system

Abstract

The system according to this embodiment aims to suggest a hairstyle that suits the user and to accurately convey this to the hairdresser. [Solution] The system according to the embodiment comprises a reception unit, a suggestion unit, a generation unit, and a provision unit. The reception unit receives the user's photo and keyword input. The suggestion unit analyzes the information received by the reception unit and suggests a hairstyle that suits the user. The generation unit generates a specific order based on the hairstyle suggested by the suggestion unit. The provision unit provides the order generated by the generation unit to the hairdresser.

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, which is 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 the 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 was a problem that due to insufficient communication with a beautician, the hairstyle desired by the user did not match the actual finish.

[0005] The system according to the embodiment aims to propose a hairstyle suitable for the user and accurately convey it to the beautician.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a suggestion unit, a generation unit, and a provision unit. The reception unit receives the user's photo and keyword input. The suggestion unit analyzes the information received by the reception unit and suggests a hairstyle that suits the user. The generation unit generates a specific order based on the hairstyle suggested by the suggestion unit. The provision unit provides the order generated by the generation unit to the hairdresser. [Effects of the Invention]

[0007] The system according to this embodiment can suggest a hairstyle that suits the user and accurately convey that to the hairdresser. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 three or more matters are connected and expressed 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 hairstyle suggestion system according to an embodiment of the present invention is a system that prevents hairstyle failures due to insufficient communication with the hairdresser by having AI suggest a hairstyle that truly suits the user and then communicating it to the hairdresser to realize the ideal hairstyle. This hairstyle suggestion system starts when the user installs a dedicated app on their smartphone, uploads a photo of themselves, and selects keywords. For example, the user selects keywords such as "make sure the bangs don't get wavy" or "increase the overall volume." This information is input into the AI. Next, the AI ​​analyzes the input information and suggests multiple hairstyles that truly suit the user based on their face, head shape, hair type, and hair color. For example, the AI ​​suggests the optimal bang length for the user's face shape and the type of perm that suits their hair type. The user chooses their favorite hairstyle from the suggested options and takes it to the hairdresser. Furthermore, the app provides specific orders to the hairdresser. For example, detailed instructions such as "cut the bangs by XX centimeters," "do the sides and back like this," "make the hair color XX," and "get a XX perm" are provided. This allows the hairdresser to create a hairstyle that meets the user's wishes. This system allows users to get the hairstyle they want and prevents mistakes caused by a lack of communication with the hairdresser. Furthermore, hair salons can secure customers and improve satisfaction by partnering with AI. In this way, the hairstyle suggestion system prevents users from getting the hairstyle they want and helps them achieve their ideal look.

[0029] The hairstyle suggestion system according to this embodiment comprises a reception unit, a suggestion unit, a generation unit, and a provision unit. The reception unit receives input of a user's photo and keywords. The user's photo may include, but is not limited to, a JPEG image with a resolution of 1080p. The reception unit allows the user to upload a photo taken with their smartphone, for example. The reception unit also receives keywords selected by the user as input. The keywords may include, but are not limited to, hairstyle styles, colors, and lengths. The suggestion unit analyzes the information received by the reception unit and suggests a hairstyle that suits the user. The suggestion unit suggests multiple hairstyle patterns that suit the user, for example, based on the user's face, head shape, hair texture, and hair color. The suggestion unit may use, for example, AI to suggest the optimal bangs length for the user's face shape and the appropriate perm method for their hair texture. The generation unit generates a specific order based on the hairstyle suggested by the suggestion unit. The generation unit generates a detailed order, for example, the length of the bangs, the style of the sides and back, the hair color, and the perm. The generation unit generates a detailed order that meets the user's preferences, for example, using AI. The provision unit provides the order generated by the generation unit to the hairdresser. The provision unit provides the order to the hairdresser, for example, through an app. The provision unit provides the generated order to the hairdresser, for example, using AI. As a result, the hairstyle suggestion system according to this embodiment can realize the ideal hairstyle by having the AI ​​suggest the optimal hairstyle based on the user's photo and keywords, and providing a specific order to the hairdresser.

[0030] The reception desk accepts user photos and keywords. User photos may include, but are not limited to, JPEG format and 1080p resolution. The reception desk allows users to upload photos taken with their smartphones, for example. Specifically, this involves the user taking photos using a dedicated app and uploading them to the system. It is recommended that photos be taken from multiple angles, such as the front, side, and back of the face. This allows the system to more accurately understand the user's face shape and hair condition. The reception desk also accepts keywords selected by the user as input. Keywords may include, but are not limited to, hairstyle styles, colors, and lengths. Users can use the in-app input form to enter their desired hairstyle style (e.g., bob, short, long), hair color (e.g., brown, blonde, black), and hair length (e.g., shoulder-length, ear-length). Furthermore, users can enter specific requests as keywords, such as the hairstyle of a particular celebrity or a hairstyle for a specific event. This allows the reception department to accurately understand the user's detailed requests and provide that information to the proposal department.

[0031] The suggestion department analyzes the information received by the reception department and proposes hairstyles that suit the user. For example, the suggestion department proposes multiple hairstyles that suit the user based on the user's face, head shape, hair type, and hair color. Specifically, it uses AI to analyze the user's face shape and identify the face type, such as round, oval, or square. Next, it considers the head shape and hair type (e.g., straight, wavy, or curly) to select the optimal hairstyle. Based on past databases, the AI ​​refers to successful examples of other users with similar face shapes and hair types to propose the optimal hairstyle. For example, it may suggest the optimal bangs length for the user's face shape or the best perm technique for their hair type. Furthermore, the suggestion department analyzes keywords entered by the user and proposes hairstyles that meet the user's preferences. For example, if the user enters "hairstyle suitable for a summer event," the AI ​​can suggest light styles and cool hair colors suitable for summer events. In this way, the suggestion department can comprehensively consider the user's individual characteristics and preferences to propose the optimal hairstyle.

