Abstract: The following relates generally to artificial intelligence (AI)-based responses to social media posts. In some embodiments, one or more processors may be configured to: (1) receive, via a chatbot, a social media post; (2) categorize, via the chatbot, the social media post; (3) determine, via the chatbot, based upon the categorization, an entity to contact; (4) build, via the chatbot, based upon the determined entity to contact, a response to the social media post; and/or (5) send, via the chatbot, the response to the entity.

Abstract: The following relates generally to AI-based review of insurance claims complaints. In some embodiments, one or more processors: (1) receive, via a chatbot, an insurance claim complaint; (2) categorize, via the chatbot, the insurance claim complaint by determining a category of the insurance claim complaint, the category comprising a tone category or a policy category; (3) build, via the chatbot, a complaint report including information of the insurance claim complaint and an indication of the category; and/or (5) send, via the chatbot, the complaint report to an insurance complaint administrator computing device.

Abstract: The following relates generally to artificial intelligence (AI)-based communication with an insurance customer. In some embodiments, one or more processors: receive, via a chatbot (or voicebot or other bot), a question in a first language; translate, via the chatbot, the question from the first language to a second language; determine, via the chatbot, an answer to the translated question, the answer being determined in the second language; translate, via the chatbot, the answer from the second language to the first language; and/or present, via the chatbot, the answer in the first language to an insurance customer.

Abstract: Methods and systems are described for generating a vehicle-to-vehicle traffic alert and updating a vehicle-usage profile. Various aspects include detecting, via one or more processors associated with a first vehicle, that an abnormal traffic condition exists in an operating environment of the first vehicle. An electronic message is generated and transmitted wirelessly, via a vehicle-mounted transceiver associated with the first vehicle, to alert a nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition. The first vehicle receives telematics data regarding operation of the nearby vehicle after the nearby vehicle received the electronic message, and transmits the telematics data to a remote server for updating a vehicle-usage profile associated with the nearby vehicle.

Abstract: Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having autonomous or semi-autonomous operation features are provided. According to certain aspects, a computer-implemented method for generating or updating usage-based insurance policies for an autonomous or semi-autonomous vehicle may be provided. A request to generate an insurance quote may be received via wireless communication, and with the customer's permission, risk levels associated with intended usage by the customer of the vehicle may be determined. An insurance policy may be adjusted based upon the risk levels and the intended vehicle usage. The insurance policy may then be presented on the customer's mobile device for review and approval. In some aspects, the vehicle may be rented, and the intended vehicle usage is measured in distance or duration of vehicle operation. Insurance discounts may be provided to risk averse vehicle owners based upon low risk levels.

Abstract: Methods and systems for alerting a user of crime-prone locations are disclosed herein. In some embodiments, the method comprises: (1) receiving, by one or more processors from a user device, location data of the user device and a destination input by the user; (2) determining, by the one or more processors, based upon the location data, that the user device is approaching the destination; (3) loading, by the one or more processors, a crime density map surrounding the destination; (4) determining, by the one or more processors, based upon the crime density map, at least one non-crime-prone parking spot, wherein the at least one non-crime-prone parking spot meets at least one predetermined criterion; and/or (5) presenting, by the one or more processors via the user device, an indication of the at least one non-crime-prone parking spot.

Abstract: Methods and systems for alerting a user of accident-prone locations are disclosed herein. In some embodiments, the method comprises: (1) receiving, by one or more processors and from a user device associated with the user, location data and motion data of the user device; (2) determining, by the one or more processors, based upon the motion data, that the user has started a trip; (3) loading, by the one or more processors, an accident density map surrounding a location of the user device based upon the location data; (4) determining, by the one or more processors, based upon the accident density map, at least one accident-prone location within a scope of the accident density map, wherein the at least one accident-prone location meets at least one predetermined criterion; and/or (5) presenting, by the one or more processors via the user device, an indication of the at least one accident-prone location.

