A computer-assisted patient navigational communication system for receiving electronic and oral communications from a patient, scanning data to determine the medical needs of the patient, and displaying relevant information to appropriate medical personnel who can immediately advise the patient of the most appropriate source of medical assistance relating to the patient's identified symptoms.

Call routing technology can route a current call from a person based on an analysis of call data related to a prior call from the same person. For example, based at least on a customer satisfaction score determined from call data related to a prior call, a call routing server can identify one or more persons to whom a current call from the same person should be routed. Next, the call routing server can route the current call to one of the identified customer service representatives.

Technology is described for routing communications between contacts and agents. Associations may be generated between agents and contacts who intend to communicate with the agents. Weightings for the associations between the agents and the contacts may be determined using a machine learning model to produce weighted associations. The weightings may represent a predicted interaction metric between the agents and the contacts. Selected pairs of agents and contacts may be determined by applying matching rules to the weightings. Communications may be routed between the contacts and the agents in accordance with the selected pairs of agents and contacts.

A computerized-method for prioritizing tenants for a service for a request-router in a cloud-based Software-as-a-Service-platform contact-center, is provided herein. When a service-monitoring module for a microservice is operating and a request-router is in a throttle-mode for each service of the microservice, the computerized-method may be operating a Tenants-Prioritization (TP) module for each tenant. The TP module may include: (i) receiving a degradation-level (ii) periodically pulling data from an Automatic-Call-Distribution system to calculate agent-activity-levels and call-queue-size; (iii) maintaining the calculated agent-activity-levels, calculated call-queue-size, tenant-trend-indicator and a preconfigured account-impact-score of the tenant; (iv) determining a tenant-tier-level based on a calculated Tenant-Request-Prioritization score; and (v) providing the tenant-tier-level and a number-of-allowed-requests per tenant-tier-level to the request-router, to provide the service to the tenant and other tenants having the determined tier-level, in a preconfigured time-window, based on the tenant tier-level and the number of allowed requests per tenant tier-level.

A computerized-method for service monitoring and rate limiting for a service for a request router in a SaaS-platform-contact-center is provided herein. When a service-monitoring-module for a microservice of the SaaS-platform-contact-center is operating and a request-router is in a throttle-mode for a service-of-the-microservice, operating a Tenants Prioritization (TP) module. The TP module includes: determining a tenant-tier-level having a determined quota-of-service-requests; receiving a degradation-level from the service-monitoring-module, for a preconfigured-period; reducing corresponding predefined total-number-of-allowed-requests by a first-threshold, when the degradation-level of the service-of-the-microservice has degraded by a second-threshold, during the preconfigured-period; increasing the corresponding predefined total-number-of-allowed-requests by the first-threshold, when the degradation-level of the service-of-the-microservice has improved by the second-threshold, during the preconfigured period; and instructing the request-router to get out of throttle-mode for the service-of-the-microservice, when the degradation-level of the service-of-the-microservice has improved by the second-threshold during the preconfigured period and there is an indication that the service-of-the-microservice has been degraded.

In a contact center apparatus, a method for generating recognized text based upon verbal data, comprising: receiving verbal data from a user; applying a natural language understanding engine the verbal data to generate phonetic representation text, the phonetic representation text configured as a phonetic representation of the verbal data; applying a language reinforcement engine to the phonetic representation text to generate recognized text, the recognized text identifying a word associated with the verbal data; and directing the user to a contact center resource based upon the recognized text.

A virtual assistant system for communicating with customers uses human intelligence to correct any errors in the system AI, while collecting data for machine learning and future improvements for more automation. The system may use a modular design, with separate components for carrying out different system functions and sub-functions, and with frameworks for selecting the component best able to respond to a given customer conversation.

A resource managing computer system for managing at least one resource in an enterprise is disclosed. The resource managing computer system includes a communication interface for establishing at least one web based chat communication session with at least one customer. The system further includes a monitoring module for monitoring one or more parameters associated with the at least one web based chat communication session. The system further includes a computing module for computing at least one confidence score based on the one or more monitored parameters of the at least one web based chat communication session. The system further includes an allocation module for allocating the at least one resource to the at least one web based chat communication session, wherein the allocation is performed based on the at least one computed confidence score.

A method related to customers orchestrating engagements with service providers. The method includes providing, by a facilitator entity, a customer journey orchestration service that includes: service providers affiliated with the customer journey orchestration service; a virtual datacenter in which cloud-based applications and databases are operably linked to communication devices of the customers and to the affiliated services providers; customer interface portals for enabling the customers to interact with the virtual datacenter, each customer interface portal being associated with a specific customer; and a customer profile database that stores customer profiles corresponding to respective ones of the customers. The method further includes configuring, by the facilitator entity, an account of the customer for participation in the customer journey orchestration service. The method further includes orchestrating, by the facilitator entity, a current engagement relating to a plurality of the preferred service providers.

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.