Hewlett-packard Development Company, L.p.

Malgorzata M Sturgill

Steven J Simske

Jason S Aronoff

Paul S Everest

Marie Vans

A Marie Vans

Matthew Donald Gaubatz

Guy Adams

Masoud K Zavarehi

Mark Jacobsen

Galia Golodetz

Marcelo Aita Riss

Shivaun Albright

David S Kellar

Mick Keyes

Cesare Gritti

Juan C Villa

Andrew Fayette Page

Julie E Hurley

Gary Maloney

Leandro M Farinati

George D Guillory

An example system includes a stage identification portion to determine a stage in a progression of a progressive identifier, and an identifier encoding portion to encode or read the progressive identifier. A number or percentage of bits of the progressive identifier encoded, or read as encoded, by the identifier encoding portion is uniquely associated with the stage in the progression.

Progressive identifier

Examples associated with printer encryption are described. One example printer includes a data store to store a one-time pad. An encryption module may encrypt a message using the one-time pad. The encryption module also transmits the encrypted message to a trusted device that stores a copy of the one-time pad. A decryption module uses the one-time pad to decrypt a received message form the trusted device. The decryption module also controls the printer to perform an action based on the received message. A refresh module replaces the one-time pad during a service event.

Printer encryption

One example is a system including a plurality of summarization engines, an evaluator, and a selector. Each of the plurality of summarization engines receives content to provide a summary of the content. The evaluator determines a value of each summary for a selected task. The selector selects a summarization engine for the selected task based on the assessed value of each summary.

Determining an optimized summarizer architecture for a selected task

Examples disclosed herein relate to item identifier assignment. For example, a processor may create a mass serialization scheme to provide a list of item identifiers to allow a subset of the identifiers to be knocked out. Each item identifier may associated with multiple containers, and each container may include two item identifiers relating to the sequence of item identifiers included within the container.

Item identifier assignment

Functional summarization of non-textual content based on a meta-algorithmic pattern is disclosed. One example is a system including a converter, a plurality of summarization engines and/or meta-algorithmic patterns, an extractor, and an evaluator. The converter converts the non-textual content into a plurality of tokens. Combinations of summarization engines and/or meta-algorithm patterns are applied to the plurality of tokens to provide a meta-summary. The extractor extracts at least one summarization term from the meta-summary, and at least one class term for each given class of a plurality of classes of non-textual content. The evaluator determines similarity values of the non-textual content over each given class, each similarity value indicative of a similarity between the at least one summarization term and the at least one class term for each given class. The selector selects a class of the plurality of classes, the selecting based on the determined similarity values.

Functional summarization of non-textual content based on a meta-algorithmic pattern

One example is a system including a plurality of combinations of summarization engines and/or meta-algorithmic patterns used to combine a plurality of summarizers, an extractor, an evaluator, and a selector. Each of the plurality of combinations of summarization engines and/or meta-algorithmic patterns receives content to provide a meta-summary of the content. The extractor generates a collection of search queries based on the content. The evaluator determines a similarity value of each combination of summarization engines and/or meta-algorithmic patterns for the collection of search queries. The selector selects an optimal combination of summarization engines and/or meta-algorithmic patterns based on the similarity value.

Data extraction based on multiple meta-algorithmic patterns

Examples herein disclose multiple engines to produce output representative of a summary of document produced by each of the multiple engines. The examples apply a weighting mechanism to the output specific to that engine to obtain a value corresponding to that output. The examples identify specialized language if the value corresponding to that output reaches at least a particular threshold.

Specialized language identification

A combinatorial summarizer includes a plurality of summarization engines, a processor in selective communication with each summarization engine, and computer readable instructions executable by the processor and embodied on a tangible, non-transitory computer readable medium. Each summarization engine is to select a respective plurality of sentences, and generate a relative rank and an associated weight for each sentence of the respective plurality of sentences. The computer readable instructions include instructions to determine a combined weight for each sentence of each respective plurality of sentences. The combined weight is based upon the respective associated weight and a respective relative human rank for each sentence in a set of sentences, including all sentences of each respective plurality of sentences. The computer readable instructions further include instructions to determine a total weight for each summarization engine based, respectively, upon the combined weights for each sentence of each respective plurality of sentences.

Combinatorial summarizer

A counterfeit identification performance attribute (SIPA) sensitivity to changes in resolution of the image for features of an image is determined. The CIPA sensitivity for the features is used to choose at least one feature to determine whether the image on a sample is a counterfeit.

Feature resolutions sensitivity for counterfeit determinations

A method and apparatus to select from an image () of a first sample (), at least one region () digitally captured at a first resolution based upon how a counterfeit identification performance attribute of each region () digitally captured at the first resolution correlate to the counterfeit identification performance attribute of the region () digitally captured at a second resolution higher than the first resolution. The selected region () is used to determine whether the image () on a second sample is a counterfeit.

Region selection for counterfeit determinations

Techniques related to data stream traffic control are disclosed herein. A bit equivalent entropy of an anonymized data stream is computed. Traffic of the data stream is controlled based on the value of the bit equivalent entropy.

Data stream traffic control

An image processing method includes applying a segmentation engine to different mappings of a digital image, and generating primitives by tessellating non-congruent segments of the different mappings, where the primitives are defined by boundaries of the non-congruent segments in the different mappings. The method further includes classifying the primitives. Classifying the primitives is accomplished by assessing, using a plurality of classifiers, a quality of each segment of each mapping, and assigning a classification to each primitive based upon the assessing. Each of applying, generating and classifying is performed by a processor programmed by machine memory encoded with data.

Applying a segmentation engine to different mappings of a digital image

Methods, and apparatus for performing methods, for classifying an image. Methods include determining a corresponding set of metrics for each region of two or more regions of a pattern of regions of an image, and classifying the image in response to at least the corresponding set of metrics for each of the two or more regions of the pattern of regions.

Classifying images

A counterfeit identification performance attribute (CIPA) sensitivity to changes in resolution of the image for features of an image is determined (). The CIPA sensitivity for the features is used to choose at least one feature to determine whether the image on a sample is a counterfeit ().

Methods, and apparatus for performing methods, for selecting a classifier engine. Methods include, for two or more portions of a set of items of known classification, classifying members of each portion using a particular classifier engine; selecting a portion of the set of items whose classifications satisfy a first criteria; classifying members of the selected portion of the set of items using two or more classifier engines; and selecting a classifier engine whose classification of the selected portion of the set of items satisfies a second criteria.

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Fort Collins, CO, United States of America

Malgorzata M Sturgill