Tata Consultancy Services Limited

Asha Rajbhoj

Padmalata Venkata Nistala

Vinay Vasant Kulkarni

Vinay Kulkarni

Ajim Innus Pathan

Shivani Soni

Balamuralidhar Purushothaman

Jayavardhana Rama Gubbi Lakshminarasimha

Shailesh Shankar Deshpande

Gomathi Ganesan

The present disclosure extracts contextual product feature model from requirement specification documents where the conventional methods fail to perform. Initially, the system receives a plurality of requirement specification documents pertaining to a product, a domain dictionary, a plurality of configuration parameters, and a plurality of extraction patterns. A product feature model is generated using a NLP based feature extraction technique. The product feature model includes a plurality of product feature elements comprising a feature area, a major feature and a plurality of features arranged hierarchically and classified into feature types. Further, a plurality of ContextType associations like core, client, geography and market are extracted for each of the plurality of features using a ContextType extraction technique. Finally, the plurality of ContextType associations is updated in the product feature model to obtain a contextual product feature model. Various types of feature exports can be generated using a natural language interface.

Method and system for extracting contextual product feature model from requirements specification documents

The present disclosure is of a method for automated authoring of purposive models from Natural Language (NL) documents. Conventional model extractors to automatically extract models from NL documents are specific to a metamodel, do not consider document structure and are not configurable. Initially, the system receives a plurality of Natural Language (NL) documents, a metamodel, a plurality of pattern trees corresponding to the metamodel, and a configurable domain dictionary. Each of the plurality of pattern trees includes a plurality of pattern elements. Further, a document information is generated from each of the plurality of NL documents using a document information reading technique. Finally, a plurality of purposive models are generated for each of the plurality of NL documents by interpreting a corresponding document information based on the plurality of pattern trees and the metamodel using a pattern interpretation technique.

Method and system for automated authoring of purposive models from natural language documents

This disclosure relates generally to impact analysis and, more particularly, to generation of impact analysis specification document for a change request. The existing state-of-art techniques for impact analysis for a change request are mostly manual, and further most of the research on impact analysis is based on source code analysis and does not address the impact of the CR at multi-granular levels. The disclosed techniques perform a fine-grained impact analysis of the CR at multi-granular levels. The fine-grained impact analysis at multi-granular includes identifying a set of impacted specification elements, a set of impacted processes, and a set of impacted features based on several steps including generating a contextual specification model and extracting a plurality of key-phrases.

Method and system for generation of impact analysis specification document for a change request

This disclosure relates to the field of project related document analysis. Conventionally, process of retrieving right information related to a stakeholder with relevant project initiation concerns involves manual intervention resulting in more time consumption. The method of the present disclosure describes a system and method for automated extraction and classification of project initiation related information from request for proposal response documents The RFP response document is parsed using document structure based parsing technique to identify questions and answers. The questions from the RFP response document are classified into different classes of interest and important information from the answers are extracted and mapped to an identified class. The method of the present disclosure demonstrates significant improvement in terms of time consumption by reducing volume of information and providing a quick access to class-specific relevant information from the RFP response document.

Automated extraction and classification of project initiation related information from request for proposal response documents

This disclosure relates to automatic creation of an image processing pipeline that has a dependency on image processing domain experts. Given the image processing problem has multiple viable solutions, search space of creating a solution for a given goal in a given constrained infrastructure is large and, choosing an optimal image processing solution is an effort, time and intellect intensive endeavor. The present disclosure models image processing goals in abstract task categories and externalizes domain knowledge as an object model and a set of rules defined over it. Rules codify task template selection criteria and algorithm selection criteria for implementing a task. Recommendations are provided to choose suitable algorithm (s) for carrying out image processing tasks. Effects of algorithm execution are recorded for subsequent tasks recommendations. The stored state of execution and trace information enables rerouting the execution and knowledge upgrade. Deployable code is generated after a validated pipeline is created.

Systems and methods for automatically creating an image processing pipeline

This disclosure relates generally to document analysis, and more particularly to a method and system for Request For Proposal (RFP) response generation. In one embodiment, the system automatically generates at least one search query in response to a RFP received as input, searches in at least one reference solution database using the generated search query, finds matching data, filters the matching data based on RFP parameter specific data, and prepares a response document. The response document is then provided as an output of the system, in response to the RFP collected as input.

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Pune, India

Asha Rajbhoj