Counting machine for manufacturing and validating event-relevant identities via an ensemble network

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.