Abstract: Techniques for calculating a location of a position consumer device is disclosed. In one example, a network server may create a fingerprint map from reference data points. Each of the reference data points may include a recorded geo-location of a position source device and signal measurements taken at that recorded geo-location. By initially estimating an initial position of the position consumer device, the network server may apply one or more threshold values to filter reference data points—candidates for interpolation. The network server may then perform an interpolation on one or more pairs of reference data points to find a pair of reference data points that is collinear with the estimated position of the position consumer device. The location of the position consumer device may then be calculated based upon geo-locations of position source devices that are associated with collinear reference data points.

Abstract: Techniques are described for filtering location data in a dataset based at least on its representation of the actual location of a user device. In some embodiments, sensor data including location data associated with a user device located in an environment may be used to identify one or more prediction factors. The one or more prediction factors may be associated to embed a user behavior vector of a user based at least on the associations. The user behavior vector may be a representation of an environment class in which the environment may be classified. The location data is associated with the environment class.

Abstract: Techniques are described herein for developing a fingerprint map that may be used for 3D indoor localization. In one example, a network server may leverage a building footprint from an open source database with signal measurements taken by probing user devices from signal sources such as access point (AP) devices. The network server may use the signal measurements to remotely calculate corresponding 3D positions of the AP devices in a particular building. Further, the network server may use the building footprint and the calculated 3D positions of the AP devices as references for developing the fingerprint map for 3D indoor localization.

Abstract: Techniques for calculating a location of a position consumer device is disclosed. In one example, a network server may create a fingerprint map from reference data points. Each of the reference data points may include a recorded geo-location of a position source device and signal measurements taken at that recorded geo-location. By initially estimating an initial position of the position consumer device, the network server may apply one or more threshold values to filter reference data points—candidates for interpolation. The network server may then perform an interpolation on one or more pairs of reference data points to find a pair of reference data points that is collinear with the estimated position of the position consumer device. The location of the position consumer device may then be calculated based upon geo-locations of position source devices that are associated with collinear reference data points.

Abstract: Techniques for locating a user device in an indoor environment include receiving an indication of an acceleration event at a start position and a deceleration event associated with the user device. Based at least on the amount of time elapsed between the acceleration event and the deceleration event, a traveled distance of the user device may be determined. Additionally, sensor data collected from the user device between the acceleration event and the deceleration event may be used to determine the direction of travel of the user device. Following the determination of the traveled distance and the direction of travel, a finish position relative to the start position of the user device in the indoor environment may be mapped using an architectural plan associated with the indoor environment.

Abstract: A telemetry data computing engine may receive the telemetry data of a user device, via a network, and it may apply a machine learning algorithm to at least one telemetry data and generate a predicted location for the user device. The predicted location may be used to generate a location pattern for the user device and a user device profile for the user device. The user device profile may be routed to the wireless carrier core network.

Abstract: A method for a base station in communication with a user device through a wireless communications network is described. The method includes communicating with a user device on a current frequency through a wireless communications network. The method may also include determining, based at least in part on a model, a probability that a higher capacity may be acquired by the user device on a frequency other than the current frequency; predicting a first recommended frequency for the user device that is likely to have a higher capacity than the current frequency; instructing the user device to scan the first recommended frequency; receiving an indication from the user device that the first recommended frequency has a higher capacity than the current frequency; and communicating with the user device on the first recommended frequency.

Abstract: Techniques for locating a user device in an indoor environment include receiving an indication of an acceleration event at a start position and a deceleration event associated with the user device. Based at least on the amount of time elapsed between the acceleration event and the deceleration event, a traveled distance of the user device may be determined. Additionally, sensor data collected from the user device between the acceleration event and the deceleration event may be used to determine the direction of travel of the user device. Following the determination of the traveled distance and the direction of travel, a finish position relative to the start position of the user device in the indoor environment may be mapped using an architectural plan associated with the indoor environment.

Abstract: A method for determining a reliability of a wireless device to estimate its location under a simulated environmental condition includes simulating an environmental condition inside a test chamber by controlling a physical parameter. The test chamber contains the wireless user device configured to estimate a location of the wireless user device based on reference signals received from a signal source. The method includes communicating a pattern of reference signals having varying signal propagation characteristics in the test chamber. The method includes receiving an indication of an estimated location calculated by the wireless user device based on the pattern of reference signals. The reliability of the wireless device to estimate its location under the simulated environmental condition is determined based on a comparison of the estimated location with the simulated location.

Abstract: Techniques are described herein for developing a fingerprint map that may be used for 3D indoor localization. In one example, a network server may leverage a building footprint from an open source database with signal measurements taken by probing user devices from signal sources such as access point (AP) devices. The network server may use the signal measurements to remotely calculate corresponding 3D positions of the AP devices in a particular building. Further, the network server may use the building footprint and the calculated 3D positions of the AP devices as references for developing the fingerprint map for 3D indoor localization.

