Abstract: One embodiment comprises data processing system comprising a data store storing a set of inventory records for a program pool of vehicles, a processor and a memory coupled to the processor storing a set of computer executable instructions. The set of computer executable instructions may be executable to receive, from a mobile application, a selection of a vehicle from the program pool, retrieve the inventory record for the selected vehicle, create, at the server, an order to capture information about a transaction, receive a request from the mobile application to view an electronic document associated with the selected vehicle; responsive to the request to view the electronic document, automatically populate the electronic document with the order information; and communicate the electronic document to the mobile application for presentation in the operator interface of a mobile device.

Abstract: Systems, methods and products for determining a consumer-facing price using a demand model that is generated based on historical transactional data. One embodiment comprises a method implemented in a pricing module of an automotive data processing system. Data that identifies a consumer (or consumer group) and a vehicle type are received and the demand model is accessed to generate a payment corresponding to the attributes of the consumer and the attributes of the vehicle type. The demand model may be implemented in a machine learning engine that maintains a set of weights ? used in a predictive demand function. The weights are adjusted by the machine learning engine to minimize a loss function which measures deviation of demand estimated by the predictive demand function from the demand indicated by a set of historical transaction data.

Abstract: A system for building, training and productionizing machine learning models is disclosed. A model training specification is received, and a plurality of sets of hyper-parameters is obtained. Sets of training data and hyper parameter sets are distributed to distributed training systems. Models are trained in parallel using different sets of training data. Models are trained using multiple sets of hyper parameters. A candidate hyper-parameter set is selected, based on a measure of estimated effectiveness of the trained predictive models, and a production predictive model is generated by training a predictive model using the selected candidate hyper-parameter set and the complete set of training data.

Abstract: A computer system that provides a low friction operator interface on a mobile device is disclosed. The computer system can comprise a mobile device processor and a mobile application. The mobile application is executable to present a graphical user interface at the mobile device, the graphical user interface comprising series of application pages having inputs for a limited set of user record information; receive the limited set of user record information via the user interface; enhance the limited set of user record information with personally identifiable information extracted from an image and send the enhanced set of user record information to a server for processing.

Abstract: One embodiment comprises data processing system comprising a data store storing a set of inventory records for a program pool of vehicles, a processor and a memory coupled to the processor storing a set of computer executable instructions. The set of computer executable instructions may be executable to receive, from a mobile application, a selection of a vehicle from the program pool, retrieve the inventory record for the selected vehicle, create, at the server, an order to capture information about a transaction, receive a request from the mobile application to view an electronic document associated with the selected vehicle; responsive to the request to view the electronic document, automatically populate the electronic document with the order information; and communicate the electronic document to the mobile application for presentation in the operator interface of a mobile device.