System

Abstract

An object of the system according to the embodiment is to specifically grasp a user's change between the past and the present and to propose effective beauty treatment.SOLUTION: A system according to an embodiment includes a photograph upload unit, an analysis unit, and a treatment suggestion unit. The photograph upload unit uploads past and current photographs. The analysis unit analyzes the photograph uploaded by the photograph upload unit. The treatment proposal unit proposes a treatment method based on the aging portion specified by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to specifically grasp changes between the past and present, making it difficult to select effective cosmetic medical treatments.

[0005] The system according to the embodiment aims to specifically grasp changes between the past and present of a patient and to propose effective cosmetic medical treatments. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo uploading unit, an analysis unit, and a treatment suggestion unit. The photo uploading unit uploads past and present photos. The analysis unit analyzes the photos uploaded by the photo uploading unit. The treatment suggestion unit suggests a treatment method based on the aging area identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can specifically grasp changes between the past and present of a patient and propose effective cosmetic medical treatments. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The beauty diagnosis app according to an embodiment of the present invention is a system that automatically compares past and present photographs of a user, identifies areas of aging, and suggests the most effective cosmetic medical treatments. This allows the user to identify areas of aging and find the most appropriate cosmetic medical treatment.

[0029] A beauty diagnosis app according to an embodiment includes a photo upload unit, an analysis unit, and a treatment suggestion unit. The photo upload unit uploads past and current photos. For example, a user uploads past and current photos to the app. The photo upload unit can accept photos in different formats. For example, photos in JPEG or PNG format can be uploaded. The analysis unit analyzes the photos uploaded by the photo upload unit. For example, a generation AI compares past and current photos to analyze changes in the face. The analysis unit can also analyze detailed changes for each facial feature. For example, it can identify wrinkles around the eyes, nasolabial folds, sagging skin, etc. The treatment suggestion unit suggests a treatment method based on the aging areas identified by the analysis unit. For example, the generation AI can suggest Botox injections for wrinkles around the eyes, hyaluronic acid injections for nasolabial folds, and lifting surgery for sagging skin. The treatment suggestion unit can select the optimal treatment method based on the user's age, skin condition, past treatment history, etc. For example, the generating AI might explain, "Considering your age and skin condition, Botox injections would be the most effective option." This allows the beauty diagnosis app of the embodiment to help users identify areas of aging and find the most suitable cosmetic treatment.

[0030] The analysis unit can analyze detailed changes for each facial feature and display the rate of change in numerical form. For example, the generation AI analyzes past and present photos and quantifies changes for each facial feature, such as the eyes, mouth, and cheeks. For example, it displays the depth of wrinkles around the eyes and the degree of sagging around the mouth numerically. When analyzing changes for each facial feature, the analysis unit also displays the rate of change for each feature in a graph. For example, it displays the rate of increase in wrinkles around the eyes and the progression of sagging in the cheeks in a line graph. The generation AI also analyzes changes for each facial feature, quantifies the rate of change, and displays it in report form. For example, the report may summarize the rate of increase in wrinkles around the eyes as 20% and the progression of sagging around the mouth as 15%. In this way, by displaying changes for each facial feature in numerical form, users can more easily understand the specific changes.

[0031] The analysis unit can input lifestyle and environmental data and analyze the impact of these factors on aging. For example, a user inputs lifestyle and environmental data into the analysis unit, and the generation AI analyzes the factors of aging based on this data. For example, the analysis unit analyzes the rate of increase in wrinkles in users who sleep shortly. The analysis unit also predicts the progression of aging based on lifestyle and environmental data. For example, the analysis unit analyzes the progression of sagging skin in users who are exposed to a lot of UV rays. The analysis unit also inputs the user's diet and exercise habits, and the generation AI analyzes the factors of aging based on this data. For example, the analysis unit analyzes changes in skin color in users who eat a nutritionally unbalanced diet. This allows users to understand the causes of aging more easily by analyzing the impact of lifestyle and environmental data on aging.

[0032] In addition to uploading photos, the photo uploading unit can also upload videos and analyze multiple frames in the video to identify detailed changes. For example, the photo uploading unit allows a user to upload a video, and the generation AI analyzes multiple frames in the video to identify detailed changes. For example, it analyzes changes in wrinkles in each frame in the video. When the photo uploading unit analyzes the video, the generation AI quantifies and displays the changes in each frame. For example, it numerically indicates the progression of skin sagging in each frame in the video. The photo uploading unit also analyzes multiple frames in the video, and the generation AI displays the changes in a graph. For example, it displays the rate of increase in wrinkles in each frame in the video in a line graph. This makes it possible to identify more detailed changes by analyzing the video.

[0033] The photo uploading unit can analyze multiple photos taken under different lighting conditions and angles to identify comprehensive areas of aging. For example, the photo uploading unit uploads multiple photos taken under different lighting conditions and angles, and the generation AI analyzes these photos to identify comprehensive areas of aging. For example, it analyzes changes in wrinkles under different lighting conditions. When the photo uploading unit analyzes multiple photos, the generation AI quantifies and displays the changes in each photo. For example, it numerically displays the progression of skin sagging in photos taken at different angles. The photo uploading unit also analyzes multiple photos taken under different lighting conditions and angles, and the generation AI displays comprehensive areas of aging in a graph. For example, it displays the rate of increase in wrinkles under different lighting conditions in a line graph. This makes it possible to identify comprehensive areas of aging by analyzing photos taken under different lighting conditions and angles.

[0034] The analysis unit can generate a 3D model of the face and analyze three-dimensional changes. For example, the analysis unit uses a generation AI to generate a 3D model of the face and analyze the three-dimensional changes. For example, the analysis unit analyzes the depth of wrinkles and the degree of sagging skin based on the 3D model of the face. The analysis unit also generates a 3D model of the face, and the generation AI quantifies and displays the three-dimensional changes. For example, the depth of wrinkles on the 3D model of the face is displayed numerically. The analysis unit also uses a generation AI to generate a 3D model of the face and display the three-dimensional changes in a graph. For example, the progression of skin sagging on the 3D model of the face is displayed in a line graph. This makes it possible to identify aging areas in more detail by generating a 3D model of the face and analyzing the three-dimensional changes.

