Digimarc Corporation

Pitney Bowes Inc.

Zulutime, LLC

Digimarc Coporation

Hugh L Brunk

Geoffrey B Rhoads

Ravi K Sharma

Kenneth Lee Levy

Vojtech Holub

Osama M Alattar

Tomas Filler

John Douglas Lord

Brett Alan Bradley

Trent J Brundage

Tony F Rodriguez

Ajith Mulki Kamath

Stephen K Decker

Jun Tian

William Y Conwell

Brett T Hannigan

J Scott Carr

Claude Zeller

Robert A Cordery

John F Stach

Adnan M Alattar

Shankar Thagadur Shivappa

Neil E Lofgren

Robert G Lyons

Scott M Long

Donald G Mackay

Aparna R Gurijala

Tyler J McKinley

Joel R Meyer

Eliot Rogers

Mark E Haynes

Tom Tewalt

Harrison Freer

Alastair M Reed

Kristyn R Falkenstern

Arlie R Conner

Ammon E Gustafson

Matthew M Weaver

Cynthia Louise Archer

William A Brosseau

William L Kilmartin

A waste stream is analyzed and sorted to segregate different items for recycling. Certain features of the technology improve the accuracy with which waste stream items are diverted to collection repositories. Other features concern adaptation of neural networks in accordance with context information sensed from the waste. Still other features serve to automate and simplify maintenance of machine vision systems used in waste sorting. Yet other aspects of the technology concern marking 2D machine readable code data on items having complex surfaces (e.g., food containers with integral ribbing for structural strength or juice pooling), to mitigate issues that such surfaces can introduce in code reading. Still other aspects of the technology concern prioritizing certain blocks of conveyor belt imagery for analysis. Yet other aspects of the technology concern joint use of near infrared spectroscopy, artificial intelligence, digital watermarking, and/or other techniques, for waste sorting. A variety of further features and arrangements are also detailed.

Methods and arrangements to aid recycling

A sequence of images depicting an object is captured, e.g., by a camera at a point-of-sale terminal in a retail store. The object is identified, such as by a barcode or watermark that is detected from one or more of the images. Once the object's identity is known, such information is used in training a classifier (e.g., a machine learning system) to recognize the object from others of the captured images, including images that may be degraded by blur, inferior lighting, etc. In another arrangement, such degraded images are processed to identify feature points useful in fingerprint-based identification of the object. Feature points extracted from such degraded imagery aid in fingerprint-based recognition of objects under real life circumstances, as contrasted with feature points extracted from pristine imagery (e.g., digital files containing label artwork for such objects). A great variety of other features and arrangements—some involving designing classifiers so as to combat classifier copying—are also detailed.

Learning systems and methods

In one aspect, an elongated lighting module includes plural colors of LEDs that flash in a cyclical sequence to provide multi-spectral illumination. Several such modules can be arranged end-to-end to span a conveyor system that transports a stream of plastic waste items in a recycling center, enabling capture of differently-illuminated image frames depicting the items. Each module may include N LEDs of a first color (e.g., red) and M LEDs of a second color (e.g., blue), where N and M are different. Drive circuitry can be simplified by configuring the LEDs in strings of common colors, but with different counts. Electrical noise due to switching transients can be reduced by operating the LEDs at a low current when not being flashed. A great variety of other features and arrangements are also detailed.

Lighting arrangements for material sorting

A plastic item, such as a beverage bottle, conveys two distinct digital watermarks, encoded using two distinct signaling protocols. A first, printed label watermark conveys a retailing payload, including a Global Trade Item Number (GTIN) used by a point-of-sale scanner in a retail store to identify and price the item when presented for checkout. A second, plastic texture watermark conveys a recycling payload, including data identifying the composition of the plastic. The use of two different signaling protocols assures that a point-of-sale scanner will not spend its limited time and computational resources working to decode the recycling watermark, which lacks the data needed for retail checkout. In some embodiments, a recycling apparatus makes advantageous use of both types of watermarks to identify the plastic composition of the item (e.g., relating GTIN to plastic type using an associated database), thereby increasing the fraction of items that are correctly identified for sorting and recycling. A great number of other features and arrangements are also detailed.

