Artificial intelligence-based system and method for predicting skin analysis of individual

Abstract

There is provided a digital imaging method of analyzing pixel data of an image of a diaper changing area associated with an individual to determine individual-specific diaper changing area skin characteristics, comprising: aggregating a training image of a diaper changing area of the individual; training a diaper changing zone skin model with the pixel data of the training image, the diaper changing zone skin model operable to determine diaper changing zone skin characteristics associated with the diaper changing zone based on the pixel data of the diaper changing zone; receiving an image of the individual captured by the digital camera; analyzing the image captured by the digital camera by a diaper changing area skin model to determine individual-specific diaper changing area skin characteristics; generating an individual-specific recommendation designed to address features identifiable within pixel data of the skin of the individual based on individual-specific diaper changing area skin characteristics; and providing the individual-specific recommendation via the user interface.

Description

Technical Field

[0001] The present disclosure relates generally to digital imaging systems and methods, and more particularly to digital imaging systems and methods for analyzing pixel data of at least one image of a diaper changing area associated with an individual to determine diaper changing area skin characteristics specific to the individual. Background Art

[0002] Users of absorbent articles experience problems or concerns related to the use of absorbent articles, the substances that the articles are designed to absorb, or the health of the users themselves. For example, typically, new parents or other guardians or caregivers experience many problems when learning to take care of babies or other individuals who need care. One such problem includes managing the often sensitive skin of newborns or other people who use diapers. Therefore, including over time, problems with infant or individual development may occur, including health, safety, health care and / or hygiene conditions or problems, wherein parents or other guardians fail to recognize such skin characteristics or changes in such skin characteristics specific to infants, children or other individuals, such as various skin conditions or skin problems (including diaper rash, and other skin irritations, dry skin, skin erythema, etc.), each of which may affect the health of infants or other individuals and is harmful to the health of infants or other individuals.

[0003] Prior art methods for dealing with such issues (including manual instructions provided to parents, guardians, and / or caregivers for changing diapers and utilizing wipes, lotions, creams, powders, etc.) can be time consuming and / or error prone (and potentially harmful). Furthermore, a parent or caregiver following such prior art methods may attempt to empirically test various products or technologies, but may not obtain satisfactory results, or may result in possible negative side effects that affect the health or safety of a particular infant or individual.

[0004] For the foregoing reasons, a need exists for a digital imaging system and method for analyzing pixel data of at least one image of a diaper changing area associated with an individual to determine individual-specific diaper changing area skin characteristics. Summary of the invention

[0005] In general, as described herein, digital imaging systems and methods for analyzing pixel data of images of an individual's diapering area (i.e., images showing the individual's skin in an area of ​​the individual's body associated with diapering) to determine individual-specific diapering area skin characteristics provide a digital imaging and artificial intelligence (AI) based solution to overcome skin conditions and / or skin problems that occur in individuals wearing absorbent articles (such as diapers). The digital systems and methods allow a user to submit a specific individual image to one or more imaging servers (e.g., including one or more processors thereof) or another computing device (e.g., such as locally on a user's mobile device), wherein the one or more imaging servers or user computing devices implement or execute a diapering area skin model trained using pixel data of potentially 10,000 (or more) images of the individual. The diapering area skin model can generate an individual-specific electronic recommendation based on the determined individual-specific diapering area skin characteristics, the individual-specific electronic recommendation being designed to address at least one feature identifiable within the pixel data of at least a portion of the diapering area of ​​the individual. For example, at least one feature may include pixels or pixel data indicative of a particular skin condition, as well as the number of skin conditions, the severity of any skin condition, and the like. In some embodiments, the individual-specific recommendations (and / or product-specific recommendations) may be transmitted to the user's user computing device via a computer network for presentation on a display screen. In other embodiments, no transmission of a particular image of the user to an imaging server occurs, wherein the individual-specific recommendations (and / or product-specific recommendations) may instead be generated by a diaper changing area skin model executed and / or implemented locally on the user's mobile device and presented on the display screen of the mobile device by a processor of the mobile device. In various embodiments, such presentation may include graphical representations, overlays, text and / or voice annotations, and the like for addressing features in the pixel data.

[0006] More specifically, as described herein, a digital imaging method for analyzing pixel data of at least one image of a diaper changing area associated with an individual to determine skin characteristics of a diaper changing area specific to the individual is provided, the digital imaging method comprising the following steps: at one or more processors communicatively coupled to one or more memories, a plurality of training images of one or more diaper changing areas associated with a corresponding plurality of individuals are gathered, each of the training images comprising pixel data of a diaper changing area associated with the corresponding individual; the one or more processors are used to train a diaper changing area skin model using the pixel data of the plurality of training images, the diaper changing area skin model being operable to determine skin characteristics of the diaper changing area associated with the diaper changing area based on the pixel data of the diaper changing area; at the one or more processors The one or more processors receive at least one image of an individual, the at least one image being captured by a digital camera, and the at least one image including pixel data of the diaper changing area associated with the individual; the diaper changing area skin model executed on the one or more processors analyzes the at least one image captured by the digital camera to determine individual-specific diaper changing area skin characteristics; the one or more processors generate at least one individual-specific recommendation based on the individual-specific diaper changing area skin characteristics, the at least one individual-specific recommendation being designed to address at least one feature identifiable within the pixel data of the individual's skin; and the at least one individual-specific recommendation is provided via a user interface of a user computing device.

[0007] In addition, as described herein, a digital imaging system is provided, which is configured to analyze pixel data of at least one image of a diaper changing area associated with an individual to determine individual-specific diaper changing area skin characteristics, the digital imaging system comprising: an imaging server, the imaging server comprising a server processor and a server memory; an imaging application (app), the imaging application configured to execute on a user computing device comprising a device processor and a device memory, the imaging application being communicatively coupled to the imaging server; and a diaper changing area skin model, the diaper changing area skin model being trained with pixel data of a plurality of training images of one or more diaper changing areas associated with a corresponding plurality of individuals, and being operable to determine individual-specific diaper changing area skin characteristics based on the pixel data of the diaper changing area. Diapering area skin characteristics associated with a diapering area, wherein the diapering area skin model is configured to execute on a server processor or a device processor so that the server processor or the device processor performs the following operations: receiving at least one image of an individual, the at least one image being captured by a digital camera, and the at least one image including pixel data of a diapering area associated with the individual; analyzing, by the diapering area skin model, the at least one image captured by the digital camera to determine individual-specific diapering area skin characteristics; generating at least one individual-specific recommendation based on the individual-specific diapering area skin characteristics, the at least one individual-specific recommendation being designed to address at least one feature identifiable within the pixel data of the individual's skin; and providing the at least one individual-specific recommendation via a user interface of a user computing device.

[0008] In addition, as described herein, a tangible, non-transitory computer-readable medium is provided, which stores instructions for analyzing pixel data of at least one image of a diaper changing area associated with an individual to determine individual-specific diaper changing area skin characteristics, and when these instructions are executed by one or more processors, the one or more processors perform the following operations: at one or more processors communicatively coupled to one or more memories, a plurality of training images of one or more diaper changing areas associated with a corresponding plurality of individuals are gathered, each of the training images includes pixel data of a diaper changing area associated with the corresponding individual; a diaper changing area skin model is trained by the one or more processors using the pixel data of the plurality of training images, and the diaper changing area skin model is operable The invention relates to a method of determining diaper changing area skin characteristics associated with a diaper changing area based on pixel data of the diaper changing area; receiving at least one image of an individual at one or more processors, the at least one image being captured by a digital camera, and the at least one image including pixel data of a diaper changing area associated with the individual; analyzing at least one image captured by the digital camera by a diaper changing area skin model executed on the one or more processors to determine individual-specific diaper changing area skin characteristics; generating at least one individual-specific recommendation based on the individual-specific diaper changing area skin characteristics by the one or more processors, the at least one individual-specific recommendation being designed to address at least one feature identifiable within the pixel data of the individual's skin; and providing at least one individual-specific recommendation via a user interface of a user computing device.

[0009] In light of the foregoing and the disclosure herein, the present disclosure includes improvements in computer functionality or improvements to other technologies, at least because the present disclosure describes, for example, improvements to an imaging server, or other computing device (e.g., a user computing device), where the intelligence or predictive capabilities of the imaging server or computing device are enhanced by a trained (e.g., machine learning trained) diapering area skin model. The diapering area skin model executed on the imaging server or computing device is capable of accurately identifying individual-specific diapering area skin characteristics and individual-specific electronic recommendations based on pixel data of other individuals, the individual-specific electronic recommendations being designed to address at least one feature identifiable within the pixel data of the diapering area of ​​a specific individual. That is, the present disclosure describes improvements in the functionality of the computer itself or in "any other technology or field of technology" because the imaging server or user computing device is enhanced by multiple training images (e.g., 10,000s of training images and associated pixel data as feature data) to accurately predict, detect, or determine the pixel data of an individual-specific image (such as a newly provided customer image). This is an improvement over the prior art, at least because prior systems lack such prediction or classification capabilities and are simply unable to accurately analyze individual-specific images to output a prediction result to address at least one feature identifiable within pixel data that includes at least one image of a diaper changing area of ​​an individual.

[0010] For similar reasons, the present disclosure relates to improvements in other technologies or areas of technology, at least because the present disclosure describes or introduces improvements to computing devices in the field of diaper use and medical care related to such diaper use, whereby a trained diaper changing area skin model executed on an imaging device or computing device improves diaper use and medical care related to such diaper use through digital and / or artificial intelligence-based analysis of user or individual images to output prediction results to address at least one feature identifiable within individual-specific pixel data of the individual's diaper changing area.

[0011] Among other things, the present disclosure includes specific features that are in addition to well-known, conventional, routine activities in the art, or that add non-routine steps that limit the claims to specific useful applications, such as analyzing pixel data of an image of an individual's diaper changing area to determine diaper changing area skin characteristics, as described herein.

[0012] The advantages will become more apparent to those skilled in the art through the following description of the preferred embodiments shown and described by way of example. As will be appreciated, embodiments of the present invention may have other and different embodiments, and their details can be modified in various respects. Therefore, the drawings and descriptions should be regarded as exemplary and non-restrictive in nature. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings described below depict various aspects of the systems and methods disclosed herein. It should be understood that each of the accompanying drawings depicts an embodiment of a specific aspect of the disclosed systems and methods, and each of the accompanying drawings is intended to be consistent with its possible embodiments. In addition, where possible, the following description refers to the figure numbers included in the following figures, wherein the features shown in multiple figures are represented by consistent figure numbers.

