# US 12,602,767 B1 · System of and method for analyzing thoroughfare conditions via oil spot analysis > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,602,767 B1 - Document ID: US12602767B1 - Status: Granted - Kind: B1 - Application number: 17/987,520 - Filed: 2022-11-15 - Granted: 2026-04-14 - Earliest claimed: 2022-11-15 - Assignees: University of South Florida - Applicants: University of South Florida - Inventors: Tristen Edward Mee; Austin Gray Mullins - Web page: https://patentgrep.com/patent/US12602767B1 ## CPC - G06T 7/0008: {checking presence/absence} - G01C 21/3415: {Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents} - G01C 21/3461: {Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types or segments such as motorways, toll roads or ferries} - G01C 21/3602: {Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera} - G01C 21/3647: {Guidance involving output of stored or live camera images or video streams} - G01C 21/3807: {characterised by the type of data} - G01C 21/3811: {Point data, e.g. Point of Interest [POI]} - G01C 21/3837: {Data obtained from a single source} - G01C 21/3841: {Data obtained from two or more sources, e.g. probe vehicles} - G01C 21/3848: {Data obtained from both position sensors and additional sensors} - G06V 10/25: Determination of region of interest [ROI] or a volume of interest [VOI] - G06V 10/82: using neural networks - G06V 20/13: Satellite images - G06V 20/176: {Urban or other man-made structures} - G06V 20/182: {Network patterns, e.g. roads or rivers} - G06T 2200/24: involving graphical user interfaces [GUIs] - G06T 2207/10032: Satellite or aerial image; Remote sensing - G06T 2207/20081: Training; Learning - G06T 2207/30184: Infrastructure - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Described herein relates to a system of and method for analyzing a condition of a thoroughfare, via dark discoloration in a surface of the thoroughfare, such as an oil formation from oil droplets dislodged from the underside of a vehicle. The oil droplets may be dislodged from the underside of a vehicle due to the sudden downward vehicle motion coupled with a rapid upward acceleration when passing over a distressed thoroughfare. The thoroughfare analysis system may be configured to input at least one thoroughfare image and compare it to at least one trained image of at least one trained dataset to determine a condition of the thoroughfare. Additionally, the thoroughfare analysis system may be configured to provide at least one alternative thoroughfare based on the condition of the thoroughfare as determined by the thoroughfare analysis system. ## Claims ### Claim 1 A method of automatically analyzing a condition of a thoroughfare, the method comprising: providing a computing device having a processor, wherein the computing device is in electrical communication with at least one server, at least one database, or both; selecting, via the processor of the computing device, at least one thoroughfare image, by: providing, via a GPS system of the computing device, a GPS path divided into a plurality of GPS coordinates based on at least one parameter regarding a travel destination of a user; receiving, via the at least one server, the at least one database, or both, at least one thoroughfare image based on at least one of the plurality of GPS coordinates; identifying, via the processor of the computing device, at least one oil formation of interest (OFI) based on object detection trained on image intensity and shape; generating, via the processor of the computing device, a bounding box surrounding the at least one OFI; comparing, via the processor of the computing device, the at least one OFI with a plurality of training images from a trained dataset to identify the at least one OFI as an ellipsoid shaped OFI or a non-ellipsoid shaped OFI; in response to identifying the at least one OFI being the ellipsoid shaped OFI: calculating, via the processor of the computing device, a likelihood of defect within the bounding box based on weighted scoring between the at least one ellipsoid shaped OFI and at least one of the plurality of training images from the trained dataset, wherein the defect is positioned prior to the ellipsoid shaped OFI and the defect results in a downward vehicle motion coupled with an upward acceleration resulting in the ellipsoid shaped OFI; and generating, via the processor of the computing device, a ranked list of thoroughfare sections most in need of repair based on the number of ellipsoid shaped OFIs per mile of thoroughfare and the calculated likelihood of defect associated with each of the ellipsoid shaped OFIs per mile, thereby prioritizing roadway repairs according to the ranked list. ### Claim 2 (depends on 1) The method of claim 1, further comprising, in response to identifying the at least one OFI being the non-ellipsoid shaped OFI: generating, via the processor of the computing device, one or more estimated wait times based upon one or more of the intensity and length of the at least one non-ellipsoid shaped OFI. ### Claim 3 (depends on 1) The method of claim 1, further comprising, after generating the bounding box surrounding the at least one OFI of the at least one thoroughfare image, highlighting the at least one OFI of the at least one thoroughfare image, wherein the highlight of the at least one OFI of the at least one thoroughfare image is brighter than at least one remaining aspect of the at least one OFI. ### Claim 4 (depends on 1) The method of claim 1, further comprising, providing, via the processor of the computing