[0035] The analysis unit can predict the rate at which aging progresses and simulate future changes. For example, the generation AI identifies areas of aging and predicts the rate at which aging progresses. For example, it predicts the rate at which wrinkles increase and the degree of skin sagging. The analysis unit also builds a system that predicts the rate at which aging progresses and simulates future changes. For example, it simulates the depth of wrinkles and the degree of skin sagging five years from now. The analysis unit also uses the generation AI to predict the rate at which aging progresses and displays future changes in a graph. For example, it shows the rate at which wrinkles increase and the degree of skin sagging ten years from now in a line graph. This makes it easier for users to predict future aging by predicting the rate at which aging progresses and simulating future changes.

[0036] The analysis unit can analyze the health condition of the skin and provide comprehensive beauty advice. For example, the generation AI of the analysis unit identifies areas of aging and analyzes the health condition of the skin. For example, it analyzes the dryness and oiliness state and provides comprehensive beauty advice. The analysis unit also builds a system in which the generation AI analyzes the health condition of the skin and provides comprehensive beauty advice. For example, it provides advice on moisturizing care for dry skin. The analysis unit also builds a system in which the generation AI analyzes areas of aging and the health condition of the skin and provides comprehensive beauty advice in the form of a report. For example, it summarizes care methods for dry skin and the condition of pores in a report. In this way, by analyzing the health condition of the skin and providing comprehensive beauty advice, users can practice more effective beauty care.

[0037] The analysis unit can compare the results of identifying aging areas with the data of other users to clarify differences from general aging patterns. For example, the analysis unit allows the generation AI to compare the results of identifying aging areas with the data of other users to clarify differences from general aging patterns. For example, the analysis unit analyzes the depth of wrinkles in comparison with users of the same age. The analysis unit also compares the results with the data of other users, and the generation AI displays a graph showing differences from general aging patterns. For example, a line graph shows the progression of sagging skin compared with users of the same age. The analysis unit also allows the generation AI to compare the results of identifying aging areas with the data of other users to provide a report showing differences from general aging patterns. For example, a report can summarize the rate of increase in wrinkles compared with users of the same age. This makes it possible to clarify differences from general aging patterns by comparing with other users' data, making it easier for users to understand their own aging characteristics.

[0038] The treatment proposal unit can refer to past treatment result data and analyze and display the success rate and risk of side effects. For example, the treatment proposal unit refers to past treatment result data for the treatment method proposed by the generation AI and analyzes and displays the success rate and risk of side effects. For example, the success rate and risk of side effects of Botox injections are displayed numerically. The treatment proposal unit also displays the success rate and risk of side effects of the treatment method proposed by the generation AI in a graph based on past treatment result data. For example, the success rate and risk of side effects of hyaluronic acid injections are displayed in a line graph. The treatment proposal unit also refers to past treatment result data and displays the success rate and risk of side effects of the treatment method proposed by the generation AI in report format. For example, the success rate and risk of side effects of lift surgery are compiled in a report. This allows users to refer to past treatment result data and analyze and display the success rate and risk of side effects, which can be useful when selecting a treatment method.

[0039] The treatment proposal unit can collect user feedback on the proposed treatment methods and improve the proposal content based on the feedback. For example, the treatment proposal unit builds a system that collects user feedback on the proposed treatment methods and improves the proposal content based on the feedback. For example, the treatment proposal unit adjusts the treatment method proposal based on user opinions. The treatment proposal unit also collects user feedback in real time, and the generation AI immediately improves the proposal content. For example, the treatment proposal unit updates the list of treatment methods based on user evaluations. The treatment proposal unit also analyzes user feedback on the proposed treatment methods, and the generation AI improves the proposal content. For example, the treatment proposal is optimized based on user satisfaction. In this way, by collecting user feedback and improving the proposal content based on it, more appropriate treatment methods can be proposed.

[0040] The treatment suggestion unit can suggest self-care methods that can be practiced in daily life in addition to the proposed treatment method. For example, the treatment suggestion unit suggests self-care methods that can be practiced in daily life in addition to the proposed treatment method. For example, it displays skin care methods and exercise routines in list format. The treatment suggestion unit also uses the generative AI to suggest self-care methods so that the user can practice them in daily life. For example, it provides daily skin care procedures and weekly exercise plans. The treatment suggestion unit also provides self-care methods in report format in addition to the proposed treatment method. For example, it compiles specific skin care procedures and detailed explanations of exercises in the report. This makes it easier for the user to perform daily beauty care by suggesting self-care methods that can be practiced in daily life.

[0041] The treatment proposal unit can compare proposed treatment methods across different price ranges and treatment periods, and provide options that fit the user's budget and schedule. For example, the treatment proposal unit compares proposed treatment methods across different price ranges and treatment periods, and provides options that fit the user's budget and schedule. For example, the price and treatment period of Botox injections are displayed in list format. The treatment proposal unit also uses the generative AI to compare treatment methods across price ranges and treatment periods, and propose the optimal option to the user. For example, the price and treatment period of hyaluronic acid injections are displayed in a graph. The treatment proposal unit also compares proposed treatment methods across price ranges and treatment periods, and provides options that fit the user's budget and schedule in report format. For example, the price and treatment period of lift surgery are summarized in a report. This makes it easier for users to select a treatment method that fits their budget and schedule by comparing treatment methods across different price ranges and treatment periods.

[0042] The treatment proposal unit can refer to past treatment result data for the selected treatment method and analyze and display the success rate and risk of side effects. For example, the treatment proposal unit refers to past treatment result data for the treatment method selected by the generation AI and analyzes and displays the success rate and risk of side effects. For example, the success rate and risk of side effects of Botox injections are displayed numerically. The treatment proposal unit also displays the success rate and risk of side effects of the treatment method selected by the generation AI in a graph based on past treatment result data. For example, the success rate and risk of side effects of hyaluronic acid injections are displayed in a line graph. The treatment proposal unit also refers to past treatment result data and displays the success rate and risk of side effects of the treatment method selected by the generation AI in report format. For example, the success rate and risk of side effects of lift surgery are summarized in a report. This allows users to refer to past treatment result data and analyze and display the success rate and risk of side effects, which can be useful when selecting a treatment method.

[0043] The treatment proposal unit can collect user feedback on the selected treatment method and improve the selection based on the feedback. For example, the treatment proposal unit collects user feedback on the selected treatment method and builds a system that improves the selection based on the feedback. For example, the treatment proposal unit adjusts the selection of the treatment method based on the user's opinion. The treatment proposal unit also collects user feedback in real time, and the generation AI immediately improves the selection. For example, the treatment proposal unit updates the list of treatment methods based on the user's evaluation. The treatment proposal unit also analyzes user feedback on the selected treatment method, and the generation AI improves the selection. For example, the selection of the treatment method is optimized based on the user's satisfaction. In this way, by collecting user feedback and improving the selection based on it, more appropriate treatment methods can be proposed.

