The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

A computer-implement method comprises: training a classifier with labeled data from a dataset; classifying, by the trained classifier, unlabeled data from the dataset; providing, by the classifier to a policy gradient, a reward signal for each data/query pair; transferring, by the classifier to a ranker, learning; training, by the policy gradient, the ranker; ranking data from the dataset based on a query; and retrieving data from the ranked data in response to the query.

A method of normalizing URLs associated with a real user session comprises extracting uniform resource locators (URLs) from ingested spans where at least a portion of the URLs comprise unique URL strings. The method also comprises decomposing each of the URLs into a sequence of tokens and grouping together subsets of related URLs. Also, the method comprises representing each subset of related URLs with a normalized URL string.

A method of normalizing URLs associated with a real user session comprises extracting uniform resource locators (URLs) from ingested spans where at least a portion of the URLs comprise unique URL strings. The method also comprises decomposing each of the URLs into a sequence of tokens and grouping together subsets of related URLs. Also, the method comprises representing each subset of related URLs with a normalized URL string.

A URL and a categorization associated with the URL are received. A key associated with the received URL is determined. An operation is performed on a database using the determined key. Examples of such operations include inserting the categorization into the database, changing a value associated with the key in the database, removing a key-value pair from the database, and querying the database.

A URL and a categorization associated with the URL are received. A key associated with the received URL is determined. An operation is performed on a database using the determined key. Examples of such operations include inserting the categorization into the database, changing a value associated with the key in the database, removing a key-value pair from the database, and querying the database.

A system and method for predictive pre-authorization of subsidiary accounts using passive biometrics which uses wireless mobile devices and biometric scanning to automatically predict pre-authorized transaction amounts for a plurality of subsidiary accounts in a secure manner without requiring the customer to handle his or her mobile device. The system and method uses a payment facilitation device at the business location which automatically detects and recognizes registered mobile devices, displays a photo of the customer to a business employee for identity confirmation, verifies the customer with a biometrics verification database, generates a pre-authorization amount with an authorization generator, and automatically deducts payments for purchases from a pre-authorized customer account. The system and method may further include capabilities for facilitating offline transactions using accounts enabled as offline accounts.

Methods, apparatus, systems, computing devices, computing entities, and/or the like for using machine-learning concepts (e.g., machine learning models) to determine predicted taxonomy-based classification scores for claims and dynamically update data fields based on the same.

Methods, apparatus, systems, computing devices, computing entities, and/or the like for using machine-learning concepts (e.g., machine learning models) to determine predicted taxonomy-based classification scores for claims and dynamically update data fields based on the same.