Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for tokenizing offers. The current disclosure presents a system and method that can present a tokenized offer for saving and retrieving from a digital wallet. The tokenized offer may be presented on a client site for selection by a user interested in taking advantage of the promotion without having to leave the current site. The current disclosure also presents a system that can retrieve the tokenized offer saved for use with a purchase. The tokenized offer may be automatically applied and on display at the merchant site and/or retrieved from the digital wallet.

Abstract: In one embodiment, a system and method is illustrated including receiving a feedback request identifying a particular user, retrieving a feedback entry in response to the feedback request, the feedback entry containing a first term, building a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term appears in a searchable data structure, mapping the first term to a graphical illustration based upon a second term associated with the graphical illustration such that the graphical illustration may be used to represent the second term, and generating a feedback page containing the first term and the graphical illustration. The method may include assigning a value to the first term so as to identify the first term, assigning the first term to the searchable data structure, and extracting the first term from the searchable data structure based, in part, upon an extraction rule.

Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for tokenizing offers. The current disclosure presents a system and method that can present a tokenized offer for saving and retrieving from a digital wallet. The tokenized offer may be presented on a client site for selection by a user interested in taking advantage of the promotion without having to leave the current site. The current disclosure also presents a system that can retrieve the tokenized offer saved for use with a purchase. The tokenized offer may be automatically applied and on display at the merchant site and/or retrieved from the digital wallet.

Abstract: In one embodiment, a system and method is illustrated including receiving a feedback request identifying a particular user, retrieving a feedback entry in response to the feedback request, the feedback entry containing a first term, building a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term appears in a searchable data structure, mapping the first term to a graphical illustration based upon a second term associated with the graphical illustration such that the graphical illustration may be used to represent the second term, and generating a feedback page containing the first term and the graphical illustration. The method may include assigning a value to the first term so as to identify the first term, assigning the first term to the searchable data structure, and extracting the first term from the searchable data structure based, in part, upon an extraction rule.

Abstract: In one embodiment, a system and method is illustrated including receiving a feedback request identifying a particular user, retrieving a feedback entry in response to the feedback request, the feedback entry containing a first term, building a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term appears in a searchable data structure, mapping the first term to a graphical illustration based upon a second term associated with the graphical illustration such that the graphical illustration may be used to represent the second term, and generating a feedback page containing the first term and the graphical illustration. The method may include assigning a value to the first term so as to identify the first term, assigning the first term to the searchable data structure, and extracting the first term from the searchable data structure based, in part, upon an extraction rule.

Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for tokenizing offers. The current disclosure presents a system and method that can present a tokenized offer for saving and retrieving from a digital wallet. The tokenized offer may be presented on a client site for selection by a user interested in taking advantage of the promotion without having to leave the current site. The current disclosure also presents a system that can retrieve the tokenized offer saved for use with a purchase. The tokenized offer may be automatically applied and on display at the merchant site and/or retrieved from the digital wallet.

Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for tokenizing offers. The current disclosure presents a system and method that can present a tokenized offer for saving and retrieving from a digital wallet. The tokenized offer may be presented on a client site for selection by a user interested in taking advantage of the promotion without having to leave the current site. The current disclosure also presents a system that can retrieve the tokenized offer saved for use with a purchase. The tokenized offer may be automatically applied and on display at the merchant site and/or retrieved from the digital wallet.

Abstract: In one embodiment, a system and method is illustrated including receiving a feedback request identifying a particular user, retrieving a feedback entry in response to the feedback request, the feedback entry containing a first term, building a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term appears in a searchable data structure, mapping the first term to a graphical illustration based upon a second term associated with the graphical illustration such that the graphical illustration may be used to represent the second term, and generating a feedback page containing the first term and the graphical illustration. The method may include assigning a value to the first term so as to identify the first term, assigning the first term to the searchable data structure, and extracting the first term from the searchable data structure based, in part, upon an extraction rule.

Abstract: In one embodiment, a system and method is illustrated including receiving a feedback request identifying a particular user, retrieving a feedback entry in response to the feedback request, the feedback entry containing a first term, building a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term appears in a searchable data structure, mapping the first term to a graphical illustration based upon a second term associated with the graphical illustration such that the graphical illustration may be used to represent the second term, and generating a feedback page containing the first term and the graphical illustration. The method may include assigning a value to the first term so as to identify the first term, assigning the first term to the searchable data structure, and extracting the first term from the searchable data structure based, in part, upon an extraction rule.

Abstract: Embodiments of computer-implemented methods and systems for activity-based recommendations are described. One example embodiment includes receiving historical activities of a plurality of users, the historical activities comprising actions associated with the items in the database, the plurality of users including the target user. A group of users is identified from the plurality of users based on historical activities of the group of users and historical activities of the target user. Generally current time activities of the group of users are received, the generally current time activities comprising activities that have occurred within a defined time window. A recommendation for one or more items is generated based on the generally current time activities, and the recommendation is provided for display to the target user.

