Patent Description:
The medical field is currently being improved by the growing availability of biomarker analysis systems. These systems, which can operate in an outpatient or clinical setting, can provide a noninvasive measurement of various protein concentrations. These protein concentrations may then be used for various treatment-related purposes. Through clinical studies, certain proteins that can be detected by these biomarker analysis systems have been identified to be linked to clinical signs of aging, as well as responsiveness/non-responsiveness to various active ingredients of skincare products. For example, preliminary studies have suggested that biomarkers such as YKL40, TG3, LCN1, IDE, and FLG2 may be correlated with clinical signs of aging and responsiveness such as shiny skin, rough skin, uneven skin tone, eye wrinkles, photo aging, loss of elasticity, dilated pores, responsiveness to retinol, and responsiveness to proxylane.

Even though these correlations have been suggested, the biomarker analysis system only provides raw protein concentration information, and does not use these correlations.

<CIT> discloses a method of generating a facial aging visualization implemented by at least one.

<CIT> and <CIT> both disclose a method for diagnosing aesthetic qualities of the skin which are also age related, as well as their progression.

What is desired are systems and methods that visualize future skin trends based on biomarker concentration information in order to improve the utility of the information gathered by the biomarker analysis system, particularly in determining a recommended skincare regimen to address the future trends predicted based on the detected biomarkers.

In order to achieve the above objects, the present invention provides a method according to independent claim <NUM> and a system according to independent claim <NUM>. The dependent claims relate to advantageous embodiments.

In some embodiments, a method of generating a facial aging visualization implemented by at least one computer processor programmed by one or more machine instructions is provided. At least one instance of a predicted facial aging trend is determined based on receiving protein biomarker concentration information. A virtual representation is generated on an electronic display indicative of the predicted facial aging trend.

In some embodiments, a system for generating a facial aging visualization is provided. The system comprises circuitry for determining at least one instance of a predicted facial aging trend based on receiving protein biomarker concentration information; and circuitry for generating a virtual representation on an electronic display indicative of the predicted facial aging trend.

In some embodiments, a computing device is provided. The computing device is configured to determine at least one instance of a predicted facial aging trend based on receiving protein biomarker concentration information; and generate a virtual representation on an electronic display indicative of the predicted facial aging trend.

<FIG> is a high-level schematic drawing that illustrates various components of an example embodiment of a system according to various aspects of the present disclosure. The system is used to obtain a sample from a user <NUM>, and to generate skin trend visualizations based on the sample. As shown, one or more sampling disks <NUM>, <NUM> are used to obtain a sample from the user <NUM>. A sampling disk <NUM> is then processed by a protein extraction device <NUM>, and a collected sample is applied to a test cartridge <NUM>. The test cartridge <NUM> is inserted into an immunoassay analyzer device <NUM>. The immunoassay analyzer device <NUM> determines concentrations of various protein biomarkers that are associated with various skin trends. The protein biomarker concentration information is then provided to a trend visualization computing device <NUM>, which generates and presents skin trend visualizations based on the protein biomarker concentration information.

<FIG> is a block diagram that illustrates further details of an example embodiment of a biomarker analysis system and an example embodiment of a trend visualization computing device according to various aspects of the present disclosure.

In some embodiments, the biomarker analysis system <NUM> includes one or more devices that provide a measurement of biomarkers sampled from a subject. In some embodiments, such sampling is done quickly and non-invasively, thus allowing the biomarker sampling to take place in an outpatient clinical or retail environment. In the illustrated embodiment, the biomarker analysis system <NUM> includes a sampling disk <NUM>, a test cartridge <NUM>, a protein extraction device <NUM>, and an immunoassay analyzer device <NUM>.

In some embodiments, the sampling disk <NUM> comprises a substrate and an adhesive. The adhesive is suitable for removably attaching the sampling disk <NUM> to the skin of a subject and obtaining a sample of skin cells therefrom. Though a sampling disk <NUM> is described, in some embodiments, an adhesive device of another shape, including but not limited to a rectangle or a tape may be used. In some embodiments, a swab, a wipe, or another device usable to collect a skin cell sample may be used instead of an adhesive device. One non-limiting example of a device that is suitable for use as a sampling disk <NUM> is a D-SQUAME® sampling disk produced by CuDerm Corporation, though other devices could be used.

In some embodiments, the protein extraction device <NUM> is configured to remove samples from sampling disks <NUM> and convert them into a form that can be provided to a test cartridge <NUM> for processing. In some embodiments, the protein extraction device <NUM> may include a container in which the sampling disk <NUM> may be placed along with a buffer solution. The protein extraction device <NUM> may also include a device for agitating, centrifuging, or otherwise processing the container such that the proteins from the collected skin sample are released from the sampling disk <NUM> and dissolved in the buffer solution.

