Abstract: A system and method for managing virtual queues. A cloud-based queue service manages a plurality of queues hosted by one or more entities. The queue service is in constant communication with the entities providing queue management, queue analysis, and queue recommendations. The queue service is likewise in direct communication with queued persons. Sending periodic updates while also motivating and incentivizing punctuality and minimizing wait times based on predictive analysis. The predictive analysis uses “Big Data” and other available data resources such as schedule data and historical throughput data for various resources associated with an entity, for which the predictions assist in the balancing of persons across multiple queues for the same event or multiple persons across a sequence of queues for sequential events.

Abstract: 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.

Abstract: 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.

Abstract: A computer-implemented method for improving performance of a service representative that provides services. The method comprises determining a performance indicator representing performance of the service representative and if the performance indicator meets a condition, starting a computing process on a computing device to interact with the service representative in order to improve the performance of the service representative.

Abstract: Provided herein are methods, systems, computing devices, and/or computing entities for generating a performance metric recommendation by generating an agent group data object for an agent profile; generating an agent assessment data object for the agent profile; generating an inferred performance gap data object for the agent profile; and generating the performance metric recommendation based at least in part on the inferred performance gap data object.

Abstract: A system for transitioning a telephony or in-person servicing to an artificial intelligence (AI) chat session. The system may receive a phone call from a user device associated with a user, and transmit a voice request for personally identifiable information associated with the user. The system may also receive and authenticate the requested personally identifiable information and, in response, generate an authentication token. The system may further receive a servicing intent from the user device, and generate a corresponding servicing intent token. Also, the system may generate an API call to an AI chatbot model, transmit the authentication token and the servicing intent token to the AI chatbot model, and map the servicing intent token to a stored servicing intent. Finally, the system may transmit a message to the user device via the AI chat session.

Abstract: 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.

Abstract: A system and method for monitoring, analyzing, and testing calls between computers of remotely working call center agents and callers in real-time. A real-time testing module implemented as a browser extension or a side-loaded application relays live call information and on-demand or periodic passive-test results to a test and dashboard server for analysis and triage. Additionally, the real-time testing module may actively test calling conditions between the agent's equipment and a distant caller by suspending the agents calls and tasks, intercepting and taking command of the agent's audio equipment, and placing a test call to a testing server. Thus, allowing actual data to be used in troubleshooting issues, rather than simulations or synthetic data. Dashboards with analytics and predictions are made available in both the browser application and an end point server for agents and operations/management personnel respectively.

Abstract: A method of assisting an agent in real-time includes receiving a call interaction between a customer and an agent; identifying words spoken in the call interaction; providing the words to a behavioral models module; computing a score for a plurality of behavioral metrics; providing a phrase formed by the words to a knowledge article selection module; providing each score for the plurality of behavioral metrics to the knowledge article selection module; providing a plurality of knowledge selection rules to the knowledge article selection module; evaluating a combination of the phrase and the scores of the plurality of behavioral metrics against each of the plurality of knowledge selection rules; matching a knowledge selection rule to the combination; selecting a knowledge article associated with the matched knowledge selection rule; generating a visual representation of the selected knowledge article; and presenting in real-time the visual representation on a graphical user interface.

Abstract: 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.

Abstract: A system and method for intent-based active callback management using enhanced callback objects, utilizing a cloud callback system comprising at least a profile manager, callback manager, interaction manager, media server, and environment analyzer, allowing users to call businesses, agents in contact centers, or other users who are connected to a cloud callback system, and, failing to connect to the individual they called, allow for an automatic callback object to be created, whereby the two users may be automatically called and bridged together at a time when both users are available.

Abstract: A method and system for automatically executing an action within a call center environment. Data is aggregated from multiple data sources into a combined data stream. One of the data sources is a source of data corresponding to at least one communication processed by the call center one of the data sources can be a source of external data representing activity that is external to the call center. The combined data stream is processed into successive batches of data corresponding to one or more communications between a call center agent and a communicating party received by the call center. A sensor data structure specifying at least one rule is applied to the batches of data. The at least one rule can include a machine learning model and a configuration data structure based on historical data from the multiple data sources. When it is determined that at least one of the batches satisfies the at least one rule, a notification message relating to the one or more communications is generated.

Abstract: There is a need for faster and more accurate predictive data analysis steps/operations. This need can be addressed by, for example, techniques for efficient predictive data analysis steps/operations. In one example, a computer-implemented method for generating a predictive output with respect to a primary audio data embedding data object associated with a primary audio data object, is provided. The method includes generating, using one or more computer processors, by utilizing a similarity determination machine learning model and based at least in part on the primary audio data embedding data object, the predictive output for the primary audio data embedding data object; generating, by the one or more computer processors, a forwarding recommendation prediction based at least in part on the predictive output; and performing, by the one or more computer processors, one or more prediction-based actions based at least in part on the forwarding recommendation prediction.

Abstract: A hardware processor can receive sets of input data describing assets associated with an entity. The hardware processor can receive inputs responsive to queries of a user. The hardware processor can individually generate predictive models based on a respective set of input data. The hardware processor can calculate predicted outcomes for the user by applying each of models to the inputs. The hardware processor can generate a user interface comprising the predictive outcomes for the user for each of the predictive models.

Abstract: A system may receive an indication that a user is accessing an ATM, receive, from the ATM, average session duration data over a predetermined period, generate, using a machine learning model, a busyness score for the ATM based on the average session duration data over the predetermined period, and determine whether the busyness score for the ATM exceeds a busyness score threshold. When the busyness score for the ATM does not exceed the busyness score threshold, the system may cause the ATM to present, via a first graphical user interface, a default ATM experience. When the busyness score for the ATM exceeds the busyness score threshold, the system may cause the ATM to present via, a second graphical user interface, a busy ATM experience.

Abstract: A computer-implemented method for providing an objective evaluation to a customer service representative regarding his performance during an interaction with a customer may include receiving a digitized data stream corresponding to a spoken conversation between a customer and a representative; converting the data stream to a text stream; generating a representative transcript that includes the words from the text stream that are spoken by the representative; comparing the representative transcript with a plurality of positive words and a plurality of negative words; and generating a score that varies according to the occurrence of each word spoken by the representative that matches one of the positive words, and/or the occurrence of each word spoken by the representative that matches one of the negative words. Tone of voice, as well as response time, during the interaction may also be monitored and analyzed to adjust the score, or generate a separate score.

Abstract: Among other things, systems and methods include a first sensor configured to detect operation of the sink; a second sensor configured to detect personal characteristics of a person operating the sink.

Abstract: A system and method for intent-based active callback management, utilizing a cloud callback system comprising at least a profile manager, callback manager, interaction manager, media server, and environment analyzer, allowing users to call businesses, agents in contact centers, or other users who are connected to a cloud callback system, and, failing to connect to the individual they called, allow for an automatic callback object to be created, whereby the two users may be automatically called and bridged together at a time when both users are available.

Abstract: Methods and systems for facilitating communication sessions between multiple communication points via a contact number. The method includes receiving a purchase request from a first user device, through a first communication point which is enabled with a context-to-call (C2C) feature. The method further includes facilitating displaying of a predetermined list of contact numbers on the first user device and receiving a contact number selected by the first user. The method also includes processing the purchase request based, at least in part, on receiving subscription fees. The method also includes assigning the contact number to a C2C-enabled account of the first user, as a dedicated C2C-enabled contact number, based, at least in part, on purchase request-processing step. The method also includes facilitating the C2C-enabled account of the first user to receive calls from second user(s), on the dedicated C2C-enabled contact number, based, at least in part, on C2C contact-assigning step.

Abstract: 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.