Patents by Inventor Yogesh Sakpal

Yogesh Sakpal has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20230385700
    Abstract: Techniques for training a classification model to improve the classification of open banking transactions are presented. The techniques include receiving raw training data from a data source. The raw training data includes historical transaction data made up of a plurality of individual transactions. The raw training data is input into the classification model. The raw training data is processed by performing a data preparation operation on the raw training data. The data preparation operation includes removing numerical characters, repeating special characters, and accent words from the textual data of each transaction. Vocabulary training is then performed on the processed training data, including tokenizing the text of each transaction and converting the tokenized text into a transformer model specific format. The classification model is then trained using a transformer model, which uses the tokenized text. The trained classification model is then stored in a database.
    Type: Application
    Filed: May 26, 2023
    Publication date: November 30, 2023
    Applicant: Mastercard International Incorporated
    Inventors: Yogesh Sakpal, Sachin Pandey, Dean Vaz, Siddhesh Dongare, Dmitriy Kontarev, Brett Ragozzine, Christopher Brousseau
  • Publication number: 20230385701
    Abstract: Techniques for training an entity resolution model are presented. The techniques include inputting raw training data into the entity resolution model. The training data includes historical transaction data including a plurality of transactions. A label dictionary is generated by performing natural language processing (NLP) on the training data. The NLP includes scanning text of each transaction, extracting one or more entities from the text, and storing the label dictionary in a database. The label dictionary includes the extracted entities. Tagged data is generated from the training data using the label dictionary. Vocabulary training is performed on the training data, including tokenizing the text of each transaction and converting the tokenized text into a transformer model specific format. The entity resolution model is then trained using a transformer model, which uses the tokenized text and the tagged data. The trained entity resolution model is then stored in a database.
    Type: Application
    Filed: May 26, 2023
    Publication date: November 30, 2023
    Applicant: Mastercard International Incorporated
    Inventors: Yogesh Sakpal, Gauri Shah Bhatnagar, Shraddha Shirke, Dean Vaz, Siddhesh Dongare, Dmitriy Kontarev, Brett Ragozzine, Christopher Brousseau