In some embodiments, the test cartridge <NUM> is approximately the size of a credit card, and includes an inlet in which a droplet (approximately 30µl) of the solution containing the proteins from the collected skin sample may be placed. The inlet may be coupled to one or more microfluidic channels through which the solution will automatically flow. In some embodiments, antibodies may be deposited within the one or more microfluidic channels, and antigens within the sample may react with the antibodies. This reaction may cause fluorescent beads associated with the antibodies to fluoresce according to the concentrations of the proteins being measured. The immunoassay analyzer device <NUM> may accept the test cartridge <NUM>, and may measure the concentrations of the proteins of interest within the sample. In some embodiments, the immunoassay analyzer device <NUM> may do so by using laser light to determine which fluorescent beads are fluorescing. Once measured, the immunoassay analyzer device <NUM> may provide the determined protein concentrations to other components of the system using any suitable technique, including but not limited to presenting the protein concentrations on a display, printing the protein concentrations on a paper receipt, and electronically transmitting the determined protein concentrations to another device. One non-limiting example of an immunoassay analyzer device <NUM> (and its associated test cartridges <NUM>) are the FREND™ System provided by NanoEnTek Inc. In some embodiments wherein the determined protein concentrations are electronically transmitted, the electronic transmission may be encrypted and/or anonymized in order to protect the privacy of the information.

The trend visualization computing device <NUM> is a computing device configured to receive protein concentration information from the biomarker analysis system <NUM>, determine one or more skin trends based on the protein concentration information, and present visualizations based on the one or more skin trends. In some embodiments, the trend visualization computing device <NUM> may be a mobile computing device such as a smartphone or a tablet computing device. In some embodiments, the trend visualization computing device <NUM> may be a desktop computing device or a laptop computing device. In some embodiments, the trend visualization computing device <NUM> may include more than one computing device, such as a user computing device configured to provide a user interface and one or more server computing devices configured to provide computational functionality (such as the functionality of the trend determination engine <NUM> and/or the trend visualization engine <NUM> described below). In such embodiments, the user computing device and the one or more server computing devices may communicate via any suitable communication technology or technologies, such as a wired technology (including but not limited to Ethernet, USB, or the Internet) or a wireless technology (including but not limited to WiFi, WiMAX, <NUM>, <NUM>, LTE, or Bluetooth).

As illustrated, the trend visualization computing device <NUM> includes a trend determination engine <NUM>, a trend visualization engine <NUM>, and a display device <NUM>.

In some embodiments, the trend determination engine <NUM> is configured to receive protein biomarker concentration information from the biomarker analysis system, and to determine skin trends using the correlation information that has been clinically determined. In some embodiments, the trend visualization engine <NUM> is configured to generate virtual representations of skin trends based on the skin trends determined by the trend determination engine <NUM>. In some embodiments, the display device <NUM> is used to present the virtual representations of skin trends generated by the trend visualization engine <NUM>. In some embodiments, the display device <NUM> may be a touch-sensitive display, and may also be used to accept input from a user to a user interface. Further details of the functionality of the components of the trend visualization computing device <NUM> are provided below.

In general, the word "engine," as used herein, refers to logic embodied in hardware or software instructions, which can be written in a programming language, such as C, C++, COBOL, JAVA™, PHP, Perl, HTML, CSS, JavaScript, VBScript, ASPX, Microsoft. NET™, and/or the like. An engine may be compiled into executable programs or written in interpreted programming languages. Software engines may be callable from other engines or from themselves. Generally, the engines described herein refer to logical modules that can be merged with other engines, or can be divided into sub-engines. The engines can be stored in any type of computer-readable medium or computer storage device and be stored on and executed by one or more general purpose computers, thus creating a special purpose computer configured to provide the engine or the functionality thereof.

<FIG> is an illustration of an example embodiment of a virtual representation created according to various aspects of the present disclosure. In the illustration, a spider diagram <NUM> is shown that illustrates the likelihood of a set of skin conditions. As shown, the spider diagram <NUM> includes scales for shininess, dehydration, visible spots, wrinkles/fine lines, invisible spots/photo-aging, redness, and pores. A dot is placed on each scale, and the dots are connected by a line <NUM> to illustrate how far along each scale the skin trends are predicted to extend. In some embodiments, the dots and the line <NUM> indicate an amount of each skin condition that is detected at a given point in time, and a slider or other user interface element may be provided to allow a user to explore how the skin conditions are predicted to change over time. In some embodiments, the dots and the line <NUM> indicate a rate of change of each skin condition. In some embodiments, the visualization may include selections for various skin treatment regimens, and the trend visualization engine <NUM> may update the trend visualization based on the biomarker concentration information and the selected skin treatment regimen so that the user can evaluate the effects of various skin treatment regimens.

