Liveperson, Inc.

Angelo D'Alessandro

Brian Haley

A system for automated account interaction has access to information about asset accounts, and receives transaction indications indicating transactions made by those accounts. The system automatically selects a first transaction indication for a first transaction made using a first asset account, for instance based on location, time, asset quantity, randomization, and/or a trained machine learning model. The system automatically selects a second asset account associated with a second user, for instance based on the second user's location, account balance, randomization, and/or a trained machine learning model. The system automatically transfers an asset quantity from the second asset account to the first asset account, and communicates an indicator indicating transfer completion. The asset quantity can be the quantity corresponding to the first transaction, or a portion thereof. The confirmation indicator can be sent to device(s) associated with either or both account(s) and/or can be published to a feed, graph, and/or index.

Method and apparatus for automated inter-account interactions

A system for automated account interaction receives historical information associated with an account corresponding to a user. The historical information identifies a transaction involving the account. The system uses one or more trained machine learning models to identify an intent for the transaction at least in part by inputting the historical information to the trained machine learning models. The system uses the trained machine learning models to generate a recommended transaction at least in part by inputting the intent for the transaction to the trained machine learning models. The system outputs the recommended transaction and receives a confirmation regarding the recommended transaction.

Automated decisioning based on predicted user intent

A context-aware account management system is described. The system receives a partial request and parses the partial request to identify one or more characters in the partial request. The system determines a plurality of valid requests that include the one or more characters, for instance in response to querying one or more data structures and/or generating the valid requests using a trained machine learning model. The system generates a suggested request by selecting one of the plurality of valid requests, and in some cases, by identifying valid values for variables within the selected one of the plurality of valid requests by querying one or more data structures and/or generating the valid values using a trained machine learning model. The system outputs the suggested request and receives a confirmation in response. The system performs an action indicated by the suggested request using a user account indicated by the suggested request.

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New York, NY, United States of America

Angelo D'Alessandro