01-03-2018 дата публикации
Номер: US20180062937A1
Принадлежит:
The adaptive learning systems described herein may include machine-learning engines, product configuration engines, and/or various other components configured to improve the efficiency of processing transactions. The systems described herein may detect and/or predict declined transactions, token deficiencies, insufficient system capacities, and/or other system anomalies. As such, the system may perform operations to generate additional tokens associated with assets, provision various bin ranges, and/or share reserve capacities, and/or other operations. Thus, the system may improve system efficiencies, ensure reliability and operability across the system, and optimize the operations for successfully processing transactions. 1. A system , comprising:a non-transitory memory; and identifying one or more token requests from a network;', 'determining, based at least on a token traffic analyzer of the system, an indication of token traffic in the network based at least on the one or more token requests;', 'determining, based at least on a monitoring platform of the system, a token depletion rate based at least on the indication of token traffic in the network;', 'generating a plurality of tokens in a token database of the system based at least on the token depletion rate; and', 'learning, based at least on an adaptive learning engine of the system, one or more indications of anomalies in the network associated with the token traffic., 'one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising2. The system of claim 1 , wherein the plurality of generated tokens is associated with a first bin range claim 1 , and wherein the operations further comprise:detecting, based at least on the monitoring platform, a low token indication based at least on the token depletion rate;provisioning a second bin range for a second plurality of tokens based at least ...
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