Abstract: A controller (120) for simultaneously tracking multiple sensors in a medical intervention includes a circuit (121-181) that causes the controller (120) to execute a process. The process executed by the circuit (121-181) includes receiving first and second signals respectively from a first and a second passive ultrasound sensor (S2) used in the medical intervention. The first and second signals respectively include first and second sensor information indicative of respective locations of the first and the second passive ultrasound sensor (S2). The process executed by the circuit (121-181) also includes combining (120) the first signal and the second signal for transmission over only one channel, and providing the first signal and the second signal over the only one channel to a system (190) that determines the location of the first passive ultrasound sensor (S1) and the location of the second passive ultrasound sensor (S2) and that has only the one channel to receive the first signal and the second signal.

Abstract: Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

Abstract: Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.

Abstract: Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine.

Abstract: Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine.

Abstract: Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine.

Abstract: Systems and methods for distributing online ads with electronic content according to a campaign strategy that is adjusted based on intraday modeling. One embodiment of the invention determines a campaign strategy for a current day allocating a daily budget to automatically bid on online ad opportunities using allocated budget amounts and distributes online ads during a first portion of the current day according to the campaign strategy. Current day data regarding use of the distributed online ads during the first portion of the current day is received and compared with historical data to determine a correction factor that accounts for a magnitude of difference between the current day data and the historical data. The campaign strategy for the current day is adjusted using the correction factor and additional online ads are distributed during a second, later portion of the current day according to the adjusted campaign strategy.

Abstract: An ensemble model is described that is usable to predict revenue metrics for one or more keywords. The ensemble model may be formed using both a historical model and a user behavior model. In one or more implementations, weights are assigned to the historical model and/or the user behavior model based on one or more criteria. Various processing techniques of the ensemble model may utilize the historical model and the user behavior model to predict revenue metrics for one or more keywords.