Abstract: Recent decades have brought tremendous advances in communication systems which have given rise to the global proliferation of smartphones and other mobile communication systems. A signal processing system implements technical solutions for determining building footprints from inputs including signal data associated with mobile communication devices.

Abstract: For machine-based prediction of visitation, a machine-learned network embeds the visitation and metadata information. Since the trace data used to show access may be sparse, another machine-learned network completes the route data. Another machine-learned network recommends effectiveness of content based on routes, the graph, metadata, and/or other information. The recommendation is based on training using counterfactual and/or other causal modeling.

Abstract: For machine-based prediction of visitation, a graph of paired mobile devices is formed where the edges for each pair are based on similarities in visitation and/or metadata for the devices. A machine-learned network embeds the visitation and metadata information, which is used to indicate the similarity. Since the trace data used to show access may be sparse, another machine-learned network completes the route based on routes used by similar devices. Another machine-learned network recommends effectiveness of content based on routes, the graph, metadata, and/or other information. The recommendation is based on training using counterfactual and/or other causal modeling.

Abstract: A verification system implements technical solutions for verifying the delivery of messages transmitted by mobile communication systems and the processing of those messages. The message verification system includes a model database and verification processing circuitry configured to receive transmitter route data, receive receiver route data, determine a viewshed for a message, and compare the viewshed to the receiver route data.

Abstract: Recent decades have brought tremendous advances in communication systems which have given rise to the global proliferation of smartphones and other mobile communication systems. A signal processing system implements technical solutions for determining building footprints from inputs including signal data associated with mobile communication devices.

Abstract: A verification system implements technical solutions for verifying the delivery of messages transmitted by mobile communication systems and the processing of those messages. The message verification system includes a model database and verification processing circuitry configured to receive transmitter route data, receive receiver route data, determine a viewshed for a message, and compare the viewshed to the receiver route data.

Abstract: For privacy-preserving attribution, the exposure and/or attribution to physical media (e.g., a surface or object) is determined on-board the mobile device. The exposure and/or attribution may be communicated remotely from the mobile device without passing specific locations and/or identifiers of the operator. The attribution information may be used by others without having access to private information, which never leaves the mobile device.

Abstract: Recent decades have brought tremendous advances in communication systems which have given rise to the global proliferation of smartphones and other mobile communication systems. A signal processing system implements technical solutions for determining building footprints from inputs including signal data associated with mobile communication devices.

Abstract: A verification system implements technical solutions for verifying the delivery of messages transmitted by mobile communication systems and the processing of those messages. The message verification system includes a model database and verification processing circuitry configured to receive transmitter route data, receive receiver route data, determine a viewshed for a message, and compare the viewshed to the receiver route data.

Abstract: Image processing of an image is used to determine the opportunity to view an object. Rather than relying on simple numbers passing an object, the opportunity to view the object is weighted based on attention, which is derived from other objects competing for attention. For the processor to more accurately determine opportunity to view as compared to using geometric information alone, a machine-learned network is used. To deal with changes in obstructions, another machine-learned network may extract obstructions from camera images. Trace data is used to allow for daily variation in base counts of viewers, allowing greater temporal resolution and determination based on information more recently acquired than counts.

Abstract: A verification system implements technical solutions for verifying the delivery of messages transmitted by mobile communication systems and the processing of those messages. The message verification system includes a model database and verification processing circuitry configured to receive transmitter route data, receive receiver route data, determine a viewshed for a message, and compare the viewshed to the receiver route data.

Abstract: Recent decades have brought tremendous advances in communication systems which have given rise to the global proliferation of smartphones and other mobile communication systems. A signal processing system implements technical solutions for determining building footprints from inputs including signal data associated with mobile communication devices.

Abstract: Despite the impressive advances made in recent decades, past digital image processing system were faced with significant technical challenges to solving important technical problems. The digital image processing system described below helps to solve these technical challenges with regard to spatial location and orientation of arbitrary objects in real-world environments. The digital image processing system performs image segmentation to accurately identify objects in an image, then locates the objects and determines their orientations.

Abstract: Despite the impressive advances made in recent decades, past digital image processing system were faced with significant technical challenges to solving important technical problems. The digital image processing system described below helps to solve these technical challenges with regard to spatial location and orientation of arbitrary objects in real-world environments. The digital image processing system performs image segmentation to accurately identify objects in an image, then locates the objects and determines their orientations.

Abstract: Recent decades have brought tremendous advances in communication systems which have given rise to the global proliferation of smartphones and other mobile communication systems. A verification system implements technical solutions for verifying the delivery of messages transmitted by mobile communication systems and the processing of those messages.

Abstract: Image processing of an image is used to determine the opportunity to view an object. Rather than relying on simple numbers passing an object, the opportunity to view the object is weighted based on attention, which is derived from other objects competing for attention. For the processor to more accurately determine opportunity to view as compared to using geometric information alone, a machine-learned network is used. To deal with changes in obstructions, another machine-learned network may extract obstructions from camera images. Trace data is used to allow for daily variation in base counts of viewers, allowing greater temporal resolution and determination based on information more recently acquired than counts.

Abstract: Image processing of an image is used to determine the opportunity to view an object. Rather than relying on simple numbers passing an object, the opportunity to view the object is weighted based on attention, which is derived from other objects competing for attention. For the processor to more accurately determine opportunity to view as compared to using geometric information alone, a machine-learned network is used. To deal with changes in obstructions, another machine-learned network may extract obstructions from camera images. Trace data is used to allow for daily variation in base counts of viewers, allowing greater temporal resolution and determination based on information more recently acquired than counts.

Abstract: For machine-based prediction of visitation, a graph of paired mobile devices is formed where the edges for each pair are based on similarities in visitation and/or metadata for the devices. A machine-learned network embeds the visitation and metadata information, which is used to indicate the similarity. Since the trace data used to show access may be sparse, another machine-learned network completes the route based on routes used by similar devices. Another machine-learned network recommends effectiveness of content based on routes, the graph, metadata, and/or other information. The recommendation is based on training using counterfactual and/or other causal modeling.

Abstract: A computing environment has a processor, a communication connection, and a memory. The memory contains instructions that, when executed by the processor, cause the computing environment to determine a plurality of parameters corresponding to each respective device in a plurality of devices. The instructions can cause the computing environment to assess a measure of similarity between a parameter corresponding to one of the devices and a parameter corresponding to each of the other devices in the plurality of devices. The executed instructions cause the computing environment to assign each pair of devices having a measure of similarity exceeding or falling below a threshold measure to the cohort. The executed instructions cause the computing environment to predict a second parameter value of at least one device assigned to the cohort based on a known first parameter value of the respective device and one or more changes in parameter value of other devices in the cohort of devices.

Abstract: Image processing of an image is used to determine the opportunity to view an object. Rather than relying on simple numbers passing an object, the opportunity to view the object is weighted based on attention, which is derived from other objects competing for attention. For the processor to more accurately determine opportunity to view as compared to using geometric information alone, a machine-learned network is used. To deal with changes in obstructions, another machine-learned network may extract obstructions from camera images. Trace data is used to allow for daily variation in base counts of viewers, allowing greater temporal resolution and determination based on information more recently acquired than counts.