Abstract: A computer system configured to recommend home projects while also identifying a homeowner's level of expertise for implementing the projects. The system may be configured to: (i) cause display of a first interface view of a frontend application to receive inputs for generating profiles of a home and the homeowner; (ii) determine a homeowner-personality type and a current level of the homeowner-personality type using the received inputs, including available features or functionalities at the home, and home improvement projects; (iii) generate a list of actions for the homeowner to take to move to a higher level for the determined homeowner-personality type; and/or (iv) cause display of a second interface view displaying the determined homeowner-personality type, the current level of the homeowner-personality type, and the list of actions to move to the higher level for determined homeowner-personality type.

Abstract: Systems and methods are described for evaluating and analyzing home data to generate a home score. The method may include: (1) retrieving home data for a property; (2) determining, based upon the home data for the property, one or more home score factors, wherein the determining may include: (i) analyzing, using a trained machine learning data evaluation model, the home data for the property to determine home characteristic data for the property, (ii) analyzing, using the trained machine learning data evaluation model, the home data for the property to determine a likelihood of loss associated with the property, and (iii) determining, based upon the home characteristic data for the property and the likelihood of loss associated with the property; and (3) generating, based upon the one or more home score factors, a home score for the property.

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: 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 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: 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) 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: 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: 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: Systems and methods are described for generating and/or displaying an overall home score for a property. The method may include: retrieving one or more attributes of a property; determining one or more home score factors from the attributes; generating an overall home score; generating a neighborhood score; and displaying the overall home score, the neighborhood score and/or a map.

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: Systems and methods are described for evaluating and gamifying maintenance for a property by a user. The method may include: (1) retrieving first home data for a first property and second home data for a second property, wherein a user is associated with the first property; (2) calculating, using a trained machine learning evaluation model, one or more second home score factors based upon the second home data; (3) detecting that the user is to be associated with the second property; (4) detecting, based upon the first home data and the second home data, one or more local environmental differences between the first property and the second property; and (5) generating a learning module for the user for improving or maintaining the one or more second home score factors based upon the one or more local environmental differences.

Abstract: Systems and methods are described for evaluating and gamifying maintenance for a property by a user. The method may include: (1) retrieving training data captured by one or more sensors associated with one or more properties or one or more users; (2) retrieving at least one of home data for a property or user data for a user; (3) determining, using a machine learning model, home score factors based upon at least one of the home data or the user data, wherein the machine learning model is trained with the training data; (4) generating, based upon the home score factors, a home score for the property; (5) determining, based upon at least one of the home data or the user data, difference factors between the property and a previous property associated with the user; and (6) causing a user device to display the home score and difference factors.

Abstract: Systems and methods are described for evaluating and gamifying maintenance for a property by a user. The method may include: (1) retrieving home data for a first property; (2) determining, using a first trained machine learning evaluation model, one or more home score factors based upon at least the home data; (3) generating, based upon the one or more home score factors, a home score for the first property; (4) determining, using a second trained machine learning evaluation model, that one or more additional properties are similar to the first property; (5) retrieving past hazard data associated with a second property of the one or more additional properties; and (6) generating, based upon at least the past hazard data and at least one of the one or more home score factors, a learning module for the first property.

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: Systems and methods are described for evaluating and analyzing home data to generate a home score. The method may include: (1) retrieving at least one of home data for a property or user data for a user; (2) determining, using a trained machine learning evaluation model, one or more home score factors based upon at least one of the home data or the user data; (3) receiving, from the user, a home modification indication; (4) modifying, based upon the home modification indication, at least one of the one or more home score factors to create one or more modified home score factors; and (5) generating, based upon the one or more modified home score factors, a home score for the property.

Abstract: Systems and methods are described for evaluating and analyzing home data to generate a home score. The method may include: (1) retrieving home data for a first property; (2) determining, using a trained machine learning evaluation model, one or more home score factors based upon at least the home data; (3) generating, based upon the one or more home score factors, a home score for the first property; (4) retrieving past hazard data associated with a second property; and (5) generating based upon at least the past hazard data, a home modification recommendation.

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: Systems and methods are described for generating and/or displaying an overall home score for a property. The method may include: retrieving one or more attributes of a property; determining one or more home score factors from the attributes; generating an overall home score; generating a neighborhood score; and displaying the overall home score, the neighborhood score and/or a map.

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.