[0014] Arrangements are shown in the drawings of the present discussion, however, it should be understood that the present embodiments are not limited to the precise arrangements and instrumentality shown, wherein:

[0015] Figure 1 An exemplary digital imaging system configured to analyze pixel data of an image of a diaper changing area of ​​an individual to determine diaper changing area skin characteristics is shown in accordance with various embodiments disclosed herein.

[0016] Figure 2A Exemplary images and their associated pixel data that may be used to train and / or implement a diaper changing area skin model according to various embodiments disclosed herein are shown.

[0017] Figure 2BAnother exemplary image and its associated pixel data that may be used to train and / or implement a diaper changing area skin model according to various embodiments disclosed herein is shown.

[0018] Figure 2C Another exemplary image and its associated pixel data that may be used to train and / or implement a diaper changing area skin model according to various embodiments disclosed herein is shown.

[0019] Figure 3 A diagram illustrating a digital imaging method of analyzing pixel data of an image of a diaper changing area of ​​an individual to determine diaper changing area skin characteristics according to various embodiments disclosed herein.

[0020] Figure 4 An exemplary user interface presented on a display screen of a user computing device is shown according to various embodiments disclosed herein.

[0021] The drawings depict preferred embodiments for purposes of illustration only.Alternative embodiments of the systems and methods shown herein may be employed without departing from the principles of the invention described herein. DETAILED DESCRIPTION

[0022] Figure 1 An exemplary digital imaging system 100 is shown that is configured to analyze pixel data of an image of a diaper changing area of ​​an individual (e.g., any one or more of images 202a, 202b, and / or 202c) to determine skin characteristics of the diaper changing area in accordance with various embodiments disclosed herein. As referred to herein, a "diaper changing area" may refer to any part of the human body, including the anus, buttocks, vulva, hips, thighs, or waist of the individual when the individual is wearing a diaper, when the individual is not wearing a diaper, or when the individual is changing a diaper. In other words, a "diaper changing area" may include any area of ​​the individual that is covered by a diaper when the diaper is worn, and any area that may be affected by a diaper or components of a diaper, or biological matter contained therein. Figure 1 In an exemplary embodiment of the present invention, the digital imaging system 100 includes a server 102, which may include one or more computer servers. In various embodiments, the server 102 includes a plurality of servers, which may include multiple, redundant or replicated servers as part of a server cluster. In other 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 and one or more computer memories 106. The server 102 may be referred to as an "imaging server" herein.

[0023] The 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. One or more memories 106 may store an operating system (OS) (e.g., Microsoft Windows, Linux, Unix, etc.) that can facilitate the functions, applications, methods, or other software discussed herein. For example, the application may include an imaging application, which may be referred to herein as a skin analyzer imaging application. The skin analyzer imaging application may include computing instructions that are configured to be implemented on an operating system of an underlying computing device (such as a user computing device and / or server). In various aspects, the skin analyzer imaging application may include a diaper changing area skin model 108, or may be additionally configured to access the diaper changing area skin model. Similar to the diaper changing area skin model 108, the skin analyzer imaging application may be stored on the memory of one or more servers 102 and / or one or more user computing devices. The one or more memories 106 may also store a diapering area skin model 108 and / or a skin analyzer imaging application, which may be an artificial intelligence-based model, such as a machine learning model trained on individual images (e.g., images 202a, 202b, and / or 202c), as described herein. Additionally or alternatively, the diapering area skin model 108 may also be stored in a database 105 that is accessible by or otherwise communicatively coupled to one or more imaging servers 102. The memory 106 may also store machine-readable instructions, including any of one or more applications, 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 method, process, element, or limitation illustrated, depicted, or described with respect to the various flowcharts, diagrams, charts, drawings, and / or other disclosures herein. For example, at least some of the applications, software components, or APIs may be, may include, or otherwise be a part of an imaging-based machine learning model or component (such as the diaper changing area skin model 108), each of which may be configured to facilitate its various functions discussed herein. It should be understood that one or more other applications executed by the processor 104 are contemplated.

[0024] The one or more processors 104 may be connected to the memory 106 via a computer bus that is responsible for transferring electronic data, data packets, or other electronic signals to and from the one or more processors 104 and the memory 106 in order to implement or execute machine-readable instructions, methods, processes, elements, or limitations as illustrated, depicted, or described with respect to the various flow charts, diagrams, charts, drawings, and / or other disclosures herein.

[0025] The processor 104 may be connected to the memory 106 via a computer bus to execute an operating system (OS). The processor 104 may also be connected to the memory 106 via a computer bus to create, read, update, delete, or otherwise access or interact with data stored in the memory 106 and / or a database 104 (e.g., a relational database such as Oracle, DB2, MySQL, or a NoSQL-based database such as MongoDB). The data stored in the memory 106 and / or the database 105 may include all or part of any of the data or information described herein, including, for example, training images and / or user images (e.g., each of the training images and / or user images including any one or more of the images 202a, 202b, and / or 202c) or other information of an individual, including demographics, age, race, skin type, etc.

[0026] The imaging server 102 may also include a communication component configured to communicate (e.g., send and receive) data to one or more network or local terminals (such as the computer network 120 and / or the terminal 109 (for presentation or visualization) described herein) via one or more external / network ports. In some embodiments, the imaging server 102 may include a client-server platform technology, such as ASP.NET, Java J2EE, Ruby on Rails, Node.js, web services, or online APIs, which respond to receiving and responding to electronic requests. The one or more imaging servers 102 may implement a client-server platform technology that may interact with one or more memories 106 (including one or more applications, one or more components, one or more APIs, data, etc. stored therein) and / or a database 105 via a computer bus to implement or execute machine-readable instructions, methods, processes, elements, or limitations as illustrated, depicted, or described with respect to the various flow charts, diagrams, charts, drawings, and / or other disclosures herein. According to some embodiments, the imaging server 102 may include or interact with one or more transceivers (e.g., WWAN, WLAN, and / or WPAN transceivers) that function according to IEEE standards, 3GPP standards, or other standards and may be used to receive and transmit data via an external / network port connected to the computer network 120. In some embodiments, the computer network 120 may include a private network or a local area network (LAN). Additionally or alternatively, the computer network 120 may include a public network, such as the Internet.

[0027] The imaging server 102 may also include or implement an operator interface configured to present information to an administrator or operator and / or receive input from an administrator or operator. Figure 1 As shown, the operator interface may provide a display screen (e.g., via terminal 109). Imaging server 102 may also provide I / O components (e.g., ports, capacitive or resistive touch-sensitive input panels, keys, buttons, lights, LEDs) that are directly accessible or attached to imaging server 102 or indirectly accessible or attached to a terminal via terminal 109. According to some embodiments, an administrator or operator may access server 102 via terminal 109 to view information, make changes, input training data or images, and / or perform other functions.

[0028] As described above, in some embodiments, the imaging server 102 may perform functions as part of a "cloud" network as discussed herein, or may otherwise communicate with other hardware or software components within the cloud to send, retrieve, or otherwise analyze data or information as described herein.

[0029] Generally speaking, a computer program or computer-based product, application or code (e.g., a model, such as an AI model, or other computing instructions described herein) may be stored on a computer-usable storage medium or a tangible, non-transitory computer-readable medium (e.g., a standard random access memory (RAM), an optical disk, a universal serial bus (USB) drive, etc.) in which such computer-readable program code or computer instructions are embodied, where the computer-readable program code or computer instructions may be installed on a processor 104 (e.g., working in conjunction with a corresponding operating system in memory 106) or otherwise adapted to be executed by the processor to facilitate, implement or perform machine-readable instructions, methods, processes, elements or limitations as illustrated, depicted or described with respect to the various flowcharts, diagrams, charts, drawings 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, byte code, interpretable source code, etc. (e.g., via Golang, Python, C, C++, C#, Objective-C, Java, Scala, ActionScript, JavaScript, HTML, CSS, XML, etc.).

[0030] like Figure 1 As shown, the imaging server 102 is communicatively connected to one or more user computing devices 111c1-111c3 and / or 112c1-112c3 via base stations 111b and 112b via a computer network 120. In some embodiments, the base stations 111b and 112b may include cellular base stations such as cellular towers, thereby communicating with the 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 mobile phone 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 communications 122 based on any one or more of a variety of wireless standards, including, as non-limiting examples, IEEE 802.11a / b / c / g (WIFI), BLUETOOTH standards, etc.

[0031] Any of the one or more user computing devices 111c1-111c3 and / or 112c1-112c3 may include a mobile device and / or client device for accessing the imaging server 102 and / or communicating with the imaging server. In various embodiments, the user computing devices 111c1-111c3 and / or 112c1-112c3 may include a cellular phone, a mobile phone, a tablet device, a personal data assistant (PDA), etc., including, as non-limiting examples, an APPLE iPhone or iPad device or a mobile phone or tablet computer based on GOOGLE ANDROID. In addition, in various embodiments, the user computing devices 111c1-111c3 and / or 112c1-112c3 may include a home or personal assistant, including, for example, GOOGLE HOME, AMAZON ALEXA, ECHO SHOW devices, etc.

[0032] In additional embodiments, the user computing devices 111c1-111c3 and / or 112c1-112c3 may include a retail computing device. The retail computing device will be configured in the same or similar manner, for example, as described herein for the user computing devices 111c1-111c3, including having a processor and a memory for implementing the diaper changing area skin model 108 as described herein or communicating with the diaper changing area skin model (e.g., via one or more servers 102). However, the retail computing device may be located, installed, or otherwise positioned within a retail environment to allow users and / or customers of the retail environment to utilize the digital imaging system and method on-site within the retail environment. For example, the retail computing device may be installed in a kiosk for user access. The user may then upload or transfer the image (e.g., from a user mobile device) to the kiosk to implement the digital imaging system and method described herein. Additionally or alternatively, the kiosk may be configured with a camera to allow the user to take a new image of himself or herself (e.g., in a private manner with authorization) for uploading and transfer. In such embodiments, the user or consumer himself or herself will be able to use the retail computing device to receive individual-specific electronic recommendations as described herein and / or have the individual-specific electronic recommendations presented on a display screen of the retail computing device. 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 for interacting with the user or consumer on site. In such embodiments, the user or consumer may be able to interact with an employee or other personnel of the retail environment via the retail computing device (e.g., by passing an image from the user's mobile device to the retail computing device or by capturing a new image by a camera of the retail computing device) to receive individual-specific electronic recommendations as described herein and / or have the individual-specific electronic recommendations presented on a display screen of the retail computing device.

