Abstract: An IP-to-Domain (IP2D) resolution system predicts which domain is most likely associated with an IP address. The resolution system generates unique source vote features (FSV) from (IP, domain, source) data. The FSV features are used to train a machine learning model that predicts which domain is most likely associated with an IP address. The domain predictions can then be used to more efficiently process events, more accurately calculate consumption scores, and more accurately detect associated company surges.

Abstract: Inferring multimodal content intent in a common geometric space in order to improve recognition of influential impacts of content includes mapping the multimodal content in a common geometric space by embedding a multimodal feature vector representing a first modality of the multimodal content and a second modality of the multimodal content and inferring intent of the multimodal content mapped into the common geometric space such that connections between multimodal content result in an improvement in recognition of the influential impact of the multimodal content.

Abstract: Embedding multimodal content in a common geometric space includes for each of a plurality of content of the multimodal content, creating a respective, first modality feature vector representative of content of the multimodal content having a first modality using a first machine learning model; for each of a plurality of content of the multimodal content, creating a respective, second modality feature vector representative of content of the multimodal content having a second modality using a second machine learning model; and semantically embedding the respective, first modality feature vectors and the respective, second modality feature vectors in a common geometric space that provides logarithm-like warping of distance space in the geometric space to capture hierarchical relationships between seemingly disparate, embedded modality feature vectors of content in the geometric space; wherein embedded modality feature vectors that are related, across modalities, are closer together in the geometric space than un