system

Abstract

The system according to this embodiment aims to enable users to effectively use self-care beauty equipment and to increase their motivation for self-care. [Solution] The system according to the embodiment comprises an advice unit, a badge acquisition unit, a plan creation unit, a challenge design unit, a feedback analysis unit, and a content generation unit. The advice unit provides advice on how to use the aesthetic equipment based on the user's data. The badge acquisition unit earns badges by clearing tasks and acquiring new techniques based on the advice provided by the advice unit. The plan creation unit creates a personalized self-care plan based on the progress obtained by the badge acquisition unit. The challenge design unit designs challenges and reward systems according to the user's progress based on the self-care plan created by the plan creation unit. The feedback analysis unit analyzes user feedback based on the challenges and reward systems designed by the challenge design unit and generates improvement suggestions and motivational messages. The content generation unit generates visual content based on the messages generated by the feedback analysis 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that users lack confidence in the effective use and self-care methods of self-esthetique devices and it is difficult to maintain motivation.

[0005] The system according to the embodiment aims to enable users to effectively use self-esthetique devices and enhance their motivation for self-care.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an advice unit, a badge acquisition unit, a plan creation unit, a challenge design unit, a feedback analysis unit, and a content generation unit. The advice unit provides advice on how to use the aesthetic equipment based on the user's data. The badge acquisition unit earns badges by completing tasks and acquiring new techniques based on the advice provided by the advice unit. The plan creation unit creates a personalized self-care plan based on the progress obtained by the badge acquisition unit. The challenge design unit designs challenges and reward systems according to the user's progress based on the self-care plan created by the plan creation unit. The feedback analysis unit analyzes user feedback based on the challenges and reward systems designed by the challenge design unit and generates improvement suggestions and motivational messages. The content generation unit generates visual content based on the messages generated by the feedback analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to effectively use self-care equipment and enhance their motivation for self-care. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memories (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

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

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

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires 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 self-care support system according to an embodiment of the present invention is a system that visualizes the progress of users who are unsure how to effectively use self-care equipment using gamification elements. Based on user data, this self-care support system uses an AI guide to provide real-time advice on specific ways to use the aesthetic equipment, the order of care, frequency, and precautions. Users earn badges by clearing challenges and acquiring new techniques. In this way, the system increases motivation for self-care and makes it enjoyable to continue. Furthermore, it uses generative AI to create personalized self-care plans and dynamically designs challenges and reward systems according to the user's progress. It also analyzes user feedback and generates improvement suggestions and motivational messages. Visual content generation visually represents results and provides education and guidance. For example, when a user uses self-care equipment, the AI ​​guide provides specific instructions such as, "Please use this equipment in this order." This allows users to understand the best self-care method for them and perform self-care with confidence. Next, users earn badges by clearing challenges and acquiring new techniques. For example, if a user acquires a specific self-care technique, they can earn a badge corresponding to that technique. This allows users to visualize their self-care progress and increase their motivation. Furthermore, it utilizes generative AI to create personalized self-care plans. Based on user data, the generative AI creates a self-care plan best suited to each individual user. For example, it can generate a self-care plan tailored to the user's skin condition and lifestyle. This allows users to practice the self-care methods that are best suited to them. It also dynamically designs challenge and reward systems based on the user's progress. The generative AI tracks the user's progress in real time and provides new challenges and rewards at the appropriate time. For example, when a user achieves a certain level of progress, a new challenge is presented, and they can earn a reward by completing that challenge. This allows users to continue self-care in an enjoyable way.Furthermore, the system analyzes user feedback and generates improvement suggestions and motivational messages. The generating AI analyzes user feedback and generates messages to improve self-care and boost motivation. For example, if a user provides feedback on self-care, the system can use that feedback to provide improvement suggestions and motivational messages. Finally, it provides education and guidance by visually representing results through the generation of visual content. The generating AI generates visual content of the user's self-care results and provides it to the user. For example, by generating and providing visual content that compares before and after photos of self-care, the user can visually confirm the effects of self-care. This allows the user to feel the effects of self-care and increase their motivation. As a result, the self-care support system can efficiently advise, manage progress, create plans, design challenges, analyze feedback, and generate visual content for the user's self-care.

[0029] The self-esthetic support system according to this embodiment comprises an advice unit, a badge acquisition unit, a plan creation unit, a challenge design unit, a feedback analysis unit, and a content generation unit. The advice unit provides advice on how to use esthetic equipment based on user data. For example, the advice unit analyzes user data and provides real-time advice on specific ways to use esthetic equipment, the order of care, frequency, and points to note. For example, the advice unit provides specific instructions such as "Please use this equipment in this order" when the user uses self-esthetic equipment. Some or all of the above processing in the advice unit may be performed using AI or not. The badge acquisition unit acquires badges by clearing tasks and acquiring new techniques based on the advice provided by the advice unit. For example, if the user acquires a specific self-care technique, the badge acquisition unit can acquire a badge corresponding to that technique. For example, the badge acquisition unit can visualize the user's progress and increase their motivation by allowing them to clear tasks. Some or all of the above processing in the badge acquisition unit may be performed using AI or not. The plan creation unit creates personalized self-care plans based on the progress obtained by the badge acquisition unit. The plan creation unit, for example, uses generative AI to create optimal self-care plans for individual users based on user data. The plan creation unit can, for example, generate self-care plans tailored to the user's skin condition and lifestyle. Some or all of the above processes in the plan creation unit may be performed using generative AI or not. The challenge design unit designs challenges and reward systems that correspond to the user's progress based on the self-care plans created by the plan creation unit. The challenge design unit, for example, uses generative AI to track the user's progress in real time and provides new challenges and rewards at appropriate times. The challenge design unit can, for example, present a new challenge when the user achieves a certain level of progress, and the user can earn a reward by completing that challenge.Some or all of the above-described processes in the Challenge Design Department may be performed using or without a generative AI. The Feedback Analysis Department analyzes user feedback based on the challenges and reward systems designed by the Challenge Design Department and generates improvement suggestions and motivational messages. For example, the Feedback Analysis Department may use a generative AI to analyze user feedback and generate messages for self-care improvement and motivational messages. For example, if a user provides feedback on self-care, the Feedback Analysis Department can use that feedback to make improvement suggestions and provide motivational messages. Some or all of the above-described processes in the Feedback Analysis Department may be performed using or without a generative AI. The Content Generation Department generates visual content based on the messages generated by the Feedback Analysis Department. For example, the Content Generation Department may use a generative AI to generate visual content of the user's self-care results and provide it to the user. For example, the Content Generation Department can generate visual content comparing before-and-after photos of self-care and provide it to the user, allowing them to visually confirm the effects of self-care. Some or all of the above-described processes in the Content Generation Department may be performed using or without a generative AI. As a result, the self-care support system according to this embodiment can efficiently advise, manage progress, create plans, design challenges, analyze feedback, and generate visual content for the user's self-care.

[0030] The advice department provides guidance on how to use aesthetic equipment based on user data. Specifically, it collects data such as the user's skin condition, past self-care history, and lifestyle, and analyzes this data to suggest the optimal way to use the aesthetic equipment. For example, if a user has dry skin, it will recommend the use of aesthetic equipment with high moisturizing effects and provide specific advice on how to use it, the order of care, frequency, and precautions. Also, if a user is using a particular aesthetic device for the first time, it will explain how to use it step by step and provide real-time instructions on precautions and effective usage. The advice department can use AI to analyze user data and generate optimal advice. For example, it can use machine learning algorithms to learn user trends from past data and provide optimal advice to each individual user. Furthermore, the advice department can collect user feedback and continuously improve the accuracy and effectiveness of its advice. In this way, the advice department can efficiently and effectively support users' self-care and improve user satisfaction.

[0031] The Badge Acquisition section allows users to earn badges by completing tasks based on advice provided by the Advice section and acquiring new skills. Specifically, when a user acquires a particular self-care technique, they can earn a badge corresponding to that technique. For example, if a user successfully performs a facial massage for the first time, they can earn the "Beginner Facial Massage" badge. The Badge Acquisition section provides a mechanism to visualize user progress and increase motivation. By checking the badges they have earned, users can feel a sense of growth and accomplishment. The Badge Acquisition section also allows users to set goals for moving to the next step, and new badges can be earned upon achievement. This increases the user's motivation to continue engaging in self-care. The Badge Acquisition section can use AI to analyze user progress and provide badges at the appropriate time. For example, it can analyze a user's self-care history and automatically award badges when certain criteria are met. The Badge Acquisition section can also collect user feedback and improve the content and method of providing badges. This allows the Badge Acquisition section to increase user motivation and promote the habit of self-care.

