Abstract: A campaign management system manages campaign datasets, wherein a campaign dataset corresponds to selections and instructions for executing an online campaign. A campaign dataset might be created by a human or a machine, or multiple humans and computers in a crowd-sourcing approach. Campaign datasets can be processed to determine an expected quality metric, a post-run quality metric, and a confidence level. A campaign comparator can compare campaign datasets to identify similar campaign datasets so that an expected quality metric, a post-run quality metric, and/or a confidence level from one campaign dataset can be imputed to another campaign dataset.

Abstract: A campaign management system manages campaign datasets, wherein a campaign dataset corresponds to selections and instructions for executing an online campaign. A campaign dataset might be created by a human or a machine, or multiple humans and computers in a crowd-sourcing approach. Campaign datasets can be processed to determine an expected quality metric, a post-run quality metric, and a confidence level. A campaign comparator can compare campaign datasets to identify similar campaign datasets so that an expected quality metric, a post-run quality metric, and/or a confidence level from one campaign dataset can be imputed to another campaign dataset.

Abstract: A computer-implemented method of generating, from an array of topic records, an output array of keywords for use in targeting online advertisements related to topics represented by the array of topic records, includes obtaining the array of topic records in computer-readable form, determining a relevancy value for words and topics, classifying the topic vectors in the plurality of topic vectors into a high-volume class or a low-volume class, generating and storing an array of seed keywords derived by sampling from an embedded space, ranking the seed keywords to form an array of ranked keywords, updating the array of ranked keywords based on keyword cost-per-click data, iterating the ranking and updating at least once, and evaluating an optimization improvement value for an iteration.

Abstract: A computer system monitors physical commodities based on sensor data about those physical commodities using a virtual vault to define a space. The computer system comprises a plurality of sensors, spatially positioned to cover a predefined delineated space containing an inventory of the physical commodities and includes a processor, coupled to receive signals from the plurality of sensors, and a memory that provides storage for sensor data. The processor can execute programming code stored in nontransitory computer readable media that includes program code for calculating a plurality of physical properties of the inventory, program code for determining one or more boundaries of the predefined delineated space, program code for mapping changes in the sensor data to changes in the physical properties of the inventory, and program code for determining a response to changes in an inventory that are based on the changes in the physical properties of the inventory.