[0032] The generation unit generates specific orders based on the hairstyles proposed by the suggestion unit. For example, the generation unit generates detailed orders for bangs length, side and back styles, hair color, and perms. Specifically, it uses AI to generate detailed orders that meet the user's preferences. For example, bangs length can be adjusted in millimeter increments, and side and back styles are described in detail, including specific cutting methods and desired finished look. For hair color, it suggests the best shade for the user's skin tone and eye color, and for perms, it provides detailed information such as the best perming method based on hair type and the duration of the perm. The generation unit compiles this information into a single order sheet and provides it in a format that hairdressers can easily understand. Furthermore, the generation unit can consider the user's past order history and feedback to generate even more accurate orders. As a result, the generation unit can generate specific and detailed orders that meet the user's preferences and provide them to hairdressers.

[0033] The service provider delivers orders generated by the generation unit to hairdressers. The service provider delivers orders to hairdressers, for example, through an app. Specifically, it sends the generated order sheet to a hairdresser-only app so that hairdressers can check it in real time. The service provider can deliver generated orders to hairdressers, for example, using AI. The AI ​​can consider the hairdresser's schedule and past treatment history to deliver orders at the optimal time. Furthermore, the service provider can also provide visual guides and video tutorials to make it easier for hairdressers to check the order details. For example, it can provide videos showing specific steps on how to cut bangs or how to apply a perm, supporting hairdressers in performing the treatment accurately. The service provider also supports real-time communication between users and hairdressers, allowing for quick questions and adjustments regarding the order details. In this way, the service provider can effectively deliver generated orders to hairdressers and support users in achieving their ideal hairstyle.

[0034] The reception desk can analyze the user's past hairstyle history and select the optimal input method. For example, the reception desk can analyze the trends of hairstyles the user has chosen in the past and automatically suggest relevant keywords. For example, the reception desk can automatically display photos of similar hairstyles based on photos the user has uploaded in the past. For example, the reception desk can suggest hairstyles that are suitable for a specific season or event based on the user's past hairstyle history. In this way, by selecting the optimal input method based on past hairstyle history, the system is made easy for the user to use. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past hairstyle history data into a generating AI and have the generating AI select the optimal input method.

[0035] The reception desk can filter the user's current hair condition and the season when they input photos and keywords. For example, the reception desk can automatically detect the user's current hair length and color and suggest appropriate keywords based on that. For example, the reception desk can consider seasonal hairstyle trends and prioritize displaying relevant keywords. For example, the reception desk can analyze the user's hair health and suggest keywords to prevent damage. This allows for the suggestion of more appropriate keywords by filtering based on the current hair condition and the season. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's hair condition data into a generating AI and have the generating AI perform the filtering.

[0036] The reception desk can prioritize retrieving highly relevant keywords by considering the user's geographical location when they input photos and keywords. For example, if the user is in an urban area, the reception desk will prioritize displaying keywords based on the latest trends. If the user is in a suburban area, the reception desk will prioritize displaying keywords related to natural style. If the user is planning to attend a specific event, the reception desk will prioritize displaying keywords related to that event. This allows for the provision of more relevant keywords by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI retrieve highly relevant keywords.

[0037] The reception desk can analyze the user's social media activity and obtain relevant keywords when photos and keywords are entered. For example, the reception desk can suggest relevant keywords based on hashtags that the user frequently uses on social media. For example, the reception desk can suggest relevant keywords based on the hairstyles of influencers that the user follows. For example, the reception desk can suggest keywords related to hairstyles that have been well-received in the past from the user's social media posts. In this way, by analyzing social media activity, it is possible to provide keywords relevant to the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI perform the task of obtaining relevant keywords.

[0038] The suggestion unit can adjust the level of detail in hairstyle suggestions based on the user's lifestyle. For example, if the user has a busy lifestyle, the suggestion unit will suggest a hairstyle that is easy to maintain. If the user has an active lifestyle, the suggestion unit will suggest a hairstyle that is easy to move in. If the user has a sociable lifestyle, the suggestion unit will suggest a stylish hairstyle. By adjusting the level of detail in suggestions based on lifestyle, it becomes possible to suggest the most suitable hairstyle for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle data into a generating AI and have the generating AI perform the adjustment of the level of detail in suggestions.

[0039] The suggestion unit can apply different suggestion algorithms depending on the user's age and gender when suggesting hairstyles. For example, the suggestion unit might suggest hairstyles based on the latest trends for younger users. For example, it might suggest more subdued hairstyles for middle-aged and older users. For example, it might suggest different hairstyles for men and women depending on gender. By applying suggestion algorithms tailored to age and gender, it becomes possible to suggest more appropriate hairstyles. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's age and gender data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0040] The suggestion unit can determine the priority of hairstyle suggestions based on the user's past hairstyle history. For example, the suggestion unit can analyze the trends of hairstyles the user has chosen in the past and prioritize suggesting related hairstyles. For example, the suggestion unit can prioritize suggesting similar hairstyles based on hairstyles that have been well-received by the user in the past. For example, the suggestion unit can prioritize suggesting hairstyles that are suitable for a specific season or event based on the user's past hairstyle history. By determining the priority of suggestions based on past hairstyle history, it becomes possible to suggest the most suitable hairstyle for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past hairstyle history data into a generating AI and have the generating AI perform the determination of the suggestion priority.

[0041] The suggestion unit can adjust the order of hairstyle suggestions based on the user's occupation and hobbies. For example, if the user is a business person, the suggestion unit will prioritize suggesting formal hairstyles. If the user is an artist, the suggestion unit will prioritize suggesting creative hairstyles. If the user enjoys sports, the suggestion unit will prioritize suggesting hairstyles that allow for easy movement. By adjusting the order of suggestions based on occupation and hobbies, it becomes possible to suggest the most suitable hairstyle for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's occupation and hobby data into a generating AI and have the generating AI adjust the order of suggestions.

