System

Abstract

A system is provided.SOLUTION: A system including means for acquiring an image of a head, means for analyzing acquired image data and evaluating a progress status of hair thinning, means for collecting lifestyle data of a user, means for analyzing the collected lifestyle data and specifying a risk factor of hair thinning, means for proposing appropriate lifestyle improvement and care goods based on the progress status of hair thinning and the risk factor, and means for presenting a proposal content to the user.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] In recent years, the number of people suffering from thinning hair has been increasing, and there is a need for guidance on appropriate lifestyle and dietary habits to stop the progression of hair loss. However, it is difficult to provide specific improvement measures according to the progress and risk factors of each individual's hair loss, and effective prevention and improvement are often not implemented. The objective of this invention is to accurately evaluate the progress of hair loss and provide users with appropriate improvement measures based on individual risk factors, so that people suffering from thinning hair can take effective measures. [Means for solving the problem]

[0005] The present invention is a system that includes a means for acquiring images of the head, a means for analyzing the acquired image data to evaluate the progression of hair loss, a means for collecting lifestyle data from the user, a means for analyzing the collected lifestyle data and identifying risk factors for hair loss, a means for suggesting appropriate lifestyle improvements and care products based on the progression of hair loss and risk factors, and a means for presenting the suggestions to the user. Specifically, the system uses an image analysis module to evaluate the amount of hair on the head and the degree of exposure of the scalp, and an AI model to analyze the lifestyle data and provide improvement measures corresponding to individual risk factors. The suggestions are also sent to the user's device and displayed, allowing the user to easily confirm and incorporate them into their daily life. Furthermore, the system includes a function for updating and reevaluating data periodically, enabling continuous measures and monitoring.

[0006] The "head image acquisition means" is a device or method for photographing the user's head and acquiring the image data.

[0007] The "means for analyzing image data" refers to algorithms or software for processing the acquired image data and evaluating the progression of hair thinning.

[0008] The "means for assessing the progression of hair loss" is a technique or method for identifying the amount of hair on the head and the degree of exposure of the scalp based on the analysis results.

[0009] The "means for collecting lifestyle habit data of users" refers to a device or platform for collecting information about lifestyle habits from users in the form of a questionnaire.

[0010] "Means for analyzing lifestyle data" refers to algorithms and AI models that analyze collected lifestyle data and identify risk factors for hair loss.

[0011] The "means for identifying risk factors" refers to a technique or method for identifying the main factors that contribute to the progression of hair loss in a user based on lifestyle data and image data.

[0012] The "means for making suggestions" is a technology or method for automatically generating suggestions for lifestyle improvements and care products according to the identified risk factors.

[0013] The "means for presenting the proposal content to the user" refers to a device or software for transmitting the generated proposal content to the user's terminal and visually displaying it.

[0014] A "reassessment means" is a technique or method for obtaining new data at regular intervals and comparing it with previous data. [Brief explanation of the drawings]

[0015] [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. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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, a 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), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] 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), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] As an embodiment of this invention, we will specifically explain a system that acquires images of a user's head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair loss. This system consists of a user terminal, a server, and a software module for analysis and feedback. The detailed operation of each element is shown below.

[0037] 1. User data collection

[0038] Head photography

[0039] The user launches the app and follows the instructions to take a photo of their head. The device stores this image data locally and prepares it for transmission to the server.

[0040] Survey responses

[0041] Users answer a questionnaire within the app and enter data about their lifestyle and eating habits, which are then collected by the device and sent to the server along with image data.

[0042] 2. Data Processing and Analysis

[0043] Image analysis

[0044] The image data received by the server is input into the image analysis module, which evaluates and quantifies the amount of hair on the user's head and the degree of exposed skin, and saves the analysis results.

[0045] Lifestyle data analysis

[0046] The server inputs lifestyle data into an AI model, which analyzes the relationship between lifestyle habits and the progression of hair loss. The AI ​​model then identifies risk factors and generates a score for each factor.

[0047] 3. Generating personalized suggestions

[0048] Proposal generation

[0049] The server combines the results of image analysis and lifestyle data analysis to generate optimal lifestyle improvement measures for the user, such as "increase protein in your diet," "importance of stress management," and "use a specific shampoo."

[0050] Submit your proposal

[0051] Once the suggestions are generated, the server sends them to the user's device, which displays them in the app for the user to review.

[0052] 4. User Feedback

[0053] Suggestion display

[0054] The device receives suggestions from the server and displays them within the app. The user checks the app and incorporates the suggestions into their daily lives.

[0055] Recording of actions taken

[0056] The user implements the suggestions and enters and records the results, which the device stores locally for the next data collection.

[0057] 5. Ongoing follow-up and feedback

[0058] Regular photo shoot

[0059] The user takes another photo of the head after a certain period of time, and the terminal prepares to send the new image data to the server.

[0060] Reevaluation

[0061] The server reanalyzes the new image data and lifestyle data and compares them with the previous proposal.

[0062] Feedback Updates

[0063] The server regenerates appropriate improvement suggestions and sends them to the terminal, which then displays the latest suggestions to the user, supporting effective management.

[0064] Specific examples

[0065] The user uses the app to take a photo of their head and answer a questionnaire about their diet and exercise habits. The device sends this information to a server, which analyzes the image and lifestyle data. The server generates suggestions such as "eat more protein" or "you need to manage your stress" and sends them to the device. The user confirms and implements these suggestions. After a certain period of time, the user takes another photo of their head and sends the latest data to the server. The server then reassesssssments and provides feedback on any improvements.

[0066] This allows users to effectively manage the progression of hair thinning and implement appropriate remedial measures.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] The user launches the app and takes a picture of their head. The device stores the captured image data locally and prepares to send it to the server.

[0070] Step 2:

[0071] The user answers a questionnaire within the app. The questions consist of dietary habits, exercise habits, stress levels, etc. The device collects the questionnaire data and prepares to send it to the server along with the image data.

[0072] Step 3:

[0073] The device sends the captured image data and the survey data to the server, which receives the data and prepares it for analysis.

[0074] Step 4:

[0075] The server inputs the image data into an image analysis module, which evaluates the amount of hair on the head and the degree of exposed skin, converts the progress of hair loss into numerical values, and stores the results in a database.

[0076] Step 5:

[0077] The server inputs the lifestyle data it receives into an AI model, which analyzes the relationship between lifestyle habits and the progression of hair loss and identifies risk factors. Scores for these risk factors are also generated and stored in a database.

[0078] Step 6:

[0079] The server integrates the results of image analysis and lifestyle data analysis. Based on the integrated data, it generates appropriate lifestyle improvement measures and skincare product recommendations. For example, specific recommendations such as "increase protein in your diet," "importance of stress management," and "use a specific shampoo" are generated.

[0080] Step 7:

[0081] The server sends the generated proposal to the user's device, which then displays the proposal received in the app so that the user can confirm and implement it.

[0082] Step 8:

[0083] The user implements the suggested improvements and records the results within the app. The device stores these execution data locally and prepares them for the next analysis.

[0084] Step 9:

[0085] After a certain period of time has passed, the user takes another image of their head to acquire new data. The device stores the new image data locally and prepares to send it to the server.

[0086] Step 10:

[0087] The device sends new image data to the server, which receives the new data and compares it with the previous data for re-evaluation.

[0088] Step 11:

[0089] The server generates appropriate improvement suggestions again based on the results of the reevaluation. The new suggestions are sent to the user's device, which displays them in the app. This allows the user to check the latest suggestions and make continuous improvements.

[0090] Example 1

[0091] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0092] Conventional hair loss prevention systems have the problem of being difficult to provide personalized advice based on the user's lifestyle and environment. Furthermore, they lack the functionality to continuously record the results of implementing the recommendations and provide feedback, making long-term hair loss management difficult. Furthermore, there is no cycle of reevaluation and improvement suggestions, meaning users cannot always receive the latest advice.

[0093] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0094] In this invention, the server includes means for acquiring an image of the head, means for analyzing the acquired image data to evaluate the progression of hair thinning, means for collecting lifestyle data of the user, means for analyzing the collected lifestyle data and identifying risk factors for hair thinning, means for suggesting appropriate lifestyle improvements and hair care products based on the progression of hair thinning and the risk factors, means for presenting the suggestions to the user, means for recording the results of implementing the suggestions, and means for continuous follow-up and reassessment. This makes it possible to provide personalized advice based on the user's individual lifestyle, and provide continuous feedback and the latest improvement suggestions.

[0095] The "means for acquiring an image of the head" is a combination of hardware and software for photographing the user's head and capturing the image data.

[0096] The "means for analyzing acquired image data to assess the progression of hair loss" refers to an algorithm or program that analyzes the amount of hair and the degree of exposure of the scalp based on the captured image of the head, and quantifies and assesses the progression of hair loss.

[0097] The "means for collecting user lifestyle data" is an interface for collecting data on the user's lifestyle habits such as diet, exercise, and sleep using questionnaires, sensors, etc.

[0098] The "means of analyzing collected lifestyle data and identifying risk factors for hair loss" is a system that uses AI models and statistical methods to identify risk factors related to the progression of hair loss based on collected lifestyle data.

[0099] The "means for suggesting appropriate lifestyle improvements and hair care products based on the progression of hair thinning and risk factors" is a component that generates lifestyle improvement measures and appropriate hair care products customized for the user based on the results of image analysis and lifestyle data analysis.

[0100] The "means for presenting the proposal content to the user" is a system that has the function of transmitting the generated proposal content to the user's device and displaying it to the user through an app or other user interface.

[0101] The "means for recording the results of implementing the suggested content" is a function that allows the user to input the results after implementing the suggested lifestyle improvements or hair care products, and save them in a database or local storage for use in the next analysis.

[0102] The "means for continuous follow-up observation and re-evaluation" is a system that re-collects the user's head images and lifestyle data at regular intervals, compares them with the previous results, and makes new analyses and proposals.

[0103] This invention describes a system that acquires images of a user's head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair loss. This system consists of a device used by the user, a server that analyzes the data, and a software module for analysis and feedback.

[0104] First, the user launches the app and takes a photo of their head. The device stores the captured image data locally and prepares it for transmission to the server. At the same time, the user answers a questionnaire within the app and enters data about their lifestyle and eating habits into the device. This data is also collected by the device and sent to the server along with the image data.

[0105] The server inputs the received image data into an image analysis module (e.g., OpenCV or TensorFlow) to evaluate and quantify the amount of hair on the user's head and the degree of exposed scalp. The analysis results are stored on the server. Next, the server inputs the lifestyle data into an AI model (e.g., scikit-learn or TensorFlow) to analyze the relationship between lifestyle habits and the progression of hair loss. The AI ​​model identifies risk factors and generates a score for each factor.

[0106] The server then combines the image analysis results with the lifestyle data analysis results to generate optimal lifestyle improvement measures for the user. These include specific suggestions such as "increase protein in your diet," "importance of stress management," and "use a specific shampoo." The generated suggestions are sent from the server to the user's device and displayed within the app. The user can review the suggestions and incorporate them into their daily lives.

[0107] After implementing the suggestions, the user enters the results into the app, and the device stores this data locally. After a certain period of time, the user takes another photo of their head and sends the new image and input data to the server. The server reanalyzes the new data, compares it with the previous suggestions to evaluate their effectiveness, and generates new feedback. This allows for continuous follow-up and feedback.

[0108] As a specific example, a user uses an app to take a photo of their head and answer a questionnaire about their diet and exercise habits. The device sends this information to a server, which analyzes the image and lifestyle data. The server uses a generative AI model to generate suggestions such as "eat more protein" or "you need to manage your stress," which are sent to the device. The user confirms and implements these suggestions. After a certain period of time, the user takes another photo of their head and sends the new data to the server. The server then reevaluates and provides feedback on any improvements.

[0109] Example prompt sentence:

[0110] Analyze data on the user's hair loss progression and suggest lifestyle improvements. Generate specific advice based on the user's dietary data and the results of head image analysis.

[0111] In this way, the present invention allows users to effectively manage the progression of hair thinning and implement appropriate remedial measures based on their individual lifestyle habits.

[0112] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0113] Step 1:

[0114] The user launches the app and takes a photo of their head.

[0115] Input: User's head image

[0116] Operation: The device activates the camera function and captures an image of the user's head. The captured image data is stored locally and prepared for transmission to the server.

[0117] Output: locally saved head image data

[0118] Step 2:

[0119] Users answer a questionnaire within the app and enter data about their lifestyle and eating habits.

[0120] Input: Lifestyle data (diet, exercise, sleep, etc.)

[0121] How it works: Users fill out survey forms within the app and their devices collect this data.

[0122] Output: Locally saved lifestyle data

[0123] Step 3:

[0124] The device sends the locally stored image data and lifestyle habit data to a server.

[0125] Input: Head image data, lifestyle data

[0126] Operation: The device communicates over the network to send the stored image data and lifestyle habit data to the server.

[0127] Output: Image data and lifestyle data sent to the server

[0128] Step 4:

[0129] The image data received by the server is input into the image analysis module, where analysis is performed.

[0130] Input: Head image data

[0131] Operation: The server uses an image analysis module (e.g., OpenCV, TensorFlow) to analyze image data and evaluate and quantify the amount of hair on the head and the degree of exposed skin.

[0132] Output: Analysis results (evaluation values ​​for hair volume and scalp exposure)

[0133] Step 5:

[0134] The lifestyle data received by the server is input into an AI model, which analyzes the relationship between lifestyle habits and the progression of hair loss.

[0135] Input: Lifestyle data

[0136] How it works: The server uses AI models (e.g., scikit-learn, TensorFlow) to analyze lifestyle data to identify risk factors and generate a score for each factor.

[0137] Output: Risk factor scoring results

[0138] Step 6:

[0139] The server integrates the results of image analysis and lifestyle data analysis to generate optimal lifestyle improvement measures for the user.

[0140] Input: Image analysis results, risk factor scores

[0141] How it works: Based on the results of image analysis and lifestyle data analysis, the server uses a generative AI model to generate appropriate lifestyle improvement measures and hair care products. For example, it generates recommendations such as "take a certain amount of protein," "stress management is necessary," and "use a specific shampoo."

[0142] Output: Generated lifestyle improvement measures and hair care product suggestions

[0143] Step 7:

[0144] Once the proposal is generated, the server sends it to the user's device.

[0145] Input: Lifestyle improvement measures and hair care product suggestions

[0146] Operation: The server sends the generated proposal to the user's device and notifies them.

[0147] Output: Suggestions sent to the device

[0148] Step 8:

[0149] The device receives suggestions from the server and displays them within the app. The user can then review the suggestions and incorporate them into their daily lives.

[0150] Input: Received proposal

[0151] Operation: The device displays the suggestion on the app screen and notifies the user.

[0152] Output: Suggestions displayed on the app screen

[0153] Step 9:

[0154] Users implement the suggested lifestyle changes and hair care products and enter their results into the app, which stores them locally on the device.

[0155] Input: Proposal execution result

[0156] How it works: The user enters their thoughts and results after implementing the suggestions into the app, which is then saved locally on the device.

[0157] Output: Locally saved proposal execution results

[0158] Step 10:

[0159] After a certain period of time, the user takes another photo of the head and sends the new image data to the server.

[0160] Input: New head image data

[0161] How it works: The user takes another photo of their head, and the device stores the new image data locally and sends it to the server at intervals.

[0162] Output: New head image data sent to the server

[0163] Step 11:

[0164] The server re-analyzes the new image data and lifestyle data, compares them with the results of the previous analysis, evaluates the effectiveness, and generates new feedback.

[0165] Input: New head image data, recollected lifestyle data

[0166] How it works: The server analyzes the images and lifestyle data again, compares them with the previous analysis results, and evaluates any changes. It then generates appropriate feedback.

[0167] Output: New feedback

[0168] Through this series of steps, users can continuously manage the progression of hair loss and implement appropriate improvements based on their individual lifestyle habits.

[0169] (Application example 1)

[0170] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0171] Conventional hair thinning treatment systems have difficulty in providing consistent progress monitoring and feedback to individual users. Furthermore, because the recommendations are not based on individual lifestyle habits or risk factors, they are unable to provide effective improvement measures. Furthermore, when used in physical salons, there is an issue of information not being shared smoothly between salon staff and customers. To solve these issues, a system is needed that provides continuous and personalized feedback and supports use in physical salons and other such stores.

[0172] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0173] In this invention, the server includes means for acquiring an image of the head, means for analyzing the acquired image data to evaluate the progress of hair thinning, means for collecting lifestyle habit data of the user, means for analyzing the collected lifestyle habit data and identifying risk factors for hair thinning, means for proposing appropriate lifestyle improvements and care products based on the progress of hair thinning and the risk factors, means for presenting the suggestions to the user, means for periodically acquiring and reanalyzing the user's progress observation data, means for updating the suggestions based on the reanalysis results, and means for sharing the suggestions between staff terminals and customer terminals in a physical store such as a beauty salon. This allows for personalized feedback to be provided to each user, and for staff and customers to share information and implement effective measures against hair thinning.

[0174] The "means for acquiring an image of the head" is a device that takes a photograph of the user's head using a digital camera or a smartphone camera and acquires the image data.

[0175] The "means for analyzing acquired image data to evaluate the progression of hair thinning" is software that uses acquired image data of the head to analyze the amount of hair and the degree of exposure of the scalp, and numerically evaluates the progression of hair thinning.

[0176] The "means for collecting user lifestyle data" is a device that collects information about the user's diet, exercise, stress, and other daily habits through questionnaires or sensors.

[0177] The "means for analyzing collected lifestyle data and identifying risk factors for hair loss" is software that analyzes collected lifestyle data of users and identifies risk factors that affect the progression of hair loss.

[0178] The "means for suggesting appropriate lifestyle improvements and care products" is software that recommends specific lifestyle improvements and care products to users based on the progression of hair loss and risk factors.

[0179] The "means for presenting the proposal content to the user" is a device that transmits the generated proposal content to the user's terminal and displays it.

[0180] The "means for periodically acquiring and reanalyzing the user's follow-up observation data" is a device that allows the user to take another photograph of the head after a certain period of time, collect the photograph together with the latest lifestyle habit data, and perform reanalysis.

[0181] The "means for updating the content of the proposals based on the reanalysis results" is software that regenerates and updates the proposals for optimal lifestyle improvements and care products based on the reanalyzed data.

[0182] A "means for sharing proposal content between staff terminals and customer terminals in a physical store such as a beauty salon" is a device that can send generated proposal content to staff terminals and customer terminals in the physical store and share information.

[0183] As an embodiment of this invention, we will specifically explain a system that acquires images of the head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair thinning. This system consists of a user terminal, a server, and a software module for analysis and feedback. In particular, it is intended for use in brick-and-mortar stores such as beauty salons.

[0184] 1. User data collection

[0185] Head photography

[0186] Users visit a hair salon and take a photo of their head using a smartphone app. This image data is immediately uploaded to a cloud server. The image of the head is used for image analysis to evaluate the user's hair volume and the degree of exposed skin.

[0187] Survey responses

[0188] Customers fill out a questionnaire about their lifestyle habits on a tablet device at the salon, and this data is also sent to the cloud server.

[0189] 2. Data Processing and Analysis

[0190] Image analysis

[0191] The server analyzes the image data uploaded to the cloud using OpenCV. Specifically, it detects the head using cv2.CascadeClassifier and evaluates the amount of hair and the degree of exposed skin within the detected area using a TensorFlow model.

[0192] Lifestyle data analysis

[0193] The questionnaire data answered by the user is input into the AI ​​model, which analyzes the relationship between lifestyle habits and the progression of hair loss, identifies risk factors, and generates a score for each factor.

[0194] 3. Generating personalized suggestions

[0195] Proposal generation

[0196] The results of image analysis and lifestyle data analysis are integrated to generate optimal lifestyle improvement measures, such as specific suggestions such as "eat a diet high in protein" or "stress management is necessary."

[0197] Submit and share your suggestions

[0198] The server sends the proposed content to the user's terminal and the salon staff terminal, allowing the salon staff to provide specific advice to the user.

[0199] 4. Follow-up and feedback

[0200] Regular imaging and reassessment

[0201] The user returns to the salon after a certain period of time and takes a new photo of their head, which is again sent to the server for re-analysis.

[0202] Feedback Updates

[0203] Based on the latest analysis results, the proposals are updated and sent to the user's terminal and the salon staff terminal.

[0204] Specific examples

[0205] When a customer visits a beauty salon, they take a photo of their head using a smartphone app. They then answer a questionnaire about their lifestyle habits on a tablet device. This data is sent to a cloud server. After analyzing the image and lifestyle data, the server generates recommendations such as "increase protein intake" or "strengthen stress management." These recommendations are shared with both the customer and the salon staff, allowing them to take specific actions.

[0206] Prompt Sentence Examples

[0207] "Develop an app for a hair salon that takes a photo of the user's head, sends it to a cloud server, and analyzes the results of a lifestyle questionnaire to generate suggestions for preventing hair loss."

[0208] In this way, the system provides personalized feedback to each user, enabling salon staff and customers to share information and implement effective measures to combat hair loss.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] Acquiring a user's head image

[0212] When a user visits a beauty salon, the salon staff takes a photo of the user's head using a dedicated smartphone app. This image data is saved in the smartphone's local storage and sent to a cloud server. The input is the user's head image data, and the output is the transmission of the image data to the cloud server.

[0213] Step 2:

[0214] Collection of lifestyle data

[0215] Users answer a questionnaire about their lifestyle habits on a tablet device at a beauty salon. Questions include, "How many times a week do you exercise?" and "How many times a day do you eat?" This data is also sent to the cloud server. The input is the user's questionnaire response data, and the output is the transmission of lifestyle habit data to the cloud server.

[0216] Step 3:

[0217] Image data analysis

[0218] The server analyzes the image data uploaded to the cloud using OpenCV. Specifically, the server detects the head using cv2.CascadeClassifier, and then evaluates the hair volume and the degree of exposed skin using a TensorFlow model. The input is the image data of the head, and the output is the evaluation results of the hair volume and the degree of exposed skin.

[0219] Step 4:

[0220] Lifestyle data analysis

[0221] The server inputs the collected lifestyle data into an AI model and analyzes the relationship between lifestyle and the progression of hair loss. The server identifies risk factors and generates a score for each factor. The input is lifestyle data, and the output is the identification of risk factors and the generation of a score.

[0222] Step 5:

[0223] Proposal generation

[0224] The server integrates the results of image analysis and lifestyle data analysis to generate optimal lifestyle improvement measures and care product recommendations. For example, suggestions include "eat a diet high in protein" and "stress management is important." The inputs are the image analysis results and lifestyle data analysis results, and the output is lifestyle improvement measures and care product recommendations.

[0225] Step 6:

[0226] Submit and share your suggestions

[0227] The server sends the generated suggestions to the user's terminal and the salon staff terminal, allowing the salon staff to provide specific advice to the user. The input is the generated suggestions, and the output is the transmission of the suggestions to the user's terminal and the salon staff terminal.

[0228] Step 7:

[0229] Regular acquisition of follow-up data

[0230] The user returns to the salon after a certain period of time and takes another photo of their head. This new image data is also sent to the cloud server. The input is the new head image data, and the output is the transmission of the image data to the cloud server.

[0231] Step 8:

[0232] Reassessment and feedback updates

[0233] The server reanalyzes the new image data and lifestyle data and compares it with the previous data. It evaluates how effective the original suggestions were and generates new suggestions. The updated suggestions are sent to the user's device and the salon staff's device. The input is the new image data and lifestyle data, and the output is the transmission of the updated suggestions.

[0234] This allows the entire process to work seamlessly to provide effective hair loss treatment to the user.

