Abstract: An exemplary embodiment of a computer-implemented method of identifying one or more products that correspond to product features of interest to a user may include: receiving an at least partially freeform or sketched illustration of a product from a user device associated with a user; determining at least one feature of the product depicted in the illustration by employing a machine-learning model trained, using (i) at least partially freeform or sketched illustrations from various users depicting products in a same product category as the product and (ii) feature labels assigned to the illustrations, to output one or more features of a depicted product in the product category in an input illustration; generating a search query based on the determined at least one feature; identifying at least one product in an inventory that corresponds to the search query; and providing information associated with the at least one identified product to the user device.

Abstract: Disclosed herein are various embodiments for a vehicle listing generation and optimization system. An embodiment operates by receiving a request to generate a vehicle listing to sell a vehicle on a website. A plurality of vehicle listings similar to the vehicle associated with the request are determined, the plurality of vehicle listings including a plurality of features providing information about a respective vehicle featured in the vehicle listing and a sales time metric specifying a time it took to sell the respective vehicle featured in a respective vehicle listing. Clustering is performed on the set of vehicle listings based on the sales time metrics, and a first cluster is selected. It is identified which of the plurality of features are common amongst at least a subset of the vehicle listings of the selected cluster. A user interface of a sales template for the vehicle associated with the request is generated.

Abstract: In certain embodiments, placement of objects within an interior space of a vehicle may be simulated. In some embodiments, one or more images of an interior space of a vehicle may be obtained. User input may be obtained, the user input indicating objects to be placed within the interior space. Based on the images of the interior space and the user input indicating objects to place within the interior space, an arrangement may be determined for the objects within the interior space. In some embodiments, the arrangement may be based upon measurements and other characteristics of the objects and the interior space. In some embodiments, a presentation simulating placement of the objects within the interior space may be simulated. In some embodiments, the presentation may comprise steps indicating an order in which to place the objects within the interior space.

Abstract: Disclosed herein are system, method, and computer program product embodiments for associating a gesture to a website action. In an embodiment, an application receives a user instruction to associate a website action with a user gesture such that the website action occurs upon receipt of the user gesture and the user gesture relates to user input on a user device's display. Thereafter, based on the user instruction, the application creates and sends an interaction request to link the user gesture to the website action. The application then receives an acceptance message from the first website that indicates that the user gesture has been successfully linked to the website action.

Abstract: Systems and methods automating the process of application code vulnerability remediation. Implementations include building a repository of code revisions as software is checked for security vulnerabilities using or more software analysis tools. In certain implementations, historical code revisions are cataloged and stored in the repository. The revisions may be tokenized and utilized to detect and automatically remediate similar issues when new software packages are submitted to the system.

Abstract: Disclosed herein are various embodiments for a vehicle listing generation and optimization system. An embodiment operates by determining a set of vehicle listings of a common vehicle type. The set of vehicle listings are clustered into a plurality of clusters based on the sales time metrics, and a first one of the clusters for which the sales time metric of the vehicle listings of the selected cluster is less than the sales time metric of the vehicle listings of a second one of the clusters is selected. The perspectives of the images in vehicle listings of the selected cluster are identified. A sales template for the vehicle type including the identified perspectives of images corresponding at least a subset of the vehicle listings of the selected cluster is generated.

Abstract: Provided herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for identifying risk using image analysis. In an embodiment, a server receives an image, including a vehicle from a camera. The server identifies an event occurring in the image. The server uses the event and the factors identify an attribute associated with the vehicle. The server generates a risk value for the vehicle based on the attribute.

Abstract: Systems, methods, and computer-readable storage media for generating a flight path for aerial drones around parked vehicles, where the flight path navigates an aerial drone to specific locations associated with predetermined views of the vehicle. A remotely located user can select from a list of predetermined views where the drone should travel, and the computer system can direct the drone to a location associated with the selected view, align the camera on the drone to capture the selected view, and relay live video of the selected view of the parked car to the user.

Abstract: According to certain aspects of the disclosure, a computer-implemented method may be used for providing vehicle recommendations based on traffic camera data. The method may include acquiring traffic data from a plurality of traffic cameras and determining vehicle information based on the traffic data. Additionally, calculating average idling time of vehicles based on the traffic data from the plurality of traffic cameras. Additionally, determining accident information based on the traffic data from the plurality of traffic cameras and selecting at least one available vehicle for purchase, wherein the at least one available vehicle matches one or more predetermined requirements based on the determined vehicle information, average idling time, and the determined accident information. Additionally, transmitting a recommendation based on the selected at least one available vehicle for purchase.

Abstract: Disclosed herein are various embodiments for a customer callback system. An embodiment operates by determining a call action indicating a customer to whom a representative is to speak. Data corresponding to an identifier associated with a computing device of the customer and an ad history indicating when a plurality of ads were presented to an application on the computing device of the customer over a defined period of time are retrieved. Based on the data, a pattern associated with when the customer is using the computing device is generated. The pattern is based on a comparison of a first period of time a second period of time during when ads were presented to the computing device. Based on the pattern, a target time indicating when to perform the call action is produced.

Abstract: The disclosure describes a system and methods for implementing a recommendation engine. The recommendation engine can at least receive a travel history log, wherein the travel history log includes a route traversed by a customer; receive a route information from a database for the route, where the route information includes an accident history for the route; generate a route traverse percentage based on the travel history log; generate a route categorization score based on the accident history for the route and the route traverse percentage; and generate a vehicle recommendation based on the route categorization score.

