US 12,507,946 B1

A computer-implemented method for tracking and predicting pigment change in human or non-human skin, comprising:

a.receiving, by a computing device, a series of digital images of a skin region captured over multiple treatment sessions;

b.processing said digital images using a processor to normalize for environmental lighting conditions, wherein said normalization comprises executing a lighting recalibration algorithm that performs a deformable registration using stable epidermal micro-landmarks and is conditioned by capture metadata comprising at least one of: focal length, exposure time, ISO, white balance, or illumination intensity;

c.quantifying a change in a pigment pattern between said images by executing a machine learning model to estimate ink particle size reduction;

d.forecasting a future pigment change by executing a non-linear regression model on a multi-dimensional dataset, said dataset comprising a plurality of variables including a Fitzpatrick skin type auto-detected from baseline images, cross-treatment interference data, fractional lattice detection data, and thermal profile mapping data; and

e.generating for display, on a user interface, a progress metric and a confidence-weighted completion date derived from a patient-specific fade curve, wherein the predictive accuracy of said completion date is a direct technical result of the processing step (b) which reduces computational complexity by providing a pre-processed, consistent data input to said machine learning model.

The method of claim 1, wherein the method further comprises receiving said series of digital images from a mobile device for at-home consumer use.

The method of claim 1, wherein said pigment change comprises ink pigment change during tattoo removal.

The method of claim 1, wherein the machine learning model is trained on a dataset comprising real-world anonymized patient data and synthetic training data.

The method of claim 1, wherein the capturing step is performed with an alignment apparatus to ensure repeatable image orientation.

The method of claim 1, wherein the progress metric comprises a fade percentage value, a confidence score, and a historical trend line.

The method of claim 1, wherein the pigment change is quantified during a treatment selected from the group consisting of: hair removal, skin rejuvenation, reduction of vascular lesions, fading of hormonal pigmentation, and stretch mark fading.

The method of claim 1, wherein the method further comprises a pre-treatment notification module configured to transmit a client-specific pre-treatment instruction to a mobile device, said instruction personalized by an AI model and dynamically adjusted based on external data sources, and further comprising a post-treatment instruction module configured to detect an end-of-treatment event and automatically generate and dispatch time-stamped aftercare instructions to a client's mobile device, said instructions dynamically adjusted based on an AI evaluation of skin type, treatment parameters, and prior healing patterns.

The method of claim 1, wherein a predictive model is trained on and incrementally improved by a federated learning model, and further comprising a license enforcement module configured to: monitor a geographic location of the client device or imaging device using a plurality of signals selected from the group consisting of: GPS, Wi-Fi triangulation, and device IP address; verify said location against a geofenced boundary defined in a license agreement; and enable or disable a core function based on the verification, wherein said license enforcement module is integral to ensuring the commercial viability and regulatory compliance of the system.

A system for tracking and predicting pigment change, comprising:

a.a memory storing computer-executable instructions;

b.a processor configured to execute the instructions to perform the following steps;

c.receiving a series of digital images of a region of skin from an imaging device;

d.operating a processing module to normalize said images for environmental factors by executing a machine learning algorithm that performs a deformable registration using stable epidermal micro-landmarks and corrects for lighting variance based on capture metadata comprising at least one of: focal length, exposure time, ISO, white balance, or illumination intensity;

e.operating an analysis module to quantify pigment change between the normalized images by executing a micro-feature-based alignment algorithm and an image analysis algorithm that estimates ink particle size reduction;

f.operating a prediction module to forecast a completion date by applying a non-linear regression model to a patient-specific fade curve, said model utilizing a plurality of variables selected from the group consisting of: a Fitzpatrick skin type auto-detected from baseline images, cross-treatment interference data, fractional lattice detection data, and thermal profile mapping data, wherein the forecasting accuracy is a technical result of the normalization performed in step (d), which improves the speed and efficiency of the computing system's processing unit; and

g.operating a display device to present the forecast and progress metrics, including a dynamically-updating confidence score, to a user.

The system of claim 10, wherein the calibrated imaging device includes a calibrated light source for controlled illumination and a fixed optical distance from the skin.

The system of claim 10, wherein the multi-modal image data includes at least two different imaging modalities selected from the group consisting of: RGB, infrared, and hyperspectral.

The system of claim 10, wherein the prediction module's confidence score is tied to at least one of: image quality, lighting consistency, or the patient's adherence to the recommended treatment protocol.

The system of claim 10, wherein the system is further configured for cross-platform interoperability to interface with external health records (EHRs) or wellness applications to synthesize a holistic patient profile.

The system of claim 10, wherein the system further comprises a closed-loop compliance enforcement system configured to: verify pre-treatment protocol compliance by analyzing real-time data from a wearable sensor or imaging device; and automatically adjust treatment parameters in real-time or prevent the treatment procedure from starting until compliance is verified.

The system of claim 10, wherein the system is further configured to autonomously initiate and perform a treatment pass after verification of safety conditions and a confirmed pre-treatment protocol compliance, without continuous operator initiation.

The system of claim 10, wherein a predictive model is trained on and incrementally improved by a federated learning model, and further comprising a license enforcement module configured to: monitor a geographic location of the client device or imaging device using a plurality of signals selected from the group consisting of: GPS, Wi-Fi triangulation, and device IP address; verify said location against a geofenced boundary defined in a license agreement; and enable or disable a core function based on the verification, wherein said license enforcement module is integral to ensuring the commercial viability and regulatory compliance of the system.