[0042] The generation unit can generate the optimal order by analyzing the user's past hairstyle history when generating an order. For example, the generation unit can analyze the trends of hairstyles the user has chosen in the past and generate a related order. For example, the generation unit can generate a similar order based on hairstyles that were well-received by the user in the past. For example, the generation unit can generate an order tailored to a specific season or event from the user's past hairstyle history. In this way, by analyzing past hairstyle history, the optimal order for the user can be generated. 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 input the user's past hairstyle history data into a generation AI and have the generation AI execute the generation of the optimal order.

[0043] The generation unit can customize the details of an order based on the user's current hair condition when generating an order. For example, the generation unit can automatically detect the user's current hair length and color and customize the order based on that. For example, the generation unit can analyze the health of the user's hair and generate an order to prevent damage. For example, the generation unit can generate an optimal perm or coloring order based on the user's hair type. This allows for the generation of more appropriate orders by customizing the order based on the current hair condition. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's hair condition data into a generation AI and have the generation AI perform the order customization.

[0044] The generation unit can generate the optimal order by considering the user's geographical location information when generating an order. For example, if the user is in an urban area, the generation unit will generate an order based on the latest trends. For example, if the user is in a suburban area, the generation unit will generate an order related to natural styles. For example, if the user has plans to attend a specific event, the generation unit will generate an order related to that event. This allows for the generation of more appropriate orders by considering geographical location information. 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 input the user's geographical location information data into a generation AI and have the generation AI perform the generation of the optimal order.

[0045] The generation unit can analyze the user's social media activity and suggest order details when generating an order. For example, the generation unit can generate relevant orders based on hashtags the user frequently uses on social media. For example, the generation unit can generate relevant orders based on the hairstyles of influencers the user follows. For example, the generation unit can generate orders related to hairstyles that have been popular in the past from the user's social media posts. In this way, by analyzing social media activity, it is possible to generate orders relevant to the user. 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 input the user's social media data into a generation AI and have the generation AI suggest order details.

[0046] The service provider can select the optimal service method by referring to the user's past hairstyle history when providing an order. For example, the service provider can analyze the trends of hairstyles the user has chosen in the past and provide relevant orders. For example, the service provider can provide similar orders based on hairstyles that were well-received by the user in the past. For example, the service provider can provide orders tailored to specific seasons or events based on the user's past hairstyle history. In this way, by referring to past hairstyle history, the service provider can provide the optimal order for the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past hairstyle history data into a generating AI and have the generating AI select the optimal service method.

[0047] The service provider can customize the means of service based on the user's current hair condition when an order is placed. For example, the service provider can automatically detect the user's current hair length and color and provide an order based on that. For example, the service provider can analyze the health of the user's hair and provide an order to prevent damage. For example, the service provider can provide an order for the optimal perm or coloring based on the user's hair type. By customizing the means of service based on the current hair condition, a more appropriate order can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's hair condition data into a generating AI and have the generating AI perform the customization of the means of service.

[0048] The service provider can select the optimal service delivery method by considering the user's geographical location when delivering an order. For example, if the user is in an urban area, the service provider may offer an order based on the latest trends. If the user is in a suburban area, the service provider may offer an order related to a natural style. If the user has plans to attend a specific event, the service provider may offer an order related to that event. By considering geographical location, the service provider can deliver a more appropriate order. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI select the optimal service delivery method.

[0049] The service provider can analyze the user's social media activity and propose a means of delivery when providing an order. For example, the service provider can provide relevant orders based on hashtags the user frequently uses on social media. For example, the service provider can provide relevant orders based on the hairstyles of influencers the user follows. For example, the service provider can provide orders related to hairstyles that have been well-received in the past, based on the user's social media posts. In this way, by analyzing social media activity, the service provider can provide orders that are relevant to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI propose a means of delivery.

[0050] The service provider can refer to the user's calendar information when providing an order and make suggestions based on their schedule. For example, the service provider can refer to the appointments registered in the user's calendar and automatically set an order. For example, the service provider can suggest an order related to a specific event from the user's calendar information. For example, the service provider can suggest the optimal order tailored to the user's schedule based on the user's calendar information. In this way, by referring to the calendar information, the service provider can provide the optimal order tailored to the user's schedule. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's calendar information data into a generating AI and have the generating AI execute suggestions based on the schedule.

[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 suggestion unit can customize hairstyle suggestions based on the user's life events. For example, if the user is preparing for a wedding, the suggestion unit will suggest a formal and elegant hairstyle. If the user is planning to participate in a sporting event, the suggestion unit will suggest a hairstyle that allows for easy movement. If the user is starting a new job, the suggestion unit will suggest a hairstyle that gives a professional impression. By customizing hairstyle suggestions based on life events, it becomes possible to suggest the most suitable hairstyle for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's life event data into a generating AI and have the generating AI perform the customization of suggestions.

[0053] The suggestion unit can increase the variety of suggestions based on the user's past hairstyle history. For example, the suggestion unit can analyze the trends of hairstyles the user has chosen in the past and suggest multiple related hairstyles. For example, the suggestion unit can suggest multiple similar hairstyles based on hairstyles that were well-received by the user in the past. For example, the suggestion unit can suggest multiple hairstyles that are suitable for a specific season or event based on the user's past hairstyle history. In this way, by increasing the variety of suggestions based on past hairstyle history, it becomes possible to suggest the most suitable hairstyle for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past hairstyle history data into a generating AI and have the generating AI perform the task of increasing the variety of suggestions.

[0054] The suggestion unit can customize hairstyle suggestions by taking into account the user's geographical location. For example, if the user is in an urban area, the suggestion unit will suggest hairstyles based on the latest trends. If the user is in a suburban area, the suggestion unit will suggest hairstyles related to natural styles. If the user has plans to attend a specific event, the suggestion unit will suggest hairstyles related to that event. This allows for more appropriate hairstyle suggestions by considering geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI perform the customization of suggestions.

