Abstract:
Contact centers employ agents to provide services to customers. In particular, artificial agents are provided that have a rich background and continuing life with the realm of social media. The artificial agent's profile is selected in accord with the profiles of current or prospective customers. As the customers age and progress in life, the artificial agent profile is updated in accord with the customers' changing profiles and similarly ages and progresses in life. When a customer desires to interact with artificial agent, for a purpose provided by a contact center, a human agent may be provided the profile and/or history of the artificial agent so as to impersonate the artificial agent and promote the relationship with the customer.
Abstract:
A contact center system can receive messages from social media sites or centers. The messages may be in a foreign language. The system can review messages by identifying content in the social media messages with negative/positive sentiment and then identify a seed term in the messages. A seed term can be a word in another language, different from the message body. The seed term is then used to find one or more other words, in the foreign language, that are correlated with the seed term. The identification of the found words in other messages can then be used to determine sentiment in the foreign language.
Abstract:
Social media websites occasionally experience a spike in activity known as a viral event or “going viral.” While some viral events are purely entertainment based, such as the latest cat video, other viral events can be specifically relevant to a domain, such as an industry or business. A social media event, such as a common thread of posts, may attract no more than the usual amount of interest or it may be about to “go viral.” As provided herein, social media events may be monitored and evaluated for virality. If an event indicates it will go viral, but has not yet done so, an affected party may be made aware of the event and take steps to curtail negative viral events or to leverage, or even promote, positive viral events.
Abstract:
An automatic timeline and topic normalization mechanism is described along with various methods and systems for administering the same. The temporal correction system proposed herein creates fully interpreted and reordered representations of events within and external to a dialog, reducing the amount of time and expensive resources typically required for reading, comprehension, and response to written communications.
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:
Automated method and systems are provided for determining a gap exists in an enterprise's knowledge base. Once a gap is determined, a question is developed in accord with the gap. An answer is then developed to answer the question and the knowledge base is updated accordingly. The source of the information may be cross-domain information such that an enterprise may include relevant information, and/or more usable information, than what could be otherwise provided by information limited to the enterprise's domain.
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:
An automated system for message analysis whereby messages within a given category may be identified and processed as a category connote. While a domain of messages may be monitored and processed in the due course of business, connote message are different. For example, a number of messages may fall into a domain of “poor airline food.” Such messages may be processed in the due course of business. However, a message with a different aspect, such as, “I found glass in my food,” may be initially identified as begin within the domain of “poor airline food,” and processed further to distinguish the message as being a connote with regard to the “poor airline food” category and warranting special handling.
Abstract:
Automated method and systems are provided for determining a gap exists in an enterprise's knowledge base. Once a gap is determined, a question is developed in accord with the gap. An answer is then developed to answer the question and the knowledge base is updated accordingly. The source of the information may be cross-domain information such that an enterprise may include relevant information, and/or more usable information, than what could be otherwise provided by information limited to the enterprise's domain.
Abstract:
A contact center system can receive messages from social media sites or centers. The messages may be in a foreign language. The system can review messages by identifying content in the social media messages with negative/positive sentiment and then identify a seed term in the messages. A seed term can be a word in another language, different from the message body. The seed term is then used to find one or more other words, in the foreign language, that are correlated with the seed term. The identification of the found words in other messages can then be used to determine sentiment in the foreign language.