system

Abstract

The system according to this embodiment aims to allow the user to intuitively understand the proposed style and try it out in real time. [Solution] The system according to the embodiment comprises a reception unit, a suggestion unit, a trial unit, and a feedback unit. The reception unit receives style input from the user. The suggestion unit suggests multiple styles based on the information received by the reception unit. The trial unit tries out the styles suggested by the suggestion unit in real time. The feedback unit provides feedback on the styles tried out by the trial unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response 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, it is difficult for a user to intuitively understand a proposed style and try it in real time, and there is room for improvement.

[0005] The system according to the embodiment aims to enable a user to intuitively understand a proposed style and try it in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a suggestion unit, a trial unit, and a feedback unit. The reception unit receives style input from the user. The suggestion unit proposes multiple styles based on the information received by the reception unit. The trial unit tries out the styles proposed by the suggestion unit in real time. The feedback unit provides feedback on the styles tried out by the trial unit. [Effects of the Invention]

[0007] The system according to this embodiment allows the user to intuitively understand the proposed style and try it out in real time. [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 labeled 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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 style suggestion system according to an embodiment of the present invention is a system that utilizes an AI agent to allow users to try out hairstyles and colors that suit them in real time. This style suggestion system allows users to compare multiple styles suggested by the AI ​​agent in front of a real mirror and intuitively understand how they look. By trying out styles tailored to hair type, bone structure, personality, and preferences, the probability of finding an ideal style increases. Hairdressers can also see the suggested styles on the actual customer, leading to smoother consultations and more accurate suggestions. Furthermore, the system provides customers with the opportunity to try out their ideal style from home, even outside of salon hours, improving customer convenience. Hairdressers can provide immediate feedback on the styles and colors suggested by the AI ​​agent, understanding customer requests and preferences and enabling them to demonstrate more advanced skills. As a result, the style suggestion system allows users to try out hairstyles and colors that suit them in real time.

[0029] The style suggestion system according to this embodiment comprises a reception unit, a suggestion unit, a trial unit, and a feedback unit. The reception unit receives style input from the user. The user can, for example, input their desired hairstyle and color. After receiving the user's input, the reception unit transmits the information to the suggestion unit. The suggestion unit proposes multiple styles based on the information received by the reception unit. The suggestion unit proposes styles based, for example, hair type, bone structure, personality, and preferences. The suggestion unit can use AI to select the most suitable style for the user. For example, the suggestion unit can use AI to analyze the user's hair type and bone structure and propose a style based on that. The suggestion unit can also use AI to learn the user's personality and preferences and propose a style based on that. The trial unit tries out the styles proposed by the suggestion unit in real time. For example, the trial unit can try out the proposed styles in front of a real mirror. The trial unit can use AI to reflect the user's appearance in real time. For example, the trial unit can use AI to analyze the user's appearance and reflect the style in real time based on that. The trial unit can use AI to track the user's movements and adjust the style in real time based on that. The feedback unit provides feedback on the styles tried by the trial unit. The feedback unit can, for example, allow a hairdresser to provide feedback on the spot. The feedback unit can use AI to understand the user's requests and preferences. The feedback unit can, for example, use AI to analyze the user's feedback and reflect it in the next suggestion. As a result, the style suggestion system according to this embodiment allows the user to try out hairstyles and colors that suit them in real time.

[0030] The reception desk receives style input from users. Users can input their desired hairstyle and color, for example. Specifically, users access a dedicated application or website using a device such as a smartphone, tablet, or PC and input their desired hairstyle and color. Input methods include text input, selection from a list of options, and image uploads. For example, a user can upload a photo of their face and specify their desired hairstyle and color on top of it. Users can also input information about styles they have tried in the past and their current hairstyle, which serves as reference information for the suggestion department to make more accurate style suggestions. After receiving the user's input, the reception desk transmits the information to the suggestion department. The transmitted information includes the user's basic information (age, gender, hair type, etc.) and details of the desired style. This allows the suggestion department to obtain basic data to make style suggestions that meet the user's needs. The reception desk also has a function to accurately receive the user's input and prompt for confirmation and correction of the input as needed. For example, if there are deficiencies in the input or if the user's desired style is ambiguous, it will provide additional questions or guidelines to support the user in providing more specific information. This allows the reception department to accurately understand user needs and provide appropriate information to the proposal department.

[0031] The suggestion department proposes multiple styles based on the information received by the reception department. For example, the suggestion department proposes styles based on hair type, bone structure, personality, and preferences. Specifically, the suggestion department uses AI to analyze the user's hair type and bone structure and proposes styles based on that analysis. The AI ​​uses image recognition technology to analyze the user's facial photograph and hair condition, identifying hair type (straight, curly, thickness, volume, etc.) and bone structure (face shape, jawline, etc.). This allows the AI ​​to select the most suitable hairstyle and color for the user. Furthermore, the suggestion department can also have the AI ​​learn the user's personality and preferences and propose styles based on that. For example, based on styles the user has previously chosen and information about their preferred fashion and lifestyle, the AI ​​learns the user's preferences and provides more personalized suggestions. The suggestion department presents the user with multiple styles and explains the characteristics and benefits of each style. For example, it provides information such as how the proposed style enhances the user's facial features, what occasions it is suitable for, and what kind of maintenance is required. This allows the user to obtain reference information to choose the style that is best suited to them. Furthermore, the suggestion function also includes a feature that allows users to provide feedback on the suggested styles, enabling the suggestion to be adjusted based on user reactions. This allows the suggestion function to provide flexible style suggestions that meet user needs.

[0032] The trial section allows users to try out styles suggested by the suggestion section in real time. For example, users can try out suggested styles in front of a real mirror. Specifically, the trial section uses AI to reflect the user's appearance in real time. Users can virtually try out suggested styles using a dedicated device or application. For example, when a user stands in front of a mirror, the AI ​​analyzes the user's face and hair condition in real time and virtually applies the suggested style. This allows the user to see how the suggested style looks on them. The trial section can also use AI to track the user's movements and adjust the style in real time based on those movements. For example, if a user moves their head or touches their hair, the AI ​​detects the movement and adjusts the style in real time. This allows the user to more accurately experience how the suggested style actually looks and feels. Furthermore, the trial section also has a function that allows users to try out multiple styles, enabling them to compare different styles and choose the one that suits them best. The trial section also has a function that records the user's trial history, which can be reviewed later. This allows users to look back at styles they've tried in the past and gain reference information to help them choose the best style for them.