[0044] The treatment suggestion unit can suggest self-care methods that can be practiced in daily life in addition to the selected treatment method. For example, the treatment suggestion unit suggests self-care methods that can be practiced in daily life in addition to the selected treatment method. For example, it displays skin care methods and exercise routines in list format. The treatment suggestion unit also uses the generative AI to suggest self-care methods so that the user can practice them in daily life. For example, it provides daily skin care procedures and weekly exercise plans. The treatment suggestion unit also provides self-care methods in report format in addition to the selected treatment method. For example, it compiles specific skin care procedures and detailed explanations of exercises in the report. This makes it easier for the user to perform daily beauty care by suggesting self-care methods that can be practiced in daily life.

[0045] The treatment proposal unit can compare selected treatment methods across different price ranges and treatment periods, and provide options that fit the user's budget and schedule. For example, the treatment proposal unit compares selected treatment methods across different price ranges and treatment periods, and provides options that fit the user's budget and schedule. For example, the price and treatment period of Botox injections are displayed in list format. The treatment proposal unit also uses the generative AI to compare treatment methods across price ranges and treatment periods, and propose the optimal option to the user. For example, the price and treatment period of hyaluronic acid injections are displayed in a graph. The treatment proposal unit also compares selected treatment methods across price ranges and treatment periods, and provides options that fit the user's budget and schedule in report format. For example, the price and treatment period of lift surgery are summarized in a report. This makes it easier for users to select a treatment method that fits their budget and schedule by comparing treatment methods across different price ranges and treatment periods.

[0046] The treatment proposal unit can automatically generate a list of questions that the user can use when consulting at a beauty clinic, based on the treatment method proposed by the generation AI. For example, the treatment proposal unit automatically generates a list of questions that the user can use when consulting at a beauty clinic, based on the treatment method proposed by the generation AI. For example, it provides a list of questions regarding Botox injections. The treatment proposal unit also builds a system in which the generation AI automatically generates a list of questions that the user can use when consulting at a beauty clinic. For example, it displays a list of questions regarding hyaluronic acid injections in list format. The treatment proposal unit also provides a list of questions that the user can use when consulting at a beauty clinic in report format, based on the treatment method proposed by the generation AI. For example, it compiles a list of questions regarding lift-up surgery into a report. In this way, the consultation can proceed smoothly by automatically generating a list of questions that the user can use when consulting at a beauty clinic.

[0047] The treatment proposal unit can collect the results of consultations at beauty clinics as feedback and improve the proposals made by the generation AI. The treatment proposal unit, for example, builds a system that collects the results of consultations at beauty clinics as feedback and improves the proposals made by the generation AI. For example, it adjusts treatment method proposals based on user opinions. The treatment proposal unit also collects consultation results in real time, and the generation AI immediately improves the proposals. For example, it updates the list of treatment methods based on user evaluations. The treatment proposal unit also analyzes the results of consultations at beauty clinics, and the generation AI improves the proposals. For example, it optimizes treatment method proposals based on user satisfaction. In this way, by collecting the results of consultations at beauty clinics as feedback and improving the proposals made by the generation AI based on that, more appropriate treatment methods can be proposed.

[0048] The treatment proposal unit provides a self-checklist that the user can complete at home before a consultation at a beauty clinic, thereby enabling the content of the consultation to be specified. The treatment proposal unit provides a self-checklist that the user can complete at home before a consultation at a beauty clinic, for example. For example, it provides a list to check skin condition and wrinkle depth. The treatment proposal unit also uses a generation AI to provide a self-checklist, allowing the user to specify the content of the consultation at home. For example, it displays a list to check sagging skin and dryness in list format. The treatment proposal unit also provides the self-checklist in report format before a consultation at a beauty clinic. For example, it compiles a list to check skin condition and wrinkle depth in a report. As a result, the user can specify the content of the consultation by completing the self-checklist at home, allowing the consultation at the beauty clinic to proceed smoothly.

[0049] The treatment proposal unit allows the user to record treatment results after a consultation at a beauty clinic, and the generation AI can use that data to improve its next proposal. The treatment proposal unit builds a system in which, for example, the user records treatment results after a consultation at a beauty clinic, and the generation AI uses that data to improve its next proposal. For example, it records the condition of the skin after treatment. The treatment proposal unit also collects treatment results after the consultation in real time, and the generation AI immediately improves the next proposal. For example, it records the depth of wrinkles and the state of sagging skin after treatment. The treatment proposal unit also records treatment results after a consultation at a beauty clinic, and the generation AI improves its next proposal. For example, it compiles the condition of the skin after treatment and the depth of wrinkles in a report. This allows the user to record treatment results, and the generation AI to improve its next proposal based on that, thereby suggesting more appropriate treatment methods.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] Beauty diagnostic apps can also collect data on users' lifestyle habits and provide advice to help improve their health. For example, users can input their diet and exercise habits, and the AI ​​can analyze their health based on this data. It can suggest ways to improve nutritional balance based on their diet, or provide an appropriate exercise plan based on their exercise habits. It can also analyze users' sleep patterns and provide advice on improving sleep quality. This can help users improve not only their beauty but also their overall health.

[0052] The analysis unit monitors the user's skin condition in real time and can issue an alert if an abnormality is detected. For example, if the skin becomes dry or red, the generative AI will detect this and suggest appropriate skin care methods to the user. It can also provide preventative advice before the skin condition worsens. For example, it can recommend using a moisturizing cream before dryness progresses. This makes it easier for users to maintain healthy skin.

[0053] The treatment suggestion unit can propose a beauty plan that matches the user's lifestyle. For example, it can propose a treatment method that will produce results in a short time to a busy user, and provide a long-term beauty plan to a user who has more time. It can also propose a treatment method that suits the user's budget. For example, it can provide a plan that combines an expensive treatment method with a relatively inexpensive self-care method. This makes it easier for the user to select beauty care that suits their lifestyle.

[0054] The treatment suggestion unit can provide a customized beauty plan based on the user's beauty goals. For example, if a user wants to look beautiful for a specific event, the unit can suggest a short-term beauty plan tailored to that goal. For long-term beauty goals, the unit can also provide a step-by-step beauty plan. For example, the unit can suggest a plan that first strengthens skin care and then adds facial treatments. This allows the user to receive effective care tailored to their beauty goals.

