# US 12,002,309 B2 · Systems and methods for managing vehicle data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,002,309 B2 - Document ID: US12002309B2 - Status: Granted - Kind: B2 - Application number: 16/983,478 - Filed: 2020-08-03 - Published: 2020-11-19 - Granted: 2024-06-04 - Earliest claimed: 2018-11-08 - Assignees: Synapse Partners, LLC - Applicants: Synapse Partners, LLC - Inventors: Evangelos Simoudis - Web page: https://patentgrep.com/patent/US12002309B2 ## CPC - G06N 5/022: Knowledge engineering; Knowledge acquisition - G07C 5/085: {using electronic data carriers} - B60K 35/00: Instruments specially adapted for vehicles; Arrangement of instruments in or on vehicles - B60K 35/10: Input arrangements, i.e. from user to vehicle, associated with vehicle functions or specially adapted therefor - B60K 35/22: Display screens - B60K 35/85: Arrangements for transferring vehicle- or driver-related data - B60W 60/001: {Planning or execution of driving tasks} - G06F 16/24573: {using data annotations, e.g. user-defined metadata} - G06N 5/02: Knowledge representation; Symbolic representation - G06N 5/04: Inference or reasoning models - G06N 20/00: Machine learning - G07C 5/008: {communicating information to a remotely located station (transmission systems for measured values G08C)} - H04W 4/40: for vehicles, e.g. vehicle-to-pedestrians [V2P] - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - 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 - B60K: ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OF TRANSMISSIONS IN VEHICLES; ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS IN VEHICLES; AUXILIARY DRIVES FOR VEHICLES; INSTRUMENTATION OR DASHBOARDS FOR VEHICLES; ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GAS EXHAUST OR FUEL SUPPLY OF PROPULSION UNITS IN VEHICLES - 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 - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## Abstract The present disclosure provides methods and systems for managing autonomous vehicle data. The method may comprise: (a) collecting said autonomous vehicle data from the autonomous vehicle, wherein the autonomous vehicle data has a size of at least 1 terabyte; (b) processing the autonomous vehicle data to generate metadata corresponding to the autonomous vehicle data, wherein the autonomous vehicle data is stored in a database; (c) using at least a portion of the metadata to retrieve a subset of the autonomous vehicle data from the database, which subset of the autonomous vehicle data has a size less than the autonomous vehicle data; and (d) storing or transmitting the subset of the autonomous vehicle data. ## Claims ### Claim 1 A computer system for managing vehicle data, wherein the vehicle data is generated and/or stored in a vehicle, the computer system comprising: a data repository comprising computer-readable data storage and configured to store (i) application data related to one or more applications that generate one or more subsets of said vehicle data, and (ii) entity data related to one or more remote entities that request said one or more subsets of said vehicle data, wherein said data repository is local to said vehicle where said vehicle data is collected or generated; a knowledge base configured to store a machine learning-based predictive model and user-defined rules, wherein the machine learning-based predictive model and the user-defined rules are used at the vehicle for determining a data transmission rule comprising: (i) a selected portion of said vehicle data to be transmitted; (ii) a timing for transmitting said selected portion of said vehicle data wherein the timing is determined based on the machine learning-based predictive model and/or the user-defined rules; and (iii) a remote entity of said one or more remote entities that is designated to receive said selected portion of said vehicle data; and a transmission module configured to transmit said selected portion of said vehicle data based at least in part on said application data, said entity data, and said data transmission rule, wherein said selected portion of said vehicle data is retrieved from said data repository based, at least in part, on metadata of said subset of said vehicle data. ### Claim 2 (depends on 1) The computer system of claim 1, wherein said data repository, said knowledge base, and said transmission module are provided onboard said vehicle. ### Claim 3 (depends on 1) The computer system of claim 1, wherein said one or more remote entities comprise a cloud application, a data center, a third-party server, or another different vehicle. ### Claim 4 (depends on 1) The computer system of claim 1, wherein said data repository is further configured to store supplemental data indicative of an availability of said one or more subsets of said vehicle data, a transmission timing