公开(公告)号：US11003717B1

公开(公告)日：2021-05-11

申请号：US15892258

申请日：2018-02-08

Applicant: Amazon Technologies, Inc.

Inventor： Dhivya Eswaran ,  Sudipto Guha ,  Nina Mishra

IPC: G06F16/00 ,  G06F16/901 ,  G06F16/21 ,  H04L29/08 ,  H04L29/12

Abstract: Techniques for detecting anomalies in streaming graph data are described. For example, an embedding technique of generating a multi-dimensional vector of summations of each weighted edge found in both a random source bounding proper subset and a random destination bounding proper subset associated with a dimension of the epoch graph is detailed. Anomaly detection is performed on the generated multi-dimensional vectors.

公开(公告)号：US10713289B1

公开(公告)日：2020-07-14

申请号：US15475458

申请日：2017-03-31

Applicant: Amazon Technologies, Inc.

Inventor： Nina Mishra ,  Yonatan Naamad

IPC: G06F16/33 ,  G06F16/332 ,  G10L15/22 ,  G10L15/18 ,  G10L15/30 ,  G06F40/295

Abstract: Systems, methods, and devices for performing interactive question answering using data source credibility and conversation entropy are disclosed. A speech-controlled device captures audio including a spoken question, and sends audio data corresponding thereto to a server(s). The server(s) performs speech processing on the audio data, and determines various stored data that can be used to determine an answer to the question. The server(s) determines which stored data to use based on the credibility of the source from which the stored data was received. The server(s) may also determine a number of user interactions needed to obtain data in order to fully answer the question and may select a question for a dialog soliciting further data based on the number of user interactions.

公开(公告)号：US11853912B1

公开(公告)日：2023-12-26

申请号：US16777589

申请日：2020-01-30

Applicant: Amazon Technologies, Inc.

Inventor： Shiva Prasad Kasiviswanathan ,  Nina Mishra ,  Yonatan Naamad

IPC: G06N5/04 ,  G06F16/28 ,  G06N5/02 ,  G06F16/23 ,  G06F16/901

CPC classification number: G06N5/04 ,  G06F16/2393 ,  G06F16/285 ,  G06F16/9024 ,  G06N5/02

Abstract: Described are systems and methods for determining causal connections between various metrics collected by wearable devices and using those causal connections to provide causal insights to other users. For example, some users may elect to perform one or more self-experiments to explore the impact certain changes in their behavior may have on metrics measured by the user's wearable device. Causal connections determined from those experiments may be used to provide causal insights relating to those metrics to other users who have not performed the same or similar experiments.

公开(公告)号：US10902062B1

公开(公告)日：2021-01-26

申请号：US15686086

申请日：2017-08-24

Applicant: Amazon Technologies, Inc.

Inventor： Sudipto Guha ,  Nina Mishra

IPC: G06F16/901 ,  G06N7/00 ,  H04L12/24 ,  H04L29/06 ,  H04L12/26

Abstract: At an artificial intelligence system, a random cut tree corresponding to a sample of a multi-dimensional data set is traversed to determine a tree-specific vector indicating respective contributions of individual dimensions to an anomaly score of a particular data point. Level-specific vectors of per-dimension contributions obtained using bounding-box analyses at each level during the traversal are aggregated to obtain the tree-specific vector. An overall anomaly score contribution for at least one dimension is obtained using respective tree-specific vectors generated from one or more random cut trees, and an indication of the overall anomaly score contribution is provided.

公开(公告)号：US12147557B2

公开(公告)日：2024-11-19

申请号：US17810306

申请日：2022-06-30

Applicant: Amazon Technologies, Inc.

Inventor： James Alexander Cook ,  Nina Mishra

IPC: G06F21/62 ,  G06F16/2455 ,  G06F21/00

Abstract: Computer systems and associated methods are disclosed to implement the non-interactive join of privacy-preserving dataset sketches. In some embodiments, an entity can publish a one-time sketch of their dataset that would enable another entity to join their data without exposing private information. The sketch can map, using a hash function, the identities associated with a first value of the dataset to a data structure, in some embodiments. A same or different entity can join the first sketch with a privacy-preserving second sketch of a second dataset that includes added noise, and can determine an estimate of a number of identities that correspond with specific values of the first and second datasets from the joined dataset. The sketch can be published just one time, and therefore does not require separate new private computations with privacy budgeting for each additional party when a join is desired, in some embodiments.

公开(公告)号：US20240005022A1

公开(公告)日：2024-01-04

申请号：US17810306

申请日：2022-06-30

Applicant: Amazon Technologies, Inc.

