Abstract: According to various embodiments, a drone is configured with a camera and is in communication with onboard control systems and/or control systems in remote devices to detect and report gas leaks during flights. The control subsystems enable uniform incident reporting and imaging using specialized optical sensors and color schemes. The presently described systems and methods enable efficient detection, annotation, and reporting of gas leaks utilizing a unique combination and configuration of drone technology, video capture, artificial intelligence (AI), and data integration.

Abstract: The present invention relates to a system and method for the system and method for modeling demand by consumer segments. In some embodiments a segment data organizer may receive transaction data. Transaction data may include transaction logs (T logs) from point of sales records from a retailer. These transaction logs, for the most part, include identification information for each transaction. The segment data organizer may also receive customer identification data which includes groupings of customers by consumer segments. The identification information within the transaction logs may be cross referenced by the customer identification data in order to generate groupings of transactions belonging to consumers in each segment. The organizer may then also aggregate the transaction logs by location, time series and product. The aggregated data may be supplied to an econometric engine capable of generating elasticity coefficients for each set of aggregate data.

Abstract: The present invention relates to a system and method for tuning demand coefficients. Transaction data for product categories is received from a store(s). Price elasticity and uncertainty values are selected for the product categories. This transaction data may be seeded with generic price elasticity and uncertainty values. Product categories where the transaction history is not sufficient enough to generate accurate demand coefficients may be identified. Tuning parameters for a product category are estimated using price elasticity and uncertainty values. The tuning parameters include price elasticity mean and price elasticity standard deviation. A modified likelihood function is generated by applying a normally distributed price elasticity term. The modified likelihood function may then be solved for its maxima, thereby generating tuned demand coefficients which may be output to a pricing optimization system for product price setting, and/or may be stored for later product categories.