system

Abstract

Provide a system. 【Solution means】 Means for collecting public data related to hair from information sources on the Internet, Means for collecting measurement data via sensors and software for obtaining the personal data of users, Means for integrating and analyzing the public data and the measurement data to extract features, Means for generating an individually optimized hair care program based on the extracted features, Means for presenting the generated hair care program to the user, Means for collecting evaluations from users and improving the hair care program, Means for automatically implementing the generated hair care program via a robotic device, Means for analyzing the condition of hair by a photographing device and providing the results in real time, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, many individuals have hair-related problems. In particular, middle-aged and elderly people are unable to find effective hair growth and anti-aging methods. In addition, since general hair care products and methods are designed for everyone, there is a lack of optimized care methods considering individual lifestyles and health conditions. Therefore, a more individualized approach is required.

Means for Solving the Problems

[0005] This invention includes means for collecting hair-related data from publicly available information on the internet, and means for collecting user-specific sensor data and information obtained through applications. This data is integrated and analyzed to generate a hair care program optimized for each individual user. Furthermore, this generated program is displayed step-by-step on a terminal to assist the user in implementing it. In addition, the program can be continuously updated based on user feedback to provide optimal care.

[0006] "Internet information sources" refer to a collection of digital data accessible on a global computer network, including scientific research, news articles, and consumer reviews.

[0007] "User personal data" refers to information related to individual users, including data on daily lifestyle habits, health status, and behavioral patterns collected through sensors and applications.

[0008] "Integration and analysis" refers to the process of combining information obtained from multiple data sources to derive consistent patterns and meanings.

[0009] A "hair care program" refers to a set of guidelines for hair care and improvement methods optimized for each individual user, generated based on analysis results.

[0010] "User feedback" refers to information based on opinions and experiences provided by users, and includes evaluations of the effectiveness and areas for improvement of hair care programs. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention is a system for providing personalized hair care programs and implements a process for effectively integrating and analyzing internet-based information sources and users' personal data. The configuration and operation of this system will be described in detail below.

[0033] Data collection and integration

[0034] The server collects publicly available hair-related data via the internet, including scientific research, industry trend information, and product reviews. Meanwhile, the device collects personal data obtained with the user's consent. This personal data includes fitness information from health apps, dietary records, and sleep data from smartphone sensors. This data is integrated into a dedicated database.

[0035] Analysis and program generation

[0036] The server analyzes the integrated data to extract unique patterns for each user. This analysis uses machine learning algorithms to identify lifestyle factors and health trends that influence hair condition. Based on these results, a personalized hair care program is generated. The program includes the necessary steps and product recommendations to improve the user's hair health.

[0037] Program provision and implementation support

[0038] The generated hair care program is provided to the user via the device. The provided program is visually displayed as a step-by-step guide for daily hair care. By following this guide and taking specific actions, the user can experience the effects.

[0039] Feedback and program updates

[0040] After performing hair care, users can provide feedback on the effects and their experience. The device collects this feedback and sends it to the server. The server uses the collected feedback and new data to continuously improve the hair care program, providing users with the latest and most effective care. This process helps users improve their hair health in a way that best suits their individual lifestyle.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server automatically collects hair-related data from publicly available information on the internet using a dedicated API and web scraping technology. This data includes the latest scientific research, product reviews, and industry news.

[0044] Step 2:

[0045] With the user's permission, the device continuously acquires individual data from sensors and apps. Specific data includes fitness data and meal logs provided by health apps, as well as sleep data from smartphone sensors.

[0046] Step 3:

[0047] The server integrates the acquired general-purpose data with personal data and performs data cleaning to remove malicious data. This improves the accuracy of the analysis.

[0048] Step 4:

[0049] The server uses machine learning algorithms to analyze the cleaned-up data. This analysis evaluates the impact of each user's lifestyle and health condition on hair, and extracts specific patterns.

[0050] Step 5:

[0051] Based on the analysis results, the server automatically generates a personalized hair care program for each user. The program includes specific product selections and daily care procedures.

[0052] Step 6:

[0053] The device notifies the user of the generated hair care program and presents it as a visually easy-to-understand step-by-step guide. This allows the user to take action based on the suggested program.

[0054] Step 7:

[0055] Users follow the presented hair care program and incorporate it into their daily lives. They can also provide feedback on the program's effectiveness and their experience.

[0056] Step 8:

[0057] The device collects user feedback and sends it to the server.

[0058] Step 9:

[0059] The server improves the hair care program based on feedback and newly collected data. This ensures that users are always provided with the latest and most effective care program.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] In recent years, there has been a growing demand for personalized care based on individual health conditions and lifestyles. However, in the case of hair care, it has been difficult to effectively utilize diverse personal data from users to provide optimal care programs. Furthermore, continuous adjustments and improvements are necessary to experience the effects of hair care, but there has been a lack of efficient feedback and update mechanisms for this purpose.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for collecting publicly available hair-related data from information sources on the Internet, means for collecting user personal data via communication devices, and means for integrating the publicly available data and the personal data and extracting patterns using machine learning algorithms. This enables the generation and provision of personalized hair care programs suited to the user's lifestyle and health condition, as well as continuous optimization through feedback.

[0065] The "Internet" is a global information network for sending and receiving information digitally around the world.

[0066] "Information sources" refer to digital platforms and databases that provide and allow access to publicly available data and knowledge.

[0067] "Hair" refers to fibrous tissue that grows on the head or body of living organisms, and in this invention, it is particularly related to hair on the head.

[0068] "Public data" refers to information that is made available on the internet in a form that anyone can access.

[0069] "Means" refer to the methods or technical elements used to achieve a specific objective.

[0070] A "user" is a person who uses a particular technology system or device, or an entity that provides data related to such use.

[0071] "Personal data" refers to information about a user, particularly information that is subject to privacy protection, including information about the user's lifestyle and health status.

[0072] "Communication equipment" refers to devices used to transmit and receive data, and includes digital devices such as smartphones and tablets.

[0073] "Integration" is the process of combining multiple data sets into a single, consistent format.

[0074] A "machine learning algorithm" is a computational method used to learn from experience and improve on specific tasks.

[0075] A "pattern" refers to a consistent feature or trend found within data.

[0076] "Visual presentation" refers to conveying information to users in an easily understandable format by displaying it in the form of images, diagrams, and other visual aids.

[0077] "Feedback" refers to the evaluations and opinions that users provide regarding the output or results of a system.

[0078] "Updating" is the process of modifying or improving existing programs or data based on new information.

[0079] This system is designed to provide users with personalized hair care programs and works as follows:

[0080] The server first collects hair-related data that is publicly available on the internet. This data includes scientific research findings, industry trends, and product reviews. Digital data acquisition techniques can be used for data collection, and specific examples include web scraping using Python libraries such as Beautiful Soup and Requests.

[0081] On the other hand, the device collects personal data with the user's consent. This includes data from health apps and food logging apps, as well as sleep data from smartphone sensors, collected using communication devices. This user data is collected using Bluetooth or Wi-Fi and transferred to a server.

[0082] The server integrates the acquired public data and personal data and stores it in a database. This integrated data is analyzed using machine learning algorithms to extract patterns related to lifestyle and health status. Existing data analysis libraries such as TENSORFLOW® and Scikit-learn are used for this analysis.

[0083] Based on the analysis results, a personalized hair care program is generated for each user. This program includes specific hair care steps and a list of recommended products. The generated program is presented to the user visually through the device's display. Users can then follow the provided guide to perform their daily hair care.

[0084] As feedback, users can send their care results and experiences to the server via their device. The server uses this feedback to update and optimize the hair care program. This allows users to continue receiving the latest care tailored to changes in their lifestyle and environment.

[0085] For example, if a user moves to a drier climate, their skincare program will be updated to recommend moisturizing products suited to the new environment.

[0086] An example of a prompt message is: "Generate a program that suggests the most suitable hair care products and procedures for the user's lifestyle based on data analysis."

[0087] In this way, the system can provide hair care best suited to the user's individual circumstances and improve their hair health.

[0088] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0089] Step 1:

[0090] The server collects publicly available hair-related data from the internet. This step uses web scraping techniques to obtain data from scientific papers, industry trends, and product reviews. The input consists of various data sources on the internet, and the output is a cleaned dataset. This dataset is stored in a database for use in the next analysis step. Specifically, Python's Beautiful Soup is used to parse HTML content and extract the necessary information.

[0091] Step 2:

[0092] The device collects personal data with the user's consent. The collected data includes fitness data from health apps, meal records, and sleep patterns. Input is raw data obtained from the user's smartphone or wearable device, and output is the transfer of this data to a server. Bluetooth and Wi-Fi are used to enable real-time data updates.

[0093] Step 3:

[0094] The server integrates public data and personal data and stores it in a database. This process involves data cleansing to standardize data formats and ensure quality. The input is the datasets collected in steps 1 and 2, and the output is the integrated data set. The integrated data is used in the next analysis step.

[0095] Step 4:

[0096] The server runs machine learning algorithms on the integrated data to extract patterns related to the user's lifestyle. This analysis uses machine learning models (e.g., TensorFlow or Scikit-learn) to analyze the data and identify factors that influence hair health. The input is the integrated dataset, and the output is the feature patterns that form the basis of the care program.

[0097] Step 5:

[0098] The server generates a personalized hair care program based on the analysis. Using a generative model, it constructs a program that includes recommended products and procedures for the user. The input is the analysis results from step 4, and the output is a specific hair care program. This program provides customized content tailored to the user's needs.

[0099] Step 6:

[0100] The terminal visually presents the generated hair care program to the user. It displays the program's contents as a step-by-step guide. The input is the care program received from the server, and the output is user-executable visual information. This allows the user to take specific hair care actions.

[0101] Step 7:

[0102] Users provide feedback on their hair care experiences and results. This feedback is entered via a terminal and sent to the server. The input is the user's experience and evaluation, and the output is feedback data. This allows the server to continuously improve the program.

[0103] (Application Example 1)

[0104] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0105] In modern life, there is a growing need for hair care tailored to individual lifestyles and health information, but existing methods only offer uniform care. Furthermore, the increasing complexity of hair care requires specialized knowledge, making it difficult for users to provide appropriate care themselves. Therefore, there is a need for automated hair care tailored to each user's condition, along with real-time feedback functionality.

[0106] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0107] In this invention, the server includes means for collecting publicly available data on hair from information sources on the Internet, means for collecting measurement data via sensors and software for acquiring the user's personal data, and means for integrating and analyzing the publicly available data and the measurement data to extract features. This enables the generation of individually optimized hair care programs and the provision of automated care to users via robotic devices.

[0108] A "server" is a computer system used to collect data from information sources on the internet and to integrate and analyze that data.

[0109] A "sensor" is a device used to measure and collect users' personal data.

[0110] "Software" refers to a group of programs used for data collection and analysis, and is particularly intended to support the creation of programs for hair care.

[0111] "Measurement data" refers to numerical data representing information about the user's lifestyle and health status, acquired through sensors and software.

[0112] "Publicly available data" refers to general information and research data about hair that is accessible on the internet.

[0113] "Characteristics" are unique trends or patterns extracted from data, and are used for personalized hair care.

[0114] A "hair care program" is a plan that includes optimized hair care steps and product recommendations, generated based on the individual user's condition.

[0115] A "robot device" is a home-use device that automatically performs hair care for the user and provides results in real time.

[0116] "Feedback" refers to information based on evaluations and experiences provided by users, which is used to improve hair care programs.

[0117] "Real-time" refers to a time frame in which data processing and results are provided immediately, thereby improving the user experience.

[0118] The hardware of the system realizing this invention includes a server, sensors, a user terminal, and a robotic device. The software includes a program for data analysis and an algorithm for generating a hair care program. Specifically, the server collects publicly available hair data from the internet, and the sensors measure and record the user's personal data.

[0119] The server integrates this data and analyzes user-specific characteristics using machine learning algorithms. Frameworks such as TensorFlow and PyTorch are used for this analysis. Furthermore, the server generates a personalized hair care program based on the analysis results. This program is sent to the user's terminal, and CSS and JavaScript (registered trademarks) are used to provide visual guidance.

[0120] The user executes the program through a terminal, and the robotic device automates hair care according to the instructions. Feedback is sent back to the server and used for continuous improvement of the program. In this way, personalized hair care becomes possible.

[0121] A concrete example would be a process where a user is presented with a suggested routine of washing their hair three times a week and using specific beauty products, and the robotic device automatically applies the products. Another example requiring flexibility is when a user has a specific hair-related concern; a possible prompt would be: "My hair hasn't been in very good condition lately. Could you give me some tips on how to maintain naturally shiny hair?"

[0122] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0123] Step 1:

[0124] The server collects publicly available data about hair from the internet. This data includes the latest research findings and product reviews, and is obtained through open APIs. The output obtained at this stage is text data on various hair care topics.

[0125] Step 2:

[0126] Sensors and the user's device collect personal data. This includes fitness information from the user's health app, sleep data, and food logs, which are transferred to the device via Bluetooth or Wi-Fi. The output of this step is user-specific lifestyle data.

[0127] Step 3:

[0128] The server integrates collected public data and personal data and stores it in a database. Based on this integrated data, machine learning algorithms are used to extract features. The input is the integrated data, and the output is a model representing unique patterns for each user. Data analysis is performed using frameworks such as TensorFlow.

[0129] Step 4:

[0130] The server generates a personalized hair care program based on the extracted features. This program includes the products to be used and the care steps involved. The input is feature data, and the output is a customized hair care plan. A generative AI model is used to generate the program.

