Stratify, Inc.

International Business Machines Corporation

Google Inc.

Other

Hewlett Packard Enterprise Development LP

Purpleyogi.com Inc.

Joy Aloysius Thomas

Pangal Pandurang Nayak

Peter Anthony Franaszek

Ramana Venkata

Ramesh Subramonian

Rakesh Mathur

Kenneth Chan

Alex David Lester

Matthew Symonds

Che Liu

Michael Christopher Yara

Hakan Ancin

Jared Scott Borner

Terrence Li

Karthik Ramachandran

Sri Harsha Vardhan Reddy Chevuru

Tsenguun Tsogbadrakh

Andrew Marsh Gardiner

Sibo Liu

John Timothy Robinson

Vamsi Salaka

David Bayer

Kumar Maddalli

Kumar Maddali

Vittorio R Castelli

John Ogden Lamping

Sridhar Rajagopalan

Jagdish Chand

Sauraj Goswami

Anant Deep Jhingran

Pantelis G Tsoucas

George Manianghat Mathew

Krishna Kumar Kesavan

Rajashekhar Goli

Ankita Bakshi

Mohana Krishna Lakhamraju

Gollakota Venkata Ramana

Methods and systems that facilitate automatic generation of Application Programming Interface (API) specification from web traffic. Methods include obtaining a plurality of API requests and responses to the plurality of API requests. Methods include processing these API requests and responses to API requests to identify one or more attributes, such as, for example, variables, query parameters, response status codes, and response schemas. Methods include identifying variables using a tree data structure to represent resource paths. Methods include identifying query parameters based on resource items in resource paths. Methods include determining that the API call does not conform to the API specification by comparing one or more attributes of the API call with the attributes of the API specification.

API specification generation

Automatically extracting profile feature attribute data from event data is disclosed, including: receiving a set of event data; receiving a feature associated with a profiling technique; determining that a plurality of events associated with a user in the set of event data corresponds to a first attribute corresponding to the feature, wherein the first attribute corresponds to a first bin having a first defined value; determining that the plurality of events associated with the user in the set of event data corresponds to a second attribute corresponding to the feature, wherein the second attribute corresponds to a second bin having a second defined value; and creating a user record corresponding to the user indicating presence in the first bin and the second bin.

Automatically extracting profile feature attribute data from event data

Documents likely to be near-duplicates are clustered based on document vectors that represent word-occurrence patterns in a relatively low-dimensional space. Edit distance between documents is defined based on comparing their document vectors. In one process, initial clusters are formed by applying a first edit-distance constraint relative to a root document of each cluster. The initial clusters can be merged subject to a second edit-distance constraint that limits the maximum edit distance between any two documents in the cluster. The second edit-distance constraint can be defined such that whether it is satisfied can be determined by comparing cluster structures rather than individual documents.

Clustering of near-duplicate documents

A pool of messages, e.g., e-mails and/or other electronic documents that each correspond to a communication from a sender to a recipient, is analyzed to identify communication chains between a source and a target. Sender and recipient identifiers extracted from the messages are used to detect direct and indirect communication links between pairs of entities. Information related to the identified communication chains can be presented to a user via an interactive network graph that supports iterative analysis of the communication-chain data.

Systems and methods for interactively analyzing communication chains based on messages

Meaningful phrases are distinguished from chance word sequences statistically, by analyzing a large number of documents and using a statistical metric such as a mutual information metric to distinguish meaningful phrases from groups of words that co-occur by chance. In some embodiments, multiple lists of candidate phrases are maintained to optimize the storage requirement of the phrase-identification algorithm. After phrase identification, a combination of words and meaningful phrases can be used to construct clusters of documents.

Phrase based document clustering with automatic phrase extraction

An automated parser for e-mail messages identifies component parts such as header, body, signature, and disclaimer. The parser uses a hidden Markov model (HMM) in which the lines making up an e mail are treated as a sequence of observations of a system that evolves according to a Markov chain having states corresponding to the component parts. The HMM is trained using a manually-annotated set of e-mail messages, then applied to parse other e-mail messages. HMM-based parsing can be further refined or expanded using heuristic post-processing techniques that exploit redundancy of some component parts (e.g., signatures, disclaimers) across a corpus of e-mail messages.

Automated parsing of e-mail messages

Documents are assigned to one or more indexes in a document indexing system on the basis of document properties such as total number of tokens in the document, number of numeric tokens in the document, number of alphabetic tokens in the document, size of the document, and metadata associated with the document. Based on statistical distributions of document properties (over a large number of documents), different indexes can be defined, and a document router can direct a particular document to one index or another based on the properties of the particular document. In some implementations, certain document properties may be used to identify a nonrelevant document, or garbage document, so that it is either not indexed or assigned to an index dedicated for such documents.

Adaptive routing of documents to searchable indexes

Phrase-based document clustering with automatic phrase extraction

Reliable identification of highly similar documents allows such documents to be treated as identical for purposes of document analysis. Identification of highly similar documents can be based on a composite hash value or other value for which the likelihood of two documents having the same value is high if and only if the documents have a high degree of similarity. Prior to performing content based analysis, the composite hash value for the current document is determined and compared to composite hash values of previously analyzed documents. If a match is found, the results of the analysis of the previous document can be applied to the current document. If no match is found, the current document is analyzed.

Composite locality sensitive hash based processing of documents

A method for predicting a subsequent resource utilization in a computer system having a plurality of devices includes the step of monitoring, over a period of time, a contemporaneous resource utilization and a number of active devices to obtain monitored values of the contemporaneous resource utilization and the number of active devices. The subsequent resource utilization is predicted, based upon the monitored values of the contemporaneous resource utilization and the number of active devices. Additionally, methods are described herein for identifying resource saturation and predicting the effects of adding a new device in a computer system.

Method and apparatus for prediction of computer system performance based on types and numbers of active devices

Techniques for organizing a corpus of electronic documents. The electronic documents are organized in a manner that facilitates review of the documents. The documents are organized into a concept-based hierarchical collection of folders based upon contents of the documents.

Techniques for organizing data to support efficient review and analysis

Techniques for sharing content information between members of a virtual user group without compromising the privacy of the members. A user can identify content information to be shared with other members of a virtual user group using a user computer system. The content information is then communicated to the other members of the virtual user group and can be accessed by members of the virtual user group in such a manner that the privacy of the user and of the other members of the virtual user group is not compromised. The present invention preserves user privacy by controlling and minimizing the amount of user-related information available/accessible to server systems hosting the virtual user groups.

Techniques for sharing content information with members of a virtual user group in a network environment without compromising user privacy

A pool of messages, e.g., e-mails and/or other electronic documents that each correspond to a communication from a sender to a recipient, is analyzed to identify communication chains between a source and a target. Sender and recipient identifiers extracted from the messages are used to detect communication links between pairs of entities. Indirect chains of any desired length can be found by iteratively tracing a communication path one step forward from the source, then one step backward from the target, and so on; at each new step, entities at end points of the forward paths and backward paths are compared to detect any entities that complete a communication chain from source to target. Information related to the identified communication chains can be presented to a user via an interactive report that supports iterative analysis of the communication-chain data.

Systems and methods for determining communication chains based on messages

Growing community of inventors

Cupertino, CA, United States of America

Joy Aloysius Thomas