device, at least one metric regarding a condition of the thoroughfare based on the calculated likelihood of defect. ### Claim 5 (depends on 4) The method of claim 4, further comprising, after providing at least one metric regarding a condition of the thoroughfare based on the calculated likelihood of defect, calculating, via the processor of the computing device, at least one alternative GPS path, at least one alternative thoroughfare, or both to the travel destination based on the at least one metric regarding the condition of the thoroughfare. ### Claim 6 (depends on 5) The method of claim 5, further comprising, after calculating at least one alternative GPS path, at least one alternative thoroughfare, or both, displaying, via the display device of the computing device, the at least one calculated alternative GPS path, alternative thoroughfare, or both by: based on a determination that the likelihood of defect of the thoroughfare is greater than the likelihood of defect of the at least one calculated alternative thoroughfare, altering the GPS path to conform to the at least one calculated alternative GPS path, alternative thoroughfare, or both; and based on a determined that the likelihood of defect of the thoroughfare is less than the likelihood of defect of the at least one calculated alternative thoroughfare, maintaining the GPS path to conform to the thoroughfare. ### Claim 7 (depends on 6) The method of claim 6, wherein the at least one metric is selected from a group consisting of a recommended speed, a number of potholes per mile on the thoroughfare, a number of bumps per mile on the thoroughfare, and a combination of thereof. ### Claim 8 (depends on 1) The method of claim 1, further comprising, after-transmitting the likelihood of defect to the display device of the computing device, and receiving an instruction to unload, from the memory of the computing device, the at least one thoroughfare image. ### Claim 9 (depends on 8) The method of claim 8, further comprising, after receiving an instruction to unload the at least one thoroughfare image, receiving at least one alternative thoroughfare image based on at least one alternative GPS path provided from at least one set of alternative parameters. ### Claim 10 A thoroughfare analysis system for automatically analyzing a condition of a thoroughfare, the thoroughfare analysis system comprising: a computing device having a processor, wherein the computing device is in electrical communication with at least one server, at least one database, or both; and a non-transitory computer-readable medium operably coupled to the processor, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the processor, cause the thoroughfare analysis system to automatically analyze the condition of the thoroughfare by executing instructions comprising: selecting, via the processor of the computing device, at least one thoroughfare image, by: providing, via a GPS system of the computing device, a GPS path divided into a plurality of GPS coordinates based on at least one parameter regarding a travel destination of a user; receiving, via the at least one server, the at least one database, or both, at least one thoroughfare image based on at least one of the plurality of GPS coordinates; identifying, via the processor of the computing device, at least one oil formation of interest (OFI) based on object detection trained on image intensity and shape; generating, via the processor of the computing device, a bounding box surrounding the at least one OFI; comparing, via the processor of the computing device, the at least one OFI with a plurality of training images from a trained dataset to identify the at least one OFI as an ellipsoid shaped OFI or a non-ellipsoid shaped OFI; in response to identifying the at least one OFI being the ellipsoid shaped OFI: calculating, via the processor of the computing device, a likelihood of defect within the bounding box based on weighted scoring between the at least one ellipsoid shaped OFI and at least one of the plurality of training images from the trained dataset, wherein the defect is positioned prior to the ellipsoid shaped OFI and the defect results in a downward vehicle motion coupled with an upward acceleration resulting in the ellipsoid shaped OFI; and generating, via the processor of the computing device, a ranked list of thoroughfare sections most in need of repair based on the number of ellipsoid shaped OFIs per mile of thoroughfare and the calculated likelihood of defect associated with each of the ellipsoid shaped OFIs per mile, thereby prioritizing roadway repairs according to the ranked list. ### Claim 11 (depends on 10) The thoroughfare analysis system of claim 10, wherein the executed instruction further includes, in response to identifying the at least one OFI being the non-ellipsoid shaped OFI: generating, via the processor of the computing device, one or more estimated wait times based upon one or more of the intensity and length of the at least one non-ellipsoid shaped OFI. ### Claim 12 (depends on 10) The thoroughfare analysis system of claim 10, further comprising, after generating the bounding box surrounding the at least one OFI of the at least one thoroughfare image, highlighting the at least one OFI of the at least one thoroughfare image, wherein the highlight of the at least one OFI of the at least one thoroughfare image is brighter than at least one remaining aspect of the at least one OFI. ### Claim 13 (depends on 10) The thoroughfare analysis system of claim 10, wherein the executed instructions