[0033] In addition, 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 Apple's iOS and / or Google's Android operating system. Any of the 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 codes (e.g., mobile applications), as described in various aspects of this document. Figure 1 As shown, the diaper changing area skin model 108 may also be stored locally on the memory of the user computing device (eg, user computing device 111c1).

[0034] The user computing devices 111c1-111c3 and / or 112c1-112c3 may include wireless transceivers to transmit wireless communications 121 and / or 122 to and receive wireless communications from base stations 111b and / or 112b. The pixel-based images 202a, 202b, and / or 202c may be transmitted to the imaging server 102 via the computer network 120 for training of models and / or imaging analysis as described herein.

[0035] In addition, one or more user computing devices 111c1-111c3 and / or 112c1-112c3 may include a digital camera and / or digital video camera for capturing or shooting digital images and / or frames (e.g., which may be any one or more of images 202a, 202b, and / or 202c). 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, a digital camera and / or digital video camera (e.g., of any one of user computing devices 111c1-111c3 and / or 112c1-112c3) may be configured to shoot, capture, or otherwise generate digital images (e.g., pixel-based images 202a, 202b, and / or 202c), and, at least in some embodiments, such images may be stored in a memory of the corresponding user computing device. In some examples, the digital camera and / or digital video camera of one or more user computer devices 111c1-111c3 may be a front-facing camera (e.g., a "selfie camera") illuminated by a front-facing lighting assembly (e.g., a "selfie light"). Additionally, in some examples, the digital camera and / or digital video camera of one or more user computer devices 111c1-111c3 may be a three-dimensional camera or may be otherwise configured to capture three-dimensional images.

[0036] Further, each of the one or more user computer devices 111c1-111c3 and / or 112c1-112c3 may include a display screen for displaying graphics, images, text, product recommendations, data, pixels, features, and / or other such visualizations or information as described herein. In various embodiments, graphics, images, text, product recommendations, data, pixels, features, and / or other such visualizations or information may be received by the imaging server 102 for display on a display screen of any one or more of the user computer devices 111c1-111c3 and / or 112c1-112c3. Additionally or alternatively, the user computer device may include, implement, access, present, or otherwise at least partially expose an interface or guide user interface (GUI) for displaying text and / or images on its display screen.

[0037] Figure 2A-2C Exemplary images 202a, 202b, and 202c are shown that may be collected or aggregated at one or more imaging servers 102 and may be analyzed and / or used for training by a diaper changing area skin model (e.g., an AI model, such as a machine learning imaging model as described herein). Each of these images may include pixel data (e.g., RGB data) representing feature data and corresponding to each of the personal attributes of the respective individuals 202au, 202bu, and 202cu within the respective images. The pixel data may be captured by a digital camera of one of the user computing devices (e.g., one or more user computer devices 111c1-111c3 and / or 112c1-112c3).

[0038] In general, as described herein, pixel data (e.g., pixel data 202ap, 202bp, and / or 202cp) includes a single point or square of data within an image, wherein each point or square represents a single pixel within the image (e.g., pixel 202ap1 and pixel 202ap2). Each pixel may be located at a specific position within the image. In addition, each pixel may have a specific size and a specific color (or lack thereof). Pixel data may be obtained in color or black and white format. Pixel color may be determined by the color format and related channel data associated with a given pixel. For example, a popular color format includes a red-green-blue (RGB) format with red, green, and blue channels. That is, in an RGB format, the data of a pixel is represented by three digital RGB components (red, green, blue), which may be referred to as channel data, to manipulate the color of a pixel region within an image. In some embodiments, the three RGB components may be represented as three 8-bit numbers for each pixel. Three 8-bit bytes (one byte for each RGB in RGB) may be used to generate 24-bit color. Each 8-bit RGB component can have 256 possible values ​​ranging from 0 to 255 (i.e., in a base 2 binary system, an 8-bit byte can contain one of 256 digital values ​​in the range of 0 to 255). The channel data (R, G, and B) can be assigned values ​​from 0 and 255 and can be used to set the color of a pixel. For example, three values ​​such as (250, 165, 0) (meaning (red=250, green=165, blue=0)) can represent an orange pixel. As another example, (red=255, green=255, blue=0) means fully saturated (255 is as bright as 8 bits can be) red and green, with no blue (zero), where the resulting color is yellow. As yet another example, the color black has RGB values ​​(red=0, green=0, blue=0) and white has RGB values ​​(red=255, green=255, blue=255), and gray has the characteristic of equal or similar RGB values. Therefore, (red=220, green=220, blue=220) is light gray (approximately white), and (red=40, green=40, blue=40) is dark gray (approximately black).

[0039] In this way, the composite of the three RGB values ​​produces the final color for a given pixel. For a 24-bit RGB color image using 3 bytes, there may be 256 shades of red and 256 shades of green and 256 shades of blue. This provides 256×256×256, i.e. 16.7 million possible combinations or colors, for a 24-bit RGB color image. In this way, the RGB data value of a pixel shows how much of each of red, green and blue constitutes the pixel. The three colors and intensity levels are combined at the image pixel, i.e. at the pixel position on the display screen, to illuminate the display screen with the color at that position. However, it should be understood that other bit sizes with fewer or more bits, such as 10 bits, can be used to produce fewer or more overall colors and ranges.

[0040] As a whole, the individual pixels positioned together in a grid pattern form a digital image (e.g., pixel data 202ap, 202bp, and / or 202cp). A single digital image may include thousands or millions of pixels. Images can be captured, generated, stored, and / or transmitted in a variety of formats, such as JPEG, TIFF, PNG, and GIF. These formats use pixels to store and represent images.

[0041] Figure 2A An exemplary image 202a and its associated pixel data (e.g., pixel data 202ap) that can be used to train and / or implement a diaper changing area skin model (e.g., diaper changing area skin model 108) according to various embodiments disclosed herein are shown. The exemplary image 202a shows an individual 202au. The image 202a is composed of pixel data including pixel data 202ap. The pixel data 202ap includes a plurality of pixels, including pixel 202ap1 and pixel 202ap2. Pixel 202ap1 is a red-toned pixel located in image 202a, which includes an area of ​​the diaper changing area of ​​the individual in which the individual 202au is experiencing a skin condition. Pixel 202ap2 is a pixel located in image 202a, which includes an area of ​​the diaper changing area of ​​the individual 202au without any skin condition. The pixel data 202ap includes various remaining pixels, which include portions of the diaper changing area of ​​the individual 202au with or without a skin condition, as well as portions of the diaper changing area of ​​the individual 202au with or without a skin condition. Figure 2A Other features shown.

[0042] Figure 2BAn exemplary image 202b and its associated pixel data (e.g., pixel data 202bp) that can be used to train and / or implement a diaper changing area skin model (e.g., diaper changing area skin model 108) according to various embodiments disclosed herein are shown. The exemplary image 202b shows an individual 202bu. The image 202b is composed of pixel data including pixel data 202bp. The pixel data 202bp includes a plurality of pixels, including pixel 202bp 1 and pixel 202bp2. Pixel 202bp 1 is a red-toned pixel located in image 202b, which includes an area of ​​the diaper changing area of ​​the individual in which the individual 202bu is experiencing a skin condition. Pixel 202bp2 is a pixel located in image 202b, which includes an area of ​​the diaper changing area of ​​the individual 202bu without any skin condition. The pixel data 202bp includes various remaining pixels, which include portions of the diaper changing area of ​​the individual 202bu with or without a skin condition, as well as pixels such as Figure 2B Other features shown.

[0043] Figure 2C An exemplary image 202c and its associated pixel data (e.g., pixel data 202cp) that can be used to train and / or implement a diaper changing area skin model (e.g., diaper changing area skin model 108) according to various embodiments disclosed herein are shown. The exemplary image 202c shows an individual 202cu. The image 202c is composed of pixel data including pixel data 202cp. The pixel data 202cp includes a plurality of pixels, including pixel 202cp 1 and pixel 202cp2. Pixel 202cp 1 is a red-toned pixel located in image 202c, which is in an area of ​​the diaper changing area of ​​the individual in which individual 202cu is experiencing a skin condition. Pixel 202cp2 is a pixel located in image 202c, which includes an area of ​​the diaper changing area of ​​individual 202cu who does not have a skin condition. The pixel data 202cp includes various remaining pixels, which include portions of the diaper changing area of ​​individual 202cu with or without a skin condition, as well as pixels such as Figure 2C Other features shown.

[0044] Figure 3A diagram of a digital imaging method 300 for analyzing pixel data of an image of a diapering area of ​​an individual (e.g., images 202a, 202b, and / or 202c) to determine diapering area skin characteristics according to various embodiments disclosed herein is shown. The method 300 may include an algorithm that implements computing instructions to perform the blocks shown and described for the method 300. In various aspects, for example, the method 300 may be implemented or otherwise performed by a skin analyzer imaging application that includes computing instructions configured to be implemented on an operating system of an underlying computing device. In various aspects, the skin analyzer imaging application may include a diapering area skin model 108, or may be additionally configured to access the diapering area skin model. Similar to the diapering area skin model 108, the skin analyzer imaging application may be stored on the memory of one or more servers 102 and / or one or more user computing devices. As described herein, the image is typically a digital image captured by a digital camera (e.g., a digital camera of the user computing device 111c1). In some embodiments, an image may include or refer to multiple images, such as multiple images (eg, frames), collected using a digital camera. Frames constitute sequential images that define motion and may constitute a movie, video, or the like.

[0045] At box 302, method 300 includes: at one or more processors communicatively coupled to one or more memories (e.g., one or more imaging servers 102 and / or user computing devices (such as user computing device 111c1)), aggregating multiple training images of one or more diaper changing areas associated with a corresponding plurality of individuals, each of the training images including pixel data of a diaper changing area associated with the corresponding individual. In some examples, the training images include indications of the pixel data of the multiple training images corresponding to areas or items that are not part of the diaper changing area of ​​the individual, such as one or more of the following: a diaper changing pad, a diaper changing area, and / or surrounding elements of the diaper changing area, or a part of a caregiver's body (such as a caregiver's hand or arm). For example, these areas or items that are not part of the diaper changing area of ​​the individual may be marked or otherwise identified as such, and in some examples, may be segmented out of (or otherwise removed from) the multiple training images before proceeding to box 304. In some examples, each of the training images may be captured within a threshold time frame of a diaper change for each respective individual (e.g., within 10 minutes of a diaper change, within 30 minutes of a diaper change, within an hour of a diaper change, etc.) For example, in some examples, the one or more processors include at least one of a server or a cloud-based computing platform, and the server or the cloud-based computing platform receives the plurality of training images of the plurality of individuals via a computer network.

