Abstract: Systems and methods for determining the effectiveness of vehicle safety features are provided. Vehicle build information (VBI) for vehicles manufactured by a plurality of OEMs may be obtained. The VBI may contain OEM-specific terminology for smart safety features associated with each vehicle. The obtained VBI may be analyzed to generate an ontology model mapping each feature to any OEM-specific terminology associated with the feature. The ontology model may be applied to the VBI to generate translated VBI for each vehicle, such that the OEM-specific terminology associated with each feature is replaced with OEM-agnostic terminology for the feature. Vehicle accident record information may be obtained for each vehicle, including, e.g., the number, frequency, severity, etc. of accidents associated with each vehicle.

Abstract: System and methods for detecting anomalous water usage at a property having a water supply system using machine learning (ML) are provided. An ML model may generate an estimated water usage of the property associated with the property data set, where the property data set may include attributes of the property influencing the water usage of the property. The ML model may be configured to learn relationships between a historical water usage data set and a historical property data set. The systems and methods may compare the water usage of the property and the estimated water usage of the property. If the comparison indicates the water usage exceeds a threshold, a notification may be generated and provided to a user device.

Abstract: The following relates generally to determining and/or displaying home scores. In some embodiments, one or more processors: (1) display, in a first portion of a display, identifying information of a home; and (2) display, in a second portion of the display: (a) an overall home score determined based upon a plurality of home subscores; (b) the plurality of home subscores comprising: a home safety subscore determined via a home safety subscore machine learning algorithm, a fire protection subscore determined via a fire protection subscore machine learning algorithm, a sustainability subscore determined via a sustainability subscore machine learning algorithm, and/or a home automation subscore determined via a home automation subscore machine learning algorithm; and (c) a relative home score comprising a comparison between the overall home score and scores of other homes.

Abstract: Systems and methods disclosed herein relates generally to using virtual reality (VR) for creating a digital twin of a home. In some embodiments, image data of a structure and an indication of a modification may be received, a virtual reality (VR) feed including a virtual representation of the modification proximate the structure may be generated, and the VR feed may be provided for presentation to a user within a VR display.

Abstract: The following relates generally to determining and/or displaying home scores. In some embodiments, one or more processors: (1) determine at least one of an overall home score, a home safety subscore, a fire protection subscore, a sustainability subscore, and/or a home automation subscore for a home; (2) identify a device; (3) determine a home score improvement that adding the device to the home would make for the overall home score, the home safety subscore, the fire protection subscore, the sustainability subscore, and/or the home automation subscore; and/or (4) display the home score improvement on a display, and/or otherwise visually, graphically, textually, audibly, or verbally outputting the home score improvement, such as via a processor, screen, voice bot, chatbot, or other bot.

Abstract: The following relates generally to determining and/or displaying home scores. In some embodiments, one or more processors: (1) receive imagery data; (2) determine a new device based upon the imagery data; (3) determine a home score improvement that adding the new device to the home would make for the overall home score, the home safety subscore, the fire protection subscore, the sustainability subscore, and/or the home automation subscore; and/or (4) display the home score improvement on a display.

Abstract: The following relates generally to determining and/or displaying home scores. In some embodiments, one or more processors: (1) provide a recommendation to purchase a new device or repair an existing device to improve an overall home score, a home safety subscore, a fire protection subscore, a sustainability subscore, and/or a home automation subscore; (2) receive a selection associated with the new device or a selection associated with the existing device; and/or (3) in response receiving the selection associated with the new device or the selection associated with the existing device, provide a tutorial of the new device or the existing device.

Abstract: Systems and methods disclosed herein relate generally to using augmented reality (AR) for receiving and displaying recommended devices proximate a structure. In some examples, underlay layer data may be received (e.g., from a camera of an AR viewer device); and overlay layer data may be received (e.g., from a different camera or other overlay layer device). An AR display may be created by correlating the underlay layer data with the overlay layer data. An improved home score indicia may be displayed based upon the recommended device.

