원래 이름 기록: State Farm Mutual Automobile Insurance Company

According to certain aspects, a computer-implemented method for operating an autonomous or semi-autonomous vehicle may be provided. With the customer's permission, an identity of a vehicle operator may be identified and a vehicle operator profile may be retrieved. Operating data regarding autonomous operation features operating the vehicle may be received from vehicle-mounted sensors. When a request to disable an autonomous feature is received, a risk level for the autonomous feature is determined and compared with a driver behavior setting for the autonomous feature stored in the vehicle operator profile. Based upon the risk level comparison, the autonomous vehicle retains control of vehicle or the autonomous feature is disengaged depending upon which is the safer driver- the autonomous vehicle or the vehicle human occupant. As a result, unsafe disengagement of self-driving functionality for autonomous vehicles may be alleviated. Insurance discounts may be provided for autonomous vehicles having this safety functionality.

In mutation testing, source code is mutated at various positions, and test suites are run against the original object code and each version of the mutated object code, to determine the quality of test suites against arbitrary changes in the object code. The present disclosure provides a mutation test manager configured to initialize multiple computing threads configuring a computing host to perform parallel computation; mutate class files within context of each computing thread; recompile mutated class files independently in each respective computing thread to generate heterogeneous mutants; and execute pending unit tests against heterogeneous mutants independently in each respective computing thread. Consequently, the mutation testing process is decoupled from computational bottlenecks which would result from linear, sequential generation, compilation, and testing of each mutation, especially in the context of JVM® programming languages configured to generate class-rich object code.

This disclosure describes techniques for enabling efficient and/or expeditious retrieval of data records stored across a clustered storage framework. An example system manages storage of data records in the storage clusters based on predefined placement rules. Frequently accessed data records are stored in a local high-performance store storage medium to reduce retrieval latency. The system determines which storage cluster to retrieve a requested data record from by selecting the cluster with the highest weight, as determined based on performance-related metrics like cluster utilization and geographic proximity.

A user analytics computing device for processing vehicle-based telematics data and generating user offerings responsive to the vehicle-based telematics data includes at least one processor in communication with a memory device. The processor is programmed to: (i) receive, from a vehicle computing device, telematics data associated with a plurality of trips taken by a first driver using a first vehicle, (ii) generate a driver profile of the first driver based at least in part upon the telematics data, (iii) access, from the memory device, contextual data associated with an environment in which the first vehicle travelled during the plurality of trips, (iv) synthesize the driver profile and the contextual data to generate a contextualized driver profile, (v) based upon the contextualized driver profile, generate a user offering to influence the driver profile, and (vi) transmit the user offering to a user computing device associated with the first driver.

Methods and systems for assessing property based on predictive analytics techniques that use external data are described herein. A server may identify a reference set of information for a reference property evaluated with a reference risk score in accordance with a risk score rule that meets a predetermined threshold. The server may obtain an external data file from an external data source, wherein the external data file includes an external set of information for a prospective property. The server may compare the reference set of information with the external set of information to identify a common feature, condition, or attribute, and if identified, may evaluate the prospective property by determining a predicted risk score based on the external set of information in accordance with the risk score rule. If the predicted risk score is determined to meet the threshold, the server may indicate the prospective property as pre-approved.

A device for evaluating aspects of health of a residential property configured to: (i) receive a first element of internal home health data captured by one or more smart devices installed within a residential property, the first element of internal home health data reflecting an aspect of operational quality of one or more assets of the residential property; (ii) determine a safety score based upon the first element of internal home health data; (iii) receive a second element of internal home health data captured by the one or more smart devices; (iv) determine a home health score based upon the first or second elements of internal home health data, the home health score representing a measure of health of the residential property; and (v) cause to be displayed, to a homeowner via a graphical user interface, a home health evaluation that includes the safety score and the home health score.