[0046] At block 304, method 300 includes training, by one or more processors (e.g., one or more processors 102), a diapering area skin model (e.g., diapering area skin model 108) with pixel data of a plurality of training images, the diapering area skin model being operable to determine diapering area skin characteristics associated with the diapering area based on the pixel data of the diapering area. In various aspects, the diapering area skin model is an artificial intelligence (AI) based model trained with at least one AI algorithm.

[0047] In some examples, the diapering skin characteristic may be a scale value. The scale may be an internalized scale unique to the diapering area skin model or an otherwise custom scale, where a minimum or small scale value may be determined from an image or set of images having a skin area where the pixel data indicates a lower number of skin conditions and / or a lower level of severity for any skin condition. Similarly, a maximum or large scale value may be determined from an image or set of images having a diapering area area or portion where the pixel data indicates that the individual's skin has a greater number of skin conditions and / or a higher level of severity for any skin condition.

[0048] In some embodiments, the diapering skin characteristic can be a percentage scale value, for example, outputting a percentage scale value from 0% to 100%, where 0% represents the least skin condition or the least severe skin condition and 100% represents the most skin condition or the most severe skin condition. The values ​​can vary within this scale range, where a percentage scale value of 67% represents one or more pixels of a skin area detected within an image having a greater number of skin conditions and / or a more severe skin condition than a percentage scale value of 10% detected for one or more pixels of skin within the same image or a different image (of the same or different individual).

[0049] In some embodiments, the diapering skin characteristics can be based on a numerical or decimal scale, such as an output value, such as from 0 to 10, where 0 represents the least skin condition and / or the least severe skin condition, and 10 represents the most skin condition and / or the most severe skin condition. The values ​​can vary within this scale, where a value of 7 represents one or more pixels of a skin area detected within an image that has a greater number of skin conditions and / or a more severe skin condition than, for example, a value of 2 detected for one or more pixels of skin within the same image or a different image (of the same or different individuals). Different areas within a diapering area can have or be associated with different characteristics and can be assigned values ​​using different scales, for example, a first diapering area can have a lower score, while a second diapering area can have a higher score.

[0050] The diapering area skin characteristics may be determined at a pixel level or for a given diapering area region or portion (e.g., one or more pixels) in an image. The diapering area skin characteristics may be calculated and / or reported as a percentage of the skin condition per body region location (anatomical region). For example, the waist total area may be 10,000 pixels, and the rash area may be 4,000 pixels within the 10,000 pixels of the waist region. Thus, the skin condition coverage area is 40% of the waist body region location.

[0051] In some embodiments, global diapering area skin properties (which may be individual-specific diapering area skin properties as described herein) may be determined by averaging (or otherwise statistically analyzing) the diapering area skin properties for one or more pixels of a given diapering area region or portion.

[0052] In some examples, the server or cloud-based computing platform discussed above with respect to block 302 trains the diaper changing area skin model with pixel data from a plurality of training images.

[0053] The training of the diaper changing area skin model 108 involves image analysis of training images used to configure the weights of the diaper changing area skin model, and its underlying algorithm (e.g., a machine learning or artificial intelligence algorithm) for predicting and / or classifying future images. For example, in various embodiments herein, the generation of the diaper changing area skin model 108 involves training the diaper changing area skin model 108 with multiple training images of multiple individuals, wherein each of the training images includes pixel data of the diaper changing area of ​​the individual. In some embodiments, the pixel data of the diaper changing area associated with the corresponding multiple individuals may include pixel data of the diaper changing area associated with the corresponding multiple individuals from multiple viewpoints and / or multiple angles. In addition, in some embodiments, personal identification information associated with the multiple individuals may be removed from the pixel data of the diaper changing area associated with the corresponding multiple individuals used to train the diaper changing area skin model 108, or otherwise not depicted therein.

[0054] In some embodiments, one or more processors of a server or cloud-based computing platform (e.g., imaging server 102) may receive a plurality of training images of a plurality of individuals via a computer network (e.g., computer network 120). In such embodiments, the server and / or cloud-based computing platform may train diaper changing area skin model 108 with pixel data of the plurality of training images.

[0055] For example, in various embodiments, supervised or unsupervised machine learning programs or algorithms can be used to train machine learning imaging models as described herein (e.g., diaper changing area skin model 108). The machine learning program or algorithm can use a neural network, which can be a convolutional neural network, a deep learning neural network, or a combined learning module or program that learns two or more features or feature data sets (e.g., pixel data) in a specific region of interest. The machine learning program or algorithm may also include deep learning neural networks, natural language processing, semantic analysis, automatic reasoning, regression analysis, support vector machine (SVM) analysis, decision tree analysis, random forest analysis, K nearest neighbor analysis, naive initial Bayes analysis, clustering, reinforcement learning and / or other machine learning algorithms and / or techniques. In some embodiments, algorithms based on artificial intelligence and / or machine learning may be included as libraries or groups executed on one or more servers 102. For example, the library may include a library based on TENSORFLOW, a PYTORCH library, and / or a SCIKIT-LEARN Python library.

[0056] Machine learning can involve identifying and recognizing patterns in existing data (such as training a model based on pixel data within an image with pixel data of a corresponding individual's body) to facilitate prediction or identification of subsequent data (such as using a model on new pixel data of a new individual's diaper changing area to determine individual-specific diaper changing area skin characteristics).

[0057] One or more machine learning models (such as the diaper changing area skin model described herein for some embodiments) may be created and trained based on exemplary data (e.g., "training data" and related pixel data) inputs or data (which may be referred to as "features" and "labels") to make effective and reliable predictions for new inputs (such as test-level or production-level data or inputs). In supervised machine learning, a machine learning program operating on a server, computing device, or another processor may be provided with exemplary inputs (e.g., "features") and their associated or observed outputs (e.g., "labels") to enable the machine learning program or algorithm to determine or discover rules, relationships, patterns, or other machine learning "models" that map such inputs (e.g., "features") to outputs (e.g., "labels"), such as by determining weights or other metrics across various feature categories and / or assigning weights or other metrics to the model. Such rules, relationships, or other models may then be provided as subsequent inputs to enable the model executing on the server, computing device, or another processor to predict the expected output based on the discovered rules, relationships, or models.

[0058] In unsupervised machine learning, a server, computing device, or another processor may be required to find its own structure in unlabeled exemplary inputs, where, for example, multiple training iterations are performed by the server, computing device, or another processor to train multiple model generations until a satisfactory model is generated, such as a model that provides sufficient predictive accuracy when given test-level or production-level data or inputs. The disclosure herein may use one or both of such supervised or unsupervised machine learning techniques.

[0059] The image analysis may include training a machine learning-based model (e.g., diaper changing area skin model 108) on pixel data of images of one or more individuals' bodies (i.e., images of the diaper changing area of ​​the individuals). Additionally or alternatively, the image analysis may include using a machine learning imaging model, such as previously trained, to determine individual-specific diaper changing area skin characteristics based on pixel data (e.g., including their RGB values) of one or more images of the diaper changing area of ​​the one or more individuals.

[0060] For example, the weights of the model may be trained based on the size and / or color of each of the individual pixels of a given image. For example, size-based training may include analysis of the size associated with each of the individual pixels of a given image. In some examples, a particular dimension of one or more pixels of a given image, or a different dimension of one or more pixels of a given image compared to other pixels in the same image corresponding to the same body part, may indicate that a portion of the diaper changing area of ​​an individual in the image is convex (indicating, for example, swelling or the presence of inflammation, acne, pustules, papules, etc.) or concave (indicating, for example, skin depressions and / or indentations), which may indicate the presence of certain skin conditions or problems in a portion of the diaper changing area of ​​an individual and / or quantify the number of such skin conditions. Color-based training may include analysis of various RGB values ​​of individual pixels of a given image. For example, a red-toned RGB value (e.g., a pixel with values ​​R=215, G=90, B=85) may indicate areas of the diaper changing area of ​​an individual with various skin conditions, as well as the number or severity of any skin conditions of the diaper changing area of ​​an individual. Lighter RGB values ​​(e.g., pixels with R=181, G=170, and B=191) can indicate lighter values, such as skin tones, i.e., areas of the diaper changing area of ​​an individual that are free of skin conditions or have fewer or less severe skin conditions. In some embodiments, the weights of the model can be trained based on different skin tones and different skin types, such as the Fitzpatrick skin type.

[0061] When a large number of pixels having a particular dimension and / or a large number of pixels having red-tinted RGB values ​​are located within a given image of a region of an individual's diaper changing area, this may indicate that the individual is experiencing a large number of skin conditions or a very severe skin condition in the region of the individual's diaper changing area shown in the image. In this way, pixel data for 10,000 training images (e.g., detailing one or more features of an individual, such as the individual's diaper changing area) may be used to train or determine individual-specific diaper changing area skin characteristics using a machine learning imaging model.

[0062] In various embodiments, the diapering area skin model (e.g., diapering area skin model 108) may be further trained by one or more processors (e.g., one or more imaging servers 102 and / or user computing devices (such as user computing device 111c1)) using pixel data of multiple training images to output one or more location identifiers indicating one or more corresponding body region locations of the respective individuals. In such embodiments, the diapering area skin model (e.g., diapering area skin model 108) executed on one or more processors (e.g., one or more imaging servers 102 and / or user computing devices (such as user computing device 111c1)) and analyzing at least one image of the individual may determine a location identifier indicating a body region location of the individual's skin. For example, the body region location within the diapering area may include the individual's perianal area, the individual's buttocks, the individual's vulva, the individual's hips, the individual's thighs, and / or the individual's waist.

[0063] refer to Figure 3 At box 306, method 300 includes: receiving at least one image of an individual at one or more processors (e.g., one or more imaging servers 102 and / or user computing devices (such as user computing device 111c 1)), the at least one image captured by a digital camera, and the at least one image includes pixel data of a diaper changing area associated with the individual. In some examples, each of the training images can be captured within a threshold time frame of a diaper change for each respective individual (e.g., within 10 minutes of removing a diaper, within 30 minutes of changing a diaper, within an hour of changing a diaper, etc.). In some examples, the digital camera is a digital video camera, and the at least one image is a plurality of images. In some examples, a server or cloud-based computing platform discussed above with respect to box 302 receives the at least one image. In addition, in some examples, a user computing device (e.g., a mobile device, a tablet computer, a handheld device, a desktop device, a personal assistant device, a home assistant device, smart / VR glasses, etc.) receives the at least one image.

