Advanced data collection, analytics and visualizations enable a structured approach to text and other data, and help you discover ideas, companies, technologies, etc. that impact what you do next. At Mergeflow we continuously invest significant resources into our technology stack to make this work. Here we describe why and how we do this.

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A 360° view of the world’s
technologies and innovations
Mergeflow’s approach to data collection,
analytics, and visualization

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Mergeflow’s challenge:
How to read 15,000 documents a day.
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24/7, Mergeflow collects news, patents, science
publications, blog posts, press releases, clinical trials,
and other data sets and sources.

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Advanced data collection, analytics and visualizations enable a structured approach to text and other
data, and help you discover ideas, companies, technologies, etc. that impact what you do next.
At Mergeflow we continuously invest significant resources into our technology stack to make this work.
Here we describe why and how we do this.
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A 360° view of the outside world
helps you innovate better and faster.
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You never know where the next great idea, company, or
technology may come from. So you need to be aware of
what is going on around you, and take a 360° perspective.

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Other companies, particularly innovative, early-
stage “under the radar” companies, may provide
products or technologies that help you address
R&D challenges. This in turn lets you bring new
solutions to market faster.
Outside experts may not only provide valuable
input to your R&D activities. You can also develop
cooperations with them. This can strengthen your
recruitment network. For example, you may hire
graduates from those experts’ labs.
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Discovering new ideas and technologies, for
example in published research, can help
overcome roadblocks in product development. It
can even inspire entirely new approaches,
technologies, or business models altogether.
Information on the role of certain technologies in
certain markets or applications is highly valuable.
For example, if you can search for a technology
and then get a list of markets in which this
technology plays a role, this will help you discover
new markets for your technologies.
Ideas & Technologies Companies & Experts Markets & Applications
The idea is to discover what inspires your next
project and then act upon this, rather than to simply
collect and file away information.
Outside ideas and technologies can impact what you do next.
No matter how big your company is, the outside world is bigger. It is therefore quite likely that there are great ideas in the outside world that your own organization is not yet aware of.

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Different data sets provide you with different insights.
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Scientific publications are not only an important
source of new ideas and technologies. They also
help identify who the experts and leading
organizations are in a given technology field.
Patents indicate intentions of their owners. For
example, if a company increases their patenting
activity in a certain technology field, it is likely that
this company will also increase their commercial
activities in that area, such as bringing new
products to market.
Similar to the more technical scientific
publications, news and blogs often provide new
ideas. But unlike scientific publications, they are
written for a general audience. This makes them
valuable if you want to learn about a new field
where you do not have in-depth expertise yet.
Scientific Publications Patents News & Blogs
Market Research
Besides providing size and growth estimates,
market research helps you connect technologies
to applications, and understand the structure of
an industry. For example, market reports often
describe the technologies etc. that play a role in a
certain market, and they mention companies that
are active in that market.
Many universities and other R&D organizations
worldwide commercialize their research through
technology transfer. Such technology transfer
offerings can help you strengthen and speed up
your own R&D projects.
Technology Transfer
Public research funding often goes to innovative,
early-stage companies. You can discover such
companies that would otherwise remain under the
radar from information about public research
projects.
Research Projects
There are many different kinds of data sets and sources that let you tap into the worldwide ‘ideas, technologies, and innovations pool’. Here are some examples.

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We need advanced analytics in order to
create a 360° view from all these data.
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Conventional search engines are great once you
already know what it is that you are looking for.
But while this is relatively straightforward,
discovering something that is relevant but that
one did not even know might exist (e.g. a
company or a new technology) is a lot harder and
very time-consuming to do with conventional
search engines.
Speed, volume, and diversity of available data
grow exponentially while the number of analysts
available for analyzing these data only grows
linearly, if at all. The result is a growing intelligence
gap.
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Similar to conventional search engines,
specialized databases (e.g. patent, company, or
research publication databases) are very useful for
finding details on known leads. What is hard is
discovering those leads in the first place. In
addition, specialized databases cannot provide
you with a 360° view.
Consultants & Analysts Conventional Search Engines
Existing solutions
fall short.
There has been and continues to be great
progress in generally available “AI analytics” or
“cognitive computing” platforms. But these
platforms are trained on specific data sets (e.g.
news or patents). Making them applicable to a
wider variety of data would require expensive and
time-consuming training data generation and
algorithm re-training.
Specialized Databases Off-the-shelf Analytics

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Built on top of its analytics, Mergeflow uses
interactive visualizations to display R&D,
investments, market data, relations between
topics and actors, and other data.
Mergeflow’s user interface is designed for 360°
discovery across, not just within, data sets. In
addition, Mergeflow uses analytics to create email
reports to help users stay in the know on relevant
developments in their fields of interest.
So we build our own.
Mergeflow collects various data sets from across
various types of sources, most of them via the
web. While some of these sources provide data
collection interfaces (APIs) for their contents, most
sources do not, and require us to build
customized crawlers.
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Manually discovering topics, people, companies,
market information, investment events and other
data from raw text is next to impossible, due to
the overwhelming amount of data. So we build
algorithms that extract such objects and events
from text.
Data Collection Analytics Visualizations & User Interface
In order to ensure data privacy and confidentiality,
we do not track what users search for, or what they
click at. This means that our analytics only look at
the contents of the data we collect, not at what
users do.

