Abstract: The current embodiments relate to a real-time automated classification system that uses machine learning system to recognize important moments in broadcast content based on log data and/or other data received from various classification systems. The real-time automated classification system may be trained to recognize correlations between the various log data to determine key moments in the broadcast content. The real-time automated logging system may determine and generate metadata that describe or give information about what is happening or appearing in the broadcast content. The real-time automated logging system may automatically generate control inputs, suggestions, recommendations, and/or edits relating to broadcast content based upon the metadata, during broadcasting of the broadcast content.

Abstract: The current embodiments relate to a real-time automated classification system that uses machine learning system to recognize important moments in broadcast content based on log data and/or other data received from various classification systems. The real-time automated classification system may be trained to recognize correlations between the various log data to determine key moments in the broadcast content. The real-time automated logging system may determine and generate metadata that describe or give information about what is happening or appearing in the broadcast content. The real-time automated logging system may automatically generate control inputs, suggestions, recommendations, and/or edits relating to broadcast content based upon the metadata, during broadcasting of the broadcast content.

Abstract: The current embodiments relate to a real-time automated classification system that uses machine learning system to recognize important moments in broadcast content based on log data and/or other data received from various classification systems. The real-time automated classification system may be trained to recognize correlations between the various log data to determine key moments in the broadcast content. The real-time automated logging system may determine and generate metadata that describe or give information about what is happening or appearing in the broadcast content. The real-time automated logging system may automatically generate control inputs, suggestions, recommendations, and/or edits relating to broadcast content based upon the metadata, during broadcasting of the broadcast content.

Abstract: The present disclosure is generally directed to a tangible, non-transitory machine-readable medium that includes machine-readable instructions that, when executed by processing circuitry, cause the processing circuitry to receive multimedia content that includes a plurality of multimedia content portions of the multimedia content. The instructions, when executed by the processing circuitry, also cause the processing circuitry to receive text data corresponding to words spoken in the multimedia content. The text data includes a plurality of text data subdivisions of the text data. Moreover, the instructions, when executed by the processing circuitry, cause the processing circuitry to align the multimedia content and the text data by determining, for each of the plurality of multimedia content portions, a corresponding subdivision of the plurality of text data subdivisions.

Abstract: The present disclosure is generally directed to a tangible, non-transitory machine-readable medium that includes machine-readable instructions that, when executed by processing circuitry, cause the processing circuitry to receive multimedia content that includes a plurality of multimedia content portions of the multimedia content. The instructions, when executed by the processing circuitry, also cause the processing circuitry to receive text data corresponding to words spoken in the multimedia content. The text data includes a plurality of text data subdivisions of the text data. Moreover, the instructions, when executed by the processing circuitry, cause the processing circuitry to align the multimedia content and the text data by determining, for each of the plurality of multimedia content portions, a corresponding subdivision of the plurality of text data subdivisions.

Abstract: The current embodiments relate to a real-time automated classification system that uses machine learning system to recognize important moments in broadcast content based on log data and/or other data received from various classification systems. The real-time automated classification system may be trained to recognize correlations between the various log data to determine key moments in the broadcast content. The real-time automated logging system may determine and generate metadata that describe or give information about what is happening or appearing in the broadcast content. The real-time automated logging system may automatically generate control inputs, suggestions, recommendations, and/or edits relating to broadcast content based upon the metadata, during broadcasting of the broadcast content.

Abstract: A communication network is operated by receiving traffic from a user device at a gateway device associated with a gateway service provider, which manages gateways to both secure and insecure networks. The gateway uses security policies to determine if traffic is destined to the secure or insecure network and applies appropriate policies which cause the traffic to be routed, dropped, or analyzed.

Abstract: Network activity in a network is regulated. Network activity scores that correspond to network usage patterns for respective network users are calculated. A network user is assigned into one of multiple risk classes responsive to a respective one of the network activity scores. A subsequent action is selected responsive to which of the risk classes the network user is assigned.

Abstract: Network activity in a network is regulated. Network activity scores that correspond to network usage patterns for respective network users are calculated. A network user is assigned into one of multiple risk classes responsive to a respective one of the network activity scores. A subsequent action is selected responsive to which of the risk classes the network user is assigned.

Abstract: Network activity in a network is regulated. Network activity scores that correspond to network usage patterns for respective network users are calculated. A network user is assigned into one of multiple risk classes responsive to a respective one of the network activity scores. A subsequent action is selected responsive to which of the risk classes the network user is assigned.

Abstract: A communication network is operated by receiving traffic from a user device at a gateway device associated with a gateway service provider, which manages gateways to both secure and insecure networks. The gateway uses security policies to determine if traffic is destined to the secure or insecure network and applies appropriate policies which cause the traffic to be routed, dropped, or analyzed.

Abstract: Network activity in a network is regulated. Network activity scores that correspond to network usage patterns for respective network users are calculated. A network user is assigned into one of multiple risk classes responsive to a respective one of the network activity scores. A subsequent action is selected responsive to which of the risk classes the network user is assigned.