IMPLEMENTATION OF SKIN ANALYSIS SYSTEMS AND METHODS

Abstract

A skin analysis system and method are described for analyzing user-specific skin data and health data to generate a user-specific skin analysis. User-specific skin data and health data of a user are received by one or more processors, and the health data includes one or more of: (1) body water content or water mass, (2) intracellular water to extracellular water ratio, (3) body mass index (BMI), (4) blood markers, (5) glucose intake level, (6) heart rate variability, or (7) heart rate. A skin analysis learning model analyzes the user-specific skin data and health data to generate a user-specific skin analysis.

Description

[Technical Field]

[0001] The present disclosure relates generally to skin analysis systems and methods, and more particularly to implementations of skin analysis systems and methods for generating a user-specific skin analysis. [Background technology]

[0002] Generally, multiple intrinsic factors of human skin, such as sweat and natural oils, have real-world effects on the overall condition and appearance of a user's skin. Additional extrinsic factors, such as wind, humidity, sunlight, and / or the use of various skin-related products, may also affect the condition of a user's skin. Unfortunately, both types of factors contribute to numerous skin conditions, such as acne, eczema, wrinkles, and general inflammation, which can adversely affect a user's skin and their perception of it. However, a user's perception of skin-related problems typically does not reflect such underlying intrinsic and / or extrinsic factors.

[0003] Therefore, when considering the number of intrinsic and / or extrinsic factors, together with the complexity of skin types, problems arise, especially when considering different users, each of which may be associated with different demographics, races, and ethnicities.This creates problems in analyzing and treating various human skin conditions and characteristics.For example, prior art methods that attempt to assist users in self-analyzing skin conditions generally lack sufficient information to generate accurate user-specific analyses, and as a result, provide broad and overly simplified recommendations.Furthermore, users may try to empirically experiment with various products or techniques, but do not achieve satisfactory results and / or cause possible negative side effects, which affect the user's skin condition or otherwise visual appearance. Summary of the Invention [Problem to be solved by the invention]

[0004] For the above reasons, there is a need for the implementation of skin analysis systems and methods that are configured to accurately generate user-specific skin analyses. [Means for solving the problem]

[0005] Generally, as described herein, implementations of skin analysis systems and methods for generating user-specific skin analyses are described. In some aspects, the artificial intelligence (AI)-based systems and methods herein are configured to input user-specific data to train an AI model to generate / predict a user-specific skin analysis. Such AI-based systems provide AI-based solutions to overcome problems arising from difficulties in identifying and treating various intrinsic and / or extrinsic factors or attributes that affect a user's skin condition.

[0006] Generally, the systems described herein enable users to submit user-specific data to a server (e.g., including one or more processors thereof) or other computing device (e.g., locally on the user's mobile device, etc.), where the server or user computing device implements or executes one or more skin analysis learning models configured to generate a user-specific skin analysis. In certain aspects, the one or more skin analysis learning models are trained using training data of potentially thousands (or more) of instances of user-specific data for each individual's skin area. The skin analysis learning model receives the user-specific data as input and generates a user-specific analysis designed to address (e.g., identify and / or treat) at least one characteristic of the user's skin area. For example, the user-specific data may include responses or other inputs indicative of sugar intake and / or other skin factors of a particular user's skin area, and one or more images / videos of the user's skin area. Further, in certain aspects, the skin analysis learning model generates a user-specific skin analysis including one or more selected from the group including: (1) skin condition, (2) a holistic score defined based on at least the skin and health data, (3) skin-related product recommendations, (4) skin-related product use recommendations, (5) supplemental product recommendations, (6) supplemental product use recommendations, (7) habit recommendations, and (8) skin predictions corresponding to the user's skin areas based on the user's skin data and health data.

[0007] Each user-submitted image and / or video may be received at a server (e.g., including its one or more processors) (also referred to herein as an “imaging server”) or otherwise at a computing device (e.g., locally on the user's mobile device, etc.), and the imaging server or user computing device implements or executes a skin analysis learning model. In certain aspects, the skin analysis learning model may be an AI-based model trained using pixel data of potentially 10,000 (or more) images depicting each individual's skin area. The AI-based skin analysis learning model may generate a user-specific analysis designed to address (e.g., identify and / or treat) at least one characteristic identifiable within the pixel data comprising the user's skin area. For example, a portion of the user's skin area may include pixels or pixel data indicative of eczema, acne, wrinkles, inflammation, and / or other skin factors of the particular user's skin area. In some aspects, the user-specific skin analysis and recommendation / score / prediction may be transmitted over a computer network to the user's user computing device for rendering on a display screen. In other aspects, transmission of the user's image to the imaging server does not occur, in which case the user-specific skin analysis and recommendations / scores / predictions may instead be generated by an AI-based skin analysis learning model, executed and / or implemented locally on the user's mobile device, and rendered by the mobile device's processor on the mobile device's display screen. In various aspects, such rendering may include graphical representations, overlays, annotations, etc. to address features in the pixel data.

[0008] More specifically, as described herein, a user-specific skin analysis method is disclosed for generating a user-specific skin analysis, the user-specific skin analysis method including receiving, by one or more processors, user skin data and receiving, by the one or more processors, user health data, the user health data including one or more of: (1) body water content or hydration, (2) intracellular to extracellular water ratio, (3) body mass index (BMI), (4) blood markers, (5) glucose intake level, (6) heart rate variability, or (7) heart rate; and analyzing the user skin data and the user health data with one or more skin analysis learning models to generate the user-specific skin analysis.

[0009] Additionally, as described herein, a user-specific skin analysis system is disclosed. The user-specific skin analysis system is configured to generate a user-specific skin analysis. The user-specific skin analysis system includes one or more processors, an analysis application (app) including computing instructions configured to execute on the one or more processors, and one or more skin analysis learning models accessible by the analysis app. The computing instructions of the analysis app, when executed by the one or more processors, cause the one or more processors to receive a user's skin data and receive the user's health data, the user's health data including one or more of: (1) body water content or hydration, (2) intracellular water to extracellular water ratio, (3) body mass index (BMI), (4) blood markers, (5) glucose intake level, (6) heart rate variability, or (7) heart rate; and analyze the user's skin data and the user's health data with the one or more skin analysis learning models to generate the user-specific skin analysis.

[0010] Further disclosed is a tangible, non-transitory computer-readable medium storing instructions for generating a user-specific skin analysis as described herein. The instructions, when executed by one or more processors, may cause the one or more processors to: receive, in an analysis application (app) executing on the one or more processors, a user's skin data; receive, in the analysis app, the user's health data, the user's health data including one or more of: (1) body water content or hydration, (2) intracellular to extracellular water ratio, (3) body mass index (BMI), (4) blood markers, (5) glucose intake level, (6) heart rate variability, or (7) heart rate; and analyze the user's skin data and the user's health data with one or more skin analysis learning models accessible by the analysis app to generate the user-specific skin analysis.

[0011] In accordance with the above and the disclosure herein, the present disclosure includes improvements in computer functionality or other technologies, at least because the present disclosure describes, for example, that a server or other computing device (e.g., a user computing device) is improved when its intelligence or deterministic capabilities are augmented by a skin analysis learning model. The skin analysis learning model running on the server or computing device is capable of identifying a user-specific skin analysis more accurately than conventional techniques based on user-specific skin and health data. Indeed, the disclosed skin analysis learning model may receive from the user one or more input images of the user's skin and one or more responses to a questionnaire, and may determine the user-specific skin data and user health data using image / video processing techniques or questionnaires or other devices. As a result, the skin analysis learning model may quickly and efficiently provide a user-specific skin analysis in a manner previously unattainable by conventional techniques.

[0012] Furthermore, the present disclosure includes improvements in computer functionality or improvements to other technologies, at least because the present disclosure describes, for example, a server or other computing device (e.g., a user computing device) being improved when its intelligence or predictive capabilities are augmented with a trained (e.g., machine learning trained) skin analysis learning model. The skin analysis learning model running on the server or computing device is capable of more accurately identifying a user-specific skin analysis designed to address at least one characteristic of the user's skin or skin area based on the skin and health data of other individuals. That is, the present disclosure describes improvements in the functionality of the computer itself or "any other technology or technical field" by virtue of the server or user computing device being augmented with multiple training data (e.g., potentially thousands (or more) of instances of skin and health data for each individual's skin area) to accurately predict, detect, or determine a user-specific skin analysis based on the user's skin and health data, such as newly provided customer responses / inputs / images, or derived therefrom. This is an improvement over the prior art at least because existing systems lack such predictive or classification capabilities and are simply unable to accurately analyze a user's skin and health data to output a predictive result to address at least one characteristic of the user's skin or skin area.

[0013] Specifically, the disclosed systems and methods feature improvements over conventional techniques by using specific health data in conjunction with skin data. By using specific health data in conjunction with skin data, the present invention provides improved accuracy of skin analysis compared to, for example, prior analyses using only skin data. Such specific health data includes one or more of: (1) body water content or amount, (2) intracellular to extracellular water ratio, (3) body mass index (BMI), (4) blood markers, (5) glucose intake level, (6) heart rate variability, or (7) heart rate. In certain aspects, the health data is selected from the group consisting of: (1) body water content or amount, (2) intracellular to extracellular water ratio, (3) body mass index (BMI), and / or mixtures thereof, in consideration of providing further improved accuracy of skin analysis, or in some aspects, the health data is selected from the group consisting of: (1) body water content or amount, (2) intracellular to extracellular water ratio, and / or mixtures thereof. Surprisingly, it has been found that these specific health data, when used together with the skin data, provide improved accuracy of skin analysis compared to other health data, such as body fat percentage. Additionally, the disclosed systems and methods feature improvements over conventional techniques by training a skin analysis learning model using multiple training data related to the skin and health data of multiple individuals. The training data generally includes individual self-assessments of the individuals' skin in the form of text questionnaire responses for each of the multiple individuals, and images of the user's skin area captured using an imaging device. Once trained using the training data, the skin analysis learning model provides highly accurate skin analysis predictions for the user to a degree unattainable using conventional techniques. Indeed, by using health data together with the skin data, the present disclosure provides improved accuracy of the resulting user-specific skin analysis compared to, for example, prior art analysis techniques that utilize only skin data.

