Accenture Global Solutions Limited

Himani Shukla

Madhura Shivaram

Swati Tata

Chung-Sheng Li

Chinnappa Guggilla

Abhishek Datta Sharma

Pratip Samanta

Sumeet Sawarkar

Suresh Venkatasubramaniyan

Ditty Mathew

Srikrishna Raamadhurai

Divyayan Dey

Srivasan Sridharan

Siddhartha Asthana

Kamlesh Narayan Chaudhari

Manash Jyoti Konwar

Keshav Bohra

Nagendra Kumar Karamala

Amit Sharma

Systems and methods for generating an output representation are disclosed. A system may include a processor including a representation generator. The representation generator may receive an input data comprising an input content and an instruction. The representation generator may include a parsing engine to parse the input data to obtain parsed information. The representation generator include a mapping engine to map the parsed information with a pre-stored base template pertaining to a pre-defined module, to obtain a mapped template. The representation generator may generate, through a machine learning (ML) model, based on the mapped template, an output representation in a pre-defined format. The output representation may correspond to the expected representation of the input content.

Generation and optimization of output representation

Examples of a content alignment system are provided. The system may receive a content record and a content creation requirement. The system may implement an artificial intelligence component to sort the content record into a plurality of objects and for identifying an object boundary for each of the plurality of objects. The system may identify a plurality of images and implement a first cognitive learning operation to identify an image boundary for each of the plurality of images. The system may identify a plurality of exhibits and implement a second cognitive learning operation to identify a data pattern associated with each of the plurality of exhibits. The system may implement a third cognitive learning operation for determining a content creation model by evaluating the plurality of objects, the plurality of images, and the plurality of exhibits. The system may generate a content creation output to resolve the content creation requirement.

Content alignment

A system for reconciliation comprises a determination engine to determine whether data is structured or unstructured, a data structuring engine to structure the data, and a rule extraction engine to determine relations between pairs of values of a first set and a second set of data. The system further comprises a matching engine to generate a confidence score for each pair of the values, a categorization engine to classify the pairs of values into matched pairs and unmatched pairs, a validation engine to validate matching and classification of the pairs based on a user feedback, and a learning engine to store details pertaining to the validation of the matching and the classification over a period of time. The learning engine forwards the details to the rule extraction engine and the categorization engine to determine the relations between subsequent pairs of values and classify the pairs based on the stored details.

Data reconciliation

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Bengaluru, India

Himani Shukla