Understand how to use Quid's companies & patents datasets for tech scouting.

1. TECH SCOUTING
In this session you will learn how to go through an end-to-end workflow to identify
emerging technologies

2. A B OU T TEC H SC OU TIN G
GOAL:
The goal of this session is to go through an
end-to-end workflow within the companies
database as well as explore advanced features
can be utilized to enhance your analysis
capabilities.
We will review searching, manipulating the
data, and drawing insights from the views
generated
1. The companies database can be used to
understand emerging sectors in a market
landscape, identify investment, map
innovation, find adjacency opportunities,
understand transaction trends, and source
companies.
2. The patents database can be used to
understand emerging sectors in a technology
market and perform due diligence on a specific
company or area of interest.
3. Academic papers can be used to understand
technology researched and developed in
universities
For this session, we will be focusing on
exploring companies and patents
METHODOLOGY:

3. COMPANIES DATASET

4. ID EN TIFYIN G EMER GIN G SEC TOR S U SIN G TH E C OMPA N IES D ATA B A SE
Understand what companies are emerging and what technology trends are
The Process:
1. Create Boolean search based on area of interest
2. Refine search using companies search page
3. Visualize the network and rename clusters
4. Identify top themes and general growth areas
5. Identify top investors & their focus areas
6. Identify innovative companies in different sectors

5. 1. C R EATE B OOLEA N SEA R C H B A SED ON A R EA OF IN TER EST
As with many other search engines, Quid utilizes Boolean search terms to query the database of news articles.
These queries are built using AND, OR, & NOT operators in conjunction with keywords in order to specify what
companies the user would like to return.
Example Question:
What companies are involved in the artificial
intelligence and internet of things space?
Keywords:
Artificial intelligence – AI, artificial intelligence
Internet of things– internet of things, iot
Query:
(AI OR “artificial intelligence”) AND (”internet of
things”~3 OR iot)

6. C OMPA N IES SPEC IFIC SEA R C H OPER ATOR S
Operator Example Description
name: name:diabetes Search by company name but not the detailed description
body: body:insulin Search the company technical description
description: body:insulin Search by the short business description
founded: founded:2000, founded > 2011 Search by founding year
kind: kind:public, kind:Public Search by Public, Private, or Investor
city: city: London Search by city (company headquarters)
state: state:alaska Search by state (company headquarters)
country: country:china Search by country (company headquarters)
acquirer: acquirer:google Search by acquirer
owner: owner:google Search by owner (parent company)
investor: investor:google, investor: (google AND intel) Search by investor
$total: $total:50M
Seach by total investment amout, logical operators (<, >, <=, >=, ==) are
available
$in: $in:100M
Search by individual funding round investment, logical operators (<, >, <=,
>=, ==) are available
$out: $out:100M
Search by a companies investments, logical operators (<, >, <=, >=, ==) are
available
exit: exit: 'Merger/Acquisition' Search by the exit type
dataset: dataset: quid or dataset: (quid OR expanded) Narrow the search results by dataset (expanded, other, quid)
keyword: keyword:cell Search by extracted keyword
While the overall search logic for companies is similar to that reviewed in with news and blogs, there are several search
operators which can help you narrow and refine you search.

7. 2. R EFIN E SEA R C H U SIN G C OMPA N IES SEA R C H PA GE
Related terms:
Related terms allow you to select other search terms that
you may want to add to your query based off of your current
search terms
Companies Returned:
Once you run a query, Quid will display how many
companies were identified in our database based on the
query run. The query will search each companies
description which is provided by our data partners and
augmented by Quid’s own propriety database.
Relevance:
Relevance is determined by looking at instances of the
search query within the company description according to 3
main components: use of the query in the title, frequency of
use in the body, and how early within the body the query
term was used.
Filtering:
There are a variety of ways to filter the data once a query has been run
Similar:
Also know as the Discover More feature – by clicking this
button Quid will search its database for other companies that
are similar to the ones you have selected. You can discover
up to 500 other similar companies.
• Amount of Funding (in a range)
• Date (in a range)
• Type of Investment
• Description Type
• Total Private Funding
• Investors
• Acquirers
• Exit Type
Within the search page, there are several ways to refine your search in order to surface the most relevant companies within a given space

