Tao Wang

7119567, Oct 10, 2006

Sep 12, 2002

10/243544

David SuitWai Ma - Cary NC, US
Tao Wang - Cary NC, US
James J. Dietz - Durham NC, US
Bing Ren - Cary NC, US

Infineon Technologies North America Corp. - San Jose CA

G01R 31/26
G01R 31/28
G01R 31/02

324763, 324765, 3241581, 714736

A testing system or method compares read data from one or more dies in a semiconductor wafer with the original data written onto the one or more dies The testing system includes one or more write registers connected to one or more dies on the semiconductor wafer. One or more comparators are connected to the dies and the write registers. The comparator generates a result in response to the original data and the read data.

7242208, Jul 10, 2007

Mar 22, 2006

11/386512

David SuitWai Ma - Cary NC, US
Tao Wang - Cary NC, US
James J. Dietz - Durham NC, US
Bing Ren - Cary NC, US

Infineon Technologies AG - Munich

G01R 31/26
G01R 31/02
G01R 31/28

324763, 324765, 3241581, 257 48, 438 18, 714736

A testing system or method compares read data from one or more dies in a semiconductor wafer with the original data written onto the one or more dies. The testing system includes one or more write registers connected to one or more dies on the semiconductor wafer. One or more comparators are connected to the dies and the write registers. The comparator generates a result in response to the original data and the read data.

7449909, Nov 11, 2008

Feb 16, 2007

11/707408

David SuitWai Ma - Cary NC, US
Tao Wang - Cary NC, US
James J. Dietz - Durham NC, US
Bing Ren - Cary NC, US

Infineon Technologies AG - Munich

G01R 31/26
G01R 31/02
G01R 31/28

324763, 324765, 3241581, 257 48, 438 18, 714736

A testing system or method compares read data from one or more dies in a semiconductor wafer with the original data written onto the one or more dies. The testing system includes one or more write registers connected to one or more dies on the semiconductor wafer. One or more comparators are connected to the dies and the write registers. The comparator generates a result in response to the original data and the read data.

20190258697, Aug 22, 2019

Apr 30, 2019

16/398690

- Cary NC, US
Tao Wang - Cary NC, US
Scott Russell Pope - Raleigh NC, US

G06F 17/18

A computing device computes a quantile value for a variable value extracted from an event block object by computing a bin number for the variable value. If the computed bin number is between a before bin number and an after bin number computed for a quantile, the quantile is identified. Frequency data is updated to include the extracted variable value as a key value. A frequency value associated with the key value indicates a number of occurrences of the variable value in previously processed data. A cumulative rank value of the identified quantile is updated. A quantile adjustment value is computed based on a comparison between the variable value and a current quantile value of the identified quantile. An updated quantile value associated with the identified quantile is computed using the updated frequency data, the computed quantile adjustment value, and the updated cumulative rank value of the identified quantile.

20190129919, May 2, 2019

Sep 25, 2018

16/140931

- Cary NC, US
Xiangqian Hu - Cary NC, US
Tao Wang - Cary NC, US
Xunlei Wu - Cary NC, US

G06F 17/18

A computing device computes a quantile value. A maximum value and a minimum value are computed for unsorted variable values to compute an upper bin value and a lower bin value for each bin of a plurality of bins. A frequency counter is computed for each bin by reading the unsorted variable values a second time. A bin number and a cumulative rank value are computed for a quantile. When an estimated memory usage value exceeds a predefined memory size constraint value, a subset of the plurality of bins are split into a plurality of bins, the frequency counter is recomputed for each bin, and the bin number and the cumulative rank value are recomputed. Frequency data is computed using the frequency counters. The quantile value is computed using the frequency data and the cumulative rank value for the quantile and output.

20180053071, Feb 22, 2018

Aug 25, 2017

15/686863

- Cary NC, US
Tao Wang - Apex NC, US

G06K 9/62
G06N 99/00
G06K 9/66

A computing device predicts occurrence of an event or classifies an object using distributed unlabeled data. Supervised data that includes a labeled subset of a plurality of observation vectors is identified. A total number of threads that will perform labeling of an unlabeled subset of the plurality of observation vectors is determined. The identified supervised data is uploaded to each thread of the total number of threads. Unlabeled observation vectors are randomly select from the unlabeled subset of the plurality of observation vectors to allocate to each thread of the total number of threads. The randomly selected, unlabeled observation vectors are uploaded to each thread of the total number of threads based on the allocation. The value of the target variable for each observation vector of the unlabeled subset of the plurality of observation vectors is determined based on a converged classification matrix and output to a labeled dataset.

20170308810, Oct 26, 2017

Oct 27, 2016

15/335530

- Cary NC, US
Tao Wang - Apex NC, US

G06N 99/00
G06N 7/00
G06N 5/00

A computing device predicts occurrence of an event or classifies an object using semi-supervised data. A label set defines permissible values for a target variable. A value of the permissible values is defined for a subset of observation vectors. A predefined number of times, a distance matrix is computed that defines a distance value between pairs of observation vectors using a distance function and a converged classification matrix; a number of observation vectors is selected that have minimum values for the distance value; a label is requested and a response is received for each of the selected observation vectors; the value of the target variable is updated for each of the selected observation vectors with the received response; and the value of the target variable is determined again by recomputing the converged classification matrix. The value of the target variable for each observation vector is output to a second dataset.

20140372339, Dec 18, 2014

Jun 18, 2013

13/920800

- Norwalk CT, US
Tao Wang - Grapevine TX, US
Sudhendu Rai - Fairport NY, US

G06Q 10/06

705348

A method of determining a maximum profit for a print production environment may include receiving, by a computing device, a flow model associated with a print production environment, applying, by the computing device, a modified Jackson Network analysis to the flow model to generate one more characteristic curves that each characterize a relationship between profit of the print production environment and job inflow rate and that each show a maximum profit value for the print production environment, and presenting, by the computing device, one or more of the generated characteristic curves to a user.