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This invention describes a computer program designed to recognize patterns. It works by linking together several different pattern-understanding "models" and then setting up a statistical "null hypothesis" to test which model is best suited for the current task. The system gathers evidence until it can confidently pick one model as the most appropriate, or until it runs out of time, and then uses that chosen model for recognition. The claims specifically detail a method for training and selecting these models.
Why it matters: Filed before the widespread adoption of deep learning and advanced meta-learning, which now provide powerful tools for dynamically selecting and combining models. Increased computational power and vast datasets since 2007 make such complex, evidence-based model selection systems highly practical.
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