[0032] The plan creation department creates personalized self-care plans based on the progress obtained by the badge acquisition department. Specifically, it collects data such as the user's skin condition, lifestyle, and self-care goals, and uses this data to generate an optimal self-care plan for each individual user. For example, if a user has dry skin, it will create a plan centered on moisturizing care and specifically suggest the aesthetic equipment to be used, the order of care, and the frequency. The plan creation department can analyze user data using generative AI to generate the optimal self-care plan. For example, it can analyze user feedback using natural language processing technology and propose a plan that meets the user's needs and preferences. In addition, the plan creation department can track the user's progress in real time and modify the plan as needed. This ensures that users can always perform self-care based on the optimal plan. Furthermore, the plan creation department can collect user feedback and improve the content and delivery method of the plan. This allows the plan creation department to efficiently and effectively support users' self-care and improve user satisfaction.

[0033] The Challenge Design Department designs challenges and reward systems that are tailored to the user's progress, based on the self-care plan created by the Planning Department. Specifically, it tracks the user's progress in real time and provides new challenges and rewards at appropriate times. For example, when a user achieves a certain level of progress, a new challenge is presented, and the user can earn a reward by completing that challenge. The Challenge Design Department can analyze the user's progress using generative AI and design optimal challenges and rewards. For example, it can analyze the user's self-care history and suggest challenges that match the user's interests and needs. The Challenge Design Department can also collect user feedback and improve the content of challenges and rewards. This increases the user's motivation to continue engaging in self-care. Furthermore, the Challenge Design Department provides a mechanism that visualizes the user's progress and allows them to feel a sense of accomplishment. By completing challenges, users can feel their own growth and promote the habit of self-care. In this way, the Challenge Design Department can increase user motivation and maximize the effectiveness of self-care.

[0034] The Feedback Analysis Department analyzes user feedback based on challenges and reward systems designed by the Challenge Design Department, generating improvement suggestions and motivational messages. Specifically, when a user provides feedback on self-care, the department uses that feedback to provide improvement suggestions and motivational messages. The Feedback Analysis Department can analyze user feedback using generative AI to generate improvement suggestions and motivational messages for self-care. For example, it can use natural language processing technology to analyze user feedback, identify user needs and challenges, and provide appropriate improvement suggestions. The Feedback Analysis Department can also track user progress in real time and provide motivational messages at the right time. This helps users maintain their motivation for self-care and continue to engage in it. Furthermore, the Feedback Analysis Department can collect user feedback and use it to improve the entire system. As a result, the Feedback Analysis Department can efficiently and effectively support users' self-care and improve user satisfaction.

[0035] The content generation unit generates visual content based on messages generated by the feedback analysis unit. Specifically, it generates and provides users with visual content showcasing the results of their self-care. For example, by generating and providing visual content that compares before-and-after photos of self-care, users can visually confirm the effectiveness of their self-care. The content generation unit can analyze user data using generation AI to generate optimal visual content. For example, it can use image recognition technology to analyze before-and-after photos of self-care and generate visual content that emphasizes the changes. The content generation unit can also collect user feedback and improve the content and delivery method of the visual content. This allows users to feel the effects of self-care and increase their motivation to continue. Furthermore, the content generation unit provides a mechanism that visualizes the user's progress and allows them to feel a sense of accomplishment. Through the visual content, users can feel their own growth and promote the habituation of self-care. This allows the content generation unit to increase user motivation and maximize the effectiveness of self-care.

[0036] The advice unit can analyze the user's past self-care history and select the most appropriate advice method. For example, the advice unit may prioritize advising on self-care methods that the user has found effective in the past. For example, the advice unit may also advise the user to avoid self-care methods that have failed in the past. For example, the advice unit may also advise on the effective frequency and timing of care based on the user's past self-care history. This allows the system to provide optimal advice based on the user's past self-care history. Some or all of the above processing in the advice unit may be performed using AI or not.

[0037] The advice unit can filter the advice based on the user's current lifestyle and areas of interest. For example, if the user is busy, the advice unit can advise on self-care methods that can be done in a short amount of time. For example, if the user is interested in a particular area of ​​beauty, the advice unit can also advise on self-care methods specific to that area. For example, the advice unit can also advise on self-care methods that are tailored to the user's lifestyle. This allows the advice unit to provide advice that is appropriate to the user's lifestyle and areas of interest. Some or all of the above processing in the advice unit may be performed using AI or not.

[0038] The advice unit can prioritize providing highly relevant advice by considering the user's geographical location when providing advice. For example, if the user lives in a cold region, the advice unit may advise on self-care methods that have a high moisturizing effect. For example, if the user lives in an urban area, the advice unit may also advise on self-care methods to combat air pollution. For example, if the user lives by the sea, the advice unit may also advise on self-care methods to protect against ultraviolet rays. This allows the system to provide highly relevant advice based on the user's geographical location. Some or all of the processing described above in the advice unit may be performed using AI or not.

[0039] The advice unit can analyze the user's social media activity and provide relevant advice when giving advice. For example, the advice unit can advise on self-care methods that the user has shown interest in on social media. The advice unit can also advise on self-care methods recommended by influencers that the user follows. The advice unit can also advise on the next steps based on the results of self-care that the user has shared on social media. This allows the advice unit to provide relevant advice based on the user's social media activity. Some or all of the above processing in the advice unit may be performed using AI or not.

[0040] The badge acquisition unit can provide the most suitable badges by referring to the user's past achievement history when a badge is acquired. For example, the badge acquisition unit can provide badges to help the user move to the next step based on tasks the user has completed in the past. For example, the badge acquisition unit can also provide new badges depending on the type of badges the user has acquired in the past. For example, the badge acquisition unit can analyze the user's past achievement history and provide badges to boost motivation. This allows the unit to provide the most suitable badges based on the user's past achievement history. Some or all of the above processing in the badge acquisition unit may be performed using AI or not.

[0041] The badge acquisition unit can provide the most suitable badge when a badge is acquired, taking into account the user's geographical location information. For example, if the user lives in a cold region, the badge acquisition unit can provide a badge related to self-care methods with high moisturizing effects. For example, if the user lives in an urban area, the badge acquisition unit can also provide a badge related to self-care methods for combating air pollution. For example, if the user lives by the sea, the badge acquisition unit can also provide a badge related to self-care methods for protecting against ultraviolet rays. This allows for the provision of the most suitable badge based on the user's geographical location information. Some or all of the processing described above in the badge acquisition unit may be performed using AI or not.

[0042] The plan creation unit can create an optimal plan by referring to the user's past self-care history. For example, the plan creation unit can create a plan that prioritizes self-care methods that the user has found effective in the past. For example, the plan creation unit can also create a plan that avoids self-care methods that the user has failed at in the past. For example, the plan creation unit can create a plan that includes the frequency and timing of effective care based on the user's past self-care history. This allows the system to provide an optimal plan based on the user's past self-care history. Some or all of the above processes in the plan creation unit may be performed using generative AI, or they may not be performed using generative AI.

[0043] The plan creation unit can customize plans based on the user's current lifestyle when creating them. For example, if the user is busy, the plan creation unit can create a plan that includes self-care methods that can be done in a short amount of time. For example, if the user is interested in a particular area of ​​beauty, the plan creation unit can also create a plan that includes self-care methods specialized in that area. For example, the plan creation unit can also create a plan that includes self-care methods tailored to the user's lifestyle. This allows for the provision of customized plans that are tailored to the user's lifestyle. Some or all of the above-described processes in the plan creation unit may be performed using generative AI, or they may not be performed using generative AI.

[0044] The plan creation unit can create an optimal plan by taking into account the user's geographical location information. For example, if the user lives in a cold region, the plan creation unit can create a plan that includes self-care methods with high moisturizing effects. For example, if the user lives in an urban area, the plan creation unit can also create a plan that includes self-care methods for dealing with air pollution. For example, if the user lives by the sea, the plan creation unit can also create a plan that includes self-care methods for dealing with ultraviolet rays. This allows the system to provide an optimal plan based on the user's geographical location information. Some or all of the above processing in the plan creation unit may be performed using generative AI, or it may be performed without using generative AI.

[0045] The planning unit can analyze the user's social media activity and customize the plan during the planning process. For example, the planning unit can create a plan that includes self-care methods the user has shown interest in on social media. The planning unit can also create a plan that includes self-care methods recommended by influencers the user follows. For example, the planning unit can create a plan that includes the next steps based on the results of self-care shared by the user on social media. This allows for the provision of customized plans based on the user's social media activity. Some or all of the above processes in the planning unit may be performed using generative AI or not.

