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PREDICTION OF FUTURE OCCURRENCES OF EVENTS USING ADAPTIVELY TRAINED ARTIFICIAL-INTELLIGENCE PROCESSES AND CONTEXTUAL DATA
PREDICTION OF FUTURE OCCURRENCES OF EVENTS USING ADAPTIVELY TRAINED ARTIFICIAL-INTELLIGENCE PROCESSES AND CONTEXTUAL DATA
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机译:预测未来发生的事件自适应训练的人工智能流程和上下文数据
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摘要
The disclosed embodiments include computer-implemented apparatuses and processes that dynamically predict future occurrences of events using adaptively trained artificial-intelligence processes and contextual data. For example, an apparatus may generate an input dataset based on first interaction data and contextual data associated with a prior temporal interval, and may apply an adaptively trained, gradient-boosted, decision-tree process to the input dataset. Based on the application of the adaptively trained, gradient-boosted, decision-tree process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of an event during a future temporal interval, which may be separated from the prior temporal interval by a corresponding buffer interval. The apparatus may also transmit a portion of the generated output data to a computing system, and the computing system may be configured to generate or modify second interaction data based on the portion of the output data.
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