[0055] The treatment suggestion unit can suggest optimal treatment methods that take into account past treatment results based on the user's beauty history. For example, it can analyze the effects and side effects of past treatments and suggest new treatment methods based on that. It can also prioritize treatment methods that are expected to have similar effects based on the past treatment history. This allows the user to receive more effective beauty care by leveraging their past experience.

[0056] The treatment suggestion unit can provide educational content to improve the user's knowledge of beauty care. For example, it can introduce the latest research results and trends in beauty and provide information that the user can use to improve their own beauty care. It can also provide specific skin care methods and exercise procedures in video format. This allows the user to deepen their knowledge of beauty and perform more effective care.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The photo uploader uploads past and current photos. For example, a user uploads past and current photos to the app. The photo uploader can also accept photos in different formats. For example, photos can be uploaded in JPEG or PNG format. Step 2: The analysis unit analyzes the photos uploaded by the photo upload unit. For example, the generation AI compares past and current photos and analyzes changes in the face. The analysis unit can also analyze detailed changes in each facial feature. For example, it can identify wrinkles around the eyes, nasolabial folds, sagging skin, etc. Step 3: The treatment suggestion unit proposes a treatment method based on the areas of aging identified by the analysis unit. For example, the generated AI may suggest Botox injections for wrinkles around the eyes, hyaluronic acid injections for nasolabial folds, and lift surgery for sagging skin. The treatment suggestion unit can also select the optimal treatment method by taking into account the user's age, skin condition, and past treatment history. For example, the generated AI might explain, "Taking into account your age and skin condition, Botox injections are the most effective."

[0059] (Example 2) The beauty diagnosis app according to an embodiment of the present invention is a system that automatically compares past and present photographs of a user, identifies areas of aging, and suggests the most effective cosmetic medical treatments. This allows the user to identify areas of aging and find the most appropriate cosmetic medical treatment.

[0060] A beauty diagnosis app according to an embodiment includes a photo upload unit, an analysis unit, and a treatment suggestion unit. The photo upload unit uploads past and current photos. For example, a user uploads past and current photos to the app. The photo upload unit can accept photos in different formats. For example, photos in JPEG or PNG format can be uploaded. The analysis unit analyzes the photos uploaded by the photo upload unit. For example, a generation AI compares past and current photos to analyze changes in the face. The analysis unit can also analyze detailed changes for each facial feature. For example, it can identify wrinkles around the eyes, nasolabial folds, sagging skin, etc. The treatment suggestion unit suggests a treatment method based on the aging areas identified by the analysis unit. For example, the generation AI can suggest Botox injections for wrinkles around the eyes, hyaluronic acid injections for nasolabial folds, and lifting surgery for sagging skin. The treatment suggestion unit can select the optimal treatment method based on the user's age, skin condition, past treatment history, etc. For example, the generating AI might explain, "Considering your age and skin condition, Botox injections would be the most effective option." This allows the beauty diagnosis app of the embodiment to help users identify areas of aging and find the most suitable cosmetic treatment.

[0061] The analysis unit can analyze detailed changes for each facial feature and display the rate of change in numerical form. For example, the generation AI analyzes past and present photos and quantifies changes for each facial feature, such as the eyes, mouth, and cheeks. For example, it displays the depth of wrinkles around the eyes and the degree of sagging around the mouth numerically. When analyzing changes for each facial feature, the analysis unit also displays the rate of change for each feature in a graph. For example, it displays the rate of increase in wrinkles around the eyes and the progression of sagging in the cheeks in a line graph. The generation AI also analyzes changes for each facial feature, quantifies the rate of change, and displays it in report form. For example, the report may summarize the rate of increase in wrinkles around the eyes as 20% and the progression of sagging around the mouth as 15%. In this way, by displaying changes for each facial feature in numerical form, users can more easily understand the specific changes.

[0062] The analysis unit can input lifestyle and environmental data and analyze the impact of these factors on aging. For example, a user inputs lifestyle and environmental data into the analysis unit, and the generation AI analyzes the factors of aging based on this data. For example, the analysis unit analyzes the rate of increase in wrinkles in users who sleep shortly. The analysis unit also predicts the progression of aging based on lifestyle and environmental data. For example, the analysis unit analyzes the progression of sagging skin in users who are exposed to a lot of UV rays. The analysis unit also inputs the user's diet and exercise habits, and the generation AI analyzes the factors of aging based on this data. For example, the analysis unit analyzes changes in skin color in users who eat a nutritionally unbalanced diet. This allows users to understand the causes of aging more easily by analyzing the impact of lifestyle and environmental data on aging.

[0063] The analysis unit can use the emotion estimation function to analyze the user's emotional reaction when viewing past and present photos and prioritize displaying parts of interest. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotional reaction when viewing past and present photos and prioritize displaying parts of interest emotionally. For example, parts that make the user feel anxious are highlighted in red. The analysis unit also analyzes the user's emotional reaction and builds a system that prioritizes displaying parts of interest emotionally. For example, parts that make the user feel surprised are highlighted. The analysis unit also uses the emotion estimation function to analyze the user's emotional reaction in real time and prioritize displaying parts of interest emotionally. For example, parts that make the user feel sad are prominently displayed. In this way, by analyzing the user's emotional reaction and prioritize displaying parts of interest, it becomes easier to understand the parts that the user is particularly concerned about.

[0064] In addition to uploading photos, the photo uploading unit can also upload videos and analyze multiple frames in the video to identify detailed changes. For example, the photo uploading unit allows a user to upload a video, and the generation AI analyzes multiple frames in the video to identify detailed changes. For example, it analyzes changes in wrinkles in each frame in the video. When the photo uploading unit analyzes the video, the generation AI quantifies and displays the changes in each frame. For example, it numerically indicates the progression of skin sagging in each frame in the video. The photo uploading unit also analyzes multiple frames in the video, and the generation AI displays the changes in a graph. For example, it displays the rate of increase in wrinkles in each frame in the video in a line graph. This makes it possible to identify more detailed changes by analyzing the video.

[0065] The photo uploading unit can analyze multiple photos taken under different lighting conditions and angles to identify comprehensive areas of aging. For example, the photo uploading unit uploads multiple photos taken under different lighting conditions and angles, and the generation AI analyzes these photos to identify comprehensive areas of aging. For example, it analyzes changes in wrinkles under different lighting conditions. When the photo uploading unit analyzes multiple photos, the generation AI quantifies and displays the changes in each photo. For example, it numerically displays the progression of skin sagging in photos taken at different angles. The photo uploading unit also analyzes multiple photos taken under different lighting conditions and angles, and the generation AI displays comprehensive areas of aging in a graph. For example, it displays the rate of increase in wrinkles under different lighting conditions in a line graph. This makes it possible to identify comprehensive areas of aging by analyzing photos taken under different lighting conditions and angles.