[0046] The Challenge Design Unit can provide optimal challenges by referring to the user's past achievement history when designing challenges. For example, the Challenge Design Unit can provide challenges to help the user move to the next step based on challenges the user has previously completed. For example, the Challenge Design Unit can also design challenges to help the user avoid challenges they have previously failed at. For example, the Challenge Design Unit can analyze the user's past achievement history and provide challenges to increase motivation. This allows the Challenge Design Unit to provide optimal challenges based on the user's past achievement history. Some or all of the above processes in the Challenge Design Unit may be performed using generative AI, or they may not be performed using generative AI.

[0047] The Challenge Design Department can customize challenges based on the user's current lifestyle when designing them. For example, if the user is busy, the Challenge Design Department can provide challenges that can be completed in a short amount of time. For example, if the user is interested in a particular beauty field, the Challenge Design Department can provide challenges specialized in that field. For example, the Challenge Design Department can provide challenges that are tailored to the user's lifestyle. This allows for the provision of customized challenges that are appropriate to the user's lifestyle. Some or all of the above-described processes in the Challenge Design Department may be performed using generative AI, or they may not be performed using generative AI.

[0048] The Challenge Design Department can provide optimal challenges by considering the user's geographical location information during the challenge design process. For example, if the user lives in a cold region, the Challenge Design Department can provide challenges that include self-care methods with high moisturizing effects. For example, if the user lives in an urban area, the Challenge Design Department can also provide challenges that include self-care methods for combating air pollution. For example, if the user lives by the sea, the Challenge Design Department can also provide challenges that include self-care methods for protecting against ultraviolet rays. This allows for the provision of optimal challenges based on the user's geographical location information. Some or all of the above processing in the Challenge Design Department may be performed using generative AI, or it may be performed without using generative AI.

[0049] The Challenge Design Department can analyze a user's social media activity and customize challenges during the challenge design process. For example, the Challenge Design Department can provide challenges that include self-care methods the user has shown interest in on social media. The Challenge Design Department can also provide challenges that include self-care methods recommended by influencers the user follows. The Challenge Design Department can also provide challenges that include the next steps based on the results of self-care shared by the user on social media. This allows for the provision of customized challenges based on the user's social media activity. Some or all of the above processing in the Challenge Design Department may be performed using generative AI or not.

[0050] The feedback analysis unit can provide optimal improvement suggestions by referring to the user's past feedback history during feedback analysis. For example, the feedback analysis unit can provide improvement suggestions for the next step based on the feedback the user has provided in the past. For example, the feedback analysis unit can also provide improvement suggestions to help the user avoid self-care methods that have failed in the past. For example, the feedback analysis unit can analyze the user's past feedback history and provide improvement suggestions to increase motivation. This allows the unit to provide optimal improvement suggestions based on the user's past feedback history. Some or all of the above processing in the feedback analysis unit may be performed using generative AI, or it may be performed without using generative AI.

[0051] The feedback analysis unit can customize feedback based on the user's current lifestyle during feedback analysis. For example, if the user is busy, the feedback analysis unit can provide feedback that includes self-care methods that can be done in a short amount of time. For example, if the user is interested in a particular area of ​​beauty, the feedback analysis unit can also provide feedback that includes self-care methods specific to that area. For example, the feedback analysis unit can also provide feedback that includes self-care methods tailored to the user's lifestyle. This allows for the provision of customized feedback that is appropriate to the user's lifestyle. Some or all of the above-described processes in the feedback analysis unit may be performed using generative AI, or they may be performed without using generative AI.

[0052] The feedback analysis unit can provide optimal feedback by considering the user's geographical location information during feedback analysis. For example, if the user lives in a cold region, the feedback analysis unit can provide feedback including self-care methods with high moisturizing effects. For example, if the user lives in an urban area, the feedback analysis unit can also provide feedback including self-care methods for dealing with air pollution. For example, if the user lives by the sea, the feedback analysis unit can also provide feedback including self-care methods for protecting against ultraviolet rays. This enables the provision of optimal feedback based on the user's geographical location information. Some or all of the above processing in the feedback analysis unit may be performed using generative AI, or it may be performed without using generative AI.

[0053] The feedback analysis unit can analyze the user's social media activity and customize the feedback during the feedback analysis process. For example, the feedback analysis unit can provide feedback that includes self-care methods the user has shown interest in on social media. The feedback analysis unit can also provide feedback that includes self-care methods recommended by influencers the user follows. For example, the feedback analysis unit can provide feedback that includes the next steps based on the results of self-care shared by the user on social media. This allows for the provision of customized feedback based on the user's social media activity. Some or all of the above processing in the feedback analysis unit may be performed using generative AI or not.

[0054] The content generation unit can provide optimal visual content by referring to the user's past self-care history during content generation. For example, the content generation unit can generate visual content that includes self-care methods the user has found effective in the past. For example, the content generation unit can also generate visual content that helps the user avoid self-care methods that have failed in the past. For example, the content generation unit can generate visual content that includes the frequency and timing of effective care based on the user's past self-care history. This allows the provision of optimal visual content based on the user's past self-care history. Some or all of the above processing in the content generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0055] The content generation unit can customize visual content based on the user's current lifestyle when generating content. For example, if the user is busy, the content generation unit can generate visual content that includes self-care methods that can be done in a short amount of time. For example, if the user is interested in a particular beauty field, the content generation unit can also generate visual content that includes self-care methods specific to that field. For example, the content generation unit can also generate visual content that includes self-care methods tailored to the user's lifestyle. This allows for the provision of customized visual content that is appropriate for the user's lifestyle. Some or all of the above-described processes in the content generation unit may be performed using a generation AI, or they may be performed without using a generation AI.

[0056] The content generation unit can provide optimal visual content by considering the user's geographical location information during content generation. For example, if the user lives in a cold region, the content generation unit can provide visual content including self-care methods with high moisturizing effects. For example, if the user lives in an urban area, the content generation unit can also provide visual content including self-care methods for combating air pollution. For example, if the user lives by the sea, the content generation unit can also provide visual content including self-care methods for protecting against ultraviolet rays. This enables the provision of optimal visual content based on the user's geographical location information. Some or all of the above-described processing in the content generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0057] The content generation unit can analyze the user's social media activity and customize the visual content during content generation. For example, the content generation unit can provide visual content that includes self-care methods the user has shown interest in on social media. The content generation unit can also provide visual content that includes self-care methods recommended by influencers the user follows. For example, the content generation unit can provide visual content that includes the next steps based on the results of self-care shared by the user on social media. This allows for the provision of customized visual content based on the user's social media activity. Some or all of the above processing in the content generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

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

[0059] The advice unit can analyze the user's past self-care history and select the most appropriate advice method. For example, it can prioritize advising on self-care methods that the user has found effective in the past. It can also advise the user to avoid self-care methods that have failed in the past. Furthermore, it can advise on the frequency and timing of effective care based on the user's past self-care history. This allows for the provision of optimal advice based on past self-care history. Some or all of the above processing in the advice unit may be performed using AI or not.

[0060] The advice unit can filter advice based on the user's current lifestyle and areas of interest. For example, if the user is busy, it can advise on self-care methods that can be done in a short amount of time. If the user is interested in a particular area of ​​beauty, it can also advise on self-care methods specific to that area. Furthermore, it can advise on self-care methods that are tailored to the user's lifestyle. This allows for the provision of advice that is appropriate to the user's lifestyle and areas of interest. Some or all of the above processing in the advice unit may be performed using AI or not.

[0061] The advice section can prioritize providing highly relevant advice by taking into account the user's geographical location. For example, if the user lives in a cold region, it can advise on self-care methods with high moisturizing effects. If the user lives in an urban area, it can advise on self-care methods to combat air pollution. Furthermore, if the user lives by the sea, it can advise on self-care methods to protect against ultraviolet rays. This allows for the provision of highly relevant advice based on the user's geographical location. Some or all of the processing described above in the advice section may be performed using AI or not.

[0062] The advice unit can analyze a user's social media activity and provide relevant advice. For example, it can advise on self-care methods the user has shown interest in on social media. It can also advise on self-care methods recommended by influencers the user follows. Furthermore, it can advise on the next steps based on the results of self-care shared by the user on social media. This allows for the provision of relevant advice based on the user's social media activity. Some or all of the above processing in the advice unit may be performed using AI or not.

[0063] The badge acquisition unit can provide the most suitable badges by referring to the user's past achievement history when a badge is acquired. For example, it can provide badges to help the user move to the next step based on tasks the user has completed in the past. It can also provide new badges depending on the types of badges the user has acquired in the past. Furthermore, it can analyze the user's past achievement history and provide badges to boost motivation. This allows for the provision of the most suitable badges based on the user's past achievement history. Some or all of the above processing in the badge acquisition unit may be performed using AI or not.