[0131] Step 5:

[0132] The terminal provides the user with a generated hair care program. This program is displayed as a visual step-by-step guide, and the interface uses HTML5 and JavaScript. The input is the hair care plan, and the output is the specific steps the user can take.

[0133] Step 6:

[0134] The user performs hair care using a robotic device. The device applies the product and performs the necessary steps in an automated process. The output is the condition of the hair after treatment, which is monitored by the device's built-in camera.

[0135] Step 7:

[0136] Users provide feedback on the results of their hair care treatment, and the device sends this feedback to the server. This feedback includes user satisfaction and areas for improvement, which are used to generate the next care program. The input for the feedback consists of user comments and ratings, and the output is data used to improve the program.

[0137] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0138] This invention provides a system that offers a hair care program individually optimized for each user. The program is generated by effectively integrating and analyzing internet-based information sources, as well as the user's personal and emotional data. Furthermore, by incorporating an emotional engine that considers the user's emotions, a more precise and detailed care program is achieved.

[0139] Data collection and emotion recognition

[0140] The server collects hair-related information from the internet using a dedicated API and web scraping technology. Simultaneously, the device acquires personal data from health apps and sensors based on the user's individual permission. Furthermore, it uses an emotion engine to acquire emotional data in real time from the user's facial expressions and voice, and adds this to the database.

[0141] Data Integration and Analysis

[0142] The server cleans the integrated general-purpose and personal data, then analyzes it using machine learning algorithms. This analysis includes a process to identify the impact of lifestyle, health status, and emotions on hair condition.

[0143] Creating a hair care program

[0144] Based on the analysis results, the server generates a personalized hair care program for the user. The user's emotional state, detected by the emotion engine, is reflected in the program's recommendations; for example, if a high stress level is detected, the program will enhance its suggestions for relaxation care.

[0145] Program provision and practical support

[0146] The generated hair care program is provided to the user via the device and presented as a visually easy-to-understand step-by-step guide. The user can then take action based on the presented steps.

[0147] Feedback and continuous improvement

[0148] After hair care is performed, users can provide feedback on the program's effectiveness, which is collected by the device. The server incorporates this feedback and newly acquired emotional data into its analysis to optimize the hair care program. By using an emotional engine, continuous and flexible care tailored to individual emotional changes is provided, resulting in greater user satisfaction.

[0149] The following describes the processing flow.

[0150] Step 1:

[0151] The server regularly collects publicly available information related to hair from the internet, such as academic papers, product reviews, and the latest news, using a dedicated API and web scraping.

[0152] Step 2:

[0153] With user consent, the device continuously acquires physical data, behavioral patterns, and dietary information from health apps and wearable devices. It also uses an emotion engine to analyze facial expressions and voice from the user's camera and microphone, acquiring emotional data in real time.

[0154] Step 3:

[0155] The server integrates the collected data in a database and performs data cleansing to remove inaccurate data. This improves the reliability of the data and the accuracy of the analysis.

[0156] Step 4:

[0157] The server uses machine learning algorithms to analyze patterns based on lifestyle, health status, and emotional state from integrated data. This analysis specifically aims to identify causal relationships related to emotions and hair.

[0158] Step 5:

[0159] The server automatically generates a personalized hair care program based on the user's analysis results. This program includes specific care procedures, recommended products, and advice tailored to the user's emotional state.

[0160] Step 6:

[0161] The device notifies the user of the generated hair care program and supports daily implementation by displaying it in a visually appealing step-by-step format.

[0162] Step 7:

[0163] Users perform daily hair care according to the provided guidelines. They can provide feedback on the effectiveness of the care and any changes in their feelings through their device.

[0164] Step 8:

[0165] The device collects user feedback and newly acquired sentiment data and sends it to the server.

[0166] Step 9:

[0167] The server reanalyzes feedback and real-time sentiment data to update the hair care program. This process ensures continuous improvement so that users always receive optimal care.

[0168] (Example 2)

[0169] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0170] In modern society, hair health is a major concern for many people, but providing hair care tailored to each individual's lifestyle and emotional state is a challenging task. There is a need to effectively integrate and analyze data from diverse sources and provide individually optimized hair care programs that even consider the user's emotions.

[0171] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0172] In this invention, the server includes means for collecting information about hair from information sources on the Internet, means for collecting users' personal information via sensors and programs, and means for obtaining users' emotional information using an emotional analysis function. This makes it possible to provide each user with an individually optimized hair treatment plan that reflects their lifestyle and emotional state.

[0173] "Information source" refers to a source of data, including information related to hair, that can be obtained via the internet.

[0174] "Personal information" refers to data that includes information about a user's health status and lifestyle.

[0175] A "detector" is hardware or a sensor used to collect data from users.

[0176] A "program" is a software application that runs on a smartphone or other computing device.

[0177] "Emotional information" refers to data that indicates the emotional state of a user, obtained from their facial expressions, voice, and other sources.

[0178] The "emotion analysis function" is a feature that identifies the user's emotional state and uses that data for analysis.

[0179] "Information integration and refinement" is the process of combining data from different sources and organizing it into a consistent format.

[0180] "Pattern derivation" is an analytical process for extracting specific trends or patterns from integrated data.

[0181] A "hair care plan" is a plan that outlines individually optimized procedures and product usage methods to achieve goals related to hair health and style.

[0182] "Supply" refers to the act of providing the generated hair treatment plan to the user.

[0183] "Evaluation information" refers to feedback provided by users, indicating the effectiveness and satisfaction level of the hair treatment plan.

[0184] "Update" refers to the process of modifying and improving the hair treatment plan based on the collected evaluation information.

[0185] This invention is a system that provides a hair care program individually optimized for each user. The central elements of the system consist of three components: a server, a terminal, and the user.

[0186] The server uses a dedicated API and web scraping techniques to collect information about hair from internet sources. Based on this collected data, it integrates personal and emotional information and analyzes the data using machine learning algorithms. Specific technologies used include real-time emotion recognition by an emotion engine and database technology for data management.

[0187] The device is responsible for acquiring the user's personal information from health apps and sensors, in a manner permitted by the user. It also provides a visual interface to the user with a generated hair care program, displaying specific steps interactively. This interface may include prompt-based guidance.

[0188] Users receive a hair care program through this system and act accordingly. After completing the program, users provide feedback, which is sent to the server via their device.

[0189] For example, if a user is troubled by dry hair, the server combines information from the internet with the user's health data to generate suggestions for appropriate moisturizing care and products to use.

[0190] An example of a prompt is: "Create a prompt to generate a stress-management-focused hair care program based on the user's health and emotional data. Specifically, we are requesting suggestions for products containing ingredients effective in reducing stress."

[0191] This system configuration makes it possible to provide users with a more effective and precise hair care program that is individually optimized for them.

[0192] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0193] Step 1:

[0194] The server retrieves publicly available data about hair from internet sources. Using a dedicated API and web scraping techniques, it collects information on the latest trends and effective ingredients in hair care. Inputs include internet URLs and search queries, and the output is structured data.

[0195] Step 2:

[0196] The device collects the user's personal information. It obtains fitness and dietary data through health apps and sensors. This collection includes taking sensor data points in real time. The input is the user's access rights to health apps and sensors, and the output is personal data.

[0197] Step 3:

[0198] The server uses an emotion engine to acquire user emotional information. It analyzes the user's facial expressions and voice in real time and evaluates their emotional state. This process involves processing data obtained through the camera and microphone. Audio and video data are taken as input, and analyzed emotional data is provided as output.

[0199] Step 4:

[0200] The server integrates and cleans the collected data. It organizes public data, personal information, and sentiment information into a consistent format to build input datasets for machine learning. This process involves deduplication and transforming the data into a standard format. The input is raw data, and the output is a formatted dataset.

[0201] Step 5:

[0202] The server analyzes the data using machine learning algorithms based on a formatted dataset. This identifies the impact of lifestyle and emotions on hair condition and derives individually optimized hair treatment plans. The input is an integrated dataset, and the output is the analysis results and hair treatment plans.

[0203] Step 6:

[0204] The server reflects the user's emotional state in the hair treatment plan and adjusts the recommendations accordingly. For example, it might recommend care that emphasizes relaxation based on the user's mental state. Inputs include analysis results and emotional data, and the output is an adjusted hair treatment plan.

[0205] Step 7:

[0206] The terminal provides the user with a generated hair treatment plan. It displays daily care steps step-by-step through a visually intuitive interface. The input is the hair treatment plan, and the output is user interaction via the interface.

[0207] Step 8:

[0208] The user inputs the results of the processing as feedback into the terminal. This feedback includes information about hair texture and satisfaction level. The received feedback is sent to the server and used for future program updates. The input is user feedback, and the output is feedback data.

[0209] Step 9:

[0210] The server reanalyzes the feedback data and continuously improves the hair treatment plan. This makes it possible to generate a care program that is more suitable for each individual user. The input is feedback data, and the output is an improved hair treatment plan.

[0211] (Application Example 2)

[0212] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0213] In modern society, many people are seeking appropriate care methods to maintain and improve the health of their hair. However, there is a problem in that many people are unable to receive effective care because there is no system in place to provide hair care optimized according to individual lifestyles and emotional states. Furthermore, there are many situations where flexible responses based on the user's emotions are required, and the lack of a system to propose appropriate care accordingly is a challenge.

[0214] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0215] In this invention, the server includes means for acquiring hair-related information from a database on the internet, means for acquiring the user's personal information using sensor devices and programs, and means for integrating and analyzing the information and the personal information to extract characteristics. This makes it possible for the home appliance to recognize the user's emotional state, create an individually optimized hair care plan, provide the plan to the user, and provide appropriate guidance.

[0216] An "internet database" is a system that provides information accessible on the web and stores a variety of data related to hair.

[0217] "Personal information" refers to data about a user's health status and lifestyle, which is acquired through sensor devices and programs.

[0218] A "sensor device" is a hardware device used to collect users' physical and environmental data.

[0219] A "program" is a part of the software that runs on a user's device and performs data collection and analysis.

[0220] "Methods for extracting characteristics" refer to analytical techniques used to identify important patterns and characteristics related to a user's hair care from the acquired information.

[0221] A "personally optimized hair care plan" is a plan that includes suggestions for the most suitable hair care based on the user's unique data.

[0222] "Household appliances" refer to robots and devices installed in the user's living space that provide guidance on hair care.

[0223] "Recognizing emotional state" refers to the process of determining the user's psychological emotions in real time based on their facial expressions and voice.

[0224] "Providing appropriate guidance" means proposing specific care methods to users based on analyzed data.

[0225] The system for implementing this invention includes an advanced system for providing users with individually optimized hair care. A server retrieves hair-related information from an internet database, while a terminal simultaneously collects the user's personal information through a sensor device. The server then integrates and analyzes the collected data. Machine learning algorithms are used for the analysis to extract characteristics and create an optimized hair care plan for each user.

[0226] The home-use device is equipped with a facial recognition camera and a voice analysis microphone to monitor the user's emotional state. This allows it to flexibly suggest care procedures and provide appropriate guidance based on the user's emotions. Specifically, the robot can play music to promote relaxation while recommending appropriate hair care products.

[0227] As a concrete example, the server evaluates the user's emotional state based on daily data and provides a relaxation plan to maintain hair health based on stress levels. An example of a prompt message in this case would be: "Please provide an optimal hair care program based on the user's emotional state. Please generate customized suggestions, including measures for when stress levels are high."

[0228] This allows users to practice the care methods best suited to them. The entire system works efficiently together to improve the user experience and provide optimal support for promoting hair health.

[0229] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0230] Step 1:

[0231] The server retrieves hair-related information from databases on the internet. The input is the URL information of the online database, and the output is raw data related to hair. To analyze this raw data, the server uses web scraping techniques to extract the data.

[0232] Step 2:

[0233] The device uses sensor equipment to collect the user's personal data. Inputs are data from the user's biosensors, and outputs are data related to health status, lifestyle, and emotional state. The device collects this data in real time and transmits it to a server.

[0234] Step 3:

[0235] The server integrates and analyzes the acquired data. Inputs include hair-related information from the internet and personal data from the device; output is the most important characteristics for the user's hair care. The server uses machine learning algorithms to extract data characteristics and analyze the user's unique trends.

[0236] Step 4:

[0237] The server creates an individually optimized hair care plan based on the extracted characteristics. The input is analyzed characteristic data, and the output is a personalized hair care suggestion. Using a generative AI model, the care content is planned according to the user's stress level and health condition.

[0238] Step 5:

[0239] The terminal provides the user with a hair care plan. The input is the hair care plan sent from the server, and the output is specific care instructions for the user. The terminal communicates the instructions to the user visually and audibly, and provides guidance on the appropriate use of products.

[0240] Step 6:

[0241] The user implements the provided hair care plan and provides feedback on its effectiveness to the device. Inputs are the user's implementation status and subjective evaluation of the effects, while output is feedback data. The device sends this feedback to a server for further analysis.

[0242] Step 7:

[0243] The server updates the hair care plan based on user feedback and new personal data. Inputs are feedback data and additional personal data, and output is a suggested improved hair care plan. This continuously optimizes the care content for the user.

[0244] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0245] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0246] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0247] [Second Embodiment]

[0248] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0249] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0250] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0251] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0252] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0253] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0254] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0255] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0256] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0257] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0258] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0259] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0260] This invention is a system for providing personalized hair care programs and implements a process for effectively integrating and analyzing internet-based information sources and users' personal data. The configuration and operation of this system will be described in detail below.