8. Once you arrive at the visualize page, you’ll be able to rename clusters using the Cluster Legend on the right
3. VISU A LIZE N ETW OR K A N D R EN A ME C LU STER S
Cluster Legend:
The legend will provide a reference for
the coloring of any visualization. This is
a dynamic panel so you can select any
of the options in the legend to highlight
the data point that the coloring applies
to. By default, the legend with reflect
cluster coloring.
Export:
Once you have a visualization that you
like or you have a curated network you
may want export the data into a
Powerpoint, CSV, or image to share with
colleagues or clients.
Information Panel:
The info panel reflects general
information on any visualization within
Quid. Depending on the dataset,
different information will be displayed.
This is a dynamic panel and will reflect
whatever is selected on the screen.
Key information that can be found here
are: keywords, investment amount,
investors, investment rounds and
company information.
Search In Network:
Allows you to search for specific
words, people, or entities within a
network.
Control Panel:
The control panel is the
navigation hub within Quid,
enabling the user to pivot and
analyze the data across different
views and lenses. The options
vary by dataset and view. Typical
views found in all three integrated
datasets include network,
scatterplot, bar chart, histogram,
and heatmap .
Tagging:
Tags displays the network tags
that you have created within the
visualization. Tags can be a great
way to add a personal touch to an
analysis. Tags can be created
through several methods including
search in network and selection.
Filtering:
The filtering menu allows you to filter the
displayed data across available attributes
within the network. This is a great way to
focus your insights and cut the data differently
for deep dives.

9. QU ID METR IC S
An important part of any analysis is understanding what data is available for your analysis. The following are
some important metrics which may help you as you create your analysis.
Q U I D G E N E R AT E D M E T R I C S
Betwenness Centrality:
Represents how connected a node is within the network. Nodes with high betweeness centrality have
many connections that extend to disparate parts of the network. Nodes with low betweeness centrality
have connections that are close together.
Degree:
Degree is a measure of the number of shared connections in a given node. A node with a degree of 5 is
connected to 5 other nodes in the current view (will not take into account filtered or deleted nodes). A
node with high Degree is highly representative of other nodes in the same area.
Initial Degree:
Initial Degree measures the number of connections for a given node prior to any filtering or network
editing.
Flow: Flow represents the combined strength of all of a node’s connections. Nodes with a high flow
have more shared language and more connections with other nodes in the network. Nodes with
a low flow can be thought of as outliers, containing language that is more peripheral to the network.
Inter-Cluster Connectivity: Inter-Cluster Connectivity is a ratio of how many direct connections to a
node are within the same cluster. A node with high Inter-Cluster Connectivity contains language that is
shared by nodes in multiple different clusters. A node with low Inter-Cluster Connectivity is not connected
to many other clusters. Clusters with high Inter-Cluster Connectivity can help you identify news stories or
companies that bridge multiple topics.

10. QU ID META D ATA : C OMPA N IES
Metadata Description
Founding Year The publically disclosed year the company was founded
Operating Status Shows whether a company is currently operating, out of business, reorganizing, a subsidiary, or acquired.
Company Type Company type denotes whether a company is public, private, an investor, or government entity.
City The city where the company is headquartered
State The state where the company is headquartered
Country The country where the company is headquartered
Industry Code A code which helps determine what industry a company is classified as by our data partners.
Investment Received Amount The disclosed dollar value of all investments that a company has received.
Investment Received Count The total count of investment rounds, including those which were undisclosed.
Investors
A list of publicly disclosed investors (the number appearing next to an investors name in parentheses denotes the number of times
they have invested)
Acquisition Made Amount The disclosed dollar value of all the acquisitions that a company has made.
Acquisition Made Count The total count of acquisitions, including those which were undisclosed, is shown in parenthesis.
Acquisition Received Amount The disclosed dollar value of all the acquisitions that a company has received.
Acquisition Received Count The total count of acquisitions, including those which were undisclosed, is shown in parenthesis.
Parent Company The parent organization, if a company is acquired or an subsidary
IPO Amount The disclosed dollar value of the IPO
IPO Count Total number of times a company has gone public
Exchange Tickers If a public company, the exchange ticker
Exit Type Information detailing whether exits were mergers, acquisitions, or IPOs.
Keywords Keywords are the most meaningful words and phrases extracted from company descriptions.

