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This invention describes a method for training a smart weighing system, such as those used in cashier-less stores, to accurately identify items. It works by continuously measuring an item's weight and adjusting that measurement based on environmental factors like temperature or humidity using a pre-recorded calibration file. The adjusted weight is then fed into a machine learning model, which has been trained to recognize specific items based on their unique calibrated weights.
Why it matters: Filed before the widespread adoption of advanced machine learning techniques like transfer learning and more efficient model architectures. These advancements could now simplify the data collection and training required for robust environmental calibration and item identification.
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