Abstract: Systems and methods for providing an aspirational safe driving incentive to drivers having the propensity or ability to improve high risk driving behaviors are provided. Drivers exhibiting high risk driving behaviors may be identified, and initial vehicle telematics data associated with each of the drivers may be collected and analyzed in order to determine which drivers have propensity or ability to improve their high risk driving behaviors. These candidate drivers may receive a probationary safe driving incentive along with feedback configured to improve the drivers' high risk driving behaviors. Updated vehicle telematics data associated with each candidate driver may be collected in order to evaluate an extent to which each candidate driver has improved his or her high risk driving behaviors. Accordingly, a candidate driver's probationary safe driving incentive may be modified or maintained based on the extent to which the candidate has improved upon high risk driving behaviors.

Abstract: An alert may be triggered to notify a pedestrian of the current operational mode of a nearby vehicle. For instance, a vehicle may operate in an autonomous or manual mode, and may occasionally switch from one mode to the other. A pedestrian who may be unaware of the current operational mode of a nearby vehicle may notice the alert and proceed accordingly. In one embodiment, an indication of the current operational mode of the nearby vehicle may be transmitted to an electronic device associated with the pedestrian. The device may generate a notification to the pedestrian based on the current operational mode. In an additional or alternative embodiment, the alert may be transmitted by the vehicle externally to be visible or audible to the pedestrian. In some embodiments, the alert may be triggered only for particular operational modes (e.g., only for autonomous or only for manual).

Abstract: Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having one or more autonomous or semi-autonomous operation features are provided. According to certain aspects, the operating status of the features, the identity of a vehicle operator, risk levels for operation of the vehicle by the vehicle operator, or damage to the vehicle may be determined based upon sensor or other data. According to further aspects, decisions regarding transferring control between the features and the vehicle operator may be made based upon sensor data and information regarding the vehicle operator. Additional aspects may recommend or install updates to the autonomous operation features based upon determined risk levels. Some aspects may include monitoring transportation infrastructure and communicating information about the infrastructure to vehicles.

Abstract: Systems and methods are provided for improving safety of one or more vehicle occupants. An example method for improving safety of one or more vehicle occupants includes accessing interior vehicle configuration data that is generated by, or derived from data generated by an interior data collection component, the data representing an interior space of a vehicle; determining, by processing the interior vehicle configuration data, location and orientation of one or more vehicle occupants; selecting a plurality of vehicle safety components to be active based on the location and orientation of the one or more vehicle occupants, and setting the plurality of vehicle safety components to an active state in which the plurality of vehicle safety components are deployed, when an emergency condition is detected.

Abstract: Vehicles may have the capability to navigate according to various levels of autonomous capabilities, the vehicle having a different set of autonomous competencies at each level. In certain situations, the vehicle may shift from one level of autonomous capability to another. The shift may require more or less driving responsibility from a human operator. Sensors inside the vehicle collect human operator parameters to determine an alertness level of the human operator. An alertness level is determined based on the human operator parameters and other data including historical data or human operator-specific data. Notifications are presented to the user based on the determined alertness level that are more or less intrusive based on the alertness level of the human operator and on the urgency of an impending change to autonomous capabilities. Notifications may be tailored to specific human operators based on human operator preference and historical performance.

Abstract: Systems and methods for determining the performance of a driver of a vehicle based on changes, over time, in the context and environment in which the vehicle operates, and any resultant driver behavior are disclosed. A set of driver response data is created from based on an analysis of time-series data indicative of the driver's operation of the vehicle in conjunction with time-series data indicative of changes in the vehicle's context/environment. The driver response data indicates the types and magnitudes of the driver's responses to various changes in the vehicle's operating context/environment and the driver's time-to-respond for each of the responses. That is, the driver response data indicates how a driver compensated his or her behavior (if at all) in response to different changes in the vehicle's context and/or environment. The driver response data may be compared to one or more thresholds to determine the driver's performance.

Abstract: Systems and methods are disclosed for operating an autonomous vehicle based on real-time operating data. The operating data may be data about vehicles, drivers, passengers, as well as relevant environmental conditions and contextual data. In some cases, historical data for the preceding data types may be used. The systems and methods may obtain a set of real-time operating data indicative of one or more behaviors of an autonomous vehicle. One or more operations may be performed on the set of real-time operating data. An instruction to modify a particular vehicle operation may be generated based on output from the operations and the one or more behaviors of the autonomous vehicle, and the instruction to modify the particular vehicle operation may be provided to a particular processor that is on-board the vehicle and that controls the particular vehicle operation, to thereby automatically modify the particular vehicle operation.

