Abstract: System trains machine learning model to determine content data, metadata, and context data for support ticket communications, in response to receiving support ticket communications. Machine learning model receives communication associated with support ticket, and determines content data, metadata, and context data for communication. System converts content data, metadata, and context data for communication into first impulse for first channel and second impulse for second channel. System determines first channel value based on first type of conversion of first impulse and any impulses for first channel that are converted from data that is determined for support ticket event. System determines second channel value based on second type of conversion of second impulse and any impulses for second channel that are converted from data that is determined for support ticket event. System uses first channel value and second channel value to generate priority associated with support ticket, and outputs priority.

Abstract: Systems and methods are described for determining customer sentiment using natural language processing in technical support communications. Communication content exchanged between a customer device and an agent device may be filtered to remove technical support syntax. Using natural language processing techniques, the processor may assign baseline values to features within the filtered communication content. To assign the baseline values, features from the filtered communication content may be identified, where the features pertain to expressed sentiments, and a trained first model may be applied to identify polarities and strengths related to the identified features. A score value may then be assigned to each identified feature, the score values being based on the polarities and strengths. A subset of the score values may then be weighted based on metadata and/or context, and the score values may be combined using a second model to determine an overall sentiment of the filtered communication content.

Abstract: System trains machine learning model to determine content data, metadata, and context data for support ticket communications, in response to receiving support ticket communications. Machine learning model receives communication associated with support ticket, and determines content data, metadata, and context data for communication. System converts content data, metadata, and context data for communication into first impulse for first channel and second impulse for second channel. System determines first channel value based on first type of conversion of first impulse and any impulses for first channel that are converted from data that is determined for support ticket event. System determines second channel value based on second type of conversion of second impulse and any impulses for second channel that are converted from data that is determined for support ticket event. System uses first channel value and second channel value to generate priority associated with support ticket, and outputs priority.

Abstract: Embodiments perform centralized input/output (I/O) path selection for hosts accessing storage devices in distributed resource sharing environments. The path selection accommodates loads along the paths through the fabric and at the storage devices. Topology changes may also be identified and automatically initiated. Some embodiments contemplate the hosts executing a plurality of virtual machines (VMs) accessing logical unit numbers (LUNs) in a storage area network (SAN).

Abstract: Embodiments perform centralized input/output (I/O) path selection for hosts accessing storage devices in distributed resource sharing environments. The path selection accommodates loads along the paths through the fabric and at the storage devices. Topology changes may also be identified and automatically initiated. Some embodiments contemplate the hosts executing a plurality of virtual machines (VMs) accessing logical unit numbers (LUNs) in a storage area network (SAN).

Abstract: Embodiments perform centralized input/output (I/O) path selection for hosts accessing storage devices in distributed resource sharing environments. The path selection accommodates loads along the paths through the fabric and at the storage devices. Topology changes may also be identified and automatically initiated. Some embodiments contemplate the hosts executing a plurality of virtual machines (VMs) accessing logical unit numbers (LUNs) in a storage area network (SAN).

Abstract: Embodiments perform centralized input/output (I/O) path selection for hosts accessing storage devices in distributed resource sharing environments. The path selection accommodates loads along the paths through the fabric and at the storage devices. Topology changes may also be identified and automatically initiated. Some embodiments contemplate the hosts executing a plurality of virtual machines (VMs) accessing logical unit numbers (LUNs) in a storage area network (SAN).