US 12,001,529 B1
Counting machine for manufacturing and validating event-relevant identities via an ensemble network
James Anderson, Morris Plains, NJ (US); Thomas J. Saleh, Stamford, CT (US); and Lawrence C. Rafsky, Juniper, FL (US)
Assigned to Validate Me LLC, Ridgefield, CT (US)
Filed by Validate Me LLC, Stamford, CT (US)
Filed on Nov. 5, 2021, as Appl. No. 17/519,634.
Int. Cl. G06F 21/31 (2013.01)
CPC G06F 21/31 (2013.01) 19 Claims
OG exemplary drawing
 
1. A counting machine comprising:
a set of counters, each of which keeps a current count, said current count initialized at the value zero and incrementing whenever a signal in the form of a data record is received by the counters;
said set of counters comprising more than one mechanical, electrical or digital counter arrayed in a distributed network with each said counter connected such that said incrementing accumulates a number of non-duplicative said data records;
said data records selected from a group consisting of:
an entity identification verification from a physical or digital data record containing an asserted purported identity of said entity;
a supporting information about said entity submitted to the distribution network by a network participant; and
users/appliers (referred to as Members) of identity validation services who also have their own data relating to said entity's identity; wherein the count of the counting machines is modified as the transaction proceeds, as additional supporting data becomes available and is supplied by network participants; wherein the counting machine utilizes a counting methodology, said counting methodology selected from a group consisting of:
EARLI (Event Associated Record Linking Identities), an assemblage of information pertinent to a particular identity-verification transaction, initiated upon receiving a request for validation of an identity validation, engaging Members and Partners in an ensemble network to control data flow for entity identification verification and supporting information processing;
BIN-NEAR, a mechanism that guides the data flow controlled by EARLI until a stopping condition is indicated by HAD-ENUF, signaling completing of non-duplicative date record accumulation and completion of a final output for identity verification; and
HAD-ENUF (Hasse Diagram Ensemble Unified Fusion), a counting method employing a supervised machine-learning approach for ensemble data fusion, automatically incrementing counts during the identity validation process and implementing a stopping rule that ends the circulation of EARLI among ensemble network participants, signifying that the final verification result has been successfully computed.