Abstract: Disclosed are systems and methods for monthly revenue commit (MRC) cost optimization, for virtual network operators whose customers utilize a network operated by a different network operator. The disclosed generally involves four stages: 1) onboarding and initial wholesale plan selection; 2) estimating what a customer's requirements (e.g., data usage) are likely to be for a month; 3) determining the least-cost wholesale plan the customer should be placed on; and 4) switching the customer's wholesale plan.

Abstract: Systems and methods for controlling industrial an industrial plant comprise: inputting an engineering diagram for a unit of the industrial plant, the engineering diagram including symbols representing assets of the industrial plant; extracting one or more assets from the engineering diagram using machine learning to recognize the one or more assets, the one or more assets including equipment, instruments, connectors, and lines, the lines relating the equipment, instruments, and connectors to one another; determining one or more relationships between the equipment, instruments, connectors, and lines to one another using machine learning to recognize the one or more relationships; and creating a flow graph from the equipment, instruments, connectors, and lines and the relationships between the equipment, instruments, connectors, and lines.

Abstract: Automated evaluation and extraction of information from piping and instrumentation diagrams (P&IDs). Aspects of the systems and methods utilize machine learning and image processing techniques to extract relevant information, such as tag names, tag numbers, and symbols, and their positions, from P&IDs. Further aspects feed errors back to a machine learning system to update its learning and improve operation of the systems and methods.

Abstract: Automated evaluation and extraction of information from piping and instrumentation diagrams (P&IDs). Aspects of the systems and methods utilize machine learning and image processing techniques to extract relevant information, such as tag names, tag numbers, and symbols, and their positions, from P&IDs. Further aspects feed errors back to a machine learning system to update its learning and improve operation of the systems and methods.

Abstract: Automated evaluation and extraction of information from piping and instrumentation diagrams (P&IDs). Aspects of the systems and methods utilize machine learning and image processing techniques to extract relevant information, such as tag names, tag numbers, and symbols, and their positions, from P&IDs. Further aspects feed errors back to a machine learning system to update its learning and improve operation of the systems and methods.

Abstract: A sponsored data system configured to operate with a wireless mobile device running an app that receives sponsored data from a content provider server is provided. The system includes an interface application configured to run on the wireless mobile device. The interface application is configured to determine availability of sponsored data. A cloud platform is configured to interface with the content provider server. The cloud platform is configured to generate a token with data usable to determine availability of sponsored data and transmit the token to the interface application. A portal is configured to create and store a package that identifies at least a portion of the data associated with the app as sponsored data.