[0033] The Feedback Department provides feedback on styles tried by the Trial Department. For example, a hairdresser can provide feedback on the spot. Specifically, the hairdresser offers professional advice on styles tried by the user in the Trial Department. Based on the user's hair type, bone structure, and lifestyle, the hairdresser explains the advantages and disadvantages of the suggested styles, supporting the user in choosing the optimal style. Furthermore, the Feedback Department can use AI to understand user requests and preferences. For example, the AI ​​analyzes user feedback and incorporates it into future suggestions. Information such as the user's impressions of the tried styles, what they liked, and what could be improved is collected, and the AI ​​analyzes this information to improve future suggestions. This allows the Feedback Department to provide more personalized suggestions tailored to the user's needs and preferences. Additionally, the Feedback Department can provide information to improve the functionality of the Suggestion Department and Trial Department based on the user feedback. For example, if a user consistently provides negative feedback on a particular style, adjustments such as reducing the frequency of that style's suggestion can be made. In this way, the Feedback Department plays a crucial role in improving user satisfaction and enhancing the overall system performance.

[0034] The suggestion unit can propose multiple styles based on hair type, bone structure, personality, and preferences. For example, the suggestion unit can propose styles based on hair type. The suggestion unit can propose styles according to hair type such as straight hair, curly hair, fine hair, and thick hair. The suggestion unit can also propose styles based on bone structure. The suggestion unit can propose styles according to bone structure such as face shape, head shape, and neck length. The suggestion unit can also propose styles based on personality and preferences. The suggestion unit can propose styles according to personality such as character, lifestyle, and hobbies. The suggestion unit can propose styles according to preferences such as color preferences, style preferences, and trend preferences. In this way, the suggestion unit can propose the most suitable style 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 data on the user's hair type, bone structure, personality, and preferences into a generating AI, which can then propose the most suitable style.

[0035] The trial unit allows users to try on suggested styles in front of a real mirror. The trial unit can try on styles using, for example, a physical mirror. The trial unit can also try on styles using a virtual mirror. The trial unit can also try on styles using a display. The trial unit can use AI to reflect the user's appearance in real time. The trial unit can, for example, have the AI ​​analyze the user's appearance and reflect the style in real time based on that. The trial unit can also have the AI ​​track the user's movements and adjust the style in real time based on that. This allows the user to intuitively check the style in the trial unit. Some or all of the above processes in the trial unit may be performed using, for example, AI, or not using AI. For example, the trial unit can input data of the user's appearance into a generating AI, and the generating AI can reflect the style in real time.

[0036] The feedback unit allows hairdressers to provide feedback on the spot. For example, the feedback unit allows hairdressers to provide real-time feedback on the user's style. The feedback unit can use AI to understand the user's requests and preferences. For example, the feedback unit uses AI to analyze the user's feedback and reflect it in the next suggestion. This allows the feedback unit to allow hairdressers to provide real-time feedback. Some or all of the above processes in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input user feedback data into a generating AI, which can then reflect it in the next suggestion.

[0037] The reception area allows customers to try out styles from home even outside of the salon's business hours. The reception area provides, for example, an interface for customers to try out styles from home. The reception area can use AI to understand customer requests and preferences. The reception area, for example, uses AI to analyze customer input and suggest styles based on that. This allows the reception area to allow customers to try out styles even from home. Some or all of the above processes in the reception area may be performed using AI, for example, or not using AI. For example, the reception area can input customer input data into a generating AI, which can then suggest styles.

[0038] The proposal department can understand customer needs and preferences and reflect them in future proposals. For example, the proposal department can learn customer needs and preferences and make future proposals based on them. The proposal department can use AI to analyze customer needs and preferences. For example, the proposal department can have AI analyze customer feedback and make future proposals based on it. This allows the proposal department to make proposals based on customer needs and preferences. Some or all of the above processes in the proposal department may be performed using AI, or not using AI. For example, the proposal department can input customer feedback data into a generating AI, which can then make future proposals.

[0039] The reception desk can analyze the user's past style history and select the optimal input method. For example, the reception desk can automatically display styles previously selected by the user as candidates. The reception desk can prioritize suggesting input methods (voice, text, etc.) previously used by the user. The reception desk can predict and suggest styles to be used during specific time periods based on the user's past style history. In this way, the reception desk can provide the optimal input method based on the user's past history. 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 past style history data into a generating AI, which can then select the optimal input method.

[0040] The reception desk can filter the user's current hair condition and preferences when they input a style. For example, the reception desk can suggest a suitable style based on the user's current hair length and color. The reception desk can prioritize displaying highly relevant styles based on the user's preferences and past choices. The reception desk can suggest styles that are gentle on the hair based on the user's hair health. This enables the reception desk to suggest styles based on the user's current hair condition and preferences. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the user's current hair condition and preferences into a generating AI, which can then perform filtering.

[0041] The reception desk can prioritize inputting highly relevant styles by considering the user's geographical location when inputting styles. For example, the reception desk can suggest popular styles based on trends in the user's area. The reception desk can suggest styles suitable for the climate of the user's area. The reception desk can suggest styles that match the culture and events of the user's area. This enables the reception desk to suggest styles based on the user's 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 information into a generating AI, which can then suggest highly relevant styles.

[0042] The reception desk can analyze the user's social media activity when inputting styles and input relevant styles. For example, the reception desk can suggest styles from influencers the user follows on social media. The reception desk can prioritize displaying styles that the user has "liked" on social media. The reception desk can suggest relevant styles based on the content of the user's social media posts. This enables the reception desk to suggest styles based on the user's social media activity. 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 activity data into a generating AI, which can then suggest relevant styles.

