Abstract: Embodiments of systems and methods for enhanced drift remediation with causal methods and online model modification are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: detect drift in an Artificial Intelligence (AI) or Machine Learning (ML) model configured to make a prediction or a causal reasoning graphical or structural inference based upon input data, identify a root cause of the drift, and tag the input data with an indication of the root cause.

Abstract: Embodiments of systems and methods for model prediction confidence utilizing drift are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: identify drift with respect to an Artificial Intelligence (AI) or Machine Learning (ML) model; and adjust a confidence score of a prediction or inference produced by the AI/ML model based, at least in part, upon the drift.

Abstract: Embodiments of systems and methods for enhanced drift remediation with causal methods and online model modification are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: detect drift in an Artificial Intelligence (AI) or Machine Learning (ML) model configured to make a prediction or a causal reasoning graphical or structural inference based upon input data, identify a root cause of the drift, and tag the input data with an indication of the root cause.

Abstract: Embodiments of systems and methods for automated identification of training datasets are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: receive a training dataset comprising a plurality of elements, tag an element of the training dataset with: (a) a first attribute representing a first characteristic detectable in the element, and (b) a second attribute representing a second characteristic not detectable in the element, and select a subset of the plurality of elements to train an Artificial Intelligence (AI) or Machine Learning (ML) model based, at least in part, upon the tag.

Abstract: Embodiments of systems and methods for unsupervised data characterization utilizing drift are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: provide input data to an Artificial Intelligence (AI) or Machine Learning (ML) drift detector, where the AI/ML drift detector is associated with a characteristic undetectable in the input data; and receive a drift confidence score from the AI/ML drift detector.

Abstract: Embodiments of systems and methods for multi-observer, consensus-based ground truth are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: detect an observation overlap between two or more devices; identify a consensus between Artificial Intelligence (AI) or Machine Intelligence (ML) model inferences made based upon data received by the two or more devices; and in response to the identification, tag at least a subset of the data with a ground truth label.

Abstract: A system for processing data is disclosed that includes an artificial intelligence (AI) model operating on a processor and configured to process an incoming data set to generate a data output. An AI model anomaly detection system operating on the processor and configured to receive the incoming data set and the data output and to generate an anomaly detection as a function of the incoming data set and the data output. An AI model anomaly analysis system operating on the processor and configured to receive the anomaly detection and the incoming data set and to generate AI model anomaly data. An AI model anomaly mitigation system operating on the processor and configured to receive the AI model anomaly data and to generate AI model anomaly correction data.

Abstract: Multiple communication types (e.g. chat, email, voice, etc.) are integrated into a call center. Embodiments can allow a single agent to handle multiple endpoints and multiple communication types. An agent user interface allows the agent to have control over accepting multiple communications and provides information to the agent about the communications and the customer. The agent can drag and drop canned responses, images, URLs, or other information into a window for immediate display on a customer's computer. The system also provides flexibility in transferring large amounts of historic and current data from one agent to another, and from storage to an active agent. The system also provides for “blending” of different communication types to provide a single interface to the agent.

Abstract: Multiple communication types are integrated into a call center. The communication types can be chat, email, Internet Protocol (IP) voice, traditional telephone, web page, digital image, digital video and other types. Features of the invention include allowing a single agent to handle multiple customers on multiple channels, or “endpoints.” Prioritizing and assigning calls to agents based on a specific criteria such as the number of endpoints assigned to an agent, the agents availability, the priority of a customer call, the efficiency of a given agent and the agent's efficiency at handling a particular communication type call. An agent user interface is described that allows the agent to have control over accepting multiple calls. The agent can drag and drop canned responses, images, URLs, or other information into a window for immediate display on a customer's computer. The system provides for detailed agent performance tracking. The system provides failure recovery by using a backup system.

Abstract: Multiple communication types are integrated into a call center. The communication types can be chat, email, Internet Protocol (IP) voice, traditional telephone, web page, digital image, digital video and other types. Features of the invention include allowing a single agent to handle multiple customers on multiple channels, or “endpoints.” Prioritizing and assigning calls to agents based on a specific criteria such as the number of endpoints assigned to an agent, the agents availability, the priority of a customer call, the efficiency of a given agent and the agent's efficiency at handling a particular communication type call. An agent user interface is described that allows the agent to have control over accepting multiple calls. The agent can drag and drop canned responses, images, URLs, or other information into a window for immediate display on a customer's computer. The system provides for detailed agent performance tracking. The system provides failure recovery by using a backup system.

Abstract: Multiple communication types are integrated into a call center. The communication types can be chat, email, Internet Protocol (IP) voice, traditional telephone, web page, digital image, digital video and other types. Features of the invention include allowing a single agent to handle multiple customers on multiple channels, or “endpoints.” Prioritizing and assigning calls to agents based on a specific criteria such as the number of endpoints assigned to an agent, the agents availability, the priority of a customer call, the efficiency of a given agent and the agent's efficiency at handling a particular communication type call. An agent user interface is described that allows the agent to have control over accepting multiple calls. The agent can drag and drop canned responses, images, URLs, or other information into a window for immediate display on a customer's computer. The system provides for detailed agent performance tracking. The system provides failure recovery by using a backup system.

Abstract: Multiple communication types are integrated into a call center. The communication types can be chat, email, Internet Protocol (IP) voice, traditional telephone, web page, digital image, digital video and other types. Features of the invention include allowing a single agent to handle multiple customers on multiple channels, or “endpoints.” Prioritizing and assigning calls to agents based on a specific criteria such as the number of endpoints assigned to an agent, the agent's availability, the priority of a customer call, the efficiency of a given agent and the agent's efficiency at handling a particular communication type call. An agent user inteface is described that allows the agent to have control over accepting multiple calls. The agent can drag and drop canned responses, images, URLs, or other information into a window for immediate display on a customer's computer. The system provides for detailed agent performance tracking. The system provides failure recovery by using a backup system.

Abstract: Multiple communication types are integrated into a call center. The communication types can be chat, email, Internet Protocol (IP) voice, traditional telephone, web page, digital image, digital video and other types. Features of the invention include allowing a single agent to handle multiple customers on multiple channels, or “endpoints.” Prioritizing and assigning calls to agents based on a specific criteria such as the number of endpoints assigned to an agent, the agent's availability, the priority of a customer call, the efficiency of a given agent and the agent's efficiency at handling a particular communication type call. An agent user inteface is described that allows the agent to have control over accepting multiple calls. The agent can drag and drop canned responses, images, URLs, or other information into a window for immediate display on a customer's computer. The system provides for detailed agent performance tracking.

Abstract: A network-based customer interface allows a customer to receive automated information from a variety of data sources, or databases, including a knowledge base and frequently asked question database. The data sources are updated based on recording ongoing customer queries and transactions with the database. The updates can be reviewed before they are used to update the data sources. Customer authentication is performed so that the customer can be given an elevated level of service and so that any prior customer sessions can be used to help human agents know about the customer's situation.