Systems and methods for generating a smart contract for a parametric event using machine learning algorithms

Abstract

Systems and methods are disclosed for generating one or more smart contracts for deployment onto a blockchain. The systems and methods may include (1) receiving vehicle sensor data generated from sensors mounted on or within (a) one or more vehicles, or (b) electronic devices; (2) analyzing the vehicle sensor data to determine one or more parametric events, wherein each of the parametric events is associated with a corresponding severity of loss; (3) generating, for each of the one or more parametric events, a corresponding smart contract that is configured to (i) receive a transaction from a computing device, and (ii) automatically execute on the blockchain when the transaction indicates that a parametric event corresponding to the smart contract has occurred; and (4) deploying the smart contract at a particular address on the blockchain.

What is claimed: 
     
         1 . A computer-implemented method for generating one or more smart contracts for deployment onto a blockchain, the method comprising:
 receiving, at one or more processors, vehicle sensor data and/or electronic device data generated from sensors mounted on or within (a) a vehicle, and/or (b) an electronic device located within the vehicle;   determining a parametric event associated with a vehicle collision or severity thereof from analysis of the vehicle sensor data and/or electronic device data by inputting, by the one or more processors, the vehicle sensor data and/or electronic device data into a trained machine learning model that is trained to identify vehicle collisions, severity of loss, severity of vehicle damage, and/or other vehicle-related events or factors;   generating, by the one or more processors and for the parametric event, a corresponding smart contract that is configured to (i) receive a transaction and/or other data from one or more computing devices, and/or (ii) automatically execute on the blockchain; and   deploying, by the one or more processors, the smart contract at a particular address on the blockchain.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein the smart contract is configured to automatically execute on the blockchain when the transaction and/or vehicle sensor data or electronic device data indicates that the parametric event (a) associated with the vehicle collision or severity thereof, and/or (b) corresponding to the smart contract has occurred. 
     
     
         3 . The computer-implemented method of  claim 1 , wherein the trained machine learning model is trained using historical vehicle collision data. 
     
     
         4 . The computer-implemented method of  claim 3 , wherein the trained machine learning model is trained to identify vehicle-related events or factors, the vehicle-related events or factors including identifying one or more of:
 a vehicle collision has occurred;   an amount of vehicle damage;   an estimated severity of the vehicle collision;   an estimated severity of personal injuries;   an estimated cost to repair the vehicle or vehicle parts;   an estimated cost to replace the vehicle or vehicle parts;   that a tow vehicle is needed to tow a damaged vehicle;   that a taxi or ride-share service is needed to transport an operator of the damaged vehicle;   that an ambulance is needed at the scene of the vehicle collision;   parts needed to repair the damaged vehicle; and/or   a nearby repair shop or body shop with the parts and expertise necessary to repair the vehicle.   
     
     
         5 . The computer-implemented method of  claim 3 , wherein the trained machine learning model identifies one or more of the following vehicle-related events or factors as the parametric event from the vehicle sensor data and/or electronic device data input:
 a vehicle collision has occurred;   an amount of vehicle damage;   an estimated severity of the vehicle collision;   an estimated severity of personal injuries;   an estimated cost to repair the vehicle or vehicle parts;   an estimated cost to replace the vehicle or vehicle parts;   that a tow vehicle is needed to tow a damaged vehicle;   that a taxi or ride-share service is needed to transport an operator of the damaged vehicle;   that an ambulance is needed at the scene of the vehicle collision;   parts needed to repair the damaged vehicle; and/or   a nearby repair shop or body shop with the parts and expertise necessary to repair the vehicle.   
     
     
         6 . The computer-implemented method of  claim 1 , wherein training the trained machine learning model includes one or more of:
 (i) Bayesian program learning;   (ii) voice recognition and synthesis;   (iii) image and/or object recognition;   (iv) optical character recognition;   (v) natural language processing;   (vi) semantic analysis; and/or   (vii) automatic reasoning.   
     
     
         7 . The computer-implemented method of  claim 1 , wherein generating the smart contract includes generating the smart contract to define an action including any one of:
 (i) initiating an instantaneous notice of loss (INOL);   (ii) communicating with an insurer entity;   (iii) communicating with an emergency response entity;   (iv) communicating with a towing service entity;   (v) communicating with a taxi or ride-share service entity;   (vi) communicating with a vehicle repair service entity; or   (vii) communicating with a vehicle salvage entity.   
     
     
         8 . A computer system for generating one or more smart contracts for deployment onto a blockchain, the computer system comprising:
 one or more processors;   a non-transitory program memory coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the computer system to:
 receive vehicle sensor data and/or electronic device data generated from sensors mounted on or within (i) a vehicle, and/or (ii) an electronic device located within the vehicle; 
 determine a parametric event associated with a vehicle collision or severity thereof from analysis of the vehicle sensor data and/or electronic device data by inputting the vehicle sensor data and/or electronic device data into a trained machine learning model that is trained to identify a vehicle collision, a severity of the vehicle collision, and/or other vehicle-related events or factors; 
 generate, for the parametric event, a corresponding smart contract that is configured to automatically execute on the blockchain when a transaction or other data is received from one or more computing devices, and/or configured to receive or store the transaction or other data that is received from the one or more computing devices; and 
 deploy the smart contract at a particular address on the blockchain. 
   
     
     
         9 . The computer system of  claim 8 , wherein the smart contract is configured to automatically execute on the blockchain when the transaction and/or vehicle sensor data or electronic device data indicates that the parametric event (a) associated with the vehicle collision or severity thereof, and/or (b) corresponding to the smart contract has occurred. 
     
     
         10 . The computer system of  claim 8 , wherein the trained machine learning model is trained using historical vehicle collision data. 
     
