Methods to reduce agent effort and improve customer experience quality through artificial intelligence. The Agent Assist tool provides contact centers with an innovative tool designed to reduce agent effort, improve quality and reduce costs by minimizing search and data entry tasks The Agent Assist tool is natively built and fully unified within the agent interface while keeping all data internally protected from third-party sharing.

A system or method for dynamic redundant call recording may include a plurality of recording devices, each recording device having a plurality of recording resources, and a resource allocator. The resource allocator may receive a request from a call receiving node for commencement of a recording session. It may then attempt to connect to a first one of the plurality of recording devices and if successful, establish a recording session between the call receiving node and the recording device, or if not successful, attempting to connect the recording session controller to a second one of the multiple recording devices. Two resource allocators may operate in parallel to establish dual recording using resources at two different recording devices. Call content may be recorded separately from call metadata and may be integrated with the metadata using a unique call ID.

Agent conversation item selection is automated by a server that automatically detects speech in a call and converts that speech to text. Software running on the server retrieves one or more items from a data store based on a determination that the text includes one or more keywords or a change in the subject of the call. The keywords can include phrases. The retrieved items include one or more of scripts, articles, manuals, daily bulletins regarding a system state, or any resource that can be used to assist with a customer call or interaction. The software running on the server generates a user interface (UI) output based on the retrieve items, and transmits the UI output to an agent device. Software running on the agent device receives the UI output and displays the retrieved items on a display of the agent device.

The distribution of incoming queries to a customer interaction center agent group is parallel processed amongst agents of that group to improve queue wait times. A threshold number of queries that may be processed by agent devices associated with the agent group at a given time are defined based on a number of agents of the agent group that are available at the given time. In response to determining that the number of queries is satisfies the threshold number of queries based on the number of agents that are available at a current time, a number of queries awaiting processing are distributed to one or more agent devices of the agent group. The threshold number of queries may be based on half of the number of agents that are available at the given time.

Calls for large call queues are handled by a system that assigns agents of a call queue to one of a first group or a second group. A size of the first group or the second group is based on a number of agents in the call queue that are online. The system batch rings each agent of the first group when an incoming call is received. If the incoming call is unanswered by the first group, the system batch rings each agent of the second group.

The number of agents to which incoming queries to a customer interaction center agent group may be distributed is limited based on skip criteria. The skip criteria is defined based on information associated with agent devices, such as locked status of a device, in-memory status of a client application at the device, or whether a telephone number provisioned for use with the device is from an external public switched telephone network. Agents which fail to satisfy the skip criteria are excluded from distributions of queries to improve wait times for customer interaction center users. Thus, queries are distributed from a queue to agents which satisfy the skip criteria.

A contact center system correlates one or more past user interactions to a current user interaction with the contact center system. The current user interaction may in some cases use a different communication modality (e.g., chat, voice, video, SMS, email, or social) than a past user interaction. The contact center system may automatically alert a supervisor agent when the system detects that a certain user warrants more attention. Real-time assistance may be provided to an agent of the contact center system based on aggregated data from a user's history of user interactions across modalities.

Techniques are described relating to automatically classifying telephone calls into a particular category using machine learning and artificial intelligence technology. As one example, calls to a customer service phone number can be classified as related to prohibited activity, or as legitimate. In particular, a number of different telephony variables as well as additional variables can be used to make such a classification, after training an appropriate machine learning model. The training process may use an externally provided call classification score that is provide by an outside entity as an input, and can be calibrated so that the output score of the trained classifier provides a score that corresponds to a real-world percentage chance of an unclassified call falling into a particular category. Thus, a classifier score of “95” can indicate that a call is in fact believed to be 95% likely to correspond to prohibited activity, for example.

A method for filtering a plurality of agent-customer interactions to determine whether one or more of a plurality of agent-customer interactions should be stored in a library of Artificial Intelligence (AI) files related to an interactive voice response system (IVR) is provided. The method may include receiving an identification of a plurality of IVR flashpoints, monitoring and/or reviewing the plurality of agent-customer interactions, and determining whether one of the plurality of agent-customer interactions meets a threshold number of the IVR flashpoints. For each of the plurality of agent-customer interactions that meets a threshold number of the IVR flashpoints, the method may further direct the IVR to convert the interaction into an IVR workflow and store the IVR workflow in the library of AI related to IVR.

A computerized-method for providing real-time lockless parallel screen recording compliance enforcement to omni-channel cloud systems, is provided herein. In a processor of a client computer configured to execute code for recording events of one or more voice or digital interactions, on one or more screens associated to the client computer, the computerized-method maintains a cache for storing a masked state. Each interaction is having an associated interaction identifier and upon receiving a screen event from a server of a cloud-based video recording that is communicating with the client computer over a communication network, operating a compliance-enforcement module. The compliance-enforcement module includes updating the cache; and operating a module of recording of screen events for the screen events of the one or more voice or digital interactions. Thus, by updating in real-time the cache on the client computer refraining from database locks in the server of the cloud-based video recording.