11. B A SIC C OMPA N Y VISU A LIZATION S
While most of the visualizations within the companies dataset are the same as news, with the exception of HeatMap, the data provided can
lead to very different insights
Network Map: Shows the overall search, relationships, emerging and prominent clusters.
• Companies colored by cluster
• Clusters colored by cluster (high level view)
• Stories colored by sub-cluster (more granular view)
5 Main Visualizations & Typical Views:
Scatterplot: Can be instrumental in looking at up to 4 variables at once. The x axis, y axis, and nodes can all represent
different variables/metadata.
• Market maturity vs. investment amount – assessing emerging vs. mature segments of the market
• Market maturity vs. investment count
Bar Chart: Bar chart view is a standard way to quantify Quid insights – with each axis being customizable
• Investors colored by cluster – to assess leading investors within the space and the places
• Acquirers colored by cluster – to assess coverage by topic
• Top company by Investment amount/count or acquisition amount/count
Timeline: Enables you to see how particular conversations and themes are changing in volume overtime – can identify
themes that are event driven or changing
• Count of investments over time colored by cluster
• Count of investment amount overtime colored by cluster
Heatmap: The Heatmap layout provides a summary view, highlighting valuable statistics across clusters, sub-clusters,
or tags within a Companies network.

12. IN TR OD U C TION TO H EATMA P
The companies Heatmap can be a powerful way to identify market opportunities. When you create a Heatmap
by cluster, you can quickly see where money is flowing (or not) within a set of companies.
Within a Heatmap, quickly see summary statistics for any of the following:
• Number of Companies
• Median Founding Year
• Total Number of Investments Received
• Total Investment Amount Received ($USD)
• Median Investment Amount Received ($USD)
• Mean Investment Amount Received ($USD)
• Investment CAGR
• Number of Exit Events
• Total Exit Amount ($USD)
• Number of Acquisition events
• Total Acquisition Amount ($USD)
• Number of IPO events
• Total IPO Amount ($USD)
We can quickly use Heatmap to answer questions like,
• Which clusters are highly saturated? (Sort descending by Num.
Companies)
• Which clusters have grown the fastest? (Sort descending by Investment
CAGR)
• Which clusters have had the largest exists? (Sort descending by Median
Exit Amount)

13. 4. ID EN TIFY TOP TH EMES A N D GEN ER A L GR OW TH A R EA S ( 1/2)
The bar chart can be used to identify top areas of investment
1. Use the Control Panel on the left to navigate to the Bar Chart view
2. Change the Bar Value within the Axes tab to represent “Investment
Received Amount” in order to visualize areas of high investment
3. Add Labels by navigating to the Labels tab in the Control Panel and
selecting “Show Data Labels”
Example: AI and IOT companies*
Steps
*Based on a Quid network of 1697 companies focused on artificial intelligence and the Internet of
Things
See legend in Appendix

14. 4. ID EN TIFY TOP TH EMES A N D GEN ER A L GR OW TH A R EA S ( 2/2)
The timeline view can be used to identify areas of investment growth and decline
1. Use the Control Panel on the left to navigate to the Timeline view
2. Select types of funding to include in timeline
3. Add Labels by navigating to the Labels tab in the Control Panel and
selecting “Show Data Labels”
Example: AI and IOT companies*
Steps
*Based on a Quid network of 1697 companies focused on artificial intelligence and the Internet of
Things
See legend in Appendix

15. 5 . I D E NTIF Y T OP I N V E STORS & T HE IR F OC US A R E AS
The bar chart can be used to find top investors and key focus areas
1. Use the Control Panel on the left to navigate to the Bar Chart
view
2. Change Bars Represent to Investors
3. Add Labels by navigating to the Labels tab in the Control
Panel and selecting “Show Data Labels”
Example: AI and IOT companies*
Steps
*Based on a Quid network of 1697 companies focused on artificial intelligence and the Internet of
Things
See legend in Appendix

16. 6. ID EN TIFY IN N OVATIVE C OMPA N IES IN D IFFER EN T SEC TOR S
The bar chart can be used to identify top companies receiving investment in different sectors
1. Use the Control Panel on the left to navigate to the Bar Chart view
2. Change Bars Represent to Company Name
3. Add Labels by navigating to the Labels tab in the Control Panel and
selecting “Show Data Labels”
Example: AI and IOT companies*
Steps
*Based on a Quid network of 1697 companies focused on artificial intelligence and the Internet of
Things
See legend in Appendix

17. ADVANCED
CAPABILITIES OF THE
COMPANIES DATASET

18. A D VA N C ED : R A N K IN G - C OMPA N IES EXA MPLE
Using Quid’s CSV output you can use Quid metrics to create a ranking system for people, companies,
institutions, and authors.
While a simple bar chart in Quid is a great way to
understand how a company performs on one metric (e.g.
investment amount) – it doesn’t allow you to assess how
several different metrics work in tandem.
By exporting Quid’s data into a CSV and manipulating
different meta data using a pivot table you can create more
complex rankings that can better reflect the nuances within
a certain concept.