[0014] For similar reasons, the present disclosure relates to improvements to other technologies or technical fields, at least because the present disclosure describes or introduces improvements to computing devices in the skin care and skin care product fields, whereby trained skin analysis learning models executed on computing devices and / or imaging devices improve underlying computing devices (e.g., servers and / or user computing devices), and such computing devices are made more efficient by configuring, adjusting, or adapting a given machine learning network architecture. For example, in some aspects, reducing the computational resources by reducing the machine learning network architecture required to analyze images, including reducing depth, width, image size, or other machine learning-based dimensionality requirements, thereby using fewer machine resources (e.g., processing cycles or memory storage). Such reduction frees up computational resources of the underlying computing system, thereby making it more efficient.

[0015] Additionally, the present disclosure includes applying some of the elements of the claims in conjunction with or by using a particular machine, e.g., an imaging device, to generate training data that can be used to train a skin analysis learning model.

[0016] Additionally, the present disclosure includes adding non-conventional steps that include specific features outside of everyday conventional activities well understood in the art or that limit the scope of the claims to particular useful applications, such as analyzing user-specific skin data and health data to generate a user-specific skin analysis designed to address (e.g., identify and / or treat) at least one characteristic of the user's skin.

[0017] The advantages will become more apparent to those skilled in the art from the following description of preferred embodiments shown and described by way of illustration. As will be understood, the present embodiments are capable of other and different embodiments, and their details are capable of modification in various respects. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive. [Brief explanation of the drawings]

[0018] The figures described below depict various aspects of the systems and methods disclosed herein. It should be understood that each figure depicts an aspect of a particular aspect of the disclosed systems and methods, and that each of the figures is intended to correspond to possible aspects thereof. Furthermore, wherever possible, the following description will refer to reference numerals contained in the following figures, wherein features depicted in multiple figures will be designated with consistent reference numerals.

[0019] Although the drawings show arrangements that are presently contemplated, it is to be understood that the present aspects are not limited to the precise arrangements and instrumentality shown. [Figure 1] 1 illustrates an example user-specific skin analysis system configured to analyze user-specific skin data and health data to generate a user-specific skin analysis, according to various aspects disclosed herein. [Figure 2] FIG. 2 is an example flow diagram depicting the operation of a skin analysis learning model from the example user-specific skin analysis system of FIG. 1 in accordance with various aspects disclosed herein. [Figure 3] 2 illustrates one embodiment of a skin analysis learning model from the exemplary user-specific skin analysis system of FIG. 1 in accordance with various aspects disclosed herein. [Figure 4] 1 illustrates an exemplary user-specific skin analysis method for generating a user-specific skin analysis according to various aspects disclosed herein. [Figure 5]1 illustrates an exemplary user interface rendered on a display screen of a user computing device, according to various embodiments disclosed herein.

[0020] These figures depict preferred embodiments for purposes of illustration only. Alternative embodiments of the systems and methods illustrated herein may be employed without departing from the inventive principles described herein. DETAILED DESCRIPTION OF THE INVENTION

[0021] 1 illustrates an exemplary user-specific skin analysis system 100 configured to analyze user-specific skin data and health data to generate a user-specific skin analysis according to various aspects disclosed herein. Generally, as referred to herein, the user-specific skin data and health data may include and / or be derived from user answers / inputs related to questions / prompts presented to a user via a display and / or user interface of a user computing device, the questions / prompts being directed to the user's skin condition and / or images captured by the user computing device depicting a skin area of the user's skin. For example, the user-specific health data may include user answers indicative of the user's sugar intake level over a particular time period (e.g., daily, weekly, etc.). Alternatively, the user-specific skin data and health data may include data obtained by processing one or more images of the user's skin area captured by the user using the user computing device. It will be appreciated that, as described herein, images of a user's skin area may include still images (e.g., individual image frames) and / or video (e.g., multiple image frames) captured using an imaging device (e.g., a user computing / mobile device).

[0022] In the exemplary embodiment of FIG. 1 , the AI-based system 100 includes a server 102, which may include one or more computer servers. In various embodiments, the server 102 includes multiple servers, which may include multiple redundant or replicated servers as part of a server farm. In still further embodiments, the server 102 may be implemented as a cloud-based server, such as a cloud-based computing platform. For example, the server 102 may be any one or more cloud-based platforms, such as MICROSOFT AZURE, AMAZON AWS, etc. The server 102 may include one or more processors 104, one or more computer memories 106, and a skin analysis learning model 108.

[0023] Memory 106 may include one or more forms of volatile and / or non-volatile fixed and / or removable memory, such as read-only memory (ROM), electronic programmable read-only memory (EPROM), random access memory (RAM), erasable electronic programmable read-only memory (EEPROM), and / or other hard drives, flash memory, MicroSD cards, etc. Memory 106 may store an operating system (OS) (e.g., Microsoft Windows, Linux, UNIX, etc.) that can facilitate functions, apps, methods, or other software as discussed herein. The memory 106 may also store the skin analysis learning model 108, as described herein, which may be a machine learning model trained on various training data (e.g., potentially thousands (or more) of instances of user-specific data for each individual's skin area), and in certain aspects, images (e.g., images 114a, 114b). Additionally or alternatively, the skin analysis learning model 108 may also be stored in a database 105, which is accessible to or otherwise communicatively coupled to the server 102.Additionally, the memory 106 may also store machine-readable instructions, including any of one or more applications (e.g., scalp and hair applications as described herein), one or more software components, and / or one or more application programming interfaces (APIs), which may be implemented to facilitate or perform the features, functions, or other disclosures described herein, such as any methods, processes, elements, or limitations, as illustrated, depicted, or described in the various flow diagrams, illustrations, diagrams, figures, and / or other disclosures herein. For example, at least some of the applications, software components, or APIs may be, include, or otherwise be part of an AI-based machine learning model or component, such as the skin analysis learning model 108, each of which may be configured to facilitate the various functions discussed herein. It should be understood that one or more other applications may be envisioned and executed by the processor 104.

[0024] The processor 104 is connected to the memory 106 via a computer bus that carries electronic data, data packets, or otherwise electronic signals between the processor 104 and the memory 106, and may implement or execute machine-readable instructions, methods, processes, elements, or limitations as illustrated, depicted, or described in the various flow diagrams, illustrations, diagrams, figures, and / or other disclosures herein.

[0025] Processor 104 may interface with memory 106 via a computer bus and execute an operating system (OS). Processor 104 may also interface with memory 106 via the computer bus and create, read, update, delete, or otherwise access or interact with data stored in memory 106 and / or database 104 (e.g., a relational database such as Oracle, DB2, MySQL, or a NoSQL-based database such as MongoDB). The data stored in memory 106 and / or database 104 may include, for example, training data (e.g., as collected by user computing devices 111c1-111c3 and / or 112c1-112c3), images and / or user images (e.g., including images 114a, 114b), and / or other information and / or images of users including demographics, age, race, skin type, etc., or all or a portion of the data or information described herein, including as otherwise described herein.

[0026] Server 102 may further include a communications component configured to communicate (e.g., send and receive) data via one or more external / network ports to one or more network or local terminals, such as computer network 120 and / or terminal 109 (for rendering or visualization) described herein. In some aspects, server 102 may include client-server platform technology, such as ASP.NET, Java J2EE, Ruby on Rails, Node.js, web services, or online APIs, responsible for receiving and responding to electronic requests. Server 102 may implement client-server platform technology that may interact with memory 106 (including applications, components, APIs, data, etc. stored therein) and / or database 105 via a computer bus to implement or perform machine-readable instructions, methods, processes, elements, or limitations as illustrated, depicted, or described in the various flow diagrams, illustrations, diagrams, figures, and / or other disclosures herein.

[0027] In various aspects, server 102 may include or interact with one or more transceivers (e.g., WWAN, WLAN, and / or WPAN transceivers) that function according to IEEE, 3GPP, or other standards and that may be used to receive and transmit data via an external / network port connected to computer network 120. In some aspects, computer network 120 may include a private network or a local area network (LAN). Additionally or alternatively, computer network 120 may include a public network, such as the Internet.

[0028] The server 102 may further include or implement an operator interface configured to present information to and / or receive input from an administrator or operator. As shown in FIG. 1 , the operator interface may provide a display screen (e.g., via terminal 109). The server 102 may also provide I / O components (e.g., ports, capacitive or resistive touch-sensitive input panels, keys, buttons, lights, LEDs), which may be directly accessible via or attached to the imaging server 102, or indirectly accessible via or attached to the terminal 109. According to some embodiments, an administrator or operator may access the server 102 via the terminal 109 to review information, make changes, input training data or images, initiate training of the skin analysis learning model 108, and / or perform other functions.

[0029] As described herein, in some aspects, server 102 may perform functions as discussed herein as part of a “cloud” network or may otherwise communicate with other hardware or software components in the cloud to transmit, retrieve, or otherwise analyze data or information as described herein.