The illustrated spider diagram <NUM> is a non-limiting example of a trend visualization or virtual representation. In some embodiments, other types of trend visualizations or virtual representations may be provided. For example, in some embodiments, similar information may be provided using numerical scales for each skin condition in a table (such as a shininess value from <NUM>-<NUM>, a dehydration value from <NUM>-<NUM>, etc.). As another example, a photo of the subject may be obtained, and the virtual representation may use a photo filter to alter the photo based on one or more of the predicted skin trends.

<FIG> is a flowchart that illustrates an example embodiment of a method of presenting a visualization of facial aging trends according to various aspects of the present disclosure. From a start block, the method <NUM> advances to procedure block <NUM>, where a procedure is executed wherein protein biomarker concentration information is obtained for a subject by a biomarker analysis system <NUM>. A non-limiting example of a biomarker analysis system <NUM> is illustrated in <FIG> and discussed above. Any suitable procedure may be used to collect the protein biomarker concentration information, including but not limited to the procedure <NUM> illustrated in <FIG> and described below.

Next, at block <NUM>, a trend determination engine <NUM> of a trend visualization computing device <NUM> receives the protein biomarker concentration information. In some embodiments, the protein biomarker concentration information is received from the biomarker analysis system <NUM> by the trend determination engine <NUM> via a network. The network may be a wireless network, including but not limited to a Wi-Fi network, a cellular network (including but not limited to a <NUM> network, a <NUM> network, a <NUM> network, or an LTE network), or a Bluetooth network; a wired network, including but not limited to an Ethernet network, a USB network, or a FireWire network; and/or any other type of network. In some embodiments, the protein biomarker concentration information may be displayed by a display device (not illustrated) of the biomarker analysis system <NUM>, and the protein biomarker concentration information may be manually entered into an interface associated with the trend visualization computing device <NUM>.

At block <NUM>, the trend determination engine <NUM> determines at least one facial aging trend based on the protein biomarker concentration information. Preliminary clinical studies have suggested linkage between five biomarkers (FLG2, TG3, IDE, LCN1, and YKL40) and clinical signs of aging. Example predictive performances (ROC curves) of these biomarkers for various clinical signs of aging are as follows:.

Preliminary studies have also suggested a link between biomarkers and whether a subject is a responder or a non-responder to retinol and proxylane. Preliminary clinical studies have suggested that the YKL40 and TG3 biomarkers indicate that a subject will be responsive to retinol for improvement of underneath eye wrinkles, that the TG3 and LCN1 biomarkers indicate that a subject will be responsive to retinol for improvement of full-face dyschromia, and that the YKL40 biomarker indicates that a subject will be responsive to proxylane for improvement of erythrosis. In some embodiments, the trend determination engine <NUM> uses one or more of these clinically suggested relationships and the biomarker concentration information to predict one or more facial aging trends based on the biomarker concentration information. In some embodiments, the trend determination engine <NUM> may update a life-time aging trend indicator based on the biomarker concentration information. In some embodiments, this life-time aging trend indicator may be stored by the trend visualization computing device <NUM>. In some embodiments, the life-time aging trend indicator may be stored by a cloud service or other server computing device. In some embodiments, the life-time aging trend indicator may later be presented to show progress in addressing the predicted facial aging trends over time.

The method <NUM> then proceeds to block <NUM>, where a trend visualization engine <NUM> of the trend visualization computing device <NUM> generates a trend visualization based on the at least one facial aging trend. In some embodiments, the trend visualization is a virtual representation, and the terms "trend visualization" and "virtual representation" may be used interchangeably. In some embodiments, the trend determination engine <NUM> may determine a degree to which the biomarker concentration information indicates the presence of one or more facial aging trends, and the trend visualization engine <NUM> may generate a trend visualization based on the indicated degrees. For example, if the biomarker concentration information indicates a <NUM>% probability of the development of rough skin, the trend determination engine <NUM> will provide the <NUM>% probability of rough skin to the trend visualization engine <NUM>, and the trend visualization engine <NUM> generates the trend visualization based on the <NUM>% probability of rough skin. In some embodiments, the trend determination engine <NUM> may determine a presence or absence of one or more facial aging trends instead of a probability, and may provide the facial aging trend information as one or more binary values to the trend visualization engine <NUM>. In some embodiments, different trend visualizations may be generated based on the use of one or more skincare products. For example, a first trend visualization may be generated to show the currently detected trend, and a second trend visualization may be generated to show how the trend would be affected by the application of a specified skincare product over time.

