The trademark ONPOINT was filed by OnPoint Technologies, LLC, a Limited Liability Company (the "Applicant"). The application was published for oppositions on July 2, 2020, and received one opposition filed on September 30, 2020 by ONEPOINT on Likelihood of confusion. the opponent was represented by Bilalian, Marguerite and proceedings were handled in English, and the oppositon was dismissed by the Euorpean Union Intellectual Property Office with decision issued September 30, 2020
The application was filed in English, and English was also language of all opposition proceedings (German was selected as the second language).
A transfer of the trademark registration was recorded on July 22, 2020. Change of name and professional address of the trademark registration was recorded on January 12, 2022. Change of name and professional address of the trademark registration was recorded on January 12, 2022.
The Opposition with reference B 003131933 was refused according to Article 8(1)(b) EUTMR with decision issued on September 23, 2024 by the EUIPO.
Goods And Services
The mark was filed in class 42 with following description of goods:
Software as a service (SaaS) services for use in the industrial manufacturing industry featuring a software portal and user interface for use in asset optimization and visualization, management and optimization of production facilities, production operations, and operations data through machine learning, data analytics, and hardware sensors for the control, prediction and alert of production unit in the field of the industrial manufacturing industry
Providing temporary use of on-line non-downloadable software for use in asset optimization and visualization, management and optimization of facilities and operations through machine learning, data analytics, and hardware sensors in the field of the industrial manufacturing industry
Technical consulting services for production units in the industrial manufacturing industry in the field of asset optimization and visualization, management and optimization of facilities, operations, and operations data through machine learning, data analytics, and hardware sensors.