[0235] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0236] As an embodiment of this invention, we will specifically explain a system that acquires images of a user's head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair loss. This system consists of a user terminal, a server, a software module that performs analysis and feedback, and an emotion engine. The detailed operation of each element is shown below.

[0237] User data collection

[0238] Head photography

[0239] The user launches the app and takes a photo by following the head guide. The device stores this image data locally and prepares it for transmission to the server.

[0240] Survey responses

[0241] Users answer a questionnaire within the app and enter data about their lifestyle and eating habits, which are then collected by the device and sent to the server along with image data.

[0242] Data processing and analysis

[0243] Image analysis

[0244] The server inputs the received image data into an image analysis module, which evaluates the amount of hair on the head and the degree of exposed skin, converts the progress of hair loss into numerical values, and stores the results in a database.

[0245] Lifestyle data analysis

[0246] The server inputs lifestyle data into the AI ​​model, which analyzes the relationship between lifestyle and the progression of hair loss. The AI ​​model identifies risk factors, generates a score for each factor, and stores it in a database.

[0247] Emotion analysis

[0248] The emotion engine collects the user's facial expression data, voice data, or text data and analyzes the user's emotional state. For example, the emotion data is acquired in real time using the smartphone's camera and microphone and sent to the emotion engine.

[0249] Generate personalized suggestions

[0250] Proposal generation

[0251] The server integrates the results of image analysis, lifestyle data analysis, and the emotional state of the user based on the emotion engine. Based on the integrated data, it generates appropriate lifestyle improvement and skincare recommendations. For example, recommendations include "increase protein in your diet," "importance of stress management," and "use a specific shampoo." The recommendations are adjusted based on the user's emotional state and presented in the most acceptable format for the user.

[0252] Submit your proposal

[0253] The server sends the generated proposal to the user's device, which then displays the proposal received in the app so that the user can confirm and implement it.

[0254] User Feedback

[0255] Suggestion display

[0256] The device receives the suggestions from the server and displays them within the app. The user checks the app and incorporates the suggestions into their daily lives. The emotion engine analyzes the user's reaction to the suggestions in real time and provides feedback.

[0257] Recording of actions taken

[0258] The user implements the suggested improvements and records the results within the app, and the device stores these execution data locally for the next data collection.

[0259] Ongoing follow-up and feedback

[0260] Regular photo shoot

[0261] The user takes another photo of their head after a certain period of time to obtain new data. The device stores the new image data locally and prepares to send it to the server.

[0262] Reevaluation

[0263] The server reanalyzes the new image data and lifestyle data, compares them with the previous data, and reassesssssments. The emotion engine reflects new feedback based on changes in the user's emotional state.

[0264] Feedback Updates

[0265] The server regenerates appropriate improvement suggestions and sends them to the device. The device displays the latest suggestions to the user, supporting effective management. The emotion engine continuously analyzes the user's reactions and evaluates the effectiveness of the suggestions.

[0266] As a specific example, a user uses an app to take a photo of their head and answer a questionnaire about their diet and exercise habits. The device sends this information to a server, which analyzes the image and lifestyle data. The server generates suggestions such as "eat more protein" or "you need to manage your stress," and sends them to the device in an optimal form that takes into account the user's emotional state using an emotion engine. The user confirms and implements these suggestions. After a certain period of time, the user takes another photo of their head and sends the latest data to the server. The server then re-evaluates and provides feedback on any improvements. The emotion engine continuously analyzes the user's emotional state and provides optimal feedback.

[0267] The processing flow will be explained below.

[0268] Step 1:

[0269] The user launches the app and takes an image of their head. The device stores this image data locally and prepares it for transmission to the server.

[0270] Step 2:

[0271] The user answers a questionnaire within the app, which asks about their eating habits, exercise habits, and stress levels. The device collects the questionnaire data and prepares to send it to the server along with the image data.

[0272] Step 3:

[0273] The device sends the captured image data and the survey data to the server, which receives the data and prepares it for analysis.

[0274] Step 4:

[0275] The server inputs the image data into an image analysis module, which evaluates the amount of hair on the head and the degree of exposed skin, converts the progress of hair loss into numerical values, and stores the results in a database.

[0276] Step 5:

[0277] The server inputs the lifestyle data it receives into an AI model, which analyzes the relationship between lifestyle habits and the progression of hair loss and identifies risk factors. Scores for these risk factors are also generated and stored in a database.

[0278] Step 6:

[0279] The emotion engine collects the user's facial expression data, voice data, or text data and analyzes the user's emotional state. For example, the emotion data is acquired in real time using a camera and a microphone and sent to the emotion engine.

[0280] Step 7:

[0281] The server integrates the results of image analysis, lifestyle data analysis, and the emotional state of the user based on the emotion engine. Based on the integrated data, it generates appropriate lifestyle improvement and skincare product recommendations. Examples include "increase protein in your diet," "importance of stress management," and "use a specific shampoo." The recommendations are adjusted based on the user's emotional state and presented in the most acceptable format for the user.

[0282] Step 8:

[0283] The server sends the generated proposal to the user's device, which then displays the proposal received in the app so that the user can confirm and implement it.

[0284] Step 9:

[0285] The user implements the suggested improvements and records the results within the app, and the device stores these execution data locally for the next data collection.

[0286] Step 10:

[0287] After a certain period of time has passed, the user takes another image of their head to acquire new data. The device stores the new image data locally and prepares to send it to the server.

[0288] Step 11:

[0289] The device sends new image data to the server, which receives the new data and compares it with the previous data for re-evaluation. This allows the progress of hair loss and the effectiveness of treatment to be monitored.

[0290] Step 12:

[0291] The server generates appropriate improvement suggestions again based on the results of the reevaluation. The new suggestions are sent to the device, which displays them within the app. This allows the user to check the latest suggestions and make continuous improvements.

[0292] Step 13:

[0293] The emotion engine analyzes the user's emotional state in real time and provides feedback: when the user expresses an emotional reaction to a suggestion, the emotion engine processes this and adjusts the suggestion as needed.

[0294] As a specific example, a user uses an app to take a photo of their head and answer a questionnaire about their diet and exercise habits. The device sends this information to a server, which analyzes the image and lifestyle data. The server generates suggestions such as "eat more protein" or "you need to manage your stress," and sends them to the device in an optimal form that takes into account the user's emotional state using an emotion engine. The user confirms and implements these suggestions. After a certain period of time, the user takes another photo of their head and sends the latest data to the server. The server then re-evaluates and provides feedback on any improvements. The emotion engine continuously analyzes the user's emotional state and provides optimal feedback.

[0295] Example 2

[0296] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0297] Many people today suffer from thinning hair and are searching for effective ways to slow its progression and improve it. However, existing methods are unable to provide personalized recommendations that take into account individual lifestyle habits and emotional states, limiting their effectiveness. Furthermore, there is a lack of systems that continuously track users' responses and provide feedback. Therefore, there is a need to propose a system that analyzes a user's head images, lifestyle habits, and emotional data, and integrates them to provide optimal solutions to improve hair loss more effectively.

[0298] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring an image of the user's head, means for analyzing the acquired image data to evaluate the progression of hair thinning, means for collecting the user's lifestyle data, means for analyzing the collected lifestyle data and identifying risk factors for hair thinning, means for collecting and analyzing the user's emotional data, means for suggesting appropriate lifestyle improvements and care products based on the progression of hair thinning, risk factors, and emotional data, means for presenting the suggestions to the user, and means for recording the user's responses and continuously analyzing the data. This makes it possible to provide personalized improvement measures tailored to the user's individual situation and effectively suppress the progression of hair thinning.

[0299] "User" refers to an individual who uses the system to take a head image and input lifestyle data.

[0300] "Head image" means photographic data of the user's head, and is primarily used to evaluate the condition of the hair.

[0301] "Lifestyle data" includes information about a user's diet, exercise, sleep, stress management, etc., and is used to identify risk factors for hair loss.

[0302] "Emotional data" refers to data about emotions obtained from a user's facial expressions, voice, or text, which is taken into account when making personalized suggestions.

[0303] "Image analysis" refers to the process of analyzing image data of the head and evaluating the amount of hair and the degree of exposure of the scalp.

[0304] "Risk factors" refer to factors that affect the progression of hair loss and are identified from lifestyle data and head images.

[0305] "Proposal content" refers to proposals for lifestyle improvement measures and care products formulated based on the results of image analysis, lifestyle data analysis, and emotional data.

[0306] "Server" means a remote computer system that receives, analyzes, and evaluates data sent by users.

[0307] "Terminal" means a device used by a user, such as a computer or smartphone, that acquires, transmits, and receives data.

[0308] "Analysis module" refers to a software component for analyzing image data and lifestyle habit data.

[0309] "Emotion Engine" means an algorithm or software for analyzing a user's emotional data and assessing their emotional state.

[0310] "Evaluation" refers to the process of quantifying or quantifying the progression of hair loss and risk factors based on the analysis results.

[0311] "Feedback" refers to the system's response and evaluation of the user's actions and reactions, and is used to reflect this in future suggestions.

[0312] The present invention relates to a system that acquires images of a user's head, collects and analyzes lifestyle data, and suggests lifestyle improvements based on the progression of hair thinning. The system is composed of a user terminal, a server, a software module that performs analysis and feedback, and an emotion engine.

[0313] User data collection

[0314] Head photography

[0315] The user launches the app and takes a photo by following the head guide. The device uses the camera function to acquire image data and saves it to local storage. This image data is then prepared for sending to the server. For the camera function, the device's standard camera app is used, and the camera is launched using an Intent. After the photo is taken, the image data is received by the onActivityResult method.

[0316] Survey responses

[0317] The user answers a questionnaire about their lifestyle and eating habits within the app. The device saves the entered information in JSON format and prepares to send it to the server along with the image data. The questionnaire UI is implemented using a mobile application development framework (e.g., Flutter or React Native).

[0318] Sending data

[0319] The image data and lifestyle habit data collected by the device are sent to the server using an HTTP POST request. Retrofit, for example, is used as the communication library.

[0320] Data processing and analysis

[0321] Image analysis

[0322] The image data received by the server is input into the image analysis module. Libraries such as OpenCV and TensorFlow are used for image analysis to evaluate the amount of hair on the head and the degree of exposed skin. The analysis results are stored in a database. Specifically, feature extraction is performed using a convolutional neural network (CNN).

[0323] Lifestyle data analysis

[0324] The server analyzes the lifestyle data and uses an AI model to identify risk factors for hair loss. The AI ​​model is trained using Scikit-learn and TensorFlow and generates a score for each risk factor. The analysis results are stored in a database.

[0325] Emotion analysis

[0326] The server uses an emotion engine to analyze the user's emotional data. Emotional data is obtained from facial expressions, voice, text, etc. NLP technologies such as Hugging Face's Transformers are used for this analysis. The emotional data obtained is analyzed in real time and the results are stored in a database.

[0327] Generate personalized suggestions

[0328] Proposal generation

[0329] The server combines the results of image analysis, lifestyle data analysis, and emotion analysis to generate recommendations for lifestyle improvements and skincare products that are optimal for the user. For example, it suggests dietary improvements or specific hair care products. This process may also incorporate rule-based systems.

[0330] Submit your proposal

[0331] The server sends the generated suggestions to the user's device either as a notification using Firebase Cloud Messaging (FCM) or as an in-app message.

[0332] User Feedback

[0333] Suggestion display

[0334] The device displays the suggestions received from the server within the app, allowing users to view the suggestions and incorporate them into their daily lives.

[0335] Recording of actions taken

[0336] The user implements the proposed improvements and records the results within the app. The device stores the execution data locally for the next data collection. Data is stored in a database such as SQLite.

[0337] Ongoing follow-up and feedback

[0338] Regular photo shoot

[0339] After a certain period of time, the user takes another photo of their head to acquire new data. The device saves the new image data in local storage and prepares to send it to the server.

[0340] Reevaluation

[0341] The server reanalyzes the new image data and lifestyle data, compares them with the previous data, and reassesssssments. The emotion engine reflects new feedback based on changes in the user's emotional state.

[0342] Feedback Updates

[0343] The server regenerates the latest improvement suggestions and sends them to the device. The device displays the latest suggestions to the user, supporting effective management. The emotion engine continuously analyzes the user's reactions and evaluates the effectiveness of the suggestions.

[0344] Specific examples and prompts for the generative AI model

[0345] As a specific example, a user uses an app to take a photo of their head, answer a questionnaire about their diet and exercise habits, and send the data to a server. The server analyzes the image and lifestyle data, generates suggestions, and sends them to the device after taking the user's emotional state into consideration using an emotion engine. The user confirms and implements the suggestions. After a certain period of time, the user takes another photo of their head and sends the updated data to the server. The server then reevaluates the results and provides new feedback.

[0346] An example of a prompt is as follows:

[0347] 1. "Assess the progression of hair loss using images of the user's head, while also taking into account questionnaire data about diet and lifestyle."

[0348] 2. "Analyze specific images and lifestyle data to suggest hair loss treatments that are suitable for the user. Also, reflect the user's emotional state."

[0349] 3. "Please analyze the new head image and the latest lifestyle data, and provide feedback on any improvements since the previous suggestion."

[0350] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0351] Step 1: Image the head

[0352] The user launches the app and takes a photo by following the photography guide on their head. The device acquires the image data using the camera function. Specifically, the device's camera app is launched using an Intent, and the image taken by the user is received by the onActivityResult method. This image data is then saved to local storage. The input is the image taken by the user, and the output is the image data saved in the device's local storage.

[0353] Step 2: Complete the survey

[0354] The user enters data about their lifestyle and eating habits into a questionnaire form within the app. The device saves this input data in JSON format and prepares to send it to the server along with image data. The input data is the lifestyle data entered by the user, and the output data is the lifestyle data saved in JSON format. The UI for the questionnaire form is developed using, for example, Flutter.

[0355] Step 3: Sending data

[0356] The image data and lifestyle habit data collected by the device are sent to the server. Specifically, an HTTP POST request is created and this data is included as the payload. The Retrofit library is used for this transmission. The input data is the image data and lifestyle habit data stored on the device, and the output data is the data sent to the server.

[0357] Step 4: Analyzing the image data

[0358] The image data received by the server is input into the image analysis module. Libraries such as OpenCV and TensorFlow are used for image analysis. The server evaluates the amount of hair on the head and the degree of exposed skin from the image data, and stores the analysis results in a database. The input data is the image data sent to the server, and the output data is the numerical data of the analysis results. Specifically, a convolutional neural network (CNN) extracts important features from the image.

[0359] Step 5: Analyze lifestyle data

[0360] The server analyzes the lifestyle data and uses an AI model to identify risk factors for hair loss. Scikit-learn and TensorFlow are used for this AI model. The server generates a score for each risk factor, which is also stored in a database. The input data is the lifestyle data sent to the server, and the output data is the risk factor score. Specifically, risk factors are scored using random forests and support vector machines (SVMs).

[0361] Step 6: Analyze the sentiment data

[0362] The server uses an emotion engine to analyze the user's facial expression data, voice data, or text data. Emotion data is acquired in real time using the smartphone's camera and microphone. NLP technologies such as Hugging Face Transformers are used for emotion analysis. The input data is the user's emotional data, and the output data is a score of the analyzed emotional state. Specifically, the server runs the facial expression recognition algorithm and voice analysis algorithm.

[0363] Step 7: Generate personalized suggestions

[0364] The server integrates the results of image analysis, lifestyle data analysis, and emotion analysis to generate recommendations for lifestyle improvements and care products that are optimal for the user. The recommendations consist of text and images generated by the server. The input data are the results of various analyses, and the output data are the recommendations. Specifically, a rule-based system or machine learning model automatically generates the recommendations based on the integrated data.

[0365] Step 8: Submit your proposal

[0366] The server sends the generated proposal to the user's device. The sending method is Firebase Cloud Messaging (FCM) or similar. The input data is the generated proposal, and the output data is the proposal displayed on the user's device. Specifically, a notification message in JSON format is created and sent via the FCM API.

[0367] Step 9: View and implement the proposal

[0368] The device will display the proposals received from the server within the app. The user will view the proposals and implement them as necessary. The input data will be the proposals sent from the server, and the output data will be the results of the implementation by the user. Specifically, we will develop a function to visualize the proposals in the app's UI.

[0369] Step 10: Recording the actions taken

[0370] The user implements the proposed improvement measures and records the results within the app. The device saves this execution data in local storage for the next analysis. The input data is the implementation data by the user, and the output data is the execution results saved on the device. Specifically, the data is managed using a database such as SQLite.

[0371] Step 11: Regular imaging and re-collection of data

[0372] After a certain period of time has passed, the user takes another photo of their head to obtain new data. The device saves the new image data and lifestyle habit data in local storage and prepares to send them to the server. The input data are the newly taken image and updated lifestyle habit data, and the output data is the new data sent to the server.

[0373] Step 12: Reassess and update feedback

[0374] The server reanalyzes the new image data and lifestyle data and compares it with the previous data for a reassessment. The emotion engine generates new feedback based on changes in the user's emotional state. The input data is the new data and the previous analysis results, and the output data is the updated feedback. Specifically, the new analysis results are saved in the database and the algorithm is rerun to update the suggestions.

[0375] Step 13: Send and view feedback

[0376] The server sends the updated feedback to the user's device, which then displays it. The input data is the feedback to be sent, and the output data is the displayed feedback. Specifically, the feedback is notified via FCM or other means, and a UI is implemented to display it within the app.

[0377] (Application example 2)

[0378] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0379] Conventional hair loss prevention systems simply analyze images of the head and collect and analyze lifestyle data, but are unable to provide suggestions based on the user's individual emotional state. This can make it difficult for users to accept the suggestions. Another drawback is that the suggestions are uniform and therefore not optimized for each user's situation.

[0380] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0381] In this invention, the server includes means for acquiring an image of the head, means for analyzing the acquired image data to evaluate the progression of hair thinning, means for collecting lifestyle data of the user, means for analyzing the collected lifestyle data and identifying risk factors for hair thinning, means for proposing appropriate lifestyle improvements and care products based on the progression of hair thinning and the risk factors, means for presenting the suggestions to the user, and means for analyzing the emotional state of the user and optimizing the suggestions. This makes it possible to make personalized suggestions based on the emotional state of each user, thereby improving the acceptance rate of suggestions and providing optimal improvement measures for each user.

[0382] The "means for acquiring an image of the head" refers to a device or software that allows a user to take an image of the head using an application and collect the image data.

[0383] The "means for analyzing acquired image data to evaluate the progression of hair thinning" refers to a device or software that analyzes captured head image data and quantifies or evaluates the progression of hair thinning, such as hair volume and the degree of scalp exposure.

[0384] The "means for collecting user lifestyle data" refers to a device or software that collects information about the user's lifestyle habits, such as diet, exercise, and sleep, through questionnaires or sensors.

[0385] The "means for analyzing collected lifestyle habit data and identifying risk factors for hair loss" refers to a device or software that analyzes collected lifestyle habit data and identifies risk factors that contribute to the progression of hair loss.

[0386] The "means for suggesting appropriate lifestyle improvements and care products" refers to a device or software that, based on the analysis results, makes suggestions to the user for improving their lifestyle and recommends care products to combat thinning hair.

[0387] The "means for presenting the proposed content to the user" refers to a device or software that notifies or displays the generated proposed content or recommendations on the user's terminal.

[0388] "Means for analyzing the user's emotional state and optimizing the content of suggestions" refers to a device or software that analyzes the user's facial expressions, voice, and text data to determine their emotional state, and then adjusts the content of suggestions in an optimal manner based on the results.

[0389] The embodiment of this invention is a system that acquires images of a user's head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair thinning. The system is composed of a user terminal, a server, a software module that performs analysis and feedback, and an emotion engine.

[0390] User data collection

[0391] Head photography

[0392] The smartphone, which acts as the user terminal, takes a picture of the user's head. The user launches the application and takes a picture of their head according to the specified guide. The acquired image data is stored in the smartphone's local storage and sent to the server.

[0393] Survey responses

[0394] Within the application, users answer a questionnaire about their lifestyle and eating habits, including questions about their diet, exercise habits, and stress. The questionnaire responses are stored on the smartphone and sent to a server along with image data.

[0395] Data processing and analysis

[0396] Image analysis

[0397] The server uses an image analysis library such as OpenCV to analyze the acquired image data. The image analysis module evaluates the amount of hair on the head and the degree of exposed skin, converts the results into numerical values, and stores them in a database.

[0398] Lifestyle data analysis

[0399] The server inputs the collected lifestyle data into a generative AI model such as TensorFlow to analyze the relationship between lifestyle habits and the progression of hair loss. This analysis identifies risk factors and generates a score for each factor. These scores are also stored in a database.

[0400] Emotion analysis

[0401] The emotion engine collects the user's facial expression, voice, and text data to analyze their emotional state. It captures emotional data in real time using the smartphone's camera and microphone and sends it to the server.

[0402] Generate personalized suggestions

[0403] Proposal generation

[0404] The server integrates the results of image analysis, lifestyle data analysis, and the emotional state calculated by the emotion engine. Based on the integrated data, it generates appropriate lifestyle improvement measures and skincare product recommendations. Specific examples of recommendations include "increasing protein in your diet," "the importance of stress management," and "using a specific shampoo." Taking into account the user's emotional state, the recommendations are adjusted to be most acceptable to the user.

[0405] Submit your proposal

[0406] The generated proposals are sent from the server to the user's device, where the smartphone application displays the received proposals and allows the user to confirm and implement them.

[0407] User Feedback

[0408] Suggestion display

[0409] The user device displays the suggestions received from the server within the application. The user can check the suggestions through the application and incorporate them into their daily lives. The emotion engine analyzes the user's reactions to the suggestions in real time and provides feedback.

[0410] Recording of actions taken

[0411] The user implements the suggested improvements and records the results within the application, and the smartphone stores these execution data locally for the next data collection.

[0412] Ongoing follow-up and feedback

[0413] Regular photo shoot

[0414] The user takes another photo of their head after a certain period of time to obtain new data. The smartphone stores the new image data locally and prepares to send it to the server.

[0415] Reevaluation

[0416] The server re-analyzes the new image data and lifestyle data, compares them with the previous data, and re-evaluates them. The emotion engine reflects new feedback based on changes in the user's emotional state.

[0417] Feedback Updates

[0418] The server regenerates appropriate improvement suggestions and sends them to the user's device. The smartphone displays the latest suggestions to the user, supporting effective management. The emotion engine continuously analyzes the user's reactions and evaluates the effectiveness of the suggestions.

[0419] Specific examples

[0420] For example, a user can use the application to take a photo of their head and answer a questionnaire about diet, exercise, and stress. This data is sent to a server, where the hair volume is analyzed, and lifestyle data is then analyzed by an AI model. Risk factors are identified, and suggestions such as "eat more protein" or "manage stress" are generated. These suggestions are optimized using sentiment analysis and presented to the user. If the user implements the suggestions and submits the data again after a certain period of time, continuous feedback and suggestions are provided.

[0421] Prompt Sentence Examples

[0422] "Analyze the user's head images and lifestyle data to identify hair volume status and associated risk factors."

[0423] "Generate dietary and lifestyle modification suggestions based on hair volume status and risk factors."

[0424] "Optimize your suggestions based on the user's emotional state."

[0425] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0426] Step 1:

[0427] Head photography using a user device

[0428] The user starts the smartphone app and takes a photo by following the head photography guide. The acquired image data is saved in local storage. The input is the captured image of the head, and the output is the image data saved in local storage.

