Abstract: Identifying a location of a mobile device is disclosed (e.g., presuming user consent to the same). One or more received signal strengths (RSSs), comprising a first RSS, may be received by a first access point (AP) from the mobile device. The RSSs may be used to identify a grid area, comprising a first grid space. A signal distance between the first grid space and the first AP may be identified using the first RSS, and combined with a first grid space distance, comprising a known distance between the first grid space and the first AP, to determine a first grid space likelihood score for the first grid space. A second grid space likelihood score may be determined for a second grid space (e.g., and a third, etc.), and the grid space comprising a desired grid space likelihood score (e.g., highest) may be selected as the mobile device location.

Abstract: Identifying a location of a mobile device is disclosed (e.g., presuming user consent to the same). One or more received signal strengths (RSSs), comprising a first RSS, may be received by a first access point (AP) from the mobile device. The RSSs may be used to identify a grid area, comprising a first grid space. A signal distance between the first grid space and the first AP may be identified using the first RSS, and combined with a first grid space distance, comprising a known distance between the first grid space and the first AP, to determine a first grid space likelihood score for the first grid space. A second grid space likelihood score may be determined for a second grid space (e.g., and a third, etc.), and the grid space comprising a desired grid space likelihood score (e.g., highest) may be selected as the mobile device location.

Abstract: Identifying a location of a mobile device is disclosed (e.g., presuming user consent to the same). One or more received signal strengths (RSSs), comprising a first RSS, may be received by a first access point (AP) from the mobile device. The RSSs may be used to identify a grid area, comprising a first grid space. A signal distance between the first grid space and the first AP may be identified using the first RSS, and combined with a first grid space distance, comprising a known distance between the first grid space and the first AP, to determine a first grid space likelihood score for the first grid space. A second grid space likelihood score may be determined for a second grid space (e.g., and a third, etc.), and the grid space comprising a desired grid space likelihood score (e.g., highest) may be selected as the mobile device location.

Abstract: Identifying a location of a mobile device is disclosed (e.g., presuming user consent to the same). One or more received signal strengths (RSSs), comprising a first RSS, may be received by a first access point (AP) from the mobile device. The RSSs may be used to identify a grid area, comprising a first grid space. A signal distance between the first grid space and the first AP may be identified using the first RSS, and combined with a first grid space distance, comprising a known distance between the first grid space and the first AP, to determine a first grid space likelihood score for the first grid space. A second grid space likelihood score may be determined for a second grid space (e.g., and a third, etc.), and the grid space comprising a desired grid space likelihood score (e.g., highest) may be selected as the mobile device location.

Abstract: Identifying a location of a mobile device is disclosed (e.g., presuming user consent to the same). One or more received signal strengths (RSSs), comprising a first RSS, may be received by a first access point (AP) from the mobile device. The RSSs may be used to identify a grid area, comprising a first grid space. A signal distance between the first grid space and the first AP may be identified using the first RSS, and combined with a first grid space distance, comprising a known distance between the first grid space and the first AP, to determine a first grid space likelihood score for the first grid space. A second grid space likelihood score may be determined for a second grid space (e.g., and a third, etc.), and the grid space comprising a desired grid space likelihood score (e.g., highest) may be selected as the mobile device location.

Abstract: One or more techniques and/or systems are disclosed for identifying a location of a mobile device (e.g., with user consent). A set of one or more indications of received signal strength (RSS) may be received, comprising a first RSS from a first access point (AP). The set of RSS indications may be used to identify a grid area, comprising a first grid space. An expected distance between the first grid space and the first AP may be identified using the first RSS. The expected distance can be combined with a first known distance between the first grid space and the first AP to determine a first grid score for the first grid space. A second grid score may be determined for a second grid space (e.g., and a third, fourth, etc.), and the grid space comprising a desired grid score (e.g., highest) may be selected as the mobile device location.

Abstract: Among other things, one or more techniques and/or systems are disclosed for identifying a proximate beacon to a mobile device. One or more first received signal strengths (RSSs), relative to the mobile device, may be received and used to determine a first average signal strength (RSS) and a first average RSS deviation for a first beacon during an observation period. An average RSS deviation for the observation period can be determined using the first average RSS deviation (e.g., and other average RSS deviations). If the average RSS deviation meets a desired deviation threshold, the first beacon may be identified as the proximate beacon. In this manner, if the user of the mobile device consents to the same, the user may be provided with relevant information (e.g., advertisements) on the mobile device while in a locale (e.g., store) corresponding to the (known) location of the beacon, for example.

Abstract: Among other things, one or more techniques and/or systems are disclosed for identifying a proximate beacon to a mobile device. One or more first received signal strengths (RSSs), relative to the mobile device, may be received and used to determine a first average signal strength (RSS) and a first average RSS deviation for a first beacon during an observation period. An average RSS deviation for the observation period can be determined using the first average RSS deviation (e.g., and other average RSS deviations). If the average RSS deviation meets a desired deviation threshold, the first beacon may be identified as the proximate beacon. In this manner, if the user of the mobile device consents to the same, the user may be provided with relevant information (e.g., advertisements) on the mobile device while in a locale (e.g., store) corresponding to the (known) location of the beacon, for example.

Abstract: Task sequences are used to manage devices. According to one aspect, a user-defined or user-selected task sequence is received. The task sequence is converted into an ordered series of steps, and the series of steps are performed, in accordance with their order, in managing a device over a network. In certain implementations, the ordered series of steps are steps for automatically deploying an operating system on the device.