[0064] At block 308, method 300 includes analyzing, by a diapering area skin model (e.g., diapering area skin model 108) executed on one or more processors (e.g., one or more imaging servers 102 and / or user computing devices (such as user computing device 111c1)), at least one image captured by a digital camera to determine individual-specific diapering area skin characteristics (e.g., based on the number or severity level of skin conditions). In some examples, the server or cloud-based computing platform discussed above with respect to block 302 analyzes at least one image to determine individual-specific diapering area skin characteristics. Further, in some examples, the user computing device discussed above with respect to block 306 analyzes at least one image to determine individual-specific diapering area skin characteristics. Additionally, in some examples, the diapering area skin model 108 may further analyze at least one image captured by the digital camera to determine a location identifier indicating a location within the diapering area, such as a specific area or portion of the individual's skin (e.g., one of the individual's perianal area, the individual's buttocks, the individual's vulva, the individual's hip, the individual's thigh, or the individual's waist, or a portion of one of these areas, etc.).

[0065] At block 310, method 300 includes generating, by one or more processors (e.g., one or more imaging servers 102 and / or user computing devices (such as user computing device 111c 1)) at least one individual-specific recommendation based on the individual-specific diaper changing area skin characteristics, the at least one individual-specific recommendation being designed to address at least one feature identifiable within pixel data of the individual's skin. Further, in some examples, the server or cloud-based computing platform discussed above with respect to block 302 generates the at least one individual-specific recommendation and transmits the individual-specific recommendation to the user computing device for presentation on a display screen of the user computing device. Further, in some examples, the user computing device discussed above with respect to block 306 generates the at least one individual-specific recommendation.

[0066] For example, an individual-specific recommendation may include at least one product recommendation for a manufactured product designed to address at least one feature identifiable within pixel data of an individual's skin. As another example, an individual-specific recommendation may include, for example, a recommended behavior selected from two or more available behaviors based on diaper changing area skin characteristics. Further, in some examples, an individual-specific recommendation may include, for example, a recommendation to use a mobile device application, a recommendation to practice one or more procedures associated with a diaper changing area, a recommendation to seek or receive medical services related to a diaper changing area, and / or a recommendation for lifestyle changes related to a diaper changing area. Additionally, in some examples, an individual-specific recommendation may include a recommendation for a visit with a medical service provider.

[0067] At block 312, method 300 includes providing at least one individual-specific recommendation via a user interface of a user computing device. The user computing device may include at least one of a mobile device, a tablet computer, a handheld device, a desktop device, a personal assistant device, or a home assistant device, for example, as described herein with respect to Figure 1 In some embodiments, at least one individual-specific recommendation may be provided as a text recommendation via a user interface of a user computing device. In addition, in some embodiments, at least one individual-specific recommendation may be provided as an audio recommendation via a user interface of a user computing device (e.g., via a speaker of the user computing device). Additionally, in the case where at least one individual-specific recommendation includes a recommendation for a visit with a healthcare provider, providing the individual-specific recommendation may include initiating a live chat, a phone call, a text message, an AI-enabled communication / interaction, or a video chat with the healthcare provider via the user interface of the user computing device.

[0068] In some embodiments, a user computing device (e.g., user computing device 111c1) may receive at least one image including pixel data of the skin of an individual. In such embodiments, the user computing device may locally execute a diapering area skin model (e.g., diapering area skin model 108) and generate individual-specific recommendations based on the output of the diapering area skin model (e.g., diapering area skin model 108). The user computing device 111c1 may then provide the individual-specific recommendations by presenting the individual-specific recommendations on its display screen.

[0069] Additionally or alternatively, in other embodiments, one or more imaging servers 102 may remotely analyze individual images from a user computing device to determine individual-specific diapering area skin characteristics and / or individual-specific electronic recommendations designed to address at least one feature identifiable within pixel data comprising the individual's skin. For example, in such embodiments, an imaging server or cloud-based computing platform (e.g., one or more imaging servers 102) receives at least one image across a computer network 120, the at least one image comprising pixel data of the individual's skin. The server or cloud-based computing platform may then execute a diapering area skin model (e.g., diapering area skin model 108) and generate individual-specific recommendations based on the output of the diapering area skin model (e.g., diapering area skin model 108). The server or cloud-based computing platform may then transmit the individual-specific recommendations to the user computing device via a computer network (e.g., computer network 120) for presentation on a display screen of the user computing device.

[0070] In some embodiments, a user may submit a new image to the diapering area skin model for analysis as described herein. In such embodiments, one or more processors (e.g., one or more imaging servers 102 and / or user computing devices (such as user computing device 111c1)) may receive a new image of an individual. The new image may be captured by a digital camera of user computing device 111c1. The new image may include pixel data of at least a portion of the individual's skin. The diapering area skin model (e.g., diapering area skin model 108) may then analyze the new image captured by the digital camera on one or more processors (e.g., one or more imaging servers 102 and / or user computing devices (such as user computing device 111c1)) to determine the new individual-specific diapering area skin characteristics. In some examples, one or more processors (e.g., one or more imaging servers 102 and / or user computing devices (such as user computing device 111c1)) may generate incremental diapering area skin characteristics based on a comparison between the new individual-specific diapering area skin characteristics and the (initial or previous) diapering area skin characteristics. Based on the new individual-specific diapering area skin characteristic or based on the incremental diapering area skin characteristic, a new individual-specific electronic recommendation or comment about at least one feature identifiable within the pixel data of the new image may be generated. The new individual-specific recommendation or comment (e.g., a message) may then be presented on a display screen of a user computing device of the user.

[0071] In some embodiments, the individual-specific electronic recommendation may be displayed on a display screen of a user computing device (e.g., user computing device 111c 1) with a graphical representation of the individual's skin annotated with one or more graphical or textual representations corresponding to the individual's specific diapering zone skin characteristics. In other embodiments, at least one individual-specific electronic recommendation may be presented in real time or near real time during or after a diapering session.

[0072] In additional embodiments, the individual-specific electronic recommendation may include a product recommendation for a manufactured product. In such embodiments, the individual-specific electronic recommendation may be displayed on a display screen of a user computing device (e.g., user computing device 111c1) along with instructions (e.g., a message) for processing at least one feature identifiable within pixel data including at least a portion of the individual's skin with the manufactured product. In further embodiments, either the user computing device 111c1 and / or the imaging server may initiate shipping of the manufactured product to the user based on the product recommendation.

[0073] Furthermore, in some embodiments, a modified image predicting how the individual's skin will behave after treating the at least one feature with the manufactured product may be generated and presented on a display screen of the user computing device 111c1 (e.g., along with an electronic recommendation for the manufactured product). Additionally, in some embodiments, a modified image depicting instructions on how to use the manufactured product to treat the at least one feature may be generated and presented on a display screen of the user computing device 111c1 (e.g., along with an electronic recommendation for the manufactured product). Furthermore, in some embodiments, a modified image depicting instructions on how to treat the at least one feature by applying a diaper to the individual's diaper changing area in a particular manner may be generated and presented on a display screen of the user computing device 111c1 (e.g., along with an electronic recommendation for the manufactured product). For example, one or more of the modified images may be presented in an augmented reality (AR) format.

[0074] Figure 4 4 shows an exemplary user interface 402 presented on a display screen 400 of a user computing device 111c1 according to various embodiments disclosed herein. Figure 4 As shown in the example of , the user interface 402 may be implemented or presented via an application program (app) executed on the user computing device 111c 1.

[0075] For example, as in Figure 4 As shown in the example of , the user interface 402 may be implemented or presented via a native application executing on the user computing device 111c 1. Figure 4 In the example of Figure 1 The user computing device, for example, 111c1 is shown as an APPLE iPhone implementing the APPLE iOS operating system and having a display screen 400. The user computing device 111c1 can execute one or more native applications (apps) on its operating system. Such native applications can be implemented or encoded (for example, as computing instructions) in a computing language (for example, SWIFT) executed by the user computing device operating system (for example, APPLE iOS) through the processor of the user computing device 111c1.

[0076] Additionally or alternatively, the user interface 402 may be implemented or presented via a web interface, such as via a web browser application, for example, a Safari and / or Google Chrome application, or other such web browsers, or the like.

[0077] like Figure 4As shown in the example of FIG. 4 , user interface 402 includes a graphical representation of an individual's skin (e.g., image 202a). Image 202a may be at least one image of an individual's skin (or a graphical representation thereof) and as analyzed by a diapering area skin model (e.g., diapering area skin model 108) as described herein. Figure 4 In an example of , a graphical representation of an individual's skin (e.g., image 202a) is annotated with one or more graphics (e.g., areas of pixel data 202ap) or textual presentations corresponding to the amount or severity of skin conditions experienced by the individual. For example, areas of pixel data 202ap may be annotated or overlaid on top of an image of the individual (e.g., image 202a) to highlight areas or one or more features identified within the pixel data (e.g., feature data and / or raw pixel data) by a diapering area skin model (e.g., diapering area skin model 108). Figure 4 In the example of FIG. 1 , the region of pixel data 202ap and the one or more features identified within the pixel data 202ap include the number of skin conditions, the severity level of any skin conditions, and other features shown in the region of pixel data 202ap. In various embodiments, pixels identified as being indicative of a particular feature of a skin condition (e.g., pixel 202ap 1 being a red-toned pixel indicative of a skin condition) may be highlighted or otherwise annotated when presented.

[0078] In some examples, the textual presentation may show an individual-specific diapering area skin characteristic (e.g., a diapering area value of 8 / 10) that shows that the individual has a higher number and / or severity of skin conditions than the average of a typical individual (e.g., where 10 corresponds to the maximum number and / or severity of skin conditions, 1 corresponds to the minimum number and / or severity of skin conditions, and 5 corresponds to the average number and / or severity of skin conditions). In particular, the value 8 / 10 indicates a higher rate of skin conditions and / or more severe skin conditions compared to other individuals. It should be understood that other textual presentation types or values ​​are contemplated herein, where the textual presentation type or value can be presented as, for example, a range of 1 to 100, such as a letter grade, or some other indication of a diapering area skin characteristic. Additionally or alternatively, a color value may be used and / or overlaid on a graphical representation shown on the user interface 402 (e.g., image 202a) to indicate a skin condition.

[0079] In some examples, in addition to or as an alternative to providing a single severity score, the user interface may provide values ​​of varying intensity found within the skin condition area, e.g., 15% of the skin condition area may be scored as 3 / 10 (less severe), 2.4% of the skin condition area may be scored as 5 / 10 (moderate severity), and 1.6% of the skin condition area may be scored as 9 / 10 (strong severity). This breakdown of severity scores may help a user or caregiver determine the actual severity of a skin condition, as well as track the progression of the skin condition and its treatment over time.