[0055] The suggestion unit can analyze a user's social media activity and suggest relevant hairstyles. For example, the suggestion unit can suggest relevant hairstyles based on hashtags the user frequently uses on social media. For example, the suggestion unit can suggest relevant hairstyles based on the hairstyles of influencers the user follows. For example, the suggestion unit can suggest hairstyles related to popular hairstyles from the user's social media posts in the past. In this way, it becomes possible to suggest hairstyles relevant to the user by analyzing their social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media data into a generating AI and have the generating AI generate suggestions for relevant hairstyles.

[0056] The suggestion unit can customize hairstyle suggestions based on the user's occupation and hobbies. For example, if the user is a business person, the suggestion unit will suggest a formal hairstyle. If the user is an artist, the suggestion unit will suggest a creative hairstyle. If the user enjoys sports, the suggestion unit will suggest a hairstyle that allows for easy movement. By customizing hairstyle suggestions based on occupation and hobbies, it becomes possible to suggest the most suitable hairstyle for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's occupation and hobby data into a generating AI and have the generating AI perform the customization of suggestions.

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

[0058] Step 1: The reception desk accepts the user's photo and keyword input. The user's photo may include, but is not limited to, a JPEG image with a resolution of 1080p. The reception desk allows the user to upload a photo, for example, taken with a smartphone. The reception desk also accepts keywords selected by the user as input. Keywords may include, but are not limited to, a hairstyle style, color, length, etc. Step 2: The suggestion department analyzes the information received by the reception department and proposes hairstyles that suit the user. For example, the suggestion department proposes multiple hairstyles that suit the user based on the user's face, head shape, hair type, and hair color. For example, the suggestion department uses AI to suggest the optimal bangs length for the user's face shape and the best way to perm their hair type. Step 3: The generation unit generates a specific order based on the hairstyle proposed by the suggestion unit. The generation unit generates detailed orders, for example, for the length of the bangs, the style of the sides and back, the hair color, and the perm. The generation unit uses AI, for example, to generate a detailed order that meets the user's preferences. Step 4: The supply unit provides the order generated by the generation unit to the hairdresser. The supply unit provides the order to the hairdresser, for example, through an app. The supply unit provides the generated order to the hairdresser, for example, using AI.

[0059] (Example of form 2) The hairstyle suggestion system according to an embodiment of the present invention is a system that prevents hairstyle failures due to insufficient communication with the hairdresser by having AI suggest a hairstyle that truly suits the user and then communicating it to the hairdresser to realize the ideal hairstyle. This hairstyle suggestion system starts when the user installs a dedicated app on their smartphone, uploads a photo of themselves, and selects keywords. For example, the user selects keywords such as "make sure the bangs don't get wavy" or "increase the overall volume." This information is input into the AI. Next, the AI ​​analyzes the input information and suggests multiple hairstyles that truly suit the user based on their face, head shape, hair type, and hair color. For example, the AI ​​suggests the optimal bang length for the user's face shape and the type of perm that suits their hair type. The user chooses their favorite hairstyle from the suggested options and takes it to the hairdresser. Furthermore, the app provides specific orders to the hairdresser. For example, detailed instructions such as "cut the bangs by XX centimeters," "do the sides and back like this," "make the hair color XX," and "get a XX perm" are provided. This allows the hairdresser to create a hairstyle that meets the user's wishes. This system allows users to get the hairstyle they want and prevents mistakes caused by a lack of communication with the hairdresser. Furthermore, hair salons can secure customers and improve satisfaction by partnering with AI. In this way, the hairstyle suggestion system prevents users from getting the hairstyle they want and helps them achieve their ideal look.

[0060] The hairstyle suggestion system according to this embodiment comprises a reception unit, a suggestion unit, a generation unit, and a provision unit. The reception unit receives input of a user's photo and keywords. The user's photo may include, but is not limited to, a JPEG image with a resolution of 1080p. The reception unit allows the user to upload a photo taken with their smartphone, for example. The reception unit also receives keywords selected by the user as input. The keywords may include, but are not limited to, hairstyle styles, colors, and lengths. The suggestion unit analyzes the information received by the reception unit and suggests a hairstyle that suits the user. The suggestion unit suggests multiple hairstyle patterns that suit the user, for example, based on the user's face, head shape, hair texture, and hair color. The suggestion unit may use, for example, AI to suggest the optimal bangs length for the user's face shape and the appropriate perm method for their hair texture. The generation unit generates a specific order based on the hairstyle suggested by the suggestion unit. The generation unit generates a detailed order, for example, the length of the bangs, the style of the sides and back, the hair color, and the perm. The generation unit generates a detailed order that meets the user's preferences, for example, using AI. The provision unit provides the order generated by the generation unit to the hairdresser. The provision unit provides the order to the hairdresser, for example, through an app. The provision unit provides the generated order to the hairdresser, for example, using AI. As a result, the hairstyle suggestion system according to this embodiment can realize the ideal hairstyle by having the AI ​​suggest the optimal hairstyle based on the user's photo and keywords, and providing a specific order to the hairdresser.

[0061] The reception desk accepts user photos and keywords. User photos may include, but are not limited to, JPEG format and 1080p resolution. The reception desk allows users to upload photos taken with their smartphones, for example. Specifically, this involves the user taking photos using a dedicated app and uploading them to the system. It is recommended that photos be taken from multiple angles, such as the front, side, and back of the face. This allows the system to more accurately understand the user's face shape and hair condition. The reception desk also accepts keywords selected by the user as input. Keywords may include, but are not limited to, hairstyle styles, colors, and lengths. Users can use the in-app input form to enter their desired hairstyle style (e.g., bob, short, long), hair color (e.g., brown, blonde, black), and hair length (e.g., shoulder-length, ear-length). Furthermore, users can enter specific requests as keywords, such as the hairstyle of a particular celebrity or a hairstyle for a specific event. This allows the reception department to accurately understand the user's detailed requests and provide that information to the proposal department.