[0261] Data collection and integration

[0262] The server collects publicly available hair-related data via the internet, including scientific research, industry trend information, and product reviews. Meanwhile, the device collects personal data obtained with the user's consent. This personal data includes fitness information from health apps, dietary records, and sleep data from smartphone sensors. This data is integrated into a dedicated database.

[0263] Analysis and program generation

[0264] The server analyzes the integrated data to extract unique patterns for each user. This analysis uses machine learning algorithms to identify lifestyle factors and health trends that influence hair condition. Based on these results, a personalized hair care program is generated. The program includes the necessary steps and product recommendations to improve the user's hair health.

[0265] Program provision and implementation support

[0266] The generated hair care program is provided to the user via the device. The provided program is visually displayed as a step-by-step guide for daily hair care. By following this guide and taking specific actions, the user can experience the effects.

[0267] Feedback and program updates

[0268] After performing hair care, users can provide feedback on the effects and their experience. The device collects this feedback and sends it to the server. The server uses the collected feedback and new data to continuously improve the hair care program, providing users with the latest and most effective care. This process helps users improve their hair health in a way that best suits their individual lifestyle.

[0269] The following describes the processing flow.

[0270] Step 1:

[0271] The server automatically collects hair-related data from publicly available information on the internet using a dedicated API and web scraping technology. This data includes the latest scientific research, product reviews, and industry news.

[0272] Step 2:

[0273] With the user's permission, the device continuously acquires individual data from sensors and apps. Specific data includes fitness data and meal logs provided by health apps, as well as sleep data from smartphone sensors.

[0274] Step 3:

[0275] The server integrates the acquired general-purpose data with personal data and performs data cleaning to remove malicious data. This improves the accuracy of the analysis.

[0276] Step 4:

[0277] The server uses machine learning algorithms to analyze the cleaned-up data. This analysis evaluates the impact of each user's lifestyle and health condition on hair, and extracts specific patterns.

[0278] Step 5:

[0279] Based on the analysis results, the server automatically generates a personalized hair care program for each user. The program includes specific product selections and daily care procedures.

[0280] Step 6:

[0281] The terminal notifies the user of the generated hair care program and presents it as a step-by-step guide that is easy to visually understand. As a result, the user can take actions based on the proposed content.

[0282] Step 7:

[0283] The user follows the presented hair care program and incorporates it into daily life for practice. The user can also provide feedback on the effects and experiences of the program.

[0284] Step 8:

[0285] The terminal collects feedback from the user and transmits it to the server.

[0286] Step 9:

[0287] The server improves the hair care program based on the feedback and newly collected data. This makes it possible to continuously provide the user with the latest and effective care programs.

[0288] (Example 1)

[0289] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0290] In recent years, the demand for personalized care based on individual health conditions and lifestyles has been increasing. However, in the case of hair care, it has been difficult to effectively utilize various personal data of users to provide an optimal care program. In addition, continuous adjustment and improvement are required to realize the effects of hair care, but there has been a lack of an efficient feedback and update mechanism for this purpose.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0292] In this invention, the server includes means for collecting publicly available hair-related data from information sources on the Internet, means for collecting user personal data via communication devices, and means for integrating the publicly available data and the personal data and extracting patterns using machine learning algorithms. This enables the generation and provision of personalized hair care programs suited to the user's lifestyle and health condition, as well as continuous optimization through feedback.

[0293] The "Internet" is a global information network for sending and receiving information digitally around the world.

[0294] "Information sources" refer to digital platforms and databases that provide and allow access to publicly available data and knowledge.

[0295] "Hair" refers to fibrous tissue that grows on the head or body of living organisms, and in this invention, it is particularly related to hair on the head.

[0296] "Public data" refers to information that is made available on the internet in a form that anyone can access.

[0297] "Means" refer to the methods or technical elements used to achieve a specific objective.

[0298] A "user" is a person who uses a particular technology system or device, or an entity that provides data related to such use.

[0299] "Personal data" refers to information about a user, particularly information that is subject to privacy protection, including information about the user's lifestyle and health status.

[0300] "Communication equipment" refers to devices used to transmit and receive data, and includes digital devices such as smartphones and tablets.

[0301] "Integration" is a process of consolidating multiple data into a single coherent format.

[0302] "Machine learning algorithm" refers to a computational method that learns from experience and is used to improve specific tasks.

[0303] "Pattern" refers to the consistent features or trends existing within data.

[0304] "Visually provided" means presenting information to the user in a form such as images or graphics and transmitting it in an easy-to-understand manner.

[0305] "Feedback" refers to the evaluations or opinions provided by the user regarding the output or results of a system.

[0306] "Update" is a process of modifying or improving existing programs or data based on new information.

[0307] This system is for providing the user with an individualized hair care program, and specifically operates as follows.

[0308] The server first collects hair-related data publicly available through the Internet. This data includes the results of scientific research, industry trends, product reviews, etc. Digital data acquisition technology can be used for data collection, and specific examples include web scraping using libraries such as Beautiful Soup and Requests in Python.

[0309] On the other hand, the terminal acquires personal data with the user's consent. This includes using communication devices and includes data from health apps and meal recording apps, sleep data from smartphone sensors, etc. These user data are collected using Bluetooth or Wi-Fi and transferred to the server.

[0310] The server integrates the acquired public data and personal data and stores it in a database. This integrated data is analyzed using machine learning algorithms to extract patterns related to lifestyle and health status. Existing data analysis libraries such as TensorFlow and Scikit-learn are used for this analysis.

[0311] Based on the analysis results, a personalized hair care program is generated for each user. This program includes specific hair care steps and a list of recommended products. The generated program is presented to the user visually through the device's display. Users can then follow the provided guide to perform their daily hair care.

[0312] As feedback, users can send their care results and experiences to the server via their device. The server uses this feedback to update and optimize the hair care program. This allows users to continue receiving the latest care tailored to changes in their lifestyle and environment.

[0313] For example, if a user moves to a drier climate, their skincare program will be updated to recommend moisturizing products suited to the new environment.

[0314] An example of a prompt message is: "Generate a program that suggests the most suitable hair care products and procedures for the user's lifestyle based on data analysis."

[0315] In this way, the system can provide hair care best suited to the user's individual circumstances and improve their hair health.

[0316] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0317] Step 1:

[0318] The server collects publicly available hair-related data from the internet. This step uses web scraping techniques to obtain data from scientific papers, industry trends, and product reviews. The input consists of various data sources on the internet, and the output is a cleaned dataset. This dataset is stored in a database for use in the next analysis step. Specifically, Python's Beautiful Soup is used to parse HTML content and extract the necessary information.

[0319] Step 2:

[0320] The device collects personal data with the user's consent. The collected data includes fitness data from health apps, meal records, and sleep patterns. Input is raw data obtained from the user's smartphone or wearable device, and output is the transfer of this data to a server. Bluetooth and Wi-Fi are used to enable real-time data updates.

[0321] Step 3:

[0322] The server integrates public data and personal data and stores it in a database. This process involves data cleansing to standardize data formats and ensure quality. The input is the datasets collected in steps 1 and 2, and the output is the integrated data set. The integrated data is used in the next analysis step.

[0323] Step 4:

[0324] The server runs machine learning algorithms on the integrated data to extract patterns related to the user's lifestyle. This analysis uses machine learning models (e.g., TensorFlow or Scikit-learn) to analyze the data and identify factors that influence hair health. The input is the integrated dataset, and the output is the feature patterns that form the basis of the care program.

[0325] Step 5:

[0326] The server generates a personalized hair care program based on the analysis. Using a generative model, it constructs a program that includes recommended products and procedures for the user. The input is the analysis results from step 4, and the output is a specific hair care program. This program provides customized content tailored to the user's needs.

[0327] Step 6:

[0328] The terminal visually presents the generated hair care program to the user. It displays the program's contents as a step-by-step guide. The input is the care program received from the server, and the output is user-executable visual information. This allows the user to take specific hair care actions.

[0329] Step 7:

[0330] Users provide feedback on their hair care experiences and results. This feedback is entered via a terminal and sent to the server. The input is the user's experience and evaluation, and the output is feedback data. This allows the server to continuously improve the program.

[0331] (Application Example 1)

[0332] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0333] In modern life, there is a growing need for hair care tailored to individual lifestyles and health information, but existing methods only offer uniform care. Furthermore, the increasing complexity of hair care requires specialized knowledge, making it difficult for users to provide appropriate care themselves. Therefore, there is a need for automated hair care tailored to each user's condition, along with real-time feedback functionality.

[0334] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0335] In this invention, the server includes means for collecting publicly available data on hair from information sources on the Internet, means for collecting measurement data via sensors and software for acquiring the user's personal data, and means for integrating and analyzing the publicly available data and the measurement data to extract features. This enables the generation of individually optimized hair care programs and the provision of automated care to users via robotic devices.

[0336] A "server" is a computer system used to collect data from information sources on the internet and to integrate and analyze that data.

[0337] A "sensor" is a device used to measure and collect users' personal data.

[0338] "Software" refers to a group of programs used for data collection and analysis, and is particularly intended to support the creation of programs for hair care.

[0339] "Measurement data" refers to numerical data representing information about the user's lifestyle and health status, acquired through sensors and software.

[0340] "Publicly available data" refers to general information and research data about hair that is accessible on the internet.

[0341] "Characteristics" are unique trends or patterns extracted from data, and are used for personalized hair care.

[0342] A "hair care program" is a plan that includes optimized hair care steps and product recommendations, generated based on the individual user's condition.

[0343] A "robot device" is a home-use device that automatically performs hair care for the user and provides results in real time.

[0344] "Feedback" refers to information based on evaluations and experiences provided by users, which is used to improve hair care programs.

[0345] "Real-time" refers to a time frame in which data processing and results are provided immediately, thereby improving the user experience.

[0346] The hardware of the system realizing this invention includes a server, sensors, a user terminal, and a robotic device. The software includes a program for data analysis and an algorithm for generating a hair care program. Specifically, the server collects publicly available hair data from the internet, and the sensors measure and record the user's personal data.

[0347] The server integrates this data and analyzes user-specific characteristics using machine learning algorithms. Frameworks such as TensorFlow and PyTorch are used for this analysis. Furthermore, the server generates a personalized hair care program based on the analysis results. This program is sent to the user's terminal, and CSS and JavaScript are used to provide visual guidance.

[0348] The user executes the program through a terminal, and the robotic device automates hair care according to the instructions. Feedback is sent back to the server and used for continuous improvement of the program. In this way, personalized hair care becomes possible.

[0349] A concrete example would be a process where a user is presented with a suggested routine of washing their hair three times a week and using specific beauty products, and the robotic device automatically applies the products. Another example requiring flexibility is when a user has a specific hair-related concern; a possible prompt would be: "My hair hasn't been in very good condition lately. Could you give me some tips on how to maintain naturally shiny hair?"

[0350] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0351] Step 1:

[0352] The server collects publicly available data about hair from the internet. This data includes the latest research findings and product reviews, and is obtained through open APIs. The output obtained at this stage is text data on various hair care topics.

[0353] Step 2:

[0354] Sensors and the user's device collect personal data. This includes fitness information from the user's health app, sleep data, and food logs, which are transferred to the device via Bluetooth or Wi-Fi. The output of this step is user-specific lifestyle data.

[0355] Step 3:

[0356] The server integrates collected public data and personal data and stores it in a database. Based on this integrated data, machine learning algorithms are used to extract features. The input is the integrated data, and the output is a model representing unique patterns for each user. Data analysis is performed using frameworks such as TensorFlow.

[0357] Step 4:

[0358] The server generates a personalized hair care program based on the extracted features. This program includes the products to be used and the care steps involved. The input is feature data, and the output is a customized hair care plan. A generative AI model is used to generate the program.

[0359] Step 5:

[0360] The terminal provides the user with a generated hair care program. This program is displayed as a visual step-by-step guide, and the interface uses HTML5 and JavaScript. The input is the hair care plan, and the output is the specific steps the user can take.

[0361] Step 6:

[0362] The user performs hair care using a robotic device. The device applies the product and performs the necessary steps in an automated process. The output is the condition of the hair after treatment, which is monitored by the device's built-in camera.

[0363] Step 7:

[0364] Users provide feedback on the results of their hair care treatment, and the device sends this feedback to the server. This feedback includes user satisfaction and areas for improvement, which are used to generate the next care program. The input for the feedback consists of user comments and ratings, and the output is data used to improve the program.

[0365] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0366] This invention provides a system that offers a hair care program individually optimized for each user. The program is generated by effectively integrating and analyzing internet-based information sources, as well as the user's personal and emotional data. Furthermore, by incorporating an emotional engine that considers the user's emotions, a more precise and detailed care program is achieved.

[0367] Data collection and emotion recognition

[0368] The server collects hair-related information from the internet using a dedicated API and web scraping technology. Simultaneously, the device acquires personal data from health apps and sensors based on the user's individual permission. Furthermore, it uses an emotion engine to acquire emotional data in real time from the user's facial expressions and voice, and adds this to the database.

[0369] Data Integration and Analysis

[0370] The server cleans the integrated general-purpose and personal data, then analyzes it using machine learning algorithms. This analysis includes a process to identify the impact of lifestyle, health status, and emotions on hair condition.

[0371] Creating a hair care program

[0372] Based on the analysis results, the server generates a personalized hair care program for the user. The user's emotional state, detected by the emotion engine, is reflected in the program's recommendations; for example, if a high stress level is detected, the program will enhance its suggestions for relaxation care.