[0066] The analysis unit can use the emotion estimation function to analyze the emotions a user feels when viewing past and present photos in real time, and provide advice to elicit positive emotions. The analysis unit, for example, uses the emotion estimation function to analyze the emotions a user feels when viewing past and present photos in real time, and provide advice to elicit positive emotions. For example, it highlights areas that make the user feel happy. The analysis unit also builds a system that analyzes the user's emotional response in real time and provides advice to elicit positive emotions. For example, it prominently displays areas that make the user feel relieved. The analysis unit also uses the emotion estimation function to analyze the user's emotional response in real time and provide advice to elicit positive emotions. For example, it highlights areas that make the user feel satisfied. In this way, the user's emotions can be analyzed in real time, and advice to elicit positive emotions can be provided.

[0067] The analysis unit can generate a 3D model of the face and analyze three-dimensional changes. For example, the analysis unit uses a generation AI to generate a 3D model of the face and analyze the three-dimensional changes. For example, the analysis unit analyzes the depth of wrinkles and the degree of sagging skin based on the 3D model of the face. The analysis unit also generates a 3D model of the face, and the generation AI quantifies and displays the three-dimensional changes. For example, the depth of wrinkles on the 3D model of the face is displayed numerically. The analysis unit also uses a generation AI to generate a 3D model of the face and display the three-dimensional changes in a graph. For example, the progression of skin sagging on the 3D model of the face is displayed in a line graph. This makes it possible to identify aging areas in more detail by generating a 3D model of the face and analyzing the three-dimensional changes.

[0068] The analysis unit can predict the rate at which aging progresses and simulate future changes. For example, the generation AI identifies areas of aging and predicts the rate at which aging progresses. For example, it predicts the rate at which wrinkles increase and the degree of skin sagging. The analysis unit also builds a system that predicts the rate at which aging progresses and simulates future changes. For example, it simulates the depth of wrinkles and the degree of skin sagging five years from now. The analysis unit also uses the generation AI to predict the rate at which aging progresses and displays future changes in a graph. For example, it shows the rate at which wrinkles increase and the degree of skin sagging ten years from now in a line graph. This makes it easier for users to predict future aging by predicting the rate at which aging progresses and simulating future changes.

[0069] The analysis unit can use the emotion estimation function to analyze the user's emotional reaction to the identified aging parts and prioritize displaying parts of concern. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotional reaction to the identified aging parts and prioritize displaying parts of concern emotionally. For example, parts that make the user feel anxious are highlighted in red. The analysis unit also builds a system that analyzes the user's emotional reaction and prioritizes displaying parts of concern emotionally. For example, parts that make the user feel surprised are highlighted. The analysis unit also uses the emotion estimation function to analyze the user's emotional reaction in real time and prioritize displaying parts of concern emotionally. For example, parts that make the user feel sad are prominently displayed. In this way, by analyzing the user's emotional reaction and prioritize displaying parts of concern, it becomes easier to understand the parts that the user is particularly concerned about.

[0070] The analysis unit can analyze the health condition of the skin and provide comprehensive beauty advice. For example, the generation AI of the analysis unit identifies areas of aging and analyzes the health condition of the skin. For example, it analyzes the dryness and oiliness state and provides comprehensive beauty advice. The analysis unit also builds a system in which the generation AI analyzes the health condition of the skin and provides comprehensive beauty advice. For example, it provides advice on moisturizing care for dry skin. The analysis unit also builds a system in which the generation AI analyzes areas of aging and the health condition of the skin and provides comprehensive beauty advice in the form of a report. For example, it summarizes care methods for dry skin and the condition of pores in a report. In this way, by analyzing the health condition of the skin and providing comprehensive beauty advice, users can practice more effective beauty care.

[0071] The analysis unit can compare the results of identifying aging areas with the data of other users to clarify differences from general aging patterns. For example, the analysis unit allows the generation AI to compare the results of identifying aging areas with the data of other users to clarify differences from general aging patterns. For example, the analysis unit analyzes the depth of wrinkles in comparison with users of the same age. The analysis unit also compares the results with the data of other users, and the generation AI displays a graph showing differences from general aging patterns. For example, a line graph shows the progression of sagging skin compared with users of the same age. The analysis unit also allows the generation AI to compare the results of identifying aging areas with the data of other users to provide a report showing differences from general aging patterns. For example, a report can summarize the rate of increase in wrinkles compared with users of the same age. This makes it possible to clarify differences from general aging patterns by comparing with other users' data, making it easier for users to understand their own aging characteristics.

[0072] The analysis unit can use the emotion estimation function to analyze the user's emotional response to the identified aging parts in real time and provide advice to elicit positive emotions. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotional response to the identified aging parts in real time and provide advice to elicit positive emotions. For example, it highlights parts that make the user feel happy. The analysis unit also builds a system that analyzes the user's emotional response in real time and provides advice to elicit positive emotions. For example, it prominently displays parts that make the user feel relieved. The analysis unit also uses the emotion estimation function to analyze the user's emotional response in real time and provide advice to elicit positive emotions. For example, it highlights parts that make the user feel satisfied. This makes it possible to analyze the user's emotions in real time and provide advice to elicit positive emotions.

[0073] The treatment proposal unit can refer to past treatment result data and analyze and display the success rate and risk of side effects. For example, the treatment proposal unit refers to past treatment result data for the treatment method proposed by the generation AI and analyzes and displays the success rate and risk of side effects. For example, the success rate and risk of side effects of Botox injections are displayed numerically. The treatment proposal unit also displays the success rate and risk of side effects of the treatment method proposed by the generation AI in a graph based on past treatment result data. For example, the success rate and risk of side effects of hyaluronic acid injections are displayed in a line graph. The treatment proposal unit also refers to past treatment result data and displays the success rate and risk of side effects of the treatment method proposed by the generation AI in report format. For example, the success rate and risk of side effects of lift surgery are compiled in a report. This allows users to refer to past treatment result data and analyze and display the success rate and risk of side effects, which can be useful when selecting a treatment method.

