20240020554. MACHINE LEARNING BASED AUTOMATED PAIRING OF INDIVIDUAL CUSTOMERS AND SMALL BUSINESSES simplified abstract (Bank of America Corporation)

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MACHINE LEARNING BASED AUTOMATED PAIRING OF INDIVIDUAL CUSTOMERS AND SMALL BUSINESSES

Organization Name

Bank of America Corporation

Inventor(s)

Siten Sanghvi of Westfield NJ (US)

Morgan S. Allen of Waxhaw NC (US)

Matthew E. Carroll of Charlotte NC (US)

Tamara S. Kingston of Peoria AZ (US)

Stephen T. Shannon of Charlotte NC (US)

MACHINE LEARNING BASED AUTOMATED PAIRING OF INDIVIDUAL CUSTOMERS AND SMALL BUSINESSES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240020554 titled 'MACHINE LEARNING BASED AUTOMATED PAIRING OF INDIVIDUAL CUSTOMERS AND SMALL BUSINESSES

Simplified Explanation

The abstract of the patent application describes a computing platform that automates the pairing of customers and businesses. It does this by analyzing the historical user activity of a user and identifying patterns in their activity. Based on these patterns, the platform anticipates the user's future purchase activity. It then matches this anticipated purchase activity with a sales offering from a vendor. The platform also considers user-defined preference rules associated with the anticipated purchase activity and determines if these rules apply to any attributes of the activity. If the rules apply, the platform triggers an action associated with the anticipated purchase activity.

  • The computing platform analyzes historical user activity to identify patterns and anticipate future purchase activity.
  • It matches the anticipated purchase activity with a sales offering from a vendor.
  • User-defined preference rules associated with the anticipated purchase activity are considered.
  • The platform determines if the preference rules apply to any attributes of the activity.
  • If the preference rules apply, the platform triggers an action associated with the anticipated purchase activity.

Potential applications of this technology:

  • E-commerce platforms can use this technology to personalize sales offerings for individual customers based on their historical activity and preferences.
  • Marketing platforms can utilize this technology to target customers with relevant sales offerings based on their anticipated purchase activity.

Problems solved by this technology:

  • It automates the process of pairing customers with businesses, saving time and effort for both parties.
  • It improves the accuracy of sales offerings by considering historical user activity and preferences.

Benefits of this technology:

  • Customers receive personalized sales offerings that match their anticipated purchase activity and preferences.
  • Businesses can increase sales by targeting customers with relevant offers based on their historical activity and preferences.
  • The automation of the pairing process saves time and resources for both customers and businesses.


Original Abstract Submitted

aspects of the disclosure relate to automated pairing of customers and businesses. a computing platform may determine, based on historical user activity of a user, a pattern of the user activity, and may identify, based on the pattern of the user activity, an anticipated purchase activity of the user. then, the computing platform may determine a sales offering by a vendor. then, the computing platform may match the anticipated purchase activity with the sales offering. then, the computing platform may retrieve user-defined preference rules associated with the anticipated purchase activity. then, the computing platform may determine whether the preference rules apply to one or more attributes of the anticipated purchase activity. subsequently, the computing platform may trigger, based on a determination that the preference rules apply to the one or more attributes of the anticipated purchase activity, an action associated with the anticipated purchase activity.

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