Abstract: The present application includes a method and system for real-time predictive scheduling. The system receives information from at least one workload input and at least one personnel input, calculating an initial schedule based on the information and on analytics rules in a scheduling analytics engine. The system then allocates incoming workloads to customer service representatives according to the initial schedule, while monitoring adherence to the initial schedule by calculating deviation from schedule adherence. If the deviation from schedule adherence exceeds an acceptable deviation from schedule adherence within the analytics rules, the system calculates an updated schedule.

Abstract: Systems and methods for assessing the skills of a customer support agent using one or more Artificial Intelligence/Machine Learning (AI/ML) models are disclosed. In at least one embodiment, one or more benchmarks against which the performance of the customer support agent is to be measured are established. The one or more benchmarks may be derived through direct and/or indirect analysis of historical customer service data by an AI/ML benchmark model. In at least one embodiment, data relating to performance of the customer support agent during a customer call is monitored. In at least one embodiment, the AI/ML benchmark model is used to determine one or more benchmark scores identifying whether the customer support agent is meeting the one or more benchmarks.

Abstract: A communication application may include a request-response mechanism, which may enable real-time interaction between multiple users executing multiple tasks at multiple locations. Using the communication application, a first user may generate a digital ticket for the tasks, which may be shared with a second user. A response associated with the digital ticket by the second user may occur in a particular structured format within the communication application. The response may include location information of the second user and status of machines executing the tasks. The communication application may bring every user action and response data associated with the tasks into one unique digital board associated with the communication application. The digital board may create and implement a collaborative network and successfully orchestrate value-exchanging interactions among the users in the data sharing ecosystem.

Abstract: A computer-implemented method for managing a graphical user interface for a computer procurement application is disclosed. The graphical user interface is configured to show data related to key performance indicators (KPIs) for an entity and those for a peer group, show recommendations for improving values of the KPIs by adjusting values of configurations associated with the KPIs for the entity based on values of the KPIs and the associated configurations for the peer group in response to a first request, and implement the adjustment in response to a second request.

Abstract: Methods and systems for determining cadences are described. Behavior information between a user and a potential customer is stored, where the behavior information comprises actions between the user and the potential customer. The behavior information is analyzed to determine an engagement score to represent time effort the potential customer spends in interacting with the user, where the analyzing utilizes model-free reinforcement learning in a generalized Markovian process. A cadence is determined utilizing the engagement score, where the cadence comprises scheduled action items for the user for future interactions with the potential customer.

Abstract: A system and method are disclosed for solving a supply chain planning problem modeled as a linear programming (LP) problem. Embodiments include receiving a matrix formulation of at least a portion of the LP problem representing a supply chain planning problem for a supply chain network, generating an image based on the matrix formulation to identify connected components, partitioning the matrix formulation based, at least in part, on the connected components constraint into at least two partitions, formulating an LP subproblem from each of the at least two partitions, and solving the LP subproblems to generate a global solution to the supply chain planning problem.

Abstract: Embodiments for using an intelligent transaction optimization assistant by a processor. One or more actions to enhance a transaction experience of one or more users may be provided according to one or more selected constraints learned via a machine learning operation from previous transaction experiences, user behavior relating to the one or more previous transaction experiences, transaction experiences shared amongst entities associated with a social network, or a combination thereof.

Abstract: An apparatus for a license orchestrator to most efficiently distribute fee-based licenses includes a processor and a memory that stores code executable by the processor to determine that a workload is scheduled to be executed by a computing device. The computing device includes a licensable resource available for execution of the workload. The code is executable to compare a per-use licensing cost associated with using the licensable resource for execution of the workload with a cost of using existing capabilities of the computing device for execution of the workload and license and use the licensable resource for execution of the workload in response to determining that the per-use licensing cost of the licensable resource is less than using the existing capabilities of the computing device.

Abstract: Systems and methods for generating visual disposition data and identifying causal event are disclosed. An example system may include an electronic mobile device to generate visual disposition data from a plurality of items in a store. The example system may further include a server to generate processed disposition data via image processing the visual disposition data and determine disposition analysis data for an item of sale from the processed disposition data. The server may further transmit the disposition analysis data, where the disposition analysis data includes an indication of a causal event for the disposal of the item of sale.

Abstract: In response to a request for currency transportation services for a retail location (e.g., providing currency to the retail location or picking-up currency from the retail location), one or more currency transportation requirements are identified and provided as input to a currency transportation model to identify and/or assign a carrier and/or a financial institution location to serve as a final destination or an origin for the currency transportation services. The output of the currency transportation model is utilized to initiate the currency transportation services by transmitting a notification to a carrier-operated computing entity to schedule the requested currency transportation services.

