# US 9,439,044 B2 · Mechanism for determining location history via multiple historical predictors > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 9,439,044 B2 - Document ID: US9439044B2 - Status: Granted - Kind: B2 - Application number: 14/596,883 - Filed: 2015-01-14 - Published: 2016-07-14 - Granted: 2016-09-06 - Earliest claimed: 2015-01-14 - Assignees: Tektronix Texas, LLC - Applicants: Tektronix, Inc. - Inventors: Geoff Hopcraft; James Blondin - Web page: https://patentgrep.com/patent/US9439044B2 ## CPC - H04W 64/003: {locating network equipment} - H04W 4/027: {using movement velocity, acceleration information} - H04W 4/029: Location-based management or tracking services - H04W 64/006: {with additional information processing, e.g. for direction or speed determination} - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## Abstract A method for determining location history of a mobile device is provided. A plurality of states corresponding to a plurality of reachable road segments associated with a road network is determined based on one or more initial estimated locations of the mobile device. Mobility model defining transition probabilities between the determined plurality of states is generated. Time series network data associated with the mobile device is received from a network data source. The time series network data is associated with a given period of time. The time series network data is processed using the generated mobility model to estimate an actual route most likely travelled by the mobile device during the given time period. ## Claims ### Claim 1 A method for determining location history of a mobile device, comprising the steps of: determining, by a processor, a plurality of states corresponding to a plurality of reachable road segments associated with a road network based on one or more initial estimated locations of the mobile device; generating, by the processor, a mobility model defining transition probabilities between the determined plurality of states; receiving, by the processor, time series network data associated with the mobile device from a network data source, wherein the time series network data is associated with a given period of time and wherein the time series network data comprises information relating to at least a plurality of signal strength measurements, a plurality of propagation path time measurements and information relating to transmission error rates associated with the mobile device; and processing, by the processor, the time series network data using the generated mobility model to estimate an actual route travelled by the mobile device along a portion of the road network during the given period of time. ### Claim 2 (depends on 1) The method of claim 1, wherein the time series network data does not conform to the Gaussian distribution. ### Claim 3 (depends on 1) The method of claim 1, wherein generating the mobility model comprises generating a directed graph data structure representing a plurality of valid travel options from the initial one or more estimated locations of the mobile device. ### Claim 4 (depends on 1) The method of claim 1, wherein processing the time series network data using the generated mobility model comprises correlating each element of the time series network data with a first subset of the plurality of states corresponding to the plurality of reachable road segments. ### Claim 5 (depends on 4) The method of claim 4, wherein generating the mobility model comprises determining a plurality of likelihood values associated with the plurality of states corresponding to the plurality of reachable road segments and wherein the correlating step comprises: multiplying each of the plurality of likelihood values associated with the plurality of states by a probability of a corresponding reachable road segment calculated based on a corresponding element of the time series network data; and selecting a second subset of the plurality of states, the second subset including states having highest cumulative probabilities based on said multiplication. ### Claim 6 (depends on 1) The method of claim 1, wherein the mobility model comprises a Markov model. ### Claim 7 (depends on 4) The method of claim 4, wherein the first subset of the plurality of states is associated with a user-configurable threshold value representing a maximum number of states being used for calculation purposes. ### Claim 8 A computer program product for determining location history of a mobile device, the computer program product comprising: one or more non-transitory computer-readable storage devices and a plurality of program instructions stored on at least one of the one or more non-transitory computer-readable storage devices, the plurality of program instructions comprising: program instructions to determine a plurality of states corresponding to a plurality of reachable road segments associated with a road network based on one or more initial estimated locations of the mobile device; program instructions to generate a mobility model defining transition probabilities between the determined plurality of states; program instructions to receive time series network data associated with the mobile device from a network data source, wherein the time series network data is associated with a given period of time and wherein the time series network data comprises information relating to at least a plurality of signal strength measurements, a plurality of propagation path time measurements and information relating to transmission error rates associated with the mobile device; and program instructions to process the time series network data using the generated mobility model to estimate an actual route travelled by the mobile device along a portion of the road network during the given period of time. ### Claim 9 (depends on 8) The computer program product of claim 8, wherein the time series network data does not conform to the Gaussian distribution. ### Claim 10 (depends on 8) The computer program product of claim 8, wherein the program instructions to generate the mobility model comprise program instructions to generate a directed graph data structure representing a plurality of valid travel options from the initial one or more estimated locations of the mobile device. ### Claim 11 (depends on 8) The computer program product of claim 8, wherein program instructions to process the time series network data using the generated mobility model comprise program instructions to correlate each element of the time series network data with a first subset of the plurality of states corresponding to the plurality of reachable road segments defined by the generated mobility model. ### Claim 12 (depends on 11) The computer program product of claim 11, wherein the program instructions to generate the mobility model comprise program instructions to determine a plurality of likelihood values associated with the plurality of states corresponding to the plurality of reachable road segments and wherein the program instructions to correlate comprise: program instructions to multiply each of the plurality of likelihood values associated with the plurality of states by a probability of a corresponding reachable road segment calculated based on a corresponding element of the time series network data; and program instructions to select a second subset of the plurality of states, the second subset including states having highest cumulative probabilities based on said multiplication. ### Claim 13 (depends on 12) The computer program product of claim 12, wherein the program instructions to generate the mobility model comprise program instructions to generate a Markov model. ### Claim 14 (depends on 11) The computer program product of claim 11, wherein the first subset of the plurality of states is associated with a user-configurable threshold value representing a maximum number of states being used for calculation purposes. ### Claim 15 A computer system for determining location history of a mobile device, the computer system comprising one or more processors, one or more non-transitory computer-readable storage devices, and a plurality of program instructions stored on at least one of the one or more non-transitory storage devices for execution by at least one of the one or more processors, the plurality of program instructions comprising: program instructions to determine a plurality of states corresponding to a plurality of reachable road segments associated with a road network based on one or more initial estimated locations of the mobile device; program instructions to generate a mobility model defining transition probabilities between the determined plurality of states; program instructions to receive time series network data associated with the mobile device from a network data source, wherein the time series network data is associated with a given period of time and wherein the time series network data comprises information relating to at least a plurality of signal strength measurements, a plurality of propagation path time measurements and information relating to transmission error rates associated with the mobile device; and program instructions to process the time series network data using the generated mobility model to estimate an actual route travelled by the mobile device along a portion of the road network during the given period of time. ### Claim 16 (depends on 15) The computer system of claim 15, wherein the time series network data does not conform to the Gaussian distribution.