[0373] Program provision and practical support

[0374] The generated hair care program is provided to the user via the device and presented as a visually easy-to-understand step-by-step guide. The user can then take action based on the presented steps.

[0375] Feedback and continuous improvement

[0376] After hair care is performed, users can provide feedback on the program's effectiveness, which is collected by the device. The server incorporates this feedback and newly acquired emotional data into its analysis to optimize the hair care program. By using an emotional engine, continuous and flexible care tailored to individual emotional changes is provided, resulting in greater user satisfaction.

[0377] The following describes the processing flow.

[0378] Step 1:

[0379] The server regularly collects publicly available information related to hair from the internet, such as academic papers, product reviews, and the latest news, using a dedicated API and web scraping.

[0380] Step 2:

[0381] With user consent, the device continuously acquires physical data, behavioral patterns, and dietary information from health apps and wearable devices. It also uses an emotion engine to analyze facial expressions and voice from the user's camera and microphone, acquiring emotional data in real time.

[0382] Step 3:

[0383] The server integrates the collected data in a database and performs data cleansing to remove inaccurate data. This improves the reliability of the data and the accuracy of the analysis.

[0384] Step 4:

[0385] The server uses machine learning algorithms to analyze patterns based on lifestyle, health status, and emotional state from integrated data. This analysis specifically aims to identify causal relationships related to emotions and hair.

[0386] Step 5:

[0387] The server automatically generates a personalized hair care program based on the user's analysis results. This program includes specific care procedures, recommended products, and advice tailored to the user's emotional state.

[0388] Step 6:

[0389] The device notifies the user of the generated hair care program and supports daily implementation by displaying it in a visually appealing step-by-step format.

[0390] Step 7:

[0391] Users perform daily hair care according to the provided guidelines. They can provide feedback on the effectiveness of the care and any changes in their feelings through their device.

[0392] Step 8:

[0393] The device collects user feedback and newly acquired sentiment data and sends it to the server.

[0394] Step 9:

[0395] The server reanalyzes feedback and real-time sentiment data to update the hair care program. This process ensures continuous improvement so that users always receive optimal care.

[0396] (Example 2)

[0397] Next, we will describe Example 2. 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".

[0398] In modern society, hair health is a major concern for many people, but providing hair care tailored to each individual's lifestyle and emotional state is a challenging task. There is a need to effectively integrate and analyze data from diverse sources and provide individually optimized hair care programs that even consider the user's emotions.

[0399] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0400] In this invention, the server includes means for collecting information about hair from information sources on the Internet, means for collecting users' personal information via sensors and programs, and means for obtaining users' emotional information using an emotional analysis function. This makes it possible to provide each user with an individually optimized hair treatment plan that reflects their lifestyle and emotional state.

[0401] "Information source" refers to a source of data, including information related to hair, that can be obtained via the internet.

[0402] "Personal information" refers to data that includes information about a user's health status and lifestyle.

[0403] A "detector" is hardware or a sensor used to collect data from users.

[0404] A "program" is a software application that runs on a smartphone or other computing device.

[0405] "Emotional information" refers to data that indicates the emotional state of a user, obtained from their facial expressions, voice, and other sources.

[0406] The "emotion analysis function" is a feature that identifies the user's emotional state and uses that data for analysis.

[0407] "Information integration and refinement" is the process of combining data from different sources and organizing it into a consistent format.

[0408] "Pattern derivation" is an analytical process for extracting specific trends or patterns from integrated data.

[0409] A "hair care plan" is a plan that outlines individually optimized procedures and product usage methods to achieve goals related to hair health and style.

[0410] "Supply" refers to the act of providing the generated hair treatment plan to the user.

[0411] "Evaluation information" refers to feedback provided by users, indicating the effectiveness and satisfaction level of the hair treatment plan.

[0412] "Update" refers to the process of modifying and improving the hair treatment plan based on the collected evaluation information.

[0413] This invention is a system that provides a hair care program individually optimized for each user. The central elements of the system consist of three components: a server, a terminal, and the user.

[0414] The server uses a dedicated API and web scraping techniques to collect information about hair from internet sources. Based on this collected data, it integrates personal and emotional information and analyzes the data using machine learning algorithms. Specific technologies used include real-time emotion recognition by an emotion engine and database technology for data management.

[0415] The device is responsible for acquiring the user's personal information from health apps and sensors, in a manner permitted by the user. It also provides a visual interface to the user with a generated hair care program, displaying specific steps interactively. This interface may include prompt-based guidance.

[0416] Users receive a hair care program through this system and act accordingly. After completing the program, users provide feedback, which is sent to the server via their device.

[0417] For example, if a user is troubled by dry hair, the server combines information from the internet with the user's health data to generate suggestions for appropriate moisturizing care and products to use.

[0418] An example of a prompt is: "Create a prompt to generate a stress-management-focused hair care program based on the user's health and emotional data. Specifically, we are requesting suggestions for products containing ingredients effective in reducing stress."

[0419] This system configuration makes it possible to provide users with a more effective and precise hair care program that is individually optimized for them.

[0420] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0421] Step 1:

[0422] The server retrieves publicly available data about hair from internet sources. Using a dedicated API and web scraping techniques, it collects information on the latest trends and effective ingredients in hair care. Inputs include internet URLs and search queries, and the output is structured data.

[0423] Step 2:

[0424] The device collects the user's personal information. It obtains fitness and dietary data through health apps and sensors. This collection includes taking sensor data points in real time. The input is the user's access rights to health apps and sensors, and the output is personal data.

[0425] Step 3:

[0426] The server uses an emotion engine to acquire user emotional information. It analyzes the user's facial expressions and voice in real time and evaluates their emotional state. This process involves processing data obtained through the camera and microphone. Audio and video data are taken as input, and analyzed emotional data is provided as output.

[0427] Step 4:

[0428] The server integrates and cleans the collected data. It organizes public data, personal information, and sentiment information into a consistent format to build input datasets for machine learning. This process involves deduplication and transforming the data into a standard format. The input is raw data, and the output is a formatted dataset.

[0429] Step 5:

[0430] The server analyzes the data using machine learning algorithms based on a formatted dataset. This identifies the impact of lifestyle and emotions on hair condition and derives individually optimized hair treatment plans. The input is an integrated dataset, and the output is the analysis results and hair treatment plans.

[0431] Step 6:

[0432] The server reflects the user's emotional state in the hair treatment plan and adjusts the recommendations accordingly. For example, it might recommend care that emphasizes relaxation based on the user's mental state. Inputs include analysis results and emotional data, and the output is an adjusted hair treatment plan.

[0433] Step 7:

[0434] The terminal provides the user with a generated hair treatment plan. It displays daily care steps step-by-step through a visually intuitive interface. The input is the hair treatment plan, and the output is user interaction via the interface.

[0435] Step 8:

[0436] The user inputs the results of the processing as feedback into the terminal. This feedback includes information about hair texture and satisfaction level. The received feedback is sent to the server and used for future program updates. The input is user feedback, and the output is feedback data.

[0437] Step 9:

[0438] The server reanalyzes the feedback data and continuously improves the hair treatment plan. This makes it possible to generate a care program that is more suitable for each individual user. The input is feedback data, and the output is an improved hair treatment plan.

[0439] (Application Example 2)

[0440] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0441] In modern society, many people are seeking appropriate care methods to maintain and improve the health of their hair. However, there is a problem in that many people are unable to receive effective care because there is no system in place to provide hair care optimized according to individual lifestyles and emotional states. Furthermore, there are many situations where flexible responses based on the user's emotions are required, and the lack of a system to propose appropriate care accordingly is a challenge.

[0442] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0443] In this invention, the server includes means for acquiring hair-related information from a database on the internet, means for acquiring the user's personal information using sensor devices and programs, and means for integrating and analyzing the information and the personal information to extract characteristics. This makes it possible for the home appliance to recognize the user's emotional state, create an individually optimized hair care plan, provide the plan to the user, and provide appropriate guidance.

[0444] An "internet database" is a system that provides information accessible on the web and stores a variety of data related to hair.

[0445] "Personal information" refers to data about a user's health status and lifestyle, which is acquired through sensor devices and programs.

[0446] A "sensor device" is a hardware device used to collect users' physical and environmental data.

[0447] A "program" is a part of the software that runs on a user's device and performs data collection and analysis.

[0448] "Methods for extracting characteristics" refer to analytical techniques used to identify important patterns and characteristics related to a user's hair care from the acquired information.

[0449] A "personally optimized hair care plan" is a plan that includes suggestions for the most suitable hair care based on the user's unique data.

[0450] "Household appliances" refer to robots and devices installed in the user's living space that provide guidance on hair care.

[0451] "Recognizing emotional state" refers to the process of determining the user's psychological emotions in real time based on their facial expressions and voice.

[0452] "Providing appropriate guidance" means proposing specific care methods to users based on analyzed data.

[0453] The system for implementing this invention includes an advanced system for providing users with individually optimized hair care. A server retrieves hair-related information from an internet database, while a terminal simultaneously collects the user's personal information through a sensor device. The server then integrates and analyzes the collected data. Machine learning algorithms are used for the analysis to extract characteristics and create an optimized hair care plan for each user.

[0454] The home-use device is equipped with a facial recognition camera and a voice analysis microphone to monitor the user's emotional state. This allows it to flexibly suggest care procedures and provide appropriate guidance based on the user's emotions. Specifically, the robot can play music to promote relaxation while recommending appropriate hair care products.

[0455] As a concrete example, the server evaluates the user's emotional state based on daily data and provides a relaxation plan to maintain hair health based on stress levels. An example of a prompt message in this case would be: "Please provide an optimal hair care program based on the user's emotional state. Please generate customized suggestions, including measures for when stress levels are high."

[0456] This allows users to practice the care methods best suited to them. The entire system works efficiently together to improve the user experience and provide optimal support for promoting hair health.

[0457] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0458] Step 1:

[0459] The server retrieves hair-related information from databases on the internet. The input is the URL information of the online database, and the output is raw data related to hair. To analyze this raw data, the server uses web scraping techniques to extract the data.

[0460] Step 2:

[0461] The device uses sensor equipment to collect the user's personal data. Inputs are data from the user's biosensors, and outputs are data related to health status, lifestyle, and emotional state. The device collects this data in real time and transmits it to a server.

[0462] Step 3:

[0463] The server integrates and analyzes the acquired data. Inputs include hair-related information from the internet and personal data from the device; output is the most important characteristics for the user's hair care. The server uses machine learning algorithms to extract data characteristics and analyze the user's unique trends.

[0464] Step 4:

[0465] The server creates an individually optimized hair care plan based on the extracted characteristics. The input is analyzed characteristic data, and the output is a personalized hair care suggestion. Using a generative AI model, the care content is planned according to the user's stress level and health condition.

[0466] Step 5:

[0467] The terminal provides the user with a hair care plan. The input is the hair care plan sent from the server, and the output is specific care instructions for the user. The terminal communicates the instructions to the user visually and audibly, and provides guidance on the appropriate use of products.

[0468] Step 6:

[0469] The user implements the provided hair care plan and provides feedback on its effectiveness to the device. Inputs are the user's implementation status and subjective evaluation of the effects, while output is feedback data. The device sends this feedback to a server for further analysis.

[0470] Step 7:

[0471] The server updates the hair care plan based on user feedback and new personal data. Inputs are feedback data and additional personal data, and output is a suggested improved hair care plan. This continuously optimizes the care content for the user.

[0472] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0473] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0474] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0475] [Third Embodiment]

[0476] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0477] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0478] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0479] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0480] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0481] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0482] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0483] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0484] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0485] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0486] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0487] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0488] This invention is a system for providing personalized hair care programs and implements a process for effectively integrating and analyzing internet-based information sources and users' personal data. The configuration and operation of this system will be described in detail below.

[0489] Data collection and integration

[0490] The server collects publicly available hair-related data via the internet, including scientific research, industry trend information, and product reviews. Meanwhile, the device collects personal data obtained with the user's consent. This personal data includes fitness information from health apps, dietary records, and sleep data from smartphone sensors. This data is integrated into a dedicated database.

[0491] Analysis and program generation

[0492] The server analyzes the integrated data to extract unique patterns for each user. This analysis uses machine learning algorithms to identify lifestyle factors and health trends that influence hair condition. Based on these results, a personalized hair care program is generated. The program includes the necessary steps and product recommendations to improve the user's hair health.

[0493] Program provision and implementation support

[0494] The generated hair care program is provided to the user via the device. The provided program is visually displayed as a step-by-step guide for daily hair care. By following this guide and taking specific actions, the user can experience the effects.

[0495] Feedback and program updates

[0496] After performing hair care, users can provide feedback on the effects and their experience. The device collects this feedback and sends it to the server. The server uses the collected feedback and new data to continuously improve the hair care program, providing users with the latest and most effective care. This process helps users improve their hair health in a way that best suits their individual lifestyle.

[0497] The following describes the processing flow.

[0498] Step 1:

[0499] The server automatically collects hair-related data from publicly available information on the internet using a dedicated API and web scraping technology. This data includes the latest scientific research, product reviews, and industry news.

[0500] Step 2:

[0501] With the user's permission, the device continuously acquires individual data from sensors and apps. Specific data includes fitness data and meal logs provided by health apps, as well as sleep data from smartphone sensors.

[0502] Step 3:

[0503] The server integrates the acquired general-purpose data with personal data and performs data cleaning to remove malicious data. This improves the accuracy of the analysis.

[0504] Step 4:

[0505] The server uses machine learning algorithms to analyze the cleaned-up data. This analysis evaluates the impact of each user's lifestyle and health condition on hair, and extracts specific patterns.

