Aman Jain

US Patent:

20090157229, Jun 18, 2009

Filed:

Dec 12, 2008

Appl. No.:

12/333893

Inventors:

Ron Rulkens - Milpitas CA, US
Nanhai Li - Pleasanton CA, US
Artur Kolics - Dublin CA, US
Aman Jain - Cupertino CA, US
Darin Birtwhistle - San Francisco CA, US
Chee Chan - Cupertino CA, US

Assignee:

LAM Research Corporation - Fremont CA

International Classification:

G05D 11/08
G05B 19/02
G05D 9/12
G05D 11/16
G01J 3/44

US Classification:

700267, 700285, 700281, 356301

Abstract:

A plating system comprises a plating solution and an apparatus for control of the plating solution, the apparatus including a Raman spectrometer for measurement of organic components, a visible light spectrometer for measurement of metallic components, and a pH probe. The plating solution can be sampled continuously or at intervals. Dosing of the plating solution adjusts for components consumed or lost in the plating process. The method of dosing is based on maintaining a desired composition of the plating solution.

US Patent:

20210224237, Jul 22, 2021

Filed:

Dec 8, 2020

Appl. No.:

17/115525

Inventors:

- McLean VA, US
Christopher J. Johnson - McLean VA, US
Peter Deng - McLean VA, US
Aman Jain - McLean VA, US

International Classification:

G06F 16/215
G06F 16/23
G06F 16/28

Abstract:

In order to facilitate entity resolution, systems and methods include a processor receiving first records associated with one or more entities, and second records associated with the one or more entities. The processor generates candidate pairs based on a similarity between first entity data and second entity data. The processor generates features for each candidate pair based on similarity measures between the first entity record and the second entity record. The processor utilizes a scoring machine learning model to determine a match score for each candidate pair based on each feature. The processor determines clusters of candidate pairs based on the match score of each feature for each candidate pair. The processor merges records of candidate pairs of each cluster into a respective entity record. The processor determines an entity associated with each entity record and updates an entity database with the entity record.

US Patent:

20210224258, Jul 22, 2021

Filed:

Jan 16, 2020

Appl. No.:

16/744919

Inventors:

- McLean VA, US
Christopher J. Johnson - McLean VA, US
Peter Deng - McLean VA, US
Aman Jain - McLean VA, US

International Classification:

G06F 16/242
G06F 16/28
G06F 16/23
G06N 20/00

Abstract:

In order to facilitate entity resolution, systems and methods include a processor receiving first records associated with one or more entities, and second records associated with the one or more entities. The processor generates candidate pairs based on a similarity between first entity data and second entity data. The processor generates features for each candidate pair based on similarity measures between the first entity record and the second entity record. The processor utilizes a scoring machine learning model to determine a match score for each candidate pair based on each feature. The processor determines clusters of candidate pairs based on the match score of each feature for each candidate pair. The processor merges records of candidate pairs of each cluster into a respective entity record. The processor determines an entity associated with each entity record and updates an entity database with the entity record.

US Patent:

20210142191, May 13, 2021

Filed:

May 29, 2020

Appl. No.:

16/888148

Inventors:

- McLean VA, US
Aman Jain - McLean VA, US
Jihan Wei - McLean VA, US
Amir Reza Rahmani - McLean VA, US
Christopher Johnson - McLean VA, US

International Classification:

G06N 5/04
G06N 5/00
G06N 20/00
G06F 16/23

Abstract:

In order to facilitate the entity resolution and entity activity tracking and indexing, systems and methods include receiving first source records from a first database and second source records from a record database. A candidate set of second source records is determined by a heuristic search in the set of second source records. A candidate pair feature vector associated with each candidate pair of first and second source records is generated. An entity matching machine learning model predicts matching first source records for each candidate second source record based on the respective candidate pair feature vector. An aggregate quantity associated with the matching first source records is aggregated from a quantity associated with each first source record, and a quantity index for each candidate second source record is determined based the aggregate quantities. Each quantity index is displayed to a user.