Abstract: A cognitive assignment engine (CAE) system attempts to infer semantic meaning from textual content of an incoming message in order to use the inferred meaning to assign the message to an appropriate responder. If the message contains insufficient textual content, the system identifies ontological structures comprised by the message's graphical content and classifies each structure as a function of the structure's location within the graphical content or of an intrinsic characteristic of the structure. The system then generates a message identifier by performing a computation on these classifications and uses the identifier to retrieve a previously stored graphical template that comprises ontological structures similar to those of the incoming message. The system associates the incoming message with a semantic meaning previously associated with the template, enabling the system to classify the message and to assign the message to the correct responder.

Abstract: A cognitive assignment engine (CAE) system attempts to infer semantic meaning from textual content of an incoming message in order to use the inferred meaning to assign the message to an appropriate responder. If the message contains insufficient textual content, the system identifies ontological structures comprised by the message's graphical content and classifies each structure as a function of the structure's location within the graphical content or of an intrinsic characteristic of the structure. The system then generates a message identifier by performing a computation on these classifications and uses the identifier to retrieve a previously stored graphical template that comprises ontological structures similar to those of the incoming message. The system associates the incoming message with a semantic meaning previously associated with the template, enabling the system to classify the message and to assign the message to the correct responder.

Abstract: A help desk management system uses segment partitioning and matching of each identified segment to a suitable problem and resolver group and then sequencing the partitioned segments based on sentiment analysis and sequence mining on historical tickets and audit logs to actuate effective resolution and pipelining of helpdesk tickets needing resolutions from multiple resolver groups. The helpdesk tickets can be in the form of e-mails.

Abstract: The approach for automatically adjusting display setting for a user that uses corrective lens or not is disclosed. The approach determines whether a user is wearing corrective visual lens. If the user is not wearing correct visual lens, then the system measures the ocular features of the user via sensors. The system then transmits data associated with the ocular features to a deep learning server wherein the deep learning server analyzes the data. The server generates a customized display setting based on the analyzed data. The system receives the display setting from the deep learning server and automatically adjusts the display setting on a device of the user based on the received display setting.

Abstract: The approach for automatically adjusting display setting for a user that uses corrective lens or not is disclosed. The approach determines whether a user is wearing corrective visual lens. If the user is not wearing correct visual lens, then the system measures the ocular features of the user via sensors. The system then transmits data associated with the ocular features to a deep learning server wherein the deep learning server analyzes the data. The server generates a customized display setting based on the analyzed data. The system receives the display setting from the deep learning server and automatically adjusts the display setting on a device of the user based on the received display setting.

Abstract: A help desk management system uses segment partitioning and matching of each identified segment to a suitable problem and resolver group and then sequencing the partitioned segments based on sentiment analysis and sequence mining on historical tickets and audit logs to actuate effective resolution and pipelining of helpdesk tickets needing resolutions from multiple resolver groups. The helpdesk tickets can be in the form of e-mails.

Abstract: A cognitive assignment engine (CAE) system attempts to infer semantic meaning from textual content of an incoming message in order to use the inferred meaning to assign the message to an appropriate responder. If the message contains insufficient textual content, the system identifies ontological structures comprised by the message's graphical content and classifies each structure as a function of the structure's location within the graphical content or of an intrinsic characteristic of the structure. The system then generates a message identifier by performing a computation on these classifications and uses the identifier to retrieve a previously stored graphical template that comprises ontological structures similar to those of the incoming message. The system associates the incoming message with a semantic meaning previously associated with the template, enabling the system to classify the message and to assign the message to the correct responder.