Ganesh H Prasad, 82New York, NY

2023009, Mar 30, 2023

Dec 16, 2021

17/552476

- PURCHASE NY, US
Hardik WADHWA - Sirsa, IN
Siddharth VIMAL - Chandigarh, IN
Siddhartha ASTHANA - Ghaziabad, IN
Ankur ARORA - New Delhi, IN
Paul John PAOLUCCI - West Harrison NY, US
Ganesh Nagendra PRASAD - Stamford CT, US
Jonathan TRIVELAS - Stormville NY, US
Samantha MEDINA - Elmsford NY, US

G06Q 20/40
G06N 3/08

Embodiments provide methods and systems for identifying a re-routed transaction. Method performed by processor includes retrieving a plurality of transaction windows from a transaction database. Each transaction window includes a transaction declined under a restricted MCC. The method includes accessing a plurality of features associated with each transaction of each transaction window from the transaction database. The method includes predicting an output dataset of a plurality of reconstructed transaction windows based on feeding the input dataset to a trained neural network model. The method includes computing a corresponding reconstruction loss value for each transaction of each transaction window. The method includes comparing the corresponding reconstruction loss value for each transaction with a pre-determined threshold value. The method includes identifying the transaction as a re-routed transaction corresponding to the transaction declined under the restricted MCC, if a corresponding reconstruction loss value for a transaction is higher than the pre-determined threshold value.

2023005, Feb 16, 2023

Aug 12, 2021

17/401048

- Purchase NY, US
Gaurav Dhama - Gurgaon, IN
Ankur Arora - New Delhi, IN
Kanishka Kayathwal - Kota, IN
Jessica Carta - Stamford CT, US
Ganesh Nagendra Prasad - West New York NJ, US

MASTERCARD INTERNATIONAL INCORPORATED - Purchase NY

G06Q 30/02
G06F 40/30
G06F 40/284
G06K 9/62

The disclosure relates to AI-based machine-learning and natural language modeling to identify semantic similarities between sets of content having natural language text. For example, a system may generate a relevance classification that indicates whether content such as articles are non-specifically relevant to charities without identifying a particular charity. If the content is non-specifically relevant to charities, the system may apply a natural language model to generate sentence embeddings based on the content and determine a level similarity between the sentence embeddings and a query embedding generated from a charity query. The charity query may itself be generated from a full description of the charity through an encoder-decoder architecture with reinforcement learning.