[0074] The treatment proposal unit can collect user feedback on the proposed treatment methods and improve the proposal content based on the feedback. For example, the treatment proposal unit builds a system that collects user feedback on the proposed treatment methods and improves the proposal content based on the feedback. For example, the treatment proposal unit adjusts the treatment method proposal based on user opinions. The treatment proposal unit also collects user feedback in real time, and the generation AI immediately improves the proposal content. For example, the treatment proposal unit updates the list of treatment methods based on user evaluations. The treatment proposal unit also analyzes user feedback on the proposed treatment methods, and the generation AI improves the proposal content. For example, the treatment proposal is optimized based on user satisfaction. In this way, by collecting user feedback and improving the proposal content based on it, more appropriate treatment methods can be proposed.

[0075] The treatment suggestion unit can use the emotion estimation function to analyze the user's emotional reaction to a proposed treatment method and preferentially suggest treatment methods that are emotionally easy to accept. The treatment suggestion unit, for example, uses the emotion estimation function to analyze the user's emotional reaction to a proposed treatment method and preferentially suggest treatment methods that are emotionally easy to accept. For example, treatment methods that the user finds reassuring are preferentially displayed. Furthermore, the treatment suggestion unit analyzes the user's emotional reaction and builds a system that preferentially suggests treatment methods that are emotionally easy to accept. For example, treatment methods that the user finds enjoyable are highlighted. Furthermore, the treatment suggestion unit uses the emotion estimation function to analyze the user's emotional reaction in real time and preferentially suggest treatment methods that are emotionally easy to accept. For example, treatment methods that the user finds satisfying are displayed in a prominent manner. In this way, by analyzing the user's emotional reaction and preferentially suggesting treatment methods that are emotionally easy to accept, the user can receive treatment with peace of mind.

[0076] The treatment suggestion unit can suggest self-care methods that can be practiced in daily life in addition to the proposed treatment method. For example, the treatment suggestion unit suggests self-care methods that can be practiced in daily life in addition to the proposed treatment method. For example, it displays skin care methods and exercise routines in list format. The treatment suggestion unit also uses the generative AI to suggest self-care methods so that the user can practice them in daily life. For example, it provides daily skin care procedures and weekly exercise plans. The treatment suggestion unit also provides self-care methods in report format in addition to the proposed treatment method. For example, it compiles specific skin care procedures and detailed explanations of exercises in the report. This makes it easier for the user to perform daily beauty care by suggesting self-care methods that can be practiced in daily life.

[0077] The treatment proposal unit can compare proposed treatment methods across different price ranges and treatment periods, and provide options that fit the user's budget and schedule. For example, the treatment proposal unit compares proposed treatment methods across different price ranges and treatment periods, and provides options that fit the user's budget and schedule. For example, the price and treatment period of Botox injections are displayed in list format. The treatment proposal unit also uses the generative AI to compare treatment methods across price ranges and treatment periods, and propose the optimal option to the user. For example, the price and treatment period of hyaluronic acid injections are displayed in a graph. The treatment proposal unit also compares proposed treatment methods across price ranges and treatment periods, and provides options that fit the user's budget and schedule in report format. For example, the price and treatment period of lift surgery are summarized in a report. This makes it easier for users to select a treatment method that fits their budget and schedule by comparing treatment methods across different price ranges and treatment periods.

[0078] The treatment suggestion unit can use the emotion estimation function to analyze the user's emotional response to a proposed treatment method in real time and provide advice for eliciting positive emotions. The treatment suggestion unit, for example, uses the emotion estimation function to analyze the user's emotional response to a proposed treatment method in real time and provide advice for eliciting positive emotions. For example, treatment methods that make the user feel relieved are highlighted. The treatment suggestion unit also builds a system that analyzes the user's emotional response in real time and provides advice for eliciting positive emotions. For example, treatment methods that make the user feel joyful are prominently displayed. The treatment suggestion unit also uses the emotion estimation function to analyze the user's emotional response in real time and provide advice for eliciting positive emotions. For example, treatment methods that make the user feel satisfied are highlighted. This makes it possible to analyze the user's emotions in real time and provide advice for eliciting positive emotions.

[0079] The treatment proposal unit can refer to past treatment result data for the selected treatment method and analyze and display the success rate and risk of side effects. For example, the treatment proposal unit refers to past treatment result data for the treatment method selected by the generation AI and analyzes and displays the success rate and risk of side effects. For example, the success rate and risk of side effects of Botox injections are displayed numerically. The treatment proposal unit also displays the success rate and risk of side effects of the treatment method selected by the generation AI in a graph based on past treatment result data. For example, the success rate and risk of side effects of hyaluronic acid injections are displayed in a line graph. The treatment proposal unit also refers to past treatment result data and displays the success rate and risk of side effects of the treatment method selected by the generation AI in report format. For example, the success rate and risk of side effects of lift surgery are summarized in a report. This allows users to refer to past treatment result data and analyze and display the success rate and risk of side effects, which can be useful when selecting a treatment method.

[0080] The treatment proposal unit can collect user feedback on the selected treatment method and improve the selection based on the feedback. For example, the treatment proposal unit collects user feedback on the selected treatment method and builds a system that improves the selection based on the feedback. For example, the treatment proposal unit adjusts the selection of the treatment method based on the user's opinion. The treatment proposal unit also collects user feedback in real time, and the generation AI immediately improves the selection. For example, the treatment proposal unit updates the list of treatment methods based on the user's evaluation. The treatment proposal unit also analyzes user feedback on the selected treatment method, and the generation AI improves the selection. For example, the selection of the treatment method is optimized based on the user's satisfaction. In this way, by collecting user feedback and improving the selection based on it, more appropriate treatment methods can be proposed.

[0081] The treatment suggestion unit can use the emotion estimation function to analyze the user's emotional reaction to the selected treatment method and preferentially select a treatment method that is emotionally easy to accept. For example, the treatment suggestion unit uses the emotion estimation function to analyze the user's emotional reaction to the selected treatment method and preferentially select a treatment method that is emotionally easy to accept. For example, the treatment suggestion unit preferentially displays a treatment method that makes the user feel relieved. Furthermore, the treatment suggestion unit builds a system that analyzes the user's emotional reaction and preferentially selects a treatment method that is emotionally easy to accept. For example, the treatment suggestion unit highlights a treatment method that makes the user feel happy. Furthermore, the treatment suggestion unit uses the emotion estimation function to analyze the user's emotional reaction in real time and preferentially selects a treatment method that is emotionally easy to accept. For example, the treatment suggestion unit prominently displays a treatment method that makes the user feel satisfied. In this way, by analyzing the user's emotional reaction and preferentially selecting a treatment method that is emotionally easy to accept, the user can receive treatment with peace of mind.

