Abstract: A computerized-method for recommending activities for an agent, in a contact center is provided herein. The computerized-method includes operating an activities-recommendation module, for agents in a contact center, during a work-shift of an agent.

Abstract: A computerized-method for gauging agent's self-assessment effectiveness, is provided herein. The computerized-method includes for each interaction (i) operating a Self-assessment Consolidation module to calculate a confidence-interval for each data-point of one or more preconfigured data-points, and (ii) operating a Self-assessment Divergence Determinant (SDD) module. The operating of the SDD includes: retrieving one or more data-points of the interaction; for each data-point retrieving the confidence interval; setting a divergence-indicator as zero, when the data point is within the confidence-interval; setting the divergence-indicator as a subtraction of the data point from the calculated lower-bound, when the data-point is lower than the lower-bound of the confidence-interval; and setting the divergence-indicator as a subtraction of the calculated upper-bound from the data-point, when the data-point is greater than the upper-bound of the confidence-interval.

Abstract: A computerized-method for calculating an After-Call-Work (ACW) factor of an interaction in a contact center, by which a related recording may be filtered for evaluation is provided herein. The method includes an After-Call-Work (ACW) factor calculation module. The operating of the ACW factor calculation module includes: (i) receiving agent recording of the interaction; (ii) aggregating data fields associated with: (a) the interaction; and (b) the customer; (iii) retrieving ACW time of the interaction; (iv) forwarding the aggregated data fields to a machine learning model; (v) operating the machine learning model to calculate a predicted ACW time, based on the aggregated data fields; (vi) calculating an ACW factor based on the received time of ACW and the calculated predicted ACW time; and (vii) sending the calculated ACW factor to a platform by which the platform is preconfigured to distribute the interaction for evaluation, based on the ACW factor.

Abstract: A computerized-method for generating a machine-learning model to determine genuineness of customer feedback to filter-out, non-genuine agent recording segments from evaluation. The computerized-method includes generating a Genuineness Opinion Score (GOS) model. The generating of GOS model includes: (a) a data manipulation phase; (b) a data visualization and analysis phase, and (c) a feature augmentation phase for sorting the variables in a set of unique and relevant variables into two categories: estimated variables and anticipated variables. The estimated variables are used for calculation of a GOS of an interaction that is received in a contact center and the anticipated variables are used for calculation of a threshold of said GOS.

Abstract: A computerized method for evaluating agent-call-logging, in a contact center, is provided herein. The method includes operating an evaluating agent-call-logging module. The operating of an evaluating agent-call-logging module includes: (i) receiving a recorded interaction between an agent and a customer, stored in the database of recorded interactions and a corresponding agent-call-logging, having ‘n’ sentences, stored in the database of call loggings; (ii) operating a speech-to-text algorithm on the received recorded interaction to yield a summarized text thereof. The yielded summarized text is comprised of sentences expressed by the customer; and (iii) operating a semantic comparison unit, to compare each sentence in the yielded summarized text with a corresponding sentence in the agent-call-logging, to yield a Sentence Similarity Score (SSS) for each sentence of the agent-call-logging.

Abstract: A computerized-method using a cloud-based computing environment for improving client service, in a contact center is provided herein. The computerized-method includes retrieving a context of a query and a time-limit from a CTI event and attempting to retrieve data to evaluate average resolution time for the received context. When the data is found, comparing the evaluated average resolution time with the received time-limit and when the received time-limit is below the evaluated average resolution time, sending a delay notice and providing the client a menu of options for querying through other channels. When the data is not found, or when the received time-limit is above the evaluated average resolution time, presenting on an agent dashboard, the time-limit of the client and accordingly updating parameters in the agent dashboard dining the inbound call, thus, improving client service, by considering the time-limit of the client before the agent addresses a query.

Abstract: A computerized method for evaluating agent-call-logging, in a contact center, is provided herein. The method includes operating an evaluating agent-call-logging module. The operating of an evaluating agent-call-logging module includes: (i) receiving a recorded interaction between an agent and a customer, stored in the database of recorded interactions and a corresponding agent-call-logging, having ‘n’ sentences, stored in the database of call loggings; (ii) operating a speech-to-text algorithm on the received recorded interaction to yield a summarized text thereof. The yielded summarized text is comprised of sentences expressed by the customer; and (iii) operating a semantic comparison unit, to compare each sentence in the yielded summarized text with a corresponding sentence in the agent-call-logging, to yield a Sentence Similarity Score (SSS) for each sentence of the agent-call-logging.

Abstract: Users often have multiple resource (e.g., devices, accounts, authorizations, permissions, etc.) that utilize or leverage a credential that may become compromised. A stolen mobile device may allow unauthorized access to the device to make and receive calls and potentially expose other resources (e.g., emails, text messages, accounts, etc.). Maintaining all these accounts is cumbersome and error prone. Having a ledger indicating resources that are blocked is provided. Copies of the ledger are maintained by other entities and updates exchanged therebetween. As a result, parties can be quickly notified when a resource should be blocked, or unblocked, if blocking is no longer warranted.

Abstract: Embodiments described herein provide an optimal communication channel recommendation engine by assessing whether the environment the customer is situated in is conducive to the channel selected by the customer. Specifically, the optimal channel recommendation engine obtains data artifacts indicative of ambient noise, motion, customer sentiment, network quality, customer focus, and/or the like to assess quality of the environment and recommend an optimal channel for the communication between the customer and the call agent. With the recommendation to switch to a different channel, the client component resumes communication with the new channel and retains the context of the interaction.