[0062] The suggestion department analyzes the information received by the reception department and proposes hairstyles that suit the user. For example, the suggestion department proposes multiple hairstyles that suit the user based on the user's face, head shape, hair type, and hair color. Specifically, it uses AI to analyze the user's face shape and identify the face type, such as round, oval, or square. Next, it considers the head shape and hair type (e.g., straight, wavy, or curly) to select the optimal hairstyle. Based on past databases, the AI ​​refers to successful examples of other users with similar face shapes and hair types to propose the optimal hairstyle. For example, it may suggest the optimal bangs length for the user's face shape or the best perm technique for their hair type. Furthermore, the suggestion department analyzes keywords entered by the user and proposes hairstyles that meet the user's preferences. For example, if the user enters "hairstyle suitable for a summer event," the AI ​​can suggest light styles and cool hair colors suitable for summer events. In this way, the suggestion department can comprehensively consider the user's individual characteristics and preferences to propose the optimal hairstyle.

[0063] The generation unit generates specific orders based on the hairstyles proposed by the suggestion unit. For example, the generation unit generates detailed orders for bangs length, side and back styles, hair color, and perms. Specifically, it uses AI to generate detailed orders that meet the user's preferences. For example, bangs length can be adjusted in millimeter increments, and side and back styles are described in detail, including specific cutting methods and desired finished look. For hair color, it suggests the best shade for the user's skin tone and eye color, and for perms, it provides detailed information such as the best perming method based on hair type and the duration of the perm. The generation unit compiles this information into a single order sheet and provides it in a format that hairdressers can easily understand. Furthermore, the generation unit can consider the user's past order history and feedback to generate even more accurate orders. As a result, the generation unit can generate specific and detailed orders that meet the user's preferences and provide them to hairdressers.

[0064] The service provider delivers orders generated by the generation unit to hairdressers. The service provider delivers orders to hairdressers, for example, through an app. Specifically, it sends the generated order sheet to a hairdresser-only app so that hairdressers can check it in real time. The service provider can deliver generated orders to hairdressers, for example, using AI. The AI ​​can consider the hairdresser's schedule and past treatment history to deliver orders at the optimal time. Furthermore, the service provider can also provide visual guides and video tutorials to make it easier for hairdressers to check the order details. For example, it can provide videos showing specific steps on how to cut bangs or how to apply a perm, supporting hairdressers in performing the treatment accurately. The service provider also supports real-time communication between users and hairdressers, allowing for quick questions and adjustments regarding the order details. In this way, the service provider can effectively deliver generated orders to hairdressers and support users in achieving their ideal hairstyle.

[0065] The reception desk can estimate the user's emotions and adjust the timing of photo and keyword input based on the estimated emotions. For example, if the user is relaxed, the reception desk provides an interface that prompts for detailed keyword input. For example, if the user is stressed, the reception desk prioritizes simple keyword input and postpones photo uploading. For example, if the user is in a hurry, the reception desk prioritizes voice input to allow for quick photo and keyword input. This allows for the collection of more relevant information by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0066] The reception desk can analyze the user's past hairstyle history and select the optimal input method. For example, the reception desk can analyze the trends of hairstyles the user has chosen in the past and automatically suggest relevant keywords. For example, the reception desk can automatically display photos of similar hairstyles based on photos the user has uploaded in the past. For example, the reception desk can suggest hairstyles that are suitable for a specific season or event based on the user's past hairstyle history. In this way, by selecting the optimal input method based on past hairstyle history, the system is made easy for the user to use. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past hairstyle history data into a generating AI and have the generating AI select the optimal input method.

[0067] The reception desk can filter the user's current hair condition and the season when they input photos and keywords. For example, the reception desk can automatically detect the user's current hair length and color and suggest appropriate keywords based on that. For example, the reception desk can consider seasonal hairstyle trends and prioritize displaying relevant keywords. For example, the reception desk can analyze the user's hair health and suggest keywords to prevent damage. This allows for the suggestion of more appropriate keywords by filtering based on the current hair condition and the season. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's hair condition data into a generating AI and have the generating AI perform the filtering.

[0068] The reception unit can estimate the user's emotions and determine the priority of keywords to be entered based on the estimated emotions. For example, if the user is relaxed, the reception unit will prioritize displaying detailed keywords. For example, if the user is stressed, the reception unit will prioritize displaying simple keywords. For example, if the user is in a hurry, the reception unit will prioritize displaying the most important keywords. This allows for the provision of more appropriate keywords by prioritizing keywords according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0069] The reception desk can prioritize retrieving highly relevant keywords by considering the user's geographical location when they input photos and keywords. For example, if the user is in an urban area, the reception desk will prioritize displaying keywords based on the latest trends. If the user is in a suburban area, the reception desk will prioritize displaying keywords related to natural style. If the user is planning to attend a specific event, the reception desk will prioritize displaying keywords related to that event. This allows for the provision of more relevant keywords by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI retrieve highly relevant keywords.

[0070] The reception desk can analyze the user's social media activity and obtain relevant keywords when photos and keywords are entered. For example, the reception desk can suggest relevant keywords based on hashtags that the user frequently uses on social media. For example, the reception desk can suggest relevant keywords based on the hairstyles of influencers that the user follows. For example, the reception desk can suggest keywords related to hairstyles that have been well-received in the past from the user's social media posts. In this way, by analyzing social media activity, it is possible to provide keywords relevant to the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI perform the task of obtaining relevant keywords.