[0043] The suggestion function can adjust the level of detail of a suggestion based on the importance of the style. For example, in the case of a significant style change, the suggestion function can provide a detailed explanation and multiple options. For a minor style change, the suggestion function can provide a concise explanation and a few options. The suggestion function can provide detailed information about styles that the user is particularly interested in. This allows the suggestion function to adjust the level of detail of the suggestion according to the importance of the style. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not using AI. For example, the suggestion function can input style importance data into a generating AI, which can then adjust the level of detail of the suggestion.

[0044] The suggestion unit can apply different suggestion algorithms depending on the style category when making suggestions. For example, for haircut suggestions, the suggestion unit may use an algorithm based on face shape and hair type. For hair color suggestions, it may use an algorithm based on skin tone and season. For hair styling suggestions, it may use an algorithm based on the user's lifestyle and events. This allows the suggestion unit to apply suggestion algorithms according to the style category. 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 style category data into a generating AI, and the generating AI can apply different suggestion algorithms.

[0045] The proposal department can prioritize proposals based on the timing of style submission. For example, if a user wants to change their style for a specific event, the proposal department can prioritize suggesting styles best suited to that event. If a user wants to change their style at the change of season, the proposal department can prioritize suggesting styles appropriate for that season. If a user is interested in a particular trend, the proposal department can prioritize suggesting styles based on that trend. This allows the proposal department to prioritize proposals based on the timing of style submission. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input style submission timing data into a generating AI, which can then determine the priority of proposals.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of styles. For example, the suggestion unit may suggest the most relevant style first based on the user's preferences. The suggestion unit may prioritize suggesting relevant styles based on the user's past choices. The suggestion unit may suggest the most suitable style first based on the user's current hair condition. This allows the suggestion unit to adjust the order of suggestions based on the relevance of styles. 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 may input style relevance data into a generating AI, which can then adjust the order of suggestions.

[0047] The trial unit can analyze the user's past style trial history during the trial period to select the optimal trial method. For example, the trial unit can suggest the optimal trial method based on the styles the user has tried in the past. The trial unit can optimize the trial order based on the user's past trial history. The trial unit can analyze the user's past trial history and suggest the most effective trial method. In this way, the trial unit can provide the optimal trial method based on the user's past history. Some or all of the above processes in the trial unit may be performed using AI, for example, or without AI. For example, the trial unit can input the user's past style trial history data into a generating AI, which can then select the optimal trial method.

[0048] The trial unit can customize the trial method based on the user's current hair condition during the trial. For example, the trial unit can suggest a suitable trial method based on the user's hair length and color. The trial unit can suggest a trial method that does not damage the hair based on the user's hair health. The trial unit can suggest the optimal trial method based on the user's hair texture. In this way, the trial unit can provide the optimal trial method based on the user's current hair condition. Some or all of the above processing in the trial unit may be performed using AI, for example, or without AI. For example, the trial unit can input the user's current hair condition data into a generating AI, which can then customize the trial method.

[0049] The trial unit can select the optimal trial method during the trial, taking into account the user's geographical location. For example, the trial unit can try out popular styles based on trends in the user's region. The trial unit can try out styles suitable for the climate of the user's region. The trial unit can try out styles that match the culture and events of the user's region. In this way, the trial unit can provide the optimal trial method based on the user's geographical location. Some or all of the above processing in the trial unit may be performed using AI, for example, or not using AI. For example, the trial unit can input the user's geographical location information into a generating AI, which can then select the optimal trial method.

[0050] The trial unit can analyze the user's social media activity during the trial and suggest trial methods. For example, the trial unit can try out the styles of influencers the user follows on social media. The trial unit can prioritize trying out styles that the user has "liked" on social media. The trial unit can try out relevant styles based on the content of the user's social media posts. In this way, the trial unit can provide the optimal trial method based on the user's social media activity. Some or all of the above processing in the trial unit may be performed using AI, for example, or not using AI. For example, the trial unit can input the user's social media activity data into a generating AI, and the generating AI can suggest trial methods.

[0051] The feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback unit can suggest optimal feedback based on the feedback the user has provided in the past. The feedback unit can optimize the content of feedback from the user's past feedback history. The feedback unit can analyze the user's past feedback history and provide the most effective feedback. In this way, the feedback unit can provide optimal feedback based on the user's past history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past feedback history data into a generating AI, which can then provide optimal feedback.

[0052] The feedback unit can customize the means of feedback based on the user's current hair condition. For example, the feedback unit can provide appropriate feedback based on the user's hair length and color. The feedback unit can provide feedback that does not damage the hair based on the user's hair health. The feedback unit can provide optimal feedback based on the user's hair texture. As a result, the feedback unit can provide optimal feedback based on the user's current hair condition. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's current hair condition data into a generating AI, which can then customize the means of feedback.

[0053] The feedback unit can select the optimal feedback method when providing feedback, taking into account the user's geographical location information. For example, the feedback unit can provide feedback based on trends in the user's region. The feedback unit can provide feedback that is appropriate for the climate of the user's region. The feedback unit can provide feedback that is tailored to the culture and events of the user's region. In this way, the feedback unit can provide optimal feedback based on the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information into a generating AI, which can then select the optimal feedback method.

[0054] The feedback unit can analyze the user's social media activity and suggest methods for providing feedback. For example, the feedback unit can provide feedback based on the styles of influencers the user follows on social media. The feedback unit can provide feedback based on the styles the user has "liked" on social media. The feedback unit can provide relevant feedback based on the content of the user's social media posts. This allows the feedback unit to provide optimal feedback based on the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's social media activity data into a generating AI, which can then suggest methods for providing feedback.

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

[0056] The suggestion unit can analyze the user's past style history and select the optimal suggestion method. For example, it can automatically display styles the user has previously selected as candidates. It can prioritize suggestion methods (voice, text, etc.) the user has used in the past. Based on the user's past style history, it can predict and suggest styles to be used during specific time periods. In this way, the suggestion unit can provide the optimal suggestion method based on the user's past history. Some or all of the above processing in the suggestion unit may be performed using AI or not.

[0057] The trial unit can customize the trial method based on the user's current hair condition. For example, it can suggest a suitable trial method based on the user's hair length and color. It can suggest a trial method that does not damage the hair based on the user's hair health. It can suggest the optimal trial method based on the user's hair texture. In this way, the trial unit can provide the optimal trial method based on the user's current hair condition. Some or all of the above processing in the trial unit may be performed using AI or not.

