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SPIDER IOT TECH BEIJING CO LTD

Overview
  • Total Patents
    12
  • GoodIP Patent Rank
    144,037
About

SPIDER IOT TECH BEIJING CO LTD has a total of 12 patent applications. Its first patent ever was published in 2018. It filed its patents most often in China. Its main competitors in its focus markets computer technology, audio-visual technology and it methods for management are GUANGZHOU HISON COMPUTER TECH CO LTD, KOFAX IMAGE PRODUCTS INC and JIANGSU TIMI INTELLIGENT TECH CO LTD.

Patent filings in countries

World map showing SPIDER IOT TECH BEIJING CO LTDs patent filings in countries
# Country Total Patents
#1 China 12

Patent filings per year

Chart showing SPIDER IOT TECH BEIJING CO LTDs patent filings per year from 1900 to 2020

Top inventors

# Name Total Patents
#1 Tian Zhibo 12
#2 Li Banggeng 6
#3 Huang Qingzhen 3
#4 Ma Mingyang 2
#5 Zhang Qiang 1
#6 Zhu Qingwei 1
#7 Zhu Bo 1
#8 Li Fengtao 1
#9 Hu Zeshuang 1

Latest patents

Publication Filing date Title
CN111530777A Self-service luggage article security inspection channel and method
CN110738178A Garden construction safety detection method and device, computer equipment and storage medium
CN110443748A Human body screen method, device and storage medium
CN110443179A It leaves the post detection method, device and storage medium
CN110399822A Action identification method of raising one's hand, device and storage medium based on deep learning
CN110443150A A kind of fall detection method, device, storage medium
CN109284740A Method, apparatus, equipment and the storage medium that mouse feelings are counted
CN109460723A The method, apparatus and storage medium counted to mouse feelings
CN109284735A Mouse feelings monitoring method, device, processor and storage medium
CN108829762A The Small object recognition methods of view-based access control model and device
CN108769690A Continuous picture management method, device, equipment and medium based on video compress
CN108648211A A kind of small target detecting method, device, equipment and medium based on deep learning