Abstract: Techniques for building and managing data models are provided. According to certain aspects, systems and methods may enable a user to input parameters associated with building one or more data models, including parameters associated with sampling, binning, and other factors. The systems and methods may automatically generate program code that corresponds to the inputted parameters and display the program code for review by the user. The systems and methods may build the data models and generate charts and plots depicting aspects of the data models. Additionally, the systems and methods may combine data models and select champion data models.

Abstract: Methods, systems, apparatus, and non-transitory computer readable media are described for conducting collaborative real-time inspections of insured properties. The system may include several workstations, each operated by a different user with a respective role in processing an insurance claim. The system also may include a server system configured to receive an indication of an insured property to be inspected by users at the workstations using an unmanned aerial vehicle (UAV) configured to collect aerial imagery, provide, substantially in real time during an inspection session, aerial imagery of the insured property to each of the workstations, receive, substantially in real time during the inspection session, information related to the insured property being inspected from the workstations, and generate a database record descriptive of the inspection session. The aerial imagery may be used to verify dimensions of insured homes or estimate home damage resulting from fire, wind, hail, water, or hurricanes.

Abstract: A system for analyzing the environment of a vehicle i) receives a plurality of data from at least one sensor associated with a vehicle, such that the plurality of data includes at least one environmental condition at a location; (ii) analyzes the plurality of data to determine the at least one environmental condition at the location; (iii) determines a condition of a building at the location based upon the at least one environmental condition; (iv) determines an insurance product for the building based upon the determined condition associated with the building; and (v) generates an insurance quote for the insurance product. As a result, the speed and accuracy of insurance providers learning about potential clients and the conditions of the potential client's property and needs is increased.

Abstract: A system and method for guiding a user after an accident and gathering relevant data for an insurance claim and/or a coverage determination. The system includes a mobile device having a memory element, processing element, communication element and user interface. The mobile device may also include a sensor and/or global positioning system. The user interface and/or sensor of the mobile device (e.g., microphone, GPS, camera, etc.) may receive information relevant to an insurance claim, including by directing a user to capture images of relevant objects and/or querying the user for verbal description(s) of facts regarding the accident. The information gathered may be saved on the memory element of the mobile device. The information may also be transmitted to a server via a network in the form of a structured incident data report.

Abstract: A technique is provided for determining a loss risk assessment score for a vehicle trip. The technique includes, at a vehicle, a computing device receiving first information indicative of operation of the vehicle. The technique also includes, at the vehicle, the computing device receiving second information indicative of an environment at a particular location and time. The computing device correlates the first information and the second information to generate a data set. The technique also includes determining a score for the vehicle trip based at least in part upon the generated data set.

Abstract: A system and method for guiding a user after an accident and gathering relevant data for an insurance claim and/or a coverage determination. The system includes a mobile device having a memory element, processing element, communication element and user interface. The mobile device may also include a sensor and/or global positioning system. The user interface and/or sensor of the mobile device (e.g., microphone. UPS, camera, etc.) may receive information relevant to an insurance claim, including by directing a user to capture images of relevant objects and/or querying the user for verbal description(s) of facts regarding the accident. The information gathered may be saved on the memory element of the mobile device. The information may also be transmitted to a server via a network in the form of a structured incident data report.

Abstract: Systems and methods are disclosed for estimating slipperiness of a road surface. This estimate may be obtained using an image sensor mounted on a vehicle. The estimated road slipperiness may be utilized when calculating a risk index for the road, or for an area including the road. If a predetermined threshold for slipperiness is exceeded, corrective actions may be taken. For instance, warnings may be generated to human drivers that are in control of driving vehicle, and autonomous vehicles may automatically adjust vehicle speed based upon road slipperiness detected.

Abstract: A remotely-controlled (RC) and/or autonomously operated inspection device, such as a ground vehicle or drone, may capture one or more sets of imaging data indicative of at least a portion of an automotive vehicle, such as all or a portion of the undercarriage. The one or more sets of imaging data may be analyzed based upon data indicative of at least one of vehicle damage or a vehicle defect being shown in the one or more sets of imaging data. Based upon the analyzing of the one or more sets of imaging data, damage to the vehicle or a defect of the vehicle may be identified. The identified damage or defect may be compared to a claimed damage or defect to determine whether the claimed damage or defect occurred.

Abstract: Systems and methods are described for generating efficient iterative electronic recommendation structures. In various aspects, one or more processors aggregate a plurality of ratings vectors, where each ratings vector is associated with a vector type and contains one or more content ranking metrics associated with one or more users. The one or more processors generate similarity pairing values from the plurality of ratings vectors, where each similarity pairing value is based on a similarity mapping between a first ratings vector and a second ratings vector. The one or more processors further generate an electronic recommendation structure based on the similarity pairing values, where the electronic recommendation structure includes a bi-directional look-up interface that is configured to return a bi-directional recommendation value after receiving a lookup request for either the vector type of the first ratings vector or the vector type of the second ratings vector.

Abstract: Computer-implemented methods and computer systems are disclosed herein as implemented by one or more local or remote processors, transceivers, servers, and/or sensors. The methods and systems include the one or more processors (i) determining an electric vehicle has a state of charge (SOC) below a predetermined threshold; (ii) if the SOC is determined to be below the predetermined threshold, determining homes capable of charging electric vehicles within a vicinity of, or a predetermined distance of, the low SOC vehicle's GPS location; (iii) from among the homes within the predetermined distance of the low SOC vehicle, ranking the homes based upon various factors; and/or (iv) scheduling a rendezvous or appointment for the low SOC vehicle with the highest ranked home for recharging the low SOC vehicle, the highest ranked home being equipped to recharge the low SOC vehicle and is available to charge the low SOC vehicle.