[0064] The plan creation unit can create an optimal plan by referring to the user's past self-care history. For example, it can create a plan that prioritizes self-care methods that the user has found effective in the past. It can also create a plan that avoids self-care methods that the user has failed at in the past. Furthermore, it can create a plan that includes the frequency and timing of effective care based on the user's past self-care history. This allows the system to provide an optimal plan based on the user's past self-care history. Some or all of the above processing in the plan creation unit may be performed using generative AI, or it may be performed without using generative AI.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The advice department provides guidance on how to use aesthetic equipment based on user data. For example, it analyzes user data and provides real-time advice on specific usage of aesthetic equipment, the order of care, frequency, and precautions. When users use self-care aesthetic equipment, it provides specific instructions such as, "Please use this equipment in this order." Step 2: In the badge acquisition section, users earn badges by completing tasks based on advice provided by the advice section and acquiring new skills. For example, if a user acquires a specific self-care technique, they can earn a badge corresponding to that technique. By completing tasks, users can visualize their progress and increase their motivation. Step 3: The plan creation unit creates a personalized self-care plan based on the progress obtained by the badge acquisition unit. For example, using generation AI, it creates an optimal self-care plan for each individual user based on user data. It can generate a self-care plan that is tailored to the user's skin condition and lifestyle. Step 4: The Challenge Design Department designs challenges and reward systems that are tailored to the user's progress, based on the self-care plan created by the Planning Department. For example, they use a generation AI to track the user's progress in real time and provide new challenges and rewards at the appropriate time. When a user achieves a certain level of progress, a new challenge is presented, and the user can earn a reward by completing that challenge. Step 5: The Feedback Analysis Department analyzes user feedback based on the challenges and reward systems designed by the Challenge Design Department, and generates improvement suggestions and motivational messages. For example, it uses a generation AI to analyze user feedback and generate messages to improve self-care and boost motivation. If a user provides feedback on self-care, it can use that feedback to provide improvement suggestions and motivational messages. Step 6: The content generation unit generates visual content based on the messages generated by the feedback analysis unit. For example, using generation AI, it generates visual content of the user's self-care results and provides it to the user. By generating and providing visual content that compares before and after photos of self-care, the user can visually confirm the effectiveness of their self-care.

[0067] (Example of form 2) The self-care support system according to an embodiment of the present invention is a system that visualizes the progress of users who are unsure how to effectively use self-care equipment using gamification elements. Based on user data, this self-care support system uses an AI guide to provide real-time advice on specific ways to use the aesthetic equipment, the order of care, frequency, and precautions. Users earn badges by clearing challenges and acquiring new techniques. In this way, the system increases motivation for self-care and makes it enjoyable to continue. Furthermore, it uses generative AI to create personalized self-care plans and dynamically designs challenges and reward systems according to the user's progress. It also analyzes user feedback and generates improvement suggestions and motivational messages. Visual content generation visually represents results and provides education and guidance. For example, when a user uses self-care equipment, the AI ​​guide provides specific instructions such as, "Please use this equipment in this order." This allows users to understand the best self-care method for them and perform self-care with confidence. Next, users earn badges by clearing challenges and acquiring new techniques. For example, if a user acquires a specific self-care technique, they can earn a badge corresponding to that technique. This allows users to visualize their self-care progress and increase their motivation. Furthermore, it utilizes generative AI to create personalized self-care plans. Based on user data, the generative AI creates a self-care plan best suited to each individual user. For example, it can generate a self-care plan tailored to the user's skin condition and lifestyle. This allows users to practice the self-care methods that are best suited to them. It also dynamically designs challenge and reward systems based on the user's progress. The generative AI tracks the user's progress in real time and provides new challenges and rewards at the appropriate time. For example, when a user achieves a certain level of progress, a new challenge is presented, and they can earn a reward by completing that challenge. This allows users to continue self-care in an enjoyable way.Furthermore, the system analyzes user feedback and generates improvement suggestions and motivational messages. The generating AI analyzes user feedback and generates messages to improve self-care and boost motivation. For example, if a user provides feedback on self-care, the system can use that feedback to provide improvement suggestions and motivational messages. Finally, it provides education and guidance by visually representing results through the generation of visual content. The generating AI generates visual content of the user's self-care results and provides it to the user. For example, by generating and providing visual content that compares before and after photos of self-care, the user can visually confirm the effects of self-care. This allows the user to feel the effects of self-care and increase their motivation. As a result, the self-care support system can efficiently advise, manage progress, create plans, design challenges, analyze feedback, and generate visual content for the user's self-care.

[0068] The self-esthetic support system according to this embodiment comprises an advice unit, a badge acquisition unit, a plan creation unit, a challenge design unit, a feedback analysis unit, and a content generation unit. The advice unit provides advice on how to use esthetic equipment based on user data. For example, the advice unit analyzes user data and provides real-time advice on specific ways to use esthetic equipment, the order of care, frequency, and points to note. For example, the advice unit provides specific instructions such as "Please use this equipment in this order" when the user uses self-esthetic equipment. Some or all of the above processing in the advice unit may be performed using AI or not. The badge acquisition unit acquires badges by clearing tasks and acquiring new techniques based on the advice provided by the advice unit. For example, if the user acquires a specific self-care technique, the badge acquisition unit can acquire a badge corresponding to that technique. For example, the badge acquisition unit can visualize the user's progress and increase their motivation by allowing them to clear tasks. Some or all of the above processing in the badge acquisition unit may be performed using AI or not. The plan creation unit creates personalized self-care plans based on the progress obtained by the badge acquisition unit. The plan creation unit, for example, uses generative AI to create optimal self-care plans for individual users based on user data. The plan creation unit can, for example, generate self-care plans tailored to the user's skin condition and lifestyle. Some or all of the above processes in the plan creation unit may be performed using generative AI or not. The challenge design unit designs challenges and reward systems that correspond to the user's progress based on the self-care plans created by the plan creation unit. The challenge design unit, for example, uses generative AI to track the user's progress in real time and provides new challenges and rewards at appropriate times. The challenge design unit can, for example, present a new challenge when the user achieves a certain level of progress, and the user can earn a reward by completing that challenge.Some or all of the above-described processes in the Challenge Design Department may be performed using or without a generative AI. The Feedback Analysis Department analyzes user feedback based on the challenges and reward systems designed by the Challenge Design Department and generates improvement suggestions and motivational messages. For example, the Feedback Analysis Department may use a generative AI to analyze user feedback and generate messages for self-care improvement and motivational messages. For example, if a user provides feedback on self-care, the Feedback Analysis Department can use that feedback to make improvement suggestions and provide motivational messages. Some or all of the above-described processes in the Feedback Analysis Department may be performed using or without a generative AI. The Content Generation Department generates visual content based on the messages generated by the Feedback Analysis Department. For example, the Content Generation Department may use a generative AI to generate visual content of the user's self-care results and provide it to the user. For example, the Content Generation Department can generate visual content comparing before-and-after photos of self-care and provide it to the user, allowing them to visually confirm the effects of self-care. Some or all of the above-described processes in the Content Generation Department may be performed using or without a generative AI. As a result, the self-care support system according to this embodiment can efficiently advise, manage progress, create plans, design challenges, analyze feedback, and generate visual content for the user's self-care.

[0069] The advice department provides guidance on how to use aesthetic equipment based on user data. Specifically, it collects data such as the user's skin condition, past self-care history, and lifestyle, and analyzes this data to suggest the optimal way to use the aesthetic equipment. For example, if a user has dry skin, it will recommend the use of aesthetic equipment with high moisturizing effects and provide specific advice on how to use it, the order of care, frequency, and precautions. Also, if a user is using a particular aesthetic device for the first time, it will explain how to use it step by step and provide real-time instructions on precautions and effective usage. The advice department can use AI to analyze user data and generate optimal advice. For example, it can use machine learning algorithms to learn user trends from past data and provide optimal advice to each individual user. Furthermore, the advice department can collect user feedback and continuously improve the accuracy and effectiveness of its advice. In this way, the advice department can efficiently and effectively support users' self-care and improve user satisfaction.

[0070] The Badge Acquisition section allows users to earn badges by completing tasks based on advice provided by the Advice section and acquiring new skills. Specifically, when a user acquires a particular self-care technique, they can earn a badge corresponding to that technique. For example, if a user successfully performs a facial massage for the first time, they can earn the "Beginner Facial Massage" badge. The Badge Acquisition section provides a mechanism to visualize user progress and increase motivation. By checking the badges they have earned, users can feel a sense of growth and accomplishment. The Badge Acquisition section also allows users to set goals for moving to the next step, and new badges can be earned upon achievement. This increases the user's motivation to continue engaging in self-care. The Badge Acquisition section can use AI to analyze user progress and provide badges at the appropriate time. For example, it can analyze a user's self-care history and automatically award badges when certain criteria are met. The Badge Acquisition section can also collect user feedback and improve the content and method of providing badges. This allows the Badge Acquisition section to increase user motivation and promote the habit of self-care.