[0058] The feedback unit can provide optimal feedback by referring to the user's past feedback history. For example, it can suggest optimal feedback based on the feedback the user has provided in the past. It can optimize the content of feedback from the user's past feedback history. It can analyze the user's past feedback history and provide the most effective feedback. In this way, the feedback unit can provide optimal feedback based on the user's past history. Some or all of the above processing in the feedback unit may be performed using AI or not.

[0059] The reception desk can prioritize inputting highly relevant styles by considering the user's geographical location. For example, it can suggest popular styles based on trends in the user's area. It can suggest styles suitable for the climate of the user's area. It can suggest styles that match the culture and events of the user's area. This enables the reception desk to suggest styles based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not.

[0060] The suggestion unit can apply different suggestion algorithms depending on the style category when making suggestions. For example, for haircut suggestions, an algorithm based on face shape and hair type can be used. For hair color suggestions, an algorithm based on skin tone and season can be used. For hair styling suggestions, an algorithm based on the user's lifestyle and events can be used. This allows the suggestion unit to apply suggestion algorithms appropriate to the style category. Some or all of the above processing in the suggestion unit may be performed using AI or not.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives style input from the user. Users can input, for example, their desired hairstyle and color. After receiving the user's input, the reception desk sends that information to the suggestion department. Step 2: The suggestion department proposes multiple styles based on the information received by the reception department. The suggestion department proposes styles based on, for example, hair type, bone structure, personality, and preferences. The suggestion department can use AI to select the most suitable style for the user. For example, the suggestion department can use AI to analyze the user's hair type and bone structure and propose styles based on that. The suggestion department can also use AI to learn the user's personality and preferences and propose styles based on that. Step 3: The trial unit tries out the style proposed by the proposal unit in real time. The trial unit can try out the proposed style in front of a real mirror, for example. The trial unit can use AI to reflect the user's appearance in real time. For example, the trial unit can use AI to analyze the user's appearance and reflect the style in real time based on that. The trial unit can also use AI to track the user's movements and adjust the style in real time based on that. Step 4: The feedback department provides feedback on the styles tried by the trial department. The feedback department can, for example, have a hairdresser provide feedback on the spot. The feedback department can use AI to understand the user's requests and preferences. The feedback department can, for example, have AI analyze the user's feedback and reflect it in the next suggestion.

[0063] (Example of form 2) The style suggestion system according to an embodiment of the present invention is a system that utilizes an AI agent to allow users to try out hairstyles and colors that suit them in real time. This style suggestion system allows users to compare multiple styles suggested by the AI ​​agent in front of a real mirror and intuitively understand how they look. By trying out styles tailored to hair type, bone structure, personality, and preferences, the probability of finding an ideal style increases. Hairdressers can also see the suggested styles on the actual customer, leading to smoother consultations and more accurate suggestions. Furthermore, the system provides customers with the opportunity to try out their ideal style from home, even outside of salon hours, improving customer convenience. Hairdressers can provide immediate feedback on the styles and colors suggested by the AI ​​agent, understanding customer requests and preferences and enabling them to demonstrate more advanced skills. As a result, the style suggestion system allows users to try out hairstyles and colors that suit them in real time.

[0064] The style suggestion system according to this embodiment comprises a reception unit, a suggestion unit, a trial unit, and a feedback unit. The reception unit receives style input from the user. The user can, for example, input their desired hairstyle and color. After receiving the user's input, the reception unit transmits the information to the suggestion unit. The suggestion unit proposes multiple styles based on the information received by the reception unit. The suggestion unit proposes styles based, for example, hair type, bone structure, personality, and preferences. The suggestion unit can use AI to select the most suitable style for the user. For example, the suggestion unit can use AI to analyze the user's hair type and bone structure and propose a style based on that. The suggestion unit can also use AI to learn the user's personality and preferences and propose a style based on that. The trial unit tries out the styles proposed by the suggestion unit in real time. For example, the trial unit can try out the proposed styles in front of a real mirror. The trial unit can use AI to reflect the user's appearance in real time. For example, the trial unit can use AI to analyze the user's appearance and reflect the style in real time based on that. The trial unit can use AI to track the user's movements and adjust the style in real time based on that. The feedback unit provides feedback on the styles tried by the trial unit. The feedback unit can, for example, allow a hairdresser to provide feedback on the spot. The feedback unit can use AI to understand the user's requests and preferences. The feedback unit can, for example, use AI to analyze the user's feedback and reflect it in the next suggestion. As a result, the style suggestion system according to this embodiment allows the user to try out hairstyles and colors that suit them in real time.

[0065] The reception desk receives style input from users. Users can input their desired hairstyle and color, for example. Specifically, users access a dedicated application or website using a device such as a smartphone, tablet, or PC and input their desired hairstyle and color. Input methods include text input, selection from a list of options, and image uploads. For example, a user can upload a photo of their face and specify their desired hairstyle and color on top of it. Users can also input information about styles they have tried in the past and their current hairstyle, which serves as reference information for the suggestion department to make more accurate style suggestions. After receiving the user's input, the reception desk transmits the information to the suggestion department. The transmitted information includes the user's basic information (age, gender, hair type, etc.) and details of the desired style. This allows the suggestion department to obtain basic data to make style suggestions that meet the user's needs. The reception desk also has a function to accurately receive the user's input and prompt for confirmation and correction of the input as needed. For example, if there are deficiencies in the input or if the user's desired style is ambiguous, it will provide additional questions or guidelines to support the user in providing more specific information. This allows the reception department to accurately understand user needs and provide appropriate information to the proposal department.