[0429] Step 2:

[0430] Answers to lifestyle questionnaire

[0431] Users answer questionnaires presented within the application and enter information about their lifestyle habits, such as diet, exercise, and stress. This data is saved in local storage. The input is the lifestyle data entered by the user in the questionnaire form, and the output is the questionnaire data saved in local storage.

[0432] Step 3:

[0433] Sending data to the server

[0434] The smartphone device sends the saved image data and survey data to the server. The input is the image data and survey data saved in the local storage, and the output is the data sent to the server.

[0435] Step 4:

[0436] Image analysis

[0437] The server analyzes the received image data using an image analysis library such as OpenCV. Specifically, it quantifies the amount of hair and the degree of exposed skin, and stores the results in a database. The input is the transmitted image data, and the output is the numerical data resulting from the analysis.

[0438] Step 5:

[0439] Lifestyle data analysis

[0440] The server inputs the received survey data into a generative AI model such as TensorFlow to identify risk factors. The analysis results are converted into a numerical score for each risk factor and stored in a database. The input is the submitted survey data, and the output is the risk factor score.

[0441] Step 6:

[0442] Emotion analysis

[0443] The server analyzes the user's facial expression and voice data to evaluate their emotional state. The smartphone's camera and microphone are used to acquire real-time emotional data, and the evaluation results are sent to the server. The input is the emotional data acquired in real time, and the output is the evaluation result of the emotional state.

[0444] Step 7:

[0445] Proposal generation

[0446] The server integrates the results of image analysis, lifestyle data analysis, and emotion analysis to generate appropriate lifestyle improvement and care product recommendations. Suggestions might include, for example, "increase protein in your diet" or "importance of stress management." The input is the analysis results obtained in the previous step, and the output is the generated recommendations.

[0447] Step 8:

[0448] Submit your proposal

[0449] The server sends the generated proposal to the user's smartphone. The input is the proposal, and the output is the proposal sent to the user's device.

[0450] Step 9:

[0451] Viewing Proposals

[0452] The smartphone displays the received suggestions in the app so that the user can check them. The input is the suggestions sent from the server, and the output is the suggestions displayed in the app.

[0453] Step 10:

[0454] Recording of actions taken

[0455] The user implements the proposed improvements and records the results within the app. The smartphone stores the implementation data locally for the next data collection. The input is the implementation data of the improvements implemented by the user, and the output is the locally stored implementation data.

[0456] 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.

[0457] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0458] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0459] [Second embodiment]

[0460] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0461] 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.

[0462] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0463] 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.

[0464] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0465] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0466] 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.

[0467] 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.

[0468] 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 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.

[0469] 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.

[0470] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0471] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0472] As an embodiment of this invention, we will specifically explain a system that acquires images of a user's head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair loss. This system consists of a user terminal, a server, and a software module for analysis and feedback. The detailed operation of each element is shown below.

[0473] 1. User data collection

[0474] Head photography

[0475] The user launches the app and follows the instructions to take a photo of their head. The device stores this image data locally and prepares it for transmission to the server.

[0476] Survey responses

[0477] Users answer a questionnaire within the app and enter data about their lifestyle and eating habits, which are then collected by the device and sent to the server along with image data.

[0478] 2. Data Processing and Analysis

[0479] Image analysis

[0480] The image data received by the server is input into the image analysis module, which evaluates and quantifies the amount of hair on the user's head and the degree of exposed skin, and saves the analysis results.

[0481] Lifestyle data analysis

[0482] The server inputs lifestyle data into an AI model, which analyzes the relationship between lifestyle habits and the progression of hair loss. The AI ​​model then identifies risk factors and generates a score for each factor.

[0483] 3. Generating personalized suggestions

[0484] Proposal generation

[0485] The server combines the results of image analysis and lifestyle data analysis to generate optimal lifestyle improvement measures for the user, such as "increase protein in your diet," "importance of stress management," and "use a specific shampoo."

[0486] Submit your proposal

[0487] Once the suggestions are generated, the server sends them to the user's device, which displays them in the app for the user to review.

[0488] 4. User Feedback

[0489] Suggestion display

[0490] The device receives suggestions from the server and displays them within the app. The user checks the app and incorporates the suggestions into their daily lives.

[0491] Recording of actions taken

[0492] The user implements the suggestions and enters and records the results, which the device stores locally for the next data collection.

[0493] 5. Ongoing follow-up and feedback

[0494] Regular photo shoot

[0495] The user takes another photo of the head after a certain period of time, and the terminal prepares to send the new image data to the server.

[0496] Reevaluation

[0497] The server reanalyzes the new image data and lifestyle data and compares them with the previous proposal.

[0498] Feedback Updates

[0499] The server regenerates appropriate improvement suggestions and sends them to the terminal, which then displays the latest suggestions to the user, supporting effective management.

[0500] Specific examples

[0501] The user uses the app to take a photo of their head and answer a questionnaire about their diet and exercise habits. The device sends this information to a server, which analyzes the image and lifestyle data. The server generates suggestions such as "eat more protein" or "you need to manage your stress" and sends them to the device. The user confirms and implements these suggestions. After a certain period of time, the user takes another photo of their head and sends the latest data to the server. The server then reassesssssments and provides feedback on any improvements.

[0502] This allows users to effectively manage the progression of hair thinning and implement appropriate remedial measures.

[0503] The processing flow will be explained below.

[0504] Step 1:

[0505] The user launches the app and takes a picture of their head. The device stores the captured image data locally and prepares to send it to the server.

[0506] Step 2:

[0507] The user answers a questionnaire within the app. The questions consist of dietary habits, exercise habits, stress levels, etc. The device collects the questionnaire data and prepares to send it to the server along with the image data.

[0508] Step 3:

[0509] The device sends the captured image data and the survey data to the server, which receives the data and prepares it for analysis.

[0510] Step 4:

[0511] The server inputs the image data into an image analysis module, which evaluates the amount of hair on the head and the degree of exposed skin, converts the progress of hair loss into numerical values, and stores the results in a database.

[0512] Step 5:

[0513] The server inputs the lifestyle data it receives into an AI model, which analyzes the relationship between lifestyle habits and the progression of hair loss and identifies risk factors. Scores for these risk factors are also generated and stored in a database.

[0514] Step 6:

[0515] The server integrates the results of image analysis and lifestyle data analysis. Based on the integrated data, it generates appropriate lifestyle improvement measures and skincare product recommendations. For example, specific recommendations such as "increase protein in your diet," "importance of stress management," and "use a specific shampoo" are generated.

[0516] Step 7:

[0517] The server sends the generated proposal to the user's device, which then displays the proposal received in the app so that the user can confirm and implement it.

[0518] Step 8:

[0519] The user implements the suggested improvements and records the results within the app. The device stores these execution data locally and prepares them for the next analysis.

[0520] Step 9:

[0521] After a certain period of time has passed, the user takes another image of their head to acquire new data. The device stores the new image data locally and prepares to send it to the server.

[0522] Step 10:

[0523] The device sends new image data to the server, which receives the new data and compares it with the previous data for re-evaluation.

[0524] Step 11:

[0525] The server generates appropriate improvement suggestions again based on the results of the reevaluation. The new suggestions are sent to the user's device, which displays them in the app. This allows the user to check the latest suggestions and make continuous improvements.

[0526] Example 1

[0527] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0528] Conventional hair loss prevention systems have the problem of being difficult to provide personalized advice based on the user's lifestyle and environment. Furthermore, they lack the functionality to continuously record the results of implementing the recommendations and provide feedback, making long-term hair loss management difficult. Furthermore, there is no cycle of reevaluation and improvement suggestions, meaning users cannot always receive the latest advice.

[0529] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0530] In this invention, the server includes means for acquiring an image of the head, means for analyzing the acquired image data to evaluate the progression of hair thinning, means for collecting lifestyle data of the user, means for analyzing the collected lifestyle data and identifying risk factors for hair thinning, means for suggesting appropriate lifestyle improvements and hair care products based on the progression of hair thinning and the risk factors, means for presenting the suggestions to the user, means for recording the results of implementing the suggestions, and means for continuous follow-up and reassessment. This makes it possible to provide personalized advice based on the user's individual lifestyle, and provide continuous feedback and the latest improvement suggestions.

[0531] The "means for acquiring an image of the head" is a combination of hardware and software for photographing the user's head and capturing the image data.

[0532] The "means for analyzing acquired image data to assess the progression of hair loss" refers to an algorithm or program that analyzes the amount of hair and the degree of exposure of the scalp based on the captured image of the head, and quantifies and assesses the progression of hair loss.

[0533] The "means for collecting user lifestyle data" is an interface for collecting data on the user's lifestyle habits such as diet, exercise, and sleep using questionnaires, sensors, etc.

[0534] The "means of analyzing collected lifestyle data and identifying risk factors for hair loss" is a system that uses AI models and statistical methods to identify risk factors related to the progression of hair loss based on collected lifestyle data.

[0535] The "means for suggesting appropriate lifestyle improvements and hair care products based on the progression of hair thinning and risk factors" is a component that generates lifestyle improvement measures and appropriate hair care products customized for the user based on the results of image analysis and lifestyle data analysis.

[0536] The "means for presenting the proposal content to the user" is a system that has the function of transmitting the generated proposal content to the user's device and displaying it to the user through an app or other user interface.

[0537] The "means for recording the results of implementing the suggested content" is a function that allows the user to input the results after implementing the suggested lifestyle improvements or hair care products, and save them in a database or local storage for use in the next analysis.

[0538] The "means for continuous follow-up observation and re-evaluation" is a system that re-collects the user's head images and lifestyle data at regular intervals, compares them with the previous results, and makes new analyses and proposals.

[0539] This invention describes a system that acquires images of a user's head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair loss. This system consists of a device used by the user, a server that analyzes the data, and a software module for analysis and feedback.

[0540] First, the user launches the app and takes a photo of their head. The device stores the captured image data locally and prepares it for transmission to the server. At the same time, the user answers a questionnaire within the app and enters data about their lifestyle and eating habits into the device. This data is also collected by the device and sent to the server along with the image data.

[0541] The server inputs the received image data into an image analysis module (e.g., OpenCV or TensorFlow) to evaluate and quantify the amount of hair on the user's head and the degree of exposed scalp. The analysis results are stored on the server. Next, the server inputs the lifestyle data into an AI model (e.g., scikit-learn or TensorFlow) to analyze the relationship between lifestyle habits and the progression of hair loss. The AI ​​model identifies risk factors and generates a score for each factor.

[0542] The server then combines the image analysis results with the lifestyle data analysis results to generate optimal lifestyle improvement measures for the user. These include specific suggestions such as "increase protein in your diet," "importance of stress management," and "use a specific shampoo." The generated suggestions are sent from the server to the user's device and displayed within the app. The user can review the suggestions and incorporate them into their daily lives.

[0543] After implementing the suggestions, the user enters the results into the app, and the device stores this data locally. After a certain period of time, the user takes another photo of their head and sends the new image and input data to the server. The server reanalyzes the new data, compares it with the previous suggestions to evaluate their effectiveness, and generates new feedback. This allows for continuous follow-up and feedback.

[0544] As a specific example, a user uses an app to take a photo of their head and answer a questionnaire about their diet and exercise habits. The device sends this information to a server, which analyzes the image and lifestyle data. The server uses a generative AI model to generate suggestions such as "eat more protein" or "you need to manage your stress," which are sent to the device. The user confirms and implements these suggestions. After a certain period of time, the user takes another photo of their head and sends the new data to the server. The server then reevaluates and provides feedback on any improvements.

[0545] Example prompt sentence:

[0546] Analyze data on the user's hair loss progression and suggest lifestyle improvements. Generate specific advice based on the user's dietary data and the results of head image analysis.

[0547] In this way, the present invention allows users to effectively manage the progression of hair thinning and implement appropriate remedial measures based on their individual lifestyle habits.

[0548] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0549] Step 1:

[0550] The user launches the app and takes a photo of their head.

[0551] Input: User's head image

[0552] Operation: The device activates the camera function and captures an image of the user's head. The captured image data is stored locally and prepared for transmission to the server.

[0553] Output: locally saved head image data

[0554] Step 2:

[0555] Users answer a questionnaire within the app and enter data about their lifestyle and eating habits.

[0556] Input: Lifestyle data (diet, exercise, sleep, etc.)

[0557] How it works: Users fill out survey forms within the app and their devices collect this data.

[0558] Output: Locally saved lifestyle data

[0559] Step 3:

[0560] The device sends the locally stored image data and lifestyle habit data to a server.

[0561] Input: Head image data, lifestyle data

[0562] Operation: The device communicates over the network to send the stored image data and lifestyle habit data to the server.

[0563] Output: Image data and lifestyle data sent to the server

[0564] Step 4:

[0565] The image data received by the server is input into the image analysis module, where analysis is performed.

[0566] Input: Head image data

[0567] Operation: The server uses an image analysis module (e.g., OpenCV, TensorFlow) to analyze image data and evaluate and quantify the amount of hair on the head and the degree of exposed skin.

[0568] Output: Analysis results (evaluation values ​​for hair volume and scalp exposure)

[0569] Step 5:

[0570] The lifestyle data received by the server is input into an AI model, which analyzes the relationship between lifestyle habits and the progression of hair loss.

[0571] Input: Lifestyle data

[0572] How it works: The server uses AI models (e.g., scikit-learn, TensorFlow) to analyze lifestyle data to identify risk factors and generate a score for each factor.

[0573] Output: Risk factor scoring results

[0574] Step 6:

[0575] The server integrates the results of image analysis and lifestyle data analysis to generate optimal lifestyle improvement measures for the user.

[0576] Input: Image analysis results, risk factor scores

[0577] How it works: Based on the results of image analysis and lifestyle data analysis, the server uses a generative AI model to generate appropriate lifestyle improvement measures and hair care products. For example, it generates recommendations such as "take a certain amount of protein," "stress management is necessary," and "use a specific shampoo."

[0578] Output: Generated lifestyle improvement measures and hair care product suggestions

[0579] Step 7:

[0580] Once the proposal is generated, the server sends it to the user's device.

[0581] Input: Lifestyle improvement measures and hair care product suggestions

[0582] Operation: The server sends the generated proposal to the user's device and notifies them.

[0583] Output: Suggestions sent to the device

[0584] Step 8:

[0585] The device receives suggestions from the server and displays them within the app. The user can then review the suggestions and incorporate them into their daily lives.

[0586] Input: Received proposal

[0587] Operation: The device displays the suggestion on the app screen and notifies the user.

[0588] Output: Suggestions displayed on the app screen

[0589] Step 9:

[0590] Users implement the suggested lifestyle changes and hair care products and enter their results into the app, which stores them locally on the device.

[0591] Input: Proposal execution result

[0592] How it works: The user enters their thoughts and results after implementing the suggestions into the app, which is then saved locally on the device.

[0593] Output: Locally saved proposal execution results

[0594] Step 10:

[0595] After a certain period of time, the user takes another photo of the head and sends the new image data to the server.

[0596] Input: New head image data

[0597] How it works: The user takes another photo of their head, and the device stores the new image data locally and sends it to the server at intervals.

[0598] Output: New head image data sent to the server

[0599] Step 11:

[0600] The server re-analyzes the new image data and lifestyle data, compares them with the results of the previous analysis, evaluates the effectiveness, and generates new feedback.

[0601] Input: New head image data, recollected lifestyle data

[0602] How it works: The server analyzes the images and lifestyle data again, compares them with the previous analysis results, and evaluates any changes. It then generates appropriate feedback.

[0603] Output: New feedback

[0604] Through this series of steps, users can continuously manage the progression of hair loss and implement appropriate improvements based on their individual lifestyle habits.

[0605] (Application example 1)

[0606] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0607] Conventional hair thinning treatment systems have difficulty in providing consistent progress monitoring and feedback to individual users. Furthermore, because the recommendations are not based on individual lifestyle habits or risk factors, they are unable to provide effective improvement measures. Furthermore, when used in physical salons, there is an issue of information not being shared smoothly between salon staff and customers. To solve these issues, a system is needed that provides continuous and personalized feedback and supports use in physical salons and other such stores.

[0608] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0609] In this invention, the server includes means for acquiring an image of the head, means for analyzing the acquired image data to evaluate the progress of hair thinning, means for collecting lifestyle habit data of the user, means for analyzing the collected lifestyle habit data and identifying risk factors for hair thinning, means for proposing appropriate lifestyle improvements and care products based on the progress of hair thinning and the risk factors, means for presenting the suggestions to the user, means for periodically acquiring and reanalyzing the user's progress observation data, means for updating the suggestions based on the reanalysis results, and means for sharing the suggestions between staff terminals and customer terminals in a physical store such as a beauty salon. This allows for personalized feedback to be provided to each user, and for staff and customers to share information and implement effective measures against hair thinning.

[0610] The "means for acquiring an image of the head" is a device that takes a photograph of the user's head using a digital camera or a smartphone camera and acquires the image data.

[0611] The "means for analyzing acquired image data to evaluate the progression of hair thinning" is software that uses acquired image data of the head to analyze the amount of hair and the degree of exposure of the scalp, and numerically evaluates the progression of hair thinning.

[0612] The "means for collecting user lifestyle data" is a device that collects information about the user's diet, exercise, stress, and other daily habits through questionnaires or sensors.

[0613] The "means for analyzing collected lifestyle data and identifying risk factors for hair loss" is software that analyzes collected lifestyle data of users and identifies risk factors that affect the progression of hair loss.

[0614] The "means for suggesting appropriate lifestyle improvements and care products" is software that recommends specific lifestyle improvements and care products to users based on the progression of hair loss and risk factors.

[0615] The "means for presenting the proposal content to the user" is a device that transmits the generated proposal content to the user's terminal and displays it.

[0616] The "means for periodically acquiring and reanalyzing the user's follow-up observation data" is a device that allows the user to take another photograph of the head after a certain period of time, collect the photograph together with the latest lifestyle habit data, and perform reanalysis.

[0617] The "means for updating the content of the proposals based on the reanalysis results" is software that regenerates and updates the proposals for optimal lifestyle improvements and care products based on the reanalyzed data.

[0618] A "means for sharing proposal content between staff terminals and customer terminals in a physical store such as a beauty salon" is a device that can send generated proposal content to staff terminals and customer terminals in the physical store and share information.

[0619] As an embodiment of this invention, we will specifically explain a system that acquires images of the head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair thinning. This system consists of a user terminal, a server, and a software module for analysis and feedback. In particular, it is intended for use in brick-and-mortar stores such as beauty salons.

[0620] 1. User data collection

[0621] Head photography

[0622] Users visit a hair salon and take a photo of their head using a smartphone app. This image data is immediately uploaded to a cloud server. The image of the head is used for image analysis to evaluate the user's hair volume and the degree of exposed skin.

[0623] Survey responses

[0624] Customers fill out a questionnaire about their lifestyle habits on a tablet device at the salon, and this data is also sent to the cloud server.

[0625] 2. Data Processing and Analysis

[0626] Image analysis

[0627] The server analyzes the image data uploaded to the cloud using OpenCV. Specifically, it detects the head using cv2.CascadeClassifier and evaluates the amount of hair and the degree of exposed skin within the detected area using a TensorFlow model.

[0628] Lifestyle data analysis

[0629] The questionnaire data answered by the user is input into the AI ​​model, which analyzes the relationship between lifestyle habits and the progression of hair loss, identifies risk factors, and generates a score for each factor.

[0630] 3. Generating personalized suggestions

[0631] Proposal generation

[0632] The results of image analysis and lifestyle data analysis are integrated to generate optimal lifestyle improvement measures, such as specific suggestions such as "eat a diet high in protein" or "stress management is necessary."

[0633] Submit and share your suggestions

[0634] The server sends the proposed content to the user's terminal and the salon staff terminal, allowing the salon staff to provide specific advice to the user.

[0635] 4. Follow-up and feedback

[0636] Regular imaging and reassessment

[0637] The user returns to the salon after a certain period of time and takes a new photo of their head, which is again sent to the server for re-analysis.

[0638] Feedback Updates

[0639] Based on the latest analysis results, the proposals are updated and sent to the user's terminal and the salon staff terminal.

[0640] Specific examples

[0641] When a customer visits a beauty salon, they take a photo of their head using a smartphone app. They then answer a questionnaire about their lifestyle habits on a tablet device. This data is sent to a cloud server. After analyzing the image and lifestyle data, the server generates recommendations such as "increase protein intake" or "strengthen stress management." These recommendations are shared with both the customer and the salon staff, allowing them to take specific actions.

[0642] Prompt Sentence Examples

[0643] "Develop an app for a hair salon that takes a photo of the user's head, sends it to a cloud server, and analyzes the results of a lifestyle questionnaire to generate suggestions for preventing hair loss."

[0644] In this way, the system provides personalized feedback to each user, enabling salon staff and customers to share information and implement effective measures to combat hair loss.

[0645] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0646] Step 1:

[0647] Acquiring a user's head image

[0648] When a user visits a beauty salon, the salon staff takes a photo of the user's head using a dedicated smartphone app. This image data is saved in the smartphone's local storage and sent to a cloud server. The input is the user's head image data, and the output is the transmission of the image data to the cloud server.

[0649] Step 2:

[0650] Collection of lifestyle data

[0651] Users answer a questionnaire about their lifestyle habits on a tablet device at a beauty salon. Questions include, "How many times a week do you exercise?" and "How many times a day do you eat?" This data is also sent to the cloud server. The input is the user's questionnaire response data, and the output is the transmission of lifestyle habit data to the cloud server.

[0652] Step 3:

[0653] Image data analysis

[0654] The server analyzes the image data uploaded to the cloud using OpenCV. Specifically, the server detects the head using cv2.CascadeClassifier, and then evaluates the hair volume and the degree of exposed skin using a TensorFlow model. The input is the image data of the head, and the output is the evaluation results of the hair volume and the degree of exposed skin.

[0655] Step 4:

[0656] Lifestyle data analysis

[0657] The server inputs the collected lifestyle data into an AI model and analyzes the relationship between lifestyle and the progression of hair loss. The server identifies risk factors and generates a score for each factor. The input is lifestyle data, and the output is the identification of risk factors and the generation of a score.

[0658] Step 5:

[0659] Proposal generation

[0660] The server integrates the results of image analysis and lifestyle data analysis to generate optimal lifestyle improvement measures and care product recommendations. For example, suggestions include "eat a diet high in protein" and "stress management is important." The inputs are the image analysis results and lifestyle data analysis results, and the output is lifestyle improvement measures and care product recommendations.

[0661] Step 6:

[0662] Submit and share your suggestions

[0663] The server sends the generated suggestions to the user's terminal and the salon staff terminal, allowing the salon staff to provide specific advice to the user. The input is the generated suggestions, and the output is the transmission of the suggestions to the user's terminal and the salon staff terminal.

[0664] Step 7:

[0665] Regular acquisition of follow-up data

[0666] The user returns to the salon after a certain period of time and takes another photo of their head. This new image data is also sent to the cloud server. The input is the new head image data, and the output is the transmission of the image data to the cloud server.

[0667] Step 8:

[0668] Reassessment and feedback updates

[0669] The server reanalyzes the new image data and lifestyle data and compares it with the previous data. It evaluates how effective the original suggestions were and generates new suggestions. The updated suggestions are sent to the user's device and the salon staff's device. The input is the new image data and lifestyle data, and the output is the transmission of the updated suggestions.

[0670] This allows the entire process to work seamlessly to provide effective hair loss treatment to the user.

[0671] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0672] As an embodiment of this invention, we will specifically explain a system that acquires images of a user's head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair loss. This system consists of a user terminal, a server, a software module that performs analysis and feedback, and an emotion engine. The detailed operation of each element is shown below.