[0030] Generally, a computer program or computer-based product, application, or code (e.g., a model such as the AI models described herein or other computing instructions) may be stored on a computer-usable storage medium having such computer-readable program code or computer instructions embodied therein, or on a tangible, non-transitory computer-readable medium (e.g., standard random access memory (RAM), an optical disk, a universal serial bus (USB) drive, etc.), and the computer-readable program code or computer instructions may be installed on or otherwise adapted to be executed by processor 104 (e.g., working in association with a respective operating system in memory 106) to facilitate, implement, or perform the machine-readable instructions, methods, processes, elements, or limitations as illustrated, depicted, or described in the various flow diagrams, illustrative figures, diagrams, illustrations, and / or other disclosures herein. In this regard, the program code may be implemented in any desired programming language and may be implemented as machine code, assembly code, bytecode, interpreted source code, or the like (e.g., via Golang, Python, C, C++, C#, Objective-C, Java, Scala, ActionScript, JavaScript, HTML, CSS, XML, etc.).

[0031] 1, server 102 is communicatively coupled to one or more user computing devices 111c1-111c3 via computer network 120 and / or to 112c1-112c3 via base stations 111b and 112b. In some aspects, base stations 111b and 112b may include cellular base stations, such as cell towers, that communicate with one or more user computing devices 111c1-111c3 and 112c1-112c3 via wireless communications 121 based on any one or more of a variety of cellular standards, including NMT, GSM, CDMA, UMMTS, LTE, 5G, etc. Additionally or alternatively, base stations 111b and 112b may include routers, wireless switches, or other such wireless connection points that communicate with one or more user computing devices 111c1-111c3 and 112c1-112c3 via wireless communication 122 based on any one or more of a variety of wireless standards, including, by way of non-limiting example, IEEE 802.11a / b / c / g (WIFI), BLUETOOTH standards, etc.

[0032] Any of one or more of user computing devices 111c1-111c3 and / or 112c1-112c3 may comprise a mobile device and / or client device for accessing and / or communicating with server 102. Such client devices may comprise one or more mobile processors and / or imaging devices for capturing images, such as images (e.g., images 114a, 114b) as described herein. In various aspects, user computing devices 111c1-111c3 and / or 112c1-112c3 may include mobile phones (e.g., cellular phones), tablet devices, personal data assistance (PDAs), etc., including, by non-limiting example, an APPLE iPhone or iPad device, or a GOOGLE ANDROID-based mobile phone or tablet.

[0033] In additional aspects, user computing devices 111c1-111c3 and / or 112c1-112c3 may comprise retail computing devices. The retail computing devices may comprise user computing devices configured in the same or similar manner as mobile devices as described herein with respect to user computing devices 111c1-111c3 and 112c1-112c3, for example, including having a processor and memory for implementing skin analysis learning model 108 as described herein or for communicating with skin analysis learning model 108 (e.g., via server 102). Additionally or alternatively, the retail computing devices may be located, installed, or otherwise positioned within a retail environment to enable users and / or customers of the retail environment to utilize the AI-based systems and methods on-site within the retail environment. For example, the retail computing devices may be installed within a kiosk for access by users. The user may then complete a questionnaire and / or upload or transfer an image to the kiosk (e.g., from the user's mobile device) to implement the AI-based systems and methods described herein. Additionally or alternatively, the kiosk may be configured with a camera to allow the user to take a new image of themselves (e.g., in a private manner, if warranted) for uploading and transfer. In such an aspect, the user or consumer themselves could use the retail computing device to receive and / or render a user-specific skin analysis for the user's skin area on a display screen of the retail computing device, as described herein.

[0034] Additionally or alternatively, the retail computing device may be a mobile device (as described herein) carried by an employee or other personnel of the retail environment to interact with users or consumers on-site. In such aspects, the user or consumer may be able to interact with the employee or other personnel of the retail environment via the retail computing device (e.g., by providing responses to a questionnaire, by transferring images from the user's mobile device to the retail computing device, or by capturing new images with a camera on the retail computing device) to receive and / or render a user-specific skin analysis for the user's skin area on a display screen of the retail computing device, as described herein.

[0035] In various aspects, one or more user computing devices 111c1-111c3 and / or 112c1-112c3 may implement or execute an operating system (OS) or mobile platform, such as the APPLE iOS and / or GOOGLE ANDROID operating systems. Any of one or more user computing devices 111c1-111c3 and / or 112c1-112c3 may include one or more processors and / or one or more memories for storing, implementing, or executing computing instructions or code, such as a mobile application or a home or personal assistant application, as described in various aspects herein. As shown in FIG. 1, the skin analysis learning model 108 and / or analysis application described herein, or at least a portion thereof, may also be stored locally on the memory of a user computing device (e.g., user computing device 111c1).

[0036] User computing devices 111c1-111c3 and / or 112c1-112c3 may include wireless transceivers for receiving and transmitting wireless communications 121 and / or 122 to and from base stations 111b and / or 112b. In various aspects, user-specific skin and health data (e.g., user responses / inputs to questionnaires presented on user computing device 111c1 and pixel-based images (e.g., images 114a, 114b) captured by user computing device 111c1) may be transmitted via computer network 120 to server 102 for training a model (e.g., skin analysis learning model 108) and / or analysis as described herein.

[0037] Additionally, one or more of user computing devices 111c1-111c3 and / or 112c1-112c3 may include an imaging device and / or a digital video camera for capturing or filming digital images and / or frames (e.g., images 114a, 114b). Each digital image may include pixel data for training or implementing a model, such as an AI or machine learning model, as described herein. For example, the imaging device and / or digital video camera of any of user computing devices 111c1-111c3 and / or 112c1-112c3 may be configured to film, capture, or otherwise generate digital images (e.g., pixel-based images 114a, 114b), and, in at least some aspects, may store such images in memory of the respective user computing device. Additionally or alternatively, such digital images may also be transmitted to and / or stored in memory 106 and / or database 105 of server 102.

[0038] Still further, each of one or more user computing devices 111c1-111c3 and / or 112c1-112c3 may include a display screen for displaying graphics, images, text, products, user-specific actions, data, pixels, features, and / or other visualizations or information as described herein. In various aspects, graphics, images, text, products, user-specific actions, data, pixels, features, and / or other such visualizations or information may be received from server 102 for display on the display screen of any one or more of user computing devices 111c1-111c3 and / or 112c1-112c3. Additionally or alternatively, a user computing device may comprise, implement, have access to, render, or otherwise expose, at least in part, an interface or guided user interface (GUI) for displaying text and / or images on its display screen.

[0039] In some aspects, computing instructions and / or applications executing on a server (e.g., server 102) and / or on a mobile device (e.g., mobile device 111c1) may be communicatively connected to analyze user-specific skin and health data, including one or more of: (1) body water content or hydration, (2) intracellular water to extracellular water ratio, (3) body mass index (BMI), (4) blood markers, (5) glucose intake level, (6) heart rate variability, or (7) heart rate, as described herein, to generate a user-specific skin analysis. For example, one or more processors (e.g., processor 104) of server 102 may be communicatively coupled to the mobile device via a computer network (e.g., computer network 120). For ease of discussion, the user's skin data and the user's health data may be collectively referred to herein as "user-specific data."

[0040] 2 is an example flow diagram 200 depicting the operation of the skin analysis learning model 108 from the example user-specific skin analysis system 100 of FIG. 1 , in accordance with various aspects disclosed herein. Generally, the skin analysis learning model 108 receives user skin and health data (“user-specific data”) as input and outputs a user-specific skin analysis. In certain aspects, the skin analysis learning model 108 may be or include one or more rule-based models, while in some aspects the model 108 may be and / or otherwise include one or more AI-based models. As such, the example flow diagram 200 may also depict an example training sequence for the skin analysis learning model 108, where the model 108 receives training data including skin and health data of a plurality of respective users as input and outputs a user-specific skin analysis corresponding to each of the respective users as output.

[0041] In aspects where user-specific data is utilized to train the skin analysis learning model 108, at least a portion of the user-specific data may be in the form of responses submitted by the user to a questionnaire. Generally, the questionnaire responses may train the skin analysis learning model 108 by enabling the model 108 to determine various correlations between the responses and the user-specific skin analysis. Furthermore, in certain aspects, the skin analysis learning model 108 may be configured to generate one or more outputs in addition to and / or as part of the user-specific skin analysis in response to receiving responses from the user, such as (1) a skin condition, (2) a holistic score defined based on at least the skin and health data, (3) skin-related product recommendations, (4) skin-related product use recommendations, (5) supplemental product recommendations, (6) supplemental product use recommendations, (7) habit recommendations, and / or (8) a skin prediction corresponding to the user's skin area.

[0042] Additionally, the user-specific data submitted by the user as input to the skin analysis learning model 108 may include image data of the user. Specifically, in certain aspects, the image data may include digital images depicting at least a portion of the user's skin area. Each image may be used to train and / or run the skin analysis learning model 108 for use across a variety of different users having a variety of different skin area features. For example, as illustrated in images 114a, 114b of FIG. 1 , the users' skin areas in these images include skin area features of the respective users' skin that are identifiable in the pixel data of images 114a, 114b. These skin area features may include, for example, skin inflammation and skin redness, which the skin analysis learning model 108 may identify in images 114a, 114b and use to generate a user-specific skin analysis for the user represented in images 114a, 114b, as described herein.

[0043] The user may run an analysis application (app), which may display a user interface that may include sections / prompts for the user to input portions of user-specific data. When the user provides answers to the survey prompts, the skin analysis learning model 108 may analyze the user-specific data to generate a user-specific skin analysis, and the analysis app may render a user interface that may include the user-specific skin analysis, a display of the user's answers, and / or the user-specific data.

