Abstract: The present disclosure provides systems and methods for generating a digital menu for an establishment. In accordance with some embodiments, a system for generating a digital menu for an establishment is configured to: retrieve menu data from a point of sale (PoS) system at the establishment; retrieve supplemental information for the retrieved menu data; generate the digital menu based on the retrieved menu data and the supplemental information; and transmit the digital menu to an electronic device for display to a user.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media that dynamically modify content distribution campaigns based on triggering conditions and actions. In particular, systems described herein can provide a user interface for display to a publisher device that includes a plurality of selectable options for setting triggering conditions and/or actions. For example, the disclosed systems can utilize a machine learning model to generate suggested triggering conditions and/or actions for one or more content distribution campaigns of a provider. Moreover, the disclosed systems can generate custom rules based on selected triggering conditions and actions and apply the custom rules during execution of digital content campaigns. For instance, the disclosed systems can monitor performance of content campaigns, detect triggering conditions, and dynamically modify digital content campaigns based on actions corresponding to the triggering conditions.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media that dynamically modify content distribution campaigns based on triggering conditions and actions. In particular, systems described herein can provide a user interface for display to a publisher device that includes a plurality of selectable options for setting triggering conditions and/or actions. For example, the disclosed systems can utilize a machine learning model to generate suggested triggering conditions and/or actions for one or more content distribution campaigns of a provider. Moreover, the disclosed systems can generate custom rules based on selected triggering conditions and actions and apply the custom rules during execution of digital content campaigns. For instance, the disclosed systems can monitor performance of content campaigns, detect triggering conditions, and dynamically modify digital content campaigns based on actions corresponding to the triggering conditions.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media that dynamically modify content distribution campaigns based on triggering conditions and actions. In particular, systems described herein can provide a user interface for display to a publisher device that includes a plurality of selectable options for setting triggering conditions and/or actions. For example, the disclosed systems can utilize a machine learning model to generate suggested triggering conditions and/or actions for one or more content distribution campaigns of a provider. Moreover, the disclosed systems can generate custom rules based on selected triggering conditions and actions and apply the custom rules during execution of digital content campaigns. For instance, the disclosed systems can monitor performance of content campaigns, detect triggering conditions, and dynamically modify digital content campaigns based on actions corresponding to the triggering conditions.

Abstract: The present disclosure provides systems and methods for generating a digital menu for an establishment. In accordance with some embodiments, a system for generating a digital menu for an establishment is configured to: retrieve menu data from a point of sale (PoS) system at the establishment; retrieve supplemental information for the retrieved menu data; generate the digital menu based on the retrieved menu data and the supplemental information; and transmit the digital menu to an electronic device for display to a user.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media that dynamically modify content distribution campaigns based on triggering conditions and actions. In particular, systems described herein can provide a user interface for display to a publisher device that includes a plurality of selectable options for setting triggering conditions and/or actions. For example, the disclosed systems can utilize a machine learning model to generate suggested triggering conditions and/or actions for one or more content distribution campaigns of a provider. Moreover, the disclosed systems can generate custom rules based on selected triggering conditions and actions and apply the custom rules during execution of digital content campaigns. For instance, the disclosed systems can monitor performance of content campaigns, detect triggering conditions, and dynamically modify digital content campaigns based on actions corresponding to the triggering conditions.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media that dynamically modify content distribution campaigns based on triggering conditions and actions. In particular, systems described herein can provide a user interface for display to a publisher device that includes a plurality of selectable options for setting triggering conditions and/or actions. For example, the disclosed systems can utilize a machine learning model to generate suggested triggering conditions and/or actions for one or more content distribution campaigns of a provider. Moreover, the disclosed systems can generate custom rules based on selected triggering conditions and actions and apply the custom rules during execution of digital content campaigns. For instance, the disclosed systems can monitor performance of content campaigns, detect triggering conditions, and dynamically modify digital content campaigns based on actions corresponding to the triggering conditions.

Abstract: The present disclosure provides systems and methods for processing customer payments for an establishment. In accordance with some embodiments, a server for processing customer payments for one or more establishments is able to connect to and interface with various establishments' PoS systems (including multiple different kinds of PoS systems) through a network (e.g. the Internet), remotely using cloud application program interfaces (APIs) of the PoS systems. The server is also configured to connect through the Internet to a payment gateway, which may be a software-as-a-service that is remotely available through APIs, for processing customer payments on behalf of an establishment.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media that dynamically modify content distribution campaigns based on triggering conditions and actions. In particular, systems described herein can provide a user interface for display to a publisher device that includes a plurality of selectable options for setting triggering conditions and/or actions. For example, the disclosed systems can utilize a machine learning model to generate suggested triggering conditions and/or actions for one or more content distribution campaigns of a provider. Moreover, the disclosed systems can generate custom rules based on selected triggering conditions and actions and apply the custom rules during execution of digital content campaigns. For instance, the disclosed systems can monitor performance of content campaigns, detect triggering conditions, and dynamically modify digital content campaigns based on actions corresponding to the triggering conditions.

Abstract: Various exemplary embodiments relate to a method of detecting anomalies in network traffic. The method includes: receiving a plurality of accounting reports from an application assurance device, the accounting reports indicating a metric of network performance; aggregating the metric from a plurality of accounting reports to determine a plurality of aggregated metrics corresponding to a plurality of intervals; storing the aggregated metrics in a database in association with the corresponding plurality of intervals; determining a rolling baseline for a current time period based on metrics of intervals corresponding to a primary partition and a sub-partition; comparing a metric for a current time period to the rolling baseline; and determining that an anomaly is occurring if the metric for the current time period differs from the rolling baseline by more than a pre-defined threshold.