[0071] The plan creation department creates personalized self-care plans based on the progress obtained by the badge acquisition department. Specifically, it collects data such as the user's skin condition, lifestyle, and self-care goals, and uses this data to generate an optimal self-care plan for each individual user. For example, if a user has dry skin, it will create a plan centered on moisturizing care and specifically suggest the aesthetic equipment to be used, the order of care, and the frequency. The plan creation department can analyze user data using generative AI to generate the optimal self-care plan. For example, it can analyze user feedback using natural language processing technology and propose a plan that meets the user's needs and preferences. In addition, the plan creation department can track the user's progress in real time and modify the plan as needed. This ensures that users can always perform self-care based on the optimal plan. Furthermore, the plan creation department can collect user feedback and improve the content and delivery method of the plan. This allows the plan creation department to efficiently and effectively support users' self-care and improve user satisfaction.

[0072] The Challenge Design Department designs challenges and reward systems that are tailored to the user's progress, based on the self-care plan created by the Planning Department. Specifically, it tracks the user's progress in real time and provides new challenges and rewards at appropriate times. For example, when a user achieves a certain level of progress, a new challenge is presented, and the user can earn a reward by completing that challenge. The Challenge Design Department can analyze the user's progress using generative AI and design optimal challenges and rewards. For example, it can analyze the user's self-care history and suggest challenges that match the user's interests and needs. The Challenge Design Department can also collect user feedback and improve the content of challenges and rewards. This increases the user's motivation to continue engaging in self-care. Furthermore, the Challenge Design Department provides a mechanism that visualizes the user's progress and allows them to feel a sense of accomplishment. By completing challenges, users can feel their own growth and promote the habit of self-care. In this way, the Challenge Design Department can increase user motivation and maximize the effectiveness of self-care.

[0073] The Feedback Analysis Department analyzes user feedback based on challenges and reward systems designed by the Challenge Design Department, generating improvement suggestions and motivational messages. Specifically, when a user provides feedback on self-care, the department uses that feedback to provide improvement suggestions and motivational messages. The Feedback Analysis Department can analyze user feedback using generative AI to generate improvement suggestions and motivational messages for self-care. For example, it can use natural language processing technology to analyze user feedback, identify user needs and challenges, and provide appropriate improvement suggestions. The Feedback Analysis Department can also track user progress in real time and provide motivational messages at the right time. This helps users maintain their motivation for self-care and continue to engage in it. Furthermore, the Feedback Analysis Department can collect user feedback and use it to improve the entire system. As a result, the Feedback Analysis Department can efficiently and effectively support users' self-care and improve user satisfaction.

[0074] The content generation unit generates visual content based on messages generated by the feedback analysis unit. Specifically, it generates and provides users with visual content showcasing the results of their self-care. For example, by generating and providing visual content that compares before-and-after photos of self-care, users can visually confirm the effectiveness of their self-care. The content generation unit can analyze user data using generation AI to generate optimal visual content. For example, it can use image recognition technology to analyze before-and-after photos of self-care and generate visual content that emphasizes the changes. The content generation unit can also collect user feedback and improve the content and delivery method of the visual content. This allows users to feel the effects of self-care and increase their motivation to continue. Furthermore, the content generation unit provides a mechanism that visualizes the user's progress and allows them to feel a sense of accomplishment. Through the visual content, users can feel their own growth and promote the habituation of self-care. This allows the content generation unit to increase user motivation and maximize the effectiveness of self-care.

[0075] The advice unit can estimate the user's emotions and adjust the content and timing of advice based on the estimated emotions. For example, if the user is feeling stressed, the advice unit will prioritize advising on self-care methods that have a relaxing effect. For example, if the user is feeling unmotivated, the advice unit can also advise on self-care methods that are effective in a short amount of time. For example, if the user is feeling agitated, the advice unit can also advise on self-care methods that should be performed in a calm state. This allows for the provision of appropriate advice tailored 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 processing described above in the advice unit may be performed using AI or not.

[0076] The advice unit can analyze the user's past self-care history and select the most appropriate advice method. For example, the advice unit may prioritize advising on self-care methods that the user has found effective in the past. For example, the advice unit may also advise the user to avoid self-care methods that have failed in the past. For example, the advice unit may also advise on the effective frequency and timing of care based on the user's past self-care history. This allows the system to provide optimal advice based on the user's past self-care history. Some or all of the above processing in the advice unit may be performed using AI or not.

[0077] The advice unit can filter the advice based on the user's current lifestyle and areas of interest. For example, if the user is busy, the advice unit can advise on self-care methods that can be done in a short amount of time. For example, if the user is interested in a particular area of ​​beauty, the advice unit can also advise on self-care methods specific to that area. For example, the advice unit can also advise on self-care methods that are tailored to the user's lifestyle. This allows the advice unit to provide advice that is appropriate to the user's lifestyle and areas of interest. Some or all of the above processing in the advice unit may be performed using AI or not.

[0078] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, if the user is feeling stressed, the advice unit will prioritize advising on self-care methods that have a relaxing effect. For example, if the user is feeling unmotivated, the advice unit may prioritize advising on self-care methods that are effective in a short amount of time. For example, if the user is feeling agitated, the advice unit may prioritize advising on self-care methods that should be performed in a calm state. This allows for the provision of advice with priorities that correspond 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 advice unit may be performed using AI or not using AI.

[0079] The advice unit can prioritize providing highly relevant advice by considering the user's geographical location when providing advice. For example, if the user lives in a cold region, the advice unit may advise on self-care methods that have a high moisturizing effect. For example, if the user lives in an urban area, the advice unit may also advise on self-care methods to combat air pollution. For example, if the user lives by the sea, the advice unit may also advise on self-care methods to protect against ultraviolet rays. This allows the system to provide highly relevant advice based on the user's geographical location. Some or all of the processing described above in the advice unit may be performed using AI or not.

[0080] The advice unit can analyze the user's social media activity and provide relevant advice when giving advice. For example, the advice unit can advise on self-care methods that the user has shown interest in on social media. The advice unit can also advise on self-care methods recommended by influencers that the user follows. The advice unit can also advise on the next steps based on the results of self-care that the user has shared on social media. This allows the advice unit to provide relevant advice based on the user's social media activity. Some or all of the above processing in the advice unit may be performed using AI or not.

[0081] The badge acquisition unit can estimate the user's emotions and adjust the criteria for acquiring badges based on the estimated emotions. For example, if the user is stressed, the badge acquisition unit may relax the criteria for acquiring badges. For example, if the user is unmotivated, the badge acquisition unit may set easily achievable tasks. For example, if the user is excited, the badge acquisition unit may set challenging tasks. This allows the criteria for acquiring badges to be adjusted 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 badge acquisition unit may be performed using AI or not using AI.

[0082] The badge acquisition unit can provide the most suitable badges by referring to the user's past achievement history when a badge is acquired. For example, the badge acquisition unit can provide badges to help the user move to the next step based on tasks the user has completed in the past. For example, the badge acquisition unit can also provide new badges depending on the type of badges the user has acquired in the past. For example, the badge acquisition unit can analyze the user's past achievement history and provide badges to boost motivation. This allows the unit to provide the most suitable badges based on the user's past achievement history. Some or all of the above processing in the badge acquisition unit may be performed using AI or not.

[0083] The badge acquisition unit can estimate the user's emotions and determine the priority of badge acquisition based on the estimated emotions. For example, if the user is stressed, the badge acquisition unit may prioritize providing badges related to relaxing self-care methods. For example, if the user is unmotivated, the badge acquisition unit may prioritize providing badges related to self-care methods that produce results in a short time. For example, if the user is excited, the badge acquisition unit may prioritize providing badges related to challenging self-care methods. This allows for the provision of badges with 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 badge acquisition unit may be performed using AI or not.

[0084] The badge acquisition unit can provide the most suitable badge when a badge is acquired, taking into account the user's geographical location information. For example, if the user lives in a cold region, the badge acquisition unit can provide a badge related to self-care methods with high moisturizing effects. For example, if the user lives in an urban area, the badge acquisition unit can also provide a badge related to self-care methods for combating air pollution. For example, if the user lives by the sea, the badge acquisition unit can also provide a badge related to self-care methods for protecting against ultraviolet rays. This allows for the provision of the most suitable badge based on the user's geographical location information. Some or all of the processing described above in the badge acquisition unit may be performed using AI or not.