[0673] User data collection

[0674] Head photography

[0675] The user launches the app and takes a photo by following the head guide. The device stores this image data locally and prepares it for transmission to the server.

[0676] Survey responses

[0677] Users answer a questionnaire within the app and enter data about their lifestyle and eating habits, which are then collected by the device and sent to the server along with image data.

[0678] Data processing and analysis

[0679] Image analysis

[0680] The server inputs the received image data into an image analysis module, which evaluates the amount of hair on the head and the degree of exposed skin, converts the progress of hair loss into numerical values, and stores the results in a database.

[0681] Lifestyle data analysis

[0682] The server inputs lifestyle data into the AI ​​model, which analyzes the relationship between lifestyle and the progression of hair loss. The AI ​​model identifies risk factors, generates a score for each factor, and stores it in a database.

[0683] Emotion analysis

[0684] The emotion engine collects the user's facial expression data, voice data, or text data and analyzes the user's emotional state. For example, the emotion data is acquired in real time using the smartphone's camera and microphone and sent to the emotion engine.

[0685] Generate personalized suggestions

[0686] Proposal generation

[0687] The server integrates the results of image analysis, lifestyle data analysis, and the emotional state of the user based on the emotion engine. Based on the integrated data, it generates appropriate lifestyle improvement and skincare recommendations. For example, recommendations include "increase protein in your diet," "importance of stress management," and "use a specific shampoo." The recommendations are adjusted based on the user's emotional state and presented in the most acceptable format for the user.

[0688] Submit your proposal

[0689] The server sends the generated proposal to the user's device, which then displays the proposal received in the app so that the user can confirm and implement it.

[0690] User Feedback

[0691] Suggestion display

[0692] The device receives the suggestions from the server and displays them within the app. The user checks the app and incorporates the suggestions into their daily lives. The emotion engine analyzes the user's reaction to the suggestions in real time and provides feedback.

[0693] Recording of actions taken

[0694] The user implements the suggested improvements and records the results within the app, and the device stores these execution data locally for the next data collection.

[0695] Ongoing follow-up and feedback

[0696] Regular photo shoot

[0697] The user takes another photo of their head after a certain period of time to obtain new data. The device stores the new image data locally and prepares to send it to the server.

[0698] Reevaluation

[0699] The server reanalyzes the new image data and lifestyle data, compares them with the previous data, and reassesssssments. The emotion engine reflects new feedback based on changes in the user's emotional state.

[0700] Feedback Updates

[0701] The server regenerates appropriate improvement suggestions and sends them to the device. The device displays the latest suggestions to the user, supporting effective management. The emotion engine continuously analyzes the user's reactions and evaluates the effectiveness of the suggestions.

[0702] As a specific example, a user uses an app to take a photo of their head and answer a questionnaire about their diet and exercise habits. The device sends this information to a server, which analyzes the image and lifestyle data. The server generates suggestions such as "eat more protein" or "you need to manage your stress," and sends them to the device in an optimal form that takes into account the user's emotional state using an emotion engine. The user confirms and implements these suggestions. After a certain period of time, the user takes another photo of their head and sends the latest data to the server. The server then re-evaluates and provides feedback on any improvements. The emotion engine continuously analyzes the user's emotional state and provides optimal feedback.

[0703] The processing flow will be explained below.

[0704] Step 1:

[0705] The user launches the app and takes an image of their head. The device stores this image data locally and prepares it for transmission to the server.

[0706] Step 2:

[0707] The user answers a questionnaire within the app, which asks about their eating habits, exercise habits, and stress levels. The device collects the questionnaire data and prepares to send it to the server along with the image data.

[0708] Step 3:

[0709] The device sends the captured image data and the survey data to the server, which receives the data and prepares it for analysis.

[0710] Step 4:

[0711] The server inputs the image data into an image analysis module, which evaluates the amount of hair on the head and the degree of exposed skin, converts the progress of hair loss into numerical values, and stores the results in a database.

[0712] Step 5:

[0713] The server inputs the lifestyle data it receives into an AI model, which analyzes the relationship between lifestyle habits and the progression of hair loss and identifies risk factors. Scores for these risk factors are also generated and stored in a database.

[0714] Step 6:

[0715] The emotion engine collects the user's facial expression data, voice data, or text data and analyzes the user's emotional state. For example, the emotion data is acquired in real time using a camera and a microphone and sent to the emotion engine.

[0716] Step 7:

[0717] The server integrates the results of image analysis, lifestyle data analysis, and the emotional state of the user based on the emotion engine. Based on the integrated data, it generates appropriate lifestyle improvement and skincare product recommendations. Examples include "increase protein in your diet," "importance of stress management," and "use a specific shampoo." The recommendations are adjusted based on the user's emotional state and presented in the most acceptable format for the user.

[0718] Step 8:

[0719] The server sends the generated proposal to the user's device, which then displays the proposal received in the app so that the user can confirm and implement it.

[0720] Step 9:

[0721] The user implements the suggested improvements and records the results within the app, and the device stores these execution data locally for the next data collection.

[0722] Step 10:

[0723] After a certain period of time has passed, the user takes another image of their head to acquire new data. The device stores the new image data locally and prepares to send it to the server.

[0724] Step 11:

[0725] The device sends new image data to the server, which receives the new data and compares it with the previous data for re-evaluation. This allows the progress of hair loss and the effectiveness of treatment to be monitored.

[0726] Step 12:

[0727] The server generates appropriate improvement suggestions again based on the results of the reevaluation. The new suggestions are sent to the device, which displays them within the app. This allows the user to check the latest suggestions and make continuous improvements.

[0728] Step 13:

[0729] The emotion engine analyzes the user's emotional state in real time and provides feedback: when the user expresses an emotional reaction to a suggestion, the emotion engine processes this and adjusts the suggestion as needed.

[0730] As a specific example, a user uses an app to take a photo of their head and answer a questionnaire about their diet and exercise habits. The device sends this information to a server, which analyzes the image and lifestyle data. The server generates suggestions such as "eat more protein" or "you need to manage your stress," and sends them to the device in an optimal form that takes into account the user's emotional state using an emotion engine. The user confirms and implements these suggestions. After a certain period of time, the user takes another photo of their head and sends the latest data to the server. The server then re-evaluates and provides feedback on any improvements. The emotion engine continuously analyzes the user's emotional state and provides optimal feedback.

[0731] Example 2

[0732] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0733] Many people today suffer from thinning hair and are searching for effective ways to slow its progression and improve it. However, existing methods are unable to provide personalized recommendations that take into account individual lifestyle habits and emotional states, limiting their effectiveness. Furthermore, there is a lack of systems that continuously track users' responses and provide feedback. Therefore, there is a need to propose a system that analyzes a user's head images, lifestyle habits, and emotional data, and integrates them to provide optimal solutions to improve hair loss more effectively.

[0734] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring an image of the user's head, means for analyzing the acquired image data to evaluate the progression of hair thinning, means for collecting the user's lifestyle data, means for analyzing the collected lifestyle data and identifying risk factors for hair thinning, means for collecting and analyzing the user's emotional data, means for suggesting appropriate lifestyle improvements and care products based on the progression of hair thinning, risk factors, and emotional data, means for presenting the suggestions to the user, and means for recording the user's responses and continuously analyzing the data. This makes it possible to provide personalized improvement measures tailored to the user's individual situation and effectively suppress the progression of hair thinning.

[0735] "User" refers to an individual who uses the system to take a head image and input lifestyle data.

[0736] "Head image" means photographic data of the user's head, and is primarily used to evaluate the condition of the hair.

[0737] "Lifestyle data" includes information about a user's diet, exercise, sleep, stress management, etc., and is used to identify risk factors for hair loss.

[0738] "Emotional data" refers to data about emotions obtained from a user's facial expressions, voice, or text, which is taken into account when making personalized suggestions.

[0739] "Image analysis" refers to the process of analyzing image data of the head and evaluating the amount of hair and the degree of exposure of the scalp.

[0740] "Risk factors" refer to factors that affect the progression of hair loss and are identified from lifestyle data and head images.

[0741] "Proposal content" refers to proposals for lifestyle improvement measures and care products formulated based on the results of image analysis, lifestyle data analysis, and emotional data.

[0742] "Server" means a remote computer system that receives, analyzes, and evaluates data sent by users.

[0743] "Terminal" means a device used by a user, such as a computer or smartphone, that acquires, transmits, and receives data.

[0744] "Analysis module" refers to a software component for analyzing image data and lifestyle habit data.

[0745] "Emotion Engine" means an algorithm or software for analyzing a user's emotional data and assessing their emotional state.

[0746] "Evaluation" refers to the process of quantifying or quantifying the progression of hair loss and risk factors based on the analysis results.

[0747] "Feedback" refers to the system's response and evaluation of the user's actions and reactions, and is used to reflect this in future suggestions.

[0748] The present invention relates to a system that acquires images of a user's head, collects and analyzes lifestyle data, and suggests lifestyle improvements based on the progression of hair thinning. The system is composed of a user terminal, a server, a software module that performs analysis and feedback, and an emotion engine.

[0749] User data collection

[0750] Head photography

[0751] The user launches the app and takes a photo by following the head guide. The device uses the camera function to acquire image data and saves it to local storage. This image data is then prepared for sending to the server. For the camera function, the device's standard camera app is used, and the camera is launched using an Intent. After the photo is taken, the image data is received by the onActivityResult method.

[0752] Survey responses

[0753] The user answers a questionnaire about their lifestyle and eating habits within the app. The device saves the entered information in JSON format and prepares to send it to the server along with the image data. The questionnaire UI is implemented using a mobile application development framework (e.g., Flutter or React Native).

[0754] Sending data

[0755] The image data and lifestyle habit data collected by the device are sent to the server using an HTTP POST request. Retrofit, for example, is used as the communication library.

[0756] Data processing and analysis

[0757] Image analysis

[0758] The image data received by the server is input into the image analysis module. Libraries such as OpenCV and TensorFlow are used for image analysis to evaluate the amount of hair on the head and the degree of exposed skin. The analysis results are stored in a database. Specifically, feature extraction is performed using a convolutional neural network (CNN).

[0759] Lifestyle data analysis

[0760] The server analyzes the lifestyle data and uses an AI model to identify risk factors for hair loss. The AI ​​model is trained using Scikit-learn and TensorFlow and generates a score for each risk factor. The analysis results are stored in a database.

[0761] Emotion analysis

[0762] The server uses an emotion engine to analyze the user's emotional data. Emotional data is obtained from facial expressions, voice, text, etc. NLP technologies such as Hugging Face's Transformers are used for this analysis. The emotional data obtained is analyzed in real time and the results are stored in a database.

[0763] Generate personalized suggestions

[0764] Proposal generation

[0765] The server combines the results of image analysis, lifestyle data analysis, and emotion analysis to generate recommendations for lifestyle improvements and skincare products that are optimal for the user. For example, it suggests dietary improvements or specific hair care products. This process may also incorporate rule-based systems.

[0766] Submit your proposal

[0767] The server sends the generated suggestions to the user's device either as a notification using Firebase Cloud Messaging (FCM) or as an in-app message.

[0768] User Feedback

[0769] Suggestion display

[0770] The device displays the suggestions received from the server within the app, allowing users to view the suggestions and incorporate them into their daily lives.

[0771] Recording of actions taken

[0772] The user implements the proposed improvements and records the results within the app. The device stores the execution data locally for the next data collection. Data is stored in a database such as SQLite.

[0773] Ongoing follow-up and feedback

[0774] Regular photo shoot

[0775] After a certain period of time, the user takes another photo of their head to acquire new data. The device saves the new image data in local storage and prepares to send it to the server.

[0776] Reevaluation

[0777] The server reanalyzes the new image data and lifestyle data, compares them with the previous data, and reassesssssments. The emotion engine reflects new feedback based on changes in the user's emotional state.

[0778] Feedback Updates

[0779] The server regenerates the latest improvement suggestions and sends them to the device. The device displays the latest suggestions to the user, supporting effective management. The emotion engine continuously analyzes the user's reactions and evaluates the effectiveness of the suggestions.

[0780] Specific examples and prompts for the generative AI model

[0781] As a specific example, a user uses an app to take a photo of their head, answer a questionnaire about their diet and exercise habits, and send the data to a server. The server analyzes the image and lifestyle data, generates suggestions, and sends them to the device after taking the user's emotional state into consideration using an emotion engine. The user confirms and implements the suggestions. After a certain period of time, the user takes another photo of their head and sends the updated data to the server. The server then reevaluates the results and provides new feedback.

[0782] An example of a prompt is as follows:

[0783] 1. "Assess the progression of hair loss using images of the user's head, while also taking into account questionnaire data about diet and lifestyle."

[0784] 2. "Analyze specific images and lifestyle data to suggest hair loss treatments that are suitable for the user. Also, reflect the user's emotional state."

[0785] 3. "Please analyze the new head image and the latest lifestyle data, and provide feedback on any improvements since the previous suggestion."

[0786] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0787] Step 1: Image the head

[0788] The user launches the app and takes a photo by following the photography guide on their head. The device acquires the image data using the camera function. Specifically, the device's camera app is launched using an Intent, and the image taken by the user is received by the onActivityResult method. This image data is then saved to local storage. The input is the image taken by the user, and the output is the image data saved in the device's local storage.

[0789] Step 2: Complete the survey

[0790] The user enters data about their lifestyle and eating habits into a questionnaire form within the app. The device saves this input data in JSON format and prepares to send it to the server along with image data. The input data is the lifestyle data entered by the user, and the output data is the lifestyle data saved in JSON format. The UI for the questionnaire form is developed using, for example, Flutter.

[0791] Step 3: Sending data

[0792] The image data and lifestyle habit data collected by the device are sent to the server. Specifically, an HTTP POST request is created and this data is included as the payload. The Retrofit library is used for this transmission. The input data is the image data and lifestyle habit data stored on the device, and the output data is the data sent to the server.

[0793] Step 4: Analyzing the image data

[0794] The image data received by the server is input into the image analysis module. Libraries such as OpenCV and TensorFlow are used for image analysis. The server evaluates the amount of hair on the head and the degree of exposed skin from the image data, and stores the analysis results in a database. The input data is the image data sent to the server, and the output data is the numerical data of the analysis results. Specifically, a convolutional neural network (CNN) extracts important features from the image.

[0795] Step 5: Analyze lifestyle data

[0796] The server analyzes the lifestyle data and uses an AI model to identify risk factors for hair loss. Scikit-learn and TensorFlow are used for this AI model. The server generates a score for each risk factor, which is also stored in a database. The input data is the lifestyle data sent to the server, and the output data is the risk factor score. Specifically, risk factors are scored using random forests and support vector machines (SVMs).

[0797] Step 6: Analyze the sentiment data

[0798] The server uses an emotion engine to analyze the user's facial expression data, voice data, or text data. Emotion data is acquired in real time using the smartphone's camera and microphone. NLP technologies such as Hugging Face Transformers are used for emotion analysis. The input data is the user's emotional data, and the output data is a score of the analyzed emotional state. Specifically, the server runs the facial expression recognition algorithm and voice analysis algorithm.

[0799] Step 7: Generate personalized suggestions

[0800] The server integrates the results of image analysis, lifestyle data analysis, and emotion analysis to generate recommendations for lifestyle improvements and care products that are optimal for the user. The recommendations consist of text and images generated by the server. The input data are the results of various analyses, and the output data are the recommendations. Specifically, a rule-based system or machine learning model automatically generates the recommendations based on the integrated data.

[0801] Step 8: Submit your proposal

[0802] The server sends the generated proposal to the user's device. The sending method is Firebase Cloud Messaging (FCM) or similar. The input data is the generated proposal, and the output data is the proposal displayed on the user's device. Specifically, a notification message in JSON format is created and sent via the FCM API.

[0803] Step 9: View and implement the proposal

[0804] The device will display the proposals received from the server within the app. The user will view the proposals and implement them as necessary. The input data will be the proposals sent from the server, and the output data will be the results of the implementation by the user. Specifically, we will develop a function to visualize the proposals in the app's UI.

[0805] Step 10: Recording the actions taken

[0806] The user implements the proposed improvement measures and records the results within the app. The device saves this execution data in local storage for the next analysis. The input data is the implementation data by the user, and the output data is the execution results saved on the device. Specifically, the data is managed using a database such as SQLite.

[0807] Step 11: Regular imaging and re-collection of data

[0808] After a certain period of time has passed, the user takes another photo of their head to obtain new data. The device saves the new image data and lifestyle habit data in local storage and prepares to send them to the server. The input data are the newly taken image and updated lifestyle habit data, and the output data is the new data sent to the server.

[0809] Step 12: Reassess and update feedback

[0810] The server reanalyzes the new image data and lifestyle data and compares it with the previous data for a reassessment. The emotion engine generates new feedback based on changes in the user's emotional state. The input data is the new data and the previous analysis results, and the output data is the updated feedback. Specifically, the new analysis results are saved in the database and the algorithm is rerun to update the suggestions.

[0811] Step 13: Send and view feedback

[0812] The server sends the updated feedback to the user's device, which then displays it. The input data is the feedback to be sent, and the output data is the displayed feedback. Specifically, the feedback is notified via FCM or other means, and a UI is implemented to display it within the app.

[0813] (Application example 2)

[0814] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0815] Conventional hair loss prevention systems simply analyze images of the head and collect and analyze lifestyle data, but are unable to provide suggestions based on the user's individual emotional state. This can make it difficult for users to accept the suggestions. Another drawback is that the suggestions are uniform and therefore not optimized for each user's situation.

[0816] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0817] In this invention, the server includes means for acquiring an image of the head, means for analyzing the acquired image data to evaluate the progression of hair thinning, means for collecting lifestyle data of the user, means for analyzing the collected lifestyle data and identifying risk factors for hair thinning, means for proposing appropriate lifestyle improvements and care products based on the progression of hair thinning and the risk factors, means for presenting the suggestions to the user, and means for analyzing the emotional state of the user and optimizing the suggestions. This makes it possible to make personalized suggestions based on the emotional state of each user, thereby improving the acceptance rate of suggestions and providing optimal improvement measures for each user.

[0818] The "means for acquiring an image of the head" refers to a device or software that allows a user to take an image of the head using an application and collect the image data.

[0819] The "means for analyzing acquired image data to evaluate the progression of hair thinning" refers to a device or software that analyzes captured head image data and quantifies or evaluates the progression of hair thinning, such as hair volume and the degree of scalp exposure.

[0820] The "means for collecting user lifestyle data" refers to a device or software that collects information about the user's lifestyle habits, such as diet, exercise, and sleep, through questionnaires or sensors.

[0821] The "means for analyzing collected lifestyle habit data and identifying risk factors for hair loss" refers to a device or software that analyzes collected lifestyle habit data and identifies risk factors that contribute to the progression of hair loss.

[0822] The "means for suggesting appropriate lifestyle improvements and care products" refers to a device or software that, based on the analysis results, makes suggestions to the user for improving their lifestyle and recommends care products to combat thinning hair.

[0823] The "means for presenting the proposed content to the user" refers to a device or software that notifies or displays the generated proposed content or recommendations on the user's terminal.

[0824] "Means for analyzing the user's emotional state and optimizing the content of suggestions" refers to a device or software that analyzes the user's facial expressions, voice, and text data to determine their emotional state, and then adjusts the content of suggestions in an optimal manner based on the results.

[0825] The embodiment of this invention is a system that acquires images of a user's head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair thinning. The system is composed of a user terminal, a server, a software module that performs analysis and feedback, and an emotion engine.

[0826] User data collection

[0827] Head photography

[0828] The smartphone, which acts as the user terminal, takes a picture of the user's head. The user launches the application and takes a picture of their head according to the specified guide. The acquired image data is stored in the smartphone's local storage and sent to the server.

[0829] Survey responses

[0830] Within the application, users answer a questionnaire about their lifestyle and eating habits, including questions about their diet, exercise habits, and stress. The questionnaire responses are stored on the smartphone and sent to a server along with image data.

[0831] Data processing and analysis

[0832] Image analysis

[0833] The server uses an image analysis library such as OpenCV to analyze the acquired image data. The image analysis module evaluates the amount of hair on the head and the degree of exposed skin, converts the results into numerical values, and stores them in a database.

[0834] Lifestyle data analysis

[0835] The server inputs the collected lifestyle data into a generative AI model such as TensorFlow to analyze the relationship between lifestyle habits and the progression of hair loss. This analysis identifies risk factors and generates a score for each factor. These scores are also stored in a database.

[0836] Emotion analysis

[0837] The emotion engine collects the user's facial expression, voice, and text data to analyze their emotional state. It captures emotional data in real time using the smartphone's camera and microphone and sends it to the server.

[0838] Generate personalized suggestions

[0839] Proposal generation

[0840] The server integrates the results of image analysis, lifestyle data analysis, and the emotional state calculated by the emotion engine. Based on the integrated data, it generates appropriate lifestyle improvement measures and skincare product recommendations. Specific examples of recommendations include "increasing protein in your diet," "the importance of stress management," and "using a specific shampoo." Taking into account the user's emotional state, the recommendations are adjusted to be most acceptable to the user.

[0841] Submit your proposal

[0842] The generated proposals are sent from the server to the user's device, where the smartphone application displays the received proposals and allows the user to confirm and implement them.

[0843] User Feedback

[0844] Suggestion display

[0845] The user device displays the suggestions received from the server within the application. The user can check the suggestions through the application and incorporate them into their daily lives. The emotion engine analyzes the user's reactions to the suggestions in real time and provides feedback.

[0846] Recording of actions taken

[0847] The user implements the suggested improvements and records the results within the application, and the smartphone stores these execution data locally for the next data collection.

[0848] Ongoing follow-up and feedback

[0849] Regular photo shoot

[0850] The user takes another photo of their head after a certain period of time to obtain new data. The smartphone stores the new image data locally and prepares to send it to the server.

[0851] Reevaluation

[0852] The server re-analyzes the new image data and lifestyle data, compares them with the previous data, and re-evaluates them. The emotion engine reflects new feedback based on changes in the user's emotional state.

[0853] Feedback Updates

[0854] The server regenerates appropriate improvement suggestions and sends them to the user's device. The smartphone displays the latest suggestions to the user, supporting effective management. The emotion engine continuously analyzes the user's reactions and evaluates the effectiveness of the suggestions.

[0855] Specific examples

[0856] For example, a user can use the application to take a photo of their head and answer a questionnaire about diet, exercise, and stress. This data is sent to a server, where the hair volume is analyzed, and lifestyle data is then analyzed by an AI model. Risk factors are identified, and suggestions such as "eat more protein" or "manage stress" are generated. These suggestions are optimized using sentiment analysis and presented to the user. If the user implements the suggestions and submits the data again after a certain period of time, continuous feedback and suggestions are provided.

[0857] Prompt Sentence Examples

[0858] "Analyze the user's head images and lifestyle data to identify hair volume status and associated risk factors."

[0859] "Generate dietary and lifestyle modification suggestions based on hair volume status and risk factors."

[0860] "Optimize your suggestions based on the user's emotional state."

[0861] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0862] Step 1:

[0863] Head photography using a user device

[0864] The user starts the smartphone app and takes a photo by following the head photography guide. The acquired image data is saved in local storage. The input is the captured image of the head, and the output is the image data saved in local storage.

[0865] Step 2:

[0866] Answers to lifestyle questionnaire

[0867] Users answer questionnaires presented within the application and enter information about their lifestyle habits, such as diet, exercise, and stress. This data is saved in local storage. The input is the lifestyle data entered by the user in the questionnaire form, and the output is the questionnaire data saved in local storage.

