Abstract: Systems, apparatus, methods, and computer program products are provided for optimized and effective leading messages, which may be an email subject that may provoke a consumer to access the body portion of an email message. A system may include circuitry configured to programmatically determine a predicted access rate for a leading message when the leading message is provided as a portion of a promotional message. Circuitry may be configured to track historical data indicating the access rates of leading messages and/or leading message terms. The circuitry may be configured to leverage the historical data to determine predicted access rates for leading messages, such as based at least in part on historical access rates associated with one or more leading message terms of the leading message.

Abstract: Embodiments provide a computer-executable method, computer system and non-transitory computer-readable medium for programmatically generating an association among two or more purchase categories based on purchase data of a plurality of consumers. The method includes programmatically accessing, from a dataset via a network device, prior purchase data associated with purchases of a plurality of commercial objects by a plurality of consumers. The method also includes programmatically identifying a plurality of categories associated with the plurality of commercial objects. The method also includes, for each consumer in the plurality of consumers, programmatically generating a total number of purchases by the consumer in each category in the plurality of categories.

Abstract: The present disclosure relates to inferring device rendered object interaction behavior. An apparatus is configured to receive historical device rendered object interaction data associated with a client device. The historical device rendered object interaction data includes a plurality of records. Each record is associated with an device rendered object category. For each record, the device rendered object category is extracted. For each device rendered object category, a penalty value is generated. The penalty value is a programmatically generated predicted network time delay during which device rendered objects associated with the device rendered object category are not to be transmitted to the client device. A penalty decision, used to determine whether to apply the penalty value to future transmissions of device rendered objects to the client device, is generated.

Abstract: The method, apparatus and computer program product described herein is configured to train and deploy a predictive model that is configured to generate a predicted ROI value for a provider with respect to a current promotion or a future promotion. An example embodiment may comprise receiving input indicative of one or more attributes of a provider or a promotion. The example embodiment may further comprise generating at least one of a predicted return on investment (ROI) value or a predicted ROI component value based at least in part on the one or more attributes of the provider or the promotion and a ROI prediction model. The method may further still comprise generating a merchant impact report including the at least one of the predicted ROI value or the predicted ROI component value for the promotion.

Abstract: A method, apparatus, and computer program product are disclosed for improved promotion targeting. An example apparatus includes a processor configured to cause retrieval of historical information regarding transactions associated with a plurality of identifier entities. The example apparatus further includes modeling circuitry configured to train a statistical model using the retrieved historical information, and estimate, using the statistical model, values for expected identifier entity transaction requests associated with each of the plurality of identifier entities. The processor of the example apparatus may further be configured to select, based on the estimated values, a subset of identifier entities to receive impressions of the promotion, and the apparatus may further include communications circuitry configured to transmit an impression of the promotion to each identifier entity in the subset.

Abstract: The unified payment system, product and method provide an effective and efficient way to better communicate to a merchant the value of running a promotion and determine a deal structure that works for the merchant, the customer, the promotion system, or any combination thereof. The unified payment system, product and method provide real-time ROI calculations that a merchant and sales representative can collaboratively simultaneously work on to identify a deal structure for the merchant to select. In this way, the merchant and sales representative may arrive at a mutually acceptable payment plan.

Abstract: The method, apparatus and computer program product described herein is configured to train and deploy a predictive model that is configured to generate a predicted ROI value for a provider with respect to a current promotion or a future promotion. An example embodiment may comprise receiving input indicative of one or more attributes of a provider or a promotion. The example embodiment may further comprise generating at least one of a predicted return on investment (ROI) value or a predicted ROI component value based at least in part on the one or more attributes of the provider or the promotion and a ROI prediction model. The method may further still comprise generating a merchant impact report including the at least one of the predicted ROI value or the predicted ROI component value for the promotion.

Abstract: A method, apparatus, and computer program product are disclosed for improved machine learning using a statistical model. In the context of an apparatus, some example embodiments include a processor configured to cause retrieval of information regarding a plurality of consumers, and modeling circuitry configured to train a statistical model of the plurality of consumers based on the retrieved information, and predict, using the statistical model, an incremental booking value associated with the promotion for each consumer of the plurality of consumers. The processor is further configured to select a subset of the plurality of consumers for receiving impressions of the promotion. Some example embodiments may further include communications circuitry configured to transmit an impression of the promotion to each consumer in the subset of the plurality of consumers.

