Computerized system, method and processor-executable code to autonomously screen mailable items to identify candidate items for content inspection or testing

A computerized system comprising:

• an artificial intelligence-based system configured to autonomously screen in real-time each respective item of a stream of mailable or shippable items being conveyed through an item processing system,
• wherein the artificial intelligence-based system is operatively coupled to acquire sensor data indicative of physical characteristics and digital information in connection with each respective item of the stream of mailable or shippable items being conveyed through the item processing system,
• wherein the artificial intelligence-based system is configured to process the data indicative of the physical characteristics and the digital information in connection with each respective item to form a respective virtual model of each respective item and generate a unique feature set for each respective item based on the respective virtual model of each respective item;
• wherein the artificial intelligence-based system is configured to comparatively analyze the unique feature set of each respective item with respect to reference feature sets;
• based on estimation of a match or near match of the unique feature set of a respective item of the stream of mailable or shippable items with at least one of the reference feature sets, the artificial intelligence-based system configured to identify the respective item of the stream of mailable or shippable items as a candidate for content inspection or testing;
• the artificial intelligence-based system configured to command the item processing system to divert the respective identified item of the stream of mailable or shippable items for content inspection or testing,
• the computerized system further comprising a machine-learning subsystem configured to generate the reference feature sets and respective match confidence thresholds for each reference feature set, wherein at least some of the reference feature sets and respective match confidence thresholds of the at least some of the reference feature sets are generated by the machine-learning subsystem in response to a training dataset configured to define constituent elements and respective relevance values of the defined constituent elements of the at least some of the reference feature sets, the defined constituent elements and the respective relevance values of each reference feature set being configured to predict items, which are candidates for content inspection or testing.