[0044] As mentioned above, the skin analysis learning model 108 running on the analysis app may be an AI-based model. Accordingly, the skin analysis learning model 108 may be trained using a supervised machine learning program or algorithm, such as multivariate regression analysis or a neural network. Generally, machine learning may involve identifying and recognizing patterns within existing data (e.g., generating a skin analysis corresponding to one or more characteristics of each individual's skin area) to facilitate prediction or discrimination of subsequent data (e.g., using the model on new user-specific data to determine or generate a skin analysis corresponding to the user's skin area). Machine learning models, such as the skin analysis learning model 108 described herein for some embodiments, may be created and trained based on example data (e.g., "training data" and associated user-specific data) inputs or data (which may be referred to as "features" and "labels") to make valid and reliable predictions of new input values, such as test-level or production-level data or input values.

[0045] In supervised machine learning, a machine learning program running on a server, computing device, or other processor is provided with example inputs (e.g., "features") and their associated or observed outputs (e.g., "labels"), and the machine learning program or algorithm may determine or discover rules, relationships, patterns, or other machine learning "models" that map such inputs (e.g., "features") to outputs (e.g., labels), for example, by determining and / or assigning weights or other measures for the model across the model's various feature categories. Such rules, relationships, or other models may then be provided with subsequent inputs and the model executed on the server, computing device, or other processor to predict expected outputs based on the discovered rules, relationships, or models.

[0046] In certain embodiments, the skin analysis learning model 108 may be trained using multiple supervised machine learning techniques, and may additionally or alternatively be trained using one or more unsupervised machine learning techniques. In unsupervised machine learning, a server, computing device, or other processor may be required to discover its own structure in unlabeled example inputs, where, for example, multiple training iterations are performed by the server, computing device, or other processor to train multiple generations of models until a satisfactory model is produced, e.g., a model that provides sufficient predictive accuracy when given test-level or production-level data or inputs.

[0047] For example, in certain embodiments, the skin analysis learning model may employ a neural network, which may be a convolutional neural network, a deep learning neural network, or a blended learning module or program that learns two or more features or feature datasets (e.g., user-specific data) within a particular area of interest. These machine learning programs or algorithms may also include natural language processing, semantic analysis, automated reasoning, support vector machine (SVM) analysis, decision tree analysis, random forest analysis, K-nearest neighbor analysis, naive Bayes analysis, clustering, reinforcement learning, and / or other machine learning algorithms and / or techniques. In some embodiments, the artificial intelligence and / or machine learning-based algorithms may be included as libraries or packages that execute on the server 102. For example, the libraries may include a TENSORFLOW-based library, a PYTORCH library, and / or a SCIKIT-LEARN Python library.

[0048] In any case, training the skin analysis learning model may also include retraining, relearning, or otherwise updating the model with new or different information, which may include information received, ingested, generated, or otherwise used over time. Further, in various aspects, the skin analysis learning model, e.g., skin analysis learning model 108, may be trained by one or more processors (e.g., one or more processors 104 of server 102 and / or a processor of a computer user device, such as a mobile device), using pixel data of multiple training images (e.g., images 114a, 114b) of each individual's skin region. In these aspects, the skin analysis learning model 108 may be additionally configured to generate a user-specific analysis corresponding to one or more features of each individual's skin region in each of the multiple training images.

[0049] Generally, the skin data included as part of the user-specific data may be derived from user-submitted images of the user's skin (e.g., skin regions). The user may submit one or more images of the user's skin, and the skin analysis learning model 108 may apply various image processing techniques to the submitted images to determine any number of skin characteristics. For example, the user may capture / submit an image of a skin region (e.g., image 114b) that includes redness and inflammation. The skin analysis learning model 108 may receive the image and apply image processing techniques such as, but not limited to, image classification, object detection, object tracking, semantic segmentation, instance segmentation, edge detection, anisotropic diffusion, pixilation, point feature mapping, and / or other suitable image processing techniques, or combinations thereof. As a result, the skin analysis learning model 108 may generate features or characteristics of the user-submitted image that enable the model 108 to determine a user-specific skin analysis based on correlations between / among the features / characteristics and known skin analyses.

[0050] Additionally or alternatively, skin data included as part of the user-specific data may be submitted by the user as part of a questionnaire response. For example, the analysis app may query the user whether the user experiences skin problems such as dryness, itchiness, redness, etc. In response to the user selecting one or more of the skin problems, the analysis app may further request the user select one or more options indicating a severity associated with the user-indicated skin problem (e.g., via rendering the options as part of the analysis app display). The user may indicate each applicable skin problem and / or corresponding severity, for example, through interaction with a user interface of the analysis app executing on the user's computing device (e.g., user computing device 111c1). For each skin problem indicated by the user, the skin analysis learning model 108 may incorporate the indicated skin problem and / or corresponding severity as part of its analysis of the user-specific data to generate a user-specific skin analysis.

[0051] Additionally, health data included as part of the user-specific data may also be derived from user-submitted images of the user's skin (e.g., skin regions) and / or may be submitted by the user as part of responses to a questionnaire. In certain aspects, a user may submit images of the user's skin as input to the user learning model 108. In these aspects, the skin analysis learning model 108 may apply various image processing techniques, as mentioned above, to determine any number of health characteristics of the user. For example, a user may submit images characterizing healthy areas of the user's skin, such that the skin analysis learning model 108 may generate health characteristics based on the images that enable the skin analysis learning model 108 to output a user-specific skin analysis. Specifically, the skin analysis learning model 108 may generate health characteristics including, but not limited to, the user's body water content / hydration, body mass index (BMI), intercellular to extracellular water ratio, glucose levels, heart rate, heart rate variability, and / or other suitable health characteristics, or combinations thereof. In any event, users may also submit health data as part of a questionnaire displayed by the analytics app, as described above.

[0052] Of course, it should be understood that the questions / prompts presented as part of the survey displayed to the user in the analytics app may include any suitable input options for the user to enter a response, such as a sliding scale and / or manually entered numbers or strings of characters indicating a skin problem or other response (e.g., by typing on a keyboard on the user's mobile device or a virtually rendered keyboard).

[0053] The user-specific analysis output by the skin analysis learning model 108 may generally include a textual and / or graphical output corresponding to the skin condition determined by the model 108. For example, the user-specific analysis may include a graphical rendering (e.g., image 114b) including text characters informing a user viewing the display that the skin area included in the submitted image and / or indicated by the user's responses to the questionnaire may have skin redness as a result of inflammation. The user-specific analysis may also include any number of various indicators, such as a skin score, which may generally indicate the current quality / condition of the user's skin area featured in the image and / or indicated in the user's responses to the questionnaire. The skin score may indicate that the current quality / condition of the user's skin area is, for example, 3.5 out of a maximum potential score of 4, which may represent that the user's skin area is relatively healthy. However, it should be understood that the skin score (and any other score or indicator included as part of the user-specific skin analysis) may be represented to the user as a graphical rendering, an alphanumeric value, a color value, and / or any other suitable representation or combination thereof.

[0054] Furthermore, the AI-based learning model may also generate a skin score description that may inform the user about the skin score (e.g., represented by a graphical score) they received. The analytics app may render the scalp score description as part of the user interface when the skin analytics learning model completes its analysis of the user-specific data. The scalp score description may include, for example, a description of the prevailing skin issues / conditions that led to the reduced score, the intrinsic / extrinsic factors causing the skin issues, and / or any other information, or a combination thereof. As an example, the skin score description may inform the user that an area of the user's skin is slightly irritated, and as a result, the user may experience unwanted skin warmth and discomfort. Further in this example, the skin score description may inform the user that irritants such as dry air may cause and / or otherwise contribute to the irritation. It should be understood that the above discussion related to skin scores may generally apply to any output of the skin analysis learning model 108 included as part of the user-specific skin analysis, such as (1) skin condition, (2) a holistic score defined based on at least skin and health data, (3) skin-related product recommendations, (4) skin-related product use recommendations, (5) supplemental product recommendations, (6) supplemental product use recommendations, (7) habit recommendations, and / or (8) skin predictions corresponding to the user's skin area.

[0055] 3 illustrates one embodiment of the skin analysis learning model 108 from the example user-specific skin analysis system 100 of FIG. 1 in accordance with various aspects disclosed herein. In the embodiment illustrated in FIG. 3, the skin analysis learning model 108 includes seven individual machine learning models (referred to herein as "engines") that may be configured to operate together. More specifically, an embodiment of the skin analysis learning model 108 may comprise an ensemble model that includes multiple AI models or sub-models configured to operate together.

[0056] Additionally or alternatively, the skin analysis learning model 108 may include a transfer learning-based set of AI models, where transfer learning involves transferring knowledge from one model to another (e.g., the output of one model is used as input to another model). Using transfer learning, a particular task (e.g., identification, classification, and / or prediction) may be solved using all or part of an already pre-trained model on a different task. Figure 3 illustrates such an ensemble AI model and / or transfer learning-based AI model that may comprise the skin analysis learning model 108. However, it should be understood that other AI models (that do not require ensemble-based learning or transfer learning-based learning) may be used.

[0057] Specifically, the skin analysis learning model 108 illustrated in FIG. 3 includes a feedback processing engine 302, a skin analysis engine 304, a holistic analysis engine 306, a skin-related product recommendation engine 308, a supplemental product recommendation engine 310, a habit recommendation engine 312, and a prediction engine 314. Each of these individual engines 302-314 may operate sequentially, with the output of one engine serving as input to another subsequent engine. For example, the feedback processing engine 302 may be trained with each individual's health data to output a respective health value, and the feedback processing engine 302 may be configured to receive the user's health data and output a user health value based on the user's health data. The user health value may be a numeric or other value representing a user's general health assessment based on the health data provided by the user (e.g., via answers and / or images).

