Truist Bank

Qspex, LLC

Qspex Technologies, Inc.

David Wright

Adam Thomas Lewis

David Pham

Kenneth William Cluff

Kai C Su

Richard Lu

Debbie Makita

A message generating system disseminates to each of multiple user devices first information items each associated with at least a respective one of multiple services made available by a first entity. When access indicative signals are received via a network connection, each prompted by a respective one of the user devices accessing at least one of the first information items, the system stores a respective awareness-stage record for each received access indicative signal, thereby recording that the respective user entity accessed at least one of the first information items. For each user entity for which a respective awareness-stage record is stored, a second item is generated and sent including information of a particular service associated with at least one first information item. User entities for which a second item was not generated are excluded as having not accessed a first item. The exclusion reduces data traffic on the network connection.

Audience segmentation prioritization in support of personalization for automated message generation

A system and method for training an artificial intelligence engine for real-time monitoring to eliminate false positives is disclosed. The system includes at least one processor, a communication interface coupled to the processor, and a memory device storing executable code. Executing the executable code causes the processor to receive data from an AI security model, receive data from a false positive database, and correlate both sets of data. The correlated data is used to generate a training dataset and a test dataset used to train a false positive identification model. After evaluating the false positive identification model, an AI engine is applied to user registration. The AI engine includes an AI security model and the false positive identification model. Additionally, a system for evaluating the security of user registration utilizing the false positive identification model is disclosed.

Training an artificial intelligence engine for real-time monitoring to eliminate false positives

A system for automating GUI digital content tracking is used to conduct a back end navigation event in which partitioned digital content blocks are sequentially displayed via an agent GUI. The system receives, in the back end navigation event, in association with at least some of the displayed partitioned digital content blocks, respective agent inputs indicating tracking markers within the partitioned digital content blocks. The system further establishes automated tracking of downstream user navigation events in which the partitioned digital content blocks are displayed in user GUIs, and generates tracking data including quantifications of user interactions, in the tracked downstream user navigation events, with the indicated tracking markers. At least a portion of the tracking data is displayed in a back end evaluation event.

Quantifying GUI digital content access using tracking markers to restrict fraud events

A system for guiding interactions with a user device requests a response from first users, stores the response as response data forming a subset of a personal data set of each of the responding first users, and generates a predictive model during training of a machine learning program with a training data set including the personal data set of each of the first users. The predictive model predicts a predicted response of a second user associated with the user device by correlating a personal data set of the second user to the personal data set of at least one of the first users. The computer sends a communication to the user device of the second user having content relating to the first product and/or service when it is determined that the predicted response indicates an interest in or preference of the second user for the first product and/or service.

Training an artificial intelligence engine to predict responses for determining appropriate action

A system for guiding interactions with a user device requests a response from a plurality of users, stores the response as response data forming a subset of a personal data set of each of the responding users, and generates a predictive model during training of a machine learning program utilizing at least one neural network with a training data set including the personal data set of each of the plurality of users. The predictive model predicts a probability of a first one of the users associated with the user device interacting with a first product and/or service by correlating a personal data set of the first one of the users to the personal data set of at least a second one of the users and sends a communication relating to the first product and/or service to the user device when the predicted probability meets or exceeds a threshold value.

Training an artificial intelligence engine for most appropriate actions

The present invention encompasses, in part, a method and apparatus for lens casting in which two molds, preferably formed of plastic, are interconnected or joined together via a ring to form a mold cavity having substantially the same dimensions of the lens to be formed therein. The invention is further directed to compositions and methods used in lens casting. In a first implementation the invention includes a mold having a ring having an interior periphery; a front mold formed of a plastic and having a lens-forming surface, an edge circumscribing the lens-forming surface that is sized to be complementarily received within a portion of the interior periphery of the ring, and a base having dimensions greater than the interior periphery; and a rear mold formed of a plastic and having a lens-forming surface.

Growing community of inventors

Roswell, GA, United States of America

David Wright