公开(公告)号：US20220237482A1

公开(公告)日：2022-07-28

申请号：US17159463

申请日：2021-01-27

申请人： Intuit Inc.

发明人： Aviv Ben Arie ,  Liat Ben Porat Roda ,  Liran Dreval

IPC分类号： G06N5/04 ,  G06N20/00

摘要： Feature randomization for securing machine learning models includes receiving an event, and altering, responsive to receiving the event, a threshold pseudo-randomly to generate an altered threshold value. Feature randomization further includes applying the altered threshold value to a threshold-dependent feature to generate an altered threshold-dependent feature value. The altered threshold-dependent feature value determined at least in part from the event. Feature randomization further includes executing a machine learning model, on the event and the altered threshold-dependent feature value, to generate a predicted event type for the event.

公开(公告)号：US12118077B2

公开(公告)日：2024-10-15

申请号：US17154293

申请日：2021-01-21

申请人： Intuit Inc.

发明人： Miriam Hanna Manevitz ,  Liat Ben Porat Roda ,  Or Basson ,  Aviv Ben Arie ,  Hagai Fine

IPC分类号： H04L9/40 ,  G06F16/901 ,  G06F17/16 ,  G06F21/55

CPC分类号： G06F21/552 ,  G06F16/9027 ,  G06F17/16 ,  H04L63/1425 ,  G06F2221/034 ,  G06F2221/2101

摘要： A plurality of graph snapshots for a plurality of consecutive periodic time samples maps between connected components in consecutive graph snapshots and describes at least one feature of each connected component. A recursively-built tree tracks an evolution of one of the connected components through the plurality of graph snapshots, the tree including a root node representing the connected component at a final one of the consecutive periodic time samples and a plurality of leaf nodes branching from the root node. A plurality of paths is extracted from the tree by traversing the tree from the root node to respective ones of the plurality of leaf nodes. Each path contains data describing an evolution of a respective one of the connected components through time as indicated by evolution of the at least one feature of the respective one of the connected components. Each of the plurality of paths is converted into a respective numerical vector of a plurality of numerical vectors that may be used as inputs to a time series anomaly detection algorithm.

公开(公告)号：US20220229903A1

公开(公告)日：2022-07-21

申请号：US17154293

申请日：2021-01-21

申请人： Intuit Inc.

发明人： Miriam Hanna Manevitz ,  Liat Ben Porat Roda ,  Or Basson ,  Aviv Ben Arie ,  Hagai Fine

IPC分类号： G06F21/55 ,  G06F16/901 ,  G06F17/16

摘要： A plurality of graph snapshots for a plurality of consecutive periodic time samples maps between connected components in consecutive graph snapshots and describes at least one feature of each connected component. A recursively-built tree tracks an evolution of one of the connected components through the plurality of graph snapshots, the tree including a root node representing the connected component at a final one of the consecutive periodic time samples and a plurality of leaf nodes branching from the root node. A plurality of paths is extracted from the tree by traversing the tree from the root node to respective ones of the plurality of leaf nodes. Each path contains data describing an evolution of a respective one of the connected components through time as indicated by evolution of the at least one feature of the respective one of the connected components. Each of the plurality of paths is converted into a respective numerical vector of a plurality of numerical vectors that may be used as inputs to a time series anomaly detection algorithm.

公开(公告)号：US11127403B2

公开(公告)日：2021-09-21

申请号：US16664480

申请日：2019-10-25

申请人： INTUIT INC.

发明人： Shlomi Medalion ,  Liron Hayman ,  Alexander Zhicharevich ,  Liat Ben Porat Roda

IPC分类号： G10L15/00 ,  G10L15/26 ,  G10L15/18 ,  G10L15/16 ,  G06N3/04 ,  G06F21/62 ,  G06K9/62 ,  G06F40/30 ,  G06F40/295

摘要： Certain aspects of the present disclosure provide techniques for detecting personally identifiable information, including: receiving a plurality of text strings, each text string of the plurality of text strings associated with a user support session; providing the plurality of text strings to one or more bidirectional long short-term memory (BiLSTM) neural network models; receiving output from the one or more BiLSTM neural network models, the output indicating one or more text data elements in the plurality of text strings comprising predicted personally identifiable information; redacting the one or more text data elements comprising the predicted personally identifiable information from the plurality of text strings to form redacted text strings; and providing, to a data repository, the redacted text strings.