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Data collection
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Smart crawling
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In order to decide whether or not to collect a
webpage, Mergeflow uses various methods.
These methods include language detection (if
possible, we try to collect English contents);
semantic topic modeling to distinguish relevant
contents (e.g. technology descriptions) from
irrelevant ones (e.g. directions); and text layout
modeling (for example, if a page looks like a
simple list of items, Mergeflow ranks it lower than
if it looks like body text).
Mergeflow’s approach
The complete website of a company, for example,
may consist of thousands of individual pages:
product descriptions, management bios, blog
posts, supplier information, directions, content in
different languages, and so on. For our purposes,
we usually only care about a subset of all these
pages. For example, we want to collect product or
technology pages, but not directions, or the same
content in multiple languages.
Selective web page collection
A crawler is software that collects data from the web. It starts off with a web page that it collects into a database,
then follows the links from that page to other pages, and collect those too. Then it moves on to the next set of pages
from there, and so on.

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Document
structuring
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We have developed an unsupervised algorithm
that can learn from a very small number of data
points (20 - 30 pages) how a source typically
structures its contents. This algorithm also learns
when a source changes their layout over time. Our
algorithm is unsupervised, which means that it
does not require human-labeled training data.
Mergeflow’s approach
There is an abundance of different formats and
layouts on the web, and these things keep
changing as well. Since we collect data from tens
of thousands of sources, and these sources
sometimes change their layouts over time,
manually specifying where to find relevant
contents is not an option. Neither is creating big
training data sets.
There are no standards
Once we have collected a document such as a web page, a blog post, or a scientific publication, we have to make
sure that we only feed relevant parts of that document into our analytics pipeline. For example, we want to extract
data from the document title or body but not from surrounding disclaimers, teasers, or other irrelevant contents. In
order to do this, we need a method of structuring a document and then determining the relevant parts of that
structure.

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Analytics
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Discovering people,
companies, topics,
and things from
texts
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We have developed various natural language
processing, semantic modeling, finite state
automata, and other methods for discovering
objects and events in texts. But rather than going
for a one-size-fits-all silver bullet, we use different
approaches to accomplish different tasks. For
example, an algorithm that discovers people
names works quite differently from one that spots
company names, or another algorithm that can
assign topics or patent classes to whole texts.
Mergeflow’s approach
Searching for and tracking known people,
companies, etc. is one thing. But discovering
companies etc. that are relevant but that one
didn’t even know might exist is something else.
Being able to do this is crucial to our customers.
After all, innovation, R&D, new business, and
deep competitive intelligence are inherently about
moving into unchartered territory.
Discovering ‘unknown unknowns’
In order to support the discovery of companies, people, technologies, materials, etc., we develop our own ‘named
entity extraction’ algorithms. We usually build upon existing approaches published in the literature, but then adapt
and expand these methods to suit our needs.

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Financial data from
texts
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Similar to our approach to detecting objects and
events (see previous page), we use an array of
methods for discovering financial data from texts.
For example, we use different natural language
processing and topic modeling methods for
detecting venture investments and market
estimates respectively. Furthermore, we have built
sophisticated disambiguation algorithms in order
to tell whether multiple mentions of an event or
data point actually refer to the same underlying
data, even if they vary in some details.
Mergeflow’s approach
Extracting financial data from texts comes with
some unique challenges. There are different
conventions for writing numbers, duplicates, etc..
For example, one and the same venture
investment in a company may be reported across
multiple sources, and these publications may be
stretched over some period of time. Market
estimates may be based on various time frames,
there may be different estimates for the same
market, and so on.
The challenges of text data
By “financial data”, we mean things like venture capital investments, market size and growth estimates, and research
fundings. Mergeflow extracts this information directly from news, press releases, company websites, information
portals, and other sources on the web.

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Visualizations & User Interface
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Interactive visualizations for financial data
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In order to ensure comparability, our analytics convert all currencies into USD.
For venture capital and research fundings, Mergeflow uses simple charts, reporting
individual funding events and a running total. Clicking on any funding event takes the
user to the documents from which a data point was extracted. All funding data are
also available as tables, which can be exported in CSV format.
Funding data Market estimate data
All market growth and size estimates are normalized to a common time frame, by
simple linear interpolation. If there are differing estimates for the same market
segment, Mergeflow reports all estimates. This lets users gauge analyst consensus
on a given market.

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Interactive visualizations for objects and events
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In order to judge the relevance of a company, a person, or a technology, it can sometimes be helpful to see how this entity relates to other entities. Mergeflow visualizes such relations
between entities, using different types of visualizations.
Co-author or co-inventor networks from science publications or patents, for example,
are useful for identifying central people in a technology field or at a company (a
competitor, for instance). Mergeflow plots such networks, and draws lines between
any two objects that appear in common contexts.
Entity networks Relations between object categories
“For my search on ‘smart city’, I want to see which emerging technologies are
connected to which companies.”
Mergeflow uses Sankey charts to visualize such relations between any two types
of object categories.

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Stay in the know on relevant developments
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While ‘360° discovery’ is a central Mergeflow use case, ‘staying in the know’ is just as important to most Mergeflow users.
When you follow a topic, Mergeflow sends you Weekly360 email update reports.
Weekly360s keep you informed on the latest developments, from across venture
investments, R&D, markets, and news and blogs.
Follow your topics
If you use Mergeflow as part of a team, Weekly360s cross-reference findings across
your team, based on the findings’ contents. If one of your findings is relevant to any
of your team members’ topics, your Weekly360 email report will indicate this to you.
Collaborative discovery

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Get in touch!
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Location
Mergeflow AG
Effnerstr. 39a
81925 Muenchen
Germany
Contact
team@mergeflow.com
WWW
www.mergeflow.com (company website)
scope.mergeflow.com (blog)
If you want to know more about our technologies,
or if you are interested in an R&D collaboration,
we would be happy to hear from you.