Automobile Monitoring Systems and Methods for Risk Determination

1 . A computer-implemented method of determining an automobile-based risk level via one or more processors, the method comprising, via one or more processors, servers, sensors, and/or transceivers:

training, via the one or more processors and/or servers, a neural network to identify risk factors within electronic automobile claim records, the neural network including a plurality of layers, an input layer of the plurality of layers including a plurality of input parameters each corresponding to a different claim attribute, and an output layer of the plurality of layers configured to output labels and weights;

receiving information corresponding to at least one of (i) an automobile, (ii) an automobile operator, or (iii) vehicle operation factors;

analyzing, via the one or more processors and/or servers, the information using the trained neural network;

identifying one or more risk factors of the risk factors applicable to the information;

generating a risk indicator for each of the one or more risk factors using the trained neural network, wherein each risk indicator includes a label and a weight output from the output layer of the trained neural network; and

determining, via the one or more processors and/or servers, a risk level corresponding to one or both of (i) the automobile, and (ii) an occupant of the automobile based upon the one or more risk indicators;

wherein training a neural network to identify risk factors within electronic automobile claim records comprises:

processing at least one of a historical set of electronic automobile claim records using natural language processing; and

training the neural network using the processed historical set of electronic automobile claim records.

2 . The computer-implemented method of claim 1 , wherein training a neural network to identify risk factors within electronic automobile claim records includes, via one or more processors:

extracting textual content from one or both of (i) audio recordings, and (ii) images.

3 . (canceled)

4 . The computer-implemented method of claim 1 , wherein training a neural network to identify risk factors within electronic automobile claim records includes, via one or more processors:

analyzing, with respect to each automobile claim in a historical set of electronic automobile claim records, one or more of (i) a make, (ii) a model, and (iii) a year, associated with each automobile claim.

5 . The computer-implemented method of claim 1 , wherein training a neural network to identify risk factors within electronic automobile claim records includes, via one or more processors:

analyzing, with respect to each automobile claim in a historical set of electronic automobile claim records, one or both of (i) payments made under the claim, and (ii) a category corresponding to the type of claim.

6 . The computer-implemented method of claim 1 , wherein generating, within the plurality of layers, one or more risk indicators corresponding to the information includes, via one or more processors:

selecting, from a set of risk indicators stored in an electronic database, at least one risk indicator.

7 . The computer-implemented method of claim 1 , wherein generating, within the plurality of layers, one or more risk indicators corresponding to the information includes, via one or more processors:

generating, based upon the information, one or more risk indicators, and

storing, in an electronic database, the one or more risk indicators.

8 . The computer-implemented method of claim 1 , wherein determining a risk level corresponding to the automobile includes, via one or more processors:

retrieving for each risk indicator among the one or more risk indicators, a set of weights, wherein each of the set of weights corresponds to a respective one of the one or more risk indicators; and

calculating, based upon the set of weights, the risk level corresponding to the automobile.

9 . The computer-implemented method of claim 1 , wherein determining a risk level corresponding to the automobile includes, via one or more processors:

determining a geographic location of the automobile; and

calculating, based upon the geographic location of the automobile, the risk level corresponding to the automobile.

10 . The computer-implemented method of claim 9 , wherein calculating, based upon the geographic location of the automobile, the risk level corresponding to the automobile includes, via one or more processors:

determining seasonal information, based upon the geographic location of the automobile.

11 . A computing system comprising:

one or more processor; and

one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to

provide a first application to a user of a client computing device, wherein the first application, when executing on the client computing device, causes the client computing device to

obtain a set of information from an input device of the client computing device, and

transmit, via a communication network interface of the client computing device, the set of information to a remote computing system;

receive, at the remote computing system, the set of information;

process, at the remote computing system, the set of information;

identify, by the remote computing system, one or more risk indications at least in part by applying the set of information to a trained neural network;

generate, by the remote computing system analyzing the one or more risk indications, a quotation; and

one or both of (i) cause, by the remote computing system, the quotation to be displayed to the user, and (ii) cause, by the remote computing system, the quotation to be provided as input to a second application.

12 . The computing system of claim 11 , wherein the instructions further cause the one or more processors to one or both of:

(i) cause, by the remote computing system, one or more audio recording in the set of information to be converted to text, and

(ii) cause, by the remote computing system, one or more image in the set of information to be converted to text.

13 . The computing system of claim 11 , wherein the instructions further cause the one or more processors to one or both of:

(i) cause, by the remote computing system, one or more text pattern matchers to be applied to one or more strings in the set of information, to produce an indication of a pattern match, and

(ii) cause, by the remote computing system, one or more natural language processors to be applied to one or more strings in the set of information, to produce an indication of a semantic relationship among a plurality of objects in the one or more strings.

14 . The computing system of claim 11 , wherein the instructions further cause the one or more processors to one or both of:

(i) cause, by the remote computing system, customer data corresponding to the user to be retrieved from an electronic database, and

(ii) cause, by the remote computing system, automobile data corresponding to an automobile of the user to be retrieved from an electronic database.

15 . The computing system of claim 11 , wherein the set of information comprises an application for an automobile insurance policy.

16 . The computing system of claim 11 , wherein the set of information comprises a claim filed under an automobile insurance policy.

17 . The computing system of claim 11 , wherein the instructions further cause the one or more processors to:

retrieve, by the one or more processors, for each risk indicator among the one or more risk indicators, a set of weights, wherein each of the set of weights corresponds to a respective one of the one or more risk indicators; and

calculate, based upon the set of weights, the risk level corresponding to the automobile.

18 . The computing system of claim 11 , wherein the instructions further cause the one or more processors to:

identify a confidence factor with respect to each of the one or more risk indications.

19 . A non-transitory computer readable medium containing program instructions that when executed, cause a computer to:

train a neural network to identify risk factors within electronic automobile claim records, wherein the neural network includes a plurality of layers, and wherein an input layer of the plurality of layers includes a plurality of input parameters each corresponding to a different claim attribute and an output layer of the plurality of layers configured to output labels and weights;

receive information corresponding to at least one of (i) an automobile, (ii) an automobile operator, or (iii) vehicle operation factors;

analyze the information using the trained neural network;

identify one or more risk factors of the risk factors applicable to the information;

generate a risk indicator for each of the one or more risk factors using the trained neural network, wherein each risk indicator includes a label and a weight output from the output layer of the trained neural network; and

determine a risk level corresponding to one or both of (ii) the automobile, and (ii) an occupant of the automobile based upon the one or more risk indicators,

wherein to train a neural network to identify risk factors within electronic automobile claim records, the program instructions that when executed, cause the computer to:

process at least one of a historical set of electronic automobile claim records using natural language processing; and

train the neural network using the processed historical set of electronic automobile claim records.

20 . The non-transitory computer readable medium of claim 19 containing further program instructions that when executed, cause the computer to:

generate, based upon analyzing the one or more risk indicators, a quotation; and

one or both of (i) cause the quotation to be displayed to a user, and (ii) cause the quotation to be provided as input to a second application.