[0085] The planning unit can estimate the user's emotions and adjust the content of the self-care plan based on the estimated emotions. For example, if the user is feeling stressed, the planning unit will create a plan that prioritizes self-care methods that have a relaxing effect. For example, if the user is feeling unmotivated, the planning unit may also create a plan that includes self-care methods that are effective in a short amount of time. For example, if the user is feeling agitated, the planning unit may also create a plan that includes self-care methods that should be performed in a calm state. This allows the system to provide a self-care plan that is tailored 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-described processes in the planning unit may be performed using generative AI or not.

[0086] The plan creation unit can create an optimal plan by referring to the user's past self-care history. For example, the plan creation unit can create a plan that prioritizes self-care methods that the user has found effective in the past. For example, the plan creation unit can also create a plan that avoids self-care methods that the user has failed at in the past. For example, the plan creation unit can create a plan that includes the frequency and timing of effective care based on the user's past self-care history. This allows the system to provide an optimal plan based on the user's past self-care history. Some or all of the above processes in the plan creation unit may be performed using generative AI, or they may not be performed using generative AI.

[0087] The plan creation unit can customize plans based on the user's current lifestyle when creating them. For example, if the user is busy, the plan creation unit can create a plan that includes self-care methods that can be done in a short amount of time. For example, if the user is interested in a particular area of ​​beauty, the plan creation unit can also create a plan that includes self-care methods specialized in that area. For example, the plan creation unit can also create a plan that includes self-care methods tailored to the user's lifestyle. This allows for the provision of customized plans that are tailored to the user's lifestyle. Some or all of the above-described processes in the plan creation unit may be performed using generative AI, or they may not be performed using generative AI.

[0088] The planning unit can estimate the user's emotions and determine the priority of plans based on the estimated emotions. For example, if the user is stressed, the planning unit can create a plan that prioritizes self-care methods that have a relaxing effect. For example, if the user is unmotivated, the planning unit can also create a plan that prioritizes self-care methods that have a quick effect. For example, if the user is agitated, the planning unit can also create a plan that prioritizes self-care methods that should be performed in a calm state. This allows the system to provide plans with priorities that correspond 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 planning unit may be performed using generative AI or not.

[0089] The plan creation unit can create an optimal plan by taking into account the user's geographical location information. For example, if the user lives in a cold region, the plan creation unit can create a plan that includes self-care methods with high moisturizing effects. For example, if the user lives in an urban area, the plan creation unit can also create a plan that includes self-care methods for dealing with air pollution. For example, if the user lives by the sea, the plan creation unit can also create a plan that includes self-care methods for dealing with ultraviolet rays. This allows the system to provide an optimal plan based on the user's geographical location information. Some or all of the above processing in the plan creation unit may be performed using generative AI, or it may be performed without using generative AI.

[0090] The planning unit can analyze the user's social media activity and customize the plan during the planning process. For example, the planning unit can create a plan that includes self-care methods the user has shown interest in on social media. The planning unit can also create a plan that includes self-care methods recommended by influencers the user follows. For example, the planning unit can create a plan that includes the next steps based on the results of self-care shared by the user on social media. This allows for the provision of customized plans based on the user's social media activity. Some or all of the above processes in the planning unit may be performed using generative AI or not.

[0091] The Challenge Design Unit can estimate the user's emotions and adjust the content and timing of challenges based on the estimated emotions. For example, if the user is feeling stressed, the Challenge Design Unit can provide challenges that include relaxing self-care methods. For example, if the user is unmotivated, the Challenge Design Unit can provide challenges that can be completed in a short time. For example, if the user is excited, the Challenge Design Unit can provide challenges that include challenging self-care methods. This allows for the provision of appropriate challenges 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 Challenge Design Unit may be performed using generative AI or not.

[0092] The Challenge Design Unit can provide optimal challenges by referring to the user's past achievement history when designing challenges. For example, the Challenge Design Unit can provide challenges to help the user move to the next step based on challenges the user has previously completed. For example, the Challenge Design Unit can also design challenges to help the user avoid challenges they have previously failed at. For example, the Challenge Design Unit can analyze the user's past achievement history and provide challenges to increase motivation. This allows the Challenge Design Unit to provide optimal challenges based on the user's past achievement history. Some or all of the above processes in the Challenge Design Unit may be performed using generative AI, or they may not be performed using generative AI.

[0093] The Challenge Design Department can customize challenges based on the user's current lifestyle when designing them. For example, if the user is busy, the Challenge Design Department can provide challenges that can be completed in a short amount of time. For example, if the user is interested in a particular beauty field, the Challenge Design Department can provide challenges specialized in that field. For example, the Challenge Design Department can provide challenges that are tailored to the user's lifestyle. This allows for the provision of customized challenges that are appropriate to the user's lifestyle. Some or all of the above-described processes in the Challenge Design Department may be performed using generative AI, or they may not be performed using generative AI.

[0094] The Challenge Design Unit can estimate the user's emotions and determine the priority of challenges based on those emotions. For example, if the user is stressed, the Challenge Design Unit will prioritize challenges that include relaxing self-care methods. If the user is unmotivated, the Challenge Design Unit may also prioritize challenges that can be completed in a short amount of time. If the user is excited, the Challenge Design Unit may also prioritize challenges that include challenging self-care methods. This allows challenges to be provided with priorities that match 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 Challenge Design Unit may be performed using generative AI or not.

[0095] The Challenge Design Department can provide optimal challenges by considering the user's geographical location information during the challenge design process. For example, if the user lives in a cold region, the Challenge Design Department can provide challenges that include self-care methods with high moisturizing effects. For example, if the user lives in an urban area, the Challenge Design Department can also provide challenges that include self-care methods for combating air pollution. For example, if the user lives by the sea, the Challenge Design Department can also provide challenges that include self-care methods for protecting against ultraviolet rays. This allows for the provision of optimal challenges based on the user's geographical location information. Some or all of the above processing in the Challenge Design Department may be performed using generative AI, or it may be performed without using generative AI.

[0096] The Challenge Design Department can analyze a user's social media activity and customize challenges during the challenge design process. For example, the Challenge Design Department can provide challenges that include self-care methods the user has shown interest in on social media. The Challenge Design Department can also provide challenges that include self-care methods recommended by influencers the user follows. The Challenge Design Department can also provide challenges that include the next steps based on the results of self-care shared by the user on social media. This allows for the provision of customized challenges based on the user's social media activity. Some or all of the above processing in the Challenge Design Department may be performed using generative AI or not.

[0097] The feedback analysis unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is feeling stressed, the feedback analysis unit can provide feedback that includes relaxing self-care methods. For example, if the user is unmotivated, the feedback analysis unit can also provide feedback that includes self-care methods that are effective in a short amount of time. For example, if the user is excited, the feedback analysis unit can also provide feedback that includes challenging self-care methods. This allows for the provision of appropriate feedback that corresponds 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-described processing in the feedback analysis unit may be performed using generative AI or not using generative AI.

[0098] The feedback analysis unit can provide optimal improvement suggestions by referring to the user's past feedback history during feedback analysis. For example, the feedback analysis unit can provide improvement suggestions for the next step based on the feedback the user has provided in the past. For example, the feedback analysis unit can also provide improvement suggestions to help the user avoid self-care methods that have failed in the past. For example, the feedback analysis unit can analyze the user's past feedback history and provide improvement suggestions to increase motivation. This allows the unit to provide optimal improvement suggestions based on the user's past feedback history. Some or all of the above processing in the feedback analysis unit may be performed using generative AI, or it may be performed without using generative AI.

[0099] The feedback analysis unit can customize feedback based on the user's current lifestyle during feedback analysis. For example, if the user is busy, the feedback analysis unit can provide feedback that includes self-care methods that can be done in a short amount of time. For example, if the user is interested in a particular area of ​​beauty, the feedback analysis unit can also provide feedback that includes self-care methods specific to that area. For example, the feedback analysis unit can also provide feedback that includes self-care methods tailored to the user's lifestyle. This allows for the provision of customized feedback that is appropriate to the user's lifestyle. Some or all of the above-described processes in the feedback analysis unit may be performed using generative AI, or they may be performed without using generative AI.

[0100] The feedback analysis unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is stressed, the feedback analysis unit can prioritize providing feedback that includes relaxing self-care methods. For example, if the user is unmotivated, the feedback analysis unit can prioritize providing feedback that includes self-care methods that are effective in a short amount of time. For example, if the user is excited, the feedback analysis unit can prioritize providing feedback that includes challenging self-care methods. This allows feedback to be provided with 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 feedback analysis unit may be performed using generative AI or not.