[0066] The suggestion department proposes multiple styles based on the information received by the reception department. For example, the suggestion department proposes styles based on hair type, bone structure, personality, and preferences. Specifically, the suggestion department uses AI to analyze the user's hair type and bone structure and proposes styles based on that analysis. The AI ​​uses image recognition technology to analyze the user's facial photograph and hair condition, identifying hair type (straight, curly, thickness, volume, etc.) and bone structure (face shape, jawline, etc.). This allows the AI ​​to select the most suitable hairstyle and color for the user. Furthermore, the suggestion department can also have the AI ​​learn the user's personality and preferences and propose styles based on that. For example, based on styles the user has previously chosen and information about their preferred fashion and lifestyle, the AI ​​learns the user's preferences and provides more personalized suggestions. The suggestion department presents the user with multiple styles and explains the characteristics and benefits of each style. For example, it provides information such as how the proposed style enhances the user's facial features, what occasions it is suitable for, and what kind of maintenance is required. This allows the user to obtain reference information to choose the style that is best suited to them. Furthermore, the suggestion function also includes a feature that allows users to provide feedback on the suggested styles, enabling the suggestion to be adjusted based on user reactions. This allows the suggestion function to provide flexible style suggestions that meet user needs.

[0067] The trial section allows users to try out styles suggested by the suggestion section in real time. For example, users can try out suggested styles in front of a real mirror. Specifically, the trial section uses AI to reflect the user's appearance in real time. Users can virtually try out suggested styles using a dedicated device or application. For example, when a user stands in front of a mirror, the AI ​​analyzes the user's face and hair condition in real time and virtually applies the suggested style. This allows the user to see how the suggested style looks on them. The trial section can also use AI to track the user's movements and adjust the style in real time based on those movements. For example, if a user moves their head or touches their hair, the AI ​​detects the movement and adjusts the style in real time. This allows the user to more accurately experience how the suggested style actually looks and feels. Furthermore, the trial section also has a function that allows users to try out multiple styles, enabling them to compare different styles and choose the one that suits them best. The trial section also has a function that records the user's trial history, which can be reviewed later. This allows users to look back at styles they've tried in the past and gain reference information to help them choose the best style for them.

[0068] The Feedback Department provides feedback on styles tried by the Trial Department. For example, a hairdresser can provide feedback on the spot. Specifically, the hairdresser offers professional advice on styles tried by the user in the Trial Department. Based on the user's hair type, bone structure, and lifestyle, the hairdresser explains the advantages and disadvantages of the suggested styles, supporting the user in choosing the optimal style. Furthermore, the Feedback Department can use AI to understand user requests and preferences. For example, the AI ​​analyzes user feedback and incorporates it into future suggestions. Information such as the user's impressions of the tried styles, what they liked, and what could be improved is collected, and the AI ​​analyzes this information to improve future suggestions. This allows the Feedback Department to provide more personalized suggestions tailored to the user's needs and preferences. Additionally, the Feedback Department can provide information to improve the functionality of the Suggestion Department and Trial Department based on the user feedback. For example, if a user consistently provides negative feedback on a particular style, adjustments such as reducing the frequency of that style's suggestion can be made. In this way, the Feedback Department plays a crucial role in improving user satisfaction and enhancing the overall system performance.

[0069] The suggestion unit can propose multiple styles based on hair type, bone structure, personality, and preferences. For example, the suggestion unit can propose styles based on hair type. The suggestion unit can propose styles according to hair type such as straight hair, curly hair, fine hair, and thick hair. The suggestion unit can also propose styles based on bone structure. The suggestion unit can propose styles according to bone structure such as face shape, head shape, and neck length. The suggestion unit can also propose styles based on personality and preferences. The suggestion unit can propose styles according to personality such as character, lifestyle, and hobbies. The suggestion unit can propose styles according to preferences such as color preferences, style preferences, and trend preferences. In this way, the suggestion unit can propose the most suitable style 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 data on the user's hair type, bone structure, personality, and preferences into a generating AI, which can then propose the most suitable style.

[0070] The trial unit allows users to try on suggested styles in front of a real mirror. The trial unit can try on styles using, for example, a physical mirror. The trial unit can also try on styles using a virtual mirror. The trial unit can also try on styles using a display. The trial unit can use AI to reflect the user's appearance in real time. The trial unit can, for example, have the AI ​​analyze the user's appearance and reflect the style in real time based on that. The trial unit can also have the AI ​​track the user's movements and adjust the style in real time based on that. This allows the user to intuitively check the style in the trial unit. Some or all of the above processes in the trial unit may be performed using, for example, AI, or not using AI. For example, the trial unit can input data of the user's appearance into a generating AI, and the generating AI can reflect the style in real time.

[0071] The feedback unit allows hairdressers to provide feedback on the spot. For example, the feedback unit allows hairdressers to provide real-time feedback on the user's style. The feedback unit can use AI to understand the user's requests and preferences. For example, the feedback unit uses AI to analyze the user's feedback and reflect it in the next suggestion. This allows the feedback unit to allow hairdressers to provide real-time feedback. Some or all of the above processes in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input user feedback data into a generating AI, which can then reflect it in the next suggestion.

[0072] The reception area allows customers to try out styles from home even outside of the salon's business hours. The reception area provides, for example, an interface for customers to try out styles from home. The reception area can use AI to understand customer requests and preferences. The reception area, for example, uses AI to analyze customer input and suggest styles based on that. This allows the reception area to allow customers to try out styles even from home. Some or all of the above processes in the reception area may be performed using AI, for example, or not using AI. For example, the reception area can input customer input data into a generating AI, which can then suggest styles.

[0073] The proposal department can understand customer needs and preferences and reflect them in future proposals. For example, the proposal department can learn customer needs and preferences and make future proposals based on them. The proposal department can use AI to analyze customer needs and preferences. For example, the proposal department can have AI analyze customer feedback and make future proposals based on it. This allows the proposal department to make proposals based on customer needs and preferences. Some or all of the above processes in the proposal department may be performed using AI, or not using AI. For example, the proposal department can input customer feedback data into a generating AI, which can then make future proposals.

[0074] The reception desk can estimate the user's emotions and adjust the timing of input based on the estimated emotions. For example, if the user is relaxed, the reception desk can provide an interface that prompts input at a slow pace. If the user is in a hurry, the reception desk can provide a concise input form to allow for quick completion. If the user is stressed, the reception desk can display guides and help functions to support input. This allows the reception desk to adjust the timing of input 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 desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI, which can then adjust the timing of input.

[0075] The reception desk can analyze the user's past style history and select the optimal input method. For example, the reception desk can automatically display styles previously selected by the user as candidates. The reception desk can prioritize suggesting input methods (voice, text, etc.) previously used by the user. The reception desk can predict and suggest styles to be used during specific time periods based on the user's past style history. In this way, the reception desk can provide the optimal input method based on the user's past history. 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 past style history data into a generating AI, which can then select the optimal input method.

