Tata Consultancy Services Limited

Sudhakara Deva Poojary

Pranav Champaklal Shah

Amit Aggrawal

Siva Prasad Pusarla

Varsha Nayak

Paras Dwivedi

Godfrey Claudin Mathais

Nidhi Harshad Shroff

Shreya Sanjay Thakare

Rahul Rana

Saad Ansari

Hemil Patel

Neelesh Vishwakarma

Saswati Soumya Tripathy

Kritika Lodha

This disclosure relates to systems and methods for multi-utterance generation of data. Embodiments of the present disclosure utilizes a smart framework that is capable of generating contextually relevant utterances with immutability regulation and punctuation-memory. More Specifically, the present disclosure generates multiple syntactically and semantically correct utterances for text input data in such a way that a provision to selectively maintain or regulate phrases or words intact is provided and punctuation consistency is maintained.

Systems and methods for multi-utterance generation of data with immutability regulation and punctuation-memory

There is no mechanism for vehicle utilization and optimization through continuous and incremental planning which ensures that transportation plans are based on real-time conditions. The present invention discloses systems and methods for vehicle utilization and optimization based on self-learning mechanism. A machine learning model for dynamic association of users to vehicles is provided that learns previously clubbed patterns of users with their corresponding locations. The learnt previously clubbed patterns are utilized for determining association between previously clubbed locations which is further utilized to obtain an optimal set of locations. The users are dynamically associated to vehicles allocated for the obtained optimal set of locations by honoring one or more social and vehicle constraints. The proposed system has self-learning capability which ensures effective vehicle utilization and optimization in real time.

Self-learning based mechanism for vehicle utilization and optimization

Embodiments provide a system and method for statistical subject identification. The system takes texts, videos, audios, and images as input for which subject needs to be identified. The system pre-process input data and generates n-grams and pre-processed text strings by removing stopwords, punctuations, selective POS tags and lemmatization. Frequency distribution of n-grams are computed, and weightage of n-grams is assigned. For each n-gram, sum of weights across all text strings is computed and a maximum weightage is identified. The computed value as a result of taking a ratio of two, is assigned to each of the n-grams. Values computed for the n-grams have a non-normal distribution, when observed statistically. Thus, the n-gram values are transformed to confidence value following a normal distribution. The system maps the n-gram domains using a domain lexicon. Finally, these domains are aggregated and converged for subject identification based on a pre-annotated mapping dictionary.

System and method for statistical subject identification from input data

Existing systems for document processing are either based on a supervised approach using annotated tags, and these systems identify section-based data from the unstructured documents without considering the statistical variations in content, which results in highly inaccurate content extraction. The disclosure herein generally relates to document processing, and, more particularly, to method and system for document indexing and retrieval. The system provides a mechanism to correlate unique words in a document with different topics identified in the document, based on a word pattern identified from the document. The correlations are captured in a knowledge graph, and can be further used in applications such as but not limited to document retrieval.

Method and system for document indexing and retrieval

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Mumbai, India

Sudhakara Deva Poojary