# US 8,788,437 B2 · System and method for implementing a learning model for predicting the geographic location of an internet protocol address > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,788,437 B2 - Document ID: US8788437B2 - Status: Granted - Kind: B2 - Application number: 13/192,686 - Filed: 2011-07-28 - Published: 2013-01-31 - Granted: 2014-07-22 - Earliest claimed: 2011-07-28 - Assignees: Quova, Inc. - Applicants: Armand Erik Prieditis - Inventors: Armand Erik Prieditis - Web page: https://patentgrep.com/patent/US8788437B2 ## CPC - G06N 20/00: Machine learning - G06N 5/04: Inference or reasoning models - H04L 61/5007: Internet protocol [IP] addresses - H04L 2101/69: using geographic information, e.g. room number - H04W 64/00: Locating users or terminals {or network equipment} for network management purposes, e.g. mobility management - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## Abstract 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. ## Claims ### Claim 1 A method for implementing a learning model for predicting a geographic location of an Internet Protocol (IP) address, the method comprising: 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 geographic coordinates of the IP address and corresponding information associated with the IP address; receiving training data including a plurality of pairs of geographic 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, the one or more parameters including a mean vector representing a pair of geographic coordinates of a baseline IP address, at least one of the one or more parameters being determined by generating at least one product of values of the training data; and returning a result including information indicative of the determined parameters. ### Claim 2 (depends on 1) The method of claim 1 wherein the model is a parametric model. ### Claim 3 (depends on 1) The method of claim 1 including using at least one mean vector generated from the training data. ### Claim 4 (depends on 1) The method of claim 1 including using at least one covariance matrix generated from the training data. ### Claim 5 (depends on 1) The method of claim 1 wherein the result includes model parameters for use with the model to predict the geographic coordinates of the target IP address. ### Claim 6 (depends on 1) The method of claim 1 including generating at least one sum of values in a column of the training data. ### Claim 7 (depends on 1) The method of claim 1 including generating at least one product of values in a pair of columns of the training data. ### Claim 8 An Internet Protocol (IP) address geo-location learning model system comprising: a processor; a model receiving component, in data communication with the processor, to receive 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 geographic coordinates of the IP address and corresponding information associated with the IP address; a training data receiving component, in data communication with the processor, to receive training data including a plurality of pairs of geographic coordinates of a target IP address and corresponding information associated with the target IP address; a parameter determining component to determine the one or more parameters based on the training data and the model, the one or more parameters including a mean vector representing a pair of geographic coordinates of a baseline IP address, at least one of the one or more parameters being determined by generating at least one product of values of the training data, and to return a result including information indicative of the determined parameters. ### Claim 9 (depends on 8) The geo-location learning model system of claim 8 wherein the model is a parametric model. ### Claim 10 (depends on 8) The geo-location learning model system of claim 8 being configured to use at least one mean vector generated from the training data. ### Claim 11 (depends on 8) The geo-location learning model system of claim 8 being configured to use at least one covariance matrix generated from the training data. ### Claim 12 (depends on 8) The geo-location learning model system of claim 8 wherein the result includes model parameters for use with the model to predict the geographic coordinates of the target IP address. ### Claim 13 (depends on 8) The geo-location learning model system of claim 8 being configured to generate at least one sum of values in a column of the training data. ### Claim 14 (depends on 8) The geo-location learning model system of claim 8 being configured to generate at least one product of values in a pair of columns of the training data. ### Claim 15 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: receive 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 geographic coordinates of the IP address and corresponding information associated with the IP address; receive training data including a plurality of pairs of geographic coordinates of a target IP address and corresponding information associated with the target IP address; determine the one or more parameters based on the training data and the model, the one or more parameters including a mean vector representing a pair of geographic coordinates of a baseline IP address, at least one of the one or more parameters being determined by generating at least one product of values of the training data; and return a result including information indicative of the determined parameters. ### Claim 16 (depends on 15) The article of manufacture of claim 15 wherein the model is a parametric model. ### Claim 17 (depends on 15) The article of manufacture of claim 15 being configured to use at least one mean vector generated from the training data. ### Claim 18 (depends on 15) The article of manufacture of claim 15 being configured to use at least one covariance matrix generated from the training data. ### Claim 19 (depends on 15) The article of manufacture of claim 15 being configured to generate at least one sum of values in a column of the training data. ### Claim 20 (depends on 15) The article of manufacture of claim 15 being configured to generate at least one product of values in a pair of columns of the training data.