[0506] Step 5:

[0507] Based on the analysis results, the server automatically generates a personalized hair care program for each user. The program includes specific product selections and daily care procedures.

[0508] Step 6:

[0509] The device notifies the user of the generated hair care program and presents it as a visually easy-to-understand step-by-step guide. This allows the user to take action based on the suggested program.

[0510] Step 7:

[0511] Users follow the presented hair care program and incorporate it into their daily lives. They can also provide feedback on the program's effectiveness and their experience.

[0512] Step 8:

[0513] The device collects user feedback and sends it to the server.

[0514] Step 9:

[0515] The server improves the hair care program based on feedback and newly collected data. This ensures that users are always provided with the latest and most effective care program.

[0516] (Example 1)

[0517] Next, we will describe Example 1. 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."

[0518] In recent years, there has been a growing demand for personalized care based on individual health conditions and lifestyles. However, in the case of hair care, it has been difficult to effectively utilize diverse personal data from users to provide optimal care programs. Furthermore, continuous adjustments and improvements are necessary to experience the effects of hair care, but there has been a lack of efficient feedback and update mechanisms for this purpose.

[0519] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0520] In this invention, the server includes means for collecting publicly available hair-related data from information sources on the Internet, means for collecting user personal data via communication devices, and means for integrating the publicly available data and the personal data and extracting patterns using machine learning algorithms. This enables the generation and provision of personalized hair care programs suited to the user's lifestyle and health condition, as well as continuous optimization through feedback.

[0521] The "Internet" is a global information network for sending and receiving information digitally around the world.

[0522] "Information sources" refer to digital platforms and databases that provide and allow access to publicly available data and knowledge.

[0523] "Hair" refers to fibrous tissue that grows on the head or body of living organisms, and in this invention, it is particularly related to hair on the head.

[0524] "Public data" refers to information that is made available on the internet in a form that anyone can access.

[0525] "Means" refer to the methods or technical elements used to achieve a specific objective.

[0526] A "user" is a person who uses a particular technology system or device, or an entity that provides data related to such use.

[0527] "Personal data" refers to information about a user, particularly information that is subject to privacy protection, including information about the user's lifestyle and health status.

[0528] "Communication equipment" refers to devices used to transmit and receive data, and includes digital devices such as smartphones and tablets.

[0529] "Integration" is the process of combining multiple data sets into a single, consistent format.

[0530] A "machine learning algorithm" is a computational method used to learn from experience and improve on specific tasks.

[0531] A "pattern" refers to a consistent feature or trend found within data.

[0532] "Visual presentation" refers to conveying information to users in an easily understandable format by displaying it in the form of images, diagrams, and other visual aids.

[0533] "Feedback" refers to the evaluations and opinions that users provide regarding the output or results of a system.

[0534] "Updating" is the process of modifying or improving existing programs or data based on new information.

[0535] This system is designed to provide users with personalized hair care programs and works as follows:

[0536] The server first collects hair-related data that is publicly available on the internet. This data includes scientific research findings, industry trends, and product reviews. Digital data acquisition techniques can be used for data collection, and specific examples include web scraping using Python libraries such as Beautiful Soup and Requests.

[0537] On the other hand, the device collects personal data with the user's consent. This includes data from health apps and food logging apps, as well as sleep data from smartphone sensors, collected using communication devices. This user data is collected using Bluetooth or Wi-Fi and transferred to a server.

[0538] The server integrates the acquired public data and personal data and stores it in a database. This integrated data is analyzed using machine learning algorithms to extract patterns related to lifestyle and health status. Existing data analysis libraries such as TensorFlow and Scikit-learn are used for this analysis.

[0539] Based on the analysis results, a personalized hair care program is generated for each user. This program includes specific hair care steps and a list of recommended products. The generated program is presented to the user visually through the device's display. Users can then follow the provided guide to perform their daily hair care.

[0540] As feedback, users can send their care results and experiences to the server via their device. The server uses this feedback to update and optimize the hair care program. This allows users to continue receiving the latest care tailored to changes in their lifestyle and environment.

[0541] For example, if a user moves to a drier climate, their skincare program will be updated to recommend moisturizing products suited to the new environment.

[0542] An example of a prompt message is: "Generate a program that suggests the most suitable hair care products and procedures for the user's lifestyle based on data analysis."

[0543] In this way, the system can provide hair care best suited to the user's individual circumstances and improve their hair health.

[0544] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0545] Step 1:

[0546] The server collects publicly available hair-related data from the internet. This step uses web scraping techniques to obtain data from scientific papers, industry trends, and product reviews. The input consists of various data sources on the internet, and the output is a cleaned dataset. This dataset is stored in a database for use in the next analysis step. Specifically, Python's Beautiful Soup is used to parse HTML content and extract the necessary information.

[0547] Step 2:

[0548] The device collects personal data with the user's consent. The collected data includes fitness data from health apps, meal records, and sleep patterns. Input is raw data obtained from the user's smartphone or wearable device, and output is the transfer of this data to a server. Bluetooth and Wi-Fi are used to enable real-time data updates.

[0549] Step 3:

[0550] The server integrates public data and personal data and stores it in a database. This process involves data cleansing to standardize data formats and ensure quality. The input is the datasets collected in steps 1 and 2, and the output is the integrated data set. The integrated data is used in the next analysis step.

[0551] Step 4:

[0552] The server runs machine learning algorithms on the integrated data to extract patterns related to the user's lifestyle. This analysis uses machine learning models (e.g., TensorFlow or Scikit-learn) to analyze the data and identify factors that influence hair health. The input is the integrated dataset, and the output is the feature patterns that form the basis of the care program.

[0553] Step 5:

[0554] The server generates a personalized hair care program based on the analysis. Using a generative model, it constructs a program that includes recommended products and procedures for the user. The input is the analysis results from step 4, and the output is a specific hair care program. This program provides customized content tailored to the user's needs.

[0555] Step 6:

[0556] The terminal visually presents the generated hair care program to the user. It displays the program's contents as a step-by-step guide. The input is the care program received from the server, and the output is user-executable visual information. This allows the user to take specific hair care actions.

[0557] Step 7:

[0558] Users provide feedback on their hair care experiences and results. This feedback is entered via a terminal and sent to the server. The input is the user's experience and evaluation, and the output is feedback data. This allows the server to continuously improve the program.

[0559] (Application Example 1)

[0560] Next, we will explain Application Example 1. In the following explanation, 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."

[0561] In modern life, there is a growing need for hair care tailored to individual lifestyles and health information, but existing methods only offer uniform care. Furthermore, the increasing complexity of hair care requires specialized knowledge, making it difficult for users to provide appropriate care themselves. Therefore, there is a need for automated hair care tailored to each user's condition, along with real-time feedback functionality.

[0562] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0563] In this invention, the server includes means for collecting publicly available data on hair from information sources on the Internet, means for collecting measurement data via sensors and software for acquiring the user's personal data, and means for integrating and analyzing the publicly available data and the measurement data to extract features. This enables the generation of individually optimized hair care programs and the provision of automated care to users via robotic devices.

[0564] A "server" is a computer system used to collect data from information sources on the internet and to integrate and analyze that data.

[0565] A "sensor" is a device used to measure and collect users' personal data.

[0566] "Software" refers to a group of programs used for data collection and analysis, and is particularly intended to support the creation of programs for hair care.

[0567] "Measurement data" refers to numerical data representing information about the user's lifestyle and health status, acquired through sensors and software.

[0568] "Publicly available data" refers to general information and research data about hair that is accessible on the internet.

[0569] "Characteristics" are unique trends or patterns extracted from data, and are used for personalized hair care.

[0570] A "hair care program" is a plan that includes optimized hair care steps and product recommendations, generated based on the individual user's condition.

[0571] A "robot device" is a home-use device that automatically performs hair care for the user and provides results in real time.

[0572] "Feedback" refers to information based on evaluations and experiences provided by users, which is used to improve hair care programs.

[0573] "Real-time" refers to a time frame in which data processing and results are provided immediately, thereby improving the user experience.

[0574] The hardware of the system realizing this invention includes a server, sensors, a user terminal, and a robotic device. The software includes a program for data analysis and an algorithm for generating a hair care program. Specifically, the server collects publicly available hair data from the internet, and the sensors measure and record the user's personal data.

[0575] The server integrates this data and analyzes user-specific characteristics using machine learning algorithms. Frameworks such as TensorFlow and PyTorch are used for this analysis. Furthermore, the server generates a personalized hair care program based on the analysis results. This program is sent to the user's terminal, and CSS and JavaScript are used to provide visual guidance.

[0576] The user executes the program through a terminal, and the robotic device automates hair care according to the instructions. Feedback is sent back to the server and used for continuous improvement of the program. In this way, personalized hair care becomes possible.

[0577] A concrete example would be a process where a user is presented with a suggested routine of washing their hair three times a week and using specific beauty products, and the robotic device automatically applies the products. Another example requiring flexibility is when a user has a specific hair-related concern; a possible prompt would be: "My hair hasn't been in very good condition lately. Could you give me some tips on how to maintain naturally shiny hair?"

[0578] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0579] Step 1:

[0580] The server collects publicly available data about hair from the internet. This data includes the latest research findings and product reviews, and is obtained through open APIs. The output obtained at this stage is text data on various hair care topics.

[0581] Step 2:

[0582] Sensors and the user's device collect personal data. This includes fitness information from the user's health app, sleep data, and food logs, which are transferred to the device via Bluetooth or Wi-Fi. The output of this step is user-specific lifestyle data.

[0583] Step 3:

[0584] The server integrates collected public data and personal data and stores it in a database. Based on this integrated data, machine learning algorithms are used to extract features. The input is the integrated data, and the output is a model representing unique patterns for each user. Data analysis is performed using frameworks such as TensorFlow.

[0585] Step 4:

[0586] The server generates a personalized hair care program based on the extracted features. This program includes the products to be used and the care steps involved. The input is feature data, and the output is a customized hair care plan. A generative AI model is used to generate the program.

[0587] Step 5:

[0588] The terminal provides the user with a generated hair care program. This program is displayed as a visual step-by-step guide, and the interface uses HTML5 and JavaScript. The input is the hair care plan, and the output is the specific steps the user can take.

[0589] Step 6:

[0590] The user performs hair care using a robotic device. The device applies the product and performs the necessary steps in an automated process. The output is the condition of the hair after treatment, which is monitored by the device's built-in camera.

[0591] Step 7:

[0592] Users provide feedback on the results of their hair care treatment, and the device sends this feedback to the server. This feedback includes user satisfaction and areas for improvement, which are used to generate the next care program. The input for the feedback consists of user comments and ratings, and the output is data used to improve the program.

[0593] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0594] This invention provides a system that offers a hair care program individually optimized for each user. The program is generated by effectively integrating and analyzing internet-based information sources, as well as the user's personal and emotional data. Furthermore, by incorporating an emotional engine that considers the user's emotions, a more precise and detailed care program is achieved.

[0595] Data collection and emotion recognition

[0596] The server collects hair-related information from the internet using a dedicated API and web scraping technology. Simultaneously, the device acquires personal data from health apps and sensors based on the user's individual permission. Furthermore, it uses an emotion engine to acquire emotional data in real time from the user's facial expressions and voice, and adds this to the database.

[0597] Data Integration and Analysis

[0598] The server cleans the integrated general-purpose and personal data, then analyzes it using machine learning algorithms. This analysis includes a process to identify the impact of lifestyle, health status, and emotions on hair condition.

[0599] Creating a hair care program

[0600] Based on the analysis results, the server generates a personalized hair care program for the user. The user's emotional state, detected by the emotion engine, is reflected in the program's recommendations; for example, if a high stress level is detected, the program will enhance its suggestions for relaxation care.

[0601] Program provision and practical support

[0602] The generated hair care program is provided to the user via the device and presented as a visually easy-to-understand step-by-step guide. The user can then take action based on the presented steps.

[0603] Feedback and continuous improvement

[0604] After hair care is performed, users can provide feedback on the program's effectiveness, which is collected by the device. The server incorporates this feedback and newly acquired emotional data into its analysis to optimize the hair care program. By using an emotional engine, continuous and flexible care tailored to individual emotional changes is provided, resulting in greater user satisfaction.

[0605] The following describes the processing flow.

[0606] Step 1:

[0607] The server regularly collects publicly available information related to hair from the internet, such as academic papers, product reviews, and the latest news, using a dedicated API and web scraping.

[0608] Step 2:

[0609] With user consent, the device continuously acquires physical data, behavioral patterns, and dietary information from health apps and wearable devices. It also uses an emotion engine to analyze facial expressions and voice from the user's camera and microphone, acquiring emotional data in real time.

[0610] Step 3:

[0611] The server integrates the collected data in a database and performs data cleansing to remove inaccurate data. This improves the reliability of the data and the accuracy of the analysis.

[0612] Step 4:

[0613] The server uses machine learning algorithms to analyze patterns based on lifestyle, health status, and emotional state from integrated data. This analysis specifically aims to identify causal relationships related to emotions and hair.

[0614] Step 5:

[0615] The server automatically generates a personalized hair care program based on the user's analysis results. This program includes specific care procedures, recommended products, and advice tailored to the user's emotional state.

[0616] Step 6:

[0617] The device notifies the user of the generated hair care program and supports daily implementation by displaying it in a visually appealing step-by-step format.

[0618] Step 7:

[0619] Users perform daily hair care according to the provided guidelines. They can provide feedback on the effectiveness of the care and any changes in their feelings through their device.

