Systems and methods of solving inquiries in an automated manner are disclosed. In some embodiments, a method, includes converting auditory data into textual data, wherein the auditory data is of a voice with speech related to an inquiry. Additionally, a target automated resolution is selected for the inquiry from a plurality of automated resolutions based on the textual data and a resolution data structure of a plurality of resolution data structures. Each of the resolution data structures relates to an automated resolution of the plurality of automated resolutions.

A method for routing civilian calls to a public-safety officer is provided herein. During operation, a call processor receives a civilian call having a source identifier number identifying a source of the call and a target identifier number identifying a target of the call. A public-safety officer is determined that is the target of the call and a public-safety officer status database is accessed to determine a current status of the public-safety officer. When the officer is able to take the call, the call is routed directly to the officer. When the officer is unable to take the call, the officer status database is accessed, and a best available officer to take the call is determined. The call is then routed to the best available officer that is able to take the call.

Disclosed herein are a system, method, and computer-readable medium with instructions for recommending a service provider based on a user's approximate current location. The service provider may be recommended for a service that is verified under a user's insurance policy and is also within proximity to the user's approximate current location. The user's approximate current location may be determined by location-based technology. The entity that receives and/or recommends the service provider may initiate a request for the service from the service provider. Further embodiments are related to routing a call to provide information to the user that is responsive to the user's location-relevant request. For example, systems, such as an automotive telematics system, may request information and be routed to the appropriate division of a user's insurance provider such that the automotive telematics system can provide services to the user that are user-specific (e.g., covered by the user's insurance policy).

Systems for and methods of determining the priority of a call interaction include receiving a call interaction from a call center; validating, by a validation and transcription engine, that the call interaction is authentic; converting, by the validation and transcription engine, the call interaction into text; extracting, by a data calculation engine, organization, location, and time information from the text; calculating, by the data calculation engine, a priority of the call interaction from the extracted information and the text by determining an important of words in the text and correlating the words to a priority class using a pre-trained algorithm that is trained on emergency-type and emergency services-type language; determining that the call interaction should be transmitted to a queue of the call center for initial handling by a call center agent; and transmitting the call interaction, the calculated priority, and the extracted information to the call center.

Techniques for behavioral pairing in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for behavioral pairing in a contact center system comprising: determining, by at least one computer processor communicatively coupled to and configured to operate in the contact center system, a plurality of contacts available for connection to an agent; determining, by the at least one computer processor, a plurality of preferred contact-agent pairings among possible pairings between the agent and the plurality of contacts; selecting, by the at least one computer processor, one of the plurality of preferred contact-agent pairings according to a probabilistic network flow model; and outputting, by the at least one computer processor, the selected one of the plurality of preferred contact-agent pairings for connection in the contact center system.

Certain exemplary aspects of the present disclosure are directed to a data-communications system including a networked contact center for which, in an example embodiment, a communication is received by a networked contact center. In determining whether to allow the communication to reach contact center resources, a contact rate value is examined. The contact rate value may represent an allowable quantity of contact within a time interval. For some example embodiments, a determination of whether the network contact center is to accept or reject the communication is made based on the contact rate value.

A system establishes an agent communication with a customer. An agent computer with a first processor and a first memory receives a batch notification identifying a batch of one or more customer records in the agent computer, for communication in the near future. The one or more customer records is associated with one or more customers. A second computer with a second processor and a second memory provides the batch notification identifying the batch of one or more customer records in the agent computer. The system connects the agent with the customer associated with a customer record selected from the batch by the agent.

Systems and methods are used to generate contact type predictions that route user customer service requests within a support platform. The contact type predictions are generated using a hybrid model that includes a deep learning component and a business logic component. The deep learning component may generate a multi-channel output based on text features and context features. The multi-channel output is modified based on one or more business rules to generate the contact type predictions.

Systems for and methods of determining the priority of a call interaction include receiving a call interaction from a call center; validating, by a validation and transcription engine, that the call interaction is authentic; converting, by the validation and transcription engine, the call interaction into text; calculating, by the data calculation engine, a priority of the call interaction from the text and organization, location, and time information in the text by determining an important of words in the text and correlating the words to a priority class using a pre-trained algorithm that is trained on emergency-type and emergency services-type language; determining that the call interaction should be transmitted to the call center for initial handling by a call center agent; and transmitting the call interaction, the calculated priority, and the extracted information to the call center.

A system and method for investigating an issue may include defining a set of phrase categories; associating each of a set of recorded interactions with at least one phrase category; receiving a selection of a first time interval and a second time interval; selecting a reference phrase category; calculating, for at least some of the phrase categories in the set of phrase categories a correlation differential based on a trend of the phrase category and a trend of the reference phrase category and, if a correlation differential of a category is larger than a threshold then including the category in the reference category.