Abstract: A system for inventory management based on transaction predictions can include memory storing transaction records. A processor can be configured to analyze a sequence of the transaction records using a neural network with a long short-term memory (LSTM) layer and at least one dense layer, wherein the LSTM layer extracts temporal purchase patterns and the dense layer generates probabilities of future purchases for multiple items and an estimated time until the next transaction. The processor can optimize the neural network by comparing generated probabilities and time estimates to actual subsequent transactions, computing cross-entropy loss for purchase probabilities and mean squared error for time estimates, and adjusting network weights accordingly. The process can apply the optimized neural network to recent transactions to predict item purchase probabilities and transaction timing, and automatically adjusts inventory levels based on the predictions to optimize inventory management.

Abstract: Methods and systems for predicting are disclosed. Records for transactions can be stored, each record comprising an indication identifying items involved in a transaction and an indication of a time elapsed between the transaction and a previous transaction. The records for transactions can be analyzed to produce a probability that the items will be involved in a next transaction.

Abstract: Systems and methods are described herein for determining user exposures to content, such as content presented by physical objects (e.g., advertisements on billboards). The systems and methods may determine a probability (an “exposure probability”) that a user has viewed or consumed content, information, or other visual media presented by a physical object, such as a billboard, vehicle, sign, or other structure, and provide the determined probability to various attribution systems, such as systems that attribute user engagements (e.g., store visits, app downloads, website visits, product purchases, and so on) to earlier content exposures.

Abstract: Systems and methods are described herein for determining user exposures to content, such as content presented by physical objects (e.g., advertisements on billboards). The systems and methods may determine a probability (an “exposure probability”) that a user has viewed or consumed content, information, or other visual media presented by a physical object, such as a billboard, vehicle, sign, or other structure, and provide the determined probability to various attribution systems, such as systems that attribute user engagements (e.g., store visits, app downloads, website visits, product purchases, and so on) to earlier content exposures.

Abstract: Methods and systems for predicting are disclosed. Records for transactions can be stored, each record comprising an indication identifying items involved in a transaction and an indication of a time elapsed between the transaction and a previous transaction. The records for transactions can be analyzed to produce a probability that the items will be involved in a next transaction.

Abstract: Systems and methods are described herein for determining user exposures to content, such as content presented by physical objects (e.g., advertisements on billboards). The systems and methods may determine a probability (an “exposure probability”) that a user has viewed or consumed content, information, or other visual media presented by a physical object, such as a billboard, vehicle, sign, or other structure, and provide the determined probability to various attribution systems, such as systems that attribute user engagements (e.g., store visits, app downloads, website visits, product purchases, and so on) to earlier content exposures.

Abstract: Systems and methods for predicting are described herein. A record for each of a plurality of events associated with user transactions can be stored. A sequential plurality of the events can be analyzed using a unidirectional long short term memory (LSTM) and first and second dense neural network layers configured to receive output from the LSTM network.

Abstract: Systems and methods are described herein for determining user exposures to content, such as content presented by physical objects (e.g., advertisements on billboards). The systems and methods may determine a probability (an “exposure probability”) that a user has viewed or consumed content, information, or other visual media presented by a physical object, such as a billboard, vehicle, sign, or other structure, and provide the determined probability to various attribution systems, such as systems that attribute user engagements (e.g., store visits, app downloads, website visits, product purchases, and so on) to earlier content exposures.

Abstract: A processor circuit may perform processing associated with storing in a memory a record for each of a plurality of transactions, each record comprising an indication identifying at least one item, of a set of items, involved in the transaction and an indication of a time elapsed between the transaction and a previous transaction. The processor circuit may perform processing associated with analyzing a sequential plurality of the transactions using at least a long short term memory (LSTM) network to produce a probability that the at least one item will be involved in a next transaction for each at least one item, and a time estimate until the next transaction occurs. The processor circuit may perform processing associated with storing each probability and the time estimate in the memory as a prediction.

Abstract: Systems and methods for predicting transactions. A record is stored for each of a plurality of transactions, each record comprising an indication identifying each item from a set of items involved in the transaction and an indication of a time elapsed between the transaction and a previous transaction. A sequential plurality of the transactions is analyzed using a unidirectional long short term memory (LSTM) network to produce a probability that each item from the set of items will be involved in a next transaction, and a time estimate value for a future time when the next transaction will occur. The probability for each item and the time estimate value is stored in the memory as a prediction.

Abstract: Systems and methods are described herein for determining user exposures to content, such as content presented by physical objects (e.g., advertisements on billboards). The systems and methods may determine a probability (an “exposure probability”) that a user has viewed or consumed content, information, or other visual media presented by a physical object, such as a billboard, vehicle, sign, or other structure, and provide the determined probability to various attribution systems, such as systems that attribute user engagements (e.g., store visits, app downloads, website visits, product purchases, and so on) to earlier content exposures.