[0076] The reception desk can filter the user's current hair condition and preferences when they input a style. For example, the reception desk can suggest a suitable style based on the user's current hair length and color. The reception desk can prioritize displaying highly relevant styles based on the user's preferences and past choices. The reception desk can suggest styles that are gentle on the hair based on the user's hair health. This enables the reception desk to suggest styles based on the user's current hair condition and preferences. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the user's current hair condition and preferences into a generating AI, which can then perform filtering.

[0077] The reception desk can estimate the user's emotions and determine the priority of input styles based on the estimated emotions. For example, if the user is relaxed, the reception desk may prioritize displaying popular styles. If the user is in a hurry, the reception desk may prioritize displaying styles that are easy to implement. If the user is stressed, the reception desk may prioritize displaying styles that have a relaxing effect. This allows the reception desk to determine style priorities 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 desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI, which can then determine the style priorities.

[0078] The reception desk can prioritize inputting highly relevant styles by considering the user's geographical location when inputting styles. For example, the reception desk can suggest popular styles based on trends in the user's area. The reception desk can suggest styles suitable for the climate of the user's area. The reception desk can suggest styles that match the culture and events of the user's area. This enables the reception desk to suggest styles based on the user's 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 information into a generating AI, which can then suggest highly relevant styles.

[0079] The reception desk can analyze the user's social media activity when inputting styles and input relevant styles. For example, the reception desk can suggest styles from influencers the user follows on social media. The reception desk can prioritize displaying styles that the user has "liked" on social media. The reception desk can suggest relevant styles based on the content of the user's social media posts. This enables the reception desk to suggest styles based on the user's social media activity. 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 activity data into a generating AI, which can then suggest relevant styles.

[0080] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide suggestions with detailed explanations. If the user is in a hurry, the suggestion unit can provide concise and to-the-point suggestions. If the user is excited, the suggestion unit can provide visually appealing suggestions. This allows the suggestion unit to adjust the way it presents its suggestions 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 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 emotion data into a generative AI, which can then adjust the way it presents its suggestions.

[0081] The suggestion function can adjust the level of detail of a suggestion based on the importance of the style. For example, in the case of a significant style change, the suggestion function can provide a detailed explanation and multiple options. For a minor style change, the suggestion function can provide a concise explanation and a few options. The suggestion function can provide detailed information about styles that the user is particularly interested in. This allows the suggestion function to adjust the level of detail of the suggestion according to the importance of the style. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not using AI. For example, the suggestion function can input style importance data into a generating AI, which can then adjust the level of detail of the suggestion.

[0082] The suggestion unit can apply different suggestion algorithms depending on the style category when making suggestions. For example, for haircut suggestions, the suggestion unit may use an algorithm based on face shape and hair type. For hair color suggestions, it may use an algorithm based on skin tone and season. For hair styling suggestions, it may use an algorithm based on the user's lifestyle and events. This allows the suggestion unit to apply suggestion algorithms according to the style category. 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 style category data into a generating AI, and the generating AI can apply different suggestion algorithms.

[0083] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide a detailed suggestion. If the user is in a hurry, the suggestion unit can provide a concise suggestion. If the user is excited, the suggestion unit can provide a visually appealing suggestion. This allows the suggestion unit to adjust the length of the suggestion 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the length of the suggestion.

[0084] The proposal department can prioritize proposals based on the timing of style submission. For example, if a user wants to change their style for a specific event, the proposal department can prioritize suggesting styles best suited to that event. If a user wants to change their style at the change of season, the proposal department can prioritize suggesting styles appropriate for that season. If a user is interested in a particular trend, the proposal department can prioritize suggesting styles based on that trend. This allows the proposal department to prioritize proposals based on the timing of style submission. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input style submission timing data into a generating AI, which can then determine the priority of proposals.

[0085] The suggestion unit can adjust the order of suggestions based on the relevance of styles. For example, the suggestion unit may suggest the most relevant style first based on the user's preferences. The suggestion unit may prioritize suggesting relevant styles based on the user's past choices. The suggestion unit may suggest the most suitable style first based on the user's current hair condition. This allows the suggestion unit to adjust the order of suggestions based on the relevance of styles. 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 may input style relevance data into a generating AI, which can then adjust the order of suggestions.

[0086] The trial unit can estimate the user's emotions and adjust the trial method based on the estimated emotions. For example, if the user is relaxed, the trial unit can conduct the trial at a slow pace. If the user is in a hurry, the trial unit can ensure that the trial is completed quickly. If the user is excited, the trial unit can conduct a visually engaging trial. This allows the trial unit to adjust the trial method 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 trial unit may be performed using AI or not using AI. For example, the trial unit can input user emotion data into a generative AI, which can then adjust the trial method.

[0087] The trial unit can analyze the user's past style trial history during the trial period to select the optimal trial method. For example, the trial unit can suggest the optimal trial method based on the styles the user has tried in the past. The trial unit can optimize the trial order based on the user's past trial history. The trial unit can analyze the user's past trial history and suggest the most effective trial method. In this way, the trial unit can provide the optimal trial method based on the user's past history. Some or all of the above processes in the trial unit may be performed using AI, for example, or without AI. For example, the trial unit can input the user's past style trial history data into a generating AI, which can then select the optimal trial method.

[0088] The trial unit can customize the trial method based on the user's current hair condition during the trial. For example, the trial unit can suggest a suitable trial method based on the user's hair length and color. The trial unit can suggest a trial method that does not damage the hair based on the user's hair health. The trial unit can suggest the optimal trial method based on the user's hair texture. In this way, the trial unit can provide the optimal trial method based on the user's current hair condition. Some or all of the above processing in the trial unit may be performed using AI, for example, or without AI. For example, the trial unit can input the user's current hair condition data into a generating AI, which can then customize the trial method.

