Abstract: In various embodiments, crowd sourcing techniques are provided to enable RTT-based positioning of UE. To address issues of discovering which beacons (e.g., Wi-Fi APs, cellular base stations, BLE transmitters, etc.) support measurement of RTT (e.g., according to IEEE 802.11mc, 3GPP Release 16, etc.), beacon RTT capabilities may be crowd-sourced from UE and maintained by a cloud-based location platform in a beacon database (or more specifically, a RTT database portion thereof). To address the issue of determining physical antenna positions, RTT measurements may be crowd-sourced from UE for those beacons that are RTT capable, and used by a trilateration algorithm (e.g., a WLS multilateration algorithm) to determine physical antenna positions, which also may be maintained in the beacon database. Accuracy of the trilateration may be enhanced by obtaining raw GNSS measurements (e.g., psuedoranges) from the UE, and performing a cloud-based RTK GNSS position fix for the UE.

Abstract: In various embodiments, crowd sourcing techniques are provided to enable RTT-based positioning of UE. To address issues of discovering which beacons (e.g., Wi-Fi APs, cellular base stations, BLE transmitters, etc.) support measurement of RTT (e.g., according to IEEE 802.11mc, 3GPP Release 16, etc.), beacon RTT capabilities may be crowd-sourced from UE and maintained by a cloud-based location platform in a beacon database (or more specifically, a RTT database portion thereof). To address the issue of determining physical antenna positions, RTT measurements may be crowd-sourced from UE for those beacons that am RTT capable, and used by a trilateration algorithm (e.g., a WLS multilateration algorithm) to determine physical antenna positions, which also may be maintained in the beacon database. Accuracy of the trilateration may be enhanced by obtaining raw GNSS measurements (e.g., psuedoranges) from the UE, and performing a cloud-based RTK GNSS position fix for the UE.

Abstract: In various embodiments, crowd sourcing techniques are provided to enable RTT-based positioning of UE. To address issues of discovering which beacons (e.g., Wi-Fi APs, cellular base stations, BLE transmitters, etc.) support measurement of RTT (e.g., according to IEEE 802.11mc, 3GPP Release 16, etc.), beacon RTT capabilities may be crowd-sourced from UE and maintained by a cloud-based location platform in a beacon database (or more specifically, a RTT database portion thereof). To address the issue of determining physical antenna positions, RTT measurements may be crowd-sourced from UE for those beacons that are RTT capable, and used by a trilateration algorithm (e.g., a WLS multilateration algorithm) to determine physical antenna positions, which also may be maintained in the beacon database. Accuracy of the trilateration may be enhanced by obtaining raw GNSS measurements (e.g., pseudoranges) from the UE, and performing a cloud-based RTK GNSS position fix for the UE.

Abstract: In various embodiments, techniques are provided for deploying a positioning applet to a SIM (e.g., a physical SIM card or an embedded SIM (eSIM)/integrated SIM (iSIM)) via an over-the-air (OTA) update or by permanent programming (i.e. “burning in”) during manufacture. The positioning applet may run solely on a processor of the SIM, functioning without support of application, OS or baseband software executing on the CPU or baseband processor of the UE, or network deployed infrastructure support. In operation, the positioning applet collects positioning measurements from a baseband processor (e.g., a baseband chipset) of the UE (e.g., via 3GPP protocols) which are sent (e.g., as an encrypted payload) to a remote location platform that compares the positioning measurements to known positioning data in a database (e.g., a crowd sourced database) to determine UE position. The remote location platform may provide an estimated position to a designated recipient system, without involvement of the UE.

Abstract: In various embodiments, techniques are provided for determining and associating multiple locations with beacons, and estimating a location of an electronic device based on beacons having multiple associated locations. To determine multiple locations of a beacon, observations are grouped into observation clusters, a probability is calculated that each observation cluster accurately describes the beacon, multiple observation clusters are selected as representative of the beacon based on the calculated probabilities, characteristics are derived for the beacon (including multiple locations) based on the selected multiple observation clusters, and at least the multiple locations for the beacon are stored in a reference database.