Abstract: A method includes determining a vendor interaction preference profile of a customer, which may include: receiving one or more vendor reviews authored by the customer; performing a content analysis process on the reviews to associate at least one review with at least one quality in a predetermined set of qualities; and performing a sentiment analysis process on the at least one associated review to determine a respective customer preference index value for each quality in the predetermined set of qualities. Additionally, the method may include comparing the vendor interaction preference profile of the customer with an interaction profile of a vendor that may include a respective interaction index value for each quality in the predetermined set of qualities. A predicted interaction metric of the vendor with the customer may be determined based on the comparing, and may be caused to be displayed on a customer device associated with the customer.

Abstract: A computer-implemented method for providing a vehicle recommendation may include obtaining characteristic data of a user-defined vehicle based on one or more interactive activities of a user with a first three-dimensional model of a model vehicle; generating a second three-dimensional model of the user-defined vehicle based on the first three-dimensional model and the characteristic data; obtaining one or more images of the second three-dimensional model; generating a selection of one or more pre-stored vehicles based on a comparison between the one or more images of the second three-dimensional model and pre-stored image data of the one or more pre-stored vehicles other than the user-defined vehicle; obtaining user feedback data based on the selection of the one or more pre-stored vehicles; generating the vehicle recommendation for the user based on the user feedback data; and transmitting, to a device associated with the user, a notification indicative of the vehicle recommendation.

Abstract: A computer-implemented method for providing product recommendations to a user may include receiving audio data from a user device. The audio data may include human speech from a user associated with the user device. The method may further include: performing a speech-to-text process on the audio data to determine text; segmenting the text into discrete conceptual units; identifying keywords that correspond words in the text; associating keywords with the conceptual units having the corresponding words; performing a first analysis process on the conceptual units to determine a first score; performing a second analysis process portions of the human speech correlated with the conceptual units to determine a second score; determining a keyword score for each keyword based on the first and second scores of the conceptual units; determining a product recommendation based on the keywords associated with conceptual units and the corresponding keyword scores; and causing the user device to output the recommendation.

Abstract: In certain embodiments, placement of objects within an interior space of a vehicle may be simulated. In some embodiments, one or more images of an interior space of a vehicle may be obtained. User input may be obtained, the user input indicating objects to be placed within the interior space. Based on the images of the interior space and the user input indicating objects to place within the interior space, an arrangement may be determined for the objects within the interior space. In some embodiments, the arrangement may be based upon measurements and other characteristics of the objects and the interior space. In some embodiments, a presentation simulating placement of the objects within the interior space may be simulated. In some embodiments, the presentation may comprise steps indicating an order in which to place the objects within the interior space.

Abstract: According to certain aspects of the disclosure, a computer-implemented method may be used for providing vehicle recommendations based on traffic camera data. The method may include acquiring traffic data from a plurality of traffic cameras and determining vehicle information based on the traffic data. Additionally, calculating average idling time of vehicles based on the traffic data from the plurality of traffic cameras. Additionally, determining accident information based on the traffic data from the plurality of traffic cameras and selecting at least one available vehicle for purchase, wherein the at least one available vehicle matches one or more predetermined requirements based on the determined vehicle information, average idling time, and the determined accident information. Additionally, transmitting a recommendation based on the selected at least one available vehicle for purchase.

Abstract: An augmented reality system including processors and storage devices storing instructions. The instructions may configure the processors to perform operations including determining a location of a mobile device, identifying a facility based on the location, requesting mapping data from a facility server, the mapping data comprising a plurality of vehicles, vehicle location data, and landmarks, identifying one location attribute in a video feed captured by an augmented reality viewer displayed in the mobile device, and determining whether the one location attribute matches the plurality of vehicles or the landmarks. The operations may also include identifying qualified vehicles from the vehicles based on qualification criteria, determining whether the qualified vehicles is in a field of view of the augmented reality viewer based on object attributes and the vehicle location data, and generating a modified video feed by providing an indication associated with qualified vehicles in the field of view.

Abstract: According to certain aspects of the disclosure, a computer-implemented method may be used for providing a vehicle recommendation based on user gestures. The method may include displaying at least one image of a vehicle to a user and receiving at least one gesture from the user performed on the at least one image of the vehicle. Additionally, the method may include assigning a value to the at least one gesture from the user and determining a feature of the vehicle based on the at least one gesture from the user. Additionally, the method may include receiving gesture information related to the at least one gesture and determining a vehicle preference of the user based on the value, the feature of the vehicle, and the gesture information. Additionally, the method may include identifying at least one available vehicle based on the vehicle preference of the user and displaying the at least one available vehicle to the user.

Abstract: A device obtains image data associated with a document. Using a first machine learning model, the device determines, for the document, a first classification of one of a plurality of document types and a first confidence score associated with the first classification, and a second classification of one of the plurality of document types and a second confidence score associated with the second classification based on the image data. The device determines a difference between the first confidence score and the second confidence score, compares the difference and a threshold value, and accept the first classification of the document when the difference satisfies the threshold value.

Abstract: Some embodiments relate to techniques for facilitating training of a prediction model for estimating a threshold score for a user. In some embodiments, a first image of at least a first portion of a first vehicle may be provided to a client device, where the first image may be associated with a first damage score. From the client device, a user-provided score for the first image may be received. Based on the user-provided score, a second image of at least a second portion of a second vehicle may be provided to the client device, where the second image may be associated with a second damage score. Training data may be generated based on the first damage score and the second damage score, and the training data may be provided to a prediction model to train the prediction model to estimate a threshold score for a user.