[0089] The trial unit can estimate the user's emotions and determine trial priorities based on those emotions. For example, if the user is relaxed, the trial unit will prioritize trying out popular styles. If the user is in a hurry, the trial unit can prioritize trying out styles that are easy to try. If the user is excited, the trial unit can prioritize trying out visually appealing styles. This allows the trial unit to determine trial priorities 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 trial unit may be performed using AI or not using AI. For example, the trial unit can input user emotion data into a generative AI, which can then determine trial priorities.

[0090] The trial unit can select the optimal trial method during the trial, taking into account the user's geographical location. For example, the trial unit can try out popular styles based on trends in the user's region. The trial unit can try out styles suitable for the climate of the user's region. The trial unit can try out styles that match the culture and events of the user's region. In this way, the trial unit can provide the optimal trial method based on the user's geographical location. Some or all of the above processing in the trial unit may be performed using AI, for example, or not using AI. For example, the trial unit can input the user's geographical location information into a generating AI, which can then select the optimal trial method.

[0091] The trial unit can analyze the user's social media activity during the trial and suggest trial methods. For example, the trial unit can try out the styles of influencers the user follows on social media. The trial unit can prioritize trying out styles that the user has "liked" on social media. The trial unit can try out relevant styles based on the content of the user's social media posts. In this way, the trial unit can provide the optimal trial method based on the user's social media activity. Some or all of the above processing in the trial unit may be performed using AI, for example, or not using AI. For example, the trial unit can input the user's social media activity data into a generating AI, and the generating AI can suggest trial methods.

[0092] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is relaxed, the feedback unit can provide detailed feedback. If the user is in a hurry, the feedback unit can provide concise feedback. If the user is excited, the feedback unit can provide visually appealing feedback. This allows the feedback unit to adjust the feedback method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into the generative AI, which can then adjust the feedback method.

[0093] The feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback unit can suggest optimal feedback based on the feedback the user has provided in the past. The feedback unit can optimize the content of feedback from the user's past feedback history. The feedback unit can analyze the user's past feedback history and provide the most effective feedback. In this way, the feedback unit can provide optimal feedback based on the user's past history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past feedback history data into a generating AI, which can then provide optimal feedback.

[0094] The feedback unit can customize the means of feedback based on the user's current hair condition. For example, the feedback unit can provide appropriate feedback based on the user's hair length and color. The feedback unit can provide feedback that does not damage the hair based on the user's hair health. The feedback unit can provide optimal feedback based on the user's hair texture. As a result, the feedback unit can provide optimal feedback based on the user's current hair condition. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's current hair condition data into a generating AI, which can then customize the means of feedback.

[0095] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is relaxed, the feedback unit may prioritize detailed feedback. If the user is in a hurry, the feedback unit may prioritize concise feedback. If the user is excited, the feedback unit may prioritize visually appealing feedback. This allows the feedback unit to determine the priority of feedback 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 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 feedback unit may be performed using AI or not using AI. For example, the feedback unit can input user emotion data into a generative AI, which can then determine the priority of feedback.

[0096] The feedback unit can select the optimal feedback method when providing feedback, taking into account the user's geographical location information. For example, the feedback unit can provide feedback based on trends in the user's region. The feedback unit can provide feedback that is appropriate for the climate of the user's region. The feedback unit can provide feedback that is tailored to the culture and events of the user's region. In this way, the feedback unit can provide optimal feedback based on the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information into a generating AI, which can then select the optimal feedback method.

[0097] The feedback unit can analyze the user's social media activity and suggest methods for providing feedback. For example, the feedback unit can provide feedback based on the styles of influencers the user follows on social media. The feedback unit can provide feedback based on the styles the user has "liked" on social media. The feedback unit can provide relevant feedback based on the content of the user's social media posts. This allows the feedback unit to provide optimal feedback based on the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's social media activity data into a generating AI, which can then suggest methods for providing feedback.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The suggestion unit can estimate the user's emotions and adjust the order of suggestions based on those emotions. For example, if the user is relaxed, suggestions with detailed explanations can be presented first. If the user is in a hurry, concise and to-the-point suggestions can be prioritized. If the user is excited, visually appealing suggestions can be presented first. This allows the suggestion unit to adjust the order of suggestions according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not.

[0100] The trial unit can estimate the user's emotions and adjust the trial method based on the estimated emotions. For example, if the user is relaxed, the trial can be conducted at a slow pace. If the user is in a hurry, the trial can be completed quickly. If the user is excited, a visually appealing trial can be conducted. This allows the trial unit to adjust the trial method according to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Some or all of the above processing in the trial unit may be performed using AI or not.

[0101] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is relaxed, it can provide detailed feedback. If the user is in a hurry, it can provide concise feedback. If the user is excited, it can provide visually appealing feedback. This allows the feedback unit to adjust the feedback method according to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Some or all of the processing described above in the feedback unit may be performed using AI or not.

[0102] The reception desk can estimate the user's emotions and determine the priority of input styles based on the estimated emotions. For example, if the user is relaxed, popular styles can be displayed preferentially. If the user is in a hurry, styles that are easy to implement can be displayed preferentially. If the user is stressed, styles with a relaxing effect can be displayed preferentially. This allows the reception desk to determine style priorities according to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Some or all of the above processing in the reception desk may be performed using AI or not.

[0103] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, it can offer suggestions that include detailed explanations. If the user is in a hurry, it can offer concise and to-the-point suggestions. If the user is excited, it can offer visually appealing suggestions. This allows the suggestion unit to adjust the way it presents suggestions according to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not.

[0104] The suggestion unit can analyze the user's past style history and select the optimal suggestion method. For example, it can automatically display styles the user has previously selected as candidates. It can prioritize suggestion methods (voice, text, etc.) the user has used in the past. Based on the user's past style history, it can predict and suggest styles to be used during specific time periods. In this way, the suggestion unit can provide the optimal suggestion method based on the user's past history. Some or all of the above processing in the suggestion unit may be performed using AI or not.

[0105] The trial unit can customize the trial method based on the user's current hair condition. For example, it can suggest a suitable trial method based on the user's hair length and color. It can suggest a trial method that does not damage the hair based on the user's hair health. It can suggest the optimal trial method based on the user's hair texture. In this way, the trial unit can provide the optimal trial method based on the user's current hair condition. Some or all of the above processing in the trial unit may be performed using AI or not.

