Accenture Global Services Gmbh

Accenture International Sarl

Anitha Chandran

Anurag Dwarakanath

Sanjay Podder

Neville Dubash

Nisha Ramachandra

Aditya Priyadarshi

Bargav Jayaraman

Bhanu Anand

Bindu Madhav Tummalapalli

Parvathy Vijay Raghavan

Shalini Chaudhari

Shubhashis Sengupta

Sanjoy Paul

Jagadeesh Chandra Bose Rantham Prabhakara

Gary Titus

Kapil Singi

Roshni Ramesh Ramnani

Subhabrata Das

David E Ingram

Brian Ahern

Sangeetha Rao

Ashwin Nayak

A device may obtain a document. The device may identify a skip value for the document. The skip value may relate to a quantity of words or a quantity of characters that are to be skipped in an n-gram. The device may determine one or more skip n-grams using the skip value for the document. A skip n-gram, of the one or more skip n-grams, may include a sequence of one or more words or one or more characters with a set of occurrences in the document. The sequence of one or more words or one or more characters may include a skip value quantity of words or characters within the sequence. The device may extract one or more terms from the document based on the one or more skip n-grams. The device may provide information identifying the one or more terms.

Automated term extraction

A device may obtain a test script document. The device may process the test script document to perform term extraction using one or more term extraction techniques to identify a set of terms of the test script document. The one or more term extraction techniques may include a skip n-gram term extraction technique. One or more terms, of the set of terms, may be located within an n-gram of the test script document. The device may process the test script document to perform hierarchy formation for results of performing term extraction. A relationship between a set of terms, of the set of terms, may be identified using hierarchy formation. The device may generate a functional diagram of the test script document based on the results of performing term extraction and results of performing hierarchy formation. The device may provide information identifying the functional diagram.

Automated functional diagram generation

A device may obtain text to be analyzed to determine semantic connections between sections of the text. The device may identify subject-verb-object (SVO) units included in the text, and may determine SVO unit information that describes the SVO units. The device may analyze the SVO unit information to determine semantic connection information that identifies one or more semantic connections between two or more of the SVO units. The one or more semantic connections may identify relationships between verbs associated with the two or more of the SVO units. The device may generate a semantic network based on the SVO unit information and the semantic connection information, and may provide information regarding the semantic network.

Generating a semantic network based on semantic connections between subject-verb-object units

A method and system for reducing demand on a power grid through demand side management includes receiving assigned priorities from a customer for smart appliances and for appliances plugged into sockets of one or more smart plugs of the customer, the assigned priorities indicative of a customer-preferred sequence for disconnection of, or adjustment of power to, the appliances from the power grid. The system determines whether to disconnect or adjust power to at least one of the appliances of the customer; and selects at least one of the appliances according to the assigned priorities to reduce demand on the power grid. The system sends one or more commands, the one or more commands indicative to the one or more smart plugs to disconnect or adjust power to the selected at least one of the appliances based on selecting at least one of the appliances.

Customer-centric demand side management for utilities

A requirements testing system facilitates the review and analysis of requirement statements for software applications. The requirements testing system automatically generates test artifacts from the requirement statements, including test scenarios, test conditions, test hints, and expected results. These test artifacts characterize the requirements statements to provide valuable analysis information that aids understanding what the intentions of the requirement statements are. The automation of the generation of these test artifacts produces numerous benefits, including fewer errors, objectivity, and no dependency on the skills and experience of a creator.

System for generating test scenarios and test conditions and expected results

A document to be segmented is converted into a common representation format, if necessary. Parsing of the document results in a document model that is analyzed based on at least one structure-dependent function to identify segments within the document. In one embodiment, the structure-dependent function may comprise a template, or a best-fit template of a plurality of templates, used for comparison with the document model. In other embodiments, the structure-dependent function may comprise table of contents information, font properties within the document model and/or an average segment size determined according to previously identified segments in one or more additional documents that are related to the document under consideration. Semantic-content dependent functions may be applied to further refine the analysis by identifying sub-segments within the extracted segments, or by identifying segments that may be properly merged according to the similarity of their respective semantic content.

Method for the logical segmentation of contents

Keyword frequency data for a plurality of document-derived segments is represented in a matrix form in which each segment is represented as a vector of dimensionality equal to the number of keywords. The matrix may be subdivided into a plurality of sub-matrices, each preferably corresponding to a non-overlapping portion of the plurality of keywords. When determining a similarity measurement between any pair of segments, at least a portion of the keyword frequency data for each sub-matrix's non-overlapping keywords are used to determine a sub-matrix dot product for the pair of segments. The resulting plurality of sub-matrix dot products are then summed together in order to provide the similarity measurement. Keywords that are synonyms of each other may be accommodated through the modification of keyword frequency data. Where the keyword frequency data in the matrix representation is relative sparse, compressed views of the matrix representation may be provided.

Techniques for computing similarity measurements between segments representative of documents

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Bengaluru, India

Anitha Chandran