Visual Iq, Inc.

Sikorsky Aircraft Corporation

The Nielsen Comapny (us), LLC

Payman Sadegh

Anto Chittilappilly

Mark W Davis

Madan Bharadwaj

Darius Jose

James P Cycon

John H Judge

Preston R Bates

Michael G Mastrianni

Parameshvyas Laxminarayan

Rakesh Pillai

Philip Gross

A computer-implemented method, simulation and prediction system, and computer program product for advertising portfolio management. Embodiments commence upon receiving data comprising a plurality of marketing stimulations and respective measured responses, both pertaining to a first time period. A computer is used to form a multi-channel simulation model, where the simulation model accepts the marketing stimulations then outputs simulated responses. The simulation model is used for determining cross-channel weights to apply to the respective measured responses pertaining to the first time period. The simulation model is updated to reflect updated marketing stimulations pertaining to a second time period. The updated marketing stimulations overwrite some of the plurality of marketing stimulations captured in the first time period. The updated simulation model is used in calculating an effectiveness value of a particular one of the updated marketing stimulations based at least in part on the cross-channel weights determined for the first time period.

Performing interactive updates to a precalculated cross-channel predictive model

Fragmented user engagement stacks are generated from users that use multiple devices to view messages. The fragmented user engagement stacks include a universal unique identifier (UUID). A computer platform stores cross-device mapping information, derived from a shared characteristic between two or more devices, that associates the UUIDs of multiple devices to a single user. The computer platform processes the cross-device mapping data to identify the UUIDs from different devices associated with a single user and to join touchpoint encounters from the single user to generate at least one cross-device user engagement stack. The computer platform uses the cross-device user engagement stack and the response data to determine attribution as a measure of influence attributed to touchpoint encounters from a single user.

Cross-device message touchpoint attribution

A measuring system and method that computes and analyzes sensor data fused with multiple mechanical and thermally induced strain measurements is provided. Further, the measuring system and method realizes physics-based relations between sensor readings due to mechanical and thermal sources by optimally de-coupling a total strain into its mechanical and thermal components. The measuring system and method also auto-tunes coefficients involved in the optimal de-coupling equations using sensor specification data and previous system test results for initialization.

Fiber optic weight sensor optimization for landing gear

A method, system, and computer program product identifies attribution of small signal stimulus in noisy response channels. Using machine-learning techniques in a computer, a small signal correlation engine correlates time series stimuli data vectors to time series response data vectors, and generates correlation coefficients that identify contributions of event notifications, including small signal attributes, to aggregated response data. Also using machine-learning techniques in a computer, a learning model simulates variations of stimuli data to predict user responses using the correlation coefficients, including computing a contribution of the small signal attributes of an event notification.

Machine learning techniques that identify attribution of small signal stimulus in noisy response channels

A system and method for allocating credit for an advertising conversion among various advertising touchpoints encounter by the consumer is provided. The system and method comprise receiving data pertaining to touchpoints and conversions of an advertising campaign across multiple channels. Users are correlated across the channels and the various conversions, touchpoints, and touchpoint attributes are identified. Each touchpoint attribute and touchpoint attribute value is assigned a weight. An attribution algorithm is selected, and coefficients are calculated using the assigned weights. The algorithm is executed and true scores corresponding to the touchpoints encountered by each converting user are computed.

Method and system for determining touchpoint attribution

A method of determining a maneuver performed by an aircraft having sensors for monitoring motion data, the method including periodically sampling the sensors to electronically determine segments of motion data of the aircraft; aggregating sequences of the segments of the motion data; comparing the aggregated segments of motion data to models of particular maneuvers; and determining the maneuver performed by the aircraft.

Growing community of inventors

Alpharetta, GA, United States of America

Payman Sadegh