Machine-learning based (ML-based) system and method for automatically processing one or more documents

A machine learning based (ML-based) system for automatically processing one or more documents, the ML-based system comprising:

• one or more hardware processors;
• a memory coupled to the one or more hardware processors, wherein the memory comprises a plurality of subsystems in form of programmable instructions executable by the one or more hardware processors, and wherein the plurality of subsystems comprises:
• a data obtaining subsystem configured to obtain data associated with at least one of:
• one or more first contents corresponding to one or more electronic mails and one or more second contents corresponding to the one or more documents attached in the one or more electronic mails, from one or more data sources;
• a data pre-processing subsystem configured to pre-process the data to generate pre-processed data associated with at least one of:
• the one or more first contents and the one or more second contents;
• a content classifying subsystem configured to classify at least one of:
• the one or more first contents as at least one of:
• one or more claim-based contents and one or more non-claim-based contents, using a machine learning (ML) model with one or more prompts;
• and
• the one or more second contents, as at least one of:
• the one or more claim-based contents and the one or more non-claim-based contents, based on one or more intents of the one or more second contents, using the ML model with the one or more prompts;
• an output subsystem configured to provide at least one of:
• the one or more claim-based contents and the one or more non-claim-based contents, as an output, to one or more users on one or more user interfaces associated with one or more electronic devices associated with the one or more users;
• and
• an accuracy assessment subsystem configured to:
• monitor performance of the ML model for a time duration;
• determine whether the performance of the ML model on the classification of at least one of:
• the one or more first contents and the one or more second contents, is below a threshold value;
• and
• fine-tune the ML model with a plurality of parameters to optimize the ML model for classifying at least one of:
• the one or more first contents and the one or more second contents, as at least one of:
• the one or more claim-based contents and the one or more non-claim-based contents.

A non-transitory computer-readable storage medium having instructions stored therein that when executed by one or more hardware processors, cause the one or more hardware processors to execute operations of:

• obtaining data associated with at least one of:
• one or more first contents corresponding to one or more electronic mails and one or more second contents corresponding to the one or more documents attached in the one or more electronic mails, from one or more data sources;
• pre-processing the data to generate pre-processed data associated with at least one of:
• the one or more first contents and the one or more second contents;
• classifying at least one of:
• the one or more first contents as at least one of:
• one or more claim-based contents and one or more non-claim-based contents, using a machine learning (ML) model with one or more prompts;
• and
• the one or more second contents, as at least one of:
• the one or more claim-based contents and the one or more non-claim-based contents, based on one or more intents of the one or more second contents, using the ML model with the one or more prompts;
• providing at least one of:
• the one or more claim-based contents and the one or more non-claim-based contents, as an output, to one or more users on one or more user interfaces associated with one or more electronic devices associated with the one or more users;
• monitoring performance of the ML model for a time duration;
• determining whether the performance of the ML model on the classification of at least one of:
• the one or more first contents and the one or more second contents, is below a threshold value;
• and
• fine-tuning the ML model with a plurality of parameters to optimize the ML model for classifying at least one of:
• the one or more first contents and the one or more second contents, as at least one of:
• the one or more claim-based contents and the one or more non-claim-based contents.