[0071] The suggestion unit can estimate the user's emotions and adjust the way hairstyle suggestions are presented based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide suggestions with detailed explanations. If the user is stressed, the suggestion unit will provide concise and to-the-point suggestions. If the user is excited, the suggestion unit will provide visually appealing suggestions. By adjusting the presentation of suggestions according to the user's emotions, more appropriate hairstyle suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0072] The suggestion unit can adjust the level of detail in hairstyle suggestions based on the user's lifestyle. For example, if the user has a busy lifestyle, the suggestion unit will suggest a hairstyle that is easy to maintain. If the user has an active lifestyle, the suggestion unit will suggest a hairstyle that is easy to move in. If the user has a sociable lifestyle, the suggestion unit will suggest a stylish hairstyle. By adjusting the level of detail in suggestions based on lifestyle, it becomes possible to suggest the most suitable hairstyle for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle data into a generating AI and have the generating AI perform the adjustment of the level of detail in suggestions.

[0073] The suggestion unit can apply different suggestion algorithms depending on the user's age and gender when suggesting hairstyles. For example, the suggestion unit might suggest hairstyles based on the latest trends for younger users. For example, it might suggest more subdued hairstyles for middle-aged and older users. For example, it might suggest different hairstyles for men and women depending on gender. By applying suggestion algorithms tailored to age and gender, it becomes possible to suggest more appropriate hairstyles. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's age and gender data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0074] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will make longer suggestions with detailed explanations. If the user is stressed, the suggestion unit will make concise and short suggestions. If the user is in a hurry, the suggestion unit will make short suggestions that get straight to the point. By adjusting the length of suggestions according to the user's emotions, it becomes possible to make more appropriate hairstyle suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0075] The suggestion unit can determine the priority of hairstyle suggestions based on the user's past hairstyle history. For example, the suggestion unit can analyze the trends of hairstyles the user has chosen in the past and prioritize suggesting related hairstyles. For example, the suggestion unit can prioritize suggesting similar hairstyles based on hairstyles that have been well-received by the user in the past. For example, the suggestion unit can prioritize suggesting hairstyles that are suitable for a specific season or event based on the user's past hairstyle history. By determining the priority of suggestions based on past hairstyle history, it becomes possible to suggest the most suitable hairstyle for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past hairstyle history data into a generating AI and have the generating AI perform the determination of the suggestion priority.

[0076] The suggestion unit can adjust the order of hairstyle suggestions based on the user's occupation and hobbies. For example, if the user is a business person, the suggestion unit will prioritize suggesting formal hairstyles. If the user is an artist, the suggestion unit will prioritize suggesting creative hairstyles. If the user enjoys sports, the suggestion unit will prioritize suggesting hairstyles that allow for easy movement. By adjusting the order of suggestions based on occupation and hobbies, it becomes possible to suggest the most suitable hairstyle for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's occupation and hobby data into a generating AI and have the generating AI adjust the order of suggestions.

[0077] The generation unit can estimate the user's emotions and adjust the order generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a detailed order. If the user is stressed, the generation unit generates a concise and to-the-point order. If the user is in a hurry, the generation unit generates an order quickly. By adjusting the order generation method according to the user's emotions, a more appropriate order can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0078] The generation unit can generate the optimal order by analyzing the user's past hairstyle history when generating an order. For example, the generation unit can analyze the trends of hairstyles the user has chosen in the past and generate a related order. For example, the generation unit can generate a similar order based on hairstyles that were well-received by the user in the past. For example, the generation unit can generate an order tailored to a specific season or event from the user's past hairstyle history. In this way, by analyzing past hairstyle history, the optimal order for the user can be generated. 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 input the user's past hairstyle history data into a generation AI and have the generation AI execute the generation of the optimal order.

[0079] The generation unit can customize the details of an order based on the user's current hair condition when generating an order. For example, the generation unit can automatically detect the user's current hair length and color and customize the order based on that. For example, the generation unit can analyze the health of the user's hair and generate an order to prevent damage. For example, the generation unit can generate an optimal perm or coloring order based on the user's hair type. This allows for the generation of more appropriate orders by customizing the order based on the current hair condition. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's hair condition data into a generation AI and have the generation AI perform the order customization.

[0080] The generation unit can estimate the user's emotions and determine the order priority based on the estimated emotions. For example, if the user is relaxed, the generation unit will prioritize generating detailed orders. If the user is stressed, the generation unit will prioritize generating concise and to-the-point orders. If the user is in a hurry, the generation unit will prioritize generating fast orders. This allows for the generation of more appropriate orders by prioritizing orders according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0081] The generation unit can generate the optimal order by considering the user's geographical location information when generating an order. For example, if the user is in an urban area, the generation unit will generate an order based on the latest trends. For example, if the user is in a suburban area, the generation unit will generate an order related to natural styles. For example, if the user has plans to attend a specific event, the generation unit will generate an order related to that event. This allows for the generation of more appropriate orders by considering geographical location information. 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 input the user's geographical location information data into a generation AI and have the generation AI perform the generation of the optimal order.

[0082] The generation unit can analyze the user's social media activity and suggest order details when generating an order. For example, the generation unit can generate relevant orders based on hashtags the user frequently uses on social media. For example, the generation unit can generate relevant orders based on the hairstyles of influencers the user follows. For example, the generation unit can generate orders related to hairstyles that have been popular in the past from the user's social media posts. In this way, by analyzing social media activity, it is possible to generate orders relevant to the user. 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 input the user's social media data into a generation AI and have the generation AI suggest order details.

[0083] The service provider can estimate the user's emotions and adjust the order delivery method based on the estimated emotions. For example, if the user is relaxed, the service provider will provide an order with detailed explanations. If the user is stressed, the service provider will provide a concise and to-the-point order. If the user is in a hurry, the service provider will deliver the order quickly. By adjusting the order delivery method according to the user's emotions, a more appropriate order can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The service provider can select the optimal service method by referring to the user's past hairstyle history when providing an order. For example, the service provider can analyze the trends of hairstyles the user has chosen in the past and provide relevant orders. For example, the service provider can provide similar orders based on hairstyles that were well-received by the user in the past. For example, the service provider can provide orders tailored to specific seasons or events based on the user's past hairstyle history. In this way, by referring to past hairstyle history, the service provider can provide the optimal order for the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past hairstyle history data into a generating AI and have the generating AI select the optimal service method.

