Abstract: Machine-learning based systems and methods are described for optimizing search engine results. A server receives, via a computer network, and associates, via a user profile, user information including search requests, transaction details, and/or profile information, for which a user receives purchasing units. The server executes a machine-learning component to predict, based on the user information, a user action score defining a probability of a user to engage in a new transaction. The server executes a search engine optimization component that receives, from the user's device, a new search request causing the search engine optimization component to generate a search engine offer associated with the new transaction. The server transmits the search engine offer to, and receives an acceptance from, computing device(s) of search engine market participant(s), the acceptance causing a targeted advertisement and search results to be returned to the user device in response to the new search request.

Abstract: Systems and methods are described for aggregating and filling Volume Attentive Trade (VAT) orders using a VAT Liquidity Builder. As described, in various aspects a first grouping of VAT orders may be aggregated by the VAT Liquidity Builder. A second grouping of VAT orders may also be aggregated by the VAT Liquidity Builder. Each of the VAT orders for the first and second groups may each include a set of VAT rules that define a limit price, a share quantity and a VAT delta. The VAT Liquidity Builder may then generate an effective limit price for each of the first group of VAT orders using the corresponding limit prices and VAT deltas, and generate an execution command to fill a particular VAT order having the largest share quantity from the second group of VAT orders with one or more qualifying VAT orders from the first group.

Abstract: Systems and methods are described for a point-of-recognition optimizer system configured to optimize onsite user purchases at a physical location. In various aspects, a purchasable-unit identifier (ID) may be received via a computer transmission, where the purchasable-unit ID, as identified by an optimizer device, is associated with a recognized purchasable-unit located onsite with the optimizer device. Based on the purchasable-unit ID, a plurality of competing purchasable-units may be identified, which may be either onsite or offsite purchasable-units. An offer is transmitted via a second computer transmission for an offered purchasable-unit to the optimizer device where the offer originates from an outbidding purchasable-unit distributor, and where the outbidding purchasable-unit distributor outbid other competing purchasable-unit distributors, each distributor corresponding to the plurality of competing purchasable-units, for an opportunity for the optimizer device to receive the offer.

Abstract: Machine-learning based systems and methods are described for optimizing search engine results. A server receives, via a computer network, and associates, via a user profile, user information including search requests, transaction details, and/or profile information, for which a user receives purchasing units. The server executes a machine-learning component to predict, based on the user information, a user action score defining a probability of a user to engage in a new transaction. The server executes a search engine optimization component that receives, from the user's device, a new search request causing the search engine optimization component to generate a search engine offer associated with the new transaction. The server transmits the search engine offer to, and receives an acceptance from, computing device(s) of search engine market participant(s), the acceptance causing a targeted advertisement and search results to be returned to the user device in response to the new search request.

Abstract: Systems and methods are described for aggregating and filling Volume Attentive Trade (VAT) orders using a VAT Liquidity Builder. As described, in various aspects a first grouping of VAT orders may be aggregated by the VAT Liquidity Builder. A second grouping of VAT orders may also be aggregated by the VAT Liquidity Builder. Each of the VAT orders for the first and second groups may each include a set of VAT rules that define a limit price, a share quantity and a VAT delta. The VAT Liquidity Builder may then generate an effective limit price for each of the first group of VAT orders using the corresponding limit prices and VAT deltas, and generate an execution command to fill a particular VAT order having the largest share quantity from the second group of VAT orders with one or more qualifying VAT orders from the first group.

Abstract: Systems and methods are described for a point-of-recognition optimizer system configured to optimize onsite user purchases at a physical location. In various aspects, a purchasable-unit identifier (ID) may be received via a computer transmission, where the purchasable-unit ID, as identified by an optimizer device, is associated with a recognized purchasable-unit located onsite with the optimizer device. Based on the purchasable-unit ID, a plurality of competing purchasable-units may be identified, which may be either onsite or offsite purchasable-units. An offer is transmitted via a second computer transmission for an offered purchasable-unit to the optimizer device where the offer originates from an outbidding purchasable-unit distributor, and where the outbidding purchasable-unit distributor outbid other competing purchasable-unit distributors, each distributor corresponding to the plurality of competing purchasable-units, for an opportunity for the optimizer device to receive the offer.