delay, a data type of said one or more subsets of said vehicle data, or a transmission protocol. ### Claim 5 (depends on 1) The computer system of claim 1, wherein said machine learning-based predictive model is based at least in part on a model tree structure. ### Claim 6 (depends on 5) The computer system of claim 5, wherein said model tree structure is used to represent one or more relationships between one or more models comprising said machine learning-based predictive model. ### Claim 7 (depends on 5) The computer system of claim 5, wherein at least one node of said model tree structure is used to represent said machine learning-based predictive model, and wherein said node includes at least one of a model architecture, a set of model parameters, a training dataset, or a test dataset. ### Claim 8 (depends on 1) The computer system of claim 1, wherein said machine learning-based predictive model is configured to be generated by a model creator located in a data center remote to said vehicle. ### Claim 9 (depends on 8) The computer system of claim 8, wherein said machine learning-based predictive model is trained and tested using metadata and said vehicle data. ### Claim 10 (depends on 9) The computer system of claim 9, wherein said vehicle data comprises sensor data, and wherein said metadata is generated from said sensor data. ### Claim 11 (depends on 10) The computer system of claim 10, wherein said metadata is associated with a sensor that captures said sensor data. ### Claim 12 (depends on 10) The computer system of claim 10, wherein said vehicle data is processed by a pipeline engine comprising one or more functional components. ### Claim 13 (depends on 12) The computer system of claim 12, wherein at least one of said one or more functional components is selected from a set of functions via a user interface. ### Claim 14 (depends on 12) The computer system of claim 12, wherein at least one of said one or more functional components is configured to create a scenario data object, wherein said scenario data object is usable for specifying a use scenario for said metadata. ### Claim 15 (depends on 9) The computer system of claim 9, wherein said metadata is usable to retrieve a training subset of said vehicle data from said remote entity for training said machine learning-based predictive model. ### Claim 16 (depends on 8) The computer system of claim 8, wherein said model creator is configured to generate one or more machine learning-based predictive models that are usable by or customized for said vehicle. ### Claim 17 (depends on 1) The computer system of claim 1, where said knowledge base is configured to store one or more machine learning-based predictive models that are usable by or customized for said vehicle. ### Claim 18 (depends on 1) The computer system of claim 1, wherein said selected portion of said vehicle data comprises an aggregation of two or more of said subsets of said vehicle data. ### Claim 19 (depends on 1) The computer system of claim 1, wherein said vehicle is (i) a connected vehicle, (ii) a connected and automated vehicle, or (iii) a connected and autonomous vehicle. ### Claim 20 A computer system for managing vehicle data, comprising: a data repository comprising computer-readable data storage and configured to store (i) application data related to one or more applications that generate one or more subsets of said vehicle data, and (ii) entity data related to one or more remote entities that request said one or more subsets of said vehicle data, wherein said data repository is local to a vehicle where said vehicle data is collected or generated, wherein said vehicle data comprises sensor data, and wherein metadata is generated from said sensor data and wherein said vehicle data is processed by a pipeline engine comprising one or more functional components; a knowledge base configured to store a machine learning-based predictive model and user-defined rules for determining a data transmission rule comprising at least one of: (i) a selected portion of said vehicle data to be transmitted; (ii) a timing for transmitting said selected portion of said vehicle data; and (iii) a remote entity of said one or more remote entities that is designated to receive said selected portion of said vehicle data, wherein said machine learning-based predictive model is configured to be generated by a model creator located in a data center remote to said vehicle and wherein said machine learning-based predictive model is trained and tested using said metadata and said vehicle data; and a transmission module configured to transmit said selected portion of said vehicle data based at least in part on said application data, said entity data, and said data transmission rule, wherein additional metadata is generated when said vehicle data is processed by said pipeline engine.