Abstract: A method and system for associating information regarding an object with image objects is disclosed, wherein each image object is a visual data containers including an image associated with the object and data items relating to the object. A user may interact with a graphical user interface to create, modify, and associate image objects by means of the associated images. Information regarding the objects may be associated therewith by means of the image objects, and image objects may be associated with other image objects to generate associations between information regarding the underlying objects. Information regarding other image objects or the associated image may be used to determine information regarding the object, which may be associated with the image object. In some embodiments, associating two or more image objects may associate the underlying objects for purposes of insurance policies or may generate a new insurance policy based on the image objects.

Abstract: The following generally relates to using image classification techniques to determine the acceptability of form documents. In some examples, an image classification model may be trained to apply a first label to form documents that are acceptable and a second label to form documents that are unacceptable. In these examples, the image classification model may include a neural network, such as a convolutional neural network. Accordingly, the systems and methods generally relate to obtaining a submitted form document, inputting the submitted form document into the trained image classification model, and/or enforcing the acceptability decision of the image classification model.

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: 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: 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: A sump pump system enables automatic determination and utilization of frequencies for mechanical shakers for sump pumps. These techniques may be implemented to detect a fault (e.g., a stuck impeller) with a sump pump and to identify a desirable frequency at which a mechanical shaker for the sump pump should vibrate to correct the fault.

Abstract: Techniques for automatically identifying frequently traveled routes are provided. An example method includes obtaining telematics data associated with routes traveled by one or more drivers; mapping the telematics data associated with each route to respective step functions representative of each of the plurality of routes; comparing, the step functions representative of each of the routes to one another in order to determine a distance between each pair of step functions, of the plurality of step functions; clustering each of the routes into one or more clusters, with each cluster including one or more routes associated with step functions having distances less than a threshold distance from one another; and identifying one or more frequently traveled routes associated with the one or more drivers based on the one or more clusters, with each frequently traveled route being included in a cluster including greater than a threshold number of routes.

Abstract: The present disclosure generally relates to generating emergency vehicle warnings, automatic control of autonomous vehicles based upon the emergency vehicle warnings. More particularly, the present disclosure relates to generating data representative of emergency vehicle warnings and alternate autonomous vehicle routing based upon real-time information related to an emergency vehicle. The information related to the emergency vehicle may include emergency vehicle origination location data, emergency vehicle current location data, emergency vehicle route data, and/or emergency vehicle destination location data. An emergency vehicle warning and/or alternate vehicle routing for autonomous vehicles may be generated based further on information related to an autonomous vehicle. In one aspect, an emergency vehicle may wirelessly communicate with the autonomous vehicle and/or an insurance provider remote server.

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: Techniques for collecting, synchronizing, and displaying various types of data relating to a road segment enable, via one or more local or remote processors, servers, transceivers, and/or sensors, (i) enhanced and contextualized analysis of vehicle events by way of synchronizing different data types, relating to a monitored road segment, collected via various different types of data sources; (ii) enhanced and contextualized analysis of filed insurance claims pertaining to a vehicle incident at a road segment; (iii) advantageous machine learning techniques for predicting a level of risk assumed for a given vehicle event or a given road segment; (iv) techniques for accounting for region-specific driver profiles when controlling autonomous vehicles; and/or (v) improved techniques for providing a GUI to display collected data in a meaningful and contextualized manner.

Abstract: The described methods and systems enable automatic detection of a driver's identity based on an analysis of position or orientation data pertaining to elements of a cockpit that the driver tends to personalize when driving according to her preferences. Example position or orientation data may include data pertaining to seat or mirror position or orientation. Some of the disclosed embodiments utilize machine-learning techniques to train a machine-learning (ML) model to automatically detect or predict a driver's identity based on learned patterns (e.g., based on preferred positions or orientations the ML model has learned for the driver.) If desired, one or more embodiments may implement unsupervised learning techniques, supervised learning techniques, or both unsupervised and supervised learning techniques.

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: 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: 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: 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: 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: The present disclosure is directed to apparatuses, systems and methods for automatically classifying images of occupants inside a vehicle. More particularly, the present disclosure is directed to apparatuses, systems and methods for automatically classifying images of occupants inside a vehicle by comparing current image feature data to previously classified image features.