公开(公告)号：US11836037B2

公开(公告)日：2023-12-05

申请号：US17476892

申请日：2021-09-16

Applicant: salesforce.com, inc.

Inventor： Amrita Saha ,  Chu Hong Hoi

IPC: G06F11/07

CPC classification number: G06F11/079 ,  G06F11/0706

Abstract: Some embodiments of the current disclosure disclose methods and systems for analyzing root causes of an incident disrupting information technology services such as cloud services. In some embodiments, a set of problem review board (PRB) documents including information about said incidents may be parsed using a natural language processing (NLP) neural model to extract structured PRB data from the unstructured investigative information contained in the PRB documents. The structured PRB data may include symptoms of the incident, root causes of the incident, resolutions of the incidents, etc., and a causal knowledge graph causally relating the symptoms, root causes, resolutions of the incidents may be generated.

公开(公告)号：US20220358005A1

公开(公告)日：2022-11-10

申请号：US17476892

申请日：2021-09-16

Applicant: salesforce.com, inc.

Inventor： Amrita Saha ,  Chu Hong Hoi

IPC: G06F11/07 ,  G06F40/279 ,  G06F16/28

Abstract: Some embodiments of the current disclosure disclose methods and systems for analyzing root causes of an incident disrupting information technology services such as cloud services. In some embodiments, a set of problem review board (PRB) documents including information about said incidents may be parsed using a natural language processing (NLP) neural model to extract structured PRB data from the unstructured investigative information contained in the PRB documents. The structured PRB data may include symptoms of the incident, root causes of the incident, resolutions of the incidents, etc., and a causal knowledge graph causally relating the symptoms, root causes, resolutions of the incidents may be generated.

公开(公告)号：US20220108169A1

公开(公告)日：2022-04-07

申请号：US17162289

申请日：2021-01-29

Applicant: salesforce.com, inc.

Inventor： Amrita Saha ,  Shafiq Rayhan Joty ,  Chu Hong Hoi

IPC: G06N3/08 ,  G06N3/04

Abstract: Embodiments described herein provide systems and methods for a partially supervised training model for questioning answering tasks. Specifically, the partially supervised training model may include two modules—a query parsing module and a program execution module. The query parsing module parses queries into a grogram, and the program execution module execute the program to reach an answer through explicit reasoning and partial supervision. In this way, the partially supervised training model can be trained with answers as supervision, obviating the need for supervision by gold program operations and gold query-span attention at each step of the program.