Abstract: Methods, systems, and apparatuses are described herein for using machine learning processes to improve synthetic monitoring of, e.g., websites. Training data may comprise sitemap information for a plurality of different websites and monitoring information that indicates whether, for each of a plurality of different portions of those websites, one or more synthetic monitoring scripts are executed. A machine learning model may be trained using that training data to output whether one or more portions of an input website should be monitored using synthetic monitoring. A sitemap of a first website may be determined and provided as input to the trained first machine learning model. Based on output from the trained first machine learning model, a synthetic monitoring script may be determined and executed to monitor at least a first portion of the first website.

Abstract: Methods and systems that use a plurality of machine learning models to both monitor user-generated data entries corresponding to differences in network traffic that may be evidence of a disruption and determine severity levels based on: (i) current and historic differences in average network traffic over the plurality of communication networks; (ii) current and historic user-generated data entries; and (iii) labeled severity levels for historic differences in average network traffic over the plurality of communication networks.

Abstract: In at least some embodiments, systems and methods of the present disclosure enable content selections for securing communications between agent devices and user devices. A request is received for a communication between an agent device and a user device. A user record associated with the user device is determined based on the characteristic. The security system may obtain content items for authenticating the agent device for the user device with the user record. A set of the plurality of content items are determined for display to the agent device. The set of the plurality of content items are transmitted to the user device associated with the user record. A content item identified by a content item selection is received. A match is determined between the selected content item and the set of content items. The communication is transmitted, responsive to the match, with a predetermined type data request for the user device to satisfy the authentication request.

Abstract: Methods and systems to detect and manage access to sensitive information. The system retrieves a dataset of entitlements for a plurality of users. The dataset of entitlements specifies one or more respective exposure vectors. The system detects a triggering event, including a release of sensitive information to a first subset of users of the plurality of users via a first exposure vector. The system then determines a second subset of users of the plurality of users that have access to the first exposure vector based on a comparison of the first exposure vector to the dataset of entitlements. The system monitors electronic communications of the second subset of users for activity related to the sensitive information to detect first activity of a first user of the second subset of users. The system generates for display a first notification to a second user based on detecting the first activity.

Abstract: In some implementations, a remote server may receive a request for authentication including a set of credentials associated with a user. The remote server may transmit, to a user device associated with the user, a request for a status associated with a browsing history of the user device or associated with recent emails viewed on the user device. The remote server may receive, from the user device, an indication of the status. The remote server may transmit a message based on the indication of the status and may determine whether to authenticate the user, using the set of credentials, based on the indication of the status.

Abstract: Disclosed herein are system, method, and computer program product embodiments for machine learning systems to predict real-time productivity changes based on prospective scheduling. A productivity predictive model is trained by a machine learning engine with meeting and productivity training data, wherein the productivity predictive model includes one or more algorithms to select a future productivity change based on a future schedule. The system receives a current schedule and current productivity measurements of a user, receives a scheduling request for the user, predicts a future productivity change and displays a graphic to a user revealing the future productivity change of the user based on accepting the scheduling request.

Abstract: In some embodiments, a computing system may monitor authentication input and modify authentication requirements based on detection of user-specific input errors. The computing system may use machine learning or other techniques to detect whether an incorrect authentication input corresponds to a common input mistake of a user. If the incorrect authentication input does correspond to a common input mistake of the user, a computing system may modify one or more authentication requirements to make the authentication process easier for the user.

Abstract: Methods and systems are described herein for predicting user access revocation for applications. The system may retrieve, based on user identifiers, user access information and user association information. The system may generate a dataset comprising entries for the user identifiers, where the entries include the user access information and the user association information. The system may input the dataset into a machine learning model to obtain predictions as to whether each user identifier requires access to one or more functions of one or more applications. In some embodiments, the machine learning model is trained to predict required user access. In response to determining that a particular prediction does not include one or more particular functions included in the user access information for a respective user identifier, the system may revoke access to the one or more particular functions from the user identifier.

Abstract: A method includes a system for improving machine-learning-based resource allocation by calibrating resource-related sentiments used to configure a dialogue generation model and updating a prior sentiment based on a response to a generated dialogue item, including a set of processors. Embodiments may also include a non-transitory, machine-readable media storing program instructions that, when executed by the set of processors, performs operations including retrieving a historical record associated with a user and a first natural language input provided by the user for a resource. Embodiments may also include determining, with a first machine learning model, an intermediate sentiment score based on the first natural language input. Embodiments may also include modifying, with the first machine learning model, the intermediate sentiment score based on the historical record to produce a new sentiment score.

