# US 10,769,482 B1 · Systems and methods for using image analysis to automatically determine vehicle information > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,769,482 B1 - Document ID: US10769482B1 - Status: Granted - Kind: B1 - Application number: 16/283,088 - Filed: 2019-02-22 - Granted: 2020-09-08 - Earliest claimed: 2016-08-29 - Assignees: State Farm Mutual Automobile Insurance Company - Applicants: State Farm Mutual Automobile Insurance Company - Inventors: Joseph Antonetti; Abid Imran; Justin Loew; Calvin Moon; Gary Foreman - Web page: https://patentgrep.com/patent/US10769482B1 ## CPC - G06V 30/1983: {Syntactic or structural pattern recognition, e.g. symbolic string recognition} - G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques} - G06F 18/254: {of classification results, e.g. of results related to same input data} - G06V 30/1916: {Validation; Performance evaluation} - G06V 30/1918: {Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion} - G06V 30/262: using context analysis, e.g. lexical, syntactic or semantic context - G06V 30/10: Character recognition - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract The present disclosure is directed to systems and methods for analyzing digital images to determine alphanumeric strings depicted in the digital images. An electronic device may generate a set of filtered images using a received digital image. The electronic device may also perform an optical character recognition (OCR) technique on the set of filtered images, and may filter out any of the set of filtered images according to a set of rules. The electronic device may further identify a set of common elements representative of the alphanumeric string depicted in the digital image, and determine a machine-encoded alphanumeric string based on the set of common elements. ## Claims ### Claim 1 A computer-implemented method of analyzing image data, the method comprising: generating, by a computer processor from a digital image depicting an alphanumeric string, at least two filtered digital images; generating, by the computer processor using an image analysis technique, at least two results respectively associated with the at least two filtered digital images and respectively indicating a set of machine-encoded strings; and analyzing the at least two results to identify a set of common elements indicating a machine-encoded alphanumeric string representative of the alphanumeric string depicted in the digital image. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, further comprising: applying a set of rules to the at least two results to remove at least a portion of the at least two results. ### Claim 3 (depends on 2) The computer-implemented method of claim 2, wherein applying the set of rules to the at least two results comprises: determining, for a result of the at least two results, that the result indicates a string comprising a number of digits that exceeds a threshold number of digits; and removing the result. ### Claim 4 (depends on 2) The computer-implemented method of claim 2, wherein applying the set of rules to the at least two results comprises: accessing a previously-reported metric associated with an account of the individual; comparing the previously-reported metric to a result of the at least two results to determine that content of the result is not possible; and removing the result. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein analyzing the at least two results to identify the set of common elements comprises: analyzing the at least two results using at least one string metric to identify the set of common elements. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein generating the at least two filtered digital images comprises: generating a first filtered digital image by applying a first digital filter to the digital image; and generating a second filtered digital image by applying a second digital filter to the digital image. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein generating the at least two results comprises: determining, from each of the at least two filtered digital images using the image analysis technique, a preliminary machine-encoded alphanumeric string depicted in the respective filtered digital image. ### Claim 8 (depends on 7) The computer-implemented method of claim 7, further comprising: calculating a confidence level of each character included in the preliminary machine-encoded alphanumeric string. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein each of the at least two results includes a confidence level for each character in the respective result, and wherein the method further comprises: determining each character of the machine-encoded alphanumeric string based on the confidence level for each character in the respective result. ### Claim 10 (depends on 1) The computer-implemented method of claim 1, further comprising: calculating a confidence level of each character included in the machine-encoded alphanumeric string; and storing, in memory, the machine-encoded alphanumeric string and the confidence level of each character included in the machine-encoded alphanumeric string. ### Claim 11 A system for analyzing image data, comprising: a memory storing a set of computer-executable instructions; and a processor interfacing with the memory, and configured to execute the computer-executable instructions to cause the processor to: generate, from a digital image depicting an alphanumeric string, at least two filtered digital images, generate, using an image analysis technique, at least two results respectively associated with the at least two filtered digital images and respectively indicating a set of machine-encoded strings, and analyze the at least two results to identify a set of common elements indicating a machine-encoded alphanumeric string representative of the alphanumeric string depicted in the digital image. ### Claim 12 (depends on 11) The system of claim 11, wherein the processor is configured to: apply a set of rules to the at least two results to remove at least a portion of the set of results. ### Claim 13 (depends on 12) The system of claim 12, wherein to apply the set of rules to the at least two results, the processor is configured to: determine, for a result of the at least two results, that the result indicates a string comprising a number of digits that exceeds a threshold number of digits, and remove the result. ### Claim 14 (depends on 12) The system of claim 12, wherein to apply the set of rules to the at least two results, the processor is configured to: access a previously-reported metric associated with an account of the individual, compare the previously-reported metric to a result of the at least two results to determine that content of the result is not possible, and remove the result. ### Claim 15 (depends on 11) The system of claim 11, wherein to analyze the at least two results to identify the set of common elements, the processor is configured to: analyze the at least two results using at least one string metric to identify the set of common elements. ### Claim 16 (depends on 11) The system of claim 11, wherein to generate the at least two filtered digital images, the processor is configured to: generate a first filtered digital image by applying a first digital filter to the digital image, and generate a second filtered digital image by applying a second digital filter to the digital image. ### Claim 17 (depends on 11) The system of claim 11, wherein to generating the at least two results, the processor is configured to: determine, from each of the at least two filtered digital images using the image analysis technique, a preliminary machine-encoded alphanumeric string depicted in the respective filtered digital image. ### Claim 18 (depends on 17) The system of claim 17, wherein the processor is further configured to: calculate a confidence level of each character included in the preliminary machine-encoded alphanumeric string. ### Claim 19 (depends on 11) The system of claim 11, wherein each of the at least two results includes a confidence level for each character in the respective result, and wherein the processor is further configured to: determine each character of the machine-encoded alphanumeric string based on the confidence level for each character in the respective result. ### Claim 20 (depends on 11) The system of claim 11, wherein the processor is further configured to: calculate a confidence level of each character included in the machine-encoded alphanumeric string, and store, in the memory, the machine-encoded alphanumeric string and the confidence level of each character included in the machine-encoded alphanumeric string.