A call analytics system and associated methods that can be used to rapidly analyze call data and provide conversational insights. The call analytics system receives audio call data of a phone call between a customer and an agent of a business, and converts the call data into one or more messages for handling by a distributed stream-processing platform. In some embodiments, the stream-processing platform is the Apache Kafka platform. The distributed platform processes the messages and communicates with various software modules to generate a variety of conversational insights. When processed by a stream-processing platform, certain analyses can occur in parallel which allows conversational insights to be provided to the businesses shortly (e.g., within seconds) after the call data is received.

A system and method for hybrid callback management and failure recovery, utilizing a callback cloud and an on-premise callback system, allowing brands to utilize a hybrid system that protects against any premise outages or cloud service faults and failures by introducing redundancies and co-maintenance of data key to callback execution.

A mobile application for a wireless handheld computing device, such as a smartphone, is disclosed in combination with a communication link establishment and management system. Systems and methods are disclosed for calling desired contacts using a smartphone that can take advantage of the power and efficiency of agent-assisted dialing provided by the communication link establishment and management system. The systems and methods automatically integrate with a customer relationship management (CRM) system connected to the communication link establishment and management system.

A hard phone, a method for implementing a traffic operation, a call center system, and a non-transitory computer-readable storage medium. The hard phone may include: a processing module (11), an input module (12), a sending module (13), a display device (14), and a receiving module (15). The display device (14) displays different types of traffic operation instructions such that a user carries out selection. The input module (12) receives the traffic operation instruction inputted by the user and an identity identification of a target attendant, and transmit to the processing module (11). The processing module (11) processes the traffic operation instruction and the identity identification according to a preset protocol, and then transmit same to the sending module (13), and send to a server via the sending module (13). The receiving module (15) receives and transmit the execution result to the processing module (11).

A system and method for integrating audio data collected, such as audio data and analytical data, to perform behavioral analysis on the audio data, using an application of acoustic signal processing and machine learning algorithms, by converting the audio data to text data and performing behavioral analysis on the text data. The behavioral analysis data from the audio application of acoustic signal processing is combined with machine learning algorithms and speech to text data to provide a call agent with feedback to assist in the next best action or insight into customer behaviors.

A system and method for authenticating an identity may include generating a first generic representation representing a stored audio content, generating a second generic representation representing input audio content, and, providing the first and second generic representations to a voice biometrics unit adapted to authenticate an identity based on the first and second generic representations.

A system and method for callback management, utilizing a callback cloud, a digital ledger, and smart callback contracts, brokering user to user communications and session establishment using an automated workflow built upon extensible root smart contract templates which can be configured to connect various users and brands. The system may comprise callback cloud, that may further comprise a callback manager, a brand interface server, an interaction manager, a media server, one or more blockchain digital ledgers, and various smart contracts which are used to autonomously respond to received user requests.

A device may receive audio data of a first call between a first user and a second user. The device may generate, based on the audio data, time series data associated with an audio signal of the first call and may process, using a first machine learning model, the time series data to generate first call insight information regarding one or more first insights associated with the first call. The device may process the audio data to generate image data associated with the audio signal and may process, using a second machine learning model, the image data to generate second call insight information regarding one or more second insights associated with the first call. The device may combine the first call insight information and the second call insight information to generate combined call insight information and cause an action to be performed based on the combined call insight information.

An adaptive cloud conversation platform that includes a consumer-oriented focus and presence management to facilitate consumer control over the platform experience. The core of the platform is an adaptive cloud conversation platform capable of making automated decisions regarding when and how to establish on-going communications with consumers so as to maximize the relationship between the consumer and a given brand. The platform is further configured to comprise a consumer-oriented control layer with advanced presence management. In some embodiments, the platform may further comprise a blockchain layer for immutable storage, smart contract management and execution, and/or cryptocurrency management.

An adaptive cloud conversation platform capable of making automated decisions regarding when and how to establish on-going communications with consumers so as to maximize the relationship between the consumer and a given brand. The system has a connection management services layer which determines what communications should be established and how they should be established, an initiation management services layer which determines when communications should be established, and a user management services layer which stores information about consumers and brands for determination of when and how communications should be established. Certain of these services have machine learning algorithms incorporated into them trained to perform analyses of the particular type of operation handled by that service. The outputs of each service can be used as inputs to other services, such that a network of machine learnings algorithms is created which determines when and how to establish on-going communications with consumers.