Inventor： James Alexander Cook ,  Nina Mishra

IPC: G06F21/62 ,  G06F16/2455

CPC classification number: G06F21/6227 ,  G06F16/2456

Abstract: Computer systems and associated methods are disclosed to implement the non-interactive join of privacy-preserving dataset sketches. In some embodiments, an entity can publish a one-time sketch of their dataset that would enable another entity to join their data without exposing private information. The sketch can map, using a hash function, the identities associated with a first value of the dataset to a data structure, in some embodiments. A same or different entity can join the first sketch with a privacy-preserving second sketch of a second dataset that includes added noise, and can determine an estimate of a number of identities that correspond with specific values of the first and second datasets from the joined dataset. The sketch can be published just one time, and therefore does not require separate new private computations with privacy budgeting for each additional party when a join is desired, in some embodiments.

公开(公告)号：US11797572B1

公开(公告)日：2023-10-24

申请号：US16056936

申请日：2018-08-07

Applicant: Amazon Technologies, Inc.

Inventor： Yonatan Naamad ,  Shiva Prasad Kasiviswanathan ,  Nina Mishra ,  Morteza Monemizadeh ,  Lauren Anne Moos ,  Joshua M. Tokle

IPC: G06F16/27 ,  G06F16/28

CPC classification number: G06F16/278 ,  G06F16/285

Abstract: Techniques for hotspot detection in a dataset are described. A hotspot being a region (or a collection of points) where the value of a function of given any region in the space measures the concentration of points in that region is significantly higher than its other regions of the dataspace. As such, a region that has a denser concentration of points than other regions of the dataspace may be considered a hotspot. In some implementations, hotspot detection includes finding two or more regions to evaluate for high-density in the dataset, a high-density region indicating a potential hotspot and extending a size of the manipulated found two or more regions to determine borders for these regions.

公开(公告)号：US20220100721A1

公开(公告)日：2022-03-31

申请号：US17549395

申请日：2021-12-13

Applicant: Amazon Technologies, Inc.

Inventor： Nina Mishra ,  Daniel Blick ,  Sudipto Guha ,  Okke Joost Schrijvers

IPC: G06F16/215 ,  G06N5/00 ,  G06N20/00

Abstract: Random cut trees are generated with respective to respective samples of a baseline set of data records of a data set for which outlier detection is to be performed. To construct a particular random cut tree, an iterative splitting technique is used, in which the attribute along which a given set of data records is split is selected based on its value range. With respect to a newly-received data record of the stream, an outlier score is determined based at least partly on a potential insertion location of a node representing the data record in a particular random cut tree, without necessarily modifying the random cut tree.

公开(公告)号：US12174807B2

公开(公告)日：2024-12-24

申请号：US17549395

申请日：2021-12-13

Applicant: Amazon Technologies, Inc.

Inventor： Nina Mishra ,  Daniel Blick ,  Sudipto Guha ,  Okke Joost Schrijvers

IPC: G06F16/00 ,  G06F16/215 ,  G06N5/01 ,  G06N20/00 ,  G06F16/2458

Abstract: Random cut trees are generated with respective to respective samples of a baseline set of data records of a data set for which outlier detection is to be performed. To construct a particular random cut tree, an iterative splitting technique is used, in which the attribute along which a given set of data records is split is selected based on its value range. With respect to a newly-received data record of the stream, an outlier score is determined based at least partly on a potential insertion location of a node representing the data record in a particular random cut tree, without necessarily modifying the random cut tree.

公开(公告)号：US11308407B1

公开(公告)日：2022-04-19

申请号：US15842291

申请日：2017-12-14

Applicant: Amazon Technologies, Inc.

Inventor： Sudipto Guha ,  Tal Wagner ,  Shiva Prasad Kasiviswanathan ,  Nina Mishra

IPC: G06N20/00 ,  G06N5/04 ,  G06F16/901

Abstract: Examples of techniques for anomaly detection with feedback are described. An instance includes a technique is receiving a plurality of unlabeled data points from an input stream; performing anomaly detection on a point of the unlabeled data points using an anomaly detection engine; pre-processing the unlabeled data point that was subjected to anomaly detection; classifying the pre-processed unlabeled data point; determining the anomaly detection was not proper based on a comparison of a result of the anomaly detection and a result of the classifying of the pre-processed unlabeled data point; and in response to determining the anomaly detection was not proper, providing feedback to the anomaly detection engine to change at least one emphasis used in anomaly detection.