An approach is provided dynamically prioritizing question requests based on extracted question data. In the approach, performed by an information handling system, a number of question requests to a question and answering (QA) system are received from a computer network, and a plurality of question priority parameters are identified, including one or more question topics and a plurality question context parameters, by performing natural language processing (NLP) analysis of each question request. The approach determines a target priority value for each question request based on the plurality of question priority parameters identified for said question request. By evaluating the target priority values for the plurality of question requests, processing of the question requests is prioritized, such as by applying an artificial intelligence (AI) learned models and rule-based logic at the information handling system to evaluate the target priority values for the plurality of question requests.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, in an information handling system comprising a processor and a memory, of dynamically prioritizing questions based on extracted question parameters, the method comprising: receiving, by the information handling system, a plurality of questions; extracting, by the information handling system, a plurality of question priority parameters comprising one or more question topics and a plurality of question context parameters for each question; determining, by the information handling system, a target priority value for each question based on the plurality of question priority parameters identified for said question; and dynamically prioritizing, by the information handling system, processing of the plurality of questions by comparing the target priority values for each of the plurality of questions. 2. The method of claim 1, wherein extracting the plurality of question priority parameters comprises performing, by the information handling system, a natural language processing (NLP) analysis of each question, wherein the NLP analysis results in one or more question topics comprising extracted named entity information from said question. 3. The method of claim 2, wherein extracting the plurality of question priority parameters further comprises comparing the extracted named entity information from each question to key priority terms contained in one or more domain dictionary databases to identify extracted named entity information that matches one or more key priority terms. 4. The method of claim 1, wherein extracting the plurality of question priority parameters comprises identifying, by the information handling system, for each question, question timing data, question source data, question location data, profile data for the user or user group submitting the question and performance requirement data for the user or user group submitting the question. 5. The method of claim 1, wherein determining a target priority value for each question comprises executing, by the information handling system, a priority prediction machine learning model against the plurality of question priority parameters identified for each question to generate a priority value for said question. 6. The method of claim 1, further comprising identifying, by the information handling system, one or more system performance parameters for the information handling system for use in determining the target priority value for each question, the system performance parameters comprising processor utilization data, available memory space data, and bandwidth utilization data. 7. The method of claim 1, wherein dynamically prioritizing processing of the plurality of questions comprises applying an artificial intelligence (AI) learned models and rule-based logic at the information handling system to sort the plurality of questions based on the target priority values for the plurality of questions. 8-20. (canceled)