SYSTEMS AND METHODS FOR LIGHT DETECTION AND RANGING (LIDAR) BASED GENERATION OF A HOMEOWNERS INSURANCE QUOTE
The following relates generally to light detection and ranging (LIDAR). In some embodiments, a homeowners insurance quote is produced based upon data received from a LIDAR camera. For instance, in some embodiments, a system: receives light detection and ranging (LIDAR) data generated from one or more LIDAR cameras; analyzes the LIDAR data to determine or identify one or more features or characteristics of a home; and generates an electronic homeowners insurance quote based upon, at least in part, the one or more features or characteristics of the home determined or identified from the LIDAR data.
1 . A computer-implemented method for providing first notice of loss based at least in part upon light detection and ranging (LIDAR) data, comprising, via one or more processors, transceivers, sensors, or servers:
receiving the LIDAR data generated from a LIDAR camera, wherein the LIDAR data includes 3D point cloud data, wherein the LIDAR camera is positioned within a home;
receiving one or more additional sources of data, wherein the one or more additional sources of data include mobile device images acquired via a mobile device;
determining if an event comprising a fire has occurred based upon processor analysis of: (i) the received LIDAR data including the 3D point cloud data, and (ii) the one or more additional sources of data including the mobile device images by determining that the event comprising a fire has occurred via a machine learning algorithm, and wherein the machine learning algorithm: (i) employs decision trees, (ii) uses Bayesian statistical analysis, or (iii) includes a neural network; and
in response to a determination that the event has occurred, generating an electronic first notice of loss.
2 . The computer-implemented method of claim 1 , wherein the one or more additional sources of data further include: smart home sensor data or images; drone sensor data or images; vehicle sensor data or images; or smart infrastructure data or images.
3 . The computer-implemented method of claim 1 , further comprising, via the one or more processors, transceivers, sensors, or servers, analyzing the received LIDAR and the one or more additional sources data to identify one or more assets that are damaged.
4 . The computer-implemented method of claim 3 , further comprising, via the one or more processors, transceivers, sensors, or servers, analyzing the received LIDAR and the one or more additional sources data to estimate an amount of damage to the identified one or more assets.
5 . The computer-implemented method of claim 3 , further comprising, via the one or more processors, transceivers, sensors, or servers, analyzing the received LIDAR and the one or more additional sources data to estimate a repair or replacement cost of the identified one or more assets.
6 . The computer-implemented method of claim 3 , wherein the identified one or more assets include the home.
7 . The computer-implemented method of claim 3 , wherein the identified one or more assets include personal articles.
8 . The computer-implemented method of claim 1 , wherein the LIDAR data is received via wireless communication or data transmission over one or more radio frequency links.
9 . The computer-implemented method of claim 1 , wherein the machine learning algorithm comprises the neural network.
10 . A computer system configured to provide first notice of loss based at least in part upon light detection and ranging (LIDAR) data, the computer system comprising one or more processors, transceivers, sensors, or servers configured to:
receive the LIDAR data generated from a LIDAR camera, wherein the LIDAR data includes 3D point cloud data, wherein the LIDAR camera is positioned within a home;
receive one or more additional sources of data, wherein the one or more additional sources of data include mobile device images acquired via a mobile device;
determine if an event comprising a fire has occurred based upon processor analysis of: (i) the received LIDAR data including the 3D point cloud data, and (ii) the one or more additional sources of data including the mobile device images by determining that the event comprising a fire has occurred via a machine learning algorithm, and wherein the machine learning algorithm: (i) employs decision trees, (ii) uses Bayesian statistical analysis, or (iii) includes a neural network; and
if the event has occurred, generate an electronic first notice of loss.
11 . The computer system of claim 10 , wherein the 3D point cloud data indicates dimensions of a room of the home.
12 . The computer system of claim 10 , wherein the one or more additional sources of data further include: smart home sensor data or images; drone sensor data or images; vehicle sensor data or images; or smart infrastructure data or images.
13 . The computer system of claim 10 , the system further configured to, via the one or more processors, transceivers, sensors, or servers, analyze the received LIDAR and the one or more additional sources data to identify one or more assets that are damaged.
14 . The computer system of claim 13 , the system further configured to, via the one or more processors, transceivers, sensors, or servers, analyze the received LIDAR and the one or more additional sources data to estimate an amount of damage to the identified one or more assets.
15 . The computer system of claim 13 , the system further configured to, via the one or more processors, transceivers, sensors, or servers, analyze the received LIDAR and the one or more additional sources data to estimate a repair or replacement cost of the identified one or more assets.
16 . The computer system of claim 13 , wherein the identified one or more assets include the home.
17 . The computer system of claim 13 , wherein the identified one or more assets include personal articles.
18 . A computer system configured to provide first notice of loss based at least in part upon light detection and ranging (LIDAR) data, comprising:
one or more processors; and
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 the LIDAR data generated from a LIDAR camera, wherein the LIDAR data includes 3D point cloud data, wherein the LIDAR camera is positioned within a home;
receive one or more additional sources of data, wherein the one or more additional sources of data include mobile device images acquired via a mobile device;
determine if an event comprising a fire has occurred based upon processor analysis of: (i) the received LIDAR data including the 3D point cloud data, and (ii) the one or more additional sources of data including the mobile device images by determining that the event comprising a fire has occurred via a machine learning algorithm, and wherein the machine learning algorithm: (i) employs decision trees, (ii) uses Bayesian statistical analysis, or (iii) includes a neural network; and
if the event has occurred, generate an electronic first notice of loss.
19 . The computer system of claim 18 , wherein the 3D point cloud data indicates dimensions of a room of the home.
20 . The computer system of claim 18 , wherein the machine learning algorithm comprises the neural network.