# US 12,762,835 B2 · Machine learning and computer vision solutions to seamless vehicle identification and environmental tracking therefor > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,762,835 B2 - Document ID: US12762835B2 - Status: Granted - Kind: B2 - Application number: 18/431,738 - Filed: 2024-02-02 - Published: 2024-06-06 - Granted: 2026-10-13 - Earliest claimed: 2022-12-06 - Assignees: Metropolis IP Holdings, LLC - Applicants: Metropolis IP Holdings, LLC - Inventors: Edwin Thomas; June Guo; Ji Sung Hwang; Anil Kumar Nayak; Todd Merle Shipway; Barry James O'Brien; Alexander David Israel - Web page: https://patentgrep.com/patent/US12762835B2 ## CPC - G06V 10/764: using classification, e.g. of video objects - G06V 2201/08: Detecting or categorising vehicles - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract A device captures a series of images over time in association with a gate, each image having a timestamp. The device determines, for a vehicle approaching the entry side, from a subset of images of the series of images featuring the vehicle, a first data set comprising a plurality of parameters that describe attributes of the vehicle by inputting the subset of images into a first machine learning model and a vehicle identifier of the vehicle by inputting images of the subset featuring a depiction of a license plate of the vehicle into a second machine learning model. The device stores the data set in association with one or more timestamps with the subset of images, determines a second data set for a second vehicle approaching the exit side, and responsive to determining that the first data set and the second data set match, instructs the gate to move. ## Claims ### Claim 1 A method comprising: capturing, using an edge device, a series of images over time featuring a vehicle approaching a parking apparatus; determining, by the edge device, from at least a subset of images of the series of images featuring the vehicle processed by the edge device: a plurality of parameters that describe attributes of the vehicle by inputting the subset of images into a first machine learning model; and a vehicle identifier of the vehicle by inputting images of the subset featuring a depiction of a license plate of the vehicle into a second machine learning model; transmitting an instruction, by the edge device, to a server remote from the edge device, for the server to verify a status of the vehicle that enables operation of the parking apparatus based on a concordance of both the plurality of parameters and the vehicle identifier together matching a data structure stored at the server; and responsive to a determination at the server that verifies the status of the vehicle that enables the operation of the parking apparatus based on the concordance of both the plurality of parameters and the vehicle identifier, instructing the parking apparatus to perform the operation. ### Claim 2 (depends on 1) The method of claim 1, wherein the plurality of parameters comprise identifying attributes of the vehicle. ### Claim 3 (depends on 2) The method of claim 2, wherein the first machine learning model is trained to output the identifying attributes of the vehicle using example data comprising images of vehicles that are labeled with one or more candidate identifying attributes. ### Claim 4 (depends on 1) The method of claim 1, wherein the vehicle identifier comprises geographical nomenclature and a string of characters. ### Claim 5 (depends on 4) The method of claim 4, wherein the second machine learning model is trained to identify the geographical nomenclature and the string of characters using training example images of license plates, where each of the training example images is labeled with its corresponding geographical nomenclature and string of characters. ### Claim 6 (depends on 1) The method of claim 1, wherein determining the vehicle identifier for first data set comprises determining that the vehicle identifier is unknown, and wherein the method further comprises: transmitting an alert to an administrator, the alert associated with at least a portion of the subset of images; and receiving, from the administrator, input that specifies the vehicle identifier. ### Claim 7 A non-transitory computer-readable medium with memory encoded thereon comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations, the instructions comprising instructions to: capture, using an edge device, a series of images over time in featuring a vehicle approaching a parking apparatus; determine, by the edge device, from at least a subset of images of the series of images featuring the vehicle processed by the edge device: a plurality of parameters that describe attributes of the vehicle by inputting the subset of images into a first machine learning model; and a vehicle identifier of the vehicle by inputting images of the subset featuring a depiction of a license plate of the vehicle into a second machine learning model; transmit an instruction, by the edge device, to a server remote from the edge device, for the server to verify a status of the vehicle that enables operation of the parking apparatus based on a concordance of both the plurality of parameters and the vehicle identifier together matching a data structure stored at the server; and responsive to a determination at the server that verifies the status of the vehicle that enables the operation of the parking apparatus based on the concordance of both the plurality of parameters and the vehicle identifier, instruct the parking apparatus to perform the operation. ### Claim 8 (depends on 7) The non-transitory computer-readable medium of claim 7, wherein the plurality of parameters comprise identifying attributes of the vehicle. ### Claim 9 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the first machine learning model is trained to output the identifying attributes of the vehicle using example data comprising images of vehicles that are labeled with one or more candidate identifying attributes. ### Claim 10 (depends on 7) The non-transitory computer-readable medium of claim 7, wherein the vehicle identifier comprises geographical nomenclature and a string of characters. ### Claim 11 (depends on 10) The non-transitory computer-readable medium of claim 10, wherein the second machine learning model is trained to identify the geographical nomenclature and the string of characters using training example images of license plates, where each of the training example images is labeled with its corresponding geographical nomenclature and string of characters. ### Claim 12 (depends on 7) The non-transitory computer-readable medium of claim 7, wherein the instructions to determine the vehicle identifier for first data set comprise instructions to determine that the vehicle identifier is unknown, and wherein the instructions further comprise instructions to: transmit an alert to an administrator, the alert associated with at least a portion of the subset of images; and receive, from the administrator, input that specifies the vehicle identifier. ### Claim 13 A system comprising: memory with instructions encoded thereon; and one or more processors that, when executing the instructions, are caused to perform operations, the operations comprising: capturing, using an edge device, a series of images over time in featuring a vehicle approaching a parking apparatus; determining, by the edge device, from at least a subset of images of the series of images featuring the vehicle processed by the edge device: a plurality of parameters that describe attributes of the vehicle by inputting the subset of images into a first machine learning model; and a vehicle identifier of the vehicle by inputting images of the subset featuring a depiction of a license plate of the vehicle into a second machine learning model; transmitting an instruction, by the edge device, to a server remote from the edge device, for the server to verify a status of the vehicle that enables operation of the parking apparatus based on a concordance of both the plurality of parameters and the vehicle identifier together matching a data structure stored at the server; and responsive to a determination at the server that verifies the status of the vehicle that enables the operation of the parking apparatus based on the concordance of both the plurality of parameters and the vehicle identifier, instructing the parking apparatus to perform the operation. ### Claim 14 (depends on 13) The system of claim 13, wherein the plurality of parameters comprise identifying attributes of the vehicle. ### Claim 15 (depends on 14) The system of claim 14, wherein the first machine learning model is trained to output the identifying attributes of the vehicle using example data comprising images of vehicles that are labeled with one or more candidate identifying attributes. ### Claim 16 (depends on 13) The system of claim 13, wherein the vehicle identifier comprises geographical nomenclature and a string of characters. ### Claim 17 (depends on 16) The system of claim 16, wherein the second machine learning model is trained to identify the geographical nomenclature and the string of characters using training example images of license plates, where each of the training example images is labeled with its corresponding geographical nomenclature and string of characters. ### Claim 18 (depends on 13) The system of claim 13, wherein determining the vehicle identifier for first data set comprises determining that the vehicle identifier is unknown, and wherein the operations further comprise: transmitting an alert to an administrator, the alert associated with at least a portion of the subset of images; and receiving, from the administrator, input that specifies the vehicle identifier.