[0080] The user interface 402 may also include or present an individual specific electronic recommendation 412. Figure 4 In an embodiment of the present invention, the individual-specific electronic recommendation 412 includes a message 412m to the user that is designed to address at least one feature identifiable within pixel data that includes a portion of the individual's skin. Figure 4 As shown in the example of , message 412m recommends that the user switch to a different type of diapers and wipes to reduce the individual's diaper rash. The product recommendation may be associated with an identified feature within the pixel data, and when a feature (e.g., a large number of skin conditions, a very severe skin condition, etc.) is identified, the user computing device 111c 1 and / or one or more servers 102 may be instructed to output a product recommendation. In one example, the pixel data may correspond to one or more identified features associated with skin depressions, and the user computing device and / or server may be instructed to output a product recommendation for a larger size diaper or article. The electronic recommendation may be a recommendation for a product, habit, or practice, such as the introduction or exclusion of certain types of foods or the maintenance of a specific fixed practice, professional consultation, and / or community support.

[0081] The user interface 402 may also include or present a portion of product recommendations 422 for manufactured products 424r (e.g., diapers, wipes, creams or lotions, baby powder, etc.). The product recommendations 422 generally correspond to the individual specific electronic recommendations 412, as described above. Figure 4 In the example of , an individual-specific electronic recommendation 412 is displayed on the display screen 400 of the user computing device 111c1 along with instructions (e.g., message 412m) for treating at least one characteristic (e.g., the number of skin conditions, the severity level of any skin conditions, etc., as shown at pixel 202ap 1) that can be identified from pixel data (e.g., pixel data 202ap) that includes the individual's skin with a manufactured product (manufactured product 424r, e.g., a specific diaper, a specific wipe, a specific lotion or cream, a specific baby powder, etc.).

[0082] like Figure 4As shown, the user interface 402 recommends products based on the individual specific electronic recommendations 412 (e.g., manufactured products 424r, such as specific diapers, specific wipes, specific lotions or creams, specific baby powders, etc.). Figure 4 In the example of FIG. 1 , output or analysis of one or more images (e.g., image 202a) of the diapering area skin model 108 (e.g., individual-specific electronic recommendations 412 and / or their associated values ​​(e.g., diapering area skin characteristic values ​​8 / 10) or associated pixel data (e.g., 202ap 1 and / or 202ap2)) may be used to generate or identify recommendations for one or more corresponding products. Such recommendations may include products such as specific diapers, specific wipes, specific lotions or creams, specific baby powders, etc., to address individual-specific issues detected by the diapering area skin model (e.g., diapering area skin model 108) within the pixel data.

[0083] exist Figure 4 In the example of , the user interface 402 presents or provides recommended products (e.g., manufactured product 424r) as determined by the diapering area skin model (e.g., diapering area skin model 108) and its associated image analysis of the image 202a and its pixel data and various features. Figure 4 In the example of , this is indicated and annotated on the user interface 402 ( 424p ).

[0084] The user interface 402 may also include a selectable UI button 424s to allow a user (e.g., a caregiver or guardian of the individual of the image 202a) to select to purchase or ship a corresponding product (e.g., manufactured product 424r). In some embodiments, selecting the selectable UI button 424s may cause one or more recommended products to be shipped to the user and / or may notify a third party that the individual is interested in one or more products. For example, the user computing device 111c 1 and / or any of the one or more imaging servers 102 may initiate shipping of a manufactured product 424r (e.g., a specific diaper, a specific wipe, a specific lotion or cream, a specific baby powder, etc.) to the user based on the individual-specific electronic recommendation 412. In such embodiments, the product may be packaged and shipped to the user.

[0085] In various embodiments, a graphical representation (e.g., image 202a) with graphical annotations (e.g., areas of pixel data 202ap), textual annotations (e.g., text 202at), and / or individual-specific electronic recommendations 412 may be transmitted to the user computing device 111c 1 via a computer network (e.g., from the imaging server 102 and / or one or more processors) for presentation on the display screen 400. In other embodiments, no transmission of a user-specific image to the imaging server occurs, wherein the individual-specific recommendations (and / or product-specific recommendations) may instead be generated locally by a diapering area skin model (e.g., diapering area skin model 108) executed and / or implemented on the user's mobile device (e.g., user computing device 111c 1) and presented on the display screen 400 of the mobile device (e.g., user computing device 111c 1) by a processor of the mobile device.

[0086] In some embodiments, any one or more of the graphical representations (e.g., image 202a) with graphical annotations (e.g., areas of pixel data 202ap), textual annotations, individual specific electronic recommendations 412, and / or product recommendations 422 may be presented in real time or near real time during or after a diaper change (e.g., locally on display screen 400). In embodiments where the images are analyzed by imaging server 102, the images may be transmitted and analyzed by imaging server 102 in real time or near real time.

[0087] In some embodiments, a user may provide new images, which may be transmitted to the one or more imaging servers 102 for updating, retraining, or reanalysis by the diaper changing area skin model 108. In other embodiments, the new images may be received locally on the computing device 111c1 and analyzed by the diaper changing area skin model 108 on the computing device 111c1.

[0088] In addition, Figure 4As shown in the example of , the user may select selectable button 412i to re-analyze the new image (e.g., locally at computing device 111c 1 or remotely at one or more imaging servers 102). Selectable button 412i may cause user interface 402 to prompt the user to attach a new image for analysis. One or more imaging servers 102 and / or a user computing device (such as user computing device 111c 1) may receive a second image of the individual. The new image may be captured by a digital camera. The new image (e.g., as image 202a) may include pixel data of the individual's skin. A diapering area skin model (e.g., diapering area skin model 108) executed on a memory of a computing device (e.g., one or more imaging servers 102) may analyze the new image captured by the digital camera to determine new individual-specific diapering area skin characteristics after the individual undergoes another diapering. The computing device (e.g., one or more imaging servers 102) may generate a new individual-specific electronic recommendation or comment about at least one characteristic identifiable within the pixel data of the new image based on the new diapering area skin characteristics. For example, a new individual-specific electronic recommendation may include a new graphical representation including graphics and / or text (eg, showing a new individual-specific diaper changing area skin characteristic, eg, 4 / 10).

[0089] The new individual-specific electronic recommendation may include additional recommendations or additional information for the individual, for example, that the individual has improved his or her diapering area skin characteristics (i.e., reduced the rate or severity of skin conditions) as detected using the pixel data of the new image. In various embodiments, the new individual-specific recommendation or comment may be transmitted to the user computing device of the user via a computer network for presentation on a display screen of the user computing device. In other embodiments, no transmission of the new image of the user to the imaging server occurs, wherein the new individual-specific recommendation (and / or product-specific recommendation) may instead be generated locally by a diapering area skin model (e.g., diapering area skin model 108) executed and / or implemented on the user's mobile device (e.g., user computing device 111c1) and presented by a processor of the mobile device on a display screen of the mobile device (e.g., user computing device 111c1).

[0090] Aspects of the Disclosure

[0091] 1. A digital imaging method for analyzing pixel data of at least one image of a diaper changing area associated with an individual to determine skin characteristics of a diaper changing area specific to the individual, the digital imaging method comprising the following steps: at one or more processors communicatively coupled to one or more memories, a plurality of training images of one or more diaper changing areas associated with a corresponding plurality of individuals are gathered, each of the training images comprising pixel data of a diaper changing area associated with the corresponding individual; the one or more processors are used to train a diaper changing area skin model using the pixel data of the plurality of training images, the diaper changing area skin model being operable to determine skin characteristics of the diaper changing area associated with the diaper changing area based on the pixel data of the diaper changing area; and at the one or more processors, Receiving at least one image of an individual at a processor, the at least one image being captured by a digital camera, and the at least one image including pixel data of the diaper changing area associated with the individual; analyzing the at least one image captured by the digital camera by the diaper changing area skin model executed on the one or more processors to determine individual-specific diaper changing area skin characteristics; generating, by the one or more processors, at least one individual-specific recommendation based on the individual-specific diaper changing area skin characteristics, the at least one individual-specific recommendation being designed to address at least one feature identifiable within the pixel data of the individual's skin; and providing the at least one individual-specific recommendation via a user interface of a user computing device.

[0092] 2. The digital imaging method of aspect 1, wherein the plurality of training images comprises images containing pixel data of diaper changing areas associated with a corresponding plurality of individuals from a plurality of viewpoints.

[0093] 3. A digital imaging method according to any one of aspects 1 or 2, wherein the plurality of training images comprises images containing pixel data of diaper changing areas associated with the respective plurality of individuals without depicting personal identifying information associated with the respective plurality of individuals.

[0094] 4. A digital imaging method according to any of aspects 1-3, wherein at least one individual-specific recommendation is displayed via a user interface of a user computing device together with a graphical representation of the individual's skin, the graphical representation of the individual's skin being annotated with one or more graphical, video, voice and / or text presentations corresponding to the individual's specific diaper changing area skin characteristics.

[0095] 5. The digital imaging method of any of aspects 1-4, wherein the at least one individual-specific recommendation is a textual recommendation displayed via a user interface of the user computing device.

[0096] 6. The digital imaging method of any of clauses 1-5, wherein the at least one individual-specific recommendation is an audio recommendation provided via a user interface of the user computing device.

[0097] 7. A digital imaging method according to any of aspects 1-6, wherein at least one individual-specific recommendation includes one or more of the following: a recommendation to use a mobile device application, a recommendation to practice one or more procedures associated with a diaper changing area, a recommendation to seek or receive medical services related to a diaper changing area, or a recommendation for lifestyle changes related to a diaper changing area.

[0098] 8. The digital imaging method of any one of aspects 1-7, wherein at least one individual-specific recommendation is a recommendation for a visit with a healthcare provider.

[0099] 9. The digital imaging method of aspect 8, further comprising the step of initiating, by one or more processors, a live chat, text message, and / or video with a healthcare provider based on a recommendation for a visit with the healthcare provider.

[0100] 10. The digital imaging method according to any one of aspects 1 to 9, wherein at least one individual specific recommendation is a recommendation for a manufactured product.

[0101] 11. The digital imaging method of aspect 10, further comprising the step of initiating, by the one or more processors, shipping of the manufactured product to an address associated with the individual based on the recommendation of the manufactured product.

[0102] 12. A digital imaging method according to any one of aspects 10 or 11, the digital imaging method also includes: generating a modified image based on at least one image by one or more processors, the modified image depicting how the individual's diaper changing area is predicted to perform after at least one feature is processed with a manufactured product; and presenting the modified image via the user interface of the user computing device.