[0058] The skin analysis engine 304 may then be configured to receive the user health values of the user from the feedback processing engine 302 and output a skin condition value (e.g., a skin score) for the user based on the user health values. As mentioned above, the user's skin condition value or skin score may generally correspond to a numerical or other value representative of the current quality / condition of the user's skin. The skin analysis engine 304 may be trained using the respective health values received from the feedback processing engine 302 to output the respective skin condition values.

[0059] The holistic analysis engine 306 may be configured to receive the user's skin condition values from the skin analysis engine 304 and output a holistic score for the user based on the user's skin condition values. The user's holistic score may generally correspond to a single (“holistic”) numeric score or otherwise representing the user's overall skin health score based on the user's skin and health data (e.g., sugar intake, heart rate, etc.). The holistic analysis engine 306 may be trained using each skin condition value received from the skin analysis engine 304 to output a respective holistic score.

[0060] The skin-related product recommendation engine 308 may then receive the user's holistic score from the holistic analysis engine 306. The skin-related product recommendation engine 308 may be configured to receive the user's skin condition values from the skin analysis engine 304, receive the user's holistic score from the holistic analysis engine 306, and output skin-related product recommendations for the user based on the user's skin condition values and the user's holistic score. In certain aspects, the skin-related product recommendations include skin-related product use recommendations configured to provide the user with product application instructions corresponding to skin-related products identified as part of the skin-related product recommendation. For example, the skin-related product recommendation engine 308 may output a recommendation suggesting that the user apply a moisturizing lotion to the user's skin area to relieve dryness / itchiness. As another example, the skin-related product recommendation engine 308 may output a recommendation suggesting that the user apply an anti-wrinkle product to the user's skin area to minimize / reduce wrinkles identified on the user's skin area. The skin-related product recommendation engine 308 may be trained using the respective skin condition values from the skin analysis engine 304 and the respective holistic scores received from the holistic analysis engine 306 to output respective skin-related product recommendations.

[0061] The supplemental product recommendation engine 310 may receive the holistic scores from the holistic analysis engine 306 to output supplemental product recommendations based on the user's holistic scores. The supplemental product recommendations may generally be and / or include supplemental products to skin-related products recommended by the skin-related product recommendation engine 308, such as vitamins or other products that may supplement a skin care regimen. Thus, in certain embodiments, the supplemental product recommendations include supplemental product use recommendations configured to provide the user with product application instructions corresponding to the supplemental products identified as part of the supplemental product recommendation. For example, the supplemental product recommendation engine 310 may output a recommendation suggesting that the user take a vitamin D supplement to improve overall skin health. The supplemental product recommendation engine 310 may be trained using each holistic score received from the holistic analysis engine 306 to output the respective supplemental product recommendations.

[0062] The habit recommendation engine 312 may receive the user's holistic score from the holistic analysis engine 306 and output habit recommendations for the user based on the user's holistic score. The habit recommendations may generally be and / or include recommended habits and / or corresponding products intended to improve the user's overall health (and, consequently, the holistic score). For example, the habit recommendation engine 312 may output habit recommendations suggesting that the user improve their sleep habits and drink more water to improve the user's overall health. The habit recommendation engine 312 may be trained using each holistic score received from the holistic analysis engine 306 to output the respective habit recommendations.

[0063] The prediction engine 314 may receive skin condition values from the skin analysis engine 304 and holistic scores from the holistic analysis engine 306, and output a skin prediction for the user based on the user's skin condition values and the user's holistic score. The skin prediction may generally be and / or may include a prediction indicating how the user's skin may change over a particular period of time (e.g., a day, a week, a month, a year) based on the skin condition values and the holistic score. In certain aspects, the skin prediction may include a visual or graphical representation of the user's skin areas that characterizes or otherwise represents changes to the user's skin as predicted by the prediction engine 314. For example, the prediction engine 314 may output a skin prediction that indicates to the user that the user's skin will begin to wrinkle in the next year based on the user's skin condition values and holistic score. Further in this example, the skin prediction may include a visual / graphical representation of the user's skin that characterizes the predicted appearance of the user's skin one year from now, and may highlight or otherwise indicate any wrinkles mentioned in the text portion of the skin prediction. The prediction engine 314 may be trained using the respective skin condition values received from the skin analysis engine 304 and the respective holistic scores received from the holistic analysis engine 306 to output respective skin predictions.

[0064] 4 illustrates an exemplary user-specific skin analysis method 400 for generating a user-specific skin analysis according to various aspects disclosed herein. User-specific data as used in method 400, and more generally as described herein, may be user responses / inputs received by a user computing device (e.g., user computing device 111c1) and / or images of the user's skin / skin area, for example, as captured by the user computing device. In some aspects, the user-specific data may include or reference multiple responses / inputs / images, such as multiple user responses collected by the user computing device while executing an analysis application (app) as described herein. Furthermore, it should be understood that the user's skin area may be any suitable part of the user's body, such as any or all of the user's arms, legs, torso, head, etc.

[0065] At block 402, method 400 includes receiving, by one or more processors (e.g., one or more processors 104 of server 102 and / or a processor of a computer user device, such as a mobile device), user skin data. More specifically, the user skin data may be received in an analysis application (app) executing on the one or more processors. The user skin data may generally define the user's skin area and, in certain aspects, may be user skin image data including image / video data of the user's skin area. For example, the skin data may include image / video data that is a digital image captured by an imaging device (e.g., an imaging device of user computing device 111c1 or 112c3). In this aspect, the image data may include pixel data of at least a portion of the user's skin area.

[0066] However, in certain embodiments, the skin data may include both image data and non-image data. Specifically, the non-image data may include user responses / inputs to a questionnaire presented as part of the execution of the analytical app. In some embodiments, at least one of the one or more processors includes at least one of a mobile device processor or a server processor.

[0067] At block 404, method 400 includes receiving, by one or more processors, user health data. The user health data may include one or more of: (1) body water content or amount, (2) intracellular to extracellular water ratio, (3) body mass index (BMI), (4) blood markers, (5) glucose intake level, (6) heart rate variability, or (7) heart rate. In certain aspects, the user health data may be selected from the group consisting of: (1) body water content or amount, (2) intracellular to extracellular water ratio, (3) body mass index (BMI), and / or a combination thereof, taking into account improved accuracy of skin analysis. In some aspects, the user health data may be selected from the group consisting of: (1) body water content or amount, (2) intracellular to extracellular water ratio, and / or a combination thereof, taking into account improved accuracy of skin analysis.

[0068] At block 406, method 400 includes analyzing the user's skin data and the user's health data with one or more skin analysis learning models (e.g., skin analysis learning model 108) to generate a user-specific skin analysis. Specifically, analysis app scalp may execute the one or more skin analysis learning models to output a user-specific skin analysis that may correspond to one or more characteristics of the user's skin area. As described above, in certain aspects, the one or more skin analysis models may comprise multiple skin analysis models, each of which may be configured to receive input data and sequentially generate output data. In certain aspects, the user-specific skin analysis includes one or more of: (1) skin condition; (2) a holistic score defined based on at least the skin and health data; (3) skin-related product recommendations; (4) skin-related product usage recommendations; (5) supplemental product recommendations; (6) supplemental product usage recommendations; (7) habit recommendations; or (8) skin predictions.

[0069] In various embodiments, the user-specific skin analysis is rendered on a display screen of a computing device (e.g., user computing device 111c1). The rendering may include instructions guiding the user to treat conditions identified based on the user's health data and skin data, as mentioned above. For example, one or more skin analysis learning models may output a user-specific skin analysis indicating that the user is sunburned. In this example, the one or more processors may additionally generate instructions for the user to purchase / apply aloe vera cream to soothe the sunburn (e.g., via skin-related product recommendation engine 308) and to proactively apply sunscreen to the user's skin to avoid future sunburns (e.g., via habit recommendation engine 312).

[0070] The user-specific skin analysis may be generated by a user computing device (e.g., user computing device 111c1) and / or by a server (e.g., server 102). For example, in some aspects, server 102 may analyze user-specific data (user skin data and health data) remote from the user computing device to determine a user-specific skin analysis corresponding to the user's skin area, as described herein with respect to FIG. 1. For example, in such aspects, a server or cloud-based computing platform (e.g., server 102) receives user-specific data defining the user's skin area, including the user's skin data and the user's health data, via computer network 120. The server or cloud-based computing platform may then execute an AI-based learning model (e.g., skin analysis learning model 108) and generate a user-specific skin analysis based on the output of the AI-based learning model. The server or cloud-based computing platform may then transmit the user-specific skin analysis to the user computing device via a computer network (e.g., computer network 120) for rendering on a display screen of the user computing device. For example, in various aspects, a user-specific skin analysis may be rendered on a display screen of a user computing device in real time or near real time during or after receipt of user-specific data defining the user's skin area.

[0071] In various aspects, the user-specific treatment may include, for example, a text-based treatment, a visual / image-based treatment, and / or a virtual rendering of the user's skin area displayed on a display screen of a user computing device (e.g., user computing device 111c1). Such a user-specific skin analysis may include a graphical representation of the user's skin area annotated with one or more graphic or text renderings corresponding to user-specific characteristics (e.g., excessive skin irritation / redness, wrinkles, etc.).

[0072] Further, in certain aspects, the analysis app may receive an image of a user, where the image may depict a skin area of the user. In these aspects, the analysis app executing on one or more processors may generate a modified image based on the image depicting how the user's skin area is predicted to look after following at least one of the recommendations included as part of the user-specific skin analysis. The analysis app may generate the modified image by manipulating one or more pixels of the user's image based on the user-specific skin analysis. As an example, the analysis app may graphically render the user-specific skin analysis for display to the user, where the user-specific skin analysis may include product / habit recommendations to increase the user's water intake and apply anti-aging cream to reduce wrinkles. The wrinkles are determined by one or more skin analysis learning models to be present on the user's skin area based on the user-specific data. In this example, the analytic app may generate a modified image of the user's skin area (as depicted in the user's image) that is wrinkle-free (or has a reduced amount of wrinkles) by manipulating (e.g., updating, smoothing, changing color) pixel values of one or more pixels of the user's image to change pixel values of pixels identified as including pixel data representative of wrinkles present in the user's skin area to pixel values that represent wrinkle-free skin present in the user's skin area. As a result, one or more processors executing the analytic app may render the modified image on a display screen of a computing device (e.g., user computing device 111c1).

