Product Description

What is the advantage of machine learning supported search over classical keyword search?
If a keyword search is comprehensive, it will typically generate a chronological list of several hundred entries that take a long time to analyse. If a keyword search generates few results, important patents will likely be missed.
With our machine learning approach, queries with up to 20,000 results can be ordered according to machine generated scores related to a specific research field.
The commercially most relevant patents appear at the top of the list while the user can go as far down in relevancy to be comfortable of not having missed anything.
Relevancy scores are calculated based on titles, abstracts, IPC classifications as well as applicant information.
Our machine relevancy rankings are superior compared to established search engines because we carefully define each machine learning model based on thousands of individual patents, based on our hands on knowledge of the energy storage sector.

What is the source of patent information provided by b-science.net?
Our database consists of more than 2 Mio. patent documents from the European Patent Office (EPO) database, published in 1980 or later, that either contain the words 'battery' or 'batteries' in the title or abstract, or to which CPC or IPC1-8 patent classification codes H01M (batteries & fuel cells) or H01G (capacitors) were assigned.
The EPO database contains patent documents filed with more than 100 patent offices across the globe.
Every patent listing contains a link to the EPO website where the full text can be accessed.

Machine translation of patent titles, abstracts and applicants
If a newly added patent in our database does not contain a family member with English title/abstract/applicants, a Google or b-science.net translation is automatically added to the database.

Non-profit organizations and early-stage startups are eligible for a discount.

Type	Description
Free Free upon registration	Triweekly patent updates (free version) 3k machine learning credits Multi-user web access
Premium 1-4 Get a quote	1-4 reviews (see below) Triweekly patent updates (incl. Excel files) 2-6 h author support 100k-400k machine learning credits Multi-user web access
Energy storage innovation & patent reviews Fuel cells (PEMFC / AEMFC, prospectus) Solid-state & semi-solid Li-ion batteries (PDF, 295 pages) High energy negative Li-ion battery electrodes (PDF, 237 pages) High energy positive Li-ion battery electrodes (PDF, 180 pages) Each review includes an Excel file with patent families ranked according to commercial relevance.
Get a quote
Machine learning credits can be used to machine rank patents according to commercial relevance. Free offerings are provided once to organizations involved in energy storage research.