Abstract: Systems and methods are described for a point-of-recognition optimizer system configured to optimize onsite user purchases at a physical location. In various aspects, a purchasable-unit identifier (ID) may be received via a computer transmission, where the purchasable-unit ID, as identified by an optimizer device, is associated with a recognized purchasable-unit located onsite with the optimizer device. Based on the purchasable-unit ID, a plurality of competing purchasable-units may be identified, which may be either onsite or offsite purchasable-units. An offer is transmitted via a second computer transmission for an offered purchasable-unit to the optimizer device where the offer originates from an outbidding purchasable-unit distributor, and where the outbidding purchasable-unit distributor outbid other competing purchasable-unit distributors, each distributor corresponding to the plurality of competing purchasable-units, for an opportunity for the optimizer device to receive the offer.

Abstract: Machine-learning based systems and methods are described for optimizing search engine results. A server receives, via a computer network, and associates, via a user profile, user information including search requests, transaction details, and/or profile information, for which a user receives purchasing units. The server executes a machine-learning component to predict, based on the user information, a user action score defining a probability of a user to engage in a new transaction. The server executes a search engine optimization component that receives, from the user's device, a new search request causing the search engine optimization component to generate a search engine offer associated with the new transaction. The server transmits the search engine offer to, and receives an acceptance from, computing device(s) of search engine market participant(s), the acceptance causing a targeted advertisement and search results to be returned to the user device in response to the new search request.

Abstract: Machine-learning based systems and methods are described for optimizing search engine results. A server receives, via a computer network, and associates, via a user profile, user information including search requests, transaction details, and/or profile information, for which a user receives purchasing units. The server executes a machine-learning component to predict, based on the user information, a user action score defining a probability of a user to engage in a new transaction. The server executes a search engine optimization component that receives, from the user's device, a new search request causing the search engine optimization component to generate a search engine offer associated with the new transaction. The server transmits the search engine offer to, and receives an acceptance from, computing device(s) of search engine market participant(s), the acceptance causing a targeted advertisement and search results to be returned to the user device in response to the new search request.

Abstract: Machine-learning based systems and methods are described for optimizing search engine results. A server receives, via a computer network, and associates, via a user profile, user information including search requests, transaction details, and/or profile information, for which a user receives purchasing units. The server executes a machine-learning component to predict, based on the user information, a user action score defining a probability of a user to engage in a new transaction. The server executes a search engine optimization component that receives, from the user's device, a new search request causing the search engine optimization component to generate a search engine offer associated with the new transaction. The server transmits the search engine offer to, and receives an acceptance from, computing device(s) of search engine market participant(s), the acceptance causing a targeted advertisement and search results to be returned to the user device in response to the new search request.

Abstract: Systems and methods are described for aggregating and filling Volume Attentive Trade (VAT) orders using a VAT Liquidity Builder. As described, in various aspects a first grouping of VAT orders may be aggregated by the VAT Liquidity Builder. A second grouping of VAT orders may also be aggregated by the VAT Liquidity Builder. Each of the VAT orders for the first and second groups may each include a set of VAT rules that define a limit price, a share quantity and a VAT delta. The VAT Liquidity Builder may then generate an effective limit price for each of the first group of VAT orders using the corresponding limit prices and VAT deltas, and generate an execution command to fill a particular VAT order having the largest share quantity from the second group of VAT orders with one or more qualifying VAT orders from the first group.

Abstract: A method of optimizing future profits may include receiving an order inquiry and/or data of the user at a server device, including one or more product of interest, and training a machine learning model by analyzing labeled data. The method may include analyzing the data of the user and at least one product of interest using the trained machine learning model to generate a discounted price for the product of interest that is lower than the credit card price of the product of interest, and/or a credit limit associated with the user. The discounted price and/or credit limit may be transmitted to a user device, and an economizer selection may be received which circumvents the usage of a credit card, and a withdrawal request and/or deposit request may be initiated in response to the economizer selection.

