Using NLP, big data and ML to identify infringers in a patent portfolio.
Every company has finite competitors, every competitor has finite products, and every patent has claims that can be analysed by NLP and learning can be enhanced by ML. Company’s internal data set includes :
• Industry standards
• Previous data maps
• Other training material
• Patent metadata
Above minefield of AI tech plus training data plus big data indicators offer a treasure for anyone interested in gaining infringement foothold from large patent datasets.
Steps to be followed :
Use tech to identify claims and specs accurately. Use ML to look at historic datasets Look at finite targets Ensure data is clean Regression models for identifying clean data sets Identify gold and claim charting.
Custom implementations on large datasets are an important part of the 2020 strategy and important learning sets to take forward.
Use of AI and data indicators to leverage monetisation and this will shape the upcoming decade.
Folks like tech transfer department members in universities, patent portfolio managers and monetisation experts can use NLP and ML models to monetise.
About us: Xlpat – Patdigger module can be trained on pre-defined data models to train models and apply monetisation on large datasets.
1. Contact us with custom patent dataset
2. Advise potential targets
3. Training dataset (if Any)
4. Let machines take over and keep a tab on infringement.
Contact us on firstname.lastname@example.org for further details.