Abstract
A technology is described for proactively managing trial subscriptions to mitigate unwanted charges that can occur when they convert to paid plans. The system can detect an initiation event, such as a confirmation email, and utilize a large language model to parse unstructured text for key parameters, for example, trial duration and renewal cost. Based on this analysis, the system can autonomously generate a restricted virtual payment token. This token may be governed by a policy that sets a low or zero-dollar spending limit and an expiration date corresponding to the trial's end. This method can provide an automated protective measure to help reduce unintended subscription fees with less need for manual user configuration, and can default to a protected state until a user approves a charge.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Yakar, Tamar and Labzovsky, Ilia, "Autonomous Generation Of Restricted Payment Tokens via LLM-Based Parsing of Unstructured Subscription Data", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9761