A computer based system and method for identifying complaint interactions, including: detecting appearances of linguistic structures related to complaints in an interaction; calculating at least one sentiment metric of the interaction; and classifying the interaction as being or not being a complaint interaction based on the detected linguistic structures and the at least one sentiment metric, for example using a trained supervised learning model.

The disclosed embodiments illustrate methods data processing for real-time text analysis. The method includes receiving text content from a plurality of user-computing devices, wherein the text content comprises at least a current text segment and a previous text segment. The method further includes extracting one or more first features from the current text segment, wherein at least a first feature of the one or more first features corresponds to a difference between timestamps associated with each of the current text segment and the previous text segment. The method further includes categorizing the current text segment into a predetermined category of one or more predetermined categories, based on at least the one or more first features, automatically by utilizing a classifier. Further, the method includes predicting a likelihood of evolution of an attribute in the text content, based on the predetermined category associated with the current text segment.

During a voice conversation, conversationally continuous voice input may be received from at least a first participant of the voice conversation. During the voice conversation, the conversationally continuous voice input may be converted into text, and the text may be analyzed to characterize content thereof. Using a graphical user interface displayed to at least a second participant of the voice conversation, a voice content animation may be rendered that visually represents the characterized content and is repeatedly updated as new content is characterized during the voice conversation.

A system and method are presented for the use of natural language processing to automate the disposition of completed interactions in an environment such as a contact center system. Communications may be inbound or outbound. A communication is received or placed by the contact center system and processed through the contact center system. After the interaction of the communication and the contact center is completed, data from the completed interaction may be processed through an automated customer interaction disposition system. Transcript data from the interaction may be processed through an NLP engine to generate data from the interaction. The data may then be automatically matched with a disposition/wrap-up code for the interaction.

A system obtains conversation data corresponding to conversations between users and agents of a client. The system identifies a set of intents from the conversations and identifies a set of contexts, explicit elements, and implied elements of these intents. The system identifies actions that can be performed to recognize new explicit and implied elements from new conversations and to address intents in these new conversations. Based on these actions, the system generates a set of recommendations that can be provided to the client. As agents communicate with users, the system monitors adherence to the set of recommendations.

Method starts with processing, by a processor, audio signal to generate audio caller utterance. Processor generates an agent action ranking score associated with the audio caller utterance and determines whether the agent action ranking score is below a minimum threshold. In response to determining that the agent action ranking score is below the minimum threshold, processor generates a transcribed caller utterance using a speech-to-text processor and generates an identified task based on the transcribed caller utterance. Using the transcribed caller utterance and a task-specific agent coaching neural network associated with the identified task, processor generates an ideal response. Processor generates a feedback result and causes the feedback result to be displayed on a display device of the agent client device. Other embodiments are disclosed herein.

Systems and methods include processing communication segments. A method may include obtaining at least one audio segment of an audio communication and distributing the at least one audio segment to a rules engine, analyzing, with the rules engine, the at least one audio segment for at least one acoustic trigger, determining at least one event that is triggered based on comparing the at least one acoustic trigger to one or more trigger rules, and communicating the at least one event to at least one end user through a web server.

Systems and methods are shown for managing call center data in accordance with one or more sets of compliance rules involving receiving data pertaining to an interaction, determining a set of compliance rules relevant to the received data, tagging the received data to identify the relevant set of compliance rules, and utilizing the data tagging, applying the relevant set of compliance rules to handling the received data. Examples also involve receiving an interaction request, obtaining the data tagging for data corresponding to the received interaction request, using the data tagging for the data corresponding to the received interaction request to obtain the corresponding relevant set of compliance rules, using the corresponding set of compliance rules to identify agents eligible to access the data corresponding to the received interaction request, and routing the received interaction request to one of the eligible agents based on a defined routing strategy.

Systems and methods for searching communications may include receiving a selection from a user of at least one of an acoustic characteristic, a language characteristic, or a category, accessing a database comprising a plurality of communications, the plurality of communications labeled with acoustic characteristics, language characteristics, and categories, restricting the plurality of communications to a subset of the plurality of communications including the user's selection of the at least one acoustic characteristic, language characteristic, or category, generating at least one of a word cloud or a tree for presenting a plurality of topics identified in the subset of the plurality of communications, and receiving a selection of one of the plurality of topics, the receiving causing a display of a further subset of the subset of the plurality of communications, the further subset having the selected topic.

A method for routing customer service requests to call centers includes collecting data associated with customer service experience between a customer and a call center regarding a completed customer call. The collected data is analyzed to determine a quality of customer service for one or more completed calls between the customer and the call center. A nature of an incoming customer call is determined. The incoming customer call is routed to a call center based upon making reference to the analyzed collected data such that the nature of the customer call matches with corresponding one or more favorable attributes of the call center.