US 11,810,213 B2
Computer platforms designed for improved electronic execution of electronic transactions and methods of use thereof
James Toffey, Newark, NJ (US); Spenser Huston, Newark, NJ (US); Vijay Mayadas, Newark, NJ (US); William Gartland, Newark, NJ (US); Thomas Duignan, Newark, NJ (US); Rick Montgomery, Newark, NJ (US); Albert John Cass, Newark, NJ (US); Suneel Nallagonda, Newark, NJ (US); and Bryan Moore, Newark, NJ (US)
Assigned to Broadridge Fixed Income Liquidity Solutions, LLC, Newark, NJ (US)
Filed by Broadridge Fixed Income Liquidity Solutions, LLC, Newark, NJ (US)
Filed on Feb. 11, 2021, as Appl. No. 17/174,229.
Application 17/174,229 is a continuation of application No. 16/879,327, filed on May 20, 2020, granted, now 10,922,773, issued on Feb. 16, 2021.
Application 16/879,327 is a continuation of application No. 16/805,401, filed on Feb. 28, 2020, abandoned.
Claims priority of provisional application 62/812,602, filed on Mar. 1, 2019.
Prior Publication US 2021/0166335 A1, Jun. 3, 2021
Int. Cl. G06Q 40/06 (2012.01); G06Q 50/18 (2012.01); G06Q 40/04 (2012.01)
CPC G06Q 50/188 (2013.01) [G06Q 40/04 (2013.01); G06Q 40/06 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
receiving, by at least one processor from a first computing device, an interest to trade a target financial instrument according to trade parameters;
wherein the trade parameters represent at least one target financial instrument feature of the target financial instrument;
accessing, by the at least one processor, a plurality of available financial instrument records comprising a plurality of available financial instrument features relating to a plurality of attributes of a plurality of available financial instruments available to trade;
determining, by the at least one processor, at least one available financial instrument record that matches the target financial instrument based at least in part on inputting the plurality of available financial instrument records and the at least one target financial instrument feature into at least one clustering machine learning model to:
cluster the plurality of available financial instrument records into a plurality of financial instrument feature groups based at least in part on at least one characteristic of the plurality of characteristics of the plurality of financial instrument features, and
classify the target financial instrument into at least one financial instrument feature group of the plurality of financial instrument feature groups based at least in part on at least one target financial instrument feature and the plurality of characteristics of the plurality of financial instrument features;
wherein at least one financial instrument feature group defines one or more similar financial instruments to the target financial instrument; and
automatically generating, by the at least one processor, a trade session initiation user interface comprising at least one user interface element presenting a list of the one or more similar financial instruments, wherein the at least one user interface element is configured to enable the user to select the one or more similar financial instruments to initiate at least one electronic execution of at least one trade of the at least one target financial instrument.