Aristocrat Technologies Australia Pty Limited

Cerebrumx Labs Private Limited

Amit Gupta

Matthew Chan

Peter Wasielewski

Sandip Ranjhan

Sarika Gupta

Erick T Ching

Philip Wanardi

The present invention provides a robust and effective solution to an entity or an organization by allowing aggregation of a set of automotive data packets pertaining to single trip using one or more common identifiers and one or more timestamps to enable enhanced data analytics and data correlation. The set of automotive data packets may be then segregated based on a plurality of sensor signals and a plurality of calculated signals to obtain a set of derived signals. The set of derived signals can provide an option of moving calculation of derived signals outside of vehicle to reduce computation cost on the vehicle. The size of the data packets may be optimized by sending the plurality of sensor signals outside of minimum signal set, only when one or more values change from the last stored value.

System and method facilitating harmonizing of automotive signals

The present invention provides a robust and effective solution to an entity or an organization for creating and standardizing an Automotive Data Quality Marker (ADQM) to determine/evaluate/predict the quality of automotive data such as Telematics, Body Control, ADAS, Diagnostics, Dashcams, and In-Vehicle Infotainment but not limited to the like generated by the vehicle (i.e., data source) using a machine learning (ML) engine associated with a processing unit. The machine learning engine comprises an amalgamation of machine learning algorithms to determine ADQM for a particular dataset. Data pertaining to vehicles is huge and repetitive. Re-training of the model for improved accuracy is a requirement as automotive data can be augmented with additional signals and data received and stored as trip objects on a regular basis.

Method and system for auto generating automotive data quality marker

The present invention provides a robust and effective solution to an entity or an organization by allowing to visualize the flow of automotive data signals from a source system to a sink system with different metric signals at each step and creating a mechanism for creating automotive signal quality marker (ASQM) for high quality automotive data signals by determining high value data parameters, inter-parameter correlation, data quality variation with trip duration, and frequency of data collection/distribution across datasets. The quality criterion thus developed is applied to data generated from a plurality of data sources. The ASQM may be calculated by converting the developed automotive data signals quality criterion into a numeric value on a specified scale. It can be used by all stakeholders in the value chain for consistent measurement of automotive data signals quality.

System and method facilitating determination of automotive signal quality marker

An electronic gaming system includes at least one processor configured to provide a reel-based wagering game. In the game, a matrix of symbol positions is displayed, and during each spin of the reels, the processor determines whether at least one symbol displayed in the matrix is a target (or collectable) symbol. When at least one target symbol is displayed, the processor also determines whether a threshold number of such target symbols have been accumulated from the matrix by the player, and if so, the processor awards a plurality of free games, each of which is evaluated against an upgraded paytable. The upgraded paytable defines a plurality of upgraded awards for a variety of symbol combinations, at least some of which incorporate the target symbol. In some cases, a different (e.g., upgraded) version of the target symbol may be displayed during free games.

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Haryana, India

Amit Gupta