Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for positioning a radio signal receiver at a first location within a three dimensional space; positioning a transmitter at a second location within the three dimensional space; transmitting a transmission signal from the transmitter to the radio signal receiver; processing, using a machine-learning network, one or more parameters of the transmission signal received at the radio signal receiver; in response to the processing, obtaining, from the machine-learning network, a prediction corresponding to a direction of arrival of the transmission signal transmitted by the transmitter; computing an error term by comparing the prediction to a set of ground truths; and updating the machine-learning network based on the error term.

Abstract: A radio-frequency (RF) receiver includes: at least n antennas, where n is an integer greater than two; m processing channels configured to receive and process n RF signals from the at least n antennas, where m is an integer greater than one and less than n; a controller configured to cause a first processing channel of the m processing channels to receive, at different corresponding times, a plurality of RF signals of the n RF signals; an indexing module configured to receive outputs from the m processing channels, and generate one or more representations of the n RF signals based on the outputs; and a spatial estimation module configured to receive the one or more representations, execute a machine learning model based on the one or more representations, and determine, based on an output of the machine learning model, a spatial estimate for an emitter of the n RF signals.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for positioning a radio signal receiver at a first location within a three dimensional space; positioning a transmitter at a second location within the three dimensional space; transmitting a transmission signal from the transmitter to the radio signal receiver; processing, using a machine-learning network, one or more parameters of the transmission signal received at the radio signal receiver; in response to the processing, obtaining, from the machine-learning network, a prediction corresponding to a direction of arrival of the transmission signal transmitted by the transmitter; computing an error term by comparing the prediction to a set of ground truths; and updating the machine-learning network based on the error term.

Abstract: Systems, methods, and computer-readable media are disclosed for estimating impressions for a digital out of home (DOOH) advertising spaces (e.g., digital billboards and screens). A DOOH advertising system may determine the location of relevant DOOH advertising spaces and the location of certain consumers with known attributes and a known location. Based on this information the DOOH advertising system may estimate a number of impressions for a given DOOH advertising space and a given consumer segment associated with attributes of consumers within a certain distance from the DOOH advertising space. Using this information, the DOOH advertising spaces having the highest estimated impressions for a given consumer segment may be identified.

Abstract: A system for maintaining product networks to provide recommendations for bid amounts to third party entity devices. A service provider entity device tracks product purchase history for customers and determines relationships associated with products purchased by the customers. When a webpage is loaded by a customer device, a third party entity device receives a bid request from a publisher device and transmits a bid recommendation request to the service provider entity device. The service provider entity device determines input features including a base bid, a propensity score, and a pacing score based on the relationships. The service provider entity device transmits a bid recommendation response with a recommended bid amount to the third party entity device, based on the input features.

Abstract: A method includes obtaining, using a specified protocol of a radio access network, low-level signal data corresponding to a radio frequency (RF) signal processed in the radio access network; providing the low-level signal data as input to at least one machine learning network; in response to providing the low-level signal data as input to the at least one machine learning network, obtaining, as an output of the at least one machine learning network, metadata providing information on one or more characteristics of the RF signal; and controlling an operation of the radio access network based on the metadata.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for positioning a radio signal receiver at a first location within a three dimensional space; positioning a transmitter at a second location within the three dimensional space; transmitting a transmission signal from the transmitter to the radio signal receiver; processing, using a machine-learning network, one or more parameters of the transmission signal received at the radio signal receiver; in response to the processing, obtaining, from the machine-learning network, a prediction corresponding to a direction of arrival of the transmission signal transmitted by the transmitter; computing an error term by comparing the prediction to a set of ground truths; and updating the machine-learning network based on the error term.

Abstract: A lip balm tube system including a container having a housing including an open top end, an inner-volume which contains a semi-soft cosmetic product such as lip balm, a content-level-indicator, and a lid, and a dispensing mechanism. The dispensing mechanism dispenses contents within the inner-volume. The content level indicator includes a transparent window which extends along a length of the housing of the container for viewing a relative content level of the contents within the inner-volume.