includes a providing unit that provides the image generated by the generation unit. A system characterized by the following features.

2. The generating unit is Generate images using AI. The system according to feature 1.

3. The generating unit is The AI ​​generates images of hairstyles or colors based on the user's preferences. The system according to feature 1.

4. The aforementioned supply unit is, The generated image is displayed on the user's smartphone or tablet. The system according to feature 1.

5. The aforementioned supply unit is, Supports the process of users reviewing generated images and requesting corrections. The system according to feature 1.

6. The generating unit is We continuously improve our generating AI by utilizing past data or user feedback. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the desired timing for receiving the request based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past request history and select the most suitable application method. The system according to feature 1.

Images