[0085] The service provider can customize the means of service based on the user's current hair condition when an order is placed. For example, the service provider can automatically detect the user's current hair length and color and provide an order based on that. For example, the service provider can analyze the health of the user's hair and provide an order to prevent damage. For example, the service provider can provide an order for the optimal perm or coloring based on the user's hair type. By customizing the means of service based on the current hair condition, a more appropriate order can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's hair condition data into a generating AI and have the generating AI perform the customization of the means of service.

[0086] The service provider can estimate the user's emotions and determine the order delivery priority based on the estimated emotions. For example, if the user is relaxed, the service provider will prioritize detailed orders. If the user is stressed, the service provider will prioritize concise and to-the-point orders. If the user is in a hurry, the service provider will prioritize quick orders. By prioritizing order delivery according to the user's emotions, more appropriate orders can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The service provider can select the optimal service delivery method by considering the user's geographical location when delivering an order. For example, if the user is in an urban area, the service provider may offer an order based on the latest trends. If the user is in a suburban area, the service provider may offer an order related to a natural style. If the user has plans to attend a specific event, the service provider may offer an order related to that event. By considering geographical location, the service provider can deliver a more appropriate order. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI select the optimal service delivery method.

[0088] The service provider can analyze the user's social media activity and propose a means of delivery when providing an order. For example, the service provider can provide relevant orders based on hashtags the user frequently uses on social media. For example, the service provider can provide relevant orders based on the hairstyles of influencers the user follows. For example, the service provider can provide orders related to hairstyles that have been well-received in the past, based on the user's social media posts. In this way, by analyzing social media activity, the service provider can provide orders that are relevant to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI propose a means of delivery.

[0089] The service provider can refer to the user's calendar information when providing an order and make suggestions based on their schedule. For example, the service provider can refer to the appointments registered in the user's calendar and automatically set an order. For example, the service provider can suggest an order related to a specific event from the user's calendar information. For example, the service provider can suggest the optimal order tailored to the user's schedule based on the user's calendar information. In this way, by referring to the calendar information, the service provider can provide the optimal order tailored to the user's schedule. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's calendar information data into a generating AI and have the generating AI execute suggestions based on the schedule.

[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 suggestion unit can estimate the user's emotions and adjust the order of hairstyle suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will first suggest a hairstyle with a detailed explanation. If the user is stressed, the suggestion unit will first suggest a hairstyle that is concise and to the point. If the user is in a hurry, the suggestion unit will first suggest the most important hairstyle. By adjusting the order of suggestions according to the user's emotions, more appropriate hairstyle suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The suggestion unit can customize hairstyle suggestions based on the user's life events. For example, if the user is preparing for a wedding, the suggestion unit will suggest a formal and elegant hairstyle. If the user is planning to participate in a sporting event, the suggestion unit will suggest a hairstyle that allows for easy movement. If the user is starting a new job, the suggestion unit will suggest a hairstyle that gives a professional impression. By customizing hairstyle suggestions based on life events, it becomes possible to suggest the most suitable hairstyle for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's life event data into a generating AI and have the generating AI perform the customization of suggestions.

[0093] The suggestion unit can estimate the user's emotions and adjust the visual representation of the suggestion based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will make a suggestion with detailed visuals. If the user is stressed, the suggestion unit will make a suggestion using simple and intuitive visuals. If the user is excited, the suggestion unit will make a suggestion using colorful and visually appealing visuals. By adjusting the visual representation according to the user's emotions, it becomes possible to make more appropriate hairstyle suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0094] The suggestion unit can increase the variety of suggestions based on the user's past hairstyle history. For example, the suggestion unit can analyze the trends of hairstyles the user has chosen in the past and suggest multiple related hairstyles. For example, the suggestion unit can suggest multiple similar hairstyles based on hairstyles that were well-received by the user in the past. For example, the suggestion unit can suggest multiple hairstyles that are suitable for a specific season or event based on the user's past hairstyle history. In this way, by increasing the variety of suggestions based on past hairstyle history, it becomes possible to suggest the most suitable hairstyle for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past hairstyle history data into a generating AI and have the generating AI perform the task of increasing the variety of suggestions.

[0095] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions slowly. If the user is stressed, the suggestion unit will provide concise and quick suggestions. If the user is in a hurry, the suggestion unit will provide the most important suggestions quickly. By adjusting the timing of suggestions according to the user's emotions, more appropriate hairstyle suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The suggestion unit can customize hairstyle suggestions by taking into account the user's geographical location. For example, if the user is in an urban area, the suggestion unit will suggest hairstyles based on the latest trends. If the user is in a suburban area, the suggestion unit will suggest hairstyles related to natural styles. If the user has plans to attend a specific event, the suggestion unit will suggest hairstyles related to that event. This allows for more appropriate hairstyle suggestions by considering geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI perform the customization of suggestions.

[0097] The suggestion unit can estimate the user's emotions and adjust the level of detail in its suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide suggestions with detailed explanations. If the user is stressed, the suggestion unit will provide concise and to-the-point suggestions. If the user is in a hurry, the suggestion unit will provide suggestions that include the most important information. By adjusting the level of detail in suggestions according to the user's emotions, more appropriate hairstyle suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0098] The suggestion unit can analyze a user's social media activity and suggest relevant hairstyles. For example, the suggestion unit can suggest relevant hairstyles based on hashtags the user frequently uses on social media. For example, the suggestion unit can suggest relevant hairstyles based on the hairstyles of influencers the user follows. For example, the suggestion unit can suggest hairstyles related to popular hairstyles from the user's social media posts in the past. In this way, it becomes possible to suggest hairstyles relevant to the user by analyzing their social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media data into a generating AI and have the generating AI generate suggestions for relevant hairstyles.