[0620] Step 8:

[0621] The device collects user feedback and newly acquired sentiment data and sends it to the server.

[0622] Step 9:

[0623] The server reanalyzes feedback and real-time sentiment data to update the hair care program. This process ensures continuous improvement so that users always receive optimal care.

[0624] (Example 2)

[0625] Next, we will describe Example 2. 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."

[0626] In modern society, hair health is a major concern for many people, but providing hair care tailored to each individual's lifestyle and emotional state is a challenging task. There is a need to effectively integrate and analyze data from diverse sources and provide individually optimized hair care programs that even consider the user's emotions.

[0627] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0628] In this invention, the server includes means for collecting information about hair from information sources on the Internet, means for collecting users' personal information via sensors and programs, and means for obtaining users' emotional information using an emotional analysis function. This makes it possible to provide each user with an individually optimized hair treatment plan that reflects their lifestyle and emotional state.

[0629] "Information source" refers to a source of data, including information related to hair, that can be obtained via the internet.

[0630] "Personal information" refers to data that includes information about a user's health status and lifestyle.

[0631] A "detector" is hardware or a sensor used to collect data from users.

[0632] A "program" is a software application that runs on a smartphone or other computing device.

[0633] "Emotional information" refers to data that indicates the emotional state of a user, obtained from their facial expressions, voice, and other sources.

[0634] The "emotion analysis function" is a feature that identifies the user's emotional state and uses that data for analysis.

[0635] "Information integration and refinement" is the process of combining data from different sources and organizing it into a consistent format.

[0636] "Pattern derivation" is an analytical process for extracting specific trends or patterns from integrated data.

[0637] A "hair care plan" is a plan that outlines individually optimized procedures and product usage methods to achieve goals related to hair health and style.

[0638] "Supply" refers to the act of providing the generated hair treatment plan to the user.

[0639] "Evaluation information" refers to feedback provided by users, indicating the effectiveness and satisfaction level of the hair treatment plan.

[0640] "Update" refers to the process of modifying and improving the hair treatment plan based on the collected evaluation information.

[0641] This invention is a system that provides a hair care program individually optimized for each user. The central elements of the system consist of three components: a server, a terminal, and the user.

[0642] The server uses a dedicated API and web scraping techniques to collect information about hair from internet sources. Based on this collected data, it integrates personal and emotional information and analyzes the data using machine learning algorithms. Specific technologies used include real-time emotion recognition by an emotion engine and database technology for data management.

[0643] The device is responsible for acquiring the user's personal information from health apps and sensors, in a manner permitted by the user. It also provides a visual interface to the user with a generated hair care program, displaying specific steps interactively. This interface may include prompt-based guidance.

[0644] Users receive a hair care program through this system and act accordingly. After completing the program, users provide feedback, which is sent to the server via their device.

[0645] For example, if a user is troubled by dry hair, the server combines information from the internet with the user's health data to generate suggestions for appropriate moisturizing care and products to use.

[0646] An example of a prompt is: "Create a prompt to generate a stress-management-focused hair care program based on the user's health and emotional data. Specifically, we are requesting suggestions for products containing ingredients effective in reducing stress."

[0647] This system configuration makes it possible to provide users with a more effective and precise hair care program that is individually optimized for them.

[0648] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0649] Step 1:

[0650] The server retrieves publicly available data about hair from internet sources. Using a dedicated API and web scraping techniques, it collects information on the latest trends and effective ingredients in hair care. Inputs include internet URLs and search queries, and the output is structured data.

[0651] Step 2:

[0652] The device collects the user's personal information. It obtains fitness and dietary data through health apps and sensors. This collection includes taking sensor data points in real time. The input is the user's access rights to health apps and sensors, and the output is personal data.

[0653] Step 3:

[0654] The server uses an emotion engine to acquire user emotional information. It analyzes the user's facial expressions and voice in real time and evaluates their emotional state. This process involves processing data obtained through the camera and microphone. Audio and video data are taken as input, and analyzed emotional data is provided as output.

[0655] Step 4:

[0656] The server integrates and cleans the collected data. It organizes public data, personal information, and sentiment information into a consistent format to build input datasets for machine learning. This process involves deduplication and transforming the data into a standard format. The input is raw data, and the output is a formatted dataset.

[0657] Step 5:

[0658] The server analyzes the data using machine learning algorithms based on a formatted dataset. This identifies the impact of lifestyle and emotions on hair condition and derives individually optimized hair treatment plans. The input is an integrated dataset, and the output is the analysis results and hair treatment plans.

[0659] Step 6:

[0660] The server reflects the user's emotional state in the hair treatment plan and adjusts the recommendations accordingly. For example, it might recommend care that emphasizes relaxation based on the user's mental state. Inputs include analysis results and emotional data, and the output is an adjusted hair treatment plan.

[0661] Step 7:

[0662] The terminal provides the user with a generated hair treatment plan. It displays daily care steps step-by-step through a visually intuitive interface. The input is the hair treatment plan, and the output is user interaction via the interface.

[0663] Step 8:

[0664] The user inputs the results of the processing as feedback into the terminal. This feedback includes information about hair texture and satisfaction level. The received feedback is sent to the server and used for future program updates. The input is user feedback, and the output is feedback data.

[0665] Step 9:

[0666] The server reanalyzes the feedback data and continuously improves the hair treatment plan. This makes it possible to generate a care program that is more suitable for each individual user. The input is feedback data, and the output is an improved hair treatment plan.

[0667] (Application Example 2)

[0668] Next, we will explain application example 2. In the following explanation, 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."

[0669] In modern society, many people are seeking appropriate care methods to maintain and improve the health of their hair. However, there is a problem in that many people are unable to receive effective care because there is no system in place to provide hair care optimized according to individual lifestyles and emotional states. Furthermore, there are many situations where flexible responses based on the user's emotions are required, and the lack of a system to propose appropriate care accordingly is a challenge.

[0670] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0671] In this invention, the server includes means for acquiring hair-related information from a database on the internet, means for acquiring the user's personal information using sensor devices and programs, and means for integrating and analyzing the information and the personal information to extract characteristics. This makes it possible for the home appliance to recognize the user's emotional state, create an individually optimized hair care plan, provide the plan to the user, and provide appropriate guidance.

[0672] An "internet database" is a system that provides information accessible on the web and stores a variety of data related to hair.

[0673] "Personal information" refers to data about a user's health status and lifestyle, which is acquired through sensor devices and programs.

[0674] A "sensor device" is a hardware device used to collect users' physical and environmental data.

[0675] A "program" is a part of the software that runs on a user's device and performs data collection and analysis.

[0676] "Methods for extracting characteristics" refer to analytical techniques used to identify important patterns and characteristics related to a user's hair care from the acquired information.

[0677] A "personally optimized hair care plan" is a plan that includes suggestions for the most suitable hair care based on the user's unique data.

[0678] "Household appliances" refer to robots and devices installed in the user's living space that provide guidance on hair care.

[0679] "Recognizing emotional state" refers to the process of determining the user's psychological emotions in real time based on their facial expressions and voice.

[0680] "Providing appropriate guidance" means proposing specific care methods to users based on analyzed data.

[0681] The system for implementing this invention includes an advanced system for providing users with individually optimized hair care. A server retrieves hair-related information from an internet database, while a terminal simultaneously collects the user's personal information through a sensor device. The server then integrates and analyzes the collected data. Machine learning algorithms are used for the analysis to extract characteristics and create an optimized hair care plan for each user.

[0682] The home-use device is equipped with a facial recognition camera and a voice analysis microphone to monitor the user's emotional state. This allows it to flexibly suggest care procedures and provide appropriate guidance based on the user's emotions. Specifically, the robot can play music to promote relaxation while recommending appropriate hair care products.

[0683] As a concrete example, the server evaluates the user's emotional state based on daily data and provides a relaxation plan to maintain hair health based on stress levels. An example of a prompt message in this case would be: "Please provide an optimal hair care program based on the user's emotional state. Please generate customized suggestions, including measures for when stress levels are high."

[0684] This allows users to practice the care methods best suited to them. The entire system works efficiently together to improve the user experience and provide optimal support for promoting hair health.

[0685] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0686] Step 1:

[0687] The server retrieves hair-related information from databases on the internet. The input is the URL information of the online database, and the output is raw data related to hair. To analyze this raw data, the server uses web scraping techniques to extract the data.

[0688] Step 2:

[0689] The device uses sensor equipment to collect the user's personal data. Inputs are data from the user's biosensors, and outputs are data related to health status, lifestyle, and emotional state. The device collects this data in real time and transmits it to a server.

[0690] Step 3:

[0691] The server integrates and analyzes the acquired data. Inputs include hair-related information from the internet and personal data from the device; output is the most important characteristics for the user's hair care. The server uses machine learning algorithms to extract data characteristics and analyze the user's unique trends.

[0692] Step 4:

[0693] The server creates an individually optimized hair care plan based on the extracted characteristics. The input is analyzed characteristic data, and the output is a personalized hair care suggestion. Using a generative AI model, the care content is planned according to the user's stress level and health condition.

[0694] Step 5:

[0695] The terminal provides the user with a hair care plan. The input is the hair care plan sent from the server, and the output is specific care instructions for the user. The terminal communicates the instructions to the user visually and audibly, and provides guidance on the appropriate use of products.

[0696] Step 6:

[0697] The user implements the provided hair care plan and provides feedback on its effectiveness to the device. Inputs are the user's implementation status and subjective evaluation of the effects, while output is feedback data. The device sends this feedback to a server for further analysis.

[0698] Step 7:

[0699] The server updates the hair care plan based on user feedback and new personal data. Inputs are feedback data and additional personal data, and output is a suggested improved hair care plan. This continuously optimizes the care content for the user.

[0700] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0701] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0702] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0703] [Fourth Embodiment]

[0704] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0705] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0706] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0707] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0708] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0709] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0710] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0711] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0712] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0713] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0714] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0715] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0716] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0717] This invention is a system for providing personalized hair care programs and implements a process for effectively integrating and analyzing internet-based information sources and users' personal data. The configuration and operation of this system will be described in detail below.

[0718] Data collection and integration

[0719] The server collects publicly available hair-related data via the internet, including scientific research, industry trend information, and product reviews. Meanwhile, the device collects personal data obtained with the user's consent. This personal data includes fitness information from health apps, dietary records, and sleep data from smartphone sensors. This data is integrated into a dedicated database.

[0720] Analysis and program generation

[0721] The server analyzes the integrated data to extract unique patterns for each user. This analysis uses machine learning algorithms to identify lifestyle factors and health trends that influence hair condition. Based on these results, a personalized hair care program is generated. The program includes the necessary steps and product recommendations to improve the user's hair health.

[0722] Program provision and implementation support

[0723] The generated hair care program is provided to the user via the device. The provided program is visually displayed as a step-by-step guide for daily hair care. By following this guide and taking specific actions, the user can experience the effects.

[0724] Feedback and program updates

[0725] After performing hair care, users can provide feedback on the effects and their experience. The device collects this feedback and sends it to the server. The server uses the collected feedback and new data to continuously improve the hair care program, providing users with the latest and most effective care. This process helps users improve their hair health in a way that best suits their individual lifestyle.

[0726] The following describes the processing flow.

[0727] Step 1:

[0728] The server automatically collects hair-related data from publicly available information on the internet using a dedicated API and web scraping technology. This data includes the latest scientific research, product reviews, and industry news.

[0729] Step 2:

[0730] With the user's permission, the device continuously acquires individual data from sensors and apps. Specific data includes fitness data and meal logs provided by health apps, as well as sleep data from smartphone sensors.

[0731] Step 3:

[0732] The server integrates the acquired general-purpose data with personal data and performs data cleaning to remove malicious data. This improves the accuracy of the analysis.

[0733] Step 4:

[0734] The server uses machine learning algorithms to analyze the cleaned-up data. This analysis evaluates the impact of each user's lifestyle and health condition on hair, and extracts specific patterns.

[0735] Step 5:

[0736] Based on the analysis results, the server automatically generates a personalized hair care program for each user. The program includes specific product selections and daily care procedures.

[0737] Step 6:

[0738] The device notifies the user of the generated hair care program and presents it as a visually easy-to-understand step-by-step guide. This allows the user to take action based on the suggested program.

[0739] Step 7:

[0740] Users follow the presented hair care program and incorporate it into their daily lives. They can also provide feedback on the program's effectiveness and their experience.

[0741] Step 8:

[0742] The device collects user feedback and sends it to the server.

[0743] Step 9:

[0744] The server improves the hair care program based on feedback and newly collected data. This ensures that users are always provided with the latest and most effective care program.

[0745] (Example 1)

[0746] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0747] In recent years, there has been a growing demand for personalized care based on individual health conditions and lifestyles. However, in the case of hair care, it has been difficult to effectively utilize diverse personal data from users to provide optimal care programs. Furthermore, continuous adjustments and improvements are necessary to experience the effects of hair care, but there has been a lack of efficient feedback and update mechanisms for this purpose.

[0748] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0749] In this invention, the server includes means for collecting publicly available hair-related data from information sources on the Internet, means for collecting user personal data via communication devices, and means for integrating the publicly available data and the personal data and extracting patterns using machine learning algorithms. This enables the generation and provision of personalized hair care programs suited to the user's lifestyle and health condition, as well as continuous optimization through feedback.

[0750] The "Internet" is a global information network for sending and receiving information digitally around the world.

[0751] "Information sources" refer to digital platforms and databases that provide and allow access to publicly available data and knowledge.

[0752] "Hair" refers to fibrous tissue that grows on the head or body of living organisms, and in this invention, it is particularly related to hair on the head.

