US 12,373,523 B2
Machine-learning for password guess risk determination
Rocio Cabrera Lozoya, Antibes (FR); Slim Trabelsi, Biot (FR); and Carlos Rafael Ocanto Davila, Neuilly sur Seine (FR)
Assigned to SAP SE, Walldorf (DE)
Filed by SAP SE, Walldorf (DE)
Filed on May 9, 2024, as Appl. No. 18/659,463.
Application 18/659,463 is a continuation of application No. 17/668,208, filed on Feb. 9, 2022, granted, now 12,001,530.
Prior Publication US 2024/0289427 A1, Aug. 29, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 21/00 (2013.01); G06F 21/31 (2013.01); G06F 40/253 (2020.01); G06F 40/263 (2020.01); G06F 40/40 (2020.01)
CPC G06F 21/31 (2013.01) [G06F 40/253 (2020.01); G06F 40/263 (2020.01); G06F 40/40 (2020.01)] 20 Claims
OG exemplary drawing
 
11. A method comprising:
accessing information about a computing resource;
accessing information about a user attempting to access the computing resource;
using the information about the computing resource to retrieve a computing domain-specific text corpus for a domain encompassing the computing resource;
using the information about the user to retrieve a user information-specific text corpus for a piece of user information in the accessed information about the user;
passing the domain-specific text corpus to a first natural language processing (NLP) machine-learned model trained specifically for the domain, the first NLP machine-learned model outputting domain-specific word embeddings;
passing the user information-specific text corpus to a second NLP machine-learned model trained specifically for the piece of user information, the second NLP machine-learned model outputting user information-specific word embeddings; and
inputting the domain-specific word embeddings and the user information-specific word embeddings into a probabilistic context free grammar (PCFG) machine-learned model, the PCFG machine-learned model updating itself based on the input and outputting a set of password guesses.