Methods and systems for data leveraging

A method for facilitating a transfer of a newly received dataset across a data network having access to a searchable global dataset, the method utilizing a computer processor and one or more non-transitory computer-readable media storing computer executable instructions, the instructions when executed by the computer processor, automatically generate data rules for said newly received dataset, the method comprising the steps of:

• said computer processor querying said global dataset to identify an existing dataset having data points overlapping with said newly received dataset, wherein the existing dataset is a dataset that references a same entity that is contained within the newly received dataset and contains data fields different from the data fields contained within the newly received dataset;
• said computer processor identifying historical data rules used for a set of data transfers relating to said existing dataset;
• and
• a machine learning engine using said historical data rules to optimize data rules for use with a data transfer relating to said newly received dataset, thereby facilitating the transfer of said newly received dataset, wherein the machine learning engine is a quantum optimization engine configured to utilize one or more quantum algorithms to process an initial data transfer rule set from the existing dataset to process the newly received data, and further optimize the data rules using the newly received data;
• wherein said machine learning engine comprises a modality selected from a group consisting of natural language processing, a trained neural network model, a deep learning model, a supervised machine learning model, and an artificial intelligence model;
• and
• said machine learning engine is a different learning engine from a learning engine used to generate the data rules.

A method for facilitating a transfer of a newly received dataset across a data network having access to a searchable global dataset, the method utilizing a computer processor and one or more non-transitory computer-readable media storing computer executable instructions, the instructions, when executed by the computer processor, automatically generate data rules for said newly received dataset, the method comprising the steps of:

• querying said global dataset to identify an existing dataset corresponding with said newly received dataset, wherein the existing dataset is a dataset that references a same entity that is contained within the newly received dataset and contains data fields different from the data fields contained within the newly received dataset;
• determining whether said existing dataset comprises at least one existing data point that matches with a corresponding data point in said newly received dataset;
• and
• deprioritizing transfer of said corresponding data point in said newly received dataset, thereby facilitating the transfer of said newly received dataset;
• wherein said computer processor utilizes a machine learning engine, and wherein the machine learning engine is a quantum optimization engine configured to utilize one or more quantum algorithms to process an initial data transfer rule set from the existing dataset to process the newly received data, and optimize the data rules using the newly received data;
• and
• wherein said machine learning engine comprises a modality selected from a group consisting of natural language processing, a trained neural network model, a deep learning model, a supervised machine learning model, and an artificial intelligence model;
• and
• said machine learning engine is a different learning engine from a learning engine used to generate the data rules.