[0753] "Public data" refers to information that is made available on the internet in a form that anyone can access.

[0754] "Means" refer to the methods or technical elements used to achieve a specific objective.

[0755] A "user" is a person who uses a particular technology system or device, or an entity that provides data related to such use.

[0756] "Personal data" refers to information about a user, particularly information that is subject to privacy protection, including information about the user's lifestyle and health status.

[0757] "Communication equipment" refers to devices used to transmit and receive data, and includes digital devices such as smartphones and tablets.

[0758] "Integration" is the process of combining multiple data sets into a single, consistent format.

[0759] A "machine learning algorithm" is a computational method used to learn from experience and improve on specific tasks.

[0760] A "pattern" refers to a consistent feature or trend found within data.

[0761] "Visual presentation" refers to conveying information to users in an easily understandable format by displaying it in the form of images, diagrams, and other visual aids.

[0762] "Feedback" refers to the evaluations and opinions that users provide regarding the output or results of a system.

[0763] "Updating" is the process of modifying or improving existing programs or data based on new information.

[0764] This system is designed to provide users with personalized hair care programs and works as follows:

[0765] The server first collects hair-related data that is publicly available on the internet. This data includes scientific research findings, industry trends, and product reviews. Digital data acquisition techniques can be used for data collection, and specific examples include web scraping using Python libraries such as Beautiful Soup and Requests.

[0766] On the other hand, the device collects personal data with the user's consent. This includes data from health apps and food logging apps, as well as sleep data from smartphone sensors, collected using communication devices. This user data is collected using Bluetooth or Wi-Fi and transferred to a server.

[0767] The server integrates the acquired public data and personal data and stores it in a database. This integrated data is analyzed using machine learning algorithms to extract patterns related to lifestyle and health status. Existing data analysis libraries such as TensorFlow and Scikit-learn are used for this analysis.

[0768] Based on the analysis results, a personalized hair care program is generated for each user. This program includes specific hair care steps and a list of recommended products. The generated program is presented to the user visually through the device's display. Users can then follow the provided guide to perform their daily hair care.

[0769] As feedback, users can send their care results and experiences to the server via their device. The server uses this feedback to update and optimize the hair care program. This allows users to continue receiving the latest care tailored to changes in their lifestyle and environment.

[0770] For example, if a user moves to a drier climate, their skincare program will be updated to recommend moisturizing products suited to the new environment.

[0771] An example of a prompt message is: "Generate a program that suggests the most suitable hair care products and procedures for the user's lifestyle based on data analysis."

[0772] In this way, the system can provide hair care best suited to the user's individual circumstances and improve their hair health.

[0773] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0774] Step 1:

[0775] The server collects publicly available hair-related data from the internet. This step uses web scraping techniques to obtain data from scientific papers, industry trends, and product reviews. The input consists of various data sources on the internet, and the output is a cleaned dataset. This dataset is stored in a database for use in the next analysis step. Specifically, Python's Beautiful Soup is used to parse HTML content and extract the necessary information.

[0776] Step 2:

[0777] The device collects personal data with the user's consent. The collected data includes fitness data from health apps, meal records, and sleep patterns. Input is raw data obtained from the user's smartphone or wearable device, and output is the transfer of this data to a server. Bluetooth and Wi-Fi are used to enable real-time data updates.

[0778] Step 3:

[0779] The server integrates public data and personal data and stores it in a database. This process involves data cleansing to standardize data formats and ensure quality. The input is the datasets collected in steps 1 and 2, and the output is the integrated data set. The integrated data is used in the next analysis step.

[0780] Step 4:

[0781] The server runs machine learning algorithms on the integrated data to extract patterns related to the user's lifestyle. This analysis uses machine learning models (e.g., TensorFlow or Scikit-learn) to analyze the data and identify factors that influence hair health. The input is the integrated dataset, and the output is the feature patterns that form the basis of the care program.

[0782] Step 5:

[0783] The server generates a personalized hair care program based on the analysis. Using a generative model, it constructs a program that includes recommended products and procedures for the user. The input is the analysis results from step 4, and the output is a specific hair care program. This program provides customized content tailored to the user's needs.

[0784] Step 6:

[0785] The terminal visually presents the generated hair care program to the user. It displays the program's contents as a step-by-step guide. The input is the care program received from the server, and the output is user-executable visual information. This allows the user to take specific hair care actions.

[0786] Step 7:

[0787] Users provide feedback on their hair care experiences and results. This feedback is entered via a terminal and sent to the server. The input is the user's experience and evaluation, and the output is feedback data. This allows the server to continuously improve the program.

[0788] (Application Example 1)

[0789] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0790] In modern life, there is a growing need for hair care tailored to individual lifestyles and health information, but existing methods only offer uniform care. Furthermore, the increasing complexity of hair care requires specialized knowledge, making it difficult for users to provide appropriate care themselves. Therefore, there is a need for automated hair care tailored to each user's condition, along with real-time feedback functionality.

[0791] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0792] In this invention, the server includes means for collecting publicly available data on hair from information sources on the Internet, means for collecting measurement data via sensors and software for acquiring the user's personal data, and means for integrating and analyzing the publicly available data and the measurement data to extract features. This enables the generation of individually optimized hair care programs and the provision of automated care to users via robotic devices.

[0793] A "server" is a computer system used to collect data from information sources on the internet and to integrate and analyze that data.

[0794] A "sensor" is a device used to measure and collect users' personal data.

[0795] "Software" refers to a group of programs used for data collection and analysis, and is particularly intended to support the creation of programs for hair care.

[0796] "Measurement data" refers to numerical data representing information about the user's lifestyle and health status, acquired through sensors and software.

[0797] "Publicly available data" refers to general information and research data about hair that is accessible on the internet.

[0798] "Characteristics" are unique trends or patterns extracted from data, and are used for personalized hair care.

[0799] A "hair care program" is a plan that includes optimized hair care steps and product recommendations, generated based on the individual user's condition.

[0800] A "robot device" is a home-use device that automatically performs hair care for the user and provides results in real time.

[0801] "Feedback" refers to information based on evaluations and experiences provided by users, which is used to improve hair care programs.

[0802] "Real-time" refers to a time frame in which data processing and results are provided immediately, thereby improving the user experience.

[0803] The hardware of the system realizing this invention includes a server, sensors, a user terminal, and a robotic device. The software includes a program for data analysis and an algorithm for generating a hair care program. Specifically, the server collects publicly available hair data from the internet, and the sensors measure and record the user's personal data.

[0804] The server integrates this data and analyzes user-specific characteristics using machine learning algorithms. Frameworks such as TensorFlow and PyTorch are used for this analysis. Furthermore, the server generates a personalized hair care program based on the analysis results. This program is sent to the user's terminal, and CSS and JavaScript are used to provide visual guidance.

[0805] The user executes the program through a terminal, and the robotic device automates hair care according to the instructions. Feedback is sent back to the server and used for continuous improvement of the program. In this way, personalized hair care becomes possible.

[0806] A concrete example would be a process where a user is presented with a suggested routine of washing their hair three times a week and using specific beauty products, and the robotic device automatically applies the products. Another example requiring flexibility is when a user has a specific hair-related concern; a possible prompt would be: "My hair hasn't been in very good condition lately. Could you give me some tips on how to maintain naturally shiny hair?"

[0807] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0808] Step 1:

[0809] The server collects publicly available data about hair from the internet. This data includes the latest research findings and product reviews, and is obtained through open APIs. The output obtained at this stage is text data on various hair care topics.

[0810] Step 2:

[0811] Sensors and the user's device collect personal data. This includes fitness information from the user's health app, sleep data, and food logs, which are transferred to the device via Bluetooth or Wi-Fi. The output of this step is user-specific lifestyle data.

[0812] Step 3:

[0813] The server integrates collected public data and personal data and stores it in a database. Based on this integrated data, machine learning algorithms are used to extract features. The input is the integrated data, and the output is a model representing unique patterns for each user. Data analysis is performed using frameworks such as TensorFlow.

[0814] Step 4:

[0815] The server generates a personalized hair care program based on the extracted features. This program includes the products to be used and the care steps involved. The input is feature data, and the output is a customized hair care plan. A generative AI model is used to generate the program.

[0816] Step 5:

[0817] The terminal provides the user with a generated hair care program. This program is displayed as a visual step-by-step guide, and the interface uses HTML5 and JavaScript. The input is the hair care plan, and the output is the specific steps the user can take.

[0818] Step 6:

[0819] The user performs hair care using a robotic device. The device applies the product and performs the necessary steps in an automated process. The output is the condition of the hair after treatment, which is monitored by the device's built-in camera.

[0820] Step 7:

[0821] Users provide feedback on the results of their hair care treatment, and the device sends this feedback to the server. This feedback includes user satisfaction and areas for improvement, which are used to generate the next care program. The input for the feedback consists of user comments and ratings, and the output is data used to improve the program.

[0822] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0823] This invention provides a system that offers a hair care program individually optimized for each user. The program is generated by effectively integrating and analyzing internet-based information sources, as well as the user's personal and emotional data. Furthermore, by incorporating an emotional engine that considers the user's emotions, a more precise and detailed care program is achieved.

[0824] Data collection and emotion recognition

[0825] The server collects hair-related information from the internet using a dedicated API and web scraping technology. Simultaneously, the device acquires personal data from health apps and sensors based on the user's individual permission. Furthermore, it uses an emotion engine to acquire emotional data in real time from the user's facial expressions and voice, and adds this to the database.

[0826] Data Integration and Analysis

[0827] The server cleans the integrated general-purpose and personal data, then analyzes it using machine learning algorithms. This analysis includes a process to identify the impact of lifestyle, health status, and emotions on hair condition.

[0828] Creating a hair care program

[0829] Based on the analysis results, the server generates a personalized hair care program for the user. The user's emotional state, detected by the emotion engine, is reflected in the program's recommendations; for example, if a high stress level is detected, the program will enhance its suggestions for relaxation care.

[0830] Program provision and practical support

[0831] The generated hair care program is provided to the user via the device and presented as a visually easy-to-understand step-by-step guide. The user can then take action based on the presented steps.

[0832] Feedback and continuous improvement

[0833] After hair care is performed, users can provide feedback on the program's effectiveness, which is collected by the device. The server incorporates this feedback and newly acquired emotional data into its analysis to optimize the hair care program. By using an emotional engine, continuous and flexible care tailored to individual emotional changes is provided, resulting in greater user satisfaction.

[0834] The following describes the processing flow.

[0835] Step 1:

[0836] The server regularly collects publicly available information related to hair from the internet, such as academic papers, product reviews, and the latest news, using a dedicated API and web scraping.

[0837] Step 2:

[0838] With user consent, the device continuously acquires physical data, behavioral patterns, and dietary information from health apps and wearable devices. It also uses an emotion engine to analyze facial expressions and voice from the user's camera and microphone, acquiring emotional data in real time.

[0839] Step 3:

[0840] The server integrates the collected data in a database and performs data cleansing to remove inaccurate data. This improves the reliability of the data and the accuracy of the analysis.

[0841] Step 4:

[0842] The server uses machine learning algorithms to analyze patterns based on lifestyle, health status, and emotional state from integrated data. This analysis specifically aims to identify causal relationships related to emotions and hair.

[0843] Step 5:

[0844] The server automatically generates a personalized hair care program based on the user's analysis results. This program includes specific care procedures, recommended products, and advice tailored to the user's emotional state.

[0845] Step 6:

[0846] The device notifies the user of the generated hair care program and supports daily implementation by displaying it in a visually appealing step-by-step format.

[0847] Step 7:

[0848] Users perform daily hair care according to the provided guidelines. They can provide feedback on the effectiveness of the care and any changes in their feelings through their device.

[0849] Step 8:

[0850] The device collects user feedback and newly acquired sentiment data and sends it to the server.

[0851] Step 9:

[0852] The server reanalyzes feedback and real-time sentiment data to update the hair care program. This process ensures continuous improvement so that users always receive optimal care.

[0853] (Example 2)

[0854] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0855] In modern society, hair health is a major concern for many people, but providing hair care tailored to each individual's lifestyle and emotional state is a challenging task. There is a need to effectively integrate and analyze data from diverse sources and provide individually optimized hair care programs that even consider the user's emotions.

[0856] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0857] In this invention, the server includes means for collecting information about hair from information sources on the Internet, means for collecting users' personal information via sensors and programs, and means for obtaining users' emotional information using an emotional analysis function. This makes it possible to provide each user with an individually optimized hair treatment plan that reflects their lifestyle and emotional state.

[0858] "Information source" refers to a source of data, including information related to hair, that can be obtained via the internet.

[0859] "Personal information" refers to data that includes information about a user's health status and lifestyle.

[0860] A "detector" is hardware or a sensor used to collect data from users.

[0861] A "program" is a software application that runs on a smartphone or other computing device.

[0862] "Emotional information" refers to data that indicates the emotional state of a user, obtained from their facial expressions, voice, and other sources.

[0863] The "emotion analysis function" is a feature that identifies the user's emotional state and uses that data for analysis.

[0864] "Information integration and refinement" is the process of combining data from different sources and organizing it into a consistent format.

[0865] "Pattern derivation" is an analytical process for extracting specific trends or patterns from integrated data.

[0866] A "hair care plan" is a plan that outlines individually optimized procedures and product usage methods to achieve goals related to hair health and style.

[0867] "Supply" refers to the act of providing the generated hair treatment plan to the user.

