Abstract: A method comprising: calculating a velocity threshold for an entity, the entity including an individual worker or a team of workers; calculating a velocity for the entity by classifying a first signature corresponding to the entity with a first machine learning (M/L) classifier, the velocity being a metric that measures a current productivity of the entity; detecting whether the velocity meets the velocity threshold; and outputting an alert when the velocity meets the velocity threshold.

Abstract: A method for use in a hosting system of a retail website, comprising: receiving respective first values for a plurality of parameters of an auto-buy campaign, the respective first values being specified by an administrator of the retail website hosting system; calculating a configuration score for the auto-buy campaign based on the respective first values for the plurality of parameters of the auto-buy campaign, the configuration score being indicative of a percentage of auto-buy bids submitted as part of the auto-buy campaign that are expected to result in a purchase; and outputting an indication of the configuration score for presentation to the administrator.

Abstract: Systems and methods display reviews of products and/or services that are most demographically congruous with the individual seeking those reviews. An individual selects a product for which reviews are to be shown. The system and method form a graph having nodes representing the individual, product reviewers, demographic characteristics, reviewed products, and product reviews. The system and method then determine demographic closeness in the graph between nodes of the individual and those of each reviewer of the selected product, where the individual may be connected to a reviewer either by a common demographic characteristic or through such a characteristic associated with another product reviewed by that reviewer. The system and method use these values to compute a demographic congruity (DC) score for each reviewer, and rank the reviews for display according to these DC scores. Statistics may be compiled for reviewers with DC scores exceeding a given threshold.

Abstract: A method, system and computer-usable medium are disclosed for implementing a machine learning system for using adaptive interrupts to control notifications to a user. In at least one embodiment, a computer-implemented method for adaptively interrupting a user with communication notifications at an information handling system is disclosed, including: receiving a communication for a user at the information handling system; intercepting a notification relating to the received communication; assessing a degree of importance of the notification using contextual information associated with the notification; assessing a degree of busyness of the user at the information handling system by actively monitoring interactions between the user and the information handling system; and selectively interrupting the user with the notification based on the busyness of the user at the information handling system and the degree of importance of the notification.

Abstract: A method, system, and computer-usable medium for implementing a system providing a customer with prescriptive remedies in response to a credit card transaction error. At least one embodiment includes receiving a credit card transaction request for purchase of a product during a purchase session from a merchant; receiving a credit card transaction error from a payment gateway; passing the credit card transaction error, customer segment information, and customer score information to a prescriptive machine learning engine; analyzing the error type, customer segment information, and customer score information at the prescriptive machine learning engine to assign a proactive response to the credit card transaction error. The proactive response is selected to increase the likelihood that a customer will continue the purchase despite the decline of the credit card.

Abstract: A method, system, and computer-usable medium for implementing a system providing a customer with prescriptive remedies in response to a credit card transaction error. At least one embodiment includes receiving a credit card transaction request for purchase of a product during a purchase session from a merchant; receiving a credit card transaction error from a payment gateway; passing the credit card transaction error, customer segment information, and customer score information to a prescriptive machine learning engine; analyzing the error type, customer segment information, and customer score information at the prescriptive machine learning engine to assign a proactive response to the credit card transaction error. The proactive response is selected to increase the likelihood that a customer will continue the purchase despite the decline of the credit card.

Abstract: A method, system and computer-usable medium are disclosed for implementing a machine learning system for using adaptive interrupts to control notifications to a user. In at least one embodiment, a computer-implemented method for adaptively interrupting a user with communication notifications at an information handling system is disclosed, including: receiving a communication for a user at the information handling system; intercepting a notification relating to the received communication; assessing a degree of importance of the notification using contextual information associated with the notification; assessing a degree of busyness of the user at the information handling system by actively monitoring interactions between the user and the information handling system; and selectively interrupting the user with the notification based on the busyness of the user at the information handling system and the degree of importance of the notification.

Abstract: A system, method, and computer-readable medium for performing a customer service channel selection operation. The customer service channel selection operation includes identifying a plurality of customer service channels associated with an organization; collecting channel data associated with each of the plurality of customer service channels; processing the channel data associated with each of the plurality of customer service channels to generate channel metrics associated with each of the plurality of customer service channels; and, generating channel insights and predictions based upon the channel data associated with each of the plurality of customer services channels.

Abstract: Some examples include a service to receive a job request that includes a job, a priority of the job, and a callback that identifies an application to execute the job. The application may be hosted by a particular virtual machine of a plurality of virtual machines being executed in a cloud hosting facility. The service may add the job request to a queue and determine, using a machine learning algorithm, a risk score associated with the job. Based at least in part on the risk score, the service may send a provisioning request to the cloud hosting facility to provision one or more additional virtual machines. After determining that the application has completed executing the job, the service may send a de-provisioning request to the cloud hosting facility to de-provision at least one virtual machine of the one or more additional virtual machines.

Abstract: A system, method, and computer-readable medium for performing a customer service channel selection operation. The customer service channel selection operation includes identifying a plurality of customer service channels associated with an organization; collecting channel data associated with each of the plurality of customer service channels; processing the channel data associated with each of the plurality of customer service channels to generate channel metrics associated with each of the plurality of customer service channels; and, generating channel insights and predictions based upon the channel data associated with each of the plurality of customer services channels.

Abstract: Some examples include a service to receive a job request that includes a job, a priority of the job, and a callback that identifies an application to execute the job. The application may be hosted by a particular virtual machine of a plurality of virtual machines being executed in a cloud hosting facility. The service may add the job request to a queue and determine, using a machine learning algorithm, a risk score associated with the job. Based at least in part on the risk score, the service may send a provisioning request to the cloud hosting facility to provision one or more additional virtual machines. After determining that the application has completed executing the job, the service may send a de-provisioning request to the cloud hosting facility to de-provision at least one virtual machine of the one or more additional virtual machines.