[0099] The suggestion unit can estimate the user's emotions and adjust the order of suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will first offer suggestions with detailed explanations. If the user is stressed, the suggestion unit will first offer concise and to-the-point suggestions. If the user is in a hurry, the suggestion unit will first offer the most important suggestions. By adjusting the order of suggestions according to the user's emotions, it becomes possible to provide more appropriate hairstyle suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0100] The suggestion unit can customize hairstyle suggestions based on the user's occupation and hobbies. For example, if the user is a business person, the suggestion unit will suggest a formal hairstyle. If the user is an artist, the suggestion unit will suggest a creative hairstyle. If the user enjoys sports, the suggestion unit will suggest a hairstyle that allows for easy movement. By customizing hairstyle suggestions based on occupation and hobbies, it becomes possible to suggest the most suitable hairstyle for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's occupation and hobby data into a generating AI and have the generating AI perform the customization of suggestions.

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

[0102] Step 1: The reception desk accepts the user's photo and keyword input. The user's photo may include, but is not limited to, a JPEG image with a resolution of 1080p. The reception desk allows the user to upload a photo, for example, taken with a smartphone. The reception desk also accepts keywords selected by the user as input. Keywords may include, but are not limited to, a hairstyle style, color, length, etc. Step 2: The suggestion department analyzes the information received by the reception department and proposes hairstyles that suit the user. For example, the suggestion department proposes multiple hairstyles that suit the user based on the user's face, head shape, hair type, and hair color. For example, the suggestion department uses AI to suggest the optimal bangs length for the user's face shape and the best way to perm their hair type. Step 3: The generation unit generates a specific order based on the hairstyle proposed by the suggestion unit. The generation unit generates detailed orders, for example, for the length of the bangs, the style of the sides and back, the hair color, and the perm. The generation unit uses AI, for example, to generate a detailed order that meets the user's preferences. Step 4: The supply unit provides the order generated by the generation unit to the hairdresser. The supply unit provides the order to the hairdresser, for example, through an app. The supply unit provides the generated order to the hairdresser, for example, using AI.

[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, proposal 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 control unit 46A of the smart device 14 and receives the user's photos and keywords. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a hairstyle that suits the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a specific order. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the order to the hairdresser. 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 signals from the user and accepts instructions from the user. 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, proposal 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 control unit 46A of the smart glasses 214 and receives the user's photo and keywords. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes a hairstyle that suits the user. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a specific order. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the order to the hairdresser. 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.

[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 signals from the user and accepts instructions from the user. 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, proposal 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 control unit 46A of the headset terminal 314 and receives the user's photo and keywords. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a hairstyle that suits the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a specific order. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the order to the hairdresser. 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 signals from the user and accepts instructions from the user. 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, proposal unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives the user's photo and keywords. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a hairstyle that suits the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a specific order. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the order to the hairdresser. 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 area that accepts user photos and keyword input, The information received by the reception unit is analyzed, and a suggestion unit proposes hairstyles that suit the user. A generation unit that generates a specific order based on the hairstyle proposed by the proposal unit, The system includes a supplying unit that provides the orders generated by the generation unit to the hairdresser. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of photo and keyword input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is The system analyzes the user's past hairstyle history and selects the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When users enter photos and keywords, the system filters the results based on their current hair condition and the season. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of keywords to be entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users input photos and keywords, the system prioritizes retrieving highly relevant keywords by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When you enter photos and keywords, the system analyzes your social media activity and retrieves relevant keywords. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the way hairstyle suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned proposal section is, When suggesting hairstyles, adjust the level of detail based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, When suggesting hairstyles, different suggestion algorithms are applied depending on the user's age and gender. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, When suggesting hairstyles, the system prioritizes suggestions based on the user's past hairstyle history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When suggesting hairstyles, the order of suggestions is adjusted based on the user's occupation and hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts the order generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When creating an order, the system analyzes the user's past hairstyle history to generate the optimal order. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When creating an order, the order details are customized based on the user's current hair condition. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and determines order priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating an order, the system takes the user's geographical location into consideration to generate the optimal order. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating an order, we analyze the user's social media activity and suggest order details. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the order delivery method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing an order, the system will refer to the user's past hairstyle history to select the most suitable method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When an order is placed, the method of delivery is customized based on the user's current hair condition. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, The system estimates the user's emotions and determines the order delivery priority based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing an order, the optimal delivery method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing an order, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing an order, we refer to the user's calendar information to make suggestions based on their schedule. 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 area that accepts user photos and keyword input, The information received by the reception unit is analyzed, and a suggestion unit proposes hairstyles that suit the user. A generation unit that generates a specific order based on the hairstyle proposed by the proposal unit, The system includes a supplying unit that provides the orders generated by the generation unit to the hairdresser. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of photo and keyword input based on the estimated user emotions. The system according to feature 1.

3. The aforementioned reception unit is The system analyzes the user's past hairstyle history and selects the optimal input method. The system according to feature 1.

4. The aforementioned reception unit is When users enter photos and keywords, the system filters the results based on their current hair condition and the season. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of keywords to be entered based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When users input photos and keywords, the system prioritizes retrieving highly relevant keywords by considering their geographical location. The system according to feature 1.

7. The aforementioned reception unit is When you enter photos and keywords, the system analyzes your social media activity and retrieves relevant keywords. The system according to feature 1.

8. The aforementioned proposal section is, The system estimates the user's emotions and adjusts the way hairstyle suggestions are presented based on those estimated emotions. The system according to feature 1.

9. The aforementioned proposal section is, When suggesting hairstyles, adjust the level of detail based on the user's lifestyle. The system according to feature 1.

10. The aforementioned proposal section is, When suggesting hairstyles, different suggestion algorithms are applied depending on the user's age and gender. The system according to feature 1.

Images