[0868] "Evaluation information" refers to feedback provided by users, indicating the effectiveness and satisfaction level of the hair treatment plan.

[0869] "Update" refers to the process of modifying and improving the hair treatment plan based on the collected evaluation information.

[0870] This invention is a system that provides a hair care program individually optimized for each user. The central elements of the system consist of three components: a server, a terminal, and the user.

[0871] The server uses a dedicated API and web scraping techniques to collect information about hair from internet sources. Based on this collected data, it integrates personal and emotional information and analyzes the data using machine learning algorithms. Specific technologies used include real-time emotion recognition by an emotion engine and database technology for data management.

[0872] The device is responsible for acquiring the user's personal information from health apps and sensors, in a manner permitted by the user. It also provides a visual interface to the user with a generated hair care program, displaying specific steps interactively. This interface may include prompt-based guidance.

[0873] Users receive a hair care program through this system and act accordingly. After completing the program, users provide feedback, which is sent to the server via their device.

[0874] For example, if a user is troubled by dry hair, the server combines information from the internet with the user's health data to generate suggestions for appropriate moisturizing care and products to use.

[0875] An example of a prompt is: "Create a prompt to generate a stress-management-focused hair care program based on the user's health and emotional data. Specifically, we are requesting suggestions for products containing ingredients effective in reducing stress."

[0876] This system configuration makes it possible to provide users with a more effective and precise hair care program that is individually optimized for them.

[0877] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0878] Step 1:

[0879] The server retrieves publicly available data about hair from internet sources. Using a dedicated API and web scraping techniques, it collects information on the latest trends and effective ingredients in hair care. Inputs include internet URLs and search queries, and the output is structured data.

[0880] Step 2:

[0881] The device collects the user's personal information. It obtains fitness and dietary data through health apps and sensors. This collection includes taking sensor data points in real time. The input is the user's access rights to health apps and sensors, and the output is personal data.

[0882] Step 3:

[0883] The server uses an emotion engine to acquire user emotional information. It analyzes the user's facial expressions and voice in real time and evaluates their emotional state. This process involves processing data obtained through the camera and microphone. Audio and video data are taken as input, and analyzed emotional data is provided as output.

[0884] Step 4:

[0885] The server integrates and cleans the collected data. It organizes public data, personal information, and sentiment information into a consistent format to build input datasets for machine learning. This process involves deduplication and transforming the data into a standard format. The input is raw data, and the output is a formatted dataset.

[0886] Step 5:

[0887] The server analyzes the data using machine learning algorithms based on a formatted dataset. This identifies the impact of lifestyle and emotions on hair condition and derives individually optimized hair treatment plans. The input is an integrated dataset, and the output is the analysis results and hair treatment plans.

[0888] Step 6:

[0889] The server reflects the user's emotional state in the hair treatment plan and adjusts the recommendations accordingly. For example, it might recommend care that emphasizes relaxation based on the user's mental state. Inputs include analysis results and emotional data, and the output is an adjusted hair treatment plan.

[0890] Step 7:

[0891] The terminal provides the user with a generated hair treatment plan. It displays daily care steps step-by-step through a visually intuitive interface. The input is the hair treatment plan, and the output is user interaction via the interface.

[0892] Step 8:

[0893] The user inputs the results of the processing as feedback into the terminal. This feedback includes information about hair texture and satisfaction level. The received feedback is sent to the server and used for future program updates. The input is user feedback, and the output is feedback data.

[0894] Step 9:

[0895] The server reanalyzes the feedback data and continuously improves the hair treatment plan. This makes it possible to generate a care program that is more suitable for each individual user. The input is feedback data, and the output is an improved hair treatment plan.

[0896] (Application Example 2)

[0897] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0898] In modern society, many people are seeking appropriate care methods to maintain and improve the health of their hair. However, there is a problem in that many people are unable to receive effective care because there is no system in place to provide hair care optimized according to individual lifestyles and emotional states. Furthermore, there are many situations where flexible responses based on the user's emotions are required, and the lack of a system to propose appropriate care accordingly is a challenge.

[0899] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0900] In this invention, the server includes means for acquiring hair-related information from a database on the internet, means for acquiring the user's personal information using sensor devices and programs, and means for integrating and analyzing the information and the personal information to extract characteristics. This makes it possible for the home appliance to recognize the user's emotional state, create an individually optimized hair care plan, provide the plan to the user, and provide appropriate guidance.

[0901] An "internet database" is a system that provides information accessible on the web and stores a variety of data related to hair.

[0902] "Personal information" refers to data about a user's health status and lifestyle, which is acquired through sensor devices and programs.

[0903] A "sensor device" is a hardware device used to collect users' physical and environmental data.

[0904] A "program" is a part of the software that runs on a user's device and performs data collection and analysis.

[0905] "Methods for extracting characteristics" refer to analytical techniques used to identify important patterns and characteristics related to a user's hair care from the acquired information.

[0906] A "personally optimized hair care plan" is a plan that includes suggestions for the most suitable hair care based on the user's unique data.

[0907] "Household appliances" refer to robots and devices installed in the user's living space that provide guidance on hair care.

[0908] "Recognizing emotional state" refers to the process of determining the user's psychological emotions in real time based on their facial expressions and voice.

[0909] "Providing appropriate guidance" means proposing specific care methods to users based on analyzed data.

[0910] The system for implementing this invention includes an advanced system for providing users with individually optimized hair care. A server retrieves hair-related information from an internet database, while a terminal simultaneously collects the user's personal information through a sensor device. The server then integrates and analyzes the collected data. Machine learning algorithms are used for the analysis to extract characteristics and create an optimized hair care plan for each user.

[0911] The home-use device is equipped with a facial recognition camera and a voice analysis microphone to monitor the user's emotional state. This allows it to flexibly suggest care procedures and provide appropriate guidance based on the user's emotions. Specifically, the robot can play music to promote relaxation while recommending appropriate hair care products.

[0912] As a concrete example, the server evaluates the user's emotional state based on daily data and provides a relaxation plan to maintain hair health based on stress levels. An example of a prompt message in this case would be: "Please provide an optimal hair care program based on the user's emotional state. Please generate customized suggestions, including measures for when stress levels are high."

[0913] This allows users to practice the care methods best suited to them. The entire system works efficiently together to improve the user experience and provide optimal support for promoting hair health.

[0914] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0915] Step 1:

[0916] The server retrieves hair-related information from databases on the internet. The input is the URL information of the online database, and the output is raw data related to hair. To analyze this raw data, the server uses web scraping techniques to extract the data.

[0917] Step 2:

[0918] The device uses sensor equipment to collect the user's personal data. Inputs are data from the user's biosensors, and outputs are data related to health status, lifestyle, and emotional state. The device collects this data in real time and transmits it to a server.

[0919] Step 3:

[0920] The server integrates and analyzes the acquired data. Inputs include hair-related information from the internet and personal data from the device; output is the most important characteristics for the user's hair care. The server uses machine learning algorithms to extract data characteristics and analyze the user's unique trends.

[0921] Step 4:

[0922] The server creates an individually optimized hair care plan based on the extracted characteristics. The input is analyzed characteristic data, and the output is a personalized hair care suggestion. Using a generative AI model, the care content is planned according to the user's stress level and health condition.

[0923] Step 5:

[0924] The terminal provides the user with a hair care plan. The input is the hair care plan sent from the server, and the output is specific care instructions for the user. The terminal communicates the instructions to the user visually and audibly, and provides guidance on the appropriate use of products.

[0925] Step 6:

[0926] The user implements the provided hair care plan and provides feedback on its effectiveness to the device. Inputs are the user's implementation status and subjective evaluation of the effects, while output is feedback data. The device sends this feedback to a server for further analysis.

[0927] Step 7:

[0928] The server updates the hair care plan based on user feedback and new personal data. Inputs are feedback data and additional personal data, and output is a suggested improved hair care plan. This continuously optimizes the care content for the user.

[0929] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0930] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0931] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0932] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0933] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0934] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0935] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0936] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0937] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0938] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0939] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0940] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0941] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0942] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0943] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0944] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0945] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0946] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0947] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0948] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0949] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0950] The following is further disclosed regarding the embodiments described above.

[0951] (Claim 1)

[0952] Methods for collecting publicly available data on hair from internet sources,

[0953] Means of collecting users' personal data through sensors and applications,

[0954] Means for integrating and analyzing the aforementioned public data and the aforementioned personal data to extract patterns,

[0955] A means for generating individually optimized hair care programs based on extracted patterns,

[0956] A means of providing the generated hair care program to the user,

[0957] A means for collecting user feedback and updating the hair care program,

[0958] A system that includes this.

[0959] (Claim 2)

[0960] The system according to claim 1, further comprising means for displaying the generated hair care program step by step on a terminal and providing support for the user to implement it.

[0961] (Claim 3)

[0962] The system according to claim 1, further comprising means for monitoring the user's execution status and improvement effects of the hair care program and for performing continuous data analysis for optimization.

[0963] "Example 1"

[0964] (Claim 1)

[0965] Methods for collecting publicly available hair-related data from internet sources,

[0966] Means of collecting users' personal data via communication devices,

[0967] A means for integrating the aforementioned public data and the aforementioned personal data and extracting patterns using a machine learning algorithm,

[0968] A means for generating an individualized hair care program based on extracted patterns,

[0969] A means of visually providing the generated hair care program through a display device,

[0970] A means for collecting user feedback and performing data analysis to update the hair care program,

[0971] A system that includes this.

[0972] (Claim 2)

[0973] The system according to claim 1, further comprising means for displaying the generated hair care program step by step on a display device and providing the user with implementation support.

[0974] (Claim 3)

[0975] The system according to claim 1, further comprising means for monitoring the user's execution status and improvement effects of the hair care program, and for performing continuous data analysis and updates for optimization.

[0976] "Application Example 1"

[0977] (Claim 1)

[0978] Methods for collecting publicly available data on hair from internet sources,

[0979] A means of collecting measurement data through sensors and software to acquire users' personal data,

[0980] A means for integrating and analyzing the aforementioned publicly available data and the aforementioned measurement data to extract features,

[0981] A means for generating individually optimized hair care programs based on extracted characteristics,

[0982] A means of presenting the generated hair care program to the user,

[0983] A means for collecting user feedback and improving the hair care program,

[0984] Means for automatically executing the generated hair care program via a robotic device,

[0985] A means of analyzing the condition of hair using a imaging device and providing the results in real time,

[0986] A system that includes this.

[0987] (Claim 2)

[0988] The system according to claim 1, further comprising means for sequentially guiding the generated hair care program on a device and providing support for the user to carry it out while receiving advice.

[0989] (Claim 3)

[0990] The system according to claim 1, further comprising means for tracking the user's implementation status and improvement effects of the hair care program and for performing continuous data analysis for optimization.

[0991] "Example 2 of combining an emotion engine"

[0992] (Claim 1)

[0993] Means of collecting information about hair from internet sources,

[0994] Means of collecting users' personal information through sensors and programs,

[0995] A means of obtaining user emotional information using an emotional analysis function,

[0996] Means for integrating and processing the aforementioned information, the aforementioned personal information, and the aforementioned emotional information to derive a pattern,

[0997] A means for constructing individually optimized hair treatment plans based on derived patterns,

[0998] A means of reflecting emotional state and adjusting recommendations in a constructed hair treatment plan,

[0999] Means for supplying the constructed hair treatment plan to the user,

[1000] A means for collecting user evaluation information and updating the hair treatment plan,

[1001] A system that includes this.

[1002] (Claim 2)

[1003] The system according to claim 1, further comprising means for displaying the constructed hair treatment plan step by step on a terminal and providing support for the user to carry it out.

[1004] (Claim 3)

[1005] The system according to claim 1, further comprising means for monitoring the user's implementation status and improvement effects of the hair treatment plan and for performing continuous information analysis for optimization.

[1006] "Application example 2 when combining with an emotional engine"

[1007] (Claim 1)

[1008] A means of obtaining information related to hair from an internet database,

[1009] Methods for acquiring users' personal information using sensor devices and programs,

[1010] A means for integrating and analyzing the aforementioned information and the aforementioned personal information to extract characteristics,

[1011] A means for creating an individually optimized hair care plan based on extracted characteristics,

[1012] A means of distributing the created hair care plan to users,

[1013] A means for collecting feedback from users and improving the aforementioned hair care plan,

[1014] A means by which a home-use device recognizes the user's emotional state and provides hair care guidance accordingly,

[1015] A system that includes this.

[1016] (Claim 2)

[1017] The system according to claim 1, further comprising means for clearly displaying the created hair care plan on the device and providing support for the user to carry it out.

[1018] (Claim 3)

[1019] The system according to claim 1, further comprising means for observing the user's implementation status and improvement effects of the hair care plan and for performing continuous information analysis for optimization. [Explanation of Symbols]

[1020] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Methods for collecting publicly available data on hair from internet sources, A means of collecting measurement data through sensors and software to acquire users' personal data, A means for integrating and analyzing the aforementioned publicly available data and the aforementioned measurement data to extract features, A means for generating individually optimized hair care programs based on extracted characteristics, A means of presenting the generated hair care program to the user, A means for collecting user feedback and improving the hair care program, Means for automatically executing the generated hair care program via a robotic device, A means of analyzing the condition of hair using a imaging device and providing the results in real time, A system that includes this.

2. The system according to claim 1, further comprising means for sequentially guiding the generated hair care program on a device and providing support for the user to carry it out while receiving advice.

3. The system according to claim 1, further comprising means for tracking the user's implementation status and improvement effects of the hair care program and for performing continuous data analysis for optimization.

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