Enforcing Most Suitable Patents in a Portfolio Using Automation
A Patent licensing attorney or a professional, dedicates a large chunk of his job in finding the answers to the below:
- What Patents in a Patent Portfolio are most valuable ?
- What Patents in a Portfolio to assert against :
- Competitor T1
- Competitor T2
- Which products of Competition are infringing a Patent Portfolio?
- Whether the Patents selected thereof are Valid?
The above problems are some of the most time consuming, tedious and any mistake in any of the queries may cost millions of dollars in good Cash.
Fortunately, solutions to most of the problems mentioned above are easier to find using our latest data crunching technologies (some of them available to the public on XLPAT 2.0 www.xlpat.com).
Companies have already yielded numerous benefits from Automation, Artificial Intelligence and Machine Learning from about 2 Trillion data points available on XLPAT.
Several indicators of go/no go and most suitable patents to enforce can be taken using a sophisticated network of :
- Data points
- Learning algorithms
- Related data sets
- Standards related information coded on to the machine
XLPAT 2.0 is able to predict the most precise evidence of infringement.
“This kind of an effort has not been attempted before and companies have already benefited from these multiple data points” says an industry expert who is closely associated with development of the product.
Further, we also understand that everything cannot be done by the machine (at least as of now) and so, we always have the Human information (ratifying the machine)
XLPAT 2.0 will be showcased at the IPO Annual meeting in San Francisco and also launched formally at an even in BAY Area.
XLPAT 2.0 will also exhibit among the world’s largest industry leaders at the DLD Tel Aviv Innovation Festival 2017.