Hewlett-packard Development Company, L.p.

International Business Machines Corporation

Hewlett Packard Enterprise Development LP

Entit Software LLC

Georgia Koutrika

Jerry J Liu

Lei Liu

Steven J Simske

Tong Zhang

Alkiviadis Simitsis

Lucian Popa

Rajasekar Krishnamurthy

Armageddon Rhabdizo Brown

Suresh Thalamati

Ryan Wisnesky

Qian Lin

Craig Peter Sayers

Eamonn O'Brien-Strain

Mauricio Antonio Hernandez-Sherrington

Rares Vernica

Mauricio A Hernandez

Alkis Simitsis

David Alan Tamez Cantu

Alejandro Guerrero Gonzalez

Dimitra Papadimitriou

Examples associated with reading difficulty level based resource recommendation are disclosed. One example may involve instructions stored on a computer readable medium. The instructions, when executed on a computer, may cause the computer to obtain a set of candidate resources related to a source document. The candidate resources may be obtained based on content extracted from the source document. The instructions may also cause the computer to identify reading difficulty levels of members of the set of candidate resources. The instructions may also cause the computer to recommend a selected candidate resource to a user. The selected candidate resource may be recommended based on subject matter similarity between the selected candidate resource and the source document. The selected candidate resource may also be recommended based on reading difficulty level similarity between the selected candidate resource and the source document.

Reading difficulty level based resource recommendation

A learning graph is generated for documents according to a sequencing approach. The learning graph includes nodes corresponding to the documents and edges. Each edge connects two of the nodes and indicates a sequencing relationship between two of the documents to which the two of the nodes correspond that specifies an order in which the two of the documents are to be reviewed in satisfaction of the learning goal. The learning graph is a directed graph specifying a learning path through the documents to achieve a learning goal in relation to a subject.

Learning graph

Examples relate to grouping students using content fields. Student data including a plurality of content fields is obtained. Each content field of the plurality of content fields includes a value that represents an unstructured marking linked to a content data collection. Student profiles are generated by assigning a student identification number to each of the plurality of content fields. Each of the student identification numbers are organized into at least one student group by analyzing the set of student profiles.

Group analysis using content data

Matching of an input document to documents in a document collection is described herein. In an example, a similarity correspondence between an input document and one or more documents in a base document collection is established. A set of base document segments and a set of message types associated to document segments in the set of base document segments is provided. The set of base document segments is derived from documents in the base document collection. The input document is segmented into input document segments corresponding to message types. Segment similarity between input document segments and base document segments corresponding to the same message types is computed. The similarity correspondence between the input document and at least one document in the base document collection is based on the computed segment similarity.

Matching of an input document to documents in a document collection

Examples relate to citation explanations. A process to provide citation explanation is provided herein. The process analyzes a primary document to extract a citation claim. The process generates a set of candidate segments of a cited document that may correspond to the citation claim. The process also analyzes the set of candidate segments.

Citation explanations

Automated content selection is disclosed. An example method includes generating a plurality of rankings for each document in a set of input documents, each ranking based on separate interesting document properties. The method also includes selecting a subset of the set of input documents, wherein each document selected for the subset is based on rankings of the selected document. The method also includes determining interesting properties of the subset. The method also includes selecting a subset with respect to parameters being optimized. The method also includes outputting a composition including the documents in the subset.

Automated content selection

Systems and methods associated with external resource identification are disclosed. One example method may be embodied on a non-transitory computer-readable medium storing computer-executable instructions. The instructions, when executed by a computer may cause the computer to perform the method. The method includes classifying a segment of a document into a member of a set of topics discussed within the document. The method also includes identifying, based on the structure of the segment and keywords from the segment, information that a reader of the document could seek upon reading the segment. The method also includes obtaining, based on the member of the set of topics, a set of candidate external resources that potentially contain the information. The method also includes presenting, in response to a user interaction with the document, a member of the set of candidate external resources identified as being likely to contain the information.

External resource identification

Examples associated with keyframe annotation are disclosed. One example includes extracting a set of keyframes from a video presentation. A subset of the keyframes is selected to present to a user based on a user preference. Annotations are generated for the subset of the keyframes. The annotations are personalized to the user. The subset of the keyframes and the annotations are presented to the user.

Keyframe annotation

Embodiments of the present invention relate to a new method of entity integration using high-level scripting languages. In one embodiment, a method of and computer product for entity integration is provided. An entity declaration is read from a machine readable medium. The entity declaration describes an entity including at least one nested entity. An index declaration is read from a machine readable medium. The index declaration describes an index of nested entities. An entity population rule is read from a machine readable medium. The entity population rule describes a mapping from an input schema to an output schema. The output schema conforms to the entity declaration. A plurality of input records is read from a first data store. The input records conform to the input schema. The entity population rule applies to the plurality of records to create a plurality of output records complying with the output schema. An index of nested entities is populated. The index complies with the index declaration.

Entity integration using high-level scripting languages

Disclosed herein are a system, non-transitory computer readable medium and method for fulfilling requests for source code. A description is associated with each section of source code text. A section of source code, whose description at least partially matches a source code request, is obtained and displayed.

Requests for source code text

Converting an input script includes obtaining an input script comprising at least one variable, obtaining at least one translation transformation rule from a library, converting the input script into a tree representation, folding the tree representation to hide a subset of variables in the input script to create a folded tree, and generating a natural language text by applying at least one translation transformation rule from the library to the folded tree.

Converting and input script to a natural language description

Methods for analyzing a Uniform Resource Locator (URL) and apparatus for performing such methods. The methods include parsing the URL into text segments and generating n-grams from the text segments. The methods further include generating annotations, each annotation corresponding to one of the n-grams and comprising a match value for its corresponding n-gram, a description of its match value, and a score. The methods still further include selecting a subset of the annotations.

Analyzing uniform resource locators

A system can include a print type classifier to receive a print message that indicates that a file has been selected to be printed. The print type classifier can also determine a print type of the file. The system can also include a print content analyzer to assign a category to the file characterizing a topic of the file based on the content of the file. The print type analyzer can also generate a content representation of the file that characterizes a summary of content of the file. The system can further include a recommendation engine to provide supplemental content based on at least one of the print type, the category and the content representation of the file, wherein the supplemental content is in a printable format.

Providing supplemental content based on a selected file

A computer-implemented method of automatically determining a name of a person appearing in an image includes receiving a collection of web pages containing a plurality of images. For each of the images, a set of names associated with the image is identified based on a text analysis of at least one of the web pages. Face detection and clustering is performed on the plurality of images to generate a plurality of face clusters. For each of the face clusters, a label for the face cluster is identified based on the set of names associated with each image in the face cluster. A name of a first person appearing in at least one of the images is determined based on the identified label for one of the face clusters associated with the first person.

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Palo Alto, CA, United States of America

Georgia Koutrika