[0103] 13. The digital imaging method of clause 12, wherein presenting the modified image comprises presenting the modified image in an augmented reality (AR) format.

[0104] 14. A digital imaging method according to aspect 7, wherein at least one individual-specific recommendation is displayed via a user interface of a user computing device along with instructions for treating at least one feature identifiable within pixel data of the individual's skin with a manufactured product.

[0105] 15. A digital imaging method according to any one of aspects 1-14, the digital imaging method also includes: generating a modified image based on at least one image by one or more processors, the modified image depicting instructions on how to process at least one feature with a manufactured product; and presenting the modified image via the user interface of the user computing device.

[0106] 16. A digital imaging method according to any one of aspects 1-15, the digital imaging method also including: generating a modified image based on at least one image by one or more processors, the modified image depicting instructions on how to process at least one feature by applying a diaper to an individual's diaper changing area in a specific manner; and presenting the modified image via the user interface of the user computing device.

[0107] 17. The digital imaging method according to any one of aspects 1 to 16, wherein at least one individual specific recommendation is presented or transmitted for presentation in real time or near real time.

[0108] 18. The digital imaging method according to any one of aspects 1 to 17, wherein the diaper changing area skin model is an artificial intelligence (AI) based model trained with at least one AI algorithm.

[0109] 19. A digital imaging method according to any one of aspects 1-18, wherein the diaper changing area skin model is also trained by one or more processors using pixel data of multiple training images to output one or more location identifiers indicating the location of one or more corresponding body areas of the corresponding individual, and wherein the diaper changing area skin model is executed on one or more processors and analyzes at least one image of the individual to determine the location identifier indicating the body area location of the individual's skin.

[0110] 20. The digital imaging method of any of aspects 1-19, wherein the diaper changing area comprises at least one of: the individual's perianal area, the individual's buttocks, the individual's vulva, the individual's hips, the individual's thighs, or the individual's waist when the individual is wearing a diaper.

[0111] 21. A digital imaging method according to any one of aspects 1-20, wherein the diaper changing area includes at least one of the following: the individual's anal area, the individual's buttocks, the individual's vulva, the individual's hips, the individual's thighs, or the individual's waist when the individual is not wearing a diaper.

[0112] 22. A method of digital imaging according to any one of clauses 1 to 21, wherein training the diaper changing area skin model comprises training the diaper changing area skin model using an indication of pixel data of a plurality of training images corresponding to one or more regions of interest.

[0113] 23. A digital imaging method according to any of aspects 1-22, wherein training the diaper changing area skin model includes training the diaper changing area skin model using indications of pixel data of multiple training images corresponding to one or more of the following: a diaper changing pad, a diaper changing area and / or surrounding elements of the diaper changing area.

[0114] 24. The digital imaging method of any of clauses 1-23, wherein training the diaper changing area skin model comprises training the diaper changing area skin model using indications of sizes associated with pixel data of a plurality of training images.

[0115] 25. The digital imaging method of any of clauses 1-24, wherein training the diapering area skin model comprises training the diapering area skin model using an indication of a comparison between two or more body regions associated with pixel data of a plurality of training images.

[0116] 26. The digital imaging method of any of clauses 1-25, wherein training the diapering area skin model comprises training the diapering area skin model using an indication of a comparison between two or more body regions associated with pixel data of a plurality of training images.

[0117] 27. A digital imaging method according to any of aspects 1-26, wherein training the diaper area skin model comprises training the diaper area skin model using indications of the location of at least one feature identifiable within pixel data of the individual's skin in a plurality of training images.

[0118] 28. The digital imaging method of any of clauses 1-27, wherein training the diaper changing area skin model comprises training the diaper changing area skin model using indications of colors associated with pixel data of a plurality of training images.

[0119] 29. A digital imaging method according to aspect 28, wherein training the diapering area skin model with indications of colors associated with pixel data of multiple training images includes training the diapering area skin model with indications of one or more of the following: colors associated with one or more diapering area skin characteristics or colors associated with one or more diapering area skin types.

[0120] 30. The digital imaging method of clause 29, wherein the one or more diaper changing area skin types comprises a Fitzpatrick skin type.

[0121] 31. The digital imaging method of any one of aspects 1-30, wherein receiving at least one image captured by a digital camera comprises receiving at least one image captured by a digital camera and illuminated by a front lighting assembly associated with the digital camera.

[0122] 32. A digital imaging method according to any of aspects 1-31, wherein the one or more processors include at least one of a server or a cloud-based computing platform, and the server or the cloud-based computing platform receives multiple training images of multiple individuals via a computer network, and wherein the server or the cloud-based computing platform trains the diaper changing area skin model using pixel data from the multiple training images.

[0123] 33. A digital imaging method according to any of aspects 1-32, wherein a server or a cloud-based computing platform receives at least one image including pixel data of a diaper changing area associated with an individual, and wherein the server or the cloud-based computing platform executes a diaper changing area skin model and generates individual-specific recommendations based on the output of the diaper changing area skin model, and transmits the individual-specific recommendations to a user computing device via a computer network for presentation via a user interface of the user computing device.

[0124] 34. The digital imaging method of any one of aspects 1-33, wherein the user computing device comprises a mobile device, a tablet computer, a handheld device, a desktop device, a personal assistant device, a home assistant device, or a retail device.

[0125] 35. A digital imaging method according to any of aspects 1-34, wherein a user computing device receives at least one image including pixel data of at least a portion of a diaper changing area associated with an individual, and wherein the user computing device executes a diaper changing area skin model and generates individual-specific recommendations based on output of the diaper changing area skin model, and presents the individual-specific recommendations via a user interface of the user computing device.

[0126] 36. The digital imaging method of any of aspects 1-35, wherein the individual-specific recommendation comprises a recommended action selected by the one or more processors from at least two available actions based on diaper changing area skin characteristics.

[0127] 37. A digital method according to any one of aspects 1-36, the digital method also includes: receiving a new image of an individual at one or more processors, the new image captured by a digital camera, and the new image includes pixel data of a diaper changing area associated with the individual; analyzing the new image captured by the digital camera by the diaper changing area skin model executed on the one or more processors to determine new individual specific diaper changing area skin characteristics; generating a new individual specific recommendation about at least one feature identifiable within the pixel data of the new image based on the new individual specific diaper changing area skin characteristics; and presenting the new individual specific recommendation or comment via a user interface of a user computing device.

[0128] 38. A digital imaging method according to aspect 37, wherein incremental diapering area skin characteristics are generated based on a comparison between new individual-specific diapering area skin characteristics and individual-specific diapering area skin characteristics, wherein the new individual-specific recommendation is also based on the incremental diapering area skin characteristics, and wherein the incremental diapering area skin characteristics are presented via a user interface of a user computing device.

[0129] 39. A method of digital imaging according to any one of clauses 37 or 38, wherein the new image of the individual is captured subsequent to capturing the image of the individual.

[0130] 40. A method of digital imaging according to clause 39, wherein the new image of the individual is captured after a threshold amount of time has elapsed after the image of the individual was captured.

[0131] 41. A method of digital imaging according to any one of clauses 1 to 40, wherein the at least one image comprises a plurality of images.

[0132] 42. A method of digital imaging according to clause 41, wherein the plurality of images are collected using a digital camera.

[0133] 43. A method of digital imaging according to any one of clauses 1 to 42, wherein at least one image comprises a three-dimensional image.

[0134] 44. The digital imaging method of any of aspects 1-43, wherein the diapering area skin property is based at least in part on the amount of the diapering area affected by the skin problem.

[0135] 45. The digital imaging method of any of aspects 1-44, wherein the diaper changing area skin characteristics are based at least in part on the severity of the skin problem affecting the diaper changing area.

[0136] 46. ​​The digital imaging method of any of clauses 1-45, wherein at least one image is captured by the digital camera within a threshold time frame after a diaper change.

[0137] 47. The digital imaging method of any of clauses 1-46, wherein at least one image is captured by the digital camera within a threshold time frame prior to a diaper change.

[0138] 48. A digital imaging method according to any of aspects 1-47, wherein generating at least one individual-specific recommendation designed to address at least one feature identifiable within pixel data of the individual's skin is also based on one or more user inputs made via a user interface associated with the individual.

[0139] 49. A digital imaging system configured to analyze pixel data of at least one image of a diaper changing area associated with an individual to determine individual-specific diaper changing area skin characteristics, the digital imaging system comprising: an imaging server, the imaging server comprising a server processor and a server memory; an imaging application (app), the imaging application configured to execute on a user computing device comprising a device processor and a device memory, the imaging application being communicatively coupled to the imaging server; and a diaper changing area skin model, the diaper changing area skin model being trained with pixel data of a plurality of training images of one or more diaper changing areas associated with a corresponding plurality of individuals, and being operable to determine individual-specific diaper changing area skin characteristics based on the pixel data of the diaper changing area. associated diaper changing area skin characteristics, wherein the diaper changing area skin model is configured to execute on a server processor or a device processor so that the server processor or the device processor performs the following operations: receiving at least one image of an individual, the at least one image being captured by a digital camera, and the at least one image including pixel data of a diaper changing area associated with the individual; analyzing, by the diaper changing area skin model, the at least one image captured by the digital camera to determine individual-specific diaper changing area skin characteristics; generating at least one individual-specific recommendation based on the individual-specific diaper changing area skin characteristics, the at least one individual-specific recommendation being designed to address at least one feature identifiable within the pixel data of the individual's skin; and providing the at least one individual-specific recommendation via a user interface of a user computing device.

[0140] 50. A tangible, non-transitory computer-readable medium storing instructions for analyzing pixel data of at least one image of a diaper changing area associated with an individual to determine skin characteristics of the diaper changing area specific to the individual, the instructions, when executed by one or more processors, causing the one or more processors to perform the following operations: at one or more processors communicatively coupled to one or more memories, a plurality of training images of one or more diaper changing areas associated with a corresponding plurality of individuals are gathered, each of the training images comprising pixel data of the diaper changing area associated with the corresponding individual; a diaper changing area skin model is trained by the one or more processors using the pixel data of the plurality of training images, the diaper changing area skin model being operable to determine skin characteristics of the diaper changing area specific to the individual based on the diaper changing area skin characteristics of the individual. pixel data of a diaper area determines diaper changing area skin characteristics associated with a diaper changing area; receiving at least one image of an individual at one or more processors, the at least one image captured by a digital camera, and the at least one image including pixel data of a diaper changing area associated with the individual; analyzing the at least one image captured by the digital camera by a diaper changing area skin model executed on the one or more processors to determine individual-specific diaper changing area skin characteristics; generating at least one individual-specific recommendation based on the individual-specific diaper changing area skin characteristics by the one or more processors, the at least one individual-specific recommendation being designed to address at least one feature identifiable within the pixel data of the individual's skin; and providing the at least one individual-specific recommendation via a user interface of a user computing device.

