Abstract:
Techniques for modeling significant locations are described. A significant location can be a location that is significant to a user of a mobile device for a variety of reasons. The mobile device can determine that a place or region is a significant location upon determining that, with sufficient certainty, the mobile device has stayed at the place or region for a sufficient amount of time. The mobile device can construct a state model that is an abstraction of one or more significant locations. The state model can include states representing the significant locations, and transitions representing movement of the mobile device between the locations. The mobile device can use the state model to provide predictive user assistance.
Abstract:
Methods, program products, and systems for using a location fingerprint database to determine a location of a mobile device are described. A mobile device can use location fingerprint data received from a server to determine a location of the mobile device at the venue. The mobile device can obtain, from a sensor of the mobile device, a vector of sensor readings, each sensor reading can measure an environment variable, e.g., a signal received by the sensor from a signal source. The mobile device can perform a statistical match between the vector and the location fingerprint data. The mobile device can then estimate a current location of the mobile device based on the statistical match.
Abstract:
A proximity fence can be a location agnostic fence defined by signal sources having no geographic location information. The proximity fence can correspond to a group of signal sources instead of a point location fixed to latitude and longitude coordinates. A signal source can be a radio frequency (RF) transmitter broadcasting a beacon signal. The beacon signal can include a payload that includes an identifier indicating a category to which the signal source belongs, and one or more labels indicating one or more subcategories to which the signal source belongs. The proximity fence defined by the group of signal sources can trigger different functions of application programs associated with the proximity fence on a mobile device, when the mobile device moves within the proximity fence and enters and exits different parts of the proximity fence corresponding to the different subcategories.
Abstract:
resumo patente de invenção: "seleção de pontos de acesso sem fio para monitoramento de geo-cerca". a presente invenção refere-se a métodos, produtos de programa e sistemas para monitorar saídas de geo-cerca usando pontos de acesso sem fio. em geral, em um aspecto, o dispositivo móvel pode selecionar, a partir de múltiplos pontos de acesso sem fio, um ou mais pontos de acesso sem fio para monitorar uma geo-cerca. a seleção de um ou mais pontos de acesso sem fio pode incluir a determinação de múltiplas regiões geográficas que correspondem à geo-cerca. o dispositivo móvel pode selecionar um ou mais pontos de acesso sem fio com base em um número total máximo de pontos de acesso sem fio a ser selecionado e uma permissão de ponto de acesso para cada uma das regiões geográficas. a permissão de ponto de acesso pode indicar um número máximo de pontos de acesso sem fio a ser selecionado para a região geográfica. o dispositivo móvel pode detectar uma entrada ou saída potencial da geo-cerca monitorando-se um ou mais pontos de acesso sem fio selecionados usando um processador sem fio. 19867197v1
Abstract:
Verfahren, Programmprodukte und Systeme zum Überwachen von Geofence-Ausgängen unter Verwendung von drahtlosen Zugangspunkten werden offenbart. Im Allgemeinen kann in einem Aspekt eine mobile Vorrichtung ein oder mehrere Eingangs-Gateways detektieren, die drahtlose Zugangspunkte sind, welche zum Überwachen eines Geofence ausgewählt wurden. Die mobile Vorrichtung kann bestimmen, dass die mobile Vorrichtung sich in dem Geofence befindet, basierend auf der Detektion. Die mobile Vorrichtung kann die Eingangs-Gateways und ein oder mehrere Ausgangs-Gateways überwachen, welche drahtlose Zugangspunkte sein können, die durch die mobile Vorrichtung überwachbar sind, wenn die mobile Vorrichtung in dem Geofence ist. Wenn die mobile Vorrichtung nach einer Anzahl von Scans unter Verwendung eines drahtlosen Prozessors bestimmt, dass die Eingangs-Gateways und die Ausgangs-Gateways nicht überwachbar sind, kann die mobile Vorrichtung einen Anwendungsprozessor verwenden, um zu bestimmen, ob die mobile Vorrichtung den Geofence verlassen hat.
Abstract:
In some implementations, a location of a mobile device can be determined by calculating an average of the locations of wireless signal transmitters that have transmitted signals received by the mobile device. In some implementations, locations are weighted with coefficients and the average is a weighted average. In some implementations, the locations of the wireless signal transmitters are determined based on identification information encoded in the wireless signals received by the mobile device. The identification information can include an identifier for a wireless signal transmitter. The identification information can include characteristics of the received wireless signal that can be used to identify wireless signal transmitters. In some implementations, identification information from one signal can be combined with identification information from another signal to determine a location of a wireless transmitter.
Abstract:
Methods, program products, and systems of location estimation using multiple wireless access gateways are disclosed. In general, in one aspect, a mobile device can scan and detect multiple wireless access gateways. The mobile device can determine an initial estimate of distance between the mobile device and each wireless access gateway. The mobile device can receive, from a server, location data of the detected wireless access gateways. The location data can include an estimated location of each wireless access gateway, an uncertainty of the estimated location, and a reach of each wireless access gateway. The mobile device can assign a weight to each estimated location using the uncertainty, the reach, and the initial estimate. The mobile device can estimate the location of the mobile device using the weighted locations. cn 0 a,) C.,D C6 C%4 00 oU U)
Abstract:
Systems, methods, and program products for providing services to a user by a mobile device based on the user's daily routine of movement. The mobile device determines whether a location cluster indicates a significant location for the user based on one or more hints that indicate an interest of the user in locations in the cluster. The mobile device can perform adaptive clustering to determine a size of area of the significant location based on how multiple locations converge in the location cluster. The mobile device can provide location-based services for calendar items, including predicting a time of arrival at an estimated location of a calendar item. The mobile device can provide various services related to a location of the mobile device or a significant location of the user through an application programming interface (API).
Abstract:
Reduced resolution location determination for improved anonymity of a user location is disclosed. In some implementations, a first location of a computing device operating in a geographic area is determined. A population density of the geographic area is estimated. A grid overlay is generated, including a number of cells based on the estimated population density. Using the grid overlay, a second location is generated for the computing device that is less precise than the first location. The less precise second location can be used in a local search or other application to improve the anonymity of the user location.
Abstract:
Methods, program products, and systems for using a location fingerprint database to determine a location of a mobile device are described. A mobile device can use location fingerprint data received from a server to determine a location of the mobile device at the venue. The mobile device can obtain, from a sensor of the mobile device, a vector of sensor readings, each sensor reading can measure an environment variable, e.g., a signal received by the sensor from a signal source. The mobile device can perform a statistical match between the vector and the location fingerprint data. The mobile device can then estimate a current location of the mobile device based on the statistical match.