Hui X Su, Age 432027 16Th Ave S, Seattle, WA 98144

2021022, Jul 22, 2021

Jul 31, 2020

16/945321

- Redmond WA, US
Yu HU - Redmond WA, US
Haiyuan CAO - Bellevue WA, US
Hui SU - Bellevue WA, US
Jinchao LI - Redmond WA, US
Xinying SONG - Redmond WA, US
Jianfeng GAO - Redmond WA, US

G06F 8/35
G06F 16/901
G06F 8/70

A workflow engine tool is disclosed that enables scientists and engineers to programmatically author workflows (e.g., a directed acyclic graph, “DAG”) with nearly no overhead, using a simpler script that needs almost no modifications for portability among multiple different workflow engines. This permits users to focus on the business logic of the project, avoiding the distracting tedious overhead related to workflow management (such as uploading modules, drawing edges, setting parameters, and other tasks). The workflow engine tool provides an abstraction layer on top of workflow engines, introducing a binding function that converts a programming language function (e.g., a normal python function) into a workflow module definition. The workflow engine tool infers module instances and induces edge dependencies automatically by inferring from a programming language script to build a DAG.

2020027, Aug 27, 2020

Feb 25, 2019

16/285180

- Redmond WA, US
Yu HU - Redmond WA, US
Haiyuan CAO - Bellevue WA, US
Hui SU - Bellevue WA, US
Jinchao LI - Redmond WA, US
Xinying SONG - Redmond WA, US
Jianfeng GAO - Redmond WA, US

G06F 8/35
G06F 16/901

A workflow engine tool is disclosed that enables scientists and engineers to programmatically author workflows (e.g., a directed acyclic graph, “DAG”) with nearly no overhead, using a simpler script that needs almost no modifications for portability among multiple different workflow engines. This permits users to focus on the business logic of the project, avoiding the distracting tedious overhead related to workflow management (such as uploading modules, drawing edges, setting parameters, and other tasks). The workflow engine tool provides an abstraction layer on top of workflow engines, introducing a binding function that converts a programming language function (e.g., a normal python function) into a workflow module definition. The workflow engine tool infers module instances and induces edge dependencies automatically by inferring from a programming language script to build a DAG.

2020027, Aug 27, 2020

Feb 25, 2019

16/285170

- Redmond WA, US
Xinying SONG - Redmond WA, US
Ah Young KIM - Redmond WA, US
Haiyuan CAO - Bellevue WA, US
Yu WANG - Redmond WA, US
Hui SU - Bellevue WA, US
Shahina FERDOUS - Redmond WA, US
Jianfeng GAO - Redmond WA, US
Karan SRIVASTAVA - Seattle WA, US
Jaideep SARKAR - Redmond WA, US

G06Q 10/10
G06Q 10/06
G06Q 10/04

Systems and methods are disclosed for intelligent scheduling of calls to sales leads, leveraging machine learning (ML) to optimize expected results. One exemplary method includes determining, using a connectivity prediction model, call connectivity rate predictions; determining timeslot resources; allocating, based at least on the call connectivity rate predictions and timeslot resources, leads to timeslots in a first time period; determining, within a timeslot and using a lead scoring model, lead prioritization among leads within the timeslot; configuring, based at least on the lead prioritization, the telephone unit with lead information for placing a phone call; and applying a contextual bandit (ML) process to update the connectivity prediction model, the lead scoring model, or both. During subsequent time periods, the updated connectivity prediction and lead scoring models are used, thereby improving expected results over time.

2020014, May 7, 2020

Jan 8, 2020

16/737474

- Redmond WA, US
Yu Wang - Redmond WA, US
Karan Srivastava - Seattle WA, US
Jinfeng Gao - Woodinville WA, US
Prabhdeep Singh - Newcastle WA, US
Haiyuan Cao - Redmond WA, US
Xinying Song - Bellevue WA, US
Hui Su - Bellevue WA, US
Jaideep Sarkar - Redmond WA, US

G06F 9/48
G06F 9/50
G06K 9/62
G06N 20/00

Generally discussed herein are devices, systems, and methods for scheduling tasks to be completed by resources. A method can include identifying features of the task, the features including a time-dependent feature and a time-independent feature, the time-dependent feature indicating a time the task is more likely to be successfully completed by the resource, converting the features to feature values based on a predefined mapping of features to feature values in a first memory device, determining, by a gradient boost tree model and based on a first current time and the feature values, a likelihood the resource will successfully complete the task, and scheduling the task to be performed by the resource based on the determined likelihood.

2019034, Nov 7, 2019

May 7, 2018

15/972968

- Redmond WA, US
Jaideep Sarkar - Redmond WA, US
Karan Srivastava - Seattle WA, US
Jianfeng Gao - Woodinville WA, US
Prabhdeep Singh - Newcastle WA, US
Hui Su - Bellevue WA, US
Jinchao Li - Redmond WA, US
Andreea Bianca Spataru - Seattle WA, US

G06F 9/50
G06F 9/48

Generally discussed herein are devices, systems, and methods for task routing. A method can include receiving, from a resource, a request for a task, in response to receiving the request, determining whether to retrieve a new task of new tasks stored in a first queue or a backlog task of backlog tasks stored in a second queue based on a combined amount of backlog tasks and new tasks relative to a capacity of the resource or the resources, retrieving the new task or the backlog task from the determined first queue or second queue, respectively, based on the determination, and providing the retrieved task to the resource.

2019030, Oct 3, 2019

Apr 2, 2018

15/943206

- Redmond WA, US
Yu Wang - Redmond WA, US
Karan Srivastava - Seattle WA, US
Jianfeng Gao - Woodinville WA, US
Prabhdeep Singh - Newcastle WA, US
Haiyuan Cao - Redmond WA, US
Xinying Song - Bellevue WA, US
Hui Su - Bellevue WA, US
Jaideep Sarkar - Redmond WA, US

G06F 9/48
G06F 9/50
G06K 9/62
G06F 15/18

Generally discussed herein are devices, systems, and methods for scheduling tasks to be completed by resources. A method can include identifying features of the task, the features including a time-dependent feature and a time-independent feature, the time-dependent feature indicating a time the task is more likely to be successfully completed by the resource, converting the features to feature values based on a predefined mapping of features to feature values in a first memory device, determining, by a gradient boost tree model and based on a first current time and the feature values, a likelihood the resource will successfully complete the task, and scheduling the task to be performed by the resource based on the determined likelihood.