公开(公告)号：US20240143607A1

公开(公告)日：2024-05-02

申请号：US18406426

申请日：2024-01-08

Applicant: Adobe Inc.

Inventor： Wei ZHANG ,  Christopher Challis

IPC: G06F16/2457 ,  G06F17/18 ,  H04L67/306 ,  H04L67/50

CPC classification number: G06F16/24578 ,  G06F17/18 ,  H04L67/306 ,  H04L67/535

Abstract: The present technology provides for facilitating efficient identification of relevant metrics. In one embodiment, a set of candidate metrics for which to determine relevance to a user is identified. For each candidate metric, a set of distribution parameters is determined, including a first distribution parameter based on implicit positive feedback associated with the metric and usage data associated with the metric and a second distribution parameter based on the usage data associated with the metric. Such usage data can efficiently facilitate identifying relevance even with an absence of negative feedback. Using the set of distribution parameters, a corresponding distribution is generated. Each distribution can then be sampled to identify a relevance score for each candidate metric indicating an extent of relevance of the corresponding metric. Based on the relevance scores for each candidate metric, a candidate metric is designated as relevant to the user.

公开(公告)号：US20220222261A1

公开(公告)日：2022-07-14

申请号：US17146651

申请日：2021-01-12

Applicant: ADOBE INC.

Inventor： Wei ZHANG ,  Christopher CHALLIS

IPC: G06F16/2457 ,  H04L29/08 ,  G06F17/18

Abstract: The present technology provides for facilitating efficient identification of relevant metrics. In one embodiment, a set of candidate metrics for which to determine relevance to a user is identified. For each candidate metric, a set of distribution parameters is determined, including a first distribution parameter based on implicit positive feedback associated with the metric and usage data associated with the metric and a second distribution parameter based on the usage data associated with the metric. Such usage data can efficiently facilitate identifying relevance even with an absence of negative feedback. Using the set of distribution parameters, a corresponding distribution is generated. Each distribution can then be sampled to identify a relevance score for each candidate metric indicating an extent of relevance of the corresponding metric. Based on the relevance scores for each candidate metric, a candidate metric is designated as relevant to the user.

公开(公告)号：US20220413839A1

公开(公告)日：2022-12-29

申请号：US17929267

申请日：2022-09-01

Applicant: Adobe Inc.

Inventor： Wei ZHANG ,  Christopher CHALLIS

IPC: G06F8/65 ,  G06F8/70 ,  G06N20/00 ,  G06K9/62 ,  G06F40/44 ,  G06F40/284

Abstract: Systems and methods for facilitating updates to software programs via machine-learning techniques are disclosed. In an example, an application generates a feature vector from a textual description of a software defect by applying a topic model to the textual description. The application uses the feature vector and one or more machine-learning models configured to predict classifications and sub-classifications of the textual description. The application integrates the classifications and the sub-classifications into a final classification of the textual description that indicates a software component responsible for causing the software defect. The final classification is usable for correcting the software defect.

公开(公告)号：US20240378400A1

公开(公告)日：2024-11-14

申请号：US18321602

申请日：2023-05-22

Applicant: ADOBE INC.

Inventor： Wei ZHANG ,  Victor Soares BURSZTYN

IPC: G06F40/40 ,  G06F40/186

Abstract: Methods and systems are provided for hallucination prevention for natural language insights. In embodiments described herein, a template-based insight with a set of facts is generated by a template-based insights engine. The set of facts are generated from a set of data and the template-based insight is generated based on a text template. A natural language insight is generated from the template-based insight through a language model. If a threshold number of facts of the template-based insight are missing from the natural language insight as determined by a hallucination gatekeeper engine then a new natural language insight is generated from the template-based insight through the language model.