[0101] The feedback analysis unit can provide optimal feedback by considering the user's geographical location information during feedback analysis. For example, if the user lives in a cold region, the feedback analysis unit can provide feedback including self-care methods with high moisturizing effects. For example, if the user lives in an urban area, the feedback analysis unit can also provide feedback including self-care methods for dealing with air pollution. For example, if the user lives by the sea, the feedback analysis unit can also provide feedback including self-care methods for protecting against ultraviolet rays. This enables the provision of optimal feedback based on the user's geographical location information. Some or all of the above processing in the feedback analysis unit may be performed using generative AI, or it may be performed without using generative AI.

[0102] The feedback analysis unit can analyze the user's social media activity and customize the feedback during the feedback analysis process. For example, the feedback analysis unit can provide feedback that includes self-care methods the user has shown interest in on social media. The feedback analysis unit can also provide feedback that includes self-care methods recommended by influencers the user follows. For example, the feedback analysis unit can provide feedback that includes the next steps based on the results of self-care shared by the user on social media. This allows for the provision of customized feedback based on the user's social media activity. Some or all of the above processing in the feedback analysis unit may be performed using generative AI or not.

[0103] The content generation unit can estimate the user's emotions and adjust the content of the visual content based on the estimated emotions. For example, if the user is stressed, the content generation unit can generate visual content that has a relaxing effect. For example, if the user is unmotivated, the content generation unit can also generate visual content that includes self-care methods that are effective in a short time. For example, if the user is excited, the content generation unit can also generate visual content that includes self-care methods with challenging content. This allows for the provision of appropriate visual content that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the content generation unit may be performed using a generative AI or not using a generative AI.

[0104] The content generation unit can provide optimal visual content by referring to the user's past self-care history during content generation. For example, the content generation unit can generate visual content that includes self-care methods the user has found effective in the past. For example, the content generation unit can also generate visual content that helps the user avoid self-care methods that have failed in the past. For example, the content generation unit can generate visual content that includes the frequency and timing of effective care based on the user's past self-care history. This allows the provision of optimal visual content based on the user's past self-care history. Some or all of the above processing in the content generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0105] The content generation unit can customize visual content based on the user's current lifestyle when generating content. For example, if the user is busy, the content generation unit can generate visual content that includes self-care methods that can be done in a short amount of time. For example, if the user is interested in a particular beauty field, the content generation unit can also generate visual content that includes self-care methods specific to that field. For example, the content generation unit can also generate visual content that includes self-care methods tailored to the user's lifestyle. This allows for the provision of customized visual content that is appropriate for the user's lifestyle. Some or all of the above-described processes in the content generation unit may be performed using a generation AI, or they may be performed without using a generation AI.

[0106] The content generation unit can estimate the user's emotions and prioritize visual content based on the estimated emotions. For example, if the user is stressed, the content generation unit will prioritize providing visual content with a relaxing effect. For example, if the user is unmotivated, the content generation unit may prioritize providing visual content that includes self-care methods that are effective in a short time. For example, if the user is excited, the content generation unit may prioritize providing visual content that includes self-care methods with challenging content. This allows for the provision of visual content with priorities that correspond 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 content generation unit may be performed using or without generative AI.

[0107] The content generation unit can provide optimal visual content by considering the user's geographical location information during content generation. For example, if the user lives in a cold region, the content generation unit can provide visual content including self-care methods with high moisturizing effects. For example, if the user lives in an urban area, the content generation unit can also provide visual content including self-care methods for combating air pollution. For example, if the user lives by the sea, the content generation unit can also provide visual content including self-care methods for protecting against ultraviolet rays. This enables the provision of optimal visual content based on the user's geographical location information. Some or all of the above-described processing in the content generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0108] The content generation unit can analyze the user's social media activity and customize the visual content during content generation. For example, the content generation unit can provide visual content that includes self-care methods the user has shown interest in on social media. The content generation unit can also provide visual content that includes self-care methods recommended by influencers the user follows. For example, the content generation unit can provide visual content that includes the next steps based on the results of self-care shared by the user on social media. This allows for the provision of customized visual content based on the user's social media activity. Some or all of the above processing in the content generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The advice unit can estimate the user's emotions and adjust the content and timing of advice based on the estimated emotions. For example, if the user is stressed, it can prioritize advising on self-care methods that have a relaxing effect. If the user is unmotivated, it can also advise on self-care methods that are effective in a short amount of time. Furthermore, if the user is agitated, it can advise on self-care methods that should be performed in a calm state. This allows for the provision of appropriate advice tailored to the user's emotions. Emotion estimation is achieved 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 processing described above in the advice unit may be performed using AI or not.

[0111] The advice unit can analyze the user's past self-care history and select the most appropriate advice method. For example, it can prioritize advising on self-care methods that the user has found effective in the past. It can also advise the user to avoid self-care methods that have failed in the past. Furthermore, it can advise on the frequency and timing of effective care based on the user's past self-care history. This allows for the provision of optimal advice based on past self-care history. Some or all of the above processing in the advice unit may be performed using AI or not.

[0112] The advice unit can filter advice based on the user's current lifestyle and areas of interest. For example, if the user is busy, it can advise on self-care methods that can be done in a short amount of time. If the user is interested in a particular area of ​​beauty, it can also advise on self-care methods specific to that area. Furthermore, it can advise on self-care methods that are tailored to the user's lifestyle. This allows for the provision of advice that is appropriate to the user's lifestyle and areas of interest. Some or all of the above processing in the advice unit may be performed using AI or not.

[0113] The advice section can prioritize providing highly relevant advice by taking into account the user's geographical location. For example, if the user lives in a cold region, it can advise on self-care methods with high moisturizing effects. If the user lives in an urban area, it can advise on self-care methods to combat air pollution. Furthermore, if the user lives by the sea, it can advise on self-care methods to protect against ultraviolet rays. This allows for the provision of highly relevant advice based on the user's geographical location. Some or all of the processing described above in the advice section may be performed using AI or not.

[0114] The advice unit can analyze a user's social media activity and provide relevant advice. For example, it can advise on self-care methods the user has shown interest in on social media. It can also advise on self-care methods recommended by influencers the user follows. Furthermore, it can advise on the next steps based on the results of self-care shared by the user on social media. This allows for the provision of relevant advice based on the user's social media activity. Some or all of the above processing in the advice unit may be performed using AI or not.

[0115] The badge acquisition unit can estimate the user's emotions and adjust the badge acquisition criteria based on the estimated emotions. For example, if the user is stressed, the badge acquisition criteria can be relaxed. Also, if the user is unmotivated, easily achievable tasks can be set. Furthermore, if the user is excited, challenging tasks can be set. This allows the badge acquisition criteria to be adjusted according to the user's emotions. Emotion estimation is achieved 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 badge acquisition unit may be performed using AI or not using AI.

[0116] The badge acquisition unit can provide the most suitable badges by referring to the user's past achievement history when a badge is acquired. For example, it can provide badges to help the user move to the next step based on tasks the user has completed in the past. It can also provide new badges depending on the types of badges the user has acquired in the past. Furthermore, it can analyze the user's past achievement history and provide badges to boost motivation. This allows for the provision of the most suitable badges based on the user's past achievement history. Some or all of the above processing in the badge acquisition unit may be performed using AI or not.

[0117] The planning unit can estimate the user's emotions and adjust the content of the self-care plan based on the estimated emotions. For example, if the user is stressed, the unit can create a plan that prioritizes self-care methods that have a relaxing effect. If the user is unmotivated, the unit can create a plan that includes self-care methods that produce results in a short time. Furthermore, if the user is agitated, the unit can create a plan that includes self-care methods that should be performed in a calm state. This allows the unit to provide a self-care plan that is tailored to the user's emotions. Emotion estimation is achieved 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-described processes in the planning unit may be performed using generative AI or not.

[0118] The plan creation unit can create an optimal plan by referring to the user's past self-care history. For example, it can create a plan that prioritizes self-care methods that the user has found effective in the past. It can also create a plan that avoids self-care methods that the user has failed at in the past. Furthermore, it can create a plan that includes the frequency and timing of effective care based on the user's past self-care history. This allows the system to provide an optimal plan based on the user's past self-care history. Some or all of the above processing in the plan creation unit may be performed using generative AI, or it may be performed without using generative AI.

