A system and method for implementing a learning model for predicting the geographic location of an Internet Protocol (IP) address are disclosed. A particular embodiment of the system and method includes receiving a model to predict a geographic coordinates position of an Internet Protocol (IP) address, the model including one or more parameters and one or more variables associated with coordinates of the IP address and corresponding information associated with the IP address; receiving training data including a plurality of pairs of coordinates of a target IP address and corresponding information associated with the target IP address; determining, by use of a processor, the one or more parameters based on the training data and the model; and returning a result including information indicative of the determined parameters.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. (canceled) 2. A method for predicting a geographical location of a selected device, comprising: obtaining a model based on a conditional multivariate normal distribution; obtaining transit time information relating the selected device and each of one or more other devices; and determining, using a hardware device, the geographical location of the selected device based on the model and the transit time information. 3. The method of claim 2, wherein the transit time information comprises one or more message transit times between the selected device and each of the one or more other devices. 4. The method of claim 2, wherein the determining adjusts a geographical location of a representative device to determine the geographical location of the selected device. 5. The method of claim 2, wherein the model based on the conditional multivariate normal distribution comprises a mean vector m and a covariance matrix s. 6. The method of claim 2, wherein the model comprises a transit times mean vector, the transit times mean vector representing a message round-trip transit time from a representative device to each of one or more source devices. 7. The method of claim 2, wherein the model comprises an m×m transit time covariance matrix, the m×m transit time covariance matrix representing a covariance of transit times associated with two or more source devices. 8. The method of claim 2, wherein the model comprises an n×m geographical location transit times covariance matrix, the n×m geographical location transit times covariance matrix representing a covariance of geographical locations of two or more source devices with transit times of the two or more source devices. 9. The method of claim 2, wherein the model comprises an n×1 geographical location mean vector, the n×1 geographical location mean vector representing values of geographical location parameters for a representative device. 10. The method of claim 2, wherein the transit times information comprises an m×1 transit times input vector, the m×1 transit times input vector representing message transit times between the selected device and each of the one or more other devices. 11. The method of claim 2, wherein the determining operation determines an n×1 predicted geographical location vector, the n×1 predicted geographical location vector representing one or more predicted geographical location parameters of the selected device. 12. The method of claim 2, wherein the determining the geographical location of the selected device further comprises: multiplying an inverted m×m transit times covariance matrix by an n×m geographical locations transit times covariance matrix to derive an n×m combined covariance matrix; subtracting an m×1 transit times mean vector from an m×1 transit times input vector to derive a transit times difference vector; multiplying the n×m combined covariance matrix and the transit times difference vector to derive a geographical location adjustment vector; and adding the geographical location adjustment vector and an n×1 geographical locations mean vector to derive an n×1 geographical location vector. 13. The method of claim 12, further comprising inverting an m×m transit times covariance matrix to derive the inverted m×m transit times covariance matrix. 14. An apparatus for predicting one or more characteristics of a selected device, the apparatus comprising: a processor; memory to store instructions that, when executed by the processor cause the processor to: obtain a model based on a conditional multivariate normal distribution; obtain transit time information relating the selected device and each of one or more other devices; and determine, using a hardware device, the geographical location of the selected device based on the model and the transit time information. 15. The apparatus of claim 14, wherein the transit time information comprises one or more message transit times between the selected device and each of the one or more other devices. 16. The apparatus of claim 14, wherein the determining adjusts a geographical location of a representative device to determine the geographical location of the selected device. 17. The apparatus of claim 14, wherein the model based on the conditional multivariate normal distribution comprises a mean vector m and a covariance matrix s. 18. The apparatus of claim 14, wherein the model comprises a transit times mean vector, the transit times mean vector representing a message round-trip transit time from a representative device to each of one or more source devices. 19. The apparatus of claim 14, wherein the model comprises an m×m transit time covariance matrix, the m×m transit time covariance matrix representing a covariance of transit times associated with two or more source devices. 20. The apparatus of claim 14, wherein the model comprises an n×m geographical location transit times covariance matrix, the n×m geographical location transit times covariance matrix representing a covariance of geographical locations of two or more source devices with transit times of the two or more source devices. 21. The apparatus of claim 14, wherein the model comprises an n×1 geographical location mean vector, the n×1 geographical location mean vector representing values of geographical location parameters for a representative device. 22. The apparatus of claim 14, wherein the transit times information comprises an m×1 transit times input vector, the m×1 transit times input vector representing message transit times between the selected device and each of the one or more other devices. 23. The apparatus of claim 14, wherein the determining operation determines an n×1 predicted geographical location vector, the n×1 predicted geographical location vector representing one or more predicted geographical parameters of the selected device. 24. The apparatus of claim 14, wherein the determining the geographical location of the selected device further comprises: multiplying an inverted m×m transit times covariance matrix by an n×m geographical locations transit times covariance matrix to derive an n×m combined covariance matrix; subtracting an m×1 transit times mean vector from an m×1 transit times input vector to derive a transit times difference vector; multiplying the n×m combined covariance matrix and the transit times difference vector to derive a geographical location adjustment vector; and adding the geographical location adjustment vector and an n×1 geographical locations mean vector to derive an n×1 geographical location vector. 25. The apparatus of claim 24, further comprising instructions that, when executed by the processor cause the processor to invert an m×m transit times covariance matrix to derive the inverted m×m transit times covariance matrix. 26. An article of manufacture comprising a non-transitory machine-readable storage medium having machine executable instructions embedded thereon, which when executed by a machine, cause the machine to: obtain a model based on a conditional multivariate normal distribution; obtain transit time information relating the selected device and each of one or more other devices; and determine, using a hardware device, the geographical location of the selected device based on the model and the transit time information.