Abstract: Embodiments of computer-implemented methods and systems for activity-based recommendations are described. One example embodiment includes receiving data indicating historical activities of a user community, the historical activities including historical activities of the target user, selecting a reference group of users from the user community based on analysis of the historical activities of the target user, receiving generally current time activities of the reference group of users, the generally current time activities including those activities that have occurred within a defined time window, and recommending items to the target user based on the generally current time activities of the reference group of users.

Abstract: Embodiments of computer-implemented methods and systems for activity-based recommendations are described. One example embodiment includes receiving data indicating historical activities of a user community, the historical activities including historical activities of the target user, selecting a reference group of users from the user community based on analysis of the historical activities of the target user, receiving generally current time activities of the reference group of users, the generally current time activities including those activities that have occurred within a defined time window, and recommending items to the target user based on the generally current time activities of the reference group of users.

Abstract: Embodiments of computer-implemented methods and systems for activity-based recommendations are described. One example embodiment includes receiving data indicating historical activities of a user community, the historical activities including historical activities of the target user, selecting a reference group of users from the user community based on analysis of the historical activities of the target user, receiving generally current time activities of the reference group of users, the generally current time activities including those activities that have occurred within a defined time window, and recommending items to the target user based on the generally current time activities of the reference group of users.

Abstract: In one embodiment, a system and method is illustrated including receiving a feedback request identifying a particular user, retrieving a feedback entry in response to the feedback request, the feedback entry containing a first term, building a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term appears in a searchable data structure, mapping the first term to a graphical illustration based upon a second term associated with the graphical illustration such that the graphical illustration may be used to represent the second term, and generating a feedback page containing the first term and the graphical illustration. The method may include assigning a value to the first term so as to identify the first term, assigning the first term to the searchable data structure, and extracting the first term from the searchable data structure based, in part, upon an extraction rule.

Abstract: In one embodiment, a system and method is illustrated including receiving a feedback request identifying a particular user, retrieving a feedback entry in response to the feedback request, the feedback entry containing a first term, building a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term appears in a searchable data structure, mapping the first term to a graphical illustration based upon a second term associated with the graphical illustration such that the graphical illustration may be used to represent the second term, and generating a feedback page containing the first term and the graphical illustration. The method may include assigning a value to the first term so as to identify the first term, assigning the first term to the searchable data structure, and extracting the first term from the searchable data structure based, in part, upon an extraction rule.

Abstract: Embodiments of computer-implemented methods and systems for activity-based recommendations are described. One example embodiment includes receiving data indicating historical activities of a user community, the historical activities including historical activities of the target user, selecting a reference group of users from the user community based on analysis of the historical activities of the target user, receiving generally current time activities of the reference group of users, the generally current time activities including those activities that have occurred within a defined time window, and recommending items to the target user based on the generally current time activities of the reference group of users.

Abstract: Embodiments of computer-implemented methods and systems for activity-based recommendations are described. One example embodiment includes receiving data indicating historical activities of a user community, the historical activities including historical activities of the target user, selecting a reference group of users from the user community based on analysis of the historical activities of the target user, receiving generally current time activities of the reference group of users, the generally current time activities including those activities that have occurred within a defined time window, and recommending items to the target user based on the generally current time activities of the reference group of users.

Abstract: A method for near real time patterns identification, in one example embodiment, comprises receiving a data stream containing information associated with a transaction and participants of the transaction and receiving an Artificial Intelligence (AI) algorithm trained to score data in the data stream. The method may further comprise receiving metadata associated with the historical information, comparing the data stream to the metadata by measuring differences between variables included in the historical metadata and the data stream. The method may further comprise modifying the data stream to suit the AI algorithm when the differences between variables are below predetermined threshold values and retraining the AI algorithm based on the data stream when the differences between the variables are greater than the predetermined threshold values. The method may further comprise feeding the data stream to the AI algorithm to classify the variables in the data stream.

Abstract: Embodiments of computer-implemented methods and systems for activity-based recommendations are described. One example embodiment includes receiving data indicating historical activities of a user community, the historical activities including historical activities of the target user, selecting a reference group of users from the user community based on analysis of the historical activities of the target user, receiving generally current time activities of the reference group of users, the generally current time activities including those activities that have occurred within a defined time window, and recommending items to the target user based on the generally current time activities of the reference group of users.

Abstract: A method for near real time patterns identification, in one example embodiment, comprises receiving a data stream containing information associated with a transaction and participants of the transaction and receiving an Artificial Intelligence (AI) algorithm trained to score data in the data stream. The method may further comprise receiving metadata associated with the historical information, comparing the data stream to the metadata by measuring differences between variables included in the historical metadata and the data stream. The method may further comprise modifying the data stream to suit the AI algorithm when the differences between variables are below predetermined threshold values and retraining the AI algorithm based on the data stream when the differences between the variables are greater than the predetermined threshold values. The method may further comprise feeding the data stream to the AI algorithm to classify the variables in the data stream.