Abstract: In one example embodiment, a certified location service enables a mobile device to access a location-based service when a determined location meets a location requirement and an overall confidence score for the determined location exceeds a confidence threshold. A data package is received including identifiers of beacons observed by the mobile device, and a location of the mobile device is determined based on a calculated location of one or more of the beacons. An overall confidence score for the determined location is calculated based on one or more individual confidence scores for the one or more beacons used in determining the location or composite confidence scores for types of the one or more beacons. The determined location and the overall confidence score are provided to one or more provider servers that allow the mobile device to access a location-based service based thereon.

Abstract: In various embodiments, crowd sourcing techniques are provided to enable RTT-based positioning of UE. To address issues of discovering which beacons (e.g., Wi-Fi APs, cellular base stations, BLE transmitters, etc.) support measurement of RTT (e.g., according to IEEE 802.11mc, 3GPP Release 16, etc.), beacon RTT capabilities may be crowd-sourced from UE and maintained by a cloud-based location platform in a beacon database (or more specifically, a RTT database portion thereof). To address the issue of determining physical antenna positions, RTT measurements may be crowd-sourced from UE for those beacons that are RTT capable, and used by a trilateration algorithm (e.g., a WLS multilateration algorithm) to determine physical antenna positions, which also may be maintained in the beacon database. Accuracy of the trilateration may be enhanced by obtaining raw GNSS measurements (e.g., psuedoranges) from the UE, and performing a cloud-based RTK GNSS position fix for the UE.

Abstract: In one example embodiment, an analysis application implements a technique for measuring a property of interest of an input dataset of location samples. The analysis application may process the input dataset in one or more multi-stage pipelines to produce values that measure a metric for the input dataset. The values that measure the metric for the input dataset may be compared with values that measure the metric for one or more labeled datasets that are generated by the analysis application, for example, using a feedback loop and sampling from a plurality of delta data sets. Each labeled datasets may have different values that measure the metric and corresponding measures of the property of interest. Based on the comparison, the analysis application may determine the measure of the property of interest for the input dataset and such measure may be returned, for example, to a remote device.

Abstract: In one example embodiment, a certified location service enables a mobile device to access a location-based service when a determined location meets a location requirement and an overall confidence score for the determined location exceeds a confidence threshold. A data package is received including identifiers of beacons observed by the mobile device, and a location of the mobile device is determined based on a calculated location of one or more of the beacons. An overall confidence score for the determined location is calculated based on one or more individual confidence scores for the one or more beacons used in determining the location or composite confidence scores for types of the one or more beacons. The determined location and the overall confidence score are provided to one or more provider servers that allow the mobile device to access a location-based service based thereon.

Abstract: In various embodiments, techniques are provided for determining one or more zones in which mobile devices are presently located and identifying or updating characteristics of on or more zones. Samples that include beacon information and/or sensor information collected by mobile devices are aggregated and dynamically organized into sample classes that are associated with zero, one or more zones. A venue is characterized by a set of zones and associated tags, which may be informed based on samples for the venue, a venue group to which the venue belongs, or all venues. To determine if a mobile device is located in one or more zones, the samples are compared to zone characteristics, and based thereon (and optionally history information) one or more zones are selected having determined likelihoods, and at least a zone having the highest likelihood of the one or more selected zones is returned.

Abstract: In one embodiment, a technique is provided for propagating network address to attribute associations between network addresses. One or more profiles are obtained that maintain an association between a first network address and one or more attributes, the association produced from network address observations of the first network address by one or more source devices in communication with a network. A second network address is determined that is associated with the first network address based on a similarity criteria. The second network address initially lacks an association with the one or more attributes. The one or more attributes are propagated from the first network address to the second address, to form an association between the second network address and the one or more attributes. The association between the second network address and the one or more attributes is then stored in an updated profile.

Abstract: In one embodiment, a processing technique is provided that utilizes multiple network address observations. One or more records are obtained that maintain network address observations, each network address observation associating one or more attributes with a network address observed by one or more source devices. Multiple network addresses from the network address observations are clustered into one or more discrete groups of network addresses based on a clustering criteria. For a selected group of network addresses, an association is formed associating one or more refined attributes derived from the selected group with an individual network address that is a member of the selected group.

Abstract: In one embodiment, a processing technique is provided for determining a refined attribute of a network address based on one or more other attributes. A network address associated with a source device in communication with a network is observed. One or more first attributes of the network address are determined, where the one or more first attributes indicate at least a spatial or temporal property of the network address. The one or more first attributes are processed to determine a second attribute to be associated with the network address, where the second attribute indicates whether the network address is a fixed or mobile network address, or is a proxy or non-proxy network address. A record is stored that maintains the association between the second attribute and the network address.

