Digimarc Corporation

Gilbert B Shaw

Geoffrey B Rhoads

Tony F Rodriguez

Bruce L Davis

William Y Conwell

J Scott Carr

Adnan M Alattar

Burt W Perry

Eliot Rogers

Joshua V Aller

A smart phone senses audio, imagery, and/or other stimulus from a user's environment, and acts autonomously to fulfill inferred or anticipated user desires. In one aspect, the detailed technology concerns phone-based cognition of a scene viewed by the phone's camera. In one detailed arrangement, image processing tasks applied to the scene are selected from among various alternatives by reference to resource costs, resource constraints, other stimulus information (e.g., audio), task substitutability, etc. The phone applies more or less resources to an image processing task depending on how successfully the task is proceeding, or based on the user's apparent interest in the task. In another detailed arrangement, data is referred to the cloud for analysis, or for gleaning. In still another detailed arrangement, cognition, and identification of appropriate device response(s), is aided by collateral information, such as context. A great number of other features and arrangements are also detailed.

Intuitive computing methods and systems

One arrangement concerns a portable device (e.g., a smartphone) that executes plural recognition agents, such as agents that perform fingerprint-based object recognition, fingerprint-based audio recognition, barcode reading, watermark decoding, etc. Each of the agents reads from and writes to a blackboard data structure, to which camera and microphone sensors also post their data. Queues of stored sensor data are thus available for the agents to process. In some arrangements, the agents also post&#x2014;to the blackboard&#x2014;estimates of the resource costs required to perform certain functions, and estimates of the quality of results that may be achieved by such functions. This allows the system to make informed decisions about how to deploy the device's limited resources (battery, processing cycles, network bandwidth, etc.). A great variety of other features and arrangements are also detailed.

Device, system and method for recognizing inputted data using memory having blackboard data structure

A smart phone senses audio, imagery, and/or other stimulus from a user's environment, and acts autonomously to fulfill inferred or anticipated user desires. In one aspect, the detailed technology concerns phone-based cognition of a scene viewed by the phone's camera. The image processing tasks applied to the scene can be selected from among various alternatives by reference to resource costs, resource constraints, other stimulus information (e.g., audio), task substitutability, etc. The phone can apply more or less resources to an image processing task depending on how successfully the task is proceeding, or based on the user's apparent interest in the task. In some arrangements, data may be referred to the cloud for analysis, or for gleaning. Cognition, and identification of appropriate device response(s), can be aided by collateral information, such as context. A great number of other features and arrangements are also detailed.

Sensor-based mobile search, related methods and systems

A document is analyzed to determine whether it is a banknote or the like, by reference to image data corresponding to the document. Two analysis techniques may be used, one based on detection of a visible pattern characteristic of a security document, the other based on detection of a steganographic digital watermark characteristic of a security document. If either characteristic is found, the image is flagged, and appropriate anti-counterfeiting steps may be taken. Detection of the visible pattern can be performed using a series of successively more rigorous tests. If the image fails a test, successive testing steps can be skipped, speeding the process. Hough transform-based pattern recognition techniques are used in some embodiments. Provision of both a visible pattern detector and a watermark detector in a single apparatus enhances detection reliability, while permitting various implementation efficiencies.

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Portland, OR, United States of America

Gilbert B Shaw