专利检索 ap:("Capital One Services, LLC") AND inv:"Matthew PERONI" 第 1 页

1.

发明公开
DISCRIMINATIVE MODEL FOR IDENTIFYING AND DEMARCATING TEXTUAL FEATURES IN RISK CONTROL DOCUMENTS 审中-公开

公开(公告)号：US20240054421A1

公开(公告)日：2024-02-15

申请号：US17884986

申请日：2022-08-10

申请人： Capital One Services, LLC

发明人： Peter TANSKI , Matthew PERONI , Deny DANIEL , Ranjith ZACHARIAH , Viji SOUNDAR , Paul VEST , Kevin ZHANG

IPC分类号： G06Q10/06 , G06F40/40 , G06F40/295 , G06F40/211

CPC分类号： G06Q10/0635 , G06F40/40 , G06F40/295 , G06F40/211

摘要： Embodiments disclosed are directed to a computing system that performs steps to automatically identify risk control features and entities in a risk control document. The computing system uses a generative machine learning (ML) model to transform a risk control document into sequences of words, classify risk control features associated with the sequences of words, and pair the sequences of words with the classified risk control features. The computing system then uses a natural language processing (NLP) model to identify syntactic characteristics of the sequences of words. Subsequently, the computing system uses a discriminative predictor system to correct the classified risk control features based on the identified syntactic characteristics, identify boundaries of the corrected classified risk control features, and pair the identified boundaries with the corrected classified risk control features.

2.

发明公开
HYBRID MODEL AND SYSTEM FOR PREDICTING QUALITY AND IDENTIFYING FEATURES AND ENTITIES OF RISK CONTROLS 审中-公开

公开(公告)号：US20230376833A1

公开(公告)日：2023-11-23

申请号：US17747445

申请日：2022-05-18

申请人： Capital One Services, LLC

发明人： Peter TANSKI , Matthew PERONI

IPC分类号： G06N20/00

CPC分类号： G06N20/00

摘要： Embodiments disclosed are directed to a computing system that performs steps to automatically identify risk control features and entities in a risk control document. The computing system regenerates, by a semantic prediction machine learning (ML) model, phrases in a risk control document. The computing system then classifies, by the semantic prediction ML model, risk control features associated with the regenerated phrases. Subsequently, the computing system corrects, by a discriminative natural language processing (NLP) model, the classified risk control features based on the phrases and the regenerated phrases.