Digimarc Corporation

Emma C Sinclair

Tony F Rodriguez

Geoffrey B Rhoads

Ravi K Sharma

William Y Conwell

Bruce L Davis

Edward Bruce Knudson

Brett Alan Bradley

Tomas Filler

John F Stach

Vojtech Holub

Joel R Meyer

Eliot Rogers

Colin P Cornaby

Tomas Denemark

Hugh L Brunk Brunk

In a next-generation retail store, the presence of a shopper at a particular shelf location, at about the time that an item was removed from that shelf location, gives rise to a hypothesis that the item may have been picked for purchase by that shopper. An ensemble of different such hypotheses, about different items removed by different shoppers, is evaluated jointly to determine which hypotheses are most likely. Other factors can influence the determinations, such as a physical distance between picked products, together with a time interval between their picking. If cameras are used to detect removal of items from shelves, hand depictions captured by such cameras are further factors that may be considered, e.g., helping associate certain item removals with a common shopper. By generating purchase lists for each shopper with such methods, shoppers can be relieved of the need of having each item scanned at a checkout. Some embodiments are privacy-preserving—neither employing nor capturing personally-identifying information. Other embodiments don't require tracking movements of shoppers through the store. A great number of other features and arrangements are also detailed.

Sensor-based maximum-likelihood estimation of item assignments

A store includes first and second racks of shelving that are respectively viewed by first and second cameras. A computer system, including one or more processors and memory, defines a first convolutional neural network (CNN) trained to recognize a first set of items stocked on the first rack of shelving, and a second CNN trained to recognize a second set of items stocked on the second rack of shelving. (The first camera provides imagery for recognition by the first CNN, and the second camera provides imagery for recognition by the second CNN.) In such arrangement, the second neural network is trained to recognize a particular item that the first neural network is not trained to recognize, and the first neural network is trained to recognize a certain item that the second neural network is not trained to recognize. Many other features and arrangements are also detailed.

A camera captures video imagery depicting a digitally-watermarked object. A reference signal in the watermark is used to discern the pose of the object relative to the camera, and this pose is used in affine-transforming and positioning a graphic on the imagery as an augmented reality overlay. Feature points are also discerned from the captured imagery, or recalled from a database indexed by the watermark. As the camera moves relative to the object, the augmented reality overlay tracks the changing object depiction, using these feature points. When feature point-based tracking fails, the watermark is again processed to determine pose, and the overlay presentation is updated accordingly. In another arrangement, feature points are extracted from images of supermarket objects captured by multiple users, and are compiled in a database in association with watermark data identifying the objects&#x2014;serving as a crowd-sourced repository of feature point data. A great number of other features and arrangements are also detailed.

Hybrid feature point/watermark-based augmented reality

Mobile devices, such as smartphones, are severely battery-limited. The capabilities of mobile device processors have increased at exponential rates, but battery technologies have improved at much slower rates. As a consequence, it is ever more important that mobile devices be operated in battery-preserving manners. An aspect of the present technology concerns methods and arrangements enabling on-going recognition-processing of imagery and audio by different detectors, without the high battery drain that has characterized the prior art. A variety of other features and arrangements are also detailed.

Method and apparatus for analyzing sensor data

Arrangements involving portable devices (e.g., smartphones and tablet computers) are disclosed. One arrangement enables a content creator to select software with which that creator's content should be rendered&#x2014;assuring continuity between artistic intention and delivery. Another utilizes a device camera to identify nearby subjects, and take actions based thereon. Others rely on near field chip (RFID) identification of objects, or on identification of audio streams (e.g., music, voice). Some technologies concern improvements to the user interfaces associated with such devices. For example, some arrangements enable discovery of both audio and visual content, without any user requirement to switch modes. Other technologies involve use of these devices in connection with shopping, text entry, and vision-based discovery. Still other improvements are architectural in nature, e.g., relating to evidence-based state machines, and blackboard systems. Yet other technologies concern computational photography. A great variety of other features and arrangements are also detailed.

Smartphone-based methods and systems

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Portland, OR, United States of America

Emma C Sinclair