[0082] The treatment suggestion unit can suggest self-care methods that can be practiced in daily life in addition to the selected treatment method. For example, the treatment suggestion unit suggests self-care methods that can be practiced in daily life in addition to the selected treatment method. For example, it displays skin care methods and exercise routines in list format. The treatment suggestion unit also uses the generative AI to suggest self-care methods so that the user can practice them in daily life. For example, it provides daily skin care procedures and weekly exercise plans. The treatment suggestion unit also provides self-care methods in report format in addition to the selected treatment method. For example, it compiles specific skin care procedures and detailed explanations of exercises in the report. This makes it easier for the user to perform daily beauty care by suggesting self-care methods that can be practiced in daily life.

[0083] The treatment proposal unit can compare selected treatment methods across different price ranges and treatment periods, and provide options that fit the user's budget and schedule. For example, the treatment proposal unit compares selected treatment methods across different price ranges and treatment periods, and provides options that fit the user's budget and schedule. For example, the price and treatment period of Botox injections are displayed in list format. The treatment proposal unit also uses the generative AI to compare treatment methods across price ranges and treatment periods, and propose the optimal option to the user. For example, the price and treatment period of hyaluronic acid injections are displayed in a graph. The treatment proposal unit also compares selected treatment methods across price ranges and treatment periods, and provides options that fit the user's budget and schedule in report format. For example, the price and treatment period of lift surgery are summarized in a report. This makes it easier for users to select a treatment method that fits their budget and schedule by comparing treatment methods across different price ranges and treatment periods.

[0084] The treatment suggestion unit can use the emotion estimation function to analyze the user's emotional response to the selected treatment method in real time and provide advice for eliciting positive emotions. The treatment suggestion unit, for example, uses the emotion estimation function to analyze the user's emotional response to the selected treatment method in real time and provide advice for eliciting positive emotions. For example, the treatment suggestion unit highlights treatment methods that make the user feel relieved. The treatment suggestion unit also constructs a system that analyzes the user's emotional response in real time and provides advice for eliciting positive emotions. For example, treatment methods that make the user feel joyful are prominently displayed. The treatment suggestion unit also uses the emotion estimation function to analyze the user's emotional response in real time and provide advice for eliciting positive emotions. For example, treatment methods that make the user feel satisfied are highlighted. This makes it possible to analyze the user's emotions in real time and provide advice for eliciting positive emotions.

[0085] The treatment proposal unit can automatically generate a list of questions that the user can use when consulting at a beauty clinic, based on the treatment method proposed by the generation AI. For example, the treatment proposal unit automatically generates a list of questions that the user can use when consulting at a beauty clinic, based on the treatment method proposed by the generation AI. For example, it provides a list of questions regarding Botox injections. The treatment proposal unit also builds a system in which the generation AI automatically generates a list of questions that the user can use when consulting at a beauty clinic. For example, it displays a list of questions regarding hyaluronic acid injections in list format. The treatment proposal unit also provides a list of questions that the user can use when consulting at a beauty clinic in report format, based on the treatment method proposed by the generation AI. For example, it compiles a list of questions regarding lift-up surgery into a report. In this way, the consultation can proceed smoothly by automatically generating a list of questions that the user can use when consulting at a beauty clinic.

[0086] The treatment proposal unit can collect the results of consultations at beauty clinics as feedback and improve the proposals made by the generation AI. The treatment proposal unit, for example, builds a system that collects the results of consultations at beauty clinics as feedback and improves the proposals made by the generation AI. For example, it adjusts treatment method proposals based on user opinions. The treatment proposal unit also collects consultation results in real time, and the generation AI immediately improves the proposals. For example, it updates the list of treatment methods based on user evaluations. The treatment proposal unit also analyzes the results of consultations at beauty clinics, and the generation AI improves the proposals. For example, it optimizes treatment method proposals based on user satisfaction. In this way, by collecting the results of consultations at beauty clinics as feedback and improving the proposals made by the generation AI based on that, more appropriate treatment methods can be proposed.

[0087] The treatment suggestion unit can use the emotion estimation function to analyze in real time the anxieties and questions the user feels when consulting at a clinic and provide appropriate advice. The treatment suggestion unit, for example, uses the emotion estimation function to analyze in real time the anxieties and questions the user feels when consulting at a clinic and provide appropriate advice. For example, it highlights areas where the user feels anxious. The treatment suggestion unit also builds a system that analyzes the user's emotional response in real time and provides appropriate advice. For example, it prominently displays treatment methods that the user feels reassured about. The treatment suggestion unit also uses the emotion estimation function to analyze the user's emotional response in real time and provide appropriate advice. For example, it highlights treatment methods that the user feels satisfied about. In this way, the anxieties and questions the user feels when consulting at a clinic can be analyzed in real time and appropriate advice can be provided, allowing the user to consult with peace of mind.

[0088] The treatment proposal unit provides a self-checklist that the user can complete at home before a consultation at a beauty clinic, thereby enabling the content of the consultation to be specified. The treatment proposal unit provides a self-checklist that the user can complete at home before a consultation at a beauty clinic, for example. For example, it provides a list to check skin condition and wrinkle depth. The treatment proposal unit also uses a generation AI to provide a self-checklist, allowing the user to specify the content of the consultation at home. For example, it displays a list to check sagging skin and dryness in list format. The treatment proposal unit also provides the self-checklist in report format before a consultation at a beauty clinic. For example, it compiles a list to check skin condition and wrinkle depth in a report. As a result, the user can specify the content of the consultation by completing the self-checklist at home, allowing the consultation at the beauty clinic to proceed smoothly.

[0089] The treatment proposal unit allows the user to record treatment results after a consultation at a beauty clinic, and the generation AI can use that data to improve its next proposal. The treatment proposal unit builds a system in which, for example, the user records treatment results after a consultation at a beauty clinic, and the generation AI uses that data to improve its next proposal. For example, it records the condition of the skin after treatment. The treatment proposal unit also collects treatment results after the consultation in real time, and the generation AI immediately improves the next proposal. For example, it records the depth of wrinkles and the state of sagging skin after treatment. The treatment proposal unit also records treatment results after a consultation at a beauty clinic, and the generation AI improves its next proposal. For example, it compiles the condition of the skin after treatment and the depth of wrinkles in a report. This allows the user to record treatment results, and the generation AI to improve its next proposal based on that, thereby suggesting more appropriate treatment methods.