[0868] Step 3:

[0869] Sending data to the server

[0870] The smartphone device sends the saved image data and survey data to the server. The input is the image data and survey data saved in the local storage, and the output is the data sent to the server.

[0871] Step 4:

[0872] Image analysis

[0873] The server analyzes the received image data using an image analysis library such as OpenCV. Specifically, it quantifies the amount of hair and the degree of exposed skin, and stores the results in a database. The input is the transmitted image data, and the output is the numerical data resulting from the analysis.

[0874] Step 5:

[0875] Lifestyle data analysis

[0876] The server inputs the received survey data into a generative AI model such as TensorFlow to identify risk factors. The analysis results are converted into a numerical score for each risk factor and stored in a database. The input is the submitted survey data, and the output is the risk factor score.

[0877] Step 6:

[0878] Emotion analysis

[0879] The server analyzes the user's facial expression and voice data to evaluate their emotional state. The smartphone's camera and microphone are used to acquire real-time emotional data, and the evaluation results are sent to the server. The input is the emotional data acquired in real time, and the output is the evaluation result of the emotional state.

[0880] Step 7:

[0881] Proposal generation

[0882] The server integrates the results of image analysis, lifestyle data analysis, and emotion analysis to generate appropriate lifestyle improvement and care product recommendations. Suggestions might include, for example, "increase protein in your diet" or "importance of stress management." The input is the analysis results obtained in the previous step, and the output is the generated recommendations.

[0883] Step 8:

[0884] Submit your proposal

[0885] The server sends the generated proposal to the user's smartphone. The input is the proposal, and the output is the proposal sent to the user's device.

[0886] Step 9:

[0887] Viewing Proposals

[0888] The smartphone displays the received suggestions in the app so that the user can check them. The input is the suggestions sent from the server, and the output is the suggestions displayed in the app.

[0889] Step 10:

[0890] Recording of actions taken

[0891] The user implements the proposed improvements and records the results within the app. The smartphone stores the implementation data locally for the next data collection. The input is the implementation data of the improvements implemented by the user, and the output is the locally stored implementation data.

[0892] 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.

[0893] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0894] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0895] [Third embodiment]

[0896] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0897] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0898] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0899] 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.

[0900] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0901] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0902] 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.

[0903] 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.

[0904] 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 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.

[0905] 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.

[0906] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0907] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0908] As an embodiment of this invention, we will specifically explain a system that acquires images of a user's head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair loss. This system consists of a user terminal, a server, and a software module for analysis and feedback. The detailed operation of each element is shown below.

[0909] 1. User data collection

[0910] Head photography

[0911] The user launches the app and follows the instructions to take a photo of their head. The device stores this image data locally and prepares it for transmission to the server.

[0912] Survey responses

[0913] Users answer a questionnaire within the app and enter data about their lifestyle and eating habits, which are then collected by the device and sent to the server along with image data.

[0914] 2. Data Processing and Analysis

[0915] Image analysis

[0916] The image data received by the server is input into the image analysis module, which evaluates and quantifies the amount of hair on the user's head and the degree of exposed skin, and saves the analysis results.

[0917] Lifestyle data analysis

[0918] The server inputs lifestyle data into an AI model, which analyzes the relationship between lifestyle habits and the progression of hair loss. The AI ​​model then identifies risk factors and generates a score for each factor.

[0919] 3. Generating personalized suggestions

[0920] Proposal generation

[0921] The server combines the results of image analysis and lifestyle data analysis to generate optimal lifestyle improvement measures for the user, such as "increase protein in your diet," "importance of stress management," and "use a specific shampoo."

[0922] Submit your proposal

[0923] Once the suggestions are generated, the server sends them to the user's device, which displays them in the app for the user to review.

[0924] 4. User Feedback

[0925] Suggestion display

[0926] The device receives suggestions from the server and displays them within the app. The user checks the app and incorporates the suggestions into their daily lives.

[0927] Recording of actions taken

[0928] The user implements the suggestions and enters and records the results, which the device stores locally for the next data collection.

[0929] 5. Ongoing follow-up and feedback

[0930] Regular photo shoot

[0931] The user takes another photo of the head after a certain period of time, and the terminal prepares to send the new image data to the server.

[0932] Reevaluation

[0933] The server reanalyzes the new image data and lifestyle data and compares them with the previous proposal.

[0934] Feedback Updates

[0935] The server regenerates appropriate improvement suggestions and sends them to the terminal, which then displays the latest suggestions to the user, supporting effective management.

[0936] Specific examples

[0937] The user uses the app to take a photo of their head and answer a questionnaire about their diet and exercise habits. The device sends this information to a server, which analyzes the image and lifestyle data. The server generates suggestions such as "eat more protein" or "you need to manage your stress" and sends them to the device. The user confirms and implements these suggestions. After a certain period of time, the user takes another photo of their head and sends the latest data to the server. The server then reassesssssments and provides feedback on any improvements.

[0938] This allows users to effectively manage the progression of hair thinning and implement appropriate remedial measures.

[0939] The processing flow will be explained below.

[0940] Step 1:

[0941] The user launches the app and takes a picture of their head. The device stores the captured image data locally and prepares to send it to the server.

[0942] Step 2:

[0943] The user answers a questionnaire within the app. The questions consist of dietary habits, exercise habits, stress levels, etc. The device collects the questionnaire data and prepares to send it to the server along with the image data.

[0944] Step 3:

[0945] The device sends the captured image data and the survey data to the server, which receives the data and prepares it for analysis.

[0946] Step 4:

[0947] The server inputs the image data into an image analysis module, which evaluates the amount of hair on the head and the degree of exposed skin, converts the progress of hair loss into numerical values, and stores the results in a database.

[0948] Step 5:

[0949] The server inputs the lifestyle data it receives into an AI model, which analyzes the relationship between lifestyle habits and the progression of hair loss and identifies risk factors. Scores for these risk factors are also generated and stored in a database.

[0950] Step 6:

[0951] The server integrates the results of image analysis and lifestyle data analysis. Based on the integrated data, it generates appropriate lifestyle improvement measures and skincare product recommendations. For example, specific recommendations such as "increase protein in your diet," "importance of stress management," and "use a specific shampoo" are generated.

[0952] Step 7:

[0953] The server sends the generated proposal to the user's device, which then displays the proposal received in the app so that the user can confirm and implement it.

[0954] Step 8:

[0955] The user implements the suggested improvements and records the results within the app. The device stores these execution data locally and prepares them for the next analysis.

[0956] Step 9:

[0957] After a certain period of time has passed, the user takes another image of their head to acquire new data. The device stores the new image data locally and prepares to send it to the server.

[0958] Step 10:

[0959] The device sends new image data to the server, which receives the new data and compares it with the previous data for re-evaluation.

[0960] Step 11:

[0961] The server generates appropriate improvement suggestions again based on the results of the reevaluation. The new suggestions are sent to the user's device, which displays them in the app. This allows the user to check the latest suggestions and make continuous improvements.

[0962] Example 1

[0963] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0964] Conventional hair loss prevention systems have the problem of being difficult to provide personalized advice based on the user's lifestyle and environment. Furthermore, they lack the functionality to continuously record the results of implementing the recommendations and provide feedback, making long-term hair loss management difficult. Furthermore, there is no cycle of reevaluation and improvement suggestions, meaning users cannot always receive the latest advice.

[0965] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0966] In this invention, the server includes means for acquiring an image of the head, means for analyzing the acquired image data to evaluate the progression of hair thinning, means for collecting lifestyle data of the user, means for analyzing the collected lifestyle data and identifying risk factors for hair thinning, means for suggesting appropriate lifestyle improvements and hair care products based on the progression of hair thinning and the risk factors, means for presenting the suggestions to the user, means for recording the results of implementing the suggestions, and means for continuous follow-up and reassessment. This makes it possible to provide personalized advice based on the user's individual lifestyle, and provide continuous feedback and the latest improvement suggestions.

[0967] The "means for acquiring an image of the head" is a combination of hardware and software for photographing the user's head and capturing the image data.

[0968] The "means for analyzing acquired image data to assess the progression of hair loss" refers to an algorithm or program that analyzes the amount of hair and the degree of exposure of the scalp based on the captured image of the head, and quantifies and assesses the progression of hair loss.

[0969] The "means for collecting user lifestyle data" is an interface for collecting data on the user's lifestyle habits such as diet, exercise, and sleep using questionnaires, sensors, etc.

[0970] The "means of analyzing collected lifestyle data and identifying risk factors for hair loss" is a system that uses AI models and statistical methods to identify risk factors related to the progression of hair loss based on collected lifestyle data.

[0971] The "means for suggesting appropriate lifestyle improvements and hair care products based on the progression of hair thinning and risk factors" is a component that generates lifestyle improvement measures and appropriate hair care products customized for the user based on the results of image analysis and lifestyle data analysis.

[0972] The "means for presenting the proposal content to the user" is a system that has the function of transmitting the generated proposal content to the user's device and displaying it to the user through an app or other user interface.

[0973] The "means for recording the results of implementing the suggested content" is a function that allows the user to input the results after implementing the suggested lifestyle improvements or hair care products, and save them in a database or local storage for use in the next analysis.

[0974] The "means for continuous follow-up observation and re-evaluation" is a system that re-collects the user's head images and lifestyle data at regular intervals, compares them with the previous results, and makes new analyses and proposals.

[0975] This invention describes a system that acquires images of a user's head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair loss. This system consists of a device used by the user, a server that analyzes the data, and a software module for analysis and feedback.

[0976] First, the user launches the app and takes a photo of their head. The device stores the captured image data locally and prepares it for transmission to the server. At the same time, the user answers a questionnaire within the app and enters data about their lifestyle and eating habits into the device. This data is also collected by the device and sent to the server along with the image data.

[0977] The server inputs the received image data into an image analysis module (e.g., OpenCV or TensorFlow) to evaluate and quantify the amount of hair on the user's head and the degree of exposed scalp. The analysis results are stored on the server. Next, the server inputs the lifestyle data into an AI model (e.g., scikit-learn or TensorFlow) to analyze the relationship between lifestyle habits and the progression of hair loss. The AI ​​model identifies risk factors and generates a score for each factor.

[0978] The server then combines the image analysis results with the lifestyle data analysis results to generate optimal lifestyle improvement measures for the user. These include specific suggestions such as "increase protein in your diet," "importance of stress management," and "use a specific shampoo." The generated suggestions are sent from the server to the user's device and displayed within the app. The user can review the suggestions and incorporate them into their daily lives.

[0979] After implementing the suggestions, the user enters the results into the app, and the device stores this data locally. After a certain period of time, the user takes another photo of their head and sends the new image and input data to the server. The server reanalyzes the new data, compares it with the previous suggestions to evaluate their effectiveness, and generates new feedback. This allows for continuous follow-up and feedback.

[0980] As a specific example, a user uses an app to take a photo of their head and answer a questionnaire about their diet and exercise habits. The device sends this information to a server, which analyzes the image and lifestyle data. The server uses a generative AI model to generate suggestions such as "eat more protein" or "you need to manage your stress," which are sent to the device. The user confirms and implements these suggestions. After a certain period of time, the user takes another photo of their head and sends the new data to the server. The server then reevaluates and provides feedback on any improvements.

[0981] Example prompt sentence:

[0982] Analyze data on the user's hair loss progression and suggest lifestyle improvements. Generate specific advice based on the user's dietary data and the results of head image analysis.

[0983] In this way, the present invention allows users to effectively manage the progression of hair thinning and implement appropriate remedial measures based on their individual lifestyle habits.

[0984] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0985] Step 1:

[0986] The user launches the app and takes a photo of their head.

[0987] Input: User's head image

[0988] Operation: The device activates the camera function and captures an image of the user's head. The captured image data is stored locally and prepared for transmission to the server.

[0989] Output: locally saved head image data

[0990] Step 2:

[0991] Users answer a questionnaire within the app and enter data about their lifestyle and eating habits.

[0992] Input: Lifestyle data (diet, exercise, sleep, etc.)

[0993] How it works: Users fill out survey forms within the app and their devices collect this data.

[0994] Output: Locally saved lifestyle data

[0995] Step 3:

[0996] The device sends the locally stored image data and lifestyle habit data to a server.

[0997] Input: Head image data, lifestyle data

[0998] Operation: The device communicates over the network to send the stored image data and lifestyle habit data to the server.

[0999] Output: Image data and lifestyle data sent to the server

[1000] Step 4:

[1001] The image data received by the server is input into the image analysis module, where analysis is performed.

[1002] Input: Head image data

[1003] Operation: The server uses an image analysis module (e.g., OpenCV, TensorFlow) to analyze image data and evaluate and quantify the amount of hair on the head and the degree of exposed skin.

[1004] Output: Analysis results (evaluation values ​​for hair volume and scalp exposure)

[1005] Step 5:

[1006] The lifestyle data received by the server is input into an AI model, which analyzes the relationship between lifestyle habits and the progression of hair loss.

[1007] Input: Lifestyle data

[1008] How it works: The server uses AI models (e.g., scikit-learn, TensorFlow) to analyze lifestyle data to identify risk factors and generate a score for each factor.

[1009] Output: Risk factor scoring results

[1010] Step 6:

[1011] The server integrates the results of image analysis and lifestyle data analysis to generate optimal lifestyle improvement measures for the user.

[1012] Input: Image analysis results, risk factor scores

[1013] How it works: Based on the results of image analysis and lifestyle data analysis, the server uses a generative AI model to generate appropriate lifestyle improvement measures and hair care products. For example, it generates recommendations such as "take a certain amount of protein," "stress management is necessary," and "use a specific shampoo."

[1014] Output: Generated lifestyle improvement measures and hair care product suggestions

[1015] Step 7:

[1016] Once the proposal is generated, the server sends it to the user's device.

[1017] Input: Lifestyle improvement measures and hair care product suggestions

[1018] Operation: The server sends the generated proposal to the user's device and notifies them.

[1019] Output: Suggestions sent to the device

[1020] Step 8:

[1021] The device receives suggestions from the server and displays them within the app. The user can then review the suggestions and incorporate them into their daily lives.

[1022] Input: Received proposal

[1023] Operation: The device displays the suggestion on the app screen and notifies the user.

[1024] Output: Suggestions displayed on the app screen

[1025] Step 9:

[1026] Users implement the suggested lifestyle changes and hair care products and enter their results into the app, which stores them locally on the device.

[1027] Input: Proposal execution result

[1028] How it works: The user enters their thoughts and results after implementing the suggestions into the app, which is then saved locally on the device.

[1029] Output: Locally saved proposal execution results

[1030] Step 10:

[1031] After a certain period of time, the user takes another photo of the head and sends the new image data to the server.

[1032] Input: New head image data

[1033] How it works: The user takes another photo of their head, and the device stores the new image data locally and sends it to the server at intervals.

[1034] Output: New head image data sent to the server

[1035] Step 11:

[1036] The server re-analyzes the new image data and lifestyle data, compares them with the results of the previous analysis, evaluates the effectiveness, and generates new feedback.

[1037] Input: New head image data, recollected lifestyle data

[1038] How it works: The server analyzes the images and lifestyle data again, compares them with the previous analysis results, and evaluates any changes. It then generates appropriate feedback.

[1039] Output: New feedback

[1040] Through this series of steps, users can continuously manage the progression of hair loss and implement appropriate improvements based on their individual lifestyle habits.

[1041] (Application example 1)

[1042] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1043] Conventional hair thinning treatment systems have difficulty in providing consistent progress monitoring and feedback to individual users. Furthermore, because the recommendations are not based on individual lifestyle habits or risk factors, they are unable to provide effective improvement measures. Furthermore, when used in physical salons, there is an issue of information not being shared smoothly between salon staff and customers. To solve these issues, a system is needed that provides continuous and personalized feedback and supports use in physical salons and other such stores.

[1044] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1045] In this invention, the server includes means for acquiring an image of the head, means for analyzing the acquired image data to evaluate the progress of hair thinning, means for collecting lifestyle habit data of the user, means for analyzing the collected lifestyle habit data and identifying risk factors for hair thinning, means for proposing appropriate lifestyle improvements and care products based on the progress of hair thinning and the risk factors, means for presenting the suggestions to the user, means for periodically acquiring and reanalyzing the user's progress observation data, means for updating the suggestions based on the reanalysis results, and means for sharing the suggestions between staff terminals and customer terminals in a physical store such as a beauty salon. This allows for personalized feedback to be provided to each user, and for staff and customers to share information and implement effective measures against hair thinning.

[1046] The "means for acquiring an image of the head" is a device that takes a photograph of the user's head using a digital camera or a smartphone camera and acquires the image data.

[1047] The "means for analyzing acquired image data to evaluate the progression of hair thinning" is software that uses acquired image data of the head to analyze the amount of hair and the degree of exposure of the scalp, and numerically evaluates the progression of hair thinning.

[1048] The "means for collecting user lifestyle data" is a device that collects information about the user's diet, exercise, stress, and other daily habits through questionnaires or sensors.

[1049] The "means for analyzing collected lifestyle data and identifying risk factors for hair loss" is software that analyzes collected lifestyle data of users and identifies risk factors that affect the progression of hair loss.

[1050] The "means for suggesting appropriate lifestyle improvements and care products" is software that recommends specific lifestyle improvements and care products to users based on the progression of hair loss and risk factors.

[1051] The "means for presenting the proposal content to the user" is a device that transmits the generated proposal content to the user's terminal and displays it.

[1052] The "means for periodically acquiring and reanalyzing the user's follow-up observation data" is a device that allows the user to take another photograph of the head after a certain period of time, collect the photograph together with the latest lifestyle habit data, and perform reanalysis.

[1053] The "means for updating the content of the proposals based on the reanalysis results" is software that regenerates and updates the proposals for optimal lifestyle improvements and care products based on the reanalyzed data.

[1054] A "means for sharing proposal content between staff terminals and customer terminals in a physical store such as a beauty salon" is a device that can send generated proposal content to staff terminals and customer terminals in the physical store and share information.

[1055] As an embodiment of this invention, we will specifically explain a system that acquires images of the head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair thinning. This system consists of a user terminal, a server, and a software module for analysis and feedback. In particular, it is intended for use in brick-and-mortar stores such as beauty salons.

[1056] 1. User data collection

[1057] Head photography

[1058] Users visit a hair salon and take a photo of their head using a smartphone app. This image data is immediately uploaded to a cloud server. The image of the head is used for image analysis to evaluate the user's hair volume and the degree of exposed skin.

[1059] Survey responses

[1060] Customers fill out a questionnaire about their lifestyle habits on a tablet device at the salon, and this data is also sent to the cloud server.

[1061] 2. Data Processing and Analysis

[1062] Image analysis

[1063] The server analyzes the image data uploaded to the cloud using OpenCV. Specifically, it detects the head using cv2.CascadeClassifier and evaluates the amount of hair and the degree of exposed skin within the detected area using a TensorFlow model.

[1064] Lifestyle data analysis

[1065] The questionnaire data answered by the user is input into the AI ​​model, which analyzes the relationship between lifestyle habits and the progression of hair loss, identifies risk factors, and generates a score for each factor.

[1066] 3. Generating personalized suggestions

[1067] Proposal generation

[1068] The results of image analysis and lifestyle data analysis are integrated to generate optimal lifestyle improvement measures, such as specific suggestions such as "eat a diet high in protein" or "stress management is necessary."

[1069] Submit and share your suggestions

[1070] The server sends the proposed content to the user's terminal and the salon staff terminal, allowing the salon staff to provide specific advice to the user.

[1071] 4. Follow-up and feedback

[1072] Regular imaging and reassessment

[1073] The user returns to the salon after a certain period of time and takes a new photo of their head, which is again sent to the server for re-analysis.

[1074] Feedback Updates

[1075] Based on the latest analysis results, the proposals are updated and sent to the user's terminal and the salon staff terminal.

[1076] Specific examples

[1077] When a customer visits a beauty salon, they take a photo of their head using a smartphone app. They then answer a questionnaire about their lifestyle habits on a tablet device. This data is sent to a cloud server. After analyzing the image and lifestyle data, the server generates recommendations such as "increase protein intake" or "strengthen stress management." These recommendations are shared with both the customer and the salon staff, allowing them to take specific actions.

[1078] Prompt Sentence Examples

[1079] "Develop an app for a hair salon that takes a photo of the user's head, sends it to a cloud server, and analyzes the results of a lifestyle questionnaire to generate suggestions for preventing hair loss."

[1080] In this way, the system provides personalized feedback to each user, enabling salon staff and customers to share information and implement effective measures to combat hair loss.

[1081] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1082] Step 1:

[1083] Acquiring a user's head image

[1084] When a user visits a beauty salon, the salon staff takes a photo of the user's head using a dedicated smartphone app. This image data is saved in the smartphone's local storage and sent to a cloud server. The input is the user's head image data, and the output is the transmission of the image data to the cloud server.

[1085] Step 2:

[1086] Collection of lifestyle data

[1087] Users answer a questionnaire about their lifestyle habits on a tablet device at a beauty salon. Questions include, "How many times a week do you exercise?" and "How many times a day do you eat?" This data is also sent to the cloud server. The input is the user's questionnaire response data, and the output is the transmission of lifestyle habit data to the cloud server.

[1088] Step 3:

[1089] Image data analysis

[1090] The server analyzes the image data uploaded to the cloud using OpenCV. Specifically, the server detects the head using cv2.CascadeClassifier, and then evaluates the hair volume and the degree of exposed skin using a TensorFlow model. The input is the image data of the head, and the output is the evaluation results of the hair volume and the degree of exposed skin.

[1091] Step 4:

[1092] Lifestyle data analysis

[1093] The server inputs the collected lifestyle data into an AI model and analyzes the relationship between lifestyle and the progression of hair loss. The server identifies risk factors and generates a score for each factor. The input is lifestyle data, and the output is the identification of risk factors and the generation of a score.

[1094] Step 5:

[1095] Proposal generation

[1096] The server integrates the results of image analysis and lifestyle data analysis to generate optimal lifestyle improvement measures and care product recommendations. For example, suggestions include "eat a diet high in protein" and "stress management is important." The inputs are the image analysis results and lifestyle data analysis results, and the output is lifestyle improvement measures and care product recommendations.

[1097] Step 6:

[1098] Submit and share your suggestions

[1099] The server sends the generated suggestions to the user's terminal and the salon staff terminal, allowing the salon staff to provide specific advice to the user. The input is the generated suggestions, and the output is the transmission of the suggestions to the user's terminal and the salon staff terminal.

[1100] Step 7:

[1101] Regular acquisition of follow-up data

[1102] The user returns to the salon after a certain period of time and takes another photo of their head. This new image data is also sent to the cloud server. The input is the new head image data, and the output is the transmission of the image data to the cloud server.

[1103] Step 8:

[1104] Reassessment and feedback updates

[1105] The server reanalyzes the new image data and lifestyle data and compares it with the previous data. It evaluates how effective the original suggestions were and generates new suggestions. The updated suggestions are sent to the user's device and the salon staff's device. The input is the new image data and lifestyle data, and the output is the transmission of the updated suggestions.

[1106] This allows the entire process to work seamlessly to provide effective hair loss treatment to the user.

[1107] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1108] As an embodiment of this invention, we will specifically explain a system that acquires images of a user's head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair loss. This system consists of a user terminal, a server, a software module that performs analysis and feedback, and an emotion engine. The detailed operation of each element is shown below.

[1109] User data collection

[1110] Head photography

[1111] The user launches the app and takes a photo by following the head guide. The device stores this image data locally and prepares it for transmission to the server.

