Abstract: Introduced herein are methods and systems for determining machine learning marketing strategy. For example, a computer-implemented method according to the disclosed technology includes steps of identifying one or more business metrics to be driven by a marketing plan; generating one or more response functions of the business metrics by performing a machine learning process on a marketing dataset; optimizing a spending subject of the marking plan subject to constraints to generate a marketing strategy based on multiple decision variables; and presenting the marketing strategy to an advertiser.

Abstract: Introduced herein are methods and systems for determining machine learning marketing strategy. For example, a computer-implemented method according to the disclosed technology includes steps of identifying one or more business metrics to be driven by a marketing plan; generating one or more response functions of the business metrics by performing a machine learning process on a marketing dataset; optimizing a spending subject of the marking plan subject to constraints to generate a marketing strategy based on multiple decision variables; and presenting the marketing strategy to an advertiser.

Abstract: Introduced herein are methods and systems for determining machine learning marketing strategy. For example, a computer-implemented method according to the disclosed technology includes steps of identifying one or more business metrics to be driven by a marketing plan; generating one or more response functions of the business metrics by performing a machine learning process on a marketing dataset; optimizing a spending subject of the marking plan subject to constraints to generate a marketing strategy based on multiple decision variables; and presenting the marketing strategy to an advertiser.

Abstract: Introduced herein are methods and systems for determining machine learning marketing strategy. For example, a computer-implemented method according to the disclosed technology includes steps of identifying one or more business metrics to be driven by a marketing plan; generating one or more response functions of the business metrics by performing a machine learning process on a marketing dataset; optimizing a spending subject of the marking plan subject to constraints to generate a marketing strategy based on multiple decision variables; and presenting the marketing strategy to an advertiser.

Abstract: Introduced herein are methods and systems for determining machine learning marketing strategy. For example, a computer-implemented method according to the disclosed technology includes steps of identifying one or more business metrics to be driven by a marketing plan; generating one or more response functions of the business metrics by performing a machine learning process on a marketing dataset; optimizing a spending subject of the marking plan subject to constraints to generate a marketing strategy based on multiple decision variables; and presenting the marketing strategy to an advertiser.

Abstract: Introduced herein are methods and systems for determining machine learning marketing strategy. For example, a computer-implemented method according to the disclosed technology includes steps of identifying one or more business metrics to be driven by a marketing plan; generating one or more response functions of the business metrics by performing a machine learning process on a marketing dataset; optimizing a spending subject of the marking plan subject to constraints to generate a marketing strategy based on multiple decision variables; and presenting the marketing strategy to an advertiser.

Abstract: Introduced herein are methods and systems for determining machine learning marketing strategy. For example, a computer-implemented method according to the disclosed technology includes steps of identifying one or more business metrics to be driven by a marketing plan; generating one or more response functions of the business metrics by performing a machine learning process on a marketing dataset; optimizing a spending subject of the marking plan subject to constraints to generate a marketing strategy based on multiple decision variables; and presenting the marketing strategy to an advertiser.

Abstract: Introduced herein are methods and systems for determining machine learning marketing strategy. For example, a computer-implemented method according to the disclosed technology includes steps of identifying one or more business metrics to be driven by a marketing plan; generating one or more response functions of the business metrics by performing a machine learning process on a marketing dataset; optimizing a spending subject of the marking plan subject to constraints to generate a marketing strategy based on multiple decision variables; and presenting the marketing strategy to an advertiser.

Abstract: Introduced herein are methods and systems for determining machine learning marketing strategy. For example, a computer-implemented method according to the disclosed technology includes steps of identifying one or more business metrics to be driven by a marketing plan; generating one or more response functions of the business metrics by performing a machine learning process on a marketing dataset; optimizing a spending subject of the marking plan subject to constraints to generate a marketing strategy based on multiple decision variables; and presenting the marketing strategy to an advertiser.

Abstract: Introduced herein are methods and systems for determining machine learning marketing strategy. For example, a computer-implemented method according to the disclosed technology includes steps of identifying one or more business metrics to be driven by a marketing plan; generating one or more response functions of the business metrics by performing a machine learning process on a marketing dataset; optimizing a spending subject of the marking plan subject to constraints to generate a marketing strategy based on multiple decision variables; and presenting the marketing strategy to an advertiser.

Abstract: Introduced herein are methods and systems for determining machine learning marketing strategy. For example, a computer-implemented method according to the disclosed technology includes steps of identifying one or more business metrics to be driven by a marketing plan; generating one or more response functions of the business metrics by performing a machine learning process on a marketing dataset; optimizing a spending subject of the marking plan subject to constraints to generate a marketing strategy based on multiple decision variables; and presenting the marketing strategy to an advertiser.

Abstract: A mobile computing device configured to communicate with an audio device comprises an interface circuit, a memory configured to store a plurality of sets of audio parameters for a device type, and a processing circuit. The processing circuit is configured to receive a device identifier, select one of the plurality of audio parameter sets based on the device identifier, generate an audio signal using the selected audio parameters, and transmit the audio signal to the audio device using the interface circuit.

Abstract: A mobile computing device configured to communicate with an audio device comprises an interface circuit, a memory configured to store a plurality of sets of audio parameters for a device type, and a processing circuit. The processing circuit is configured to receive a device identifier, select one of the plurality of audio parameter sets based on the device identifier, generate an audio signal using the selected audio parameters, and transmit the audio signal to the audio device using the interface circuit.

Abstract: Data is communicated between a first computing device and a second computing device over a transport employing a communication protocol in a multiplexed manner. The second computing device also includes wireless communication capabilities. Data is received from a first application and a second application through a first virtual port and a second virtual port, respectively, and packets are generated from the data. Tags corresponding to the first application or the second application are added to each packet. The packets are multiplexed and transmitted over the transport according to the communication protocol. The transmitted packets are de-multiplexed based upon the tags to reconstruct the data corresponding to the first or second application.

Abstract: Radio logging data of a smartphone is collected on the smartphone itself using an on-device radio logging application that runs on the smartphone itself. The collected radio logging data is provided to a host computer at a later time after the radio logging data is saved, or as the radio logging data is collected, or to a remote computer as the radio logging data is collected.

Abstract: Data is communicated between a first computing device and a second computing device over a transport employing a communication protocol in a multiplexed manner. The second computing device also includes wireless communication capabilities. Data is received from a first application and a second application through a first virtual port and a second virtual port, respectively, and packets are generated from the data. Tags corresponding to the first application or the second application are added to each packet. The packets are multiplexed and transmitted over the transport according to the communication protocol. The transmitted packets are de-multiplexed based upon the tags to reconstruct the data corresponding to the first or second application.

Abstract: The present invention introduces an aliased selection system with audible cues to allow a user of a handheld computer system locate a desired item from a list of item. The aliased selection system allows a user to spell out a desired item by activating an input that specifies a subset that containing a next letter. In one embodiment, two different subsets are used: A to M and N to Z. When the user has entered information on enough letters such that the number of possibilities fits entirely on a display screen then a first audible cue is given. The user may enter additional information on until a single list item is uniquely identified. Once a single item is uniquely identified, the system emits a second audible cue that informs the user that a single item has been specified. The aliased selection system allows a user to select a desired item from a list with a single hand and without looking at the display screen. However, the user may shorten the selection process by looking at the display screen.