Abstract: Methods and systems for using reinforcement learning to optimize promotions are disclosed. A promotion can be offered to a customer computing device, using a reinforcement learning model with a sensitivity parameter. A customer's reaction to the promotion can be observed.

Abstract: Systems and methods for identifying item substitutions. History information can be collected. The history information can be transformed into a matrix of observed substitutions. A neural network can be trained on the matrix of observed substitutions to generate item embeddings. A substitution similarity between the item and another item based on the item embeddings can be identified.

Abstract: Systems and methods for optimizing cyber security projects. A method may include identifying cyber security related projects used to protect entity assets; and solving an optimization programming problem algorithm, using an optimization engine comprising an evolutionary algorithm, to identify project synergies to select a subset of cyber security related projects that optimize expected loss reduction from cyber security related threat.

Abstract: Systems and methods for identifying item substitutions. History information can be collected. The history information can be transformed into a matrix of observed substitutions. A neural network can be trained on the matrix of observed substitutions to generate item embeddings. A substitution similarity between the item and another item based on the item embeddings can be identified.

Abstract: Systems and methods are disclosed for displaying information related to items in a store. Stock keeping unit (SKU) information for items in the store and combination information indicating which SKUs were sold together can be determined.

Abstract: Systems and methods for identifying item substitutions. History information can be collected. The history information can be transformed into a matrix of observed substitutions. A neural network can be trained on the matrix of observed substitutions to generate item embeddings. A substitution similarity between the item and another item based on the item embeddings can be identified.

Abstract: Methods and Systems for context-sensitive manipulation of an object via a presentation software are disclosed.

Abstract: Systems and methods for identifying item substitutions. History information can be collected. The history information can include one or more episodes from one or more customers. Each episode can include one or more items. The history information can be transformed into a matrix of observed substitutions. A neural network can be trained on the matrix of observed substitutions to generate item embeddings. Input including an item can be received. A substitution similarity between the item and another item based on the item embeddings can be identified.

Abstract: Systems and methods for managing data. Person and position data can be copied from a database and it can be determined whether a person is aligned to any positions. Information can be displayed representing the person and indicating any positions to which the person is aligned.

Abstract: A method for screening documents. The order of the text in the documents can be changed in order to create additional documents. A vocabulary of words from the text in the documents can be built. The words can be coded as vectors. Vector relationships can be defined. The documents can be classified using the vector relationships.

Abstract: Systems and methods for managing data. Person and position data can be copied from a database and it can be determined whether a person is aligned to any positions. Information can be displayed representing the person and indicating any positions to which the person is aligned.

Abstract: Methods and systems of using reinforcement learning to optimizing promotions. A promotion can be offered to a user using a reinforcement learning model with a sensitivity parameter, the reinforcement module estimating a time period during which the user will respond to the first information. The user's reaction to the promotion can be observed. The reinforcement learning model can be adapted based on the user's reaction.

Abstract: Systems and methods for identifying item substitutions. History information can be collected. The history information can include one or more episodes from one or more customers. Each episode can include one or more items. The history information can be transformed into a matrix of observed substitutions. A neural network can be trained on the matrix of observed substitutions to generate item embeddings. Input including an item can be received. A substitution similarity between the item and another item based on the item embeddings can be identified. The item embeddings can be clustered to generate need states.

Abstract: Systems and methods are disclosed for displaying information related to items in a store. Stock keeping unit (SKU) information for items in the store and combination information indicating which SKUs were sold together can be determined.

Abstract: Systems and methods for joining a video conference. A first function can be found, wherein the first function can interact with a user interface element and can be used to call a video conference bot. The first function can be replaced with a second function; wherein the second function can be used to call the video conference bot but may not interact with the user interface element. The second function can be used to join the video conference without interacting with the DOM.

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 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: Methods and systems for providing a network graphical representation of database records. Database records of a record class can be selected according to descriptive criteria. An attribute of the record class can be identified from different data sources, and a network node can be associated to an instance of the attribute from the database records. The network node can be connected with a network link that designates the network node and comprises a common instance of the attribute, wherein the network link can be based on a co-occurrence of attribute values. Additional network nodes can be connected to other network nodes with network links that designate associations between the network nodes. The database records can be visualized using network nodes and network links.

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.