[0073] In certain aspects, the user's skin data is the user's first skin data, and the user's health data is the user's first health data. In these aspects, one or more processors may receive the user's first skin data and the user's first health data at a first time and receive the user's second skin data and the user's second health data at a second time. One or more skin analysis models (e.g., skin analysis learning model 108) may analyze the user's second skin data and the user's second health data and generate a new user-specific skin analysis based on a comparison of the user's second skin data and the user's second health data with the user's first skin data and the user's first health data. In this manner, when executed on one or more processors, the analysis app may track changes to the user's skin area (and the user's skin in general) over time. The new user-specific analysis may be the same as or different from the user-specific skin analysis based on the first health data and the first skin data. For example, the user-specific skin analysis may include recommendations the user receives each night suggesting increasing the amount of sleep. In this example, the user may have gotten the recommended amount of sleep between the first time and the second time, and as a result, the new user-specific skin analysis may not include that suggestion because the user's skin area may not contain features indicative of lack of sleep.

[0074] As referred to herein in various aspects, an AI-based learning model (e.g., skin analysis learning model 108) may be trained using each individual's skin data and health data to output a user-specific skin analysis. More specifically, in certain aspects, each of the one or more skin analysis learning models may be trained using digital image data of a plurality of training images depicting each individual's skin area and each individual's health data, such that the one or more skin analysis models are configured to output one or more skin analyses corresponding to one or more characteristics of each individual's skin area. Furthermore, in various aspects, each of the one or more skin analysis learning models may be trained using a plurality of training images and a plurality of non-image training data corresponding to each individual.

[0075] For example, a first set of training data corresponding to each first individual may include skin data representing damaged (e.g., sunburned) skin and health data indicating that the first individual is relatively healthy (e.g., low BMI, healthy glucose levels, healthy heart rate, etc.). Further, in this example, a second set of training data corresponding to each second individual may include skin data representing older (e.g., wrinkled) skin and health data indicating that the second individual is moderately unhealthy (e.g., medium / high BMI, moderate glucose levels, slightly elevated heart rate, etc.). Finally, in this example, a third set of data corresponding to each third individual may include skin data representing healthy skin and health data indicating that the third individual is very unhealthy (e.g., high BMI, high glucose levels, very elevated heart rate, etc.). In this example, one or more skin analysis learning models may be trained using the first, second, and third sets of training data to better identify / correlate each of the conditions represented in the respective training data sets.

[0076] FIG. 5 illustrates an exemplary user interface 500 rendered on a display screen 502 of a user computing device (e.g., user computing device 112c1) in accordance with various aspects disclosed herein. For example, as shown in the example of FIG. 5, user interface 500 may be implemented or rendered via an application (app) running on user computing device 112c1. For example, as shown in the example of FIG. 5, user interface 500 may be implemented or rendered via a native app running on user computing device 112c1. In the example of FIG. 5, user computing device 112c1 is the user computing device described with respect to FIG. 1; for example, 112c1 is illustrated as an APPLE iPhone implementing the APPLE iOS operating system and having display screen 502. User computing device 112c1 may run one or more native applications (apps) on its operating system, including, for example, an analytics app described herein. Such native apps may be implemented or coded (e.g., as computing instructions) in a computing language (e.g., SWIFT) executable by a processor of the user computing device 112c1 and by the user computing device operating system (e.g., APPLE iOS).

[0077] Additionally or alternatively, the user interface 500 may be implemented or rendered via a web interface, such as via a web browser application, such as the Safari and / or Google Chrome app, or other such web browser.

[0078] As shown in the example of FIG. 5, the user interface 500 includes a graphical representation (e.g., image 114b) of a user's skin area 506. The image 114b may include an image of the user (or its graphical representation 506) including pixel data (e.g., pixel data 114ap) of at least a portion of the user's skin area, as described herein. In the example of FIG. 5, the graphical representation 506 of the user's skin area (e.g., image 114b) is annotated with one or more graphics (e.g., areas of pixel data 114ap) or textual renderings (e.g., text 114at) corresponding to various features identifiable within the pixel data comprising the portion of the user's skin area. For example, the areas of pixel data 114ap may be annotated or overlaid on the user's image (e.g., image 114b) to highlight areas or features identified within the pixel data (e.g., feature data and / or raw pixel data) by an AI-based learning model (e.g., skin analysis learning model 108). 5, the area of pixel data 114ap is indicative of features defined in pixel data 114ap, including skin redness / irritation indicative of inflammation (e.g., of pixels 114ap1-3), and may be indicative of other features (e.g., skin wrinkles, sunburn, etc.) shown in the area of pixel data 114ap as described herein. In various aspects, pixels identified as unique features (e.g., pixels 114ap1-3) may be highlighted or otherwise annotated when rendered on the display screen 502.

[0079] As an example, the first pixel 114ap1 may represent an area of the user's skin that is characterized by a high level of dryness and / or irritation relative to the user's healthy skin contained in image 114b and / or relative to the individual healthy skin images used to train an AI-based learning model (e.g., skin analysis learning model 108). The second pixel 114ap2 may represent an area of the user's skin that is characterized by a high level of inflammation relative to the user's healthy skin contained in image 114b and / or relative to the individual healthy skin images used to train an AI-based learning model (e.g., skin analysis learning model 108). The third pixel 114ap3 may represent an area of the user's skin that is characterized by a high level of redness relative to the user's healthy skin contained in image 114b and / or relative to the individual healthy skin images used to train an AI-based learning model (e.g., skin analysis learning model 108). Thus, in this example, when one or more skin analysis learning models evaluate each of these pixels (in addition to other pixels contained within the remainder of image 114b within the area of pixel data 114ap), the models may output a user-specific skin analysis (e.g., user-specific skin score and analysis 510) that informs the user that their skin is characterized by these characteristics (e.g., dryness / irritation, redness, etc.) that are likely the result of inflammation.

[0080] A text rendering (e.g., text 114at) indicates a user-specific skin score (e.g., 75 for pixels 114ap1-3), which may indicate that the user has an above-average skin score (75) as a result of inflammation. A score of 75 indicates that the user has a relatively low degree of redness as a result of inflammation present on the user's skin area and, therefore, would likely benefit from cleansing the user's skin with a soothing body cleanser specifically designed to improve the health / quality / condition of the user's skin (e.g., reduce the amount / degree of redness / inflammation). It should be understood that other text rendering types or values are contemplated herein, in which case the text rendering type or value may be rendered, for example, a skin quality score, a holistic score, etc. Additionally or alternatively, a color value may be used and / or overlaid on a graphical representation (e.g., a graphical representation of the user's skin area 506) shown on user interface 500 to indicate the degree or quality of a given score, for example, a low score of 25 or a high score of 90. Scores may be provided as raw scores, absolute scores, percentage-based scores, and / or any other suitable presentation style. Additionally or alternatively, such scores may be presented with a text or graphic indicator indicating whether the score represents a positive result (good skin health), a negative result (poor skin health), or an acceptable result (average or acceptable skin health / skin care).

[0081] The user interface 500 may also include or render a user-specific skin score and analysis 510. In the embodiment of Figure 5, the user-specific skin score and analysis 510 includes a message 510m to the user designed to indicate to the user the user-specific skin analysis and / or skin / holistic score, along with a brief explanation of any reasons that led to the user-specific skin analysis and / or skin / holistic score. As shown in the example of Figure 5, the message 510m indicates to the user that the user-specific skin score is "75" and further indicates to the user that the user-specific skin analysis corresponds to the user-specific skin score because the user's skin area has "mild redness likely caused by inflammation."

[0082] 5, the user-specific treatment recommendation 512 may be and / or include any of skin-related product recommendations (via the skin-related product recommendation engine 308), supplemental product recommendations (via the supplemental product recommendation engine 310), habit recommendations (via the habit recommendation engine 312), skin predictions (via the prediction engine 314), and / or any other suitable recommendation / value / score described herein, or combinations thereof. The user-specific treatment recommendation 512 may include a message 512m to the user designed to address at least one feature identifiable within the user-specific data defining the user's skin area.

[0083] As shown in the example of FIG. 5, message 512m recommends that the user cleanse their skin with a soothing body cleanser to improve skin health / quality / condition by reducing redness due to inflammation. The recommendation of a soothing body cleanser can be based on an above-average user-specific skin score (e.g., 75) suggesting that the user's image depicts mild redness due to inflammation, and the soothing body cleanser product is designed to address inflammation / redness detected or classified in the pixel data of image 114b or otherwise predicted based on the user's user-specific data (e.g., skin data and health data). The product recommendation can be correlated with identified features within the user-specific data and / or pixel data, and user computing device 112c1 and / or server 102 can be instructed (via skin-related product recommendation engine 308 / supplemental product recommendation engine 310) to output a product recommendation when a feature (e.g., mild skin redness / irritation) is identified.

