Identifying components and patterns in digital designs

A computer-implemented method comprising:

• receiving, by one or more processors, an interface design data object, the interface design data object comprising a text-based hierarchical code structure defining a plurality of frames associated with one or more components of a graphical user interface;
• generating, by one or more processors, an input data object using the interface design data object, the input data object configured to be input to a graph neural network, the input data object comprising at least one matrix comprising one or more vectorized representations of a plurality of nodes corresponding to the plurality of frames and related to each other based on the interface design data object indicating at least one parent or child relationship among the plurality of nodes;
• applying, by one or more processors, the at least one matrix of the input data object to the graph neural network configured to generate at least one predicted classification associated with at least one of the plurality of nodes identifying at least one of the one or more components as noncompliant with one or more preapproved components, the graph neural network trained using a training data set comprising the one or more preapproved components;
• and
• based on the at least one predicted classification, generating, by one or more processors, an update to the graphical user interface, the update comprising a replacement component configured to replace the at least one of the one or more components in response to the at least one predicted classification.

At least one non-transitory computer readable medium comprising computer coded instructions therein, wherein the computer coded instructions are configured to, when executed by at least one processor:

• receive an interface design data object, the interface design data object comprising a text-based hierarchical code structure defining a plurality of frames associated with one or more components of a graphical user interface;
• generate an input data object using the interface design data object, the input data object configured to be input to a graph neural network, the input data object comprising at least one matrix comprising one or more vectorized representations of a plurality of nodes corresponding to the plurality of frames and related to each other based on the interface design data object indicating at least one parent or child relationship among the plurality of nodes;
• apply the at least one matrix of the input data object to the graph neural network configured to generate at least one predicted classification associated with at least one of the plurality of nodes identifying at least one of the one or more components as noncompliant with one or more preapproved components, the graph neural network trained using a training data set comprising the one or more preapproved components;
• and
• based on the at least one predicted classification, generate an update to the graphical user interface, the update comprising a replacement component configured to replace the at least one of the one or more components in response to the at least one predicted classification.