     
         11 . The computer system of  claim 10 , wherein the trained machine learning model is trained to identify vehicle-related events or factors, the vehicle-related events or factors including identifying one or more of:
 a vehicle collision has occurred;   an amount of vehicle damage;   an estimated severity of the vehicle collision;   an estimated severity of personal injuries;   an estimated cost to repair the vehicle or vehicle parts;   an estimated cost to replace the vehicle or vehicle parts;   that a tow vehicle is needed to tow a damaged vehicle;   that a taxi or ride-share service is needed to transport an operator of the damaged vehicle;   that an ambulance is needed at the scene of the vehicle collision;   parts needed to repair the damaged vehicle; and/or   a nearby repair shop or body shop with the parts and expertise necessary to repair the vehicle.   
     
     
         12 . The computer system of  claim 11 , wherein the trained machine learning model identifies one or more of the following vehicle-related events or factors as the parametric event from the vehicle sensor data and/or electronic device data input:
 a vehicle collision has occurred;   an amount of vehicle damage;   an estimated severity of the vehicle collision;   an estimated severity of personal injuries;   an estimated cost to repair the vehicle or vehicle parts;   an estimated cost to replace the vehicle or vehicle parts;   that a tow vehicle is needed to tow a damaged vehicle;   that a taxi or ride-share service is needed to transport an operator of the damaged vehicle;   that an ambulance is needed at the scene of the vehicle collision;   parts needed to repair the damaged vehicle; and/or   a nearby repair shop or body shop with the parts and expertise necessary to repair the vehicle.   
     
     
         13 . The computer system of  claim 8 , wherein training the trained machine learning model includes one or more of:
 (i) Bayesian program learning;   (ii) voice recognition and synthesis;   (iii) image and/or object recognition;   (iv) optical character recognition;   (v) natural language processing;   (vi) semantic analysis; and/or   (vii) automatic reasoning.   
     
     
         14 . The computer system of  claim 12 , wherein the executable instructions, when executed by the one or more processors, cause the computer system to generate the smart contract to define an action including any one of:
 (i) initiating an instantaneous notice of loss (INOL);   (ii) communicating with an insurer entity;   (iii) communicating with an emergency response entity;   (iv) communicating with a towing service entity;   (v) communicating with a taxi or ride-share service entity;   (vi) communicating with a vehicle repair service entity; or   (vii) communicating with a vehicle salvage entity.   
     
     
         15 . A tangible, non-transitory computer-readable medium storing executable instructions for generating one or more smart contracts for deployment onto a blockchain, the computer system comprising, that when executed by one or more processors of a computer system, cause the computer system to:
 receive vehicle sensor data and/or electronic device data generated from sensors mounted on or within (i) a vehicle, and/or (ii) an electronic device located within the vehicle;   determine a parametric event associated with a vehicle collision or severity thereof from analysis of the vehicle sensor data and/or electronic device data by inputting the vehicle sensor data and/or electronic device data into a trained machine learning model that is trained to identify a vehicle collision, a severity of the vehicle collision, and/or other vehicle-related events or factors;   generate, for the parametric event, a corresponding smart contract that is configured to automatically execute on the blockchain when a transaction or other data is received from one or more computing devices, and/or configured to receive or store the transaction or other data that is received from the one or more computing devices; and   deploy the smart contract at a particular address on the blockchain.   
     
     
         16 . The tangible, non-transitory computer-readable medium of  claim 15 , wherein the smart contract is configured to automatically execute on the blockchain when the transaction and/or vehicle sensor data or electronic device data indicates that the parametric event (a) associated with the vehicle collision or severity thereof, and/or (b) corresponding to the smart contract has occurred. 
     
     
         17 . The tangible, non-transitory computer-readable medium of  claim 15 , wherein the trained machine learning model is trained using historical vehicle collision data. 
     
     
         18 . The tangible, non-transitory computer-readable medium of  claim 17 , wherein the trained machine learning model is trained to identify vehicle-related events or factors, the vehicle-related events or factors including identifying one or more of:
 a vehicle collision has occurred;   an amount of vehicle damage;   an estimated severity of the vehicle collision;   an estimated severity of personal injuries;   an estimated cost to repair the vehicle or vehicle parts;   an estimated cost to replace the vehicle or vehicle parts;   that a tow vehicle is needed to tow a damaged vehicle;   that a taxi or ride-share service is needed to transport an operator of the damaged vehicle;   that an ambulance is needed at the scene of the vehicle collision;   parts needed to repair the damaged vehicle; and/or   a nearby repair shop or body shop with the parts and expertise necessary to repair the vehicle.   
     
     
         19 . The tangible, non-transitory computer-readable medium of  claim 18 , wherein the trained machine learning model identifies one or more of the following vehicle-related events or factors as the parametric event from the vehicle sensor data and/or electronic device data input:
 a vehicle collision has occurred;   an amount of vehicle damage;   an estimated severity of the vehicle collision;   an estimated severity of personal injuries;   an estimated cost to repair the vehicle or vehicle parts;   an estimated cost to replace the vehicle or vehicle parts;   that a tow vehicle is needed to tow a damaged vehicle;   that a taxi or ride-share service is needed to transport an operator of the damaged vehicle;   that an ambulance is needed at the scene of the vehicle collision;   parts needed to repair the damaged vehicle; and/or   a nearby repair shop or body shop with the parts and expertise necessary to repair the vehicle.   
     
     
         20 . The tangible, non-transitory computer-readable medium of  claim 15 , wherein training the trained machine learning model includes one or more of:
 (i) Bayesian program learning;   (ii) voice recognition and synthesis;   (iii) image and/or object recognition;   (iv) optical character recognition;   (v) natural language processing;   (vi) semantic analysis; and/or   (vii) automatic reasoning.