[1112] Survey responses

[1113] Users answer a questionnaire within the app and enter data about their lifestyle and eating habits, which are then collected by the device and sent to the server along with image data.

[1114] Data processing and analysis

[1115] Image analysis

[1116] The server inputs the received image data into an image analysis module, which evaluates the amount of hair on the head and the degree of exposed skin, converts the progress of hair loss into numerical values, and stores the results in a database.

[1117] Lifestyle data analysis

[1118] The server inputs lifestyle data into the AI ​​model, which analyzes the relationship between lifestyle and the progression of hair loss. The AI ​​model identifies risk factors, generates a score for each factor, and stores it in a database.

[1119] Emotion analysis

[1120] The emotion engine collects the user's facial expression data, voice data, or text data and analyzes the user's emotional state. For example, the emotion data is acquired in real time using the smartphone's camera and microphone and sent to the emotion engine.

[1121] Generate personalized suggestions

[1122] Proposal generation

[1123] The server integrates the results of image analysis, lifestyle data analysis, and the emotional state of the user based on the emotion engine. Based on the integrated data, it generates appropriate lifestyle improvement and skincare recommendations. For example, recommendations include "increase protein in your diet," "importance of stress management," and "use a specific shampoo." The recommendations are adjusted based on the user's emotional state and presented in the most acceptable format for the user.

[1124] Submit your proposal

[1125] The server sends the generated proposal to the user's device, which then displays the proposal received in the app so that the user can confirm and implement it.

[1126] User Feedback

[1127] Suggestion display

[1128] The device receives the suggestions from the server and displays them within the app. The user checks the app and incorporates the suggestions into their daily lives. The emotion engine analyzes the user's reaction to the suggestions in real time and provides feedback.

[1129] Recording of actions taken

[1130] The user implements the suggested improvements and records the results within the app, and the device stores these execution data locally for the next data collection.

[1131] Ongoing follow-up and feedback

[1132] Regular photo shoot

[1133] The user takes another photo of their head after a certain period of time to obtain new data. The device stores the new image data locally and prepares to send it to the server.

[1134] Reevaluation

[1135] The server reanalyzes the new image data and lifestyle data, compares them with the previous data, and reassesssssments. The emotion engine reflects new feedback based on changes in the user's emotional state.

[1136] Feedback Updates

[1137] The server regenerates appropriate improvement suggestions and sends them to the device. The device displays the latest suggestions to the user, supporting effective management. The emotion engine continuously analyzes the user's reactions and evaluates the effectiveness of the suggestions.

[1138] As a specific example, a user uses an app to take a photo of their head and answer a questionnaire about their diet and exercise habits. The device sends this information to a server, which analyzes the image and lifestyle data. The server generates suggestions such as "eat more protein" or "you need to manage your stress," and sends them to the device in an optimal form that takes into account the user's emotional state using an emotion engine. The user confirms and implements these suggestions. After a certain period of time, the user takes another photo of their head and sends the latest data to the server. The server then re-evaluates and provides feedback on any improvements. The emotion engine continuously analyzes the user's emotional state and provides optimal feedback.

[1139] The processing flow will be explained below.

[1140] Step 1:

[1141] The user launches the app and takes an image of their head. The device stores this image data locally and prepares it for transmission to the server.

[1142] Step 2:

[1143] The user answers a questionnaire within the app, which asks about their eating habits, exercise habits, and stress levels. The device collects the questionnaire data and prepares to send it to the server along with the image data.

[1144] Step 3:

[1145] The device sends the captured image data and the survey data to the server, which receives the data and prepares it for analysis.

[1146] Step 4:

[1147] The server inputs the image data into an image analysis module, which evaluates the amount of hair on the head and the degree of exposed skin, converts the progress of hair loss into numerical values, and stores the results in a database.

[1148] Step 5:

[1149] The server inputs the lifestyle data it receives into an AI model, which analyzes the relationship between lifestyle habits and the progression of hair loss and identifies risk factors. Scores for these risk factors are also generated and stored in a database.

[1150] Step 6:

[1151] The emotion engine collects the user's facial expression data, voice data, or text data and analyzes the user's emotional state. For example, the emotion data is acquired in real time using a camera and a microphone and sent to the emotion engine.

[1152] Step 7:

[1153] The server integrates the results of image analysis, lifestyle data analysis, and the emotional state of the user based on the emotion engine. Based on the integrated data, it generates appropriate lifestyle improvement and skincare product recommendations. Examples include "increase protein in your diet," "importance of stress management," and "use a specific shampoo." The recommendations are adjusted based on the user's emotional state and presented in the most acceptable format for the user.

[1154] Step 8:

[1155] The server sends the generated proposal to the user's device, which then displays the proposal received in the app so that the user can confirm and implement it.

[1156] Step 9:

[1157] The user implements the suggested improvements and records the results within the app, and the device stores these execution data locally for the next data collection.

[1158] Step 10:

[1159] After a certain period of time has passed, the user takes another image of their head to acquire new data. The device stores the new image data locally and prepares to send it to the server.

[1160] Step 11:

[1161] The device sends new image data to the server, which receives the new data and compares it with the previous data for re-evaluation. This allows the progress of hair loss and the effectiveness of treatment to be monitored.

[1162] Step 12:

[1163] The server generates appropriate improvement suggestions again based on the results of the reevaluation. The new suggestions are sent to the device, which displays them within the app. This allows the user to check the latest suggestions and make continuous improvements.

[1164] Step 13:

[1165] The emotion engine analyzes the user's emotional state in real time and provides feedback: when the user expresses an emotional reaction to a suggestion, the emotion engine processes this and adjusts the suggestion as needed.

[1166] As a specific example, a user uses an app to take a photo of their head and answer a questionnaire about their diet and exercise habits. The device sends this information to a server, which analyzes the image and lifestyle data. The server generates suggestions such as "eat more protein" or "you need to manage your stress," and sends them to the device in an optimal form that takes into account the user's emotional state using an emotion engine. The user confirms and implements these suggestions. After a certain period of time, the user takes another photo of their head and sends the latest data to the server. The server then re-evaluates and provides feedback on any improvements. The emotion engine continuously analyzes the user's emotional state and provides optimal feedback.

[1167] Example 2

[1168] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1169] Many people today suffer from thinning hair and are searching for effective ways to slow its progression and improve it. However, existing methods are unable to provide personalized recommendations that take into account individual lifestyle habits and emotional states, limiting their effectiveness. Furthermore, there is a lack of systems that continuously track users' responses and provide feedback. Therefore, there is a need to propose a system that analyzes a user's head images, lifestyle habits, and emotional data, and integrates them to provide optimal solutions to improve hair loss more effectively.

[1170] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring an image of the user's head, means for analyzing the acquired image data to evaluate the progression of hair thinning, means for collecting the user's lifestyle data, means for analyzing the collected lifestyle data and identifying risk factors for hair thinning, means for collecting and analyzing the user's emotional data, means for suggesting appropriate lifestyle improvements and care products based on the progression of hair thinning, risk factors, and emotional data, means for presenting the suggestions to the user, and means for recording the user's responses and continuously analyzing the data. This makes it possible to provide personalized improvement measures tailored to the user's individual situation and effectively suppress the progression of hair thinning.

[1171] "User" refers to an individual who uses the system to take a head image and input lifestyle data.

[1172] "Head image" means photographic data of the user's head, and is primarily used to evaluate the condition of the hair.

[1173] "Lifestyle data" includes information about a user's diet, exercise, sleep, stress management, etc., and is used to identify risk factors for hair loss.

[1174] "Emotional data" refers to data about emotions obtained from a user's facial expressions, voice, or text, which is taken into account when making personalized suggestions.

[1175] "Image analysis" refers to the process of analyzing image data of the head and evaluating the amount of hair and the degree of exposure of the scalp.

[1176] "Risk factors" refer to factors that affect the progression of hair loss and are identified from lifestyle data and head images.

[1177] "Proposal content" refers to proposals for lifestyle improvement measures and care products formulated based on the results of image analysis, lifestyle data analysis, and emotional data.

[1178] "Server" means a remote computer system that receives, analyzes, and evaluates data sent by users.

[1179] "Terminal" means a device used by a user, such as a computer or smartphone, that acquires, transmits, and receives data.

[1180] "Analysis module" refers to a software component for analyzing image data and lifestyle habit data.

[1181] "Emotion Engine" means an algorithm or software for analyzing a user's emotional data and assessing their emotional state.

[1182] "Evaluation" refers to the process of quantifying or quantifying the progression of hair loss and risk factors based on the analysis results.

[1183] "Feedback" refers to the system's response and evaluation of the user's actions and reactions, and is used to reflect this in future suggestions.

[1184] The present invention relates to a system that acquires images of a user's head, collects and analyzes lifestyle data, and suggests lifestyle improvements based on the progression of hair thinning. The system is composed of a user terminal, a server, a software module that performs analysis and feedback, and an emotion engine.

[1185] User data collection

[1186] Head photography

[1187] The user launches the app and takes a photo by following the head guide. The device uses the camera function to acquire image data and saves it to local storage. This image data is then prepared for sending to the server. For the camera function, the device's standard camera app is used, and the camera is launched using an Intent. After the photo is taken, the image data is received by the onActivityResult method.

[1188] Survey responses

[1189] The user answers a questionnaire about their lifestyle and eating habits within the app. The device saves the entered information in JSON format and prepares to send it to the server along with the image data. The questionnaire UI is implemented using a mobile application development framework (e.g., Flutter or React Native).

[1190] Sending data

[1191] The image data and lifestyle habit data collected by the device are sent to the server using an HTTP POST request. Retrofit, for example, is used as the communication library.

[1192] Data processing and analysis

[1193] Image analysis

[1194] The image data received by the server is input into the image analysis module. Libraries such as OpenCV and TensorFlow are used for image analysis to evaluate the amount of hair on the head and the degree of exposed skin. The analysis results are stored in a database. Specifically, feature extraction is performed using a convolutional neural network (CNN).

[1195] Lifestyle data analysis

[1196] The server analyzes the lifestyle data and uses an AI model to identify risk factors for hair loss. The AI ​​model is trained using Scikit-learn and TensorFlow and generates a score for each risk factor. The analysis results are stored in a database.

[1197] Emotion analysis

[1198] The server uses an emotion engine to analyze the user's emotional data. Emotional data is obtained from facial expressions, voice, text, etc. NLP technologies such as Hugging Face's Transformers are used for this analysis. The emotional data obtained is analyzed in real time and the results are stored in a database.

[1199] Generate personalized suggestions

[1200] Proposal generation

[1201] The server combines the results of image analysis, lifestyle data analysis, and emotion analysis to generate recommendations for lifestyle improvements and skincare products that are optimal for the user. For example, it suggests dietary improvements or specific hair care products. This process may also incorporate rule-based systems.

[1202] Submit your proposal

[1203] The server sends the generated suggestions to the user's device either as a notification using Firebase Cloud Messaging (FCM) or as an in-app message.

[1204] User Feedback

[1205] Suggestion display

[1206] The device displays the suggestions received from the server within the app, allowing users to view the suggestions and incorporate them into their daily lives.

[1207] Recording of actions taken

[1208] The user implements the proposed improvements and records the results within the app. The device stores the execution data locally for the next data collection. Data is stored in a database such as SQLite.

[1209] Ongoing follow-up and feedback

[1210] Regular photo shoot

[1211] After a certain period of time, the user takes another photo of their head to acquire new data. The device saves the new image data in local storage and prepares to send it to the server.

[1212] Reevaluation

[1213] The server reanalyzes the new image data and lifestyle data, compares them with the previous data, and reassesssssments. The emotion engine reflects new feedback based on changes in the user's emotional state.

[1214] Feedback Updates

[1215] The server regenerates the latest improvement suggestions and sends them to the device. The device displays the latest suggestions to the user, supporting effective management. The emotion engine continuously analyzes the user's reactions and evaluates the effectiveness of the suggestions.

[1216] Specific examples and prompts for the generative AI model

[1217] As a specific example, a user uses an app to take a photo of their head, answer a questionnaire about their diet and exercise habits, and send the data to a server. The server analyzes the image and lifestyle data, generates suggestions, and sends them to the device after taking the user's emotional state into consideration using an emotion engine. The user confirms and implements the suggestions. After a certain period of time, the user takes another photo of their head and sends the updated data to the server. The server then reevaluates the results and provides new feedback.

[1218] An example of a prompt is as follows:

[1219] 1. "Assess the progression of hair loss using images of the user's head, while also taking into account questionnaire data about diet and lifestyle."

[1220] 2. "Analyze specific images and lifestyle data to suggest hair loss treatments that are suitable for the user. Also, reflect the user's emotional state."

[1221] 3. "Please analyze the new head image and the latest lifestyle data, and provide feedback on any improvements since the previous suggestion."

[1222] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1223] Step 1: Image the head

[1224] The user launches the app and takes a photo by following the photography guide on their head. The device acquires the image data using the camera function. Specifically, the device's camera app is launched using an Intent, and the image taken by the user is received by the onActivityResult method. This image data is then saved to local storage. The input is the image taken by the user, and the output is the image data saved in the device's local storage.

[1225] Step 2: Complete the survey

[1226] The user enters data about their lifestyle and eating habits into a questionnaire form within the app. The device saves this input data in JSON format and prepares to send it to the server along with image data. The input data is the lifestyle data entered by the user, and the output data is the lifestyle data saved in JSON format. The UI for the questionnaire form is developed using, for example, Flutter.

[1227] Step 3: Sending data

[1228] The image data and lifestyle habit data collected by the device are sent to the server. Specifically, an HTTP POST request is created and this data is included as the payload. The Retrofit library is used for this transmission. The input data is the image data and lifestyle habit data stored on the device, and the output data is the data sent to the server.

[1229] Step 4: Analyzing the image data

[1230] The image data received by the server is input into the image analysis module. Libraries such as OpenCV and TensorFlow are used for image analysis. The server evaluates the amount of hair on the head and the degree of exposed skin from the image data, and stores the analysis results in a database. The input data is the image data sent to the server, and the output data is the numerical data of the analysis results. Specifically, a convolutional neural network (CNN) extracts important features from the image.

[1231] Step 5: Analyze lifestyle data

[1232] The server analyzes the lifestyle data and uses an AI model to identify risk factors for hair loss. Scikit-learn and TensorFlow are used for this AI model. The server generates a score for each risk factor, which is also stored in a database. The input data is the lifestyle data sent to the server, and the output data is the risk factor score. Specifically, risk factors are scored using random forests and support vector machines (SVMs).

[1233] Step 6: Analyze the sentiment data

[1234] The server uses an emotion engine to analyze the user's facial expression data, voice data, or text data. Emotion data is acquired in real time using the smartphone's camera and microphone. NLP technologies such as Hugging Face Transformers are used for emotion analysis. The input data is the user's emotional data, and the output data is a score of the analyzed emotional state. Specifically, the server runs the facial expression recognition algorithm and voice analysis algorithm.

[1235] Step 7: Generate personalized suggestions

[1236] The server integrates the results of image analysis, lifestyle data analysis, and emotion analysis to generate recommendations for lifestyle improvements and care products that are optimal for the user. The recommendations consist of text and images generated by the server. The input data are the results of various analyses, and the output data are the recommendations. Specifically, a rule-based system or machine learning model automatically generates the recommendations based on the integrated data.

[1237] Step 8: Submit your proposal

[1238] The server sends the generated proposal to the user's device. The sending method is Firebase Cloud Messaging (FCM) or similar. The input data is the generated proposal, and the output data is the proposal displayed on the user's device. Specifically, a notification message in JSON format is created and sent via the FCM API.

[1239] Step 9: View and implement the proposal

[1240] The device will display the proposals received from the server within the app. The user will view the proposals and implement them as necessary. The input data will be the proposals sent from the server, and the output data will be the results of the implementation by the user. Specifically, we will develop a function to visualize the proposals in the app's UI.

[1241] Step 10: Recording the actions taken

[1242] The user implements the proposed improvement measures and records the results within the app. The device saves this execution data in local storage for the next analysis. The input data is the implementation data by the user, and the output data is the execution results saved on the device. Specifically, the data is managed using a database such as SQLite.

[1243] Step 11: Regular imaging and re-collection of data

[1244] After a certain period of time has passed, the user takes another photo of their head to obtain new data. The device saves the new image data and lifestyle habit data in local storage and prepares to send them to the server. The input data are the newly taken image and updated lifestyle habit data, and the output data is the new data sent to the server.

[1245] Step 12: Reassess and update feedback

[1246] The server reanalyzes the new image data and lifestyle data and compares it with the previous data for a reassessment. The emotion engine generates new feedback based on changes in the user's emotional state. The input data is the new data and the previous analysis results, and the output data is the updated feedback. Specifically, the new analysis results are saved in the database and the algorithm is rerun to update the suggestions.

[1247] Step 13: Send and view feedback

[1248] The server sends the updated feedback to the user's device, which then displays it. The input data is the feedback to be sent, and the output data is the displayed feedback. Specifically, the feedback is notified via FCM or other means, and a UI is implemented to display it within the app.

[1249] (Application example 2)

[1250] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1251] Conventional hair loss prevention systems simply analyze images of the head and collect and analyze lifestyle data, but are unable to provide suggestions based on the user's individual emotional state. This can make it difficult for users to accept the suggestions. Another drawback is that the suggestions are uniform and therefore not optimized for each user's situation.

[1252] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1253] In this invention, the server includes means for acquiring an image of the head, means for analyzing the acquired image data to evaluate the progression of hair thinning, means for collecting lifestyle data of the user, means for analyzing the collected lifestyle data and identifying risk factors for hair thinning, means for proposing appropriate lifestyle improvements and care products based on the progression of hair thinning and the risk factors, means for presenting the suggestions to the user, and means for analyzing the emotional state of the user and optimizing the suggestions. This makes it possible to make personalized suggestions based on the emotional state of each user, thereby improving the acceptance rate of suggestions and providing optimal improvement measures for each user.

[1254] The "means for acquiring an image of the head" refers to a device or software that allows a user to take an image of the head using an application and collect the image data.

[1255] The "means for analyzing acquired image data to evaluate the progression of hair thinning" refers to a device or software that analyzes captured head image data and quantifies or evaluates the progression of hair thinning, such as hair volume and the degree of scalp exposure.

[1256] The "means for collecting user lifestyle data" refers to a device or software that collects information about the user's lifestyle habits, such as diet, exercise, and sleep, through questionnaires or sensors.

[1257] The "means for analyzing collected lifestyle habit data and identifying risk factors for hair loss" refers to a device or software that analyzes collected lifestyle habit data and identifies risk factors that contribute to the progression of hair loss.

[1258] The "means for suggesting appropriate lifestyle improvements and care products" refers to a device or software that, based on the analysis results, makes suggestions to the user for improving their lifestyle and recommends care products to combat thinning hair.

[1259] The "means for presenting the proposed content to the user" refers to a device or software that notifies or displays the generated proposed content or recommendations on the user's terminal.

[1260] "Means for analyzing the user's emotional state and optimizing the content of suggestions" refers to a device or software that analyzes the user's facial expressions, voice, and text data to determine their emotional state, and then adjusts the content of suggestions in an optimal manner based on the results.

[1261] The embodiment of this invention is a system that acquires images of a user's head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair thinning. The system is composed of a user terminal, a server, a software module that performs analysis and feedback, and an emotion engine.

[1262] User data collection

[1263] Head photography

[1264] The smartphone, which acts as the user terminal, takes a picture of the user's head. The user launches the application and takes a picture of their head according to the specified guide. The acquired image data is stored in the smartphone's local storage and sent to the server.

[1265] Survey responses

[1266] Within the application, users answer a questionnaire about their lifestyle and eating habits, including questions about their diet, exercise habits, and stress. The questionnaire responses are stored on the smartphone and sent to a server along with image data.

[1267] Data processing and analysis

[1268] Image analysis

[1269] The server uses an image analysis library such as OpenCV to analyze the acquired image data. The image analysis module evaluates the amount of hair on the head and the degree of exposed skin, converts the results into numerical values, and stores them in a database.

[1270] Lifestyle data analysis

[1271] The server inputs the collected lifestyle data into a generative AI model such as TensorFlow to analyze the relationship between lifestyle habits and the progression of hair loss. This analysis identifies risk factors and generates a score for each factor. These scores are also stored in a database.

[1272] Emotion analysis

[1273] The emotion engine collects the user's facial expression, voice, and text data to analyze their emotional state. It captures emotional data in real time using the smartphone's camera and microphone and sends it to the server.

[1274] Generate personalized suggestions

[1275] Proposal generation

[1276] The server integrates the results of image analysis, lifestyle data analysis, and the emotional state calculated by the emotion engine. Based on the integrated data, it generates appropriate lifestyle improvement measures and skincare product recommendations. Specific examples of recommendations include "increasing protein in your diet," "the importance of stress management," and "using a specific shampoo." Taking into account the user's emotional state, the recommendations are adjusted to be most acceptable to the user.

[1277] Submit your proposal

[1278] The generated proposals are sent from the server to the user's device, where the smartphone application displays the received proposals and allows the user to confirm and implement them.

[1279] User Feedback

[1280] Suggestion display

[1281] The user device displays the suggestions received from the server within the application. The user can check the suggestions through the application and incorporate them into their daily lives. The emotion engine analyzes the user's reactions to the suggestions in real time and provides feedback.

[1282] Recording of actions taken

[1283] The user implements the suggested improvements and records the results within the application, and the smartphone stores these execution data locally for the next data collection.

[1284] Ongoing follow-up and feedback

[1285] Regular photo shoot

[1286] The user takes another photo of their head after a certain period of time to obtain new data. The smartphone stores the new image data locally and prepares to send it to the server.

[1287] Reevaluation

[1288] The server re-analyzes the new image data and lifestyle data, compares them with the previous data, and re-evaluates them. The emotion engine reflects new feedback based on changes in the user's emotional state.

[1289] Feedback Updates

[1290] The server regenerates appropriate improvement suggestions and sends them to the user's device. The smartphone displays the latest suggestions to the user, supporting effective management. The emotion engine continuously analyzes the user's reactions and evaluates the effectiveness of the suggestions.

[1291] Specific examples

[1292] For example, a user can use the application to take a photo of their head and answer a questionnaire about diet, exercise, and stress. This data is sent to a server, where the hair volume is analyzed, and lifestyle data is then analyzed by an AI model. Risk factors are identified, and suggestions such as "eat more protein" or "manage stress" are generated. These suggestions are optimized using sentiment analysis and presented to the user. If the user implements the suggestions and submits the data again after a certain period of time, continuous feedback and suggestions are provided.

[1293] Prompt Sentence Examples

[1294] "Analyze the user's head images and lifestyle data to identify hair volume status and associated risk factors."

[1295] "Generate dietary and lifestyle modification suggestions based on hair volume status and risk factors."

[1296] "Optimize your suggestions based on the user's emotional state."

[1297] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1298] Step 1:

[1299] Head photography using a user device

[1300] The user starts the smartphone app and takes a photo by following the head photography guide. The acquired image data is saved in local storage. The input is the captured image of the head, and the output is the image data saved in local storage.

[1301] Step 2:

[1302] Answers to lifestyle questionnaire

[1303] Users answer questionnaires presented within the application and enter information about their lifestyle habits, such as diet, exercise, and stress. This data is saved in local storage. The input is the lifestyle data entered by the user in the questionnaire form, and the output is the questionnaire data saved in local storage.

[1304] Step 3:

[1305] Sending data to the server

[1306] The smartphone device sends the saved image data and survey data to the server. The input is the image data and survey data saved in the local storage, and the output is the data sent to the server.