Abstract: The following relates generally to determining and/or displaying home scores. In some embodiments, one or more processors: (1) identify an upgrade and/or service to a home; (2) determine a home score improvement that the upgrade and/or service would make for an overall home score, a home safety subscore, a fire protection subscore, a sustainability subscore, and/or a home automation subscore; and/or (3) display the home score improvement along with an indication of the upgrade and/or service on a display.

Abstract: Systems and methods disclosed herein relates generally to using augmented reality (AR) for visualizing recommended updates to devices proximate a structure. In some examples, underlay layer data may be received (e.g., from a camera of an AR viewer device); and overlay layer data may be received (e.g., from a different camera or other overlay layer device). An AR display may be created by correlating the underlay layer data with the overlay layer data. An improved home score indicia may be displayed based upon the recommended update.

Abstract: This disclosure provides a method for determining male fertility status. The method comprises determining GM1 localization patterns following induced sperm capacitation, identifying the percentage of various patterns, particularly the ratio of [(AA+APM)/total number of GM1 localization patterns] and determining if the percentage of certain GM1 localization patterns in response to induced capacitation is altered. Based on the change in the percentage of localization patterns of certain patterns in response to induced capacitation, alone or in combination with other sperm attributes, male fertility status can be identified.

Abstract: Systems and methods for determining the effectiveness of vehicle safety features are provided. Vehicle build information (VBI) for vehicles manufactured by a plurality of OEMs may be obtained. The VBI may contain OEM-specific terminology for smart safety features associated with each vehicle. The obtained VBI may be analyzed to generate an ontology model mapping each feature to any OEM-specific terminology associated with the feature. The ontology model may be applied to the VBI to generate translated VBI for each vehicle, such that the OEM-specific terminology associated with each feature is replaced with OEM-agnostic terminology for the feature. Vehicle accident record information may be obtained for each vehicle, including, e.g., the number, frequency, severity, etc. of accidents associated with each 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: This disclosure provides a method for determining male fertility status. The method comprises determining GM1 localization patterns following induced sperm capacitation, identifying the percentage of various patterns, particularly the ratio of [(AA+APM)/total number of GM1 localization patterns] and determining if the percentage of certain GM1 localization patterns in response to induced capacitation is altered. Based on the change in the percentage of localization patterns of certain patterns in response to induced capacitation, alone or in combination with other sperm attributes, male fertility status can be identified.

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: An autonomous vehicle (AV) including a vehicle body, and a vehicle computing device is provided. The vehicle computing device includes a processor in communication with a memory device. The processor is configured to identify a time and a geographic location of a traffic collision involving the AV, retrieve map data and contextual data associated with the time and the geographic location of the traffic collision, retrieve vehicle telematics data collected by sensors coupled to the vehicle body, determine for each of a plurality of moments in time during the traffic collision a position and an orientation of the AV during the traffic collision, generate a simulation of the traffic collision including a representation of the AV based upon the map data, the contextual data, and the vehicle telematics data, and provide content to enable display of the simulation on a display device.

Abstract: In one aspect, a graphical user interface (GUI) for a dynamic model creation (DMC) system may be provided. The GUI may include: (i) a first display area programmed to display the at least one model including a visual representation of a determined location where a collision occurred, and/or (ii) a second display area programmed to display at least one data table corresponding to the at least one model, wherein movement of a visual representation of a vehicle on the GUI during a simulation of the at least one model is controlled at least by telematics data, and wherein the GUI is configured to receive user inputs such that the visual representations in the first display area and the at least one data table in the second display area are dynamically modifiable by a user.

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: An autonomous vehicle (AV) including a vehicle body, and a vehicle computing device is provided. The vehicle computing device includes a processor in communication with a memory device. The processor is configured to identify a time and a geographic location of a traffic collision involving the AV, retrieve map data and contextual data associated with the time and the geographic location of the traffic collision, retrieve vehicle telematics data collected by sensors coupled to the vehicle body, determine for each of a plurality of moments in time during the traffic collision a position and an orientation of the AV during the traffic collision, generate a simulation of the traffic collision including a representation of the AV based upon the map data, the contextual data, and the vehicle telematics data, and provide content to enable display of the simulation on a display device.

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.