Abstract: The unified payment system, product and method provide an effective and efficient way to better communicate to a merchant the value of running a promotion and determine a deal structure that works for the merchant, the customer, the promotion system, or any combination thereof. The unified payment system, product and method provide real-time ROI calculations that a merchant and sales representative can collaboratively simultaneously work on to identify a deal structure for the merchant to select. In this way, the merchant and sales representative may arrive at a mutually acceptable payment plan.

Abstract: A method, apparatus, and computer program product are disclosed for improved machine learning using a statistical model. In the context of an apparatus, some example embodiments include a processor configured to cause retrieval of information regarding a plurality of consumers, and modeling circuitry configured to train a statistical model of the plurality of consumers based on the retrieved information, and predict, using the statistical model, an incremental booking value associated with the promotion for each consumer of the plurality of consumers. The processor is further configured to select a subset of the plurality of consumers for receiving impressions of the promotion. Some example embodiments may further include communications circuitry configured to transmit an impression of the promotion to each consumer in the subset of the plurality of consumers.

Abstract: The unified payment system, product and method provide an effective and efficient way to better communicate to a merchant the value of running a promotion and determine a deal structure that works for the merchant, the customer, the promotion system, or any combination thereof. The unified payment system, product and method provide real-time ROI calculations that a merchant and sales representative can collaboratively simultaneously work on to identify a deal structure for the merchant to select. In this way, the merchant and sales representative may arrive at a mutually acceptable payment plan.

Abstract: A computer-implemented method of optimizing real time profit for guaranteed deal bidding includes receiving a plurality of inputs for a guaranteed deal. The computer-implemented method also includes formulating an expected profit for the guaranteed deal based on the plurality of inputs. Further, the computer-implemented method includes optimizing the expected profit dynamically by varying a bid amount. Furthermore, the computer-implemented method includes rendering an advertisement corresponding to a bidder to attain a maximum profit.

Abstract: A method, computer system, and computer program product for determining N disulphide bond connections in a protein having 2N bonded sulphur atoms. Each sulphur atom is available for forming the N disulphide bond connections through use of an undirected graph having the 2N sulphur atoms as nodes and having an edge connecting each pair of nodes. Each sulphur atom is comprised by a cysteine of the protein. The cysteines are sequenced along a chain in the protein as ordered list of position indexes. A weight W of each edge between cysteine position indexes I1 and I2 is calculated as decreasing with increasing separation d between I1 and I2, wherein d is |I1?I2|/100. The N disulphide bond connections are determined by optimizing an objective function formed from the calculated weights W.

Abstract: A method, computer system, and computer program product for determining N disulphide bond connections in a protein having 2N bonded sulphur atoms. Each sulphur atom is available for forming the N disulphide bond connections through use of an undirected graph having the 2N sulphur atoms as nodes and having an edge connecting each pair of nodes. Each sulphur atom is comprised by a cysteine of the protein. The cysteines are sequenced along a chain in the protein as ordered list of position indexes. A weight W of each edge between cysteine position indexes I1 and I2 is calculated as decreasing with increasing separation d between I1 and I2, wherein d is |I1?I2|/100. The N disulphide bond connections are determined by optimizing an objective function formed from the calculated weights W.

Abstract: A method, computer system, and computer program product for determining N disulphide bond connections in a protein having 2N bonded sulphur atoms. Each sulphur atom is available for forming the N disulphide bond connections through use of an undirected graph having the 2N sulphur atoms as nodes and having an edge connecting each pair of nodes. Each sulphur atom is comprised by a cysteine of the protein. The cysteines are sequenced along a chain in the protein as ordered list of position indexes. A weight W of each edge between cysteine position indexes I1 and I2 is calculated as decreasing with increasing separation d between I1 and I2, wherein d is |I1?I2|/100. The N disulphide bond connections are determined by optimizing an objective function formed from the calculated weights W.

Abstract: A method, computer system, and computer program product for determining N disulphide bond connections in a protein having 2N bonded sulphur atoms. Each sulphur atom is available for forming the N disulphide bond connections through use of an undirected graph having the 2N sulphur atoms as nodes and having an edge connecting each pair of nodes. Each sulphur atom is comprised by a cysteine of the protein. The cysteines are sequenced along a chain in the protein as ordered list of position indexes. A weight W of each edge between cysteine position indexes I1 and I2 is calculated as decreasing with increasing separation d between I1 and I2, wherein d is |I1?I2|/100. The N disulphide bond connections are determined by optimizing an objective function formed from the calculated weights W.