Jun He

US Patent:

8637778, Jan 28, 2014

Filed:

Apr 8, 2010

Appl. No.:

12/756748

Inventors:

Qing Ma - Saratoga CA, US
Jun He - Portland OR, US
Patrick Morrow - Portland OR, US
Paul B. Fischer - Portland OR, US
Sridhar Balakrishnan - Portland OR, US
Satish Radhakrishnan - Hillsboro OR, US
Tatyana Tanya Andryushchenko - Portland OR, US
Guanghai Xu - Portland OR, US

Assignee:

Intel Corporation - Santa Clara CA

International Classification:

H05K 1/03
H05K 7/02

US Classification:

174260, 174255, 174263, 361792, 361807, 361809

Abstract:

The present subject matter relates to the field of fabricating microelectronic devices. In at least one embodiment, the present subject matter relates to forming an interconnect that has a portion thereof which becomes debonded from the microelectronic device during cooling after attachment to an external device. The debonded portion allows the interconnect to flex and absorb stress.

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:

20230129808, Apr 27, 2023

Filed:

Oct 25, 2021

Appl. No.:

17/509885

Inventors:

- San Jose CA, US
Wuyang Dai - Santa Clara CA, US
Jun He - Fremont CA, US
Hsiang-Yu Yang - Palo Alto CA, US
Zhenyu Yan - Cupertino CA, US

International Classification:

H04L 12/58
G06K 9/62
G06N 20/00

Abstract:

Methods and systems are provided for facilitating time zone prediction using electronic communication data. Electronic message data associated with a message recipient of electronic communications is obtained. The electronic message data includes message delivery data associated with an electronic message and message response data associated with a response, by the message recipient, to a received electronic message. Using a machine learning model and based on the message delivery data and the message response data, a time-zone score is determined for a time zone. Such a time-zone score can indicate a probability the time zone corresponds with the message recipient. Based on the time-zone score, the time zone is identified as corresponding with the message recipient.

US Patent:

20230005023, Jan 5, 2023

Filed:

Jul 2, 2021

Appl. No.:

17/366910

Inventors:

- San Jose CA, US
Lijun Yu - San Jose CA, US
Jun He - Fremont CA, US
Zhenyu Yan - Cupertino CA, US
Wuyang Dai - San Jose CA, US

International Classification:

G06Q 30/02

Abstract:

Methods and systems are provided for improved electronic communication campaign technologies, which can automatically balance objectives or goals of an electronic communication campaign against an overall opt-out rate for the electronic communication campaign. An electronic communications frequency optimizer can generate individual contact frequencies for individual email recipients. Embodiments can avoid unnecessary or counterproductive communications while achieving overall campaign goals, and can use processes to improve the efficiency of systems. In some cases, embodiments cluster communication recipients into different groups based on their past actions, then optimizes the communication contact frequency on different groups, to avoid performing optimization directly on millions of recipients. Some embodiments automatically self-update, for example with recipients' recent responses, to generate and/or implement campaign communication schedules on an individual level.

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.