# US 12,111,865 B2 · Video analysis for efficient sorting of event data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,111,865 B2 - Document ID: US12111865B2 - Status: Granted - Kind: B2 - Application number: 17/975,251 - Filed: 2022-10-27 - Published: 2023-04-06 - Granted: 2024-10-08 - Earliest claimed: 2018-01-11 - Assignees: Lytx, Inc. - Applicants: Lytx, Inc. - Inventors: Brett Hoye; Stephen Krotosky - Web page: https://patentgrep.com/patent/US12111865B2 ## CPC - G06F 16/783: using metadata automatically derived from the content - G06V 20/41: {Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items (segmenting video sequences G06V20/49)} - G06V 20/44: {Event detection} - G07C 5/0866: {the electronic data carrier being a digital video recorder in combination with video camera} - B60W 2040/0881: {Seat occupation; Driver or passenger presence} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE - B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT ## Abstract A system for sorting event data includes an interface and a processor. The interface is configured to receive a small set of video frame data from a vehicle event recorder. The processor is configured to determine that the small set of video frame data indicates to fetch more vehicle event recorder data; determine a categorization based at least in part on the more vehicle event recorder data; and in response to the categorization, route the more vehicle event recorder data to a reviewing queue. ## Claims ### Claim 1 A system for sorting event data, comprising: an interface configured to: receive a small set of video frame data from a vehicle event recorder; and a processor configured to: determine that the small set of video frame data indicates to fetch more vehicle event recorder data, comprises to: determine that the small set of video frame data indicates that an anomalous event has occurred using a model, wherein the model implements a machine learning technique, wherein the machine learning technique includes machine vision, and wherein the determining that the small set of video frame data indicates that the anomalous event has occurred comprises to: determine, using the model, that the small set of video frame data indicates that a trash bin is overflowing; and in response a determination that the small set of video frame data indicates that the anomalous event has occurred, fetch the more vehicle event recorder data, comprising to: determine that a score associated with the model falls between a first threshold and a second threshold, wherein in the event the score falls below the first threshold, determine that the anomalous event has not occurred and omit fetching the more vehicle event recorder data, wherein in the event the score falls above the second threshold, determine that the anomalous event has occurred and omit fetching the more vehicle event recorder data; and in the event that the score falls between the first threshold and the second threshold, fetch the more vehicle event recorder data; determine a categorization based at least in part on the more vehicle event recorder data, wherein the categorization includes human review; and in response to the categorization, route the more vehicle event recorder data to a reviewing queue to determine whether the trash bin is overflowing. ### Claim 2 (depends on 1) The system of claim 1, wherein the processor is further configured to provide an indication to have the vehicle event recorder send more vehicle event recorder data. ### Claim 3 (depends on 1) The system of claim 1, wherein the processor is further configured to receive the more vehicle event recorder data. ### Claim 4 (depends on 1) The system of claim 1, wherein a small set of video frame data comprises N video frames. ### Claim 5 (depends on 4) The system of claim 4, wherein N is 6. ### Claim 6 (depends on 1) The system of claim 1, wherein fetching more vehicle event recorder data comprises fetching S seconds of video. ### Claim 7 (depends on 6) The system of claim 6, wherein S is 12. ### Claim 8 (depends on 1) The system of claim 1, wherein fetching more vehicle event recorder data comprises fetching GPS data. ### Claim 9 (depends on 1) The system of claim 1, wherein determining a categorization uses machine vision. ### Claim 10 (depends on 1) The system of claim 1, wherein determining that the small set of video frame data indicates to fetch more vehicle event recorder data uses machine vision analysis. ### Claim 11 (depends on 1) The system of claim 1, further comprises determining whether to perform a review of the small set of video frame data. ### Claim 12 (depends on 11) The system of claim 11, wherein in response to determining to perform the review of the small set of video frame data, indicating to perform a review of the small set of video frame data. ### Claim 13 (depends on 1) The system of claim 1, further comprising determining whether an analysis of the more vehicle event recorder data indicates nothing unusual. ### Claim 14 (depends on 1) The system of claim 1, further comprising determining whether an analysis of the more vehicle event recorder data indicates an event. ### Claim 15 (depends on 14) The system of claim 14, wherein in response to determining that the analysis of the more vehicle event recorder data indicates to perform a review of the more vehicle event recorder data. ### Claim 16 (depends on 1) The system of claim 1, wherein the reviewing queue comprises a human reviewing queue or a machine vision reviewing queue. ### Claim 17 A method for sorting event data, comprising: receiving a small set of video frame data from a vehicle event recorder; determining, using a processor, that the small set of video frame data indicates to fetch more vehicle event recorder data, comprises: determining that the small set of video frame data indicates that an anomalous event has occurred using a model, wherein the model implements a machine learning technique, wherein the machine learning technique includes machine vision, and wherein the determining that the small set of video frame data indicates that the anomalous event has occurred comprises: determining, using the model, that the small set of video frame data indicates that a trash bin is overflowing; and in response a determination that the small set of video frame data indicates that the anomalous event has occurred, fetching the more vehicle event recorder data, comprising: determining that a score associated with the model falls between a first threshold and a second threshold, wherein in the event the score falls below the first threshold, determine that the anomalous event has not occurred and omit fetching the more vehicle event recorder data, wherein in the event the score falls above the second threshold, determine that the anomalous event has occurred and omit fetching the more vehicle event recorder data; and in the event that the score falls between the first threshold and the second threshold, fetching the more vehicle event recorder data; determining a categorization based at least in part on the more vehicle event recorder data, wherein the categorization includes human review; and in response to the categorization, routing the more vehicle event recorder data to a reviewing queue to determine whether the trash bin is overflowing. ### Claim 18 A computer program product for sorting event data, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for: receiving a small set of video frame data from a vehicle event recorder; determining that the small set of video frame data indicates to fetch more vehicle event recorder data, comprises: determining that the small set of video frame data indicates that an anomalous event has occurred using a model, wherein the model implements a machine learning technique, wherein the machine learning technique includes a deep machine vision, and wherein the determining that the small set of video frame data indicates that the anomalous event has occurred comprises: determining, using the model, that the small set of video frame data indicates that a trash bin is overflowing; and in response a determination that the small set of video frame data indicates that the anomalous event has occurred, fetching the more vehicle event recorder data, comprising: determining that a score associated with the model falls between a first threshold and a second threshold, wherein in the event the score falls below the first threshold, determine that the anomalous event has not occurred and omit fetching the more vehicle event recorder data, wherein in the event the score falls above the second threshold, determine that the anomalous event has occurred and omit fetching the more vehicle event recorder data; and in the event that the score falls between the first threshold and the second threshold, fetching the more vehicle event recorder data; determining a categorization based at least in part on the more vehicle event recorder data, wherein the categorization includes human review; and in response to the categorization, routing the more vehicle event recorder data to a reviewing queue to determine whether the trash bin is overflowing.