Abstract: A virtual environment may be provided, the virtual environment including a procedurally generated virtual landscape. The virtual landscape may include various components, such as natural terrain elements, infrastructural elements, and residential/commercial properties, that are automatically generated based upon procedural rules so as to provide uniqueness of any one virtual landscape. A user may enter the virtual environment and, via various virtual tools, identify aspects of the generated virtual landscape that affect risk to one or more of the properties therein.

Abstract: A method and system provide areas with lifestyle preferences selected by a user. Generally, a computing device receives a selection of one or more lifestyle preferences from the user. In response to the selected lifestyle preferences, a visual display of one or more areas of a geographic map that includes one or more of the lifestyle preferences that favorably compare to the lifestyle preferences of the user is presented to the user via a user interface of the computing device. Additionally, the user may submit a review of the lifestyle preferences available in a particular location.

Abstract: A method of training and using a machine learning model that controls for consideration of undesired factors which might otherwise be considered by the trained model during its subsequent analyses of new data. For example, the model may be a neural network trained on a set of training images to evaluate an insurance applicant based upon an image or audio data of the insurance applicant as part of an underwriting process to determine an appropriate life or health insurance premium. The model is trained to probabilistically correlate an aspect of the applicant's appearance with a personal and/or health-related characteristic. Any undesired factors, such as age, sex, ethnicity, and/or race, are identified for exclusion. The trained model receives the image (e.g., a “selfie”) of the insurance applicant, analyzes the image without considering the identified undesired factors, and suggests the appropriate insurance premium based only on the remaining desired factors.

Abstract: 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.

Abstract: Systems and methods are provided for controlling operation of a vehicle. An example system for controlling operation of a vehicle includes one or more data collection components and one or more processors. The one or more data collection components are configured to collect data representative of a physical configuration of an interior vehicle component. The one or more processors are configured to access the collected data, determine, by processing the collected data, the physical configuration of the interior vehicle component, select a manner of operation based upon the determined physical configuration of the interior vehicle component, and cause the vehicle to operate according to the manner of operation.

Abstract: A system and computer-implemented method detect and act upon operator reliance to vehicle alerts. The system and method include receiving user profile data of an operator that includes a baseline of at least one driving activity aided by activation of an alert from a feature of an Advanced Driver Assistance System (ADAS). The system and method may include receiving historical ADAS alert frequency data including a history of at least one driving activity aided by activation of the alert from the ADAS feature. The system and method may compare the user profile data with the historical ADAS alert frequency data, determine a reliance level based upon the comparing, and set at least a portion of an operator profile associated with the operator with the reliance level. As a result, a risk averse driver, and/or proper responsiveness to vehicle alerts may be rewarded with insurance-cost savings, such as increased discounts.

Abstract: A method for autonomous vehicle driver situational awareness recovery training includes receiving an input parameter; determining a simulation; receiving user performance data; and analyzing the user performance data to determine a driving competency score. A method for autonomous vehicle driver situational awareness recovery training includes receiving an input parameter; executing a simulation; and recording user performance data. A system includes a processor a display device; and a non-transitory computer-readable medium storing instructions that, when executed by the one or more processors, cause the system to receive an input parameter; execute a simulation; and record user performance data.

Abstract: Systems and methods for situational modification of autonomous vehicle operation are disclosed. According to aspects, a computing device may detect the occurrence of an emergency event and may determine a current operation of an autonomous vehicle that may be associated with the emergency event. The computing device may determine a modification to operation of the autonomous vehicle, where the modification may represent a violation of a roadway regulation that may enable effective handling of the emergency event. The computing device may generate a set of instructions for the autonomous vehicle to execute to cause the autonomous vehicle to undertake the operation modification.

Abstract: Embodiments described herein generate a first set of prompts, the first set of prompts configured to prompt a vehicle owner for a first set of answers used to learn preferred vehicle renter characteristics; receive the first set of answers; generate, based upon the first set of answers, a second set of prompts, the second set of prompts configured to prompt the owner for a second set of answers used to learn additional preferred vehicle renter characteristics; receive the second set of answers; predict user preference value(s) of a profile of the owner based upon the second set of answers, wherein the user preference value(s) define criteria for sharing a vehicle associated with the profile with vehicle renters who satisfy the criteria; apply the criteria to potential vehicle renters; and cause an indication of the vehicle to be displayed only to the potential vehicle renters who satisfy the criteria.

Abstract: Techniques for identifying faults in a collaborative computing system including a plurality of disparate, geographically separated computing systems are described herein. An intelligent monitoring (IM) server computing system may receive data from the plurality of computing devices and may monitor the health of the collaborative computing system. The IM server computing system may analyze the data and identify one or more faults associated with a portion of the collaborative system (e.g., an associated computing device, platform, network, etc.). In some examples, the IM server computing system may be configured to identify potential future faults associated with the portion of the collaborative system. Based on the fault, the IM server computing device may determine an action to take to remedy the fault and/or prevent the potential future fault. The IM server computing device may either automatically perform the action or send a notification to the associated computing system to perform the action.