Hewlett-packard Development Company, L.p.

Lei Liu

Jerry J Liu

Georgia Koutrika

Steven J Simske

Shanchan Wu

Ning Ge

Tong Zhang

Anita Rogacs

Jian Fan

Helen Ann Holder

Hector A Lopez

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 system that uses biometric data to facilitate learning is provided herein. The system receives at least two data points via a biometric sensor. Information is extracted from the at least two data points and a set of features are identified. Data analysis is then performed on the set of features.

Biometric data to facilitate learning

Examples associated with user authentication are described. One example method includes authenticating a user of a device using a static authentication technique. A behavior profile associated with the user is loaded. The behavior profile describes a pattern of device usage behavior by the user in a three-dimensional space over a time slice. The behavior profile also identifies distinctive user habits. Usage of the device is monitored, and a behavior similarity index is periodically updated. The behavior similarity index describes a similarity between the usage of the device and the pattern of device usage behavior. The behavior similarity index is weighted based on the distinctive user habits. Access to the device is restricted when the behavior similarity index reaches a predefined threshold.

User authentication

A method may include sensing first data samples from a first set of different subjects having a membership in a target class and sensing second data samples from a second set of different subjects not having a membership in the target class, wherein each of the first data samples and the second data samples includes a composite of individual data features. The individual data features from each composite of the first data samples and the second data samples are extracted and quantified. Sets of features and associated weightings of a target class model are identified based upon quantified values of the individual features from each composite of the first samples and the second samples to create a model representing a fingerprint of the target class to determine membership status of a sample having an unknown membership status with respect to the target class.

Target class feature model

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

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 disclosed herein relate to selecting an image based on text topic and image explanatory value. In one implementation, a processor selects an image to associate with a text based on a criteria indicating the explanatory value of a context information related to the image in relation to the topic of the text. The processor may output the selected image.

Image selection based on text topic and image explanatory value

Examples herein disclose capturing an image of printed text and a handwritten annotation and determining a topic as related to the printed text in the captured image. The examples disclose identifying a resource based on the handwritten annotation.

Identifying a resource based on a handwritten annotation

Examples herein disclose obtaining regions of digital content and determining a correlation measurement between the multiple regions of digital content adjacently located to each other. The examples disclose identifying a breakpoint in the digital content based on the determined correlation measurement.

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Palo Alto, CA, United States of America

Lei Liu