further comprise, providing, via the processor of the computing device, at least one metric regarding a condition of the thoroughfare based on the calculated likelihood of defect. ### Claim 14 (depends on 13) The thoroughfare analysis system of claim 13, wherein the executed instructions further include, after providing at least one metric regarding a condition of the thoroughfare based on the calculated likelihood of defect, calculating, via the processor of the computing device, at least one alternative GPS path, at least one alternative thoroughfare, or both to the travel destination based on the at least one metric regarding the condition of the thoroughfare. ### Claim 15 (depends on 14) The thoroughfare analysis system of claim 14, wherein the executed instructions further comprise after calculating at least one alternative GPS path, at least one alternative thoroughfare, or both, displaying, via the display device of the computing device, the at least one calculated alternative GPS path, alternative thoroughfare, or both by: based on a determination that the likelihood of defect of the thoroughfare is greater than the likelihood of defect of the at least one calculated alternative thoroughfare, altering the GPS path to conform to the at least one calculated alternative GPS path, alternative thoroughfare, or both; and based on a determined that the likelihood of defect of the thoroughfare is less than the likelihood of defect of the at least one calculated alternative thoroughfare, maintaining the GPS path to conform to the thoroughfare. ### Claim 16 (depends on 15) The thoroughfare analysis system of claim 15, wherein the at least one metric is selected from a group consisting of a recommended speed, a number of potholes per mile on the thoroughfare, a number of bumps per mile on the thoroughfare, and a combination of thereof. ### Claim 17 (depends on 10) The thoroughfare analysis system of claim 10, wherein the executed instructions further comprise transmitting the likelihood of defect to the display device of the computing device, and receiving an instruction to unload, from the memory of the computing device, the at least one thoroughfare image. ### Claim 18 (depends on 17) The thoroughfare analysis system of claim 17, wherein the executed instructions further comprise, after receiving an instruction to unload the at least one thoroughfare image, receiving at least one alternative thoroughfare image based on at least one alternative GPS path provided from at least one set of alternative parameters. ### Claim 19 A method of automatically providing an optimized GPS path to a travel destination based on a condition of a thoroughfare, the method comprising: providing a computing device having a processor, wherein the computing device is in electrical communication with at least one server, at least one database, or both; selecting, via the processor of the computing device, at least one thoroughfare image, by: providing, via a GPS system of the computing device, a GPS path divided into a plurality of GPS coordinates based on at least one parameter regarding a travel destination of a user; receiving, via the at least one server, the at least one database, or both, at least one thoroughfare image based on at least one of the plurality of GPS coordinates; identifying, via the processor of the computing device, at least one oil formation of interest (OFI) based on object detection trained on image intensity and shape; generating, via the processor of the computing device, a bounding box surrounding the at least one OFI; comparing, via the processor of the computing device, the at least one detected OFI with a plurality of training images from a trained dataset to identify the at least one OFI as an ellipsoid shaped OFI or a non-ellipsoid shaped OFI; in response to identifying the at least one OFI being the ellipsoid shaped OFI: calculating, via the processor of the computing device, a likelihood of defect within the bounding box based on weighted scoring between the at least one ellipsoid shaped OFI and at least one of the plurality of training images from the trained dataset, wherein the defect is positioned prior to the ellipsoid shaped OFI and the defect results in a downward vehicle motion coupled with an upward acceleration resulting in the ellipsoid shaped OFI; calculating, via the processor of the computing device, at least one alternative GPS path, at least one alternative thoroughfare, or both to the travel destination based on the likelihood of defect of the thoroughfare; and automatically displaying, via the display device of the computing device, the GPS path, the at least one calculated alternative GPS path, or both by: based on a determination that the likelihood of defect of the thoroughfare is greater than the likelihood of defect of the at least one calculated alternative thoroughfare, altering the GPS path to conform to the at least one calculated alternative GPS path, alternative thoroughfare, or both; and based on a determined that the likelihood of defect of the thoroughfare is less than the likelihood of defect of the at least one calculated alternative thoroughfare, maintaining the GPS path to conform to the thoroughfare. ### Claim 20 (depends on 19) The method of claim 19, further comprising: receiving an instruction to unload, from the memory of the computing device, the at least one thoroughfare image; and receiving at least one alternative thoroughfare image based on at least one alternative GPS path provided from at least one set of alternative parameters.