Abstract: Systems and methods are described for aggregating and filling Volume Attentive Trade (VAT) orders using a VAT Liquidity Builder. As described, in various aspects a first grouping of VAT orders may be aggregated by the VAT Liquidity Builder. A second grouping of VAT orders may also be aggregated by the VAT Liquidity Builder. Each of the VAT orders for the first and second groups may each include a set of VAT rules that define a limit price, a share quantity and a VAT delta. The VAT Liquidity Builder may then generate an effective limit price for each of the first group of VAT orders using the corresponding limit prices and VAT deltas, and generate an execution command to fill a particular VAT order having the largest share quantity from the second group of VAT orders with one or more qualifying VAT orders from the first group.

Abstract: Systems and methods are described for a point-of-recognition optimizer system configured to optimize onsite user purchases at a physical location. In various aspects, a purchasable-unit identifier (ID) may be received via a computer transmission, where the purchasable-unit ID, as identified by an optimizer device, is associated with a recognized purchasable-unit located onsite with the optimizer device. Based on the purchasable-unit ID, a plurality of competing purchasable-units may be identified, which may be either onsite or offsite purchasable-units. An offer is transmitted via a second computer transmission for an offered purchasable-unit to the optimizer device where the offer originates from an outbidding purchasable-unit distributor, and where the outbidding purchasable-unit distributor outbid other competing purchasable-unit distributors, each distributor corresponding to the plurality of competing purchasable-units, for an opportunity for the optimizer device to receive the offer.

Abstract: Systems and methods are described for a point-of-recognition optimizer system configured to optimize onsite user purchases at a physical location. In various aspects, a purchasable-unit identifier (ID) may be received via a computer transmission, where the purchasable-unit ID, as identified by an optimizer device, is associated with a recognized purchasable-unit located onsite with the optimizer device. Based on the purchasable-unit ID, a plurality of competing purchasable-units may be identified, which may be either onsite or offsite purchasable-units. An offer is transmitted via a second computer transmission for an offered purchasable-unit to the optimizer device where the offer originates from an outbidding purchasable-unit distributor, and where the outbidding purchasable-unit distributor outbid other competing purchasable-unit distributors, each distributor corresponding to the plurality of competing purchasable-units, for an opportunity for the optimizer device to receive the offer.

Abstract: Systems and methods are described for aggregating and filling Volume Attentive Trade (VAT) orders using a VAT Liquidity Builder. As described, in various aspects a first grouping of VAT orders may be aggregated by the VAT Liquidity Builder. A second grouping of VAT orders may also be aggregated by the VAT Liquidity Builder. Each of the VAT orders for the first and second groups may each include a set of VAT rules that define a limit price, a share quantity and a VAT delta. The VAT Liquidity Builder may then generate an effective limit price for each of the first group of VAT orders using the corresponding limit prices and VAT deltas, and generate an execution command to fill a particular VAT order having the largest share quantity from the second group of VAT orders with one or more qualifying VAT orders from the first group.

Abstract: Systems and methods are described for aggregating and filling Volume Attentive Trade (VAT) orders using a VAT Liquidity Builder. As described, in various aspects a first grouping of VAT orders may be aggregated by the VAT Liquidity Builder. A second grouping of VAT orders may also be aggregated by the VAT Liquidity Builder. Each of the VAT orders for the first and second groups may each include a set of VAT rules that define a limit price, a share quantity and a VAT delta. The VAT Liquidity Builder may then generate an effective limit price for each of the first group of VAT orders using the corresponding limit prices and VAT deltas, and generate an execution command to fill a particular VAT order having the largest share quantity from the second group of VAT orders with one or more qualifying VAT orders from the first group.

Abstract: Systems and methods are described for a point-of-recognition optimizer system configured to optimize onsite user purchases at a physical location. In various aspects, a purchasable-unit identifier (ID) may be received via a computer transmission, where the purchasable-unit ID, as identified by an optimizer device, is associated with a recognized purchasable-unit located onsite with the optimizer device. Based on the purchasable-unit ID, a plurality of competing purchasable-units may be identified, where the plurality of competing purchasable-units includes the recognized purchasable-unit and one or more additional purchasable-units, which may be either onsite or offsite purchasable-units.