Learn more

CHEMESSEN INC

Overview
  • Total Patents
    47
  • GoodIP Patent Rank
    202,906
About

CHEMESSEN INC has a total of 47 patent applications. Its first patent ever was published in 2011. It filed its patents most often in Republic of Korea and WIPO (World Intellectual Property Organization). Its main competitors in its focus markets computer technology, transport and measurement are CAMBRICON TECH CORP LTD, MANHATTAN ENG INCORPORATED and CANTIN JASON F.

Patent filings in countries

World map showing CHEMESSEN INCs patent filings in countries

Patent filings per year

Chart showing CHEMESSEN INCs patent filings per year from 1900 to 2020

Focus industries

Top inventors

# Name Total Patents
#1 Park Tae Yun 44
#2 Kwon Yun Kyung 44
#3 Kim Yang Soo 42
#4 Jeon Jeong Jae 42
#5 Jung Won Chon 42
#6 Cho Jun Hyuk 41
#7 Sung Ae Ri 41
#8 Kwon Oh Yung 41
#9 Park Jae Hyun 4
#10 Kim Yangsoo 3

Latest patents

Publication Filing date Title
KR20200105012A Drone with autonomous collision avoidance function and the method thereof
KR20200105008A Method for autonomous swarm flight of drone through single controlling flight path and synchronizing time
KR20190091670A System for analysing lc-ms/ms data of nature products
KR20190091668A Method for analysing lc-ms/ms data of nature products
KR20140145753A Method for expecting property of each molecules of compound by using underlying corelation between molecules on property and molecular descriptor of of standard molecules
WO2012177108A2 Model, method and system for predicting, processing and servicing online physicochemical and thermodynamic properties of pure compound
KR20120085178A Method for predicting a property of compound and system for predicting a property of compound
KR20120085160A Multiple linear regression-artificial neural network hybrid model predicting heat of vaporization of pure organic compound at normal boiling point
KR20120085169A Multiple linear regression-artificial neural network hybrid model predicting enthalpy of fusion at melting point of pure organic compound
KR20120085174A Multiple linear regression-artificial neural network hybrid model predicting critical volume of pure organic compound
KR20120085164A Multiple linear regression-artificial neural network model predicting polarizability of pure organic compound
KR20120085168A Multiple linear regression-artificial neural network hybrid model predicting critical temperature of pure organic compound
KR20120085163A Svrc model predicting gas viscosity of pure organic
KR20120085167A Multiple linear regression-artificial neural network hybrid model predicting critical pressure of pure organic compound
KR20120085175A Multiple linear regression-artificial neural network hybrid model predicting saturated liquid density of pure rganic compound at 298.15k
KR20120085157A Multiple linear regression-artificial neural network hybrid model predicting normal boiling point of pure organic compound
KR20120085161A Svrc model predicting vapor pressure of liquid of pure organic compound
KR20120085166A Multiple linear regression-artificial neural network hybrid model predicting acentric factor of pure organic compound
KR20120085171A Multiple linear regression-artificial neural network hybrid model predicting parachor of pure organic compound
KR20120085172A Svrc model predicting heat capacity of liquid of pure organic compound