公开(公告)号：US20220036248A1

公开(公告)日：2022-02-03

申请号：US16944415

申请日：2020-07-31

Applicant: Oath Inc.

Inventor： Gal Lalouche ,  Ran Wolff

IPC: G06N20/20 ,  G06N5/04

Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. A check is performed on a level of available bidding currency for bidding a training sample that is used to train a model via machine learning. A bid in an amount within the available bidding currency is sent, to a source of the training sample, for the training sample. The training sample is received from the source when the bid is successful. A prediction is then generated in accordance with the training sample based on one or more parameters associated with the model and is sent to the source.

公开(公告)号：US20210191997A1

公开(公告)日：2021-06-24

申请号：US16726548

申请日：2019-12-24

Applicant: Oath Inc.

Inventor： Dan Pelleg ,  Ran Wolff ,  Avihai Mejer

IPC: G06F16/9535 ,  G06F16/9538 ,  G06F16/21

Abstract: The present teaching relates to a method, system, and programming for content personalization. A request is received from a user to obtain a content item. Information indicative of a literacy-level of the user is obtained and the content item to be provided to the user is retrieved. The content item is modified by updating information included in the content item based on the literacy-level of the user to generate an updated content item. The updated content item is provided to the user in response to the request.

公开(公告)号：US20220036247A1

公开(公告)日：2022-02-03

申请号：US16944324

申请日：2020-07-31

Applicant: Oath Inc.

Inventor： Gal Lalouche ,  Ran Wolff

IPC: G06N20/20 ,  G06N5/04

Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. A training sample is first received from a source. A prediction is generated according to the training sample and based on one or more parameters associated with a model. A metric characterizing the prediction is also determined. The prediction and the metric are transmitted to the source to facilitate a determination on whether a ground truth label for the training sample is to be provided. When the ground truth label is received from the source, the one or more parameters of the model are updated based on the prediction and the ground truth label.

公开(公告)号：US10558822B2

公开(公告)日：2020-02-11

申请号：US14969201

申请日：2015-12-15

Applicant: Oath Inc.

Inventor： Liane Lewin-Eytan ,  Dotan Di Castro ,  Eyal Zohar ,  Yoelle Maarek ,  Ran Wolff ,  Doug Sharp

IPC: G06F17/20 ,  G06F21/62 ,  G06F17/22 ,  H04L12/58

Abstract: Methods, systems, and computer-readable media for anonymizing electronic documents. In accordance with one or more embodiments, structurally-similar electronic documents can be identified among a group of electronic documents (e.g., e-mail messages, documents containing HTML formatting, etc.). A hash function can be specifically tailored to identify the similarly structured documents. The structurally-similar electronic documents can be grouped into a same equivalence class. Masked anonymized document samples can be generated from the structurally-similar electronic documents utilizing the same equivalence class, thereby ensuring that the anonymized document samples when viewed as a part of an audit remain anonymous. An online process is provided to guarantee k-anonymity of the users over the entire lifetime of the auditing process. An auditor's productivity can be measured based on the amount of content revealed to the auditor within the samples he is assigned. The auditor's productivity is maximized while ensuring anonymization over the lifetime of the audit.

公开(公告)号：US11861652B2

公开(公告)日：2024-01-02

申请号：US16235077

申请日：2018-12-28

Applicant: Oath Inc.

Inventor： Ariel Raviv ,  Ran Wolff ,  Joel Oren ,  Noa Avigdor-Elgrabli ,  Michael Viderman ,  Ian Michael McCarthy

IPC: G06Q30/02 ,  G06Q30/0207 ,  G06N20/00 ,  G06Q30/0251

CPC classification number: G06Q30/0239 ,  G06N20/00 ,  G06Q30/0261 ,  G06Q30/0269

Abstract: One or more computing devices, systems, and/or methods for mailbox-based coupon display are provided. For example, a plurality of email messages stored in a mailbox of a first email account of a first user may be identified, and may be evaluated to identify a plurality of coupons. The plurality of coupons may be analyzed to determine one or more coupon features. User data and/or activity data associated with the first user may be analyzed to determine one or more user features. A predictive model may be tuned based upon the one or more coupon features and/or the one or more user features. A set of scores may be generated for the plurality of coupons based upon the predictive model. A graphical user interface may be controlled based upon the set of scores to display one or more coupons.

公开(公告)号：US20220036249A1

公开(公告)日：2022-02-03

申请号：US16944459

申请日：2020-07-31

Applicant: Oath Inc.

Inventor： Gal Lalouche ,  Ran Wolff

IPC: G06N20/20 ,  G06N5/04

Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. A training sample is sent to an expert for training a model representative of the expert. A prediction is received, which is generated by the expert in accordance with the training sample and based on one or more parameters associated with the model. A metric with respect to the prediction characterizing the prediction received from the expert is analyzed. When the metric satisfies a first criterion, a ground truth label associated with the training sample is sent to the expert to facilitate the training.

公开(公告)号：US20220036138A1

公开(公告)日：2022-02-03

申请号：US16944503

申请日：2020-07-31

Applicant: Oath Inc.

Inventor： Gal Lalouche ,  Ran Wolff

IPC: G06K9/62 ,  G06N20/20

Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. A bid is received, from an expert during training, for a training sample with an amount within a level of available bidding currency associated with the expert. The training sample is used for training a model associated with the expert. It is determined whether the expert is among at least one winner selected based on bids from one or more experts. If the expert is among the at least one winner, the training sample is sent to the expert. The at least one winner is selected based on one or more criteria aiming at expert diversification.