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SCOTT CUNNINGHAM: Hello, this is Scott Cunningham.
And you're joining me today at the faculty
of technology, policy, and management at the Delft University of Technology.
In the last two setting the stage videos,
we discussed the large volumes of text data available in the world
and how you could apply these data to monitoring high technology development.
This week, let's put these ideas of competitive intelligence
directly into action.
For instance, what new things are going on today at Apple?
At the site Patently Apple, you can follow a technology journalist
as they blog and tweet about new developments at Apple.
Patently Apple tracks the trademarks, patent grants,
and patent applications for Apple.
Then they discuss and interpret these findings for their followers.
What sort of things does Patently Apple learn about technology development
at Apple?
Here is one specific invention which is reported by the site.
An Apple iPhone already has a light sensor
and wireless communication capabilities.
Why not replace the light sensor with a solar cell?
That way, the extra electricity you harvest from ambient light
could be used to keep your battery all topped up.
And if you've already got a full charge, then that excess electricity
can be transmitted to other nearby devices.
That's innovation on the page from Apple.
You've probably already heard of the five W's.
Those are the who, the what, when, where,
and why of any journalist's story.
Those are the basic questions used by any journalist in uncovering the story.
If you know the answer to those questions, you've got a whole picture.
And those are the same questions that you can
find by looking at patent applications.
Let's take a quick look at this new Apple
invention I was just telling you about.
Incidentally, if you want to follow along, take a look yourself.
Go to uspto.gov and search their free database of patent filings.
This particular patent number is 20150199062.
That's 20150199062.
We can run down the five W's and see what
there is to be learned from this particular patent.
OK now, who is involved?
Well, there's Apple for sure.
They're the assignee for the patent.
Another important who is the inventor.
They're listed right there on the patent as well.
And next, we might ask what.
What has been patented exactly?
The patent describes the technology, and it also
acknowledges all the work that came before it.
That's known as the prior art.
When was it patented?
This grant application was made in the first quarter of 2014.
If you monitor this long enough, you'll also
find out when the patent is eventually granted or denied.
Those are really key dates in the life cycle of a new technology.
Another question is, where was it patented?
Here, the patent lists the company's research and development labs
in Cupertino, California.
Another where that you'd really like to know
is, which world patent office was it patented?
Regardless of their actual home, companies
may patent in the United States, or in Europe, or in Japan.
So why was it patented?
The patent application includes all the patent claims, the new and novel things
that make this invention, and no other invention, special.
Some journalists also include a how question.
How is the technology intended to be used?
This is often something that you can read right off a patent as well.
So there you have them-- five W's and an H. For any given emerging technology,
these questions provide a remarkably complete picture
of technological development.
The answers to these questions are free from the reading
at the US and other patent offices all across the world.
There was a lot of information on that patent.
But is there enough information to really understand
the full capabilities of Apple?
Perhaps not.
But there are 1,000 more patent applications to be read and analyzed,
and that's just for Apple alone.
Continuously monitoring the patent office
to find new information about Apple is quite a lot of work.
Understanding and compiling this information is still harder.
Fortunately, this is the sort of work which can be automated,
and which scales well when performed by computer.
And that's where text mining comes into the picture.
In this week of the course, I'll be giving you
the tools you need to extract insights out of text data
to answer these and other questions.
We'll be using pandas, which is the leading Python
solution for producing useful standardized tables,
queries, and reports.
We'll also be previewing the Natural Language Toolkit, or NLTK.
This package helps you tokenize full text resources
so that you can find where one patent claim ends and another begins.
We'll also be taking a look at regular expressions,
a technique for finding and extracting significant patterns from your data.
Join us in the forums while we track Europe's largest patenters.
We'll be watching as they develop the key enabling technologies.
Those are technologies like photonics, new materials, biotechnology,
and microelectronics.
I hope you'll add your voice to the forum.
This is Scott Cunningham.
Thank you for joining me today.
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