Abstract: A method for improving communications in a digital collaboration environment by receiving a communication directed to a first user, determining that the first user is unavailable, in response to determining that the first user is unavailable, determining a second user based on an attribute, and notifying the second user about the communication directed to the first user.

Abstract: Disclosed is a task map providing apparatus for displaying one or more tasks, the apparatus including a memory and a processor electrically connected with the memory. The processor may be configured to generate the one or more tasks by receiving, from a task instructor who instructs recording a note through the one or more tasks, information on a task performer instructed to record the note corresponding to the one or more asks, generate progressive bars to correspond the one or more tasks, the progressive bar representing how many days have passed and remain with respect to a note recording due date by which the task performer needs to record the note.

Abstract: A method for improving performance of a computer procurement application includes using the procurement control system computer, determining a peer group associated with a first entity; using a procurement control system computer, obtaining, from client computers respectively associated with the entities, configurations that instruct a procurement application how to perform procurement tasks for the respective entities of the entities in the peer group; using a procurement control system computer, obtaining, from a first client computer associated with the first entity, a first configuration that instruct the procurement application how to perform procurement tasks for the first entity; obtaining a desired end result from the first entity; using the procurement control system computer, using a machine learning algorithm, determining configurations of the peer groups that have a causal relationship to the desired end result; providing a description of the configuration value to the client computer associated w

Abstract: A signal relationship is defined between a granular data value and a target data value. At least a portion of the granular data value corresponds to a granular latency value that is smaller than a target data latency value corresponding to the target data value. Granular data corresponding to the granular data value is interpreted. The granular data is aggregated in response to the signal relationship. A value of the target data value for a selected time reference is estimated, and the estimated value of the target data value is provided as a nowcasting prediction of the target data value.

Abstract: A crew planning system includes a demand forecasting module and an optimization module. The system forecasts anticipated reserve demand levels, and determines suitable reserve staffing approaches to meet anticipated reserve demand. The forecasting is based upon probabilistic distribution models which take into account variability associated with reserve demand for a particular day. Via use of the crew planning system, reserve staffing expenses may be reduced and/or reserve demand may be met with a higher degree of probability.

Abstract: Systems and methods for scheduling tasks to field professionals are provided. In one implementation, the system receives real-time information about a progress of a field professional assigned to a set of tasks. The real-time information may reflect a likelihood the field professional will complete the assigned a set of tasks. The system dynamically determines a window of opportunity to assign an additional task to the field professional based on the real-time information. Thereafter, the system identifies a plurality of optional tasks that the field professional can complete during the window of opportunity. In some cases, the window of opportunity includes an unplanned event likely to interfere with at least one scheduled task. The system can either present the optional tasks to the field professional and assign a task based on the field professional's selection; or automatically select a task for assignment to the field professional based on historical data.

Abstract: Embodiments are directed to managing user associations with service providers. A status value for an association between a user and a service provider may be provided if a user may be non-compliant with one or more terms of the association. Treatment plans for the user may be determined based on the classification of the user. Response information associated with the user may be generated based on execution of the treatment plans such that the user information may be updated based on the response information. During execution of the treatment plans at each evaluation point, result models may be employed to classify the response information or the updated user information. In response to non-compliance by the user with one or more conditions of a current treatment plan, other treatment plans may be determined and executed based on the updated user information.

Abstract: Techniques are described for machine learning-based generation of target segments is leveraged in a digital medium environment. A segment targeting system generates training data to train a machine learning model to predict strength of correlation between a set of users and a defined demographic. Further, a machine learning model is trained with visit statistics for the users to predict the likelihood that the users will visit a particular digital content platform. Those users with the highest predicted correlation with the defined demographic and the highest likelihood to visit the digital content platform can be selected and placed within a target segment, and digital content targeted to the defined demographic can be delivered to users in the target segment.

Abstract: Methods, apparatuses, systems, and computer program products for facilitating human intervention in an autonomous device are disclosed. In a particular embodiment, a method of facilitating human intervention in an autonomous device includes a service controller selecting from a first plurality of human interventionists, by a service controller, a first set of human interventionists to respond to a request associated with an autonomous device; transmitting, by the service controller, the request to a first set of interventionist devices, each interventionist device of the first set of interventionist devices associated with a particular human interventionist in the first set of human interventionists; and receiving from the first set of interventionist devices, by the service controller, a first set of interventionist responses to the request.

Abstract: An exemplary embodiment of the present application is a system and method for work allocation optimization. In the present disclosure, analytics are applied to work items while the work items are waiting in a work queue in order to optimize the routing and allocation of work items to agents in the most efficient manner possible, while optimizing the utilization of agents. By performing a look ahead at more than the initial work item, the system assesses the agent skills required by imminent work items in the work queue. This is then compared to a skillset of each available and/or soon to be available agent in order to achieve the optimal allocation of the work items to maximize utilization of agents. The work items are then routed to the agents accordingly.