Abstract: Systems and methods are disclosed herein for training machine learning models using precision data based on movement and timing of data collection. The system trains the machine learning model to predict object locations in an environment. The training data includes location data, timing data, and motion data, collection of which is triggered by a user input indicating that a particular object was located. The system generates precision parameters for the locations by assigning initial values to entries within the training data and may lower precision parameters based on the timing data indicating that an object was located faster than a threshold time or based on the motion data indicating that a rate of motion exceeded a threshold rate when a corresponding object was located. The system may update the training data with the lowered precision parameters and train the machine learning model to predict the object locations within the environment.

Abstract: Aspects described herein may allow displaying of augmented reality content items associated with selected vehicle models, as well as price or budget information associated with the displayed vehicle to facilitate the user to make purchasing or financing decisions. For example, a computing device may detect a plurality of physical anchors attached to a vehicle husk and determine location information for each of the plurality of physical anchors. The computing device may receive one or more augmented reality (AR) content items corresponding to one or more vehicle features for the selected vehicle model and display via an AR interface, the one or more AR content items positioned relative to the vehicle husk based on the location of each of the plurality of physical anchors, as well as a price associated with the selected vehicle model having the one or more vehicle features.

Abstract: In some embodiments, a computing system may monitor authentication input and modify authentication requirements based on detection of user-specific input errors. The computing system may use machine learning or other techniques to detect whether an incorrect authentication input corresponds to a common input mistake of a user. If the incorrect authentication input does correspond to a common input mistake of the user, a computing system may modify one or more authentication requirements to make the authentication process easier for the user.

Abstract: In some embodiments, a computing system may monitor authentication input and modify authentication requirements based on detection of user-specific input errors. The computing system may use machine learning or other techniques to detect whether an incorrect authentication input corresponds to a common input mistake of a user. If the incorrect authentication input does correspond to a common input mistake of the user, a computing system may modify one or more authentication requirements to make the authentication process easier for the user.

Abstract: Methods and systems that use a plurality of machine learning models to both monitor user-generated data entries corresponding to differences in network traffic that may be evidence of a disruption and determine severity levels based on: (i) current and historic differences in average network traffic over the plurality of communication networks; (ii) current and historic user-generated data entries; and (iii) labeled severity levels for historic differences in average network traffic over the plurality of communication networks.

Abstract: Methods and systems are described herein for generating communication modifications to reduce inaccurate machine-learning-based communication suppressions. The system may receive a candidate communication to be sent to a user device or user account. The system may generate a prediction indicating whether a negative action is likely to be taken by a machine learning model with respect to the candidate communication. Based on a prediction that a negative action is likely to be taken by a machine learning model with respect to the candidate communication, the system may modify the candidate communication.

Abstract: Methods and systems are described herein for a resource allocation system. The resource allocation system may obtain a corresponding quantity of resources (e.g., memory, processor, storage, etc.) needed to be allocated for each resource class (e.g., for a given performance class) for a particular time period (e.g., for one month). Furthermore, the resource allocation system may track allocation of each class of resources and may predict that some classes of resources will be oversubscribed. Based on the prediction, the resource allocation system may, using a machine learning model, identify supplemental classes for each resource class predicted to be oversubscribed and generate a warning when a resource of a supplemental class is predicted to be used.

Abstract: Methods and systems that use a plurality of machine learning models to both monitor user-generated data entries corresponding to differences in network traffic that may be evidence of a disruption and determine severity levels based on: (i) current and historic differences in average network traffic over the plurality of communication networks; (ii) current and historic user-generated data entries; and (iii) labeled severity levels for historic differences in average network traffic over the plurality of communication networks.

Abstract: Aspects described herein may allow displaying of augmented reality content items associated with selected vehicle models, as well as price or budget information associated with the displayed vehicle to facilitate the user to make purchasing or financing decisions. For example, a computing device may detect a plurality of physical anchors attached to a vehicle husk and determine location information for each of the plurality of physical anchors. The computing device may receive one or more augmented reality (AR) content items corresponding to one or more vehicle features for the selected vehicle model and display via an AR interface, the one or more AR content items positioned relative to the vehicle husk based on the location of each of the plurality of physical anchors, as well as a price associated with the selected vehicle model having the one or more vehicle features.

Abstract: Aspects described herein may use machine learning models to establishing severity designations for associating with a potential occurrence of an incident of an entity. Asset ownership data, development operations tools metric data, and severity matrix data are compiled and a relationship between the compiled data and new metric data is determined. Based upon the determined relationship, a new entry to add to the severity matrix data is predicted and a notification of the same is thereafter outputted.

Abstract: Methods and systems are described herein for generating vehicle profiles for detected vehicles based on engine sound recordings. Based on those vehicle profiles, an enterprise is enabled to generate a user profile for customers. To generate the vehicle profile, a vehicle profiling system may be used. The vehicle profiling system may receive sound data with range information. The vehicle profiling system may input the sound data into a trained machine learning model and receive vehicle model information for the sound data. Based on the output, the profiling system may retrieve metadata associated with the vehicle model and generate a profile based on the metadata.