Abstract: A computerized-method using a cloud-based computing environment for improving client service, in a contact center is provided herein. The computerized-method includes: retrieving a context of a query and a time-limit from a CTI event and attempting to retrieve data to evaluate average resolution time for the received context. When the data is found, comparing the evaluated average resolution time with the received time-limit and when the received time-limit is below the evaluated average resolution time, sending a delay notice and providing the client a menu of options for querying through other channels. When the data is not found, or when the received time-limit is above the evaluated average resolution time, presenting on an agent dashboard, the time-limit of the client and accordingly updating parameters in the agent dashboard during the inbound call, thus, improving client service, by considering the time-limit of the client before the agent addresses a query.

Abstract: A computerized-method for calculating a hold factor of an interaction in a call center, by which related agent recording segments may be filtered for evaluation is provided herein. The computerized-method include: operating a Hold Factor Calculation (HFC) model for an interaction. The HFC model include receiving agent recording segments of the interaction and then collecting data fields of: (i) skills of agent; and (ii) interaction metadata. Then, checking to determine if hold time has occurred in the received agent recording segments and when it is determined that hold time has occurred the HFC is: (a) calculating a hold-ratio; (b) calculating a conversation score based on the collected data fields; (c) dividing the calculated hold ratio by the calculated conversation score to yield a hold factor; and (d) sending the yielded hold factor to a quality planner microservice by which the quality planner is preconfigured to distribute the interaction for evaluation.

Abstract: Assigning an appropriate channel in an omni-channel contact center environment includes receiving, by a computer of the contact center, an incoming contact via a first communication channel, the incoming contact assignable to a plurality of available communication channels; queuing, by the computer, the incoming contact in a first queue associated with the first communication channel; determining, by the computer, a preferable communication channel from among the plurality of communication channels and the first communication channel; and when the preferable communication channel is one of the plurality of communication channels, determining whether to assign the incoming contact to the preferable communication channel.

Abstract: Assigning an appropriate channel in an omni-channel contact center environment includes receiving, by a computer of the contact center, an incoming contact via a first communication channel, the incoming contact assignable to a plurality of available communication channels; queueing, by the computer, the incoming contact in a first queue associated with the first communication channel; determining, by the computer, a preferable communication channel from among the plurality of communication channels and the first communication channel; and when the preferable communication channel is one of the plurality of communication channels, determining whether to assign the incoming contact to the preferable communication channel.

Abstract: Multiple vendors often contribute a portion of their hardware and/or software to a customer to enable a particular feature. When issues occur, a contact center for more than one vendor may be required to perform an action in order to identify a resolution to the issue and apply the resolution. By coordinating multiple contact centers with smart contracts via a distributed leger, rules may be developed for the creation of blocks for notifications, escalations, tasks, and other actions or informational notifications to resolve the issue. Additionally, the blockchain may serve as a permanent record provide a history of actions taken, by whom, and when.

Abstract: A computerized-method using a cloud-based computing environment for improving client service, in a contact center is provided herein. The computerized-method includes: retrieving a context of a query and a time-limit from a CTI event and attempting to retrieve data to evaluate average resolution time for the received context. When the data is found, comparing the evaluated average resolution time with the received time-limit and when the received time-limit is below the evaluated average resolution time, sending a delay notice and providing the client a menu of options for querying through other channels. When the data is not found, or when the received time-limit is above the evaluated average resolution time, presenting on an agent dashboard, the time-limit of the client and accordingly updating parameters in the agent dashboard during the inbound call, thus, improving client service, by considering the time-limit of the client before the agent addresses a query.

Abstract: A computerized-method for generating a machine-learning model to determine genuineness of customer feedback to filter-out, non-genuine agent recording segments from evaluation. The computerized-method includes generating a Genuineness Opinion Score (GOS) model. The generating of GOS model includes: (a) a data manipulation phase; (b) a data visualization and analysis phase, and (c) a feature augmentation phase for sorting the variables in a set of unique and relevant variables into two categories: estimated variables and anticipated variables. The estimated variables are used for calculation of a GOS of an interaction that is received in a contact center and the anticipated variables are used for calculation of a threshold of said GOS.

Abstract: A determination is made that a real-time communication session (e.g., a voice call) between a first agent terminal and a customer communication endpoint has been placed on hold by a first contact center agent. An estimated hold time for the real-time communication is determined, based on a rule, in response to determining that the real-time communication session has been placed on hold. It is determined that the estimated hold time for the real-time communication session has expired. In response to determining that the estimated hold time for the real-time communication session has expired, one or more actions occur. For example, the real-time communication session is automatically transferred to a second agent terminal.

Abstract: A determination is made that a real-time communication session (e.g., a voice call) between a first agent terminal and a customer communication endpoint has been placed on hold by a first contact center agent. An estimated hold time for the real-time communication is determined, based on a rule, in response to determining that the real-time communication session has been placed on hold. It is determined that the estimated hold time for the real-time communication session has expired. In response to determining that the estimated hold time for the real-time communication session has expired, one or more actions occur. For example, the real-time communication session is automatically transferred to a second agent terminal.

Abstract: Electronic conferences are a common method of conducting a meeting when the participants are not at the same location. When a conference drops a portion of the audio, such carrying speech from a speaker, participants may be excluded from the full content of the conference. By having a device associated with a speaking participant convey both audio and generated text from the speech provided by the speaking participant, a server may be able to determine that speech is missing from the audio portion and automatically insert text content. As a result, poor audio quality may be mitigated with text and omitting unwanted text when the audio quality is sufficient.

Abstract: Agents in a contact center are often connected to communications (e.g., calls, real-time text chats) automatically and without human intervention. By analyzing the communication and/or a history associated with the customer on the communication, an auto-answer delay may be determined that is better able to provide the agent with sufficient time to prepare for the communication. The auto-answer delay may be determined as a variation from a default auto-answer delay.