Abstract: A case assistant is provided to client support professionals, which utilizes robotic process automation (RPA) technologies to analyze large amounts of data related to historical client cases that are similar to current open cases, data related to skilled experts associated with similar client cases, and data related to business exceptions. Several processes are utilized to provide this data to client support professionals, including a document similarity finder that utilizes a vector data collector, a tokenizer, a stop word remover, a relevance finder, and a similarity finder, several of which utilize a variety of machine learning technologies. Additional processes include a skilled experts finder and a business exceptions finder.

Abstract: A multi-dimensional human resource allocation adviser integrates with one or more employee skill set data sources and processes and aggregates both initial/static and dynamic skill set data from those sources. Machine learning algorithms are then used to normalize and rank the aggregated employee skills with respect to the skill set and requirements associated with a given task, project, or case. The set of employees determined to have employee skill sets that most closely match the skill set and other requirements associated with the given project, task, or case are then filtered based on rules and constraints determined by the requirements of the business and/or the client. The best employee match, or matches, remaining after the rules and constraints filtering are then recommended for assignment/allocation to the given task, project, or case.

Abstract: A service provider system receives case data of a client from a client service system. Vector data is collected from the case data through integration and aggregation. Signals of anomalies or sentiments are detected through machine learning from the integrated and aggregated vector data. The signals are validated, consolidated, and associated with case, contact, and client object types. A user interface presents the validated and consolidated signals to users who proactively take action based on the signals. The user interface includes dashboards, notifications, and indicators.

Abstract: A service provider system receives case data of a client from a client service system. Vector data is collected from the case data through integration and aggregation. Signals of anomalies or sentiments are detected through machine learning from the integrated and aggregated vector data. The signals are validated, consolidated and associated with case, contact, and client object types. A user interface presents the validated and consolidated signals to users who proactively take action based on the signals. The user interface includes dashboards, notifications, and indicators.

Abstract: A multi-dimensional human resource allocation adviser integrates with one or more employee skill set data sources and processes and aggregates both initial/static and dynamic skill set data from those sources. Machine learning algorithms are then used to normalize and rank the aggregated employee skills with respect to the skill set and requirements associated with a given task, project, or case. The set of employees determined to have employee skill sets that most closely match the skill set and other requirements associated with the given project, task, or case are then filtered based on rules and constraints determined by the requirements of the business and/or the client. The best employee match, or matches, remaining after the rules and constraints filtering are then recommended for assignment/allocation to the given task, project, or case.