[0119] The Challenge Design Unit can estimate the user's emotions and adjust the content and timing of challenges based on those emotions. For example, if a user is feeling stressed, it can provide challenges that include relaxing self-care methods. If a user is unmotivated, it can provide challenges that can be completed in a short time. Furthermore, if a user is excited, it can provide challenges that include challenging self-care methods. This allows for the provision of appropriate challenges tailored to the user's emotions. Emotion estimation is achieved 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-described processes in the Challenge Design Unit may be performed using generative AI or not.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The advice department provides guidance on how to use aesthetic equipment based on user data. For example, it analyzes user data and provides real-time advice on specific usage of aesthetic equipment, the order of care, frequency, and precautions. When users use self-care aesthetic equipment, it provides specific instructions such as, "Please use this equipment in this order." Step 2: In the badge acquisition section, users earn badges by completing tasks based on advice provided by the advice section and acquiring new skills. For example, if a user acquires a specific self-care technique, they can earn a badge corresponding to that technique. By completing tasks, users can visualize their progress and increase their motivation. Step 3: The plan creation unit creates a personalized self-care plan based on the progress obtained by the badge acquisition unit. For example, using generation AI, it creates an optimal self-care plan for each individual user based on user data. It can generate a self-care plan that is tailored to the user's skin condition and lifestyle. Step 4: The Challenge Design Department designs challenges and reward systems that are tailored to the user's progress, based on the self-care plan created by the Planning Department. For example, they use a generation AI to track the user's progress in real time and provide new challenges and rewards at the appropriate time. When a user achieves a certain level of progress, a new challenge is presented, and the user can earn a reward by completing that challenge. Step 5: The Feedback Analysis Department analyzes user feedback based on the challenges and reward systems designed by the Challenge Design Department, and generates improvement suggestions and motivational messages. For example, it uses a generation AI to analyze user feedback and generate messages to improve self-care and boost motivation. If a user provides feedback on self-care, it can use that feedback to provide improvement suggestions and motivational messages. Step 6: The content generation unit generates visual content based on the messages generated by the feedback analysis unit. For example, using generation AI, it generates visual content of the user's self-care results and provides it to the user. By generating and providing visual content that compares before and after photos of self-care, the user can visually confirm the effectiveness of their self-care.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the advice unit, badge acquisition unit, plan creation unit, challenge design unit, feedback analysis unit, and content generation unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the advice unit is implemented by the control unit 46A of the smart device 14 and provides advice on how to use the aesthetic equipment based on the user's data. The badge acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and allows the user to acquire badges by clearing challenges and acquiring new skills. The plan creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a personalized self-care plan based on the user's data. The challenge design unit is implemented by the control unit 46A of the smart device 14 and designs challenges and reward systems according to the user's progress. The feedback analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes user feedback and generates improvement suggestions and motivational messages. The content generation unit is implemented by the control unit 46A of the smart device 14 and generates visual content. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the advice unit, badge acquisition unit, plan creation unit, challenge design unit, feedback analysis unit, and content generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the advice unit is implemented by the control unit 46A of the smart glasses 214 and provides advice on how to use the aesthetic equipment based on the user's data. The badge acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and allows the user to acquire badges by clearing challenges and acquiring new skills. The plan creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a personalized self-care plan based on the user's data. The challenge design unit is implemented by the control unit 46A of the smart glasses 214 and designs challenges and reward systems according to the user's progress. The feedback analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes user feedback and generates improvement suggestions and motivational messages. The content generation unit is implemented by the control unit 46A of the smart glasses 214 and generates visual content. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the advice unit, badge acquisition unit, plan creation unit, challenge design unit, feedback analysis unit, and content generation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the advice unit is implemented by the control unit 46A of the headset terminal 314 and provides advice on how to use the aesthetic equipment based on the user's data. The badge acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and allows the user to acquire badges by clearing challenges and acquiring new skills. The plan creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a personalized self-care plan based on the user's data. The challenge design unit is implemented by the control unit 46A of the headset terminal 314 and designs challenges and reward systems according to the user's progress. The feedback analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes user feedback and generates improvement suggestions and motivational messages. The content generation unit is implemented, for example, by the control unit 46A of the headset terminal 314, and generates visual content. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the advice unit, badge acquisition unit, plan creation unit, challenge design unit, feedback analysis unit, and content generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the advice unit is implemented by the control unit 46A of the robot 414 and provides advice on how to use the aesthetic equipment based on the user's data. The badge acquisition unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and allows the user to acquire badges by clearing challenges and acquiring new skills. The plan creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a personalized self-care plan based on the user's data. The challenge design unit is implemented by, for example, the control unit 46A of the robot 414 and designs challenges and reward systems according to the user's progress. The feedback analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes user feedback and generates improvement suggestions and motivational messages. The content generation unit is implemented by, for example, the control unit 46A of the robot 414 and generates visual content. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) The advice department provides guidance on how to use aesthetic equipment based on user data, The Badge Acquisition Unit earns badges by clearing challenges and acquiring new skills based on advice provided by the aforementioned Advice Unit, A plan creation unit that creates a personalized self-care plan based on the progress obtained by the badge acquisition unit, Based on the self-care plan created by the aforementioned plan creation unit, the challenge design unit designs challenges and reward systems according to the user's progress. Based on the challenges and reward systems designed by the aforementioned Challenge Design Department, the Feedback Analysis Department analyzes user feedback and generates improvement suggestions and motivational messages. The system includes a content generation unit that generates visual content based on messages generated by the feedback analysis unit. A system characterized by the following features. (Note 2) The aforementioned advice section, It estimates the user's emotions and adjusts the content and timing of advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned advice section, Analyze the user's past self-care history and select the most appropriate advice method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned advice section, When providing advice, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice section, When providing advice, we prioritize offering highly relevant advice by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned advice section, When providing advice, we analyze the user's social media activity and offer relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned badge acquisition unit is We estimate the user's sentiment and adjust the criteria for earning badges based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned badge acquisition unit is When a user earns a badge, the system provides the most suitable badge by referencing their past achievement history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned badge acquisition unit is The system estimates user sentiment and prioritizes badge acquisition based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned badge acquisition unit is When a user earns a badge, the system will provide the most suitable badge based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned plan creation unit, The system estimates the user's emotions and adjusts the content of the self-care plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned plan creation unit, When creating a plan, we refer to the user's past self-care history to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned plan creation unit, When creating a plan, customize it based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned plan creation unit, It estimates user sentiment and determines plan priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned plan creation unit, When creating a plan, we take the user's geographical location into consideration to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned plan creation unit, When creating a plan, analyze the user's social media activity to customize the plan. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned Challenge Design Department The system estimates the user's emotions and adjusts the content and timing of challenges based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned Challenge Design Department When designing challenges, we provide the most suitable challenges by referring to the user's past achievement history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned Challenge Design Department When designing challenges, customize them based on the user's current life circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned Challenge Design Department It estimates the user's emotions and prioritizes challenges based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned Challenge Design Department When designing challenges, we provide the optimal challenge by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned Challenge Design Department When designing a challenge, analyze users' social media activity to customize the challenge. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback analysis unit, It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback analysis unit, During feedback analysis, we refer to the user's past feedback history to provide optimal improvement suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback analysis unit, During feedback analysis, customize the feedback based on the user's current life situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback analysis unit, 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 28) The aforementioned feedback analysis unit, When analyzing feedback, we provide optimal feedback by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback analysis unit, During feedback analysis, we analyze users' social media activity to customize the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 30) The content generation unit, It estimates the user's emotions and adjusts the content of the visuals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The content generation unit, When generating content, the system provides optimal visual content by referencing the user's past self-care history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The content generation unit, When generating content, the visual content is customized based on the user's current life situation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The content generation unit, It estimates user emotions and prioritizes visual content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The content generation unit, When generating content, we provide optimal visual content by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The content generation unit, When generating content, the system analyzes users' social media activity to customize the visual content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0194] 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. The advice department provides guidance on how to use aesthetic equipment based on user data, The Badge Acquisition Unit earns badges by clearing challenges and acquiring new skills based on advice provided by the aforementioned Advice Unit, A plan creation unit that creates a personalized self-care plan based on the progress obtained by the badge acquisition unit, Based on the self-care plan created by the aforementioned plan creation unit, the challenge design unit designs challenges and reward systems according to the user's progress. Based on the challenges and reward systems designed by the aforementioned Challenge Design Department, the Feedback Analysis Department analyzes user feedback and generates improvement suggestions and motivational messages. The system includes a content generation unit that generates visual content based on messages generated by the feedback analysis unit. A system characterized by the following features.

2. The aforementioned advice section, It estimates the user's emotions and adjusts the content and timing of advice based on those estimated emotions. The system according to feature 1.

3. The aforementioned advice section, Analyze the user's past self-care history and select the most appropriate advice method. The system according to feature 1.

4. The aforementioned advice section, When providing advice, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system according to feature 1.

6. The aforementioned advice section, When providing advice, we prioritize offering highly relevant advice by taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned advice section, When providing advice, we analyze the user's social media activity and offer relevant advice. The system according to feature 1.

8. The aforementioned badge acquisition unit is We estimate the user's sentiment and adjust the criteria for earning badges based on that estimated sentiment. The system according to feature 1.

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