[0141] Additional considerations

[0142] Although the present disclosure sets forth a number of different embodiments, it should be understood that the legal scope of this specification is defined by the claims set forth at the end of this patent and their equivalents. The specific embodiments are to be understood to be exemplary only and not to describe every possible embodiment, as it would be impractical to describe every possible embodiment. Numerous alternative embodiments may be implemented using current technology or technology developed after the filing date of this patent, which alternative embodiments will still fall within the scope of the claims.

[0143] The following additional considerations apply to the foregoing discussion. Throughout this specification, multiple instances may implement components, operations or structures described as single instances. Although the individual operations of one or more methods are shown and described as separate operations, one or more operations in each operation may be performed simultaneously, and it is not necessary to perform these operations in the order shown. The structure and function displayed as a separate component in the exemplary configuration may be implemented as a combined structure or component. Similarly, the structure and function displayed as a single component may be implemented as a separate component. These modifications, additions and improvements and other variations, modifications, additions and improvements all belong within the scope of this article.

[0144] In addition, certain embodiments are described herein as including logic or multiple routines, subroutines, applications, or instructions. These may constitute software (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware. In hardware, routines, etc. are tangible units that can perform certain operations and may be configured or arranged in a certain manner. In an exemplary embodiment, one or more computer systems (e.g., independent client or server computer systems) or one or more hardware modules of a computer system (e.g., a processor or a processor group) may be configured by software (e.g., an application or an application portion) as a hardware module for performing certain operations as described herein.

[0145] The various operations of the exemplary 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 relevant operations. Such processors, whether temporarily or permanently configured, may constitute processor-implemented modules for performing one or more operations or functions. In some exemplary embodiments, the modules mentioned herein may include processor-implemented modules.

[0146] Similarly, the methods or routines described herein may be implemented at least in part by a processor. For example, at least some of the operations of the method may be performed by one or more processors or processor-implemented hardware modules. The execution of certain operations in the operation may be assigned to one or more processors, which not only reside in a single machine, but are also deployed between multiple machines. In some exemplary embodiments, one or more processors may be located at a single location, while in other embodiments, the processors may be distributed at multiple locations.

[0147] The execution of certain operations in the operation may be assigned to one or more processors, which reside not only in a single machine, but also deployed between multiple machines. In some exemplary embodiments, 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 embodiments, one or more processors or processor-implemented modules may be distributed in multiple geographic locations.

[0148] This detailed description is to be understood as being exemplary only and does not describe every possible implementation, because describing every possible implementation is impractical even if possible. Numerous alternative implementations can be implemented by one of ordinary skill in the art using current technology or technology developed after the filing date of this application.

[0149] Those skilled in the art will appreciate that various modifications, changes and combinations may be made to the above-described embodiments without departing from the scope of the present invention, and these modifications, changes and combinations should be considered to fall within the scope of the present invention.

[0150] The patent claims at the end of this patent application are not intended to be interpreted under 35 U.S.C. §112(f) unless expressly referenced to conventional means-plus-function language, such as "means for" or "step for" language expressly referenced in the claims. The systems and methods described herein relate to improvements in computer functionality, as well as improving the functionality of conventional computers.

[0151] The dimensions and values ​​disclosed herein should not be understood as being strictly limited to the exact numerical values ​​cited. Instead, unless otherwise indicated, each such dimension is intended to represent the stated value and a functionally equivalent range around that value. For example, a dimension disclosed as "40 mm" is intended to mean "about 40 mm".

[0152] Unless expressly excluded or otherwise limited, each document cited herein, including any cross-referenced or related patent or patent application and any patent application or patent to which this application claims priority or the benefit of, is hereby incorporated by reference in its entirety. The citation of any document is not an admission that it is prior art to any of the present invention disclosed or claimed herein, or an admission that it, by itself or in combination with any one or more of the references, proposes, suggests, or discloses any such invention. In addition, to the extent that any meaning or definition of a term in this invention 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 invention shall govern.

[0153] Although specific embodiments of the present invention have been illustrated and described, it will be apparent to those skilled in the art that various other changes and modifications may be made without departing from the spirit and scope of the present invention. Therefore, it is intended that all such changes and modifications within the scope of the present invention be covered in the appended claims.

Claims

1. A digital imaging method for analyzing pixel data of at least one image of a diaper changing area associated with an individual to determine skin characteristics of a diaper changing area specific to the individual, the digital imaging method comprising the steps of: Aggregating, at one or more processors communicatively coupled to one or more memories, a plurality of training images of one or more diaper changing areas associated with a respective plurality of individuals, each of the training images comprising pixel data of a diaper changing area associated with a respective individual; training, by the one or more processors, a diaper changing area skin model using the pixel data of the plurality of training images, the diaper changing area skin model being operable to determine diaper changing area skin characteristics associated with a diaper changing area based on the pixel data of the diaper changing area; receiving, at the one or more processors, at least one image of an individual, the at least one image captured by a digital camera, and the at least one image including pixel data of the diaper changing area associated with the individual; analyzing, by the diaper changing area skin model executed on the one or more processors, the at least one image captured by the digital camera to determine individual specific diaper changing area skin characteristics; generating, by the one or more processors, at least one individual-specific recommendation based on the individual-specific diaper changing area skin characteristics, the at least one individual-specific recommendation being designed to address at least one feature identifiable within the pixel data of the individual's skin; as well as The at least one individual-specific recommendation is provided via a user interface of a user computing device.

2. The digital imaging method of claim 1, wherein the plurality of training images comprises images containing pixel data of the diaper changing area associated with the corresponding plurality of individuals from a plurality of viewpoints.

3. The digital imaging method of claim 1, wherein the plurality of training images comprises images containing pixel data of the diaper changing area associated with the respective plurality of individuals without depicting personal identifying information associated with the respective plurality of individuals.

4. A digital imaging method according to claim 1, wherein the at least one individual-specific recommendation is displayed via the user interface of the user computing device together with a graphical representation of the individual's skin, the graphical representation of the individual's skin being annotated with one or more graphical or textual presentations corresponding to the individual's specific diaper changing area skin characteristics. 5 . The digital imaging method of claim 1 , wherein the at least one individual-specific recommendation is a textual recommendation displayed via the user interface of the user computing device.

6. The digital imaging method of claim 1, wherein the at least one individual-specific recommendation is an audio recommendation provided via the user interface of the user computing device.

7. A digital imaging method according to claim 1, wherein the at least one individual-specific recommendation includes one or more of the following: a recommendation to use a mobile device application, a recommendation to practice one or more procedures associated with the diaper changing area, a recommendation to seek or receive medical services related to the diaper changing area, or a recommendation for lifestyle changes related to the diaper changing area.

8. The digital imaging method of claim 1, wherein the at least one individual-specific recommendation is a recommendation for a visit with a healthcare provider.

9. The digital imaging method according to claim 8, further comprising the following steps: A live chat or video with the healthcare provider is initiated, by the one or more processors, based on the recommendation for the visit with the healthcare provider.

10. The digital imaging method of claim 1, wherein the at least one individual-specific recommendation is a recommendation for a manufactured product.

11. The digital imaging method according to claim 10, further comprising the following steps: Shipping of the manufactured product to an address associated with the individual is initiated by the one or more processors based on the recommendation for the manufactured product.

12. The digital imaging method according to claim 10, further comprising: generating, by the one or more processors, based on the at least one image, a modified image depicting how the individual's diaper changing area is predicted to perform after the at least one feature is manipulated with the manufactured product and presenting the modified image via the user interface of the user computing device.

13. The digital imaging method of claim 12, wherein presenting the modified image comprises presenting the modified image in an augmented reality (AR) format.

14. A digital imaging method according to claim 10, wherein the at least one individual-specific recommendation is displayed via the user interface of the user computing device together with instructions for processing at least one feature identifiable within the pixel data of the individual's skin with the manufactured product.

15. The digital imaging method of claim 1, wherein the diaper changing area skin model is an artificial intelligence (AI) based model trained with at least one AI algorithm.

16. The digital imaging method of claim 1, wherein training the diapering area skin model comprises training the diapering area skin model with indications of pixel data of the plurality of training images corresponding to one or more regions of interest.

17. A digital imaging method according to claim 1, wherein training the diaper changing area skin model includes training the diaper changing area skin model with an indication of the pixel data of the plurality of training images corresponding to one or more of the following: a diaper changing pad, a diaper changing area, or surrounding elements of the diaper changing area.

18. The digital imaging method of claim 1, wherein training the diaper changing area skin model comprises training the diaper changing area skin model with indications of sizes associated with the pixel data of the plurality of training images.

19. The digital imaging method of claim 1, wherein training the diapering area skin model comprises training the diapering area skin model with an indication of a comparison between two or more body regions associated with the pixel data of the plurality of training images.

20. The digital imaging method of claim 1, wherein training the diaper changing area skin model comprises training the diaper changing area skin model with an indication of colors associated with the pixel data of the plurality of training images.

21. A digital imaging method according to any of the preceding claims, wherein training the diapering area skin model with indications of colors associated with the pixel data of the plurality of training images comprises training the diapering area skin model with indications of one or more of the following: colors associated with one or more diapering area skin characteristics or colors associated with one or more diapering area skin types.

22. The digital imaging method according to any one of claims 1 to 20, further comprising: receiving, at the one or more processors, a new image of the individual, the new image captured by the digital camera, and the new image including pixel data of the diaper changing area associated with the individual; analyzing, by the diapering area skin model executed on the one or more processors, the new image captured by the digital camera to determine new individual specific diapering area skin characteristics; generating a new individual-specific recommendation regarding at least one feature identifiable within the pixel data of the new image based on the new individual-specific diaper changing area skin characteristic; and The new individual-specific recommendation or review is presented via the user interface of the user computing device.

23. A digital imaging method according to claim 22, wherein incremental diapering area skin characteristics are generated based on a comparison between the new individual-specific diapering area skin characteristics and the individual-specific diapering area skin characteristics, wherein the new individual-specific recommendation is also based on the incremental diapering area skin characteristics, and wherein the incremental diapering area skin characteristics are presented via the user interface of the user computing device.

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