Capital One Services, LLC (20240338715). INTEGRATING DATA FROM MULTIPLE UNRELATED DATA STRUCTURES simplified abstract

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INTEGRATING DATA FROM MULTIPLE UNRELATED DATA STRUCTURES

Organization Name

Capital One Services, LLC

Inventor(s)

Matthew Nowak of Midlothian VA (US)

Alexander Gurfinkel of Frisco TX (US)

Anna Husain of Chevy Chase MD (US)

Kamari Clark of Arlington VA (US)

INTEGRATING DATA FROM MULTIPLE UNRELATED DATA STRUCTURES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240338715 titled 'INTEGRATING DATA FROM MULTIPLE UNRELATED DATA STRUCTURES

The abstract of this patent application describes a device that can retrieve various types of data, such as exchange data, account data, record data, interaction data, and metaverse data. The device can also obtain location data and wireless network data related to a user device and two different entities. Based on this data, the device can determine the probability of the user acquiring an item in the future and transmit information accordingly.

  • The device retrieves exchange data, account data, record data, interaction data, and metaverse data.
  • It obtains location data and wireless network data associated with different entities.
  • It calculates the probability of the user acquiring an item in the future based on the collected data.
  • The device can transmit information based on the probability of the user acquiring the item.

Potential Applications: This technology could be used in predictive analytics for e-commerce platforms, targeted advertising, and personalized recommendations based on user behavior.

Problems Solved: This technology helps in predicting user behavior and preferences, allowing for more targeted and effective marketing strategies.

Benefits: The technology can lead to increased sales, improved user engagement, and enhanced customer satisfaction by providing personalized recommendations and offers.

Commercial Applications: Predictive analytics software for e-commerce platforms, targeted advertising solutions for businesses, and personalized recommendation engines for online retailers could benefit from this technology.

Questions about the technology: 1. How does this technology ensure user data privacy and security? 2. What are the potential challenges in implementing this predictive analytics system in real-world scenarios?


Original Abstract Submitted

in some implementations, a device may retrieve one or more of: exchange data, account data, record data, interaction data, or metaverse data. the device may obtain at least one of location data or wireless network data, where the location data indicates a location, of a user device, associated with a first entity, and where the wireless network data indicates a wireless network, to which the user device has connected, associated with a second entity. the device may determine a probability of the user acquiring an item in a future time interval based on at least one of the exchange data, the account data, the record data, the interaction data, or the metaverse data, and at least one of first information relating to the first entity or second information relating to the second entity. the device may transmit information based on the probability of the user acquiring the item.

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