Hewlett-packard Development Company, L.P.

Hewlett Packard Enterprise Development LP

Box Limited

Entit Software LLC

Claudio Bartolini

Mehmet Kivanc Ozonat

Hamid Reza Motahari Nezhad

Andrew Robert Byde

Marianne Hickey

Maher Rahmouni

Parag Mulendra Joshi

David Trastour

Andrew Kahn

Denali Lumma

Alan Hersh Karp

Sven Graupner

Kannan Govindarajan

Arindam Banerji

Abdel Boulmakoul

Gregory Pogossiants

Christopher William Preist

Shamik Sharma

Dorothea Beringer

Mathias Jean Rene Salle

Scott L Williams

Anup Kumar Kalia

Rosalynn J Tuggle

Jason Waugh

Embodiments provide systems and methods for performing automated browser testing on different native file types by receiving files of different types. A preview version of each file can be generated and rendered in an output file type. Generating the preview version can be performed by a preview application executed by the testing system and rendering the preview version of the first file can be performed by a browser application executed by the testing system. The output file type can be different from the received file type. For example, the received file type can be a native file type of a first application different from the browser and the output file type comprises an HyperText Markup Language (HTML) file type. A test can be executed on the rendered preview version based on one or more assertions on the first file.

Automated browser testing assertion on native file formats

A project-comparator subsystem generates a similarity metric for input electronically-represented projects input to the project-comparator subsystem. A project search engine receives data that represents a first electronically-represented project, identifies, by pairwise comparison of the stored electronically-represented projects with the first electronically-represented project using the project-comparator subsystem, a stored electronically-represented project with greatest similarity to the first electronically-represented project, and outputs an indication of the identified stored electronically-represented project.

Comparing projects

Identifying a subset of signifiers to analyze can include determining a set of distance metrics between a first signifier and each of a plurality of second signifiers, identifying a subset of the plurality of second signifiers to analyze based on the set of distance metrics using a computing device, and determining a relation between the subset of the plurality of second signifiers and the first signifier based a subset of the set of distance metrics.

Identifying subsets of signifiers to analyze

Suggesting email tags. A non-transitory machine-readable storage device includes executable instructions that, when executed, cause one or more processors to provide a suggestion for at least one suggested tag based on content of an email, receive a selection of a selected tag, store the email in a computer database, thus creating a stored email, and associate the selected tag with the stored email in the computer database.

Email tags

A particular case is matched to further cases, where the matching is based on plural attributes contained in the particular case and in the further cases, wherein one of the plural attributes relates to a flow of activities taken to address the respective case.

Matching of cases based on attributes including an attribute relating to flow of activities

Clustering signifiers in a semantics graph can comprise coarsening a semantics graph associated with an enterprise communication network containing a plurality of nodes into a number of sub-graphs containing supernodes; partitioning each of the number of sub-graphs into a number of clusters; and iteratively refining the number of clusters to reduce an edge-cut of the semantics graph, based on the number of clusters.

Clustering signifiers in a semantics graph

Building a semantics graph for an enterprise communication network can include extracting a first signifier and a second signifier from the enterprise communication network, determining a semantic proximity of the first signifier and the second signifier using an engine executing computer readable instructions, and building the semantics graph, wherein the first signifier and the second signifier are represented as nodes connected by an edge representing the semantic proximity of the first signifier and the second signifier.

Building a semantics graph for an enterprise communication network

Information relating to project metric data and occurrence of events in a plurality of projects is received (). Uncertainty data is determined () based on the received information, where the uncertainty data represents a probability distribution of at least one metric associated with the active project. Input relating to a scenario to perform what-if analysis is received (), and based on the input, what-if analysis is performed ().

Performing what-if analysis

An example of identifying tasks and commitments can include receiving a communication message. A task and a parameter can be identified in the communication message. Information related to the task can be extracted from the communication message using natural language processing (NLP) and machine learning (ML). A commitment related to the task can be identified using NLP extracted information. A state of the commitment can be identified using NLP and ML based on the extracted information.

Identifying tasks and commitments using natural language processing and machine learning

Display screen with graphical user interface

A method for calculating a content subset can include crawling a number of webpages for content, determining a relevance to a particular domain of the content, determining a penalty value for each of the number of webpages; and calculating, utilizing a data tree-based model, a subset of the content to analyze based on the relevance and the penalty value.

Calculating a content subset

One embodiment of the present invention is directed to a project search system, implemented on one or more computer systems, comprising two or more stored projects represented by electronically stored, computer-accessible data that are stored in a project-database component; a project-comparator subsystem, executed on the one or more computer systems, that generates a similarity metric for two electronically-represented projects input to the project-comparator subsystem; and a project search engine, executed on the one or more computer systems, that receives data that represents an input project, identifies, by pairwise comparison of the stored projects with the input project using the project-comparator subsystem, one or more stored projects with greatest similarity to the input project, and outputs, to a graphical interface displayed on a computer display or to an application program, indications of the one or more stored projects with greatest similarity to the input project.

Method and system for comparing and locating projects

Building a semantics graph for an enterprise communication network can include calculating a distance metric between a first signifier and a second signifier associated with an enterprise communication network, wherein the distance metric includes a plurality of relationships defined based on a frequency of co-occurrences of the first signifier and the second signifier, and building a semantics graph for the enterprise communication network using the calculated distance metric.

Semantics graphs for enterprise communication networks

A method and system for project and portfolio management is disclosed. The method may include tagging one or more data records of projects of a plurality of projects with one or more tags, and displaying on a display device the one or more tagged data records with information relating to said one or more tags. A computer program product is also disclosed.

Growing community of inventors

Palo Alto, CA, United States of America

Claudio Bartolini