Stateful, Real-Time, Interactive, and Predictive Knowledge Pattern Machine

A method for generating and processing knowledge patterns performed by a system comprising a real-time-updated signal database comprising quantifiable and/or qualifiable metrics and signal properties thereof, a real-time-updated event database comprising singular or repeated occurrences and event properties thereof, a real-time-updated knowledge graph comprising information entities including a set of signals, a set of events, properties thereof, and relationships between the set of signals, the set of events, and the properties, a model library comprising a set of trained artificial intelligence models, a memory for storing computer instructions, and at least one processor, the method comprising:

• automatically updating the set of artificial intelligence models;
• automatically updating the signals, signal properties, events, and event properties in the knowledge graph and time-stamp the update;
• precomputing and timestamping in real-time the signal predictions and/or event predictions of signals and events in the real-time-updated knowledge graph by automatically selecting, fetching, and running artificially intelligent models from the model library;
• automatically storing the signal predictions and the event predictions in the signal database and event database, respectively;
• executing a user query process in combination with the real-time updated knowledge graph to process a free-form user query to extract at least one signal and/or at least one event;
• automatically generating a predictive answer to the free-form user query via a simulation process based on signal predictions and/or event predictions corresponding to the extracted at least one signal and/or at least one event;
• and
• automatically converting the predictive answer into a report by selecting a reporting format according to the predictive answer.