[1307] Step 4:

[1308] Image analysis

[1309] The server analyzes the received image data using an image analysis library such as OpenCV. Specifically, it quantifies the amount of hair and the degree of exposed skin, and stores the results in a database. The input is the transmitted image data, and the output is the numerical data resulting from the analysis.

[1310] Step 5:

[1311] Lifestyle data analysis

[1312] The server inputs the received survey data into a generative AI model such as TensorFlow to identify risk factors. The analysis results are converted into a numerical score for each risk factor and stored in a database. The input is the submitted survey data, and the output is the risk factor score.

[1313] Step 6:

[1314] Emotion analysis

[1315] The server analyzes the user's facial expression and voice data to evaluate their emotional state. The smartphone's camera and microphone are used to acquire real-time emotional data, and the evaluation results are sent to the server. The input is the emotional data acquired in real time, and the output is the evaluation result of the emotional state.

[1316] Step 7:

[1317] Proposal generation

[1318] The server integrates the results of image analysis, lifestyle data analysis, and emotion analysis to generate appropriate lifestyle improvement and care product recommendations. Suggestions might include, for example, "increase protein in your diet" or "importance of stress management." The input is the analysis results obtained in the previous step, and the output is the generated recommendations.

[1319] Step 8:

[1320] Submit your proposal

[1321] The server sends the generated proposal to the user's smartphone. The input is the proposal, and the output is the proposal sent to the user's device.

[1322] Step 9:

[1323] Viewing Proposals

[1324] The smartphone displays the received suggestions in the app so that the user can check them. The input is the suggestions sent from the server, and the output is the suggestions displayed in the app.

[1325] Step 10:

[1326] Recording of actions taken

[1327] The user implements the proposed improvements and records the results within the app. The smartphone stores the implementation data locally for the next data collection. The input is the implementation data of the improvements implemented by the user, and the output is the locally stored implementation data.

[1328] 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.

[1329] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1330] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1331] [Fourth embodiment]

[1332] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1333] 7, a 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.

[1334] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1335] 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.

[1336] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1337] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1338] 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.

[1339] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1340] 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.

[1341] 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 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.

[1342] 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.

[1343] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1344] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1345] As an embodiment of this invention, we will specifically explain a system that acquires images of a user's head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair loss. This system consists of a user terminal, a server, and a software module for analysis and feedback. The detailed operation of each element is shown below.

[1346] 1. User data collection

[1347] Head photography

[1348] The user launches the app and follows the instructions to take a photo of their head. The device stores this image data locally and prepares it for transmission to the server.

[1349] Survey responses

[1350] Users answer a questionnaire within the app and enter data about their lifestyle and eating habits, which are then collected by the device and sent to the server along with image data.

[1351] 2. Data Processing and Analysis

[1352] Image analysis

[1353] The image data received by the server is input into the image analysis module, which evaluates and quantifies the amount of hair on the user's head and the degree of exposed skin, and saves the analysis results.

[1354] Lifestyle data analysis

[1355] The server inputs lifestyle data into an AI model, which analyzes the relationship between lifestyle habits and the progression of hair loss. The AI ​​model then identifies risk factors and generates a score for each factor.

[1356] 3. Generating personalized suggestions

[1357] Proposal generation

[1358] The server combines the results of image analysis and lifestyle data analysis to generate optimal lifestyle improvement measures for the user, such as "increase protein in your diet," "importance of stress management," and "use a specific shampoo."

[1359] Submit your proposal

[1360] Once the suggestions are generated, the server sends them to the user's device, which displays them in the app for the user to review.

[1361] 4. User Feedback

[1362] Suggestion display

[1363] The device receives suggestions from the server and displays them within the app. The user checks the app and incorporates the suggestions into their daily lives.

[1364] Recording of actions taken

[1365] The user implements the suggestions and enters and records the results, which the device stores locally for the next data collection.

[1366] 5. Ongoing follow-up and feedback

[1367] Regular photo shoot

[1368] The user takes another photo of the head after a certain period of time, and the terminal prepares to send the new image data to the server.

[1369] Reevaluation

[1370] The server reanalyzes the new image data and lifestyle data and compares them with the previous proposal.

[1371] Feedback Updates

[1372] The server regenerates appropriate improvement suggestions and sends them to the terminal, which then displays the latest suggestions to the user, supporting effective management.

[1373] Specific examples

[1374] The user uses the app to take a photo of their head and answer a questionnaire about their diet and exercise habits. The device sends this information to a server, which analyzes the image and lifestyle data. The server generates suggestions such as "eat more protein" or "you need to manage your stress" and sends them to the device. The user confirms and implements these suggestions. After a certain period of time, the user takes another photo of their head and sends the latest data to the server. The server then reassesssssments and provides feedback on any improvements.

[1375] This allows users to effectively manage the progression of hair thinning and implement appropriate remedial measures.

[1376] The processing flow will be explained below.

[1377] Step 1:

[1378] The user launches the app and takes a picture of their head. The device stores the captured image data locally and prepares to send it to the server.

[1379] Step 2:

[1380] The user answers a questionnaire within the app. The questions consist of dietary habits, exercise habits, stress levels, etc. The device collects the questionnaire data and prepares to send it to the server along with the image data.

[1381] Step 3:

[1382] The device sends the captured image data and the survey data to the server, which receives the data and prepares it for analysis.

[1383] Step 4:

[1384] The server inputs the image data into an image analysis module, which evaluates the amount of hair on the head and the degree of exposed skin, converts the progress of hair loss into numerical values, and stores the results in a database.

[1385] Step 5:

[1386] The server inputs the lifestyle data it receives into an AI model, which analyzes the relationship between lifestyle habits and the progression of hair loss and identifies risk factors. Scores for these risk factors are also generated and stored in a database.

[1387] Step 6:

[1388] The server integrates the results of image analysis and lifestyle data analysis. Based on the integrated data, it generates appropriate lifestyle improvement measures and skincare product recommendations. For example, specific recommendations such as "increase protein in your diet," "importance of stress management," and "use a specific shampoo" are generated.

[1389] Step 7:

[1390] The server sends the generated proposal to the user's device, which then displays the proposal received in the app so that the user can confirm and implement it.

[1391] Step 8:

[1392] The user implements the suggested improvements and records the results within the app. The device stores these execution data locally and prepares them for the next analysis.

[1393] Step 9:

[1394] After a certain period of time has passed, the user takes another image of their head to acquire new data. The device stores the new image data locally and prepares to send it to the server.

[1395] Step 10:

[1396] The device sends new image data to the server, which receives the new data and compares it with the previous data for re-evaluation.

[1397] Step 11:

[1398] The server generates appropriate improvement suggestions again based on the results of the reevaluation. The new suggestions are sent to the user's device, which displays them in the app. This allows the user to check the latest suggestions and make continuous improvements.

[1399] Example 1

[1400] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1401] Conventional hair loss prevention systems have the problem of being difficult to provide personalized advice based on the user's lifestyle and environment. Furthermore, they lack the functionality to continuously record the results of implementing the recommendations and provide feedback, making long-term hair loss management difficult. Furthermore, there is no cycle of reevaluation and improvement suggestions, meaning users cannot always receive the latest advice.

[1402] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1403] In this invention, the server includes means for acquiring an image of the head, means for analyzing the acquired image data to evaluate the progression of hair thinning, means for collecting lifestyle data of the user, means for analyzing the collected lifestyle data and identifying risk factors for hair thinning, means for suggesting appropriate lifestyle improvements and hair care products based on the progression of hair thinning and the risk factors, means for presenting the suggestions to the user, means for recording the results of implementing the suggestions, and means for continuous follow-up and reassessment. This makes it possible to provide personalized advice based on the user's individual lifestyle, and provide continuous feedback and the latest improvement suggestions.

[1404] The "means for acquiring an image of the head" is a combination of hardware and software for photographing the user's head and capturing the image data.

[1405] The "means for analyzing acquired image data to assess the progression of hair loss" refers to an algorithm or program that analyzes the amount of hair and the degree of exposure of the scalp based on the captured image of the head, and quantifies and assesses the progression of hair loss.

[1406] The "means for collecting user lifestyle data" is an interface for collecting data on the user's lifestyle habits such as diet, exercise, and sleep using questionnaires, sensors, etc.

[1407] The "means of analyzing collected lifestyle data and identifying risk factors for hair loss" is a system that uses AI models and statistical methods to identify risk factors related to the progression of hair loss based on collected lifestyle data.

[1408] The "means for suggesting appropriate lifestyle improvements and hair care products based on the progression of hair thinning and risk factors" is a component that generates lifestyle improvement measures and appropriate hair care products customized for the user based on the results of image analysis and lifestyle data analysis.

[1409] The "means for presenting the proposal content to the user" is a system that has the function of transmitting the generated proposal content to the user's device and displaying it to the user through an app or other user interface.

[1410] The "means for recording the results of implementing the suggested content" is a function that allows the user to input the results after implementing the suggested lifestyle improvements or hair care products, and save them in a database or local storage for use in the next analysis.

[1411] The "means for continuous follow-up observation and re-evaluation" is a system that re-collects the user's head images and lifestyle data at regular intervals, compares them with the previous results, and makes new analyses and proposals.

[1412] This invention describes a system that acquires images of a user's head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair loss. This system consists of a device used by the user, a server that analyzes the data, and a software module for analysis and feedback.

[1413] First, the user launches the app and takes a photo of their head. The device stores the captured image data locally and prepares it for transmission to the server. At the same time, the user answers a questionnaire within the app and enters data about their lifestyle and eating habits into the device. This data is also collected by the device and sent to the server along with the image data.

[1414] The server inputs the received image data into an image analysis module (e.g., OpenCV or TensorFlow) to evaluate and quantify the amount of hair on the user's head and the degree of exposed scalp. The analysis results are stored on the server. Next, the server inputs the lifestyle data into an AI model (e.g., scikit-learn or TensorFlow) to analyze the relationship between lifestyle habits and the progression of hair loss. The AI ​​model identifies risk factors and generates a score for each factor.

[1415] The server then combines the image analysis results with the lifestyle data analysis results to generate optimal lifestyle improvement measures for the user. These include specific suggestions such as "increase protein in your diet," "importance of stress management," and "use a specific shampoo." The generated suggestions are sent from the server to the user's device and displayed within the app. The user can review the suggestions and incorporate them into their daily lives.

[1416] After implementing the suggestions, the user enters the results into the app, and the device stores this data locally. After a certain period of time, the user takes another photo of their head and sends the new image and input data to the server. The server reanalyzes the new data, compares it with the previous suggestions to evaluate their effectiveness, and generates new feedback. This allows for continuous follow-up and feedback.

[1417] As a specific example, a user uses an app to take a photo of their head and answer a questionnaire about their diet and exercise habits. The device sends this information to a server, which analyzes the image and lifestyle data. The server uses a generative AI model to generate suggestions such as "eat more protein" or "you need to manage your stress," which are sent to the device. The user confirms and implements these suggestions. After a certain period of time, the user takes another photo of their head and sends the new data to the server. The server then reevaluates and provides feedback on any improvements.

[1418] Example prompt sentence:

[1419] Analyze data on the user's hair loss progression and suggest lifestyle improvements. Generate specific advice based on the user's dietary data and the results of head image analysis.

[1420] In this way, the present invention allows users to effectively manage the progression of hair thinning and implement appropriate remedial measures based on their individual lifestyle habits.

[1421] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1422] Step 1:

[1423] The user launches the app and takes a photo of their head.

[1424] Input: User's head image

[1425] Operation: The device activates the camera function and captures an image of the user's head. The captured image data is stored locally and prepared for transmission to the server.

[1426] Output: locally saved head image data

[1427] Step 2:

[1428] Users answer a questionnaire within the app and enter data about their lifestyle and eating habits.

[1429] Input: Lifestyle data (diet, exercise, sleep, etc.)

[1430] How it works: Users fill out survey forms within the app and their devices collect this data.

[1431] Output: Locally saved lifestyle data

[1432] Step 3:

[1433] The device sends the locally stored image data and lifestyle habit data to a server.

[1434] Input: Head image data, lifestyle data

[1435] Operation: The device communicates over the network to send the stored image data and lifestyle habit data to the server.

[1436] Output: Image data and lifestyle data sent to the server

[1437] Step 4:

[1438] The image data received by the server is input into the image analysis module, where analysis is performed.

[1439] Input: Head image data

[1440] Operation: The server uses an image analysis module (e.g., OpenCV, TensorFlow) to analyze image data and evaluate and quantify the amount of hair on the head and the degree of exposed skin.

[1441] Output: Analysis results (evaluation values ​​for hair volume and scalp exposure)

[1442] Step 5:

[1443] The lifestyle data received by the server is input into an AI model, which analyzes the relationship between lifestyle habits and the progression of hair loss.

[1444] Input: Lifestyle data

[1445] How it works: The server uses AI models (e.g., scikit-learn, TensorFlow) to analyze lifestyle data to identify risk factors and generate a score for each factor.

[1446] Output: Risk factor scoring results

[1447] Step 6:

[1448] The server integrates the results of image analysis and lifestyle data analysis to generate optimal lifestyle improvement measures for the user.

[1449] Input: Image analysis results, risk factor scores

[1450] How it works: Based on the results of image analysis and lifestyle data analysis, the server uses a generative AI model to generate appropriate lifestyle improvement measures and hair care products. For example, it generates recommendations such as "take a certain amount of protein," "stress management is necessary," and "use a specific shampoo."

[1451] Output: Generated lifestyle improvement measures and hair care product suggestions

[1452] Step 7:

[1453] Once the proposal is generated, the server sends it to the user's device.

[1454] Input: Lifestyle improvement measures and hair care product suggestions

[1455] Operation: The server sends the generated proposal to the user's device and notifies them.

[1456] Output: Suggestions sent to the device

[1457] Step 8:

[1458] The device receives suggestions from the server and displays them within the app. The user can then review the suggestions and incorporate them into their daily lives.

[1459] Input: Received proposal

[1460] Operation: The device displays the suggestion on the app screen and notifies the user.

[1461] Output: Suggestions displayed on the app screen

[1462] Step 9:

[1463] Users implement the suggested lifestyle changes and hair care products and enter their results into the app, which stores them locally on the device.

[1464] Input: Proposal execution result

[1465] How it works: The user enters their thoughts and results after implementing the suggestions into the app, which is then saved locally on the device.

[1466] Output: Locally saved proposal execution results

[1467] Step 10:

[1468] After a certain period of time, the user takes another photo of the head and sends the new image data to the server.

[1469] Input: New head image data

[1470] How it works: The user takes another photo of their head, and the device stores the new image data locally and sends it to the server at intervals.

[1471] Output: New head image data sent to the server

[1472] Step 11:

[1473] The server re-analyzes the new image data and lifestyle data, compares them with the results of the previous analysis, evaluates the effectiveness, and generates new feedback.

[1474] Input: New head image data, recollected lifestyle data

[1475] How it works: The server analyzes the images and lifestyle data again, compares them with the previous analysis results, and evaluates any changes. It then generates appropriate feedback.

[1476] Output: New feedback

[1477] Through this series of steps, users can continuously manage the progression of hair loss and implement appropriate improvements based on their individual lifestyle habits.

[1478] (Application example 1)

[1479] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1480] Conventional hair thinning treatment systems have difficulty in providing consistent progress monitoring and feedback to individual users. Furthermore, because the recommendations are not based on individual lifestyle habits or risk factors, they are unable to provide effective improvement measures. Furthermore, when used in physical salons, there is an issue of information not being shared smoothly between salon staff and customers. To solve these issues, a system is needed that provides continuous and personalized feedback and supports use in physical salons and other such stores.

[1481] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1482] In this invention, the server includes means for acquiring an image of the head, means for analyzing the acquired image data to evaluate the progress of hair thinning, means for collecting lifestyle habit data of the user, means for analyzing the collected lifestyle habit data and identifying risk factors for hair thinning, means for proposing appropriate lifestyle improvements and care products based on the progress of hair thinning and the risk factors, means for presenting the suggestions to the user, means for periodically acquiring and reanalyzing the user's progress observation data, means for updating the suggestions based on the reanalysis results, and means for sharing the suggestions between staff terminals and customer terminals in a physical store such as a beauty salon. This allows for personalized feedback to be provided to each user, and for staff and customers to share information and implement effective measures against hair thinning.

[1483] The "means for acquiring an image of the head" is a device that takes a photograph of the user's head using a digital camera or a smartphone camera and acquires the image data.

[1484] The "means for analyzing acquired image data to evaluate the progression of hair thinning" is software that uses acquired image data of the head to analyze the amount of hair and the degree of exposure of the scalp, and numerically evaluates the progression of hair thinning.

[1485] The "means for collecting user lifestyle data" is a device that collects information about the user's diet, exercise, stress, and other daily habits through questionnaires or sensors.

[1486] The "means for analyzing collected lifestyle data and identifying risk factors for hair loss" is software that analyzes collected lifestyle data of users and identifies risk factors that affect the progression of hair loss.

[1487] The "means for suggesting appropriate lifestyle improvements and care products" is software that recommends specific lifestyle improvements and care products to users based on the progression of hair loss and risk factors.

[1488] The "means for presenting the proposal content to the user" is a device that transmits the generated proposal content to the user's terminal and displays it.

[1489] The "means for periodically acquiring and reanalyzing the user's follow-up observation data" is a device that allows the user to take another photograph of the head after a certain period of time, collect the photograph together with the latest lifestyle habit data, and perform reanalysis.

[1490] The "means for updating the content of the proposals based on the reanalysis results" is software that regenerates and updates the proposals for optimal lifestyle improvements and care products based on the reanalyzed data.

[1491] A "means for sharing proposal content between staff terminals and customer terminals in a physical store such as a beauty salon" is a device that can send generated proposal content to staff terminals and customer terminals in the physical store and share information.

[1492] As an embodiment of this invention, we will specifically explain a system that acquires images of the head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair thinning. This system consists of a user terminal, a server, and a software module for analysis and feedback. In particular, it is intended for use in brick-and-mortar stores such as beauty salons.

[1493] 1. User data collection

[1494] Head photography

[1495] Users visit a hair salon and take a photo of their head using a smartphone app. This image data is immediately uploaded to a cloud server. The image of the head is used for image analysis to evaluate the user's hair volume and the degree of exposed skin.

[1496] Survey responses

[1497] Customers fill out a questionnaire about their lifestyle habits on a tablet device at the salon, and this data is also sent to the cloud server.

[1498] 2. Data Processing and Analysis

[1499] Image analysis

[1500] The server analyzes the image data uploaded to the cloud using OpenCV. Specifically, it detects the head using cv2.CascadeClassifier and evaluates the amount of hair and the degree of exposed skin within the detected area using a TensorFlow model.

[1501] Lifestyle data analysis

[1502] The questionnaire data answered by the user is input into the AI ​​model, which analyzes the relationship between lifestyle habits and the progression of hair loss, identifies risk factors, and generates a score for each factor.

[1503] 3. Generating personalized suggestions

[1504] Proposal generation

[1505] The results of image analysis and lifestyle data analysis are integrated to generate optimal lifestyle improvement measures, such as specific suggestions such as "eat a diet high in protein" or "stress management is necessary."

[1506] Submit and share your suggestions

[1507] The server sends the proposed content to the user's terminal and the salon staff terminal, allowing the salon staff to provide specific advice to the user.

[1508] 4. Follow-up and feedback

[1509] Regular imaging and reassessment

[1510] The user returns to the salon after a certain period of time and takes a new photo of their head, which is again sent to the server for re-analysis.

[1511] Feedback Updates

[1512] Based on the latest analysis results, the proposals are updated and sent to the user's terminal and the salon staff terminal.

[1513] Specific examples

[1514] When a customer visits a beauty salon, they take a photo of their head using a smartphone app. They then answer a questionnaire about their lifestyle habits on a tablet device. This data is sent to a cloud server. After analyzing the image and lifestyle data, the server generates recommendations such as "increase protein intake" or "strengthen stress management." These recommendations are shared with both the customer and the salon staff, allowing them to take specific actions.

[1515] Prompt Sentence Examples

[1516] "Develop an app for a hair salon that takes a photo of the user's head, sends it to a cloud server, and analyzes the results of a lifestyle questionnaire to generate suggestions for preventing hair loss."

[1517] In this way, the system provides personalized feedback to each user, enabling salon staff and customers to share information and implement effective measures to combat hair loss.

[1518] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1519] Step 1:

[1520] Acquiring a user's head image

[1521] When a user visits a beauty salon, the salon staff takes a photo of the user's head using a dedicated smartphone app. This image data is saved in the smartphone's local storage and sent to a cloud server. The input is the user's head image data, and the output is the transmission of the image data to the cloud server.

[1522] Step 2:

[1523] Collection of lifestyle data

[1524] Users answer a questionnaire about their lifestyle habits on a tablet device at a beauty salon. Questions include, "How many times a week do you exercise?" and "How many times a day do you eat?" This data is also sent to the cloud server. The input is the user's questionnaire response data, and the output is the transmission of lifestyle habit data to the cloud server.

[1525] Step 3:

[1526] Image data analysis

[1527] The server analyzes the image data uploaded to the cloud using OpenCV. Specifically, the server detects the head using cv2.CascadeClassifier, and then evaluates the hair volume and the degree of exposed skin using a TensorFlow model. The input is the image data of the head, and the output is the evaluation results of the hair volume and the degree of exposed skin.

[1528] Step 4:

[1529] Lifestyle data analysis

[1530] The server inputs the collected lifestyle data into an AI model and analyzes the relationship between lifestyle and the progression of hair loss. The server identifies risk factors and generates a score for each factor. The input is lifestyle data, and the output is the identification of risk factors and the generation of a score.

[1531] Step 5:

[1532] Proposal generation

[1533] The server integrates the results of image analysis and lifestyle data analysis to generate optimal lifestyle improvement measures and care product recommendations. For example, suggestions include "eat a diet high in protein" and "stress management is important." The inputs are the image analysis results and lifestyle data analysis results, and the output is lifestyle improvement measures and care product recommendations.

[1534] Step 6:

[1535] Submit and share your suggestions

[1536] The server sends the generated suggestions to the user's terminal and the salon staff terminal, allowing the salon staff to provide specific advice to the user. The input is the generated suggestions, and the output is the transmission of the suggestions to the user's terminal and the salon staff terminal.

[1537] Step 7:

[1538] Regular acquisition of follow-up data

[1539] The user returns to the salon after a certain period of time and takes another photo of their head. This new image data is also sent to the cloud server. The input is the new head image data, and the output is the transmission of the image data to the cloud server.

[1540] Step 8:

[1541] Reassessment and feedback updates

[1542] The server reanalyzes the new image data and lifestyle data and compares it with the previous data. It evaluates how effective the original suggestions were and generates new suggestions. The updated suggestions are sent to the user's device and the salon staff's device. The input is the new image data and lifestyle data, and the output is the transmission of the updated suggestions.

[1543] This allows the entire process to work seamlessly to provide effective hair loss treatment to the user.

[1544] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1545] As an embodiment of this invention, we will specifically explain a system that acquires images of a user's head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair loss. This system consists of a user terminal, a server, a software module that performs analysis and feedback, and an emotion engine. The detailed operation of each element is shown below.

[1546] User data collection

[1547] Head photography

[1548] The user launches the app and takes a photo by following the head guide. The device stores this image data locally and prepares it for transmission to the server.

[1549] Survey responses

[1550] Users answer a questionnaire within the app and enter data about their lifestyle and eating habits, which are then collected by the device and sent to the server along with image data.

