公开(公告)号：US11878327B2

公开(公告)日：2024-01-23

申请号：US17470674

申请日：2021-09-09

Applicant: Digimarc Corporation

Inventor： Ravi K Sharma ,  Tomas Filler ,  Vojtech Holub ,  Osama M. Alattar ,  Hugh L. Brunk ,  John D. Lord ,  Geoffrey B. Rhoads ,  William Y. Conwell ,  Ajith M. Kamath

IPC: B07C5/34 ,  B07C5/342

CPC classification number: B07C5/3412 ,  B07C5/3422 ,  B07C2501/0045

Abstract: 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.

公开(公告)号：US20190261914A1

公开(公告)日：2019-08-29

申请号：US16289167

申请日：2019-02-28

Applicant: Digimarc Corporation

Inventor： Bruce L. Davis ,  Tony F. Rodriguez ,  Alastair M. Reed ,  John F. Stach ,  Geoffrey B. Rhoads ,  William Y. Conwell ,  Shankar Thagadur Shivappa ,  Ravi K Sharma ,  Richard F. Gibson

IPC: A61B5/00 ,  G06F16/248 ,  G16H50/20 ,  G16H40/67 ,  A61B5/103 ,  G06F16/245 ,  G16H50/70 ,  G06T5/40 ,  G06T7/00 ,  G10L19/018

Abstract: Reference imagery of dermatological conditions is compiled in a crowd-sourced database (contributed by clinicians and/or the lay public), together with associated diagnosis information. A user later submits a query image to the system (e.g., captured with a smartphone). Image-based derivatives for the query image are determined (e.g., color histograms, FFT-based metrics, etc.), and are compared against similar derivatives computed from the reference imagery. This comparison identifies diseases that are not consistent with the query image, and such information is reported to the user. Depending on the size of the database, and the specificity of the data, 90% or more of candidate conditions may be effectively ruled-out, possibly sparing the user from expensive and painful biopsy procedures, and granting some peace of mind (e.g., knowledge that an emerging pattern of small lesions on a forearm is probably not caused by shingles, bedbugs, malaria or AIDS). A great number of other features and arrangements are also detailed.