Correlated vector metrics for image embedding processing

A computing device, comprising:

• one or more processors;
• and
• one or more memories storing computer-executable instructions that, when executed by the one or more processors, causes the one or more processors to:
• receive an image associated with an article representing a transfer of at least one resource from a first entity to a second entity, wherein the image represents a user-defined feature of the article;
• provide, to a generative model, the image associated with the article representing the transfer of the at least one resource from the first entity to the second entity, wherein the generative model is configured to generate image embeddings from input images;
• receive, from the generative model, an image embedding associated with the image associated with the article representing the transfer of the at least one resource from the first entity to the second entity, the image embedding encoding at least the user-defined feature of the article;
• determine a vector distance metric based at least in part on a vector distance comparison between the image embedding and a prior image embedding;
• determine whether an association exists between the first entity and the second entity based at least in part on the user-defined feature and a known parameter associated with the user-defined feature;
• and
• control one or more operations of a computer system to cause a distribution of the at least one resource from the first entity to the second entity based at least in part on the association and the vector distance metric.

A non-transitory computer-readable storage medium storing computer-executable instructions that, when executed with one or more processors of a computing device, causes the one or more processors to:

• receive an image associated with an article representing a transfer of at least one resource from a first entity to a second entity, wherein the image represents a user-defined feature of the article;
• provide, to a generative model, the image associated with the article representing the transfer of the at least one resource from the first entity to the second entity, wherein the generative model is configured to generate image embeddings from input images;
• receive, from the generative model, an image embedding associated with the image associated with the article representing the transfer of the at least one resource from the first entity to the second entity, the image embedding encoding at least the user-defined feature of the article;
• determine a vector distance metric based at least in part on a vector distance comparison between the image embedding and a prior image embedding;
• determine whether an association exists between the first entity and the second entity based at least in part on the user-defined feature and a known parameter associated with the user-defined feature;
• and
• control one or more operations of a computer system to cause a distribution of the at least one resource from the first entity to the second entity based at least in part on the association and the vector distance metric.