[0084] User interface 500 may also include or render a section for product recommendations 522 for manufactured product 524r (e.g., a soothing body cleanser as described above). Product recommendations 522 may correspond to user-specific treatment recommendations 512, as described above. For example, in the example of FIG. 5, user-specific treatment recommendations 512 may be displayed on display screen 502 of user computing device 112c1 along with instructions (e.g., message 512m) for treating at least one feature (e.g., an above-average user skin score of 75 associated with redness / inflammation at pixels 114ap1-3) that is predicted and / or identifiable based on user-specific data (including pixel data 114ap) including pixel data of at least a portion of the user's skin area with a manufactured product (manufactured product 524r (e.g., a soothing body cleanser)). The predicted or identified feature is shown and annotated (524p) on user interface 500.

[0085] As shown in FIG. 5 , the user interface 500 recommends a product (e.g., a manufactured product 524r (e.g., a soothing body cleanser)) based on the user-specific treatment recommendation 512. In the example of FIG. 5 , the output or analysis of the user-specific data by the AI-based learning model (e.g., skin analysis learning model 108), such as the user-specific skin score and analysis 510 and / or its associated values (e.g., user-specific skin score 75) or associated pixel data (e.g., 114ap1, 114ap2, and / or 114ap3), and / or the user-specific treatment recommendation 512, may be used to generate or identify a corresponding product recommendation. Such recommendations may include products such as body cleansers, anti-aging products, antioxidant products, anti-wrinkle products, hydrating products, anti-inflammatory products, shampoos, conditioners, etc., to address the user-specific concerns detected or predicted from the user-specific data.

[0086] User interface 500 may further include selectable UI buttons 524s that allow a user (e.g., a user of image 114b) to select a corresponding product (e.g., manufactured product 524r) for purchase or shipment. In some aspects, selection of selectable UI button 524s may cause the recommended product to be shipped to the user and / or may notify a third party that the individual is interested in the product. For example, either user computing device 112c1 and / or server 102 may initiate shipment of manufactured product 524r (e.g., a soothing body cleanser) to the user based on the user-specific skin score and analysis 510 and / or the user-specific treatment recommendation 512. In such aspects, the product may be packaged and shipped to the user.

[0087] In various aspects, the graphical representation (e.g., a graphical representation of the user's skin area 506) with the graphical annotations (e.g., areas of pixel data 114ap), text annotations (e.g., text 114at), and the user-specific skin score and analysis 510 and user-specific treatment recommendations 512 may be transmitted over a computer network (e.g., from the server 102 and / or one or more processors) to the user computing device 112c1 for rendering on the display screen 502. In other aspects, transmission of the user-specific image to a server does not occur, in which case the user-specific skin score and analysis 510 and user-specific treatment recommendations 512 (and / or product-specific recommendations) may instead be generated locally by an AI-based learning model (e.g., skin analysis learning model 108) running and / or implemented on the user's mobile device (e.g., the user computing device 112c1) and rendered on the display screen 502 of the mobile device (e.g., the user computing device 112c1) by a processor of the mobile device.

[0088] In some aspects, any one or more of the graphical representation (e.g., a graphical representation of the user's skin area 506) with the graphical annotation (e.g., an area of pixel data 114ap), text annotation (e.g., text 114at), user-specific skin score and analysis 510, user-specific treatment recommendation 512, and / or product recommendation 522 may be rendered (e.g., rendered locally on the display screen 502) in real time or near real time during or after receipt of the user-specific data. In aspects in which the user-specific data is analyzed by the server 102, the user-specific data may be transmitted and analyzed by the server 102 in real time or near real time.

[0089] In some aspects, a user may provide new user-specific data that may be transmitted to the server 102 for updating, retraining, or reanalysis by the skin analysis learning model 108. In other aspects, the new user-specific data may be received locally on the computing device 112c1 and analyzed by the skin analysis learning model 108 on the computing device 112c1.

[0090] 5, the user may select selectable button 512i to reanalyze new user-specific data (e.g., locally at computing device 112c1 or remotely at server 102). Selectable button 512i may cause user interface 500 to prompt the user to input / attach new user-specific data for analysis. A user computing device, such as server 102 and / or user computing device 112c1, may receive the new user-specific data, including data defining the user's skin area. Specifically, the new user-specific data may be received / captured by user computing device 112c1 (e.g., via an integrated digital camera of user computing device 112c1). A new image (e.g., similar to image 114b) included as part of the new user-specific data may include pixel data of a portion of the user's skin area. An AI-based learning model (e.g., skin analysis learning model 108) executing on the memory of the computing device (e.g., server 102) may analyze the new user-specific data received / captured by the user computing device 112c1 to generate a new user-specific skin analysis. The computing device (e.g., server 102) may generate the new user-specific skin analysis based on a comparison of the new user-specific data with the user-specific data. For example, the new user-specific skin analysis may include a new graphical representation including graphics and / or text (e.g., showing a new skin score, e.g., 85, after the user cleansed their skin with a soothing body cleanser). The new user-specific skin analysis may include an additional quality score, e.g., that the user successfully cleansed their skin to reduce skin redness / irritation, as detected in the new user-specific data. A comment may include that the user needs to correct an additional feature, e.g., dry skin, detected in the new user-specific data by applying an additional product, e.g., moisturizing lotion.

[0091] In various aspects, the new user-specific skin analysis and / or the new user-specific treatment recommendation may be transmitted over a computer network from the server 102 to the user's user computing device for rendering on the display screen 502 of the user computing device (e.g., user computing device 112c1).

[0092] In other aspects, the user's new user-specific data is not transmitted to the server, in which case the new user-specific skin analysis and / or new user-specific treatment recommendations (and / or product / habit-specific recommendations) may instead be generated locally by an AI-based learning model (e.g., skin analysis learning model 108) running and / or implemented on the user's mobile device (e.g., user computing device 112c1) and rendered on the display screen 502 of mobile device 112c1 by the mobile device's processor. [Example]

[0093] The user-specific skin analysis system shown in Figure 1 was used to perform a user-specific skin analysis method using health data, particularly the user's body water content or hydration, along with the user's skin image data to generate a user-specific skin analysis. The user-specific skin analysis, which is a skin condition and skin prediction, particularly a prediction of pigmented spots and pigmented blemishes, was provided on a display screen of a computing device. The method and system of the present invention improves the accuracy of the skin analysis compared to skin analysis using only the skin data without the health data, and compared to skin analysis using other health data, such as body fat percentage.

[0094] Aspects of the Disclosure The following aspects are provided as examples according to the disclosure herein and are not intended to limit the scope of the disclosure. 1. A user-specific skin analysis method for generating a user-specific skin analysis, the method including: receiving, by one or more processors, user skin data; and receiving, by one or more processors, user health data, the user health data including one or more of: (1) body water content or hydration, (2) intracellular to extracellular water ratio, (3) body mass index (BMI), (4) blood markers, (5) glucose intake level, (6) heart rate variability, or (7) heart rate; and analyzing the user skin data and the user health data with one or more skin analysis learning models to generate the user-specific skin analysis. 2. The method of aspect 1, wherein the user's health data is selected from the group consisting of body water content or water mass, intracellular to extracellular water ratio, body mass index (BMI), and mixtures thereof. 3. The method of aspect 1 or 2, wherein the user's health data is selected from the group consisting of body water content or water volume, intracellular to extracellular water ratio, and mixtures thereof. 4. The method of any one of aspects 1-3, wherein one or more skin analysis learning models are trained using each individual's skin and health data to output a user-specific skin analysis. 5. The method of any one of aspects 1-4, further comprising rendering, by the one or more processors, the user-specific skin analysis on a display screen of a computing device. 6. The method of any one of aspects 1-5, further comprising: receiving, by one or more processors, an image depicting the user's skin area; generating, by the one or more processors, a modified image based on the image, the modified image depicting how the user's skin area is predicted to appear after following at least one of the recommendations; and rendering, by the one or more processors, the modified image on a display screen of a computing device. 7. The method of any one of aspects 1-6, wherein the user's skin data is user's first skin data and the user's health data is user's first health data, and the method further includes receiving, by one or more processors, the user's first skin data and the user's first health data at a first time; receiving, by one or more processors, the user's second skin data and the user's second health data at a second time; analyzing the user's second skin data and the user's second health data with one or more skin analysis models; and generating a new user-specific skin analysis based on a comparison of the user's second skin data and the user's second health data with the user's first skin data and the user's first health data. 8. The method of any one of aspects 1-7, wherein at least one of the one or more processors comprises at least one of a processor of a mobile device or a processor of a server. 9. The method of any one of aspects 1 to 8, wherein the user's skin data is user's skin image data. 10. A user-specific skin analysis system configured to generate a user-specific skin analysis, the system comprising: one or more processors; an analysis application (app) including computing instructions configured to execute on the one or more processors; and one or more skin analysis learning models accessible by the analysis app, wherein the computing instructions of the analysis app, when executed by the one or more processors, cause the one or more processors to receive a user's skin data and receive the user's health data, the user's health data including one or more of: (1) body water content or hydration, (2) intracellular to extracellular water ratio, (3) body mass index (BMI), (4) blood markers, (5) glucose intake level, (6) heart rate variability, or (7) heart rate; and analyze the user's skin data and the user's health data with the one or more skin analysis learning models to generate the user-specific skin analysis. 11. The system of aspect 10, wherein the user's health data is selected from the group consisting of body water content or water mass, intracellular to extracellular water ratio, body mass index (BMI), and mixtures thereof. 12. The system of aspect 10 or 11, wherein the user's health data is selected from the group consisting of body water content or water volume, intracellular to extracellular water ratio, and mixtures thereof. 13. The system of any one of aspects 10-12, wherein one or more skin analysis learning models are trained using each individual's skin and health data to output a user-specific skin analysis. 14. The system of any one of aspects 10 to 14, wherein the computing instructions, when executed by the one or more processors, further cause the one or more processors to render a user-specific skin analysis on a display screen of the computing device. 15. The system of any one of aspects 10 to 14, wherein the computing instructions, when executed by one or more processors, further cause the one or more processors to: receive an image depicting a user's skin area; generate a modified image based on the image, the modified image depicting how the user's skin area is predicted to look after following at least one of the recommendations; and render the modified image on a display screen of the computing device. 16. The system of any one of aspects 10 to 15, wherein the user's skin data is user's first skin data, the user's health data is user's first health data, and the computing instructions, when executed by the one or more processors, further cause the one or more processors to: receive the user's first skin data and the user's first health data at a first time; receive the user's second skin data and the user's second health data at a second time; analyze the user's second skin data and the user's second health data with one or more skin analysis models; and generate a new user-specific skin analysis based on a comparison of the user's second skin data and the user's second health data with the user's first skin data and the user's first health data. 17. The system of any one of aspects 10-16, wherein at least one of the one or more processors includes at least one of a processor of a mobile device or a processor of a server. 18. The system of any one of aspects 10 to 17, wherein the user's skin data is user's skin image data. 19. A tangible, non-transitory computer-readable medium storing instructions for generating a user-specific skin analysis, the instructions, when executed by one or more processors, causing the one or more processors to: receive, in an analysis application (app) executing on the one or more processors, a user's skin data; receive, in the analysis app, the user's health data, where the user's health data includes one or more of: (1) body water content or hydration, (2) intracellular to extracellular water ratio, (3) body mass index (BMI), (4) blood markers, (5) glucose intake level, (6) heart rate variability, or (7) heart rate; and analyze the user's skin data and the user's health data with one or more skin analysis learning models accessible by the analysis app to generate the user-specific skin analysis. 20. The tangible, non-transitory computer-readable medium of aspect 19, wherein the user's skin data is user's skin image data.

