	US 12,462,289 B2
	Device, method, and computer-readable media for recommendation networking based on connections, characteristics, and assets using machine learning
Micah Price, The Colony, TX (US); Avid Ghamsari, Carrollton, TX (US); Qiaochu Tang, Frisco, TX (US); Geoffrey Dagley, McKinney, TX (US); and Staeven Duckworth, Oak Point, TX (US)
Assigned to Capital One Services, LLC, McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Jan. 9, 2024, as Appl. No. 18/407,739.
Application 18/407,739 is a continuation of application No. 17/488,505, filed on Sep. 29, 2021, granted, now 11,907,997.
Prior Publication US 2024/0161166 A1, May 16, 2024
Int. Cl. G06Q 30/06 (2023.01); G06N 3/04 (2023.01); G06Q 30/0601 (2023.01); H04L 51/52 (2022.01)

CPC G06Q 30/0631 (2013.01) [G06N 3/04 (2013.01); G06Q 30/0625 (2013.01); H04L 51/52 (2022.05)]

20 Claims

9. A method comprising:

determining, for a demographic of users, training data indicating;

first connections between a first plurality of users that are members of the demographic,

first characteristics of the first plurality of users that are members of the demographic,

a first plurality of assets purchased by the first plurality of users that are members of the demographic, and

a history of communications between the first plurality of users relating to the first plurality of assets;

training, using the training data, a machine learning model, implemented via an artificial neural network and comprising a plurality of nodes, to:

receive input indicating second connections between a third plurality of users, second characteristics of the third plurality of users, and a second plurality of assets purchased by the third plurality of users, and

output, based on the input, one or more of the third plurality of users, wherein training the machine learning model comprises modifying, based on the training data, a weight between at least two of the plurality of nodes such that, when provided at least a portion of the training data, the machine learning model outputs one of the plurality of members based on the connections between the plurality of members and the assets purchased by the plurality of members;

collecting web activity data by monitoring web browsing activity of a first user, wherein the web activity data indicates preferences of the first user corresponding to a type of asset;

determining social networking data that comprises a plurality of associations between a second plurality of users;

identifying, based on purchase history data corresponding to the type of asset, one or more merchants where the second plurality of users purchased the one or more assets;

inputting, to an input node of the plurality of nodes of the trained machine learning model, input data comprising:

the web activity data,

an indication of the one or more merchants,

the social networking data, and

the purchase history data;

determining, based on output received via an output node of the plurality of nodes of the trained machine learning model, at least one second user of the second plurality of users, wherein the at least one second user has purchased a second asset that is the type of asset;

generating, for the at least one second user, a notification prompting the second user to contact the first user regarding an intention of the first user to acquire the type of asset; and

causing the notification to be transmitted to the second user.