Abstract: A property analyzer device may include (1) a receiver configured to receive property identification information from a mobile device, the received property identification information including a picture of the property and/or a property location; (2) a retriever configured to retrieve property-related information from multiple property sources (such as public record databases, websites, or an internal company database) based upon the received property identification information; (3) a processor configured to consolidate the retrieved property-related information into a consolidated property-related image of the property; and (4) a transmitter configured to transmit the consolidated property-related image to the mobile device for review by a user.

Abstract: Described herein are methods and systems for generating and applying vehicle maintenance models to vehicle telematics data. A vehicle telematics analytics (VTA) computing device is configured to receive vehicle telematics data associated with operation of a subject vehicle, and apply the received vehicle telematics data to a vehicle maintenance model to determine whether the operation of the subject vehicle is better or worse than standard operation of like vehicles. The VTA computing device may adjust a maintenance cost and/or determine if a user of the vehicle is eligible for a reward based upon this determination. The VTA computing device is configured to transmit an itemized report to the user including the adjusted maintenance cost and/or the reward as well as one or more other vehicle costs.

Abstract: This disclosure describes example techniques for automatically configuring domain name system (DNS) servers to handle custom hostnames assigned to hosted zones. The techniques monitor DNS record sets of the hosted zones for special DNS records registering new hostnames. When such a special DNS record is detected, the central DNS server checks a database to see if that hostname is already assigned to another hosted zone. If the hostname is not assigned to another hosted zone, the central DNS server automatically updates its own DNS configuration data pair the new hostname with the Internet Protocol (IP) address of the hosted zone requesting the hostname. This allows the central DNS server to resolve future DNS queries for the new hostname by routing requests to the proper zone.

Abstract: A transportation system for generating a transportation recommendation may (1) receive a transportation request; (2) identify, using the transportation request, a first geographic location and a second geographic location associated with the transportation request; (3) determine, using the first geographic location and the second geographic location, a plurality of routes between the first geographic location and the second geographic location: and/or (4) generate a sustainability impact score for each of the plurality of routes, The sustainability impact score may indicate an estimated climate impact of using at least one transportation modality to travel along the route. The transportation system may also (5) select a recommended route from the plurality of routes using the sustainability impact scores of the plurality of routes; and/or (6) generate a user interface providing the recommended route or otherwise present the recommend route and other information on a user device.

Abstract: Test cases written to test a software application can be dynamically distributed among different sets of test cases that can be executed simultaneously in different parallel threads, thereby speeding up testing relative to executing the test cases sequentially in a single thread. Although executing the test cases in parallel threads can cause the test cases to execute in a different order than intended by developers, any test cases that fail due to database conflicts or other errors as a result of the parallelization can be retried at least once. Re-executing failed test cases at a later time can lead to the full set of test cases ultimately succeeding, despite errors that may have been caused by the parallelization.

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.

Abstract: A system and method for measuring a driver's actual driving behaviors (e.g., acceleration, deceleration) in a manual driving mode to determine their preferred driving style, and then causing an autonomous or semi-autonomous vehicle to operate itself, within limits, in accordance with the drivers' driving style when operating in a self-driving mode, thereby providing a more familiar and comfortable driving experience for the driver. Data is collected on the actual driving behavior, any pre-existing data is accessed on the actual driving behavior, and the collected data and the pre-existing data are combined. A custom control is then created based upon the combined data, and the custom control is applied to manage the self-driving behavior of the autonomous or semi-autonomous vehicle in a self-driving mode. Additional data continues to be collected on the actual driving behavior, and the custom control is adjusted based upon the collected additional data.

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: Systems and methods may be provided for providing dynamic user experience (UX) applications that may reduce burden upon application users by allowing querying and sharing of user information across multiple application interactions associated with a user. Furthermore, the systems and methods herein may provide security restrictions that protect potentially sensitive user information from erroneous user authentication.

Abstract: Annotations associated with test cases written to test a software application can allow related test cases to be dynamically distributed among different sets of test cases that can be executed simultaneously in different parallel threads, thereby speeding up testing relative to executing the test cases sequentially in a single thread. The annotations can also allow test cases to be identified that are relevant to code changes, such that testing times can be further reduced by executing a subset of test cases relevant to code changes instead of a full set of test cases.

Abstract: This disclosure describes systems, devices, and techniques for managing resources in an enterprise network using lifecycle prediction. In an example method, a resource may be identified as operational within an enterprise network. An initial lifecycle of the resource may be estimated based at least partly on one or more characteristics of the resource. At least one trigger condition associated with the resource may be identified as having occurred. In response to identifying that the at least one trigger condition has occurred, an updated lifecycle of the resource may be estimated by applying at least one rule associated with the at least one trigger condition to the initial lifecycle. An expiration time of the resource may be determined based on the updated lifecycle. The resource may be replaced in advance of the expiration date of the resource.

