Abstract:
A semantic translation model system is described along with various methods and mechanisms for administering the same. The semantic translation model system proposed herein creates an intermediate representation and a knowledge base in multiple languages, reducing the amount of time and expensive resources typically required for translation and automatic response to written communications. The system also removes the problem of a translation being influenced by a person's writing style and human misinterpretation and provides ongoing translation to keep the system current.
Abstract:
Contact centers may benefit from routing messages to agents who have similar, or complementary, attributes as the customer of the message. In a text message, certain message attributes provide artifacts that may be common to one particular customer attribute. Messages containing that particular message attribute provide a derived customer attribute and the message routed accordingly. In addition, agents responding to a customer may be provided with guidance to ensure their response is appropriate for the derived customer attribute of the customer.
Abstract:
A semantic translation model system is described along with various methods and mechanisms for administering the same. The semantic translation model system proposed herein creates an intermediate representation and a knowledge base in multiple languages, reducing the amount of time and expensive resources typically required for translation and automatic response to written communications. The system also removes the problem of a translation being influenced by a person's writing style and human misinterpretation and provides ongoing translation to keep the system current.
Abstract:
The sentiment of a message may not be obtainable from the message itself. However, many messages have an associated context that provides information useful in determining the sentiment of a message. Messages may include links to other resources, such as graphics or videos, which in turn include titles, comments, viewer ratings or other attributes that may provide a sentiment of the message.
Abstract:
Contact centers may incorporate automated agents to respond to inquiries. The inquiries may solicit a substantive response, for example, by providing a time when the inquiry asks for the departure time for a flight. Such responses omit the normal conversational subject matter used to embellish person-to-person conversations and appear are very machine-like. Herein, a source of user context, such as a social media website, customer database, or other data, is accessed. Certain aspects of the customer may then be identified and used to embellish the reply with additional and/or alternative content. As a result, the reply may be more conversational.
Abstract:
A contact center system can receive messages from social media sites or centers. The system can review long messages by identifying content in the long message with negative sentiment. The content with negative sentiment is further analyzed to determine whether the identified content is actionable. If the identified content is actionable, the communication system can automatically routed the long message to an agent for response.
Abstract:
A dialog aggregator provided by a contact center communication system for text-based interaction chains is described along with various methods and mechanisms for administering the same. The dialog aggregator produces a summary, in real-time, of questions posed and existing answers in the interaction chain while identifying outstanding questions that have not been answered for display to an agent. The display includes any current answer the agent is working on as well as completed items and additionally executes rules based on the status of the remaining questions. The display in canonical form of the summary and outstanding question set enables a contact center agent or other observer of the interaction to quickly and efficiently assess the interaction history.
Abstract:
A contact center system can receive messages from consumers. The system can then interact with the consumer or customer using a dialog. Before conducting the session with the consumer, past interactions using the dialog are reviewed to determine words, phrases, and other information that caused the dialog to be successful. The information is stored as norms. Upon beginning a new interaction with the dialog, the norms and the past successful dialogs are retrieved and compared to the active dialog while the interaction is on-going. The comparison is then used to ensure that the present active dialog will lead to a successful outcome or to resolve any issued if the outcome is not likely to be successful.
Abstract:
The sentiment of a message may not be obtainable from the message itself. However, many messages have an associated context that provides information useful in determining the sentiment of a message. Messages may include links to other resources, such as graphics or videos, which in turn include titles, comments, viewer ratings or other attributes that may provide a sentiment of the message.
Abstract:
A contact center system can receive messages from social media sites or centers. The messages may include derogatory or nefarious content. The system can review messages to identify the message as nefarious and identify the poster as a social media provocateur. The system may then automatically respond to the nefarious content. Further, the system may prevent future nefarious conduct by the identified social media provocateur by executing one or more automated procedures.