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IP Cases & Commentary – Details

04 June 2013

USPTO ‘Patents for Humanity’ – Winners Announced

Cathrine McGowan

In April 2012 we reported on the US Patent and Trademark Office’s (USPTO’s) competition ‘Patents for Humanity’. The project, an initiative of the Obama administration, sought to promote and reward humanitarian applications of technologies patented in the US. On its launch, the competition anticipated up to fifty winners, but in the end just ten entrants have been rewarded – two in each of the five categories – plus six honourable mentions across three of the categories. Perhaps the smaller number of winners (and a two month extended deadline for entries) indicate fewer entrants than expected. Nevertheless, the competition has been applauded by a number of senior US government officials and has itself received the ‘National IP and Technology Transfer Policy Award’ from the non-profit group Licensing Executives Society International (LESI). The winners in each category are:

Medical – Medicines and Vaccines

• Gilead Sciences: distribution of HIV drugs in Africa and Asia.

• University of California, Berkeley: low-cost production of anti-malarial compounds.

Medical – Diagnostics and Devices

• SIGN Fracture Care International: distribution of low-cost fracture implants in developing world hospitals.

• Becton Dickinson: placement of new tuberculosis diagnosis machines in the TB ‘high burden’ countries.

Food and Nutrition

• DuPont Pioneer: development of a fortified strain of sorghum for sub-Saharan Africa.

• Intermark Partners Strategic Management LLP: extraction of edible protein and vitamins from waste rice bran in Latin America.

Clean Technology

• Proctor & Gamble: worldwide distribution of a small chemical water purification packet.

• Nokero: provision of solar light bulbs and telephone chargers to villages without electricity.

Information Technology

• Sproxil, Inc: deployment in sub- Saharan Africa of a cell phone system for identifying counterfeit drugs.

• Microsoft Corporation: provision of machine learning tools for analysis of large health data sets.

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