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: Aspects described herein may use machine learning models to predict one or more remediation actions to mitigate reoccurrence of an incident that has become restored based upon previous incidents of an entity. Historical incident data is compiled into two incident datasets: one representative of incidents that were assigned a remediation action to mitigate reoccurrence of the incident, and a second representative of incidents that were not assigned a remediation action. A machine learning model matches relationships between data in the two datasets and outputs scores representative of similarities. Based on the scores, one or more remediation actions are mapped to an incident in the second dataset and the remediation action is performed for the incident.

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 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: 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 predict one or more remediation actions to mitigate reoccurrence of an incident that has become restored based upon previous incidents of an entity. Historical incident data is compiled into two incident datasets: one representative of incidents that were assigned a remediation action to mitigate reoccurrence of the incident, and a second representative of incidents that were not assigned a remediation action. A machine learning model matches relationships between data in the two datasets and outputs scores representative of similarities. Based on the scores, one or more remediation actions are mapped to an incident in the second dataset and the remediation action is performed for the incident.

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.

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: Aspects described herein may use machine learning models to predict one or more remediation actions to mitigate occurrence of an incident based upon previous incidents of an entity. Asset ownership data and development operations tools metric data are compiled and a relationship between the compiled data and an occurrence of previous incidents is determined. A machine learning model predicts relationships between the occurrence of a previous incident and assets data. One or more remediation actions are assigned to an asset and a notification is outputted regarding the same.

Abstract: Aspects described herein may use machine learning models to predict one or more remediation actions to mitigate reoccurrence of an incident that has become restored based upon previous incidents of an entity. Historical incident data is compiled into two incident datasets: one representative of incidents that were assigned a remediation action to mitigate reoccurrence of the incident, and a second representative of incidents that were not assigned a remediation action. A machine learning model matches relationships between data in the two datasets and outputs scores representative of similarities. Based on the scores, one or more remediation actions are mapped to an incident in the second dataset and the remediation action is performed for the incident.

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 predict individuals or teams to assign to a discussion group in response to the occurrence of a new incident of an entity. A first machine learning model recognizes relationships between data concerning previous incidents, including remediation actions and individuals assigned to a discussion group on the corresponding incident, and a new incident. A second machine learning model predicts individuals to assign to a discussion group to address the new incident and schedules a conference bridge based upon known scheduling data of the individuals.