Abstract: Among other things, one or more techniques and/or systems are disclosed for identifying a proximate beacon to a mobile device. One or more first received signal strengths (RSSs), relative to the mobile device, may be received and used to determine a first average signal strength (RSS) and a first average RSS deviation for a first beacon during an observation period. An average RSS deviation for the observation period can be determined using the first average RSS deviation (e.g., and other average RSS deviations). If the average RSS deviation meets a desired deviation threshold, the first beacon may be identified as the proximate beacon. In this manner, if the user of the mobile device consents to the same, the user may be provided with relevant information (e.g., advertisements) on the mobile device while in a locale (e.g., store) corresponding to the (known) location of the beacon, for example.

Abstract: Identifying a location of a mobile device is disclosed (e.g., presuming user consent to the same). One or more received signal strengths (RSSs), comprising a first RSS, may be received by a first access point (AP) from the mobile device. The RSSs may be used to identify a grid area, comprising a first grid space. A signal distance between the first grid space and the first AP may be identified using the first RSS, and combined with a first grid space distance, comprising a known distance between the first grid space and the first AP, to determine a first grid space likelihood score for the first grid space. A second grid space likelihood score may be determined for a second grid space (e.g., and a third, etc.), and the grid space comprising a desired grid space likelihood score (e.g., highest) may be selected as the mobile device location.

Abstract: One or more techniques and/or systems are disclosed for identifying a location of a mobile device (e.g., with user consent). A set of one or more indications of received signal strength (RSS) may be received, comprising a first RSS from a first access point (AP). The set of RSS indications may be used to identify a grid area, comprising a first grid space. An expected distance between the first grid space and the first AP may be identified using the first RSS. The expected distance can be combined with a first known distance between the first grid space and the first AP to determine a first grid score for the first grid space. A second grid score may be determined for a second grid space (e.g., and a third, fourth, etc.), and the grid space comprising a desired grid score (e.g., highest) may be selected as the mobile device location.

Abstract: Some implementations construct a quality score table based on historic data collected for a plurality of ad-keyword pairs. An ad-keyword pair may be selected for determining a quality score. One or more advertisement parameters may be determined for the selected ad-keyword pair. Based on the one or more advertisement parameters, the quality score for the selected ad-keyword pair may be determined from the quality score table. In some implementations, the quality score table is constructed by iteratively cutting a directed graph representing the advertisement parameters and the historic data. Further, in some implementations, the table may be smoothed using a smoothing operation.

Abstract: Some implementations generate a mapping function using one or more historic performance indicators for a set of ad-keyword pairs and one or more advertisement metrics extracted from the set of ad-keyword pairs. The mapping function may be applied to map one or more advertisement metrics of a particular ad-keyword pair to determine a quality score for the particular ad-keyword pair. For example, the quality score may be used when determining whether to select an advertisement for display or may be provided as feedback to an advertiser. Additionally, in some implementations, the mapping function may be applied to determine a quality score for a new ad-keyword pair that has not yet accumulated historic information.

Abstract: Some implementations provide techniques for estimating impression numbers. For example, a log of advertisement bidding data may be used to generate and train an impression estimation model. In some implementations, an impression estimation component may use a boost regression technique to determine a predicted impression value range based on a proposed bid received from an advertiser. For example, the predicted impression value range may be determined based on a predicted estimation error. Additionally, in some instances, the predicted impression value range may be evaluated using one or more evaluation metrics.

Abstract: A client data distribution service may receive messages from a client device requesting data sets from the data distribution service. The data sets may include a client certificate used to protect in verified subsequent communications between the client device in the data distribution service as well as provisioning packets used by the client device to enable operation by the user. Specific messages between the client device in the data distribution service include hardware identifiers, initialization keys, last used sequence numbers, and Underwriter product identifiers. Corresponding acknowledgment messages may be used by the data distribution service to remove completed transactions.

Abstract: Methods and a program of instruction provide a packet schema framework for communication between elements of a pay-as-you-go business model including a provisioning server, an adapted electronic device, and a service provider. The packet schema defines provisioning instructions and content types to support service provisioning, including electronic device configuration and state, time-metering, and other types of functional and administrative tasks as well as to provide a foundation for any future messages needed for product evolution. The schema also defines security at multiple levels to guard against malicious users who may try to hook into the system to fraudulently use and/or configure the electronic devices for their own use and gain.

Abstract: According to one aspect of task sequence interfaces, a method includes exposing an interface that allows task sequences to be performed on a plurality of remote devices, and receiving requests regarding the task sequences and the plurality of remote devices via the interface. The interface comprises an object model including device objects that represent physical devices in a network, and jobs objects that represent task sequences performed on one or more of the plurality of remote devices.

Abstract: Task sequences are used to manage devices. According to one aspect, a user-defined or user-selected task sequence is received. The task sequence is converted into an ordered series of steps, and the series of steps are performed, in accordance with their order, in managing a device over a network. In certain implementations, the ordered series of steps are steps for automatically deploying an operating system on the device.

Abstract: Task sequences are used to manage devices. According to one aspect, a user-defined or user-selected task sequence is received. The task sequence is converted into an ordered series of steps, and the series of steps are performed, in accordance with their order, in managing a device over a network. In certain implementations, the ordered series of steps are steps for automatically deploying an operating system on the device.