[0095] Additional Considerations While this disclosure provides detailed descriptions of many different aspects, it should be understood that the legal scope of this description is defined by the terms of the claims at the end of this patent and any equivalents. This detailed description should be construed as exemplary only and does not describe every possible aspect, as describing every possible aspect would be impractical. Many alternative aspects may be implemented using either current technology or technology developed after the filing date of this patent, and such aspects would still fall within the scope of the claims.

[0096] The following additional considerations apply to the foregoing discussion: Throughout this specification, multiple examples may implement components, operations, or structures that are described as a single example. While individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed simultaneously, and the operations need not be performed in the order illustrated. Structures and functions presented as separate components in example configurations may be implemented as combined structures or components. Similarly, structures and functions presented as a single component may also be implemented as separate components. These and other variations, modifications, additions, and improvements are included within the scope of the present subject matter herein.

[0097] Additionally, certain aspects are described herein as including logic or certain routines, subroutines, applications, or instructions. These may be constructed as either software (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware. In hardware, routines, etc., are tangible units capable of performing certain operations and may be configured or arranged in a particular manner. In exemplary aspects, one or more computer systems (e.g., standalone, client, or server computer systems) or one or more hardware modules (e.g., processors or groups of processors) of a computer system may be configured by software (e.g., applications or application portions) as hardware modules that operate to perform certain operations as described herein.

[0098] Various operations of the example methods described herein may be performed, at least in part, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the associated operations. Whether temporarily or permanently configured, such processors may form processor-implemented modules that operate to perform one or more operations or functions. Modules referred to herein may, in some example aspects, include processor-implemented modules.

[0099] Similarly, the methods or routines described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented hardware modules. Certain performance of those operations may be distributed to one or more processors and may reside within a single machine as well as spread across multiple machines. In some exemplary aspects, the processor(s) may be located in a single location, whereas in other aspects, the processor(s) may be distributed across several locations.

[0100] Certain performance of those operations may be distributed across one or more processors and may reside within a single machine as well as be spread across multiple machines. In some exemplary aspects, one or more processors or processor-implemented modules may be located in a single geographic location (e.g., in a home environment, an office environment, or a server farm). In other aspects, one or more processors or processor-implemented modules may be distributed across multiple geographic locations.

[0101] This detailed description should be construed as illustrative only and does not describe every possible embodiment, as describing every possible embodiment would be impractical, if not impossible. Those skilled in the art will be able to implement numerous alternative embodiments using either current technology or technology developed after the filing date of this application.

[0102] Those skilled in the art will recognize that numerous modifications, variations, and combinations can be made with respect to the above-described aspects without departing from the scope of the present invention, and that such modifications, variations, and combinations can be considered to be within the circumstance of the inventive concept.

[0103] The claims at the end of this patent application are not intended to be construed under 35 U.S.C. § 112(f) unless conventional means-plus-function language, such as "means for" or "step for," is expressly recited. The systems and methods described herein are directed to improving computer functionality and improve upon the functionality of conventional computers.

[0104] Dimensions and values disclosed herein should not be understood as being strictly limited to the exact numerical values recited. Instead, unless otherwise indicated, each such dimension is intended to mean both the recited value and a functionally equivalent range surrounding that value. For example, a dimension disclosed as "40 mm" is intended to mean "about 40 mm."

[0105] All documents cited herein, including any cross-referenced or related patents or patent applications, and any patent applications or patents to which this application claims priority or benefit, are incorporated herein by reference in their entirety, unless expressly stated to the contrary. The citation of any document shall not be deemed to be prior art to any invention disclosed or claimed herein, or to teach, suggest, or disclose any such invention, either alone or in combination with any other reference(s). Furthermore, to the extent that any meaning or definition of a term in this document conflicts with any meaning or definition of the same term in a document incorporated by reference, the meaning or definition assigned to that term in this document shall govern.

[0106] While particular embodiments of the present invention have been illustrated and described, it would be obvious to those skilled in the art that various other changes and modifications can be made without departing from the spirit and scope of the invention. It is therefore intended to cover in the appended claims all such changes and modifications that are within the scope of this invention.

Claims

1. 1. A user-specific skin analysis method for generating a user-specific skin analysis, comprising: receiving, by one or more processors, skin data of the user, wherein the skin data of the user is skin image data of the user; receiving, by the one or more processors, health data of the user, the health data of the user including body water content or hydration and intracellular to extracellular water ratio; and analyzing the skin data of the user and the health data of the user with one or more skin analysis learning models to generate a user-specific skin analysis.

2. 10. The method of claim 1, wherein the one or more skin analysis learning models are trained using individual skin and health data to output the user-specific skin analysis.

3. The method of claim 1 , further comprising rendering, by the one or more processors, the user-specific skin analysis on a display screen of a computing device.

4. receiving, by the one or more processors, an image depicting a skin area of the user; generating, by the one or more processors, a modified image based on the image, the modified image depicting how the skin area of the user is predicted to appear after following at least one of the recommendations; The method of claim 1 , further comprising: rendering, by the one or more processors, the modified image on a display screen of a computing device.

5. The skin data of the user is first skin data of the user, and the health data of the user is first health data of the user, and the method includes: receiving, by the one or more processors, the first skin data of the user and the first health data of the user at a first time; receiving, by the one or more processors, second skin data of the user and second health data of the user at a second time; analyzing the second skin data of the user and the second health data of the user with the one or more skin analysis models; 5. The method of claim 1, further comprising: generating a new user-specific skin analysis based on a comparison of the second skin data of the user and the second health data of the user with the first skin data of the user and the first health data of the user.

6. The method of any one of claims 1 to 4, wherein at least one of the one or more processors comprises at least one of a processor of a mobile device or a processor of a server.

7. 1. A user-specific skin analysis system configured to generate a user-specific skin analysis, the user-specific skin analysis system comprising: one or more processors; an analytics application (app) including computing instructions configured to execute on the one or more processors; and one or more skin analysis learning models accessible by the analysis app; When the computing instructions of the analytical app are executed by the one or more processors, the one or more processors are receiving skin data of the user, wherein the skin data of the user is skin image data of the user; receiving health data of the user, the health data of the user including body water content or water mass and intracellular to extracellular water ratio; and analyzing the skin data of the user and the health data of the user with the one or more skin analysis learning models to generate a user-specific skin analysis.

8. 8. The system of claim 7, wherein the one or more skin analysis learning models are trained using individual skin and health data to output the user-specific skin analysis.

9. The computing instructions, when executed by the one or more processors, cause the one or more processors to: The system of claim 7 , further comprising rendering the user-specific skin analysis on a display screen of a computing device.

10. The computing instructions, when executed by the one or more processors, cause the one or more processors to: receiving an image depicting a skin area of the user; generating a modified image based on the image, the modified image depicting how the skin area of the user is predicted to look after following at least one of the recommendations; 10. The system of claim 7, further comprising: rendering the modified image on a display screen of a computing device.

11. the skin data of the user is first skin data of the user, and the health data of the user is first health data of the user; and the computing instructions, when executed by the one or more processors, cause the one or more processors to: receiving the first skin data of the user and the first health data of the user at a first time; receiving second skin data of the user and second health data of the user at a second time; analyzing the second skin data of the user and the second health data of the user with the one or more skin analysis models; and generating a new user-specific skin analysis based on a comparison of the second skin data of the user and the second health data of the user with the first skin data of the user and the first health data of the user.

12. The system of any one of claims 7 to 10, wherein at least one of the one or more processors comprises at least one of a processor of a mobile device or a processor of a server.

13. 1. A tangible, non-transitory computer-readable medium storing instructions for generating a user-specific skin analysis, the instructions, when executed by one or more processors, causing the one or more processors to: receiving, in an analysis application (app) running on one or more processors, skin data of the user, wherein the skin data of the user is skin image data of the user; receiving, in the analysis app, health data of the user, the health data of the user including body water content or water mass and intracellular to extracellular water ratio; and analyzing the user's skin data and the user's health data with the one or more skin analysis learning models accessible by the analysis app to generate a user-specific skin analysis.

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