In some embodiments, the trend visualization engine <NUM> may create a visualization that is based on probabilities received from the trend determination engine <NUM>. For example, if the visualization is a graph visualization such as the spider diagram <NUM> illustrated in <FIG>, the probability of each skin trend may be indicated as a value from zero (the center of the spider diagram <NUM>) to <NUM> (the outer edge of the spider diagram <NUM>). As another example, if the visualization is a manipulated version of a photo of the subject, the probability may be used to change the intensity of a filter applied to the photo, or to change an amount of time over which a change illustrated by the filter is intended to represent. Accordingly, a determined <NUM>% probability of the appearance of wrinkles or fine lines may cause a filter to be applied and a photo visualization to be presented with an indication that it shows a predicted appearance after five years, while a determined <NUM>% probability of the appearance of wrinkles or fine lines may cause a filter to be applied and a similar photo visualization to be presented, but with an indication that it shows a predicted appearance after ten years instead of five years. These values are examples only, and in some embodiments, different values or techniques may be used.

In some embodiments, more than one skin trend may be determined by the trend determination engine <NUM>, and the trend visualization engine <NUM> may generate a combined trend visualization showing the effects of all of the skin trends. In some embodiments, the trend visualization engine <NUM> may generate separate visualizations for each detected skin trend. For example, the spider diagram <NUM> illustrated in <FIG> concurrently visualizes multiple predicted skin trends. As another example, a photo visualization on which filters are applied to show predicted trends may apply a single filter at a time to separately illustrate the predicted skin trends. As yet another example, multiple filters may be applied in sequence to a photo to concurrently illustrate multiple skin trends.

At block <NUM>, the trend visualization computing device <NUM> presents the trend visualization on a display device <NUM> of the trend visualization computing device <NUM>. In some embodiments, the trend visualization computing device <NUM> may instead transmit the trend visualization to another computing device for presentation or storage. For example, if the trend visualization computing device <NUM> is a server computing device, the trend visualization computing device <NUM> may generate the trend visualization and transmit it to a mobile computing device or a desktop computing device for presentation. In some embodiments, the transmission of the trend visualization between the devices may be encrypted and/or anonymized.

The method <NUM> then proceeds to an end block and terminates.

<FIG> is a flowchart that illustrates an example embodiment of a procedure for obtaining protein biomarker concentration information according to various aspects of the present disclosure. The procedure <NUM> is a non-limiting example of a procedure suitable for use at block <NUM> of <FIG> for obtaining protein biomarker concentration information for a subject.

From a start block, the procedure <NUM> advances to block <NUM>, where a sample is obtained from the subject using a sampling disk <NUM>. In some embodiments, the sampling disk <NUM> may be attached to the skin of the subject via an adhesive. In some embodiments, the sampling disk <NUM> may absorb substances such as sweat, sebum, and other skin secretions. In some embodiments, the adhesive of the sampling disk <NUM> may collect skin cells from the skin of the subject. In some embodiments, the sampling disk <NUM> may be placed on the skin and removed from the skin multiple times to collect the skin sample. In some embodiments, a weight or pressure may be applied on top of the sampling disk <NUM>, and/or the sampling disk <NUM> may be left in place for a specific amount of time, in order to obtain a consistent sample.

Next, at block <NUM>, a protein extraction device <NUM> extracts proteins from the sampling disk into a solution. In some embodiments, the protein extraction device <NUM> may apply the solution to the sampling disk in order to dissolve the skin cells, skin secretions, or other sampled matter into the solution. In some embodiments, the sampling disk <NUM> may be placed in a buffer solution. and may then be shaken to cause the skin sample to be dissolved in the solution.

Claim 1:
A method (<NUM>) of generating a facial aging visualization implemented by at least one computer processor programmed by one or more machine instructions, the method (<NUM>) comprising:
determining a predicted future presence of at least one clinical sign of skin aging for the subject based on receiving protein biomarker concentration information generated from a sample collected from the subject;
generating a virtual representation on an electronic display indicative of the predicted future presence of the at least one clinical sign of skin aging in the absence of any treatment;
wherein determining the at least one instance of the predicted facial aging trend based on receiving protein biomarker concentration information includes determining the at least one instance of the predicted facial aging trend based on receiving information about a concentration of or information indicative of a presence or absence of an FLG2 biomarker, a TG3 biomarker, an IDE biomarker, and a YKL40 biomarker.