Recycling methods and systems, and related plastic containers

Consumer product labels sometime include 2D machine readable codes, such as digital watermark patterns. When such label artwork is printed on a plastic sleeve that is then heat-shrunk to conform to a plastic container, the code can become distorted and unreadable. Various techniques are detailed to cope with this problem. Additionally, methods are disclosed to assess—before a heat-shrink label is applied to a product—the expected final readability of any machine readable code included in the label artwork. A variety of other features and arrangements are also detailed.

Pre-press warping arrangements for watermarks and associated graphic artworks, with related quality control techniques

A thermoplastic resin, such as PET, is molded to define a 2D code signal, such as a digital watermark pattern. The mold can comprise an array of hole or spike features, some of which are directly vented to atmospheric pressure. A network of channels can link the other features to the directly-vented features, so all features are vented. A mold comprising spike features can form a digital watermark pattern on an item such that the watermark payload is decodable both from the side of the item that contacted the mold, and also from the opposite, non-contact side of the item. To aid entry of viscous thermoplastic among the very fine elemental features of a mold representing a watermark signal pattern, the features can be overlapped, forming a connected binary mark having larger features. A variety of other improvements and arrangements are also detailed.

Signaling arrangements employing molded thermoplastics

A surface is laser-etched to convey a 2D machine-readable code pattern. Various strategies are detailed to minimize the etching time. Some strategies include modifying the code pattern to reduce a path length traveled by the laser. Some strategies include modifying the code pattern to make it sub-optimal, i.e., making the code pattern a less-faithful approximation of an ideal code pattern. In some embodiments the etched surface is the surface of a plastic container, and the code pattern conveys information indicating the type of plastic of which the container is manufactured. A variety of other features and arrangements are also detailed.

Laser marking of machine-readable codes

The parameters of an optical code are optimized to achieve improved signal robustness, reliability, capacity and/or visual quality. An optimization program can determine spatial density, dot distance, dot size and signal component priority to optimize robustness. An optical code generator employs these parameters to produce an optical code at the desired spatial density and robustness. The optical code is merged into a host image, such as imagery, text and graphics of a package or label, or it may be printed by itself, e.g., on an otherwise blank label or carton. A great number of other features and arrangements are also detailed.

Generating reading optical codes with variable density to adapt for visual quality and reliability

A plastic item, such as a beverage bottle, can convey two distinct digital watermarks, encoded using two distinct signaling protocols. A first, printed label watermark conveys a retailing payload, including a Global Trade Item Number (GTIN) used by a point-of-sale scanner in a retail store to identify and price the item when presented for checkout. A second, plastic texture watermark may convey a recycling payload, including data identifying the composition of the plastic. The use of two different signaling protocols assures that a point-of-sale scanner will not spend its limited time and computational resources working to decode the recycling watermark, which may lack data needed for retail checkout. In some embodiments, a recycling apparatus makes advantageous use of both types of watermarks to identify the plastic composition of the item (e.g., relating GTIN to plastic type using an associated database), thereby increasing the fraction of items that are correctly identified for sorting and recycling. In other embodiments the plastic item (or a label thereon) bears only a single watermark. A great number of other features and arrangements are also detailed.

Methods and arrangements for sorting items, useful in recycling

Methods and arrangements involving electronic devices, such as smartphones, tablet computers, wearable devices, etc., are disclosed. One arrangement involves a low-power processing technique for discerning cues from audio input. Another involves a technique for detecting audio activity based on the Kullback-Liebler divergence (KLD) (or a modified version thereof) of the audio input. Still other arrangements concern techniques for managing the manner in which policies are embodied on an electronic device. Others relate to distributed computing techniques. A great variety of other features are also detailed.

Methods and system for cue detection from audio input, low-power data processing and related arrangements

Generating and reading optical codes with variable density to adapt for visual quality and reliability

A sequence of images depicting an object is captured, e.g., by a camera at a point-of-sale terminal in a retail store. The object is identified, such as by a barcode or watermark that is detected from one or more of the images. Once the object's identity is known, such information is used in training a classifier (e.g., a machine learning system) to recognize the object from others of the captured images, including images that may be degraded by blur, inferior lighting, etc. In another arrangement, such degraded images are processed to identify feature points useful in fingerprint-based identification of the object. Feature points extracted from such degraded imagery aid in fingerprint-based recognition of objects under real life circumstances, as contrasted with feature points extracted from pristine imagery (e.g., digital files containing label artwork for such objects). A great variety of other features and arrangements&#x2014;some involving designing classifiers so as to combat classifier copying&#x2014;are also detailed.

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Portland, OR, United States of America

Hugh L Brunk