Abstract: In one embodiment, techniques are provided to establish and use semantic associations between location profiles and ambient profiles. One or more location profiles are selected from a location database. A first plurality of ambient profiles is selected for a first area surrounding one or more geographic locations of the location profiles. One or more patterns are extracted from the first plurality of ambient profiles and are used to generate associations between location profiles and ambient profiles in an association database which semantically associates location profiles with ambient profiles independent of geographic location. The associations may be used, among other things, to service requests from mobile devices and/or update ambient profiles or location profiles.

Abstract: In one embodiment, a mapping/translation technique is provided for generating an association between an observed network address and one or more attributes that are not directly observed. A network address is observed that is associated with a source device. A first attribute is determined, the first attribute being a directly observed attribute. The first attribute is mapped to a second attribute based on at least a predefined spatial, temporal, or identity-related correspondence between the first attribute and the second attribute, wherein the second attribute was not directly observed in connection with the network address. An association is generated between the second attribute and the network address. A record that maintains the association between the second attribute and the network address is stored.

Abstract: In various embodiments, position of a user device is estimated by scanning for WLAN packets transmitted within range of the user device, the scanning to include a plurality of phases which are progressed through until WLAN information sufficient to identify at least a threshold number of WLAN APs is obtained, the plurality of phases including an active scanning phase in which the RF module transmits probe request packets and receives one or more probe response packets, and one or more passive scanning phases in which the RF module listens for one or more packets without first transmitting request packets, extracting WLAN information indicating an identity of one or more WLAN APs from the one or more probe response packets or the one or more listened for packets, and providing the WLAN information to a WPS to obtain an estimate of the position of the user device.

Abstract: In one embodiment, a filtering technique is provided for ensuring data quality of network address observations. A network address observation is obtained of a network address associated with a source device, the network address observation associating the network address with one or more directly observed attributes. The network address observation is filtered based on a comparison of a selected one of the one or more directly observed attributes to a predetermined criteria, and using a result of the comparison as indicative of whether the network address observation should be used for association of the network address with one or more directly observed attributes. The filtering either associates one or more indicators with the network address observation, or removes the network address observation. A network address to attribute association system executed on one or more electronic devices stores a record that maintains any network address observation that has not been removed and any indicator.

Abstract: In one embodiment, techniques are provided for estimating demographic information. A current device demographic profile for a mobile device is retrieved. An estimated geographic location of the mobile device and a time at which the mobile device visited the estimated geographic location is determined. Based on this, a location demographic profile for a geographic area that includes the estimated geographic location and for a time frame that includes the determined time is retrieved. The current device demographic profile for the mobile device is updated based on the location demographic profile. Further, the location demographic profile is updated based on a plurality of device demographic profiles of a plurality of mobile devices that visit geographic locations within the geographic area, the plurality of mobile devices including the mobile device.

Abstract: In one embodiment, a technique is provided for estimating and using an expected error of a position estimate of a WLAN-enabled mobile device produced by a WLAN positioning system. The WLAN-enabled mobile device receives signals transmitted by a plurality of WLAN access points in range of the WLAN-enabled device. The position of the WLAN-enabled device is estimated based on the received signals from the WLAN access points in range of the WLAN-enabled device. An expected error of the position estimate is based on at least one of a spatial spread associated with geographic positions of the WLAN access points, signal coverage areas of the WLAN access points, or a number of the WLAN access points. The expected error is used in providing one or more location-based services to a user of the WLAN-enabled mobile device.

Abstract: The present disclosure relates to a method for determining the position of a WLAN positioning system (WPS) and satellite positioning system (SPS) enabled device. The method can include determining an initial WPS position of the device using WPS, calculating an error region around the initial WPS position of the device, dividing the error region into a plurality of points, obtaining satellite measurements from at least two satellites in view of the device, determining a variation in a receiver clock bias for each point within the error region based on the satellite measurements from at least two satellites, selecting the point with the lowest variation in the receiver clock bias, and determining whether or not to use the point with the lowest variation in receiver clock bias to refine the initial WPS position of the device.