US 12,399,924 B1
Robust methods for multi-domain signal evaluation systems systems
Gaurav Iyer, Delhi (IN); Arturo Devesa, New York, NY (US); Ashish Kudaisya, Delhi (IN); Solmaz Torabi, Austin, TX (US); Somya Rai, Faridabad (IN); Ankit Malviya, Ujjain (IN); Raunak Nitin Rathi, Pune (IN); Md Sharique, Bihar Sharif (IN); and Manish Singh Mahra, Noida (IN)
Assigned to ExlService Holdings, Inc., New York, NY (US)
Filed by ExlService Holdings, Inc., New York, NY (US)
Filed on Mar. 6, 2025, as Appl. No. 19/072,917.
Claims priority of application No. 202411068819 (IN), filed on Sep. 11, 2024.
Int. Cl. G06F 16/338 (2019.01); G06F 3/0484 (2022.01); G06F 9/451 (2018.01); G06F 16/334 (2025.01)
CPC G06F 16/338 (2019.01) [G06F 3/0484 (2013.01); G06F 9/453 (2018.02); G06F 16/3344 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method performed by a multi-domain signal evaluation system for processing alphanumeric signals, the method comprising:
receiving a first digital artifact comprising unstructured alphanumeric signal data indicating contextual information associated with a decision logic condition,
wherein the first digital artifact corresponds to a set of compliance parameters that define one or more acceptable content elements of the unstructured alphanumeric signal data;
responsive to identifying a set of non-compliant alphanumeric signals from the unstructured alphanumeric signal data that fail to satisfy the set of compliance parameters:
generating a set of masking elements comprising a mapping to the set of non-compliant alphanumeric signals, and
generating a second digital artifact comprising alphanumeric signal data that substitutes or supplements the identified non-compliant alphanumeric signals of the unstructured alphanumeric signal data with the set of masking elements;
determining a set of signal domain categories for the second digital artifact, each signal domain category corresponding to one or more alphanumeric signal properties that are satisfied by the alphanumeric signal data of the second digital artifact;
using the determined set of signal domain categories, generating, using a machine learning model, a composite alphanumeric signal for the alphanumeric signal data of the second digital artifact; and
generating and configuring for display, at a user interface, a set of guidance artifacts generated using the composite alphanumeric signal, each displayed guidance artifact comprising:
(1) a human-readable narrative that recommends invocation of at least one available user action with respect to the decision logic condition, and
(2) a predicted outcome of executing the recommended invocation of the at least one available user action.