At&t Intellectual Property I, L.p.

Andrew Campbell

Prince Paulraj

Ana Armenta

Edmond J Abrahamian

Elijah Hall

Maisam Shahid Wasti

Jean Luo

Brandon Bolong Lee

Anomalies associated with events relating to users or user accounts can be detected. An anomaly detection management component (ADMC) determines embedded arrays comprising data bit groups representative of groups of properties and groups of relationships between properties associated with users, based on analysis of data related to events associated with users. ADMC trains a neural network (NN) based on applying embedded arrays to NN, in accordance with an artificial intelligence (AI) analysis process. ADMC determines an embedded array comprising data bits representative of properties and relationships between properties associated with a user based on analysis of data associated with the user. Trained NN can determine a pattern relating to the properties and relationships associated with the user based on AI-based analysis of the embedded array. Trained NN can detect an anomaly in the pattern based on AI-based analysis of the pattern, wherein the anomaly relates to an event.

Anomaly detection relating to communications using information embedding

A processing system may obtain a request to add at least a first feature to a feature graph database, where the request comprises a first feature ontology of the first feature, and where the first feature ontology comprises: a label of the first feature and a relationship of the first feature to a concept or to another feature. The processing system may then identify whether the first feature is a duplicate of a second feature in the feature graph database based at least upon the first feature ontology and a second feature ontology of the second feature and generate an indication of whether the first feature is a duplicate in response to the identifying.

Disambiguation of feature graph databases

Mitigation of temporal generalization losses a target machine learning model is disclosed. Mitigation can be based on identifying, removing, modifying, transforming, etc., features, explanatory variables, models, etc., that can have an unstable relationship with a target outcome over time. Implementation of a more stable representation can be initiated. Temporal stability measures (TSMs) for one or more model feature(s) can be determined based on one or more variable performance metrics (VPMs). A group of one or more VPMs can be selected based on features of a model in either a development or production environment. Model feature modification can be recommended based on a TSM, which can prune a feature, transform a feature, add a feature, etc. Temporal stability information can be presented, e.g., via a dashboard-type user interface. Models can be updated based on mutations of a model comprising a feature modification(s), including competitive champion/challenger model updating.

Mitigating temporal generalization for a machine learning model

A method for detecting threat pathways using sequence graphs includes constructing a sequence graph from a set of data containing information about activities in a telecommunications service provider network, where the sequence graph represents a subset of the activities that occurs as a sequence, providing an embedding of the sequence graph as input to a machine learning model, wherein the machine learning model has been trained to detect when an input embedding of a sequence graph is likely to indicate a threat activity, determining, based on an output of the machine learning model, whether the subset of the activities is indicative of the threat activity, and initiating a remedial action to mitigate the threat activity.

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Pleasant Hill, CA, United States of America

Andrew Campbell