公开(公告)号：US11797700B2

公开(公告)日：2023-10-24

申请号：US17103581

申请日：2020-11-24

Applicant: Alcon Inc.

Inventor： Uma Chandrashekhar

IPC: G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G06F21/62 ,  G06N20/20 ,  G06F21/60

CPC classification number: G06F21/6218 ,  G06F21/604 ,  G06N20/20

Abstract: Techniques for controlling data access using machine learning are provided. In one aspect, first, second, and third training data sets are generated from a set of historical access records and a set of historical data records, where the access records correspond to requests for data and comprise information identifying whether the request satisfies one or more data access rules, and the data records correspond to data elements and comprise information identifying whether the data element satisfies the one or more data access rules. One or more machine learning models are trained based on the first, second, and third training data sets to generate an output identifying whether requests for data should be granted.

公开(公告)号：US12287895B2

公开(公告)日：2025-04-29

申请号：US18466119

申请日：2023-09-13

Applicant: Alcon Inc.

Inventor： Uma Chandrashekhar

IPC: G06F21/55 ,  G06F21/31 ,  G06F21/60 ,  G06F21/62 ,  G06N20/20 ,  H04L9/40

Abstract: Techniques for controlling data access using machine learning are provided. In one aspect, first, second, and third training data sets are generated from a set of historical access records and a set of historical data records, where the access records correspond to requests for data and comprise information identifying whether the request satisfies one or more data access rules, and the data records correspond to data elements and comprise information identifying whether the data element satisfies the one or more data access rules. One or more machine learning models are trained based on the first, second, and third training data sets to generate an output identifying whether requests for data should be granted.

公开(公告)号：US20210165901A1

公开(公告)日：2021-06-03

申请号：US17103581

申请日：2020-11-24

Applicant: Alcon Inc.

Inventor： Uma Chandrashekhar

IPC: G06F21/62 ,  G06F21/60 ,  G06N20/20

Abstract: Techniques for controlling data access using machine learning are provided. In one aspect, first, second, and third training data sets are generated from a set of historical access records and a set of historical data records, where the access records correspond to requests for data and comprise information identifying whether the request satisfies one or more data access rules, and the data records correspond to data elements and comprise information identifying whether the data element satisfies the one or more data access rules. One or more machine learning models are trained based on the first, second, and third training data sets to generate an output identifying whether requests for data should be granted.

公开(公告)号：US20250165595A1

公开(公告)日：2025-05-22

申请号：US18951860

申请日：2024-11-19

Applicant: Alcon, Inc.

Inventor： Shruti Siva Kumar ,  Thomas E. Jackson ,  Sinchan Bhattacharya ,  Ramesh Sarangapani ,  Uma Chandrashekhar ,  Pranava Kumar Vemula

IPC: G06F21/55 ,  H04L9/40

Abstract: A system includes a machine learning (ML) engine in which a data store is coupled to a processing system. The processing system executes code to receive a dataset from the data store, and produces three models each comprising three tests. Each of the tests seeks to detect one of three anomaly types corresponding to each of the models. The processing system performs at least two of the three tests relating to each of the three anomaly types. Separately for each anomaly type, the processing system detects an anomaly when two-out-of-three (2oo3) tests conclude that the anomaly is present in the dataset. The dataset including the flagged anomalies is stored in a data repository. The anomaly is filtered from the dataset. The processing system is configured to use data from the dataset to retrain an existing trained ML model.

公开(公告)号：US20230418967A1

公开(公告)日：2023-12-28

申请号：US18466119

申请日：2023-09-13

Applicant: Alcon Inc.

Inventor： Uma Chandrashekhar

IPC: G06F21/62 ,  G06N20/20 ,  G06F21/60

CPC classification number: G06F21/6218 ,  G06F21/604 ,  G06N20/20

Abstract: Techniques for controlling data access using machine learning are provided. In one aspect, first, second, and third training data sets are generated from a set of historical access records and a set of historical data records, where the access records correspond to requests for data and comprise information identifying whether the request satisfies one or more data access rules, and the data records correspond to data elements and comprise information identifying whether the data element satisfies the one or more data access rules. One or more machine learning models are trained based on the first, second, and third training data sets to generate an output identifying whether requests for data should be granted.