Abstract: Systems and methods for providing ticket support using a machine learning model trained using clusters of support tickets that are clustered based on similarity of resolution commands are provided. The system extracts commands used to resolve prior tickets and creates clusters of resolved tickets based on similarity of the commands. For each cluster, problem statements are extracted from the resolved tickets. The system trains a machine learning model with the extracted problem statements to identify a cluster number for each cluster. With a new support ticket, the system extracts a problem statement from the new ticket and identifies a predicted cluster number by applying the trained machine learning mode! to the problem statement from the new ticket. Based on the predicted cluster number, one or more commands used to resolve the prior tickets in the cluster corresponding to the predicted cluster number are accessed and provided to a requesting user.

Abstract: Systems and methods for providing ticket support using a machine learning model trained using clusters of support tickets that are clustered based on similarity of resolution commands are provided. The system extracts commands used to resolve prior tickets and creates clusters of resolved tickets based on similarity of the commands. For each cluster, problem statements are extracted from the resolved tickets. The system trains a machine learning model with the extracted problem statements to identify a cluster number for each cluster. With a new support ticket, the system extracts a problem statement from the new ticket and identifies a predicted cluster number by applying the trained machine learning mode! to the problem statement from the new ticket. Based on the predicted cluster number, one or more commands used to resolve the prior tickets in the cluster corresponding to the predicted cluster number are accessed and provided to a requesting user.

Abstract: A method for training a neural network for linking incident management tickets is provided. A first training set of linked pairs of incident management tickets is generated. Each pair is labeled as being linked and comprises first and second tickets having first text features and second features. A Siamese neural network model is trained using the first text features as inputs to an input layer of the model. The input layer is configured to generate first and second input embeddings for the first and second tickets, respectively. The model is trained using the first and second input embeddings and the second features as inputs to an output layer of the model. The output layer is configured to generate first and second output embeddings for the first and second tickets, respectively. The model is trained using a contrastive loss function between the first and second output embeddings.

Abstract: Techniques for managing support computing services in computing systems are disclosed. One example technique includes analyzing data representing a service ticket having data representing multiple messages exchanged between the user and a support entity of a support tier in the computing system. Based on the analysis, an interaction pattern in the multiple messages can be identified. The interaction pattern includes a sequence of words or phrases arranged in a chronological order. The method can further include determining whether the identified interaction pattern matches one or more interaction patterns corresponding to previously escalated service tickets and triggering a notification of the immediate escalation of the service ticket to another support entity in another support tier in response to determining that the interaction pattern matches one of the one or more interaction patterns corresponding to previously escalated service tickets.

Abstract: A system may include a processing unit; a storage device comprising instructions, which when executed by the processing unit, configure the processing unit to perform operations comprising: retrieving a meeting count of meetings scheduled during a future time period; generating a predicted usage level of a service during the future time period based on the meeting count; determining a resource modification for the service based on the predicted usage level; and implementing the resource modification prior to the future time period.

Abstract: Techniques for managing support computing services in computing systems are disclosed. One example technique includes analyzing data representing a service ticket having data representing multiple messages exchanged between the user and a support entity of a support tier in the computing system. Based on the analysis, an interaction pattern in the multiple messages can be identified. The interaction pattern includes a sequence of words or phrases arranged in a chronological order. The method can further include determining whether the identified interaction pattern matches one or more interaction patterns corresponding to previously escalated service tickets and triggering a notification of the immediate escalation of the service ticket to another support entity in another support tier in response to determining that the interaction pattern matches one of the one or more interaction patterns corresponding to previously escalated service tickets.

Abstract: A system may include a processing unit; a storage device comprising instructions, which when executed by the processing unit, configure the processing unit to perform operations comprising: receiving a service outage alert for a service used by an entity; retrieving a current count of non-recurring meetings for the entity; determining that the service outage alert is a false positive based on a current service load for the service and the current count; and based on the determining, suppressing the service outage alert.

Abstract: A system may include a processing unit; a storage device comprising instructions, which when executed by the processing unit, configure the processing unit to perform operations comprising: retrieving a meeting count of meetings scheduled during a future time period; generating a predicted usage level of a service during the future time period based on the meeting count; determining a resource modification for the service based on the predicted usage level; and implementing the resource modification prior to the future time period.

Abstract: A system may include a processing unit; a storage device comprising instructions, which when executed by the processing unit, configure the processing unit to perform operations comprising: receiving a service outage alert for a service used by an entity; retrieving a current count of non-recurring meetings for the entity; determining that the service outage alert is a false positive based on a current service load for the service and the current count; and based on the determining, suppressing the service outage alert.

Abstract: A method, process, and associated systems for policy-based development and runtime control of mobile applications. Security objects that describe or enforce security policies are embedded into the source code of an enhanced application while the application is being developed. When a user attempts to launch the enhanced application on a mobile device, the security objects are updated to match a latest valid version of the objects stored on an enterprise server. The security objects may be further updated at other times. Global security policies, which affect the entire enterprise and which may deny the application permission to launch, are enforced by a global security policy stored within one of the updated security objects. If the application does run, application-specific security policies contained in the updated security objects modify application behavior at runtime in order to enforce application-specific security policies.

Abstract: A method, process, and associated systems for policy-based development and runtime control of mobile applications. Security objects that describe or enforce security policies are embedded into the source code of an enhanced application while the application is being developed. When a user attempts to launch the enhanced application on a mobile device, the security objects are updated to match a latest valid version of the objects stored on an enterprise server. The security objects may be further updated at other times. Global security policies, which affect the entire enterprise and which may deny the application permission to launch, are enforced by a global security policy stored within one of the updated security objects. If the application does run, application-specific security policies contained in the updated security objects modify application behavior at runtime in order to enforce application-specific security policies.

Abstract: A method, process, and associated systems for policy-based development and runtime control of mobile applications. Security objects that describe or enforce security policies are embedded into the source code of an enhanced application while the application is being developed. When a user attempts to launch the enhanced application on a mobile device, the security objects are updated to match a latest valid version of the objects stored on an enterprise server. The security objects may be further updated at other times. Global security policies, which affect the entire enterprise and which may deny the application permission to launch, are enforced by a global security policy stored within one of the updated security objects. If the application does run, application-specific security policies contained in the updated security objects modify application behavior at runtime in order to enforce application-specific security policies.

Abstract: A method, process, and associated systems for policy-based development and runtime control of mobile applications. Security objects that describe or enforce security policies are embedded into the source code of an enhanced application while the application is being developed. When a user attempts to launch the enhanced application on a mobile device, the security objects are updated to match a latest valid version of the objects stored on an enterprise server. The security objects may be further updated at other times. Global security policies, which affect the entire enterprise and which may deny the application permission to launch, are enforced by a global security policy stored within one of the updated security objects. If the application does run, application-specific security policies contained in the updated security objects modify application behavior at runtime in order to enforce application-specific security policies.