Abstract: In one aspect, a computer system for vehicle configuration verification, and/or detecting unauthorized vehicle modification may be provided. In some exemplary embodiments, the computer system may include a processor and a non-transitory, tangible, computer-readable storage medium having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations including: (1) receiving a vehicle image, including a vehicle identifier and at least one software module; (2) calculating a configuration hash value of the at least one software module; generating a first data block including the configuration hash value, a first index value, the vehicle identifier, and a digital signature; (3) storing the first data block in a memory; and/or (4) transmitting the first data block to any number of network participants using a distributed network to facilitate vehicle software configuration verification.

Abstract: Methods and systems for monitoring use and determining risks associated with operation of a vehicle having one or more autonomous operation features are provided. According to certain aspects, operating data may be recorded during operation of the vehicle. This may include information regarding the vehicle, the vehicle environment, use of the autonomous operation features, and/or control decisions made by the features. The control decisions may include actions the feature would have taken to control the vehicle, but which were not taken because a vehicle operator was controlling the relevant aspect of vehicle operation at the time. The operating data may be recorded in a log, which may then be used to determine risk levels associated with vehicle operation based upon risk levels associated with the autonomous operation features. The risk levels may further be used to adjust an insurance policy associated with the vehicle.

Abstract: Provided herein is a digital care circle platform for electronic communication between a senior and virtual care circle members. The digital care circle platform may include a chatbot and an application associated with the senior, a mobile application associated with the plurality of virtual care circle members, at least one processor in communication with at least one memory device, and one or more servers, sensors, wearables, and transceivers. The at least one processor may be configured to: (i) accept event posts from the senior via the chatbot and application and from each virtual care circle member of the plurality of virtual care circle members via the mobile application, (ii) detect pro-active check-ins from the senior, and (iii) generate, once a pro-active check-in is detected, an electronic communication detailing the pro-active check-in as a pro-active check-in event, and transmit the pro-active check-in event to the virtual care circle members.

Abstract: Users may engage in a target user activity via digital systems, such as a website, and/or non-digital systems. Users may user various digital channels to arrive at the digital systems or non-digital systems. Users may also arrive at digital systems, such as the website, via different entry pages. A propensity analyzer can, based on activity data associated with users, determine propensities of one or more of the digital channels, digital systems, non-digital channels, and/or entry pages to drive users to perform a target user activity. The propensity analyzer can generate recommendations for revising digital channels, digital systems, non-digital channels, and/or entry pages to increase their propensities to drive users to perform the target user activity.

Abstract: In one aspect, a system for receiving communications while a user operates a motor vehicle is provided. The system may include (1) a BLUETOOTH interface configured to establish a connection to a BLUETOOTH device associated with the motor vehicle, (2) a wireless communication interface configured to receive a wireless communication from a remote device, and (3) a processor coupled to the wireless communication interface and the BLUETOOTH interface, and configured to: (a) determine the user is operating the motor vehicle based upon the connection to the BLUETOOTH device, and (b) suppress a notification of the user of receipt of the wireless communication while the user is operating the motor vehicle.

Abstract: A rental self-service (“RSS”) computer system, including a processor and a memory, is provided. The processor is programmed to: (i) retrieve policyholder insurance data using a claim identifier and a policyholder identifier; (ii) generate a customized link including the claim identifier and the policyholder identifier, wherein the customized link is executable on a user device to cause display of a searchable interface configured to enable the claimant to search for rentable vehicles from a plurality of vendors; (iii) in response to receiving the customized link from the user device, retrieve rental vehicle data including at least one rental vendor location and vehicle rental cost information; (iv) cause the retrieved rental vehicle data to be displayed on the user device to enable the claimant to view available rental options; and (v) receive confirmation of a rental reservation request of a selected rental option at a selected rental vendor location.

Abstract: A computer-implemented method can include, identifying a subset of drivers from among a group of other drivers. The computer-implemented method can also include, receiving a first set of telematics data associated with a vehicle operated by a driver and a second set of telematics data associated with a group of other vehicles operated by the subset of drivers from among the group of other drivers. The computer-implemented method can further include, ranking the driver and drivers of the subset of drivers by comparing the first set of telematics data and the second set of telematics data. The computer-implemented method can additionally include, transmitting for display on a graphical user interface of an electronic device of the driver, a ranking of the driver.

Abstract: A system for notifying emergency services of a vehicular crash may (i) receive sensor data of a vehicular crash from at least one mobile device associated with a user; (ii) generate a scenario model of the vehicular crash based upon the received sensor data; (iii) store the scenario model; and/or (iv) transmit a message to one or more emergency services based upon the scenario model. As a result, the speed and accuracy of deploying emergency services to the vehicular crash location is increased. The system may also utilize vehicle occupant positional data, and internal and external sensor data to detect potential imminent vehicle collisions, take corrective actions, automatically engage autonomous or semi-autonomous vehicle features, and/or generate virtual reconstructions of the vehicle collision.

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.