Jun Heng He

US Patent:

6838299, Jan 4, 2005

Filed:

Nov 28, 2001

Appl. No.:

09/997086

Inventors:

Rose A. Mulligan - Chandler AZ, US
Jun He - Portland OR, US
Thomas Marieb - Portland OR, US
Susanne Menezes - Portland OR, US
Steven Towle - Phoenix AZ, US

Assignee:

Intel Corporation - Santa Clara CA

International Classification:

H01L 2100
H01L 2144

US Classification:

438 33, 438114, 438465

Abstract:

A method of dicing a microelectronic device wafer comprising forming at least one trench in at least one dicing street on the microelectronic device wafer, wherein the trench prevents cracking and/or delamination problems in the interconnect layer of the microelectronic device wafers caused by a subsequent dicing by a wafer saw.

US Patent:

8542898, Sep 24, 2013

Filed:

Dec 16, 2011

Appl. No.:

13/328879

Inventors:

Mark Bathe - Cambridge MA, US
Jun He - San Diego CA, US
Nilah Monnier - Brookline MA, US

Assignee:

Massachusetts Institute of Technology - Cambridge MA

International Classification:

G06K 9/00

US Classification:

382128, 703 11

Abstract:

Techniques for inferring particle dynamics from certain data include determining multiple models for motion of particles in a biological sample. Each model includes a corresponding set of one or more parameters. Measured data is obtained based on measurements at one or more voxels of an imaging system sensitive to motion of particles in the biological sample; and, determining noise correlation of the measured data. Based at least in part on the noise correlation, a marginal likelihood is determined of the measured data given each model of the multiple models. A relative probability for each model is determined based on the marginal likelihood. Based at least in part on the relative probability for each model, a value is determined for at least one parameter of the set of one or more parameters corresponding to a selected model of the multiple models.

US Patent:

20220357891, Nov 10, 2022

Filed:

Apr 19, 2022

Appl. No.:

17/724149

Inventors:

- Mountain View CA, US
MARK FAY - MOUNTAIN VIEW CA, US
JUN HE - MOUNTAIN VIEW CA, US
RENJIE FAN - SAN JOSE CA, US
JOHN COLGROVE - LOS ALTOS CA, US

International Classification:

G06F 3/06
G06F 11/07
G06F 11/10
G06F 11/34
G06F 12/0804
G06F 12/0866

Abstract:

A method for efficient reads by reconstruction may determining an expected read latency for reading data from a primary read location of a plurality of storage devices, determining an expected reconstruction latency for reconstructing the data using reconstruction data, wherein portions of the reconstruction data are stored at a plurality of alternative read locations of the plurality of storage devices, reading the portions of the reconstruction data from the plurality of alternative read locations of the plurality of storage devices, and reconstructing the data stored at the primary read location using the reconstruction data, wherein the expected reconstruction latency is lower than the expected read latency.

US Patent:

20220261286, Aug 18, 2022

Filed:

Apr 29, 2022

Appl. No.:

17/732867

Inventors:

- MOUNTAIN VIEW CA, US
MARK FAY - MOUNTAIN VIEW CA, US
JUN HE - MOUNTAIN VIEW CA, US
RENJIE FAN - SAN JOSE CA, US
KIRON VIJAYASANKAR - LOS ALTOS CA, US
YUVAL FRANDZEL - FOSTER CITY CA, US

International Classification:

G06F 9/50
G06F 3/06
G06F 9/48
G06F 13/16

Abstract:

A method of scheduling input/output operations for a storage system including determining a deadline for a storage operation, wherein the deadline is dependent on an expected latency of the storage operation; adding the storage operation to a queue of storage operations; and reordering the queue dependent upon the deadline of the storage operation and one or more deadlines of one or more storage operations in the queue of storage operations.

US Patent:

20210075875, Mar 11, 2021

Filed:

Sep 9, 2019

Appl. No.:

16/564768

Inventors:

- San Jose CA, US
Jun He - Fremont CA, US
Zhenyu Yan - Cupertino CA, US
Wuyang Dai - San Jose CA, US
Abhishek Pani - San Francisco CA, US

Assignee:

Adobe Inc. - San Jose CA

International Classification:

H04L 29/08
G06F 16/2457
G06N 3/04
G06N 3/08

Abstract:

The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times for distributing digital content to client devices utilizing a recommendation system approach. For example, the disclosed systems can utilize a recommendation system model such as a matrix factorization model, a factorization machine model, and/or a neural network to implement collaborative filtering to generate predicted response rates for particular candidate send times. Based on the predicted response rates indicating likelihoods of receiving responses for particular send times, the disclosed system can generate a distribution schedule to provide electronic communications at one or more of the send times.

US Patent:

20200327419, Oct 15, 2020

Filed:

Apr 15, 2019

Appl. No.:

16/384558

Inventors:

- San Jose CA, US
Jun He - Fremont CA, US
Zhenyu Yan - Cupertino CA, US
Wuyang Dai - San Jose CA, US
Abhishek Pani - Sunnyvale CA, US

International Classification:

G06N 3/08
G06F 17/18
G06N 3/10

Abstract:

The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a target distribution schedule for providing electronic communications based on predicted behavior rates by utilizing a genetic algorithm and one or more objective functions. For example, the disclosed systems can generate predicted behavior rates by training and utilizing one or more behavior prediction models. Based on the predicted behavior rates, the disclosed systems can further utilize a genetic algorithm to apply objective functions to generate one or more candidate distribution schedules. In accordance with the genetic algorithm, the disclosed systems can select a target distribution schedule for a particular user/client device. The disclosed systems can thus provide one or more electronic communications to individual users based on respective target distribution schedules.

US Patent:

20200110981, Apr 9, 2020

Filed:

Oct 4, 2018

Appl. No.:

16/152227

Inventors:

- San Jose CA, US
Jun He - Fremont CA, US
Fei Tan - Harrison NJ, US
Xiang Wu - Mountain View CA, US
Bo Peng - Santa Clara CA, US
Abhishek Pani - San Francisco CA, US

Assignee:

Adobe Inc. - San Jose CA

International Classification:

G06N 3/04
G06N 3/08

Abstract:

A hybrid deep-learning action prediction architecture system is described that predicts actions. The architecture includes a main path and an auxiliary path. The main path may contain multiple layers of convolutional neural networks for further aggregation to coarser time spans. The resultant data produced by the convolutional neural networks is passed to multiple layers of LSTMs. The outputs from LSTMs are then combined with the profile in the auxiliary path to predict an action label.

US Patent:

20180101788, Apr 12, 2018

Filed:

Oct 30, 2015

Appl. No.:

15/030854

Inventors:

- Palo Alto CA, US
Jun HE - Fremont CA, US

International Classification:

G06N 99/00
G06F 17/18
G06N 7/00
G06N 5/04

Abstract:

A new approach is proposed to support two variants of the approach to effectively generate reason codes for an ensemble model. Both variants involve treating the ensemble model as a black box, identifying trivial values for input variables to the ensemble model, replacing each of the input variables to the ensemble model with its corresponding trivial values and then evaluating the impact on a score of the model after the trivial value replacement. The evaluation result of the impact is then used to generate the reason codes for the ensemble model. Specifically, the first variant is configured to perform one round of replacement, wherein variables with top drops in the score will be the reason codes. The second variant is configured to perform multiple rounds of replacement, which in each round, keeps the identified reason codes variables replaced with trivial values and analyzes incremental drops of replacing remaining variables.