[1551] Data processing and analysis

[1552] Image analysis

[1553] The server inputs the received image data into an image analysis module, which evaluates the amount of hair on the head and the degree of exposed skin, converts the progress of hair loss into numerical values, and stores the results in a database.

[1554] Lifestyle data analysis

[1555] The server inputs lifestyle data into the AI ​​model, which analyzes the relationship between lifestyle and the progression of hair loss. The AI ​​model identifies risk factors, generates a score for each factor, and stores it in a database.

[1556] Emotion analysis

[1557] The emotion engine collects the user's facial expression data, voice data, or text data and analyzes the user's emotional state. For example, the emotion data is acquired in real time using the smartphone's camera and microphone and sent to the emotion engine.

[1558] Generate personalized suggestions

[1559] Proposal generation

[1560] The server integrates the results of image analysis, lifestyle data analysis, and the emotional state of the user based on the emotion engine. Based on the integrated data, it generates appropriate lifestyle improvement and skincare recommendations. For example, recommendations include "increase protein in your diet," "importance of stress management," and "use a specific shampoo." The recommendations are adjusted based on the user's emotional state and presented in the most acceptable format for the user.

[1561] Submit your proposal

[1562] The server sends the generated proposal to the user's device, which then displays the proposal received in the app so that the user can confirm and implement it.

[1563] User Feedback

[1564] Suggestion display

[1565] The device receives the suggestions from the server and displays them within the app. The user checks the app and incorporates the suggestions into their daily lives. The emotion engine analyzes the user's reaction to the suggestions in real time and provides feedback.

[1566] Recording of actions taken

[1567] The user implements the suggested improvements and records the results within the app, and the device stores these execution data locally for the next data collection.

[1568] Ongoing follow-up and feedback

[1569] Regular photo shoot

[1570] The user takes another photo of their head after a certain period of time to obtain new data. The device stores the new image data locally and prepares to send it to the server.

[1571] Reevaluation

[1572] The server reanalyzes the new image data and lifestyle data, compares them with the previous data, and reassesssssments. The emotion engine reflects new feedback based on changes in the user's emotional state.

[1573] Feedback Updates

[1574] The server regenerates appropriate improvement suggestions and sends them to the device. The device displays the latest suggestions to the user, supporting effective management. The emotion engine continuously analyzes the user's reactions and evaluates the effectiveness of the suggestions.

[1575] As a specific example, a user uses an app to take a photo of their head and answer a questionnaire about their diet and exercise habits. The device sends this information to a server, which analyzes the image and lifestyle data. The server generates suggestions such as "eat more protein" or "you need to manage your stress," and sends them to the device in an optimal form that takes into account the user's emotional state using an emotion engine. The user confirms and implements these suggestions. After a certain period of time, the user takes another photo of their head and sends the latest data to the server. The server then re-evaluates and provides feedback on any improvements. The emotion engine continuously analyzes the user's emotional state and provides optimal feedback.

[1576] The processing flow will be explained below.

[1577] Step 1:

[1578] The user launches the app and takes an image of their head. The device stores this image data locally and prepares it for transmission to the server.

[1579] Step 2:

[1580] The user answers a questionnaire within the app, which asks about their eating habits, exercise habits, and stress levels. The device collects the questionnaire data and prepares to send it to the server along with the image data.

[1581] Step 3:

[1582] The device sends the captured image data and the survey data to the server, which receives the data and prepares it for analysis.

[1583] Step 4:

[1584] The server inputs the image data into an image analysis module, which evaluates the amount of hair on the head and the degree of exposed skin, converts the progress of hair loss into numerical values, and stores the results in a database.

[1585] Step 5:

[1586] The server inputs the lifestyle data it receives into an AI model, which analyzes the relationship between lifestyle habits and the progression of hair loss and identifies risk factors. Scores for these risk factors are also generated and stored in a database.

[1587] Step 6:

[1588] The emotion engine collects the user's facial expression data, voice data, or text data and analyzes the user's emotional state. For example, the emotion data is acquired in real time using a camera and a microphone and sent to the emotion engine.

[1589] Step 7:

[1590] The server integrates the results of image analysis, lifestyle data analysis, and the emotional state of the user based on the emotion engine. Based on the integrated data, it generates appropriate lifestyle improvement and skincare product recommendations. Examples include "increase protein in your diet," "importance of stress management," and "use a specific shampoo." The recommendations are adjusted based on the user's emotional state and presented in the most acceptable format for the user.

[1591] Step 8:

[1592] The server sends the generated proposal to the user's device, which then displays the proposal received in the app so that the user can confirm and implement it.

[1593] Step 9:

[1594] The user implements the suggested improvements and records the results within the app, and the device stores these execution data locally for the next data collection.

[1595] Step 10:

[1596] After a certain period of time has passed, the user takes another image of their head to acquire new data. The device stores the new image data locally and prepares to send it to the server.

[1597] Step 11:

[1598] The device sends new image data to the server, which receives the new data and compares it with the previous data for re-evaluation. This allows the progress of hair loss and the effectiveness of treatment to be monitored.

[1599] Step 12:

[1600] The server generates appropriate improvement suggestions again based on the results of the reevaluation. The new suggestions are sent to the device, which displays them within the app. This allows the user to check the latest suggestions and make continuous improvements.

[1601] Step 13:

[1602] The emotion engine analyzes the user's emotional state in real time and provides feedback: when the user expresses an emotional reaction to a suggestion, the emotion engine processes this and adjusts the suggestion as needed.

[1603] As a specific example, a user uses an app to take a photo of their head and answer a questionnaire about their diet and exercise habits. The device sends this information to a server, which analyzes the image and lifestyle data. The server generates suggestions such as "eat more protein" or "you need to manage your stress," and sends them to the device in an optimal form that takes into account the user's emotional state using an emotion engine. The user confirms and implements these suggestions. After a certain period of time, the user takes another photo of their head and sends the latest data to the server. The server then re-evaluates and provides feedback on any improvements. The emotion engine continuously analyzes the user's emotional state and provides optimal feedback.

[1604] Example 2

[1605] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1606] Many people today suffer from thinning hair and are searching for effective ways to slow its progression and improve it. However, existing methods are unable to provide personalized recommendations that take into account individual lifestyle habits and emotional states, limiting their effectiveness. Furthermore, there is a lack of systems that continuously track users' responses and provide feedback. Therefore, there is a need to propose a system that analyzes a user's head images, lifestyle habits, and emotional data, and integrates them to provide optimal solutions to improve hair loss more effectively.

[1607] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring an image of the user's head, means for analyzing the acquired image data to evaluate the progression of hair thinning, means for collecting the user's lifestyle data, means for analyzing the collected lifestyle data and identifying risk factors for hair thinning, means for collecting and analyzing the user's emotional data, means for suggesting appropriate lifestyle improvements and care products based on the progression of hair thinning, risk factors, and emotional data, means for presenting the suggestions to the user, and means for recording the user's responses and continuously analyzing the data. This makes it possible to provide personalized improvement measures tailored to the user's individual situation and effectively suppress the progression of hair thinning.

[1608] "User" refers to an individual who uses the system to take a head image and input lifestyle data.

[1609] "Head image" means photographic data of the user's head, and is primarily used to evaluate the condition of the hair.

[1610] "Lifestyle data" includes information about a user's diet, exercise, sleep, stress management, etc., and is used to identify risk factors for hair loss.

[1611] "Emotional data" refers to data about emotions obtained from a user's facial expressions, voice, or text, which is taken into account when making personalized suggestions.

[1612] "Image analysis" refers to the process of analyzing image data of the head and evaluating the amount of hair and the degree of exposure of the scalp.

[1613] "Risk factors" refer to factors that affect the progression of hair loss and are identified from lifestyle data and head images.

[1614] "Proposal content" refers to proposals for lifestyle improvement measures and care products formulated based on the results of image analysis, lifestyle data analysis, and emotional data.

[1615] "Server" means a remote computer system that receives, analyzes, and evaluates data sent by users.

[1616] "Terminal" means a device used by a user, such as a computer or smartphone, that acquires, transmits, and receives data.

[1617] "Analysis module" refers to a software component for analyzing image data and lifestyle habit data.

[1618] "Emotion Engine" means an algorithm or software for analyzing a user's emotional data and assessing their emotional state.

[1619] "Evaluation" refers to the process of quantifying or quantifying the progression of hair loss and risk factors based on the analysis results.

[1620] "Feedback" refers to the system's response and evaluation of the user's actions and reactions, and is used to reflect this in future suggestions.

[1621] The present invention relates to a system that acquires images of a user's head, collects and analyzes lifestyle data, and suggests lifestyle improvements based on the progression of hair thinning. The system is composed of a user terminal, a server, a software module that performs analysis and feedback, and an emotion engine.

[1622] User data collection

[1623] Head photography

[1624] The user launches the app and takes a photo by following the head guide. The device uses the camera function to acquire image data and saves it to local storage. This image data is then prepared for sending to the server. For the camera function, the device's standard camera app is used, and the camera is launched using an Intent. After the photo is taken, the image data is received by the onActivityResult method.

[1625] Survey responses

[1626] The user answers a questionnaire about their lifestyle and eating habits within the app. The device saves the entered information in JSON format and prepares to send it to the server along with the image data. The questionnaire UI is implemented using a mobile application development framework (e.g., Flutter or React Native).

[1627] Sending data

[1628] The image data and lifestyle habit data collected by the device are sent to the server using an HTTP POST request. Retrofit, for example, is used as the communication library.

[1629] Data processing and analysis

[1630] Image analysis

[1631] The image data received by the server is input into the image analysis module. Libraries such as OpenCV and TensorFlow are used for image analysis to evaluate the amount of hair on the head and the degree of exposed skin. The analysis results are stored in a database. Specifically, feature extraction is performed using a convolutional neural network (CNN).

[1632] Lifestyle data analysis

[1633] The server analyzes the lifestyle data and uses an AI model to identify risk factors for hair loss. The AI ​​model is trained using Scikit-learn and TensorFlow and generates a score for each risk factor. The analysis results are stored in a database.

[1634] Emotion analysis

[1635] The server uses an emotion engine to analyze the user's emotional data. Emotional data is obtained from facial expressions, voice, text, etc. NLP technologies such as Hugging Face's Transformers are used for this analysis. The emotional data obtained is analyzed in real time and the results are stored in a database.

[1636] Generate personalized suggestions

[1637] Proposal generation

[1638] The server combines the results of image analysis, lifestyle data analysis, and emotion analysis to generate recommendations for lifestyle improvements and skincare products that are optimal for the user. For example, it suggests dietary improvements or specific hair care products. This process may also incorporate rule-based systems.

[1639] Submit your proposal

[1640] The server sends the generated suggestions to the user's device either as a notification using Firebase Cloud Messaging (FCM) or as an in-app message.

[1641] User Feedback

[1642] Suggestion display

[1643] The device displays the suggestions received from the server within the app, allowing users to view the suggestions and incorporate them into their daily lives.

[1644] Recording of actions taken

[1645] The user implements the proposed improvements and records the results within the app. The device stores the execution data locally for the next data collection. Data is stored in a database such as SQLite.

[1646] Ongoing follow-up and feedback

[1647] Regular photo shoot

[1648] After a certain period of time, the user takes another photo of their head to acquire new data. The device saves the new image data in local storage and prepares to send it to the server.

[1649] Reevaluation

[1650] The server reanalyzes the new image data and lifestyle data, compares them with the previous data, and reassesssssments. The emotion engine reflects new feedback based on changes in the user's emotional state.

[1651] Feedback Updates

[1652] The server regenerates the latest improvement suggestions and sends them to the device. The device displays the latest suggestions to the user, supporting effective management. The emotion engine continuously analyzes the user's reactions and evaluates the effectiveness of the suggestions.

[1653] Specific examples and prompts for the generative AI model

[1654] As a specific example, a user uses an app to take a photo of their head, answer a questionnaire about their diet and exercise habits, and send the data to a server. The server analyzes the image and lifestyle data, generates suggestions, and sends them to the device after taking the user's emotional state into consideration using an emotion engine. The user confirms and implements the suggestions. After a certain period of time, the user takes another photo of their head and sends the updated data to the server. The server then reevaluates the results and provides new feedback.

[1655] An example of a prompt is as follows:

[1656] 1. "Assess the progression of hair loss using images of the user's head, while also taking into account questionnaire data about diet and lifestyle."

[1657] 2. "Analyze specific images and lifestyle data to suggest hair loss treatments that are suitable for the user. Also, reflect the user's emotional state."

[1658] 3. "Please analyze the new head image and the latest lifestyle data, and provide feedback on any improvements since the previous suggestion."

[1659] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1660] Step 1: Image the head

[1661] The user launches the app and takes a photo by following the photography guide on their head. The device acquires the image data using the camera function. Specifically, the device's camera app is launched using an Intent, and the image taken by the user is received by the onActivityResult method. This image data is then saved to local storage. The input is the image taken by the user, and the output is the image data saved in the device's local storage.

[1662] Step 2: Complete the survey

[1663] The user enters data about their lifestyle and eating habits into a questionnaire form within the app. The device saves this input data in JSON format and prepares to send it to the server along with image data. The input data is the lifestyle data entered by the user, and the output data is the lifestyle data saved in JSON format. The UI for the questionnaire form is developed using, for example, Flutter.

[1664] Step 3: Sending data

[1665] The image data and lifestyle habit data collected by the device are sent to the server. Specifically, an HTTP POST request is created and this data is included as the payload. The Retrofit library is used for this transmission. The input data is the image data and lifestyle habit data stored on the device, and the output data is the data sent to the server.

[1666] Step 4: Analyzing the image data

[1667] The image data received by the server is input into the image analysis module. Libraries such as OpenCV and TensorFlow are used for image analysis. The server evaluates the amount of hair on the head and the degree of exposed skin from the image data, and stores the analysis results in a database. The input data is the image data sent to the server, and the output data is the numerical data of the analysis results. Specifically, a convolutional neural network (CNN) extracts important features from the image.

[1668] Step 5: Analyze lifestyle data

[1669] The server analyzes the lifestyle data and uses an AI model to identify risk factors for hair loss. Scikit-learn and TensorFlow are used for this AI model. The server generates a score for each risk factor, which is also stored in a database. The input data is the lifestyle data sent to the server, and the output data is the risk factor score. Specifically, risk factors are scored using random forests and support vector machines (SVMs).

[1670] Step 6: Analyze the sentiment data

[1671] The server uses an emotion engine to analyze the user's facial expression data, voice data, or text data. Emotion data is acquired in real time using the smartphone's camera and microphone. NLP technologies such as Hugging Face Transformers are used for emotion analysis. The input data is the user's emotional data, and the output data is a score of the analyzed emotional state. Specifically, the server runs the facial expression recognition algorithm and voice analysis algorithm.

[1672] Step 7: Generate personalized suggestions

[1673] The server integrates the results of image analysis, lifestyle data analysis, and emotion analysis to generate recommendations for lifestyle improvements and care products that are optimal for the user. The recommendations consist of text and images generated by the server. The input data are the results of various analyses, and the output data are the recommendations. Specifically, a rule-based system or machine learning model automatically generates the recommendations based on the integrated data.

[1674] Step 8: Submit your proposal

[1675] The server sends the generated proposal to the user's device. The sending method is Firebase Cloud Messaging (FCM) or similar. The input data is the generated proposal, and the output data is the proposal displayed on the user's device. Specifically, a notification message in JSON format is created and sent via the FCM API.

[1676] Step 9: View and implement the proposal

[1677] The device will display the proposals received from the server within the app. The user will view the proposals and implement them as necessary. The input data will be the proposals sent from the server, and the output data will be the results of the implementation by the user. Specifically, we will develop a function to visualize the proposals in the app's UI.

[1678] Step 10: Recording the actions taken

[1679] The user implements the proposed improvement measures and records the results within the app. The device saves this execution data in local storage for the next analysis. The input data is the implementation data by the user, and the output data is the execution results saved on the device. Specifically, the data is managed using a database such as SQLite.

[1680] Step 11: Regular imaging and re-collection of data

[1681] After a certain period of time has passed, the user takes another photo of their head to obtain new data. The device saves the new image data and lifestyle habit data in local storage and prepares to send them to the server. The input data are the newly taken image and updated lifestyle habit data, and the output data is the new data sent to the server.

[1682] Step 12: Reassess and update feedback

[1683] The server reanalyzes the new image data and lifestyle data and compares it with the previous data for a reassessment. The emotion engine generates new feedback based on changes in the user's emotional state. The input data is the new data and the previous analysis results, and the output data is the updated feedback. Specifically, the new analysis results are saved in the database and the algorithm is rerun to update the suggestions.

[1684] Step 13: Send and view feedback

[1685] The server sends the updated feedback to the user's device, which then displays it. The input data is the feedback to be sent, and the output data is the displayed feedback. Specifically, the feedback is notified via FCM or other means, and a UI is implemented to display it within the app.

[1686] (Application example 2)

[1687] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1688] Conventional hair loss prevention systems simply analyze images of the head and collect and analyze lifestyle data, but are unable to provide suggestions based on the user's individual emotional state. This can make it difficult for users to accept the suggestions. Another drawback is that the suggestions are uniform and therefore not optimized for each user's situation.

[1689] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1690] In this invention, the server includes means for acquiring an image of the head, means for analyzing the acquired image data to evaluate the progression of hair thinning, means for collecting lifestyle data of the user, means for analyzing the collected lifestyle data and identifying risk factors for hair thinning, means for proposing appropriate lifestyle improvements and care products based on the progression of hair thinning and the risk factors, means for presenting the suggestions to the user, and means for analyzing the emotional state of the user and optimizing the suggestions. This makes it possible to make personalized suggestions based on the emotional state of each user, thereby improving the acceptance rate of suggestions and providing optimal improvement measures for each user.

[1691] The "means for acquiring an image of the head" refers to a device or software that allows a user to take an image of the head using an application and collect the image data.

[1692] The "means for analyzing acquired image data to evaluate the progression of hair thinning" refers to a device or software that analyzes captured head image data and quantifies or evaluates the progression of hair thinning, such as hair volume and the degree of scalp exposure.

[1693] The "means for collecting user lifestyle data" refers to a device or software that collects information about the user's lifestyle habits, such as diet, exercise, and sleep, through questionnaires or sensors.

[1694] The "means for analyzing collected lifestyle habit data and identifying risk factors for hair loss" refers to a device or software that analyzes collected lifestyle habit data and identifies risk factors that contribute to the progression of hair loss.

[1695] The "means for suggesting appropriate lifestyle improvements and care products" refers to a device or software that, based on the analysis results, makes suggestions to the user for improving their lifestyle and recommends care products to combat thinning hair.

[1696] The "means for presenting the proposed content to the user" refers to a device or software that notifies or displays the generated proposed content or recommendations on the user's terminal.

[1697] "Means for analyzing the user's emotional state and optimizing the content of suggestions" refers to a device or software that analyzes the user's facial expressions, voice, and text data to determine their emotional state, and then adjusts the content of suggestions in an optimal manner based on the results.

[1698] The embodiment of this invention is a system that acquires images of a user's head, collects and analyzes lifestyle data, and makes lifestyle improvement suggestions based on the progression of hair thinning. The system is composed of a user terminal, a server, a software module that performs analysis and feedback, and an emotion engine.

[1699] User data collection

[1700] Head photography

[1701] The smartphone, which acts as the user terminal, takes a picture of the user's head. The user launches the application and takes a picture of their head according to the specified guide. The acquired image data is stored in the smartphone's local storage and sent to the server.

[1702] Survey responses

[1703] Within the application, users answer a questionnaire about their lifestyle and eating habits, including questions about their diet, exercise habits, and stress. The questionnaire responses are stored on the smartphone and sent to a server along with image data.

[1704] Data processing and analysis

[1705] Image analysis

[1706] The server uses an image analysis library such as OpenCV to analyze the acquired image data. The image analysis module evaluates the amount of hair on the head and the degree of exposed skin, converts the results into numerical values, and stores them in a database.

[1707] Lifestyle data analysis

[1708] The server inputs the collected lifestyle data into a generative AI model such as TensorFlow to analyze the relationship between lifestyle habits and the progression of hair loss. This analysis identifies risk factors and generates a score for each factor. These scores are also stored in a database.

[1709] Emotion analysis

[1710] The emotion engine collects the user's facial expression, voice, and text data to analyze their emotional state. It captures emotional data in real time using the smartphone's camera and microphone and sends it to the server.

[1711] Generate personalized suggestions

[1712] Proposal generation

[1713] The server integrates the results of image analysis, lifestyle data analysis, and the emotional state calculated by the emotion engine. Based on the integrated data, it generates appropriate lifestyle improvement measures and skincare product recommendations. Specific examples of recommendations include "increasing protein in your diet," "the importance of stress management," and "using a specific shampoo." Taking into account the user's emotional state, the recommendations are adjusted to be most acceptable to the user.

[1714] Submit your proposal

[1715] The generated proposals are sent from the server to the user's device, where the smartphone application displays the received proposals and allows the user to confirm and implement them.

[1716] User Feedback

[1717] Suggestion display

[1718] The user device displays the suggestions received from the server within the application. The user can check the suggestions through the application and incorporate them into their daily lives. The emotion engine analyzes the user's reactions to the suggestions in real time and provides feedback.

[1719] Recording of actions taken

[1720] The user implements the suggested improvements and records the results within the application, and the smartphone stores these execution data locally for the next data collection.

[1721] Ongoing follow-up and feedback

[1722] Regular photo shoot

[1723] The user takes another photo of their head after a certain period of time to obtain new data. The smartphone stores the new image data locally and prepares to send it to the server.

[1724] Reevaluation

[1725] The server re-analyzes the new image data and lifestyle data, compares them with the previous data, and re-evaluates them. The emotion engine reflects new feedback based on changes in the user's emotional state.

[1726] Feedback Updates

[1727] The server regenerates appropriate improvement suggestions and sends them to the user's device. The smartphone displays the latest suggestions to the user, supporting effective management. The emotion engine continuously analyzes the user's reactions and evaluates the effectiveness of the suggestions.

[1728] Specific examples

[1729] For example, a user can use the application to take a photo of their head and answer a questionnaire about diet, exercise, and stress. This data is sent to a server, where the hair volume is analyzed, and lifestyle data is then analyzed by an AI model. Risk factors are identified, and suggestions such as "eat more protein" or "manage stress" are generated. These suggestions are optimized using sentiment analysis and presented to the user. If the user implements the suggestions and submits the data again after a certain period of time, continuous feedback and suggestions are provided.

[1730] Prompt Sentence Examples

[1731] "Analyze the user's head images and lifestyle data to identify hair volume status and associated risk factors."

[1732] "Generate dietary and lifestyle modification suggestions based on hair volume status and risk factors."

[1733] "Optimize your suggestions based on the user's emotional state."

[1734] The flow of the specific processing in the application example 2...

Claims

1. means for acquiring an image of the head; A means for analyzing the acquired image data to evaluate the progression of hair thinning; means for collecting lifestyle data of a user; A method for analyzing collected lifestyle data and identifying risk factors for hair loss, Based on the progression of hair loss and risk factors, we will propose appropriate lifestyle changes and care products. means for presenting the suggestions to the user; A system including:

2. 2. The system according to claim 1, wherein the analyzing means evaluates the amount of hair on the head and the degree of exposure of the scalp.

3. 2. The system according to claim 1, wherein said suggesting means transmits the content of the suggestion to a terminal of the user and displays it thereon.

4. 2. The system according to claim 1, further comprising means for periodically updating and reassessing the lifestyle habit data of the user.

5. 10. The system of claim 1, wherein the means for providing suggestions includes suggestions regarding dietary habits, exercise habits, and stress management.

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