[0106] The feedback unit can provide optimal feedback by referring to the user's past feedback history. For example, it can suggest optimal feedback based on the feedback the user has provided in the past. It can optimize the content of feedback from the user's past feedback history. It can analyze the user's past feedback history and provide the most effective feedback. In this way, the feedback unit can provide optimal feedback based on the user's past history. Some or all of the above processing in the feedback unit may be performed using AI or not.

[0107] The reception desk can prioritize inputting highly relevant styles by considering the user's geographical location. For example, it can suggest popular styles based on trends in the user's area. It can suggest styles suitable for the climate of the user's area. It can suggest styles that match the culture and events of the user's area. This enables the reception desk to suggest styles based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not.

[0108] The suggestion unit can apply different suggestion algorithms depending on the style category when making suggestions. For example, for haircut suggestions, an algorithm based on face shape and hair type can be used. For hair color suggestions, an algorithm based on skin tone and season can be used. For hair styling suggestions, an algorithm based on the user's lifestyle and events can be used. This allows the suggestion unit to apply suggestion algorithms appropriate to the style category. Some or all of the above processing in the suggestion unit may be performed using AI or not.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk receives style input from the user. Users can input, for example, their desired hairstyle and color. After receiving the user's input, the reception desk sends that information to the suggestion department. Step 2: The suggestion department proposes multiple styles based on the information received by the reception department. The suggestion department proposes styles based on, for example, hair type, bone structure, personality, and preferences. The suggestion department can use AI to select the most suitable style for the user. For example, the suggestion department can use AI to analyze the user's hair type and bone structure and propose styles based on that. The suggestion department can also use AI to learn the user's personality and preferences and propose styles based on that. Step 3: The trial unit tries out the style proposed by the proposal unit in real time. The trial unit can try out the proposed style in front of a real mirror, for example. The trial unit can use AI to reflect the user's appearance in real time. For example, the trial unit can use AI to analyze the user's appearance and reflect the style in real time based on that. The trial unit can also use AI to track the user's movements and adjust the style in real time based on that. Step 4: The feedback department provides feedback on the styles tried by the trial department. The feedback department can, for example, have a hairdresser provide feedback on the spot. The feedback department can use AI to understand the user's requests and preferences. The feedback department can, for example, have AI analyze the user's feedback and reflect it in the next suggestion.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the reception unit, proposal unit, trial unit, and feedback unit, is implemented, for example, by 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, where the user can input their desired hairstyle and color. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which uses AI to propose the optimal style to the user. The trial unit is implemented, for example, by the control unit 46A of the smart device 14, where the user can try out the proposed style in front of a real mirror. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which understands the user's requests and preferences and reflects them in the next proposal. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the reception unit, proposal unit, trial unit, and feedback unit, is implemented 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, where the user can input their desired hairstyle and color. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, where AI is used to suggest the optimal style to the user. The trial unit is implemented by the control unit 46A of the smart glasses 214, where the user can try out the suggested style in front of a real mirror. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, where the user's requests and preferences are understood and reflected in the next proposal. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the reception unit, proposal unit, trial unit, and feedback unit, is implemented by, for example, 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, where the user can input their desired hairstyle and color. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses AI to propose the optimal style to the user. The trial unit is implemented by, for example, the control unit 46A of the headset terminal 314, where the proposed style can be tried in front of a real mirror. The feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which grasps the user's requests and preferences and reflects them in the next proposal. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the reception unit, proposal unit, trial unit, and feedback 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, where the user can input their desired hairstyle and color. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses AI to propose the optimal style to the user. The trial unit is implemented by, for example, the control unit 46A of the robot 414, where the proposed style can be tried in front of a real mirror. The feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which grasps the user's requests and preferences and reflects them in the next proposal. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A reception area that accepts style input from users, Based on the information received by the aforementioned reception department, the proposal department proposes multiple styles, A trial unit that tests the style proposed by the aforementioned proposal unit in real time, The system includes a feedback unit that provides feedback on the style tested by the aforementioned testing unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We propose multiple styles based on hair type, bone structure, personality, and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned trial section is, Try out the suggested style in front of a real mirror. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is The hairdresser provides feedback on the spot. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Customers can try out styles from home even outside of salon hours. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Understand customer needs and preferences and incorporate them into future proposals. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of style input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past style history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering a style, filtering is performed based on the user's current hair condition and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input styles based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering styles, the system prioritizes the input of styles that are most relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering styles, the system analyzes the user's social media activity and inputs relevant styles. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the style. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the style category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the styles were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the styles. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned trial section is, It estimates the user's emotions and adjusts the trial method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned trial section is, During the trial period, the system analyzes the user's past style trial history to select the optimal trial method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned trial section is, During the trial period, the trial method is customized based on the user's current hair condition. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned trial section is, It estimates the user's emotions and determines the priority of trials based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned trial section is, During the trial period, the optimal trial method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned trial section is, During the trial period, we analyze the user's social media activity and suggest ways to conduct the trial. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When providing feedback, we refer to the user's past feedback history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is When providing feedback, customize the feedback method based on the user's current hair condition. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, the optimal feedback method is selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and suggest ways to provide feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 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 style input from users, Based on the information received by the aforementioned reception department, the proposal department proposes multiple styles, A trial unit that tests the style proposed by the aforementioned proposal unit in real time, The system includes a feedback unit that provides feedback on the style tested by the aforementioned testing unit. A system characterized by the following features.

2. The aforementioned proposal section is, We propose multiple styles based on hair type, bone structure, personality, and preferences. The system according to feature 1.

3. The aforementioned trial section is, Try out the suggested style in front of a real mirror. The system according to feature 1.

4. The aforementioned feedback unit is The hairdresser provides feedback on the spot. The system according to feature 1.

5. The aforementioned reception unit is Customers can try out styles from home even outside of salon hours. The system according to feature 1.

6. The aforementioned proposal section is, Understand customer needs and preferences and incorporate them into future proposals. The system according to feature 1.

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

8. The aforementioned reception unit is Analyze the user's past style history and select the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When entering a style, filtering is performed based on the user's current hair condition and preferences. The system according to feature 1.

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