Abstract: Embodiments relate to configuring artificial-intelligence (AI) decision nodes throughout a communication decision tree. The decision nodes can support successive iteration of AI models to dynamically define iteration data that corresponds to a trajectory through the tree.

Abstract: Embodiments relate to configuring artificial-intelligence (AI) decision nodes throughout a communication decision tree. The decision nodes can support successive iteration of AI models to dynamically define iteration data that corresponds to a trajectory through the tree.

Abstract: Embodiments relate to configuring artificial-intelligence (AI) decision nodes throughout a communication decision tree. The decision nodes can support successive iteration of AI models to dynamically define iteration data that corresponds to a trajectory through the tree.

Abstract: Embodiments relate to configuring artificial-intelligence (AI) decision nodes throughout a communication decision tree.

Abstract: Embodiments relate to de-obfuscated data to process at one or more artificial-intelligence (AI) decision nodes throughout a communication decision tree. Through processing directed by the communication decision tree, additional de-obfuscated data is collected and securely transmitted in de-obfuscated or re-obfuscated form to enrich underlying data structures.

Abstract: Embodiments relate to configuring artificial-intelligence (AI) decision nodes throughout a communication decision tree. The decision nodes can support successive iteration of AI models to dynamically define iteration data that corresponds to a trajectory through the tree.

Abstract: Embodiments relate to configuring artificial-intelligence (AI) decision nodes throughout a communication decision tree. The decision nodes can support successive iteration of AI models to dynamically define iteration data that corresponds to a trajectory through the tree.

Abstract: Embodiments relate to configuring artificial-intelligence (AI) decision nodes throughout a communication decision tree. The decision nodes can support successive iteration of AI models to dynamically define iteration data that corresponds to a trajectory through the tree.

Abstract: Embodiments relate to configuring artificial-intelligence (AI) decision nodes throughout a communication decision tree. The decision nodes can support successive iteration of AI models to dynamically define iteration data that corresponds to a trajectory through the tree.

Abstract: The present disclosure relates to an intelligent user interface that predicts tasks for users to complete using a trained machine-learning model. In some implementations, when a user accesses the intelligent user interface, the available tasks and a user profile can be inputted into the trained machine-learning model to output a prediction of one or more tasks for the user to complete. Advantageously, the trained machine-learning model outputs a prediction of tasks that the user will likely need to complete, based at least in part on the user's profile and previous interactions with applications.

Abstract: Techniques for using machine learning to configure social media campaigns are disclosed. A social relationship management (SRM) service performs supervised machine learning to generate a learned model, at least by: generating feature vectors based on training data including campaign configuration data and one or more campaign success metrics; and performing pattern recognition on the feature vectors to determine one or more preferred campaign configurations. The SRM service publishes messages to one or more social media platforms and receives user interaction data associated with users' interactions with the messages. The SRM service performs unsupervised machine learning to update the learned model based at least in part on the user interaction data.

Abstract: Systems for using social media sites in recruiting activities. Potential candidates for job openings are considered using computer-implemented analysis of an online social referral graph and a rule base. Content from one or more social data sources is retrieved and analyzed to identify potential candidates for a job opening. The potential candidate's social data comprises information used as a reputation score pertaining to the potential candidate and as a reputation score pertaining to the potential candidate's social connections. An employee database is cross referenced to find a match between employees and any of the potential candidate's social connections. A referral score based on an influence contribution measure, a clout contribution measure, a generosity contribution measure, an activity contribution measure, an impact contribution measure, or an engagement contribution measure is calculated.

Abstract: Embodiments relate to de-obfuscated data to process at one or more artificial-intelligence (AI) decision nodes throughout a communication decision tree. Through processing directed by the communication decision tree, additional de-obfuscated data is collected and securely transmitted in de-obfuscated or re-obfuscated form to enrich underlying data structures.

Abstract: Embodiments relate to de-obfuscated data to process at one or more artificial-intelligence (AI) decision nodes throughout a communication decision tree. Through processing directed by the communication decision tree, additional de-obfuscated data is collected and securely transmitted in de-obfuscated or re-obfuscated form to enrich underlying data structures.

Abstract: Techniques for using machine learning to configure social media campaigns are disclosed. A social relationship management (SRM) service performs supervised machine learning to generate a learned model, at least by: generating feature vectors based on training data including campaign configuration data and one or more campaign success metrics; and performing pattern recognition on the feature vectors to determine one or more preferred campaign configurations. The SRM service publishes messages to one or more social media platforms and receives user interaction data associated with users' interactions with the messages. The SRM service performs unsupervised machine learning to update the learned model based at least in part on the user interaction data.

Abstract: Embodiments relate to de-obfuscated data to process at one or more artificial-intelligence (AI) decision nodes throughout a communication decision tree. Through processing directed by the communication decision tree, additional de-obfuscated data is collected and securely transmitted in de-obfuscated or re-obfuscated form to enrich underlying data structures.

Abstract: Embodiments relate to configuring artificial-intelligence (AI) decision nodes throughout a communication decision tree.

Abstract: Embodiments relate to configuring artificial-intelligence (AI) decision nodes throughout a communication decision tree. The decision nodes can support successive iteration of AI models to dynamically define iteration data that corresponds to a trajectory through the tree.

Abstract: Embodiments relate to configuring artificial-intelligence (AI) decision nodes throughout a communication decision tree. The decision nodes can support successive iteration of AI models to dynamically define iteration data that corresponds to a trajectory through the tree.

Abstract: Systems, methods, and software applications for managing slide presentation content are described in the present disclosure. According to one embodiment, among many, a computer readable medium is configured to store instructions that are executable by a processing device. The computer readable medium comprises logic adapted to enable a user to enter one or more search terms via a graphical user interface, the one or more search terms being used to initiate a text search of individual slides disassembled from one or more slide presentations and stored in an external database. Additional logic is adapted to display results of the text search on the graphical user interface, which displays a visual representation of a plurality of the individual slides resulting from the text search. The computer readable medium also includes logic adapted to enable a user to select one or more of the individual slides displayed on the graphical user interface.