Abstract: Apparatuses, systems and methods are provided for checking code for errors. The apparatuses, systems and methods may send a target code and a prompt for code checking to a machine learning (ML) chatbot to cause the ML chatbot to check the target code for errors. The apparatuses, systems and methods may determine whether there is an error in the target code based at least partially on a response from the ML chatbot. The apparatuses, systems and methods may, responsive to determining that there is an error in the target code, determine, via an interaction with the ML chatbot, a solution to fix the error. The apparatuses, systems and methods may analyze the solution to determine a number of at least one of (i) a set of steps or (ii) a set of interactions required by the solution. The apparatuses, systems and methods may, responsive to determining that the number exceeds a predetermined threshold, fix the error by implementing the solution with respect to the target code.

Abstract: Apparatuses, systems and methods are provided for checking code for errors. The apparatuses, systems and methods may send a target code and a prompt for code checking to a machine learning (ML) chatbot to cause the ML chatbot to check the target code for errors. The apparatuses, systems and methods may determine whether there is an error in the target code based at least partially on a response from the ML chatbot. The apparatuses, systems and methods may, responsive to determining that there is an error in the target code, determine, via an interaction with the ML chatbot, whether there is a solution to fix the error. The apparatuses, systems and methods may, responsive to determining that there is a solution to fix the error, cause the ML chatbot to (i) fix the error, and/or (ii) present the error and/or the solution to a user.

Abstract: Apparatuses, systems and methods are provided for generating customized code. The apparatuses, systems and methods may cause a chatbot to generate customized code. The apparatuses, systems and methods may integrate the customized code in an insurance application. The customized code, when executed by one or more processors, may cause the one or more processors to collect user information from a user device, determine whether a change of insurance policy for the user is required, and responsive to determining that a change of insurance policy is required, automatically update the insurance policy in the insurance application based upon the required change.

Abstract: Disclosed systems and methods incorporate machine learning to assess damage to vehicles. An example method includes: accessing image data representing one or more digital images of damage to a vehicle; selecting, using one or more processors, an applicable machine learning algorithm from a plurality of different trained machine learning algorithms, wherein the plurality of different machine learning algorithms are trained for respective different combinations of one or more of vehicle make, vehicle model, vehicle year, or area of damage; processing the image data, with the applicable machine learning algorithm using one or more processors, to determine assessed damage for the vehicle; accessing actual damage information for the vehicle; determining, using one or more processors, differences between the actual damage and the assessed damage; and iterating, using one or more processors, the applicable machine learning algorithm based on the differences to improve its damage assessment accuracy.

Abstract: Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having autonomous or semi-autonomous operation features are provided. According to certain aspects, a computer-implemented method for generating or updating usage-based insurance policies for autonomous or semi-autonomous vehicles may be provided. A request to generate an insurance quote may be received via wireless communication, and with the customer's permission, risk levels associated with intended usage by the customer of an autonomous or semi-autonomous vehicle may be determined. An insurance policy may be adjusted based upon the risk levels and the intended vehicle usage. The insurance policy may then be presented on the customer's mobile device for review and approval. In some aspects, the vehicle may be rented, and the intended vehicle usage is measured in distance or duration of vehicle operation. Insurance discounts may be provided to risk averse vehicle owners based upon low risk levels.

Abstract: The following relates generally to using artificial intelligence (AI) and/or machine learning (ML) to identify patentable ideas from company emails. In some embodiments, one or more processors: access one or more email messages; identify, via a machine learning (ML) chatbot trained to analyze text and generate text, one or more patentable concepts in the one or more email messages; compare the one or more patentable concepts to a patent database including published patent applications with respective publication dates on or before the date of database access, wherein comparing includes generating a comparison document indicating at least one difference and/or at least one similarity between the one or more patentable concepts and the patent applications of the patent database, wherein the comparing includes using the ML chatbot; and generate, based upon the comparison, one or more novelty scores for each of the one or more patentable concepts.