Abstract: Techniques are described herein for developing a fingerprint map that may be used for 3D indoor localization. In one example, a network server may leverage a building footprint from an open source database with signal measurements taken by probing user devices from signal sources such as access point (AP) devices. The network server may use the signal measurements to remotely calculate corresponding 3D positions of the AP devices in a particular building. Further, the network server may use the building footprint and the calculated 3D positions of the AP devices as references for developing the fingerprint map for 3D indoor localization.

Abstract: A computer-implemented method for determining a path of a patient in a hospital includes receiving administrative and/or subjective data of the patient; comparing said administrative and subjective data with administrative and subjective data of previously treated patient stored in a database to determine previously treated patients having a given level of similarity with the patient, wherein said database comprises, for each previously treated patient, a recorded path; collecting hospital resources availability data; and determining an initial path of the patient in the hospital wherein said initial path includes calculated probabilities of using specific hospital resources in a given sequence, based on the recorded path of said previously treated patients having said level of similarity with the patient, and said hospital resources availability data.

Abstract: Techniques for calculating a location of a position consumer device is disclosed. In one example, a network server may create a fingerprint map from reference data points. Each of the reference data points may include a recorded geo-location of a position source device and signal measurements taken at that recorded geo-location. By initially estimating an initial position of the position consumer device, the network server may apply one or more threshold values to filter reference data points—candidates for interpolation. The network server may then perform an interpolation on one or more pairs of reference data points to find a pair of reference data points that is collinear with the estimated position of the position consumer device. The location of the position consumer device may then be calculated based upon geo-locations of position source devices that are associated with collinear reference data points.

Abstract: Techniques are described for filtering location data in a dataset based at least on its representation of the actual location of a user device. In some embodiments, sensor data including location data associated with a user device located in an environment may be used to identify one or more prediction factors. The one or more prediction factors may be associated to embed a user behavior vector of a user based at least on the associations. The user behavior vector may be a representation of an environment class in which the environment may be classified. The location data is associated with the environment class.

Abstract: Techniques for locating a user device in an indoor environment include receiving an indication of an acceleration event at a start position and a deceleration event associated with the user device. Based at least on the amount of time elapsed between the acceleration event and the deceleration event, a traveled distance of the user device may be determined. Additionally, sensor data collected from the user device between the acceleration event and the deceleration event may be used to determine the direction of travel of the user device. Following the determination of the traveled distance and the direction of travel, a finish position relative to the start position of the user device in the indoor environment may be mapped using an architectural plan associated with the indoor environment.

Abstract: Techniques are described herein for developing a fingerprint map that may be used for 3D indoor localization. In one example, a network server may leverage a building footprint from an open source database with signal measurements taken by probing user devices from signal sources such as access point (AP) devices. The network server may use the signal measurements to remotely calculate corresponding 3D positions of the AP devices in a particular building. Further, the network server may use the building footprint and the calculated 3D positions of the AP devices as references for developing the fingerprint map for 3D indoor localization.

Abstract: A telemetry data computing engine may receive the telemetry data of a user device, via a network, and it may apply a machine learning algorithm to at least one telemetry data and generate a predicted location for the user device. The predicted location may be used to generate a location pattern for the user device and a user device profile for the user device. The user device profile may be routed to the wireless carrier core network.

Abstract: Techniques are disclosed for locating a user device in an indoor environment such as a building based at least on a building topology model using one or more Internet of Things (IoT) devices. Certain aspects of the techniques include detecting a presence of a user device in communication with an IoT device in a building, wherein the IoT device is associated with device attributes. The building is identified based at least on a building topology model that is associated with the building and the device attributes. The location of the IoT device is determined based at least on the building topology model. The user device is located relative to the location of the IoT device.

Abstract: Techniques are described for locating user devices in an indoor environment based at least on a building topology model. The techniques include detecting a presence of a user device in a building. The building may include ingress and egress points connecting defined spaces within the building. The locations of the individual ingress and egress points are identified based at least on the movement data of the user device. A building topology model may be generated using the locations of the individual ingress and egress points. To locate the user device at a given timestamp, one or more of the ingress and egress points passed through by the user device may be identified. The location of the user device may be determined based at least on the locations of the one or more ingress and egress points passed through by the user device mapped to the building topology model.

Abstract: The remote analysis of mobile device quality of experience diagnostic files includes analyzing diagnostic files. A diagnostic driver application resident at the mobile device is remotely activated to generate and send diagnostic files to one or more network resident servers for analysis. The diagnostic files may be analyzed to determine the mobile device quality of experience, and to determine a root cause and geographic and/or network location of a problem, such as dropped calls or poor data connectivity. In some embodiments, the diagnostic files may be aggregated to form a database of aggregated diagnostics, which can be used to further analyze a telecommunications network to determine the root cause of a network problem.