[0090] The treatment suggestion unit can use the emotion estimation function to analyze in real time the anxieties and questions the user feels during consultation at the clinic and provide advice to elicit positive emotions. The treatment suggestion unit, for example, uses the emotion estimation function to analyze in real time the anxieties and questions the user feels during consultation at the clinic and provide advice to elicit positive emotions. For example, it highlights treatment methods that make the user feel relieved. The treatment suggestion unit also builds a system that analyzes the user's emotional responses in real time and provides advice to elicit positive emotions. For example, it prominently displays treatment methods that make the user feel happy. The treatment suggestion unit also uses the emotion estimation function to analyze the user's emotional responses in real time and provide advice to elicit positive emotions. For example, it highlights treatment methods that make the user feel satisfied. In this way, the anxieties and questions the user feels during consultation at the clinic can be analyzed in real time and advice to elicit positive emotions can be provided, allowing the user to consult with peace of mind.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] Beauty diagnostic apps can also collect data on users' lifestyle habits and provide advice to help improve their health. For example, users can input their diet and exercise habits, and the AI ​​can analyze their health based on this data. It can suggest ways to improve nutritional balance based on their diet, or provide an appropriate exercise plan based on their exercise habits. It can also analyze users' sleep patterns and provide advice on improving sleep quality. This can help users improve not only their beauty but also their overall health.

[0093] The analysis unit monitors the user's skin condition in real time and can issue an alert if an abnormality is detected. For example, if the skin becomes dry or red, the generative AI will detect this and suggest appropriate skin care methods to the user. It can also provide preventative advice before the skin condition worsens. For example, it can recommend using a moisturizing cream before dryness progresses. This makes it easier for users to maintain healthy skin.

[0094] The analysis unit can estimate the user's emotions and analyze their stress level. For example, it can analyze the user's emotional reactions when looking at past and present photos and quantify their stress level. If stress is high, it can suggest relaxation methods and activities to relieve stress. It can also analyze the impact of stress on the skin based on the user's emotional data and suggest appropriate skin care methods. This allows users to manage stress and take care of their beauty at the same time.

[0095] The treatment suggestion unit can propose a beauty plan that matches the user's lifestyle. For example, it can propose a treatment method that will produce results in a short time to a busy user, and provide a long-term beauty plan to a user who has more time. It can also propose a treatment method that suits the user's budget. For example, it can provide a plan that combines an expensive treatment method with a relatively inexpensive self-care method. This makes it easier for the user to select beauty care that suits their lifestyle.

[0096] The analysis unit can estimate the user's emotions and provide beauty advice to elicit positive emotions. For example, it can analyze the user's emotional reactions when looking at past and present photos and provide advice to elicit positive emotions. It can highlight areas that bring joy to the user and provide advice to further enhance the beauty of those areas. It can also prominently display areas that make the user feel at ease and suggest care methods to maintain those areas. This allows the user to engage in beauty care while maintaining positive emotions.

[0097] The treatment suggestion unit can provide a customized beauty plan based on the user's beauty goals. For example, if a user wants to look beautiful for a specific event, the unit can suggest a short-term beauty plan tailored to that goal. For long-term beauty goals, the unit can also provide a step-by-step beauty plan. For example, the unit can suggest a plan that first strengthens skin care and then adds facial treatments. This allows the user to receive effective care tailored to their beauty goals.

[0098] The analysis unit can estimate the user's emotions and suggest beauty products that are emotionally acceptable. For example, it can analyze the user's emotional reactions when looking at past and present photos and suggest beauty products that elicit positive emotions. It can prioritize the display of skin care products that contain ingredients that the user finds reassuring and provide detailed instructions on how to use the products. It can also suggest products with scents and textures that the user finds pleasurable. This makes it easier for the user to select beauty products that are emotionally satisfying.

[0099] The treatment suggestion unit can suggest optimal treatment methods that take into account past treatment results based on the user's beauty history. For example, it can analyze the effects and side effects of past treatments and suggest new treatment methods based on that. It can also prioritize treatment methods that are expected to have similar effects based on the past treatment history. This allows the user to receive more effective beauty care by leveraging their past experience.

[0100] The analysis unit can estimate the user's emotions and suggest beauty clinics that are emotionally acceptable. For example, it can analyze the user's emotional response when looking at past and present photos and suggest beauty clinics that elicit positive emotions. Clinics that the user feels comfortable with are displayed preferentially, and details of the clinic's characteristics and treatment track record are explained. It is also possible to display reviews and ratings of clinics that the user finds enjoyable. This makes it easier for the user to select a beauty clinic that satisfies them emotionally.

[0101] The treatment suggestion unit can provide educational content to improve the user's knowledge of beauty care. For example, it can introduce the latest research results and trends in beauty and provide information that the user can use to improve their own beauty care. It can also provide specific skin care methods and exercise procedures in video format. This allows the user to deepen their knowledge of beauty and perform more effective care.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The photo uploader uploads past and current photos. For example, a user uploads past and current photos to the app. The photo uploader can also accept photos in different formats. For example, photos can be uploaded in JPEG or PNG format. Step 2: The analysis unit analyzes the photos uploaded by the photo upload unit. For example, the generation AI compares past and current photos and analyzes changes in the face. The analysis unit can also analyze detailed changes in each facial feature. For example, it can identify wrinkles around the eyes, nasolabial folds, sagging skin, etc. Step 3: The treatment suggestion unit proposes a treatment method based on the areas of aging identified by the analysis unit. For example, the generated AI may suggest Botox injections for wrinkles around the eyes, hyaluronic acid injections for nasolabial folds, and lift surgery for sagging skin. The treatment suggestion unit can also select the optimal treatment method by taking into account the user's age, skin condition, and past treatment history. For example, the generated AI might explain, "Taking into account your age and skin condition, Botox injections are the most effective."

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

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

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

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

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A photo upload section where you can upload past and present photos; an analysis unit that analyzes the photos uploaded by the photo upload unit; a treatment suggestion unit that suggests a treatment method based on the aging area identified by the analysis unit. A system characterized by:

2. The analysis unit Analyzes detailed changes for each facial part and displays the rate of change numerically 2. The system of claim 1.

3. The analysis unit Enter lifestyle and environmental data and analyze the impact of these factors on aging 2. The system of claim 1.

4. The analysis unit Analyzes the user's emotional response when viewing past and present photos, and prioritizes displaying the areas of interest.

2. The system of claim 1.

5. The photo upload unit In addition to uploading the photos, videos may be uploaded and multiple frames within the video analyzed to identify detailed changes.

2. The system of claim 1.

Images