19. A D VA N C ED : EXPOR TIN G TR A N SA C TION D ATA & R EPOR TS
The companies is comprised of a variety of different data tables. Fundamentally, Quid analyzes the
descriptions of the companies, but uses transactional data – “Events” – to layer on temporal investment data
• Select the down arrow icon to the right of the
“Create Network” button
• A drop down will appear with 9 options
• Select “Create Report”
• A pop-up will appear titled “Generate Report”
• Input the kind of report: “Company Profile,” “Event,”
etc.
• Input the desired date range*
*Exports are allowed within the previous 5 years

20. A D VA N C ED : D ISC OVER MOR E C OMPA N IES
Another unique features of the companies dataset is ”Discover More” which allows you expand your search
beyond your current parameters.
The Process:
This is helpful once you've identified a sub-cluster of interest within an
existing network, or if you have a particularly undefined space or set of
companies and are trying to explore that space further.
To do so, click the "Visualize" button, and then click "Run in Background" to
bring you back to the project page. On the right hand side, click on the
arrow pointing down and select "Discover More".
You can add up to 500 additional companies. Note that when your original
network is smaller, you may not want to add 5x as many companies as this
increases the likelihood of noise/irrelevant companies being brought into
your network.
You can choose to add these companies to your current portfolio or create
an entirely new portfolio to keep them separate. Once you've determined
how many additional companies to add, click Discover.
To visualize this network, first click "Create Network" on the right hand side.

21. A D VA N C ED : U PLOA D IN G YOU R OW N LIST OF C OMPA N IES
In addition to querying for a portfolio of companies, you can alternatively analyze a set of predefined
companies - bypassing the search process and use Quid to visualize a custom list of companies.
The Process:
First, you will need a csv with either company names (Quid can attempt to
match the names to the companies contained the database) or company
database ids (can be found in the CSV export of any companies network).
Save a CSV with your names or IDs in on column under the “id” OR “name”
(note that you can only upload 1,000 companies).
To upload the list – you will need to click new search and select the upload
button next to the companies option.
Quid will return a list of all the companies and their matches – it is
important to identify any companies you want to deselect and confirm with
“add selected”.
Once your portfolio has been saved you will be returned to the original
company search page and you can go ahead and visualize the network.

22. PATENTS DATASET

23. ID EN TIFYIN G EMER GIN G SEC TOR S U SIN G TH E PATEN TS D ATA B A SE
Understand patent trends
The Process:
1. Create Boolean search based on area of interest
2. Refine search using patents search page
3. Visualize the network and rename clusters
4. Identify top themes
5. Identify growth areas
6. Identify citations incoming/outgoing

24. 1. C R EATE B OOLEA N SEA R C H B A SED ON A R EA OF IN TER EST
As with many other search engines, Quid utilizes Boolean search terms to query the database of news articles.
These queries are built using AND, OR, & NOT operators in conjunction with keywords in order to specify what
patents the user would like to return.
Example Question:
What patents are being filed in the artificial
intelligence and internet of things space?
Keywords:
Artificial intelligence – AI, artificial intelligence,
machine learning
Query:
(AI OR “artificial intelligence” OR ”machine
learning”)

25. PATEN TS SPEC IFIC SEA R C H OPER ATOR S
Patents Example Description
title: title: "Flexible Display" Search the invention title
abstract: abstract: "display controller connected" Search the invention abstract
applicationDate: applicationDate: 2014-01-01 Search by application date
publicationCountry: publicationCountry: FR
Search by 2 letter country code for where any of the member patents were
issued
applicationCountry: applicationCountry: US
Search by 2 letter country code for where an application was filed for any of
the member patents
assignee: assignee: Google Search by assignee or assignees
inventor: inventor: "Jobs S" Search by the patents inventor or inventors
IPC: A43* Search by the IPC code, use a star if you don't want to search on the full code
While the overall search logic for companies is similar to that reviewed in with news and blogs, there
are several search operators which can help you narrow and refine you search.

26. 2. R EFIN E SEA R C H U SIN G PATEN TS SEA R C H PA GE
Related terms:
Related terms allow you to select other search terms that
you may want to add to your query based off of your current
search terms
Companies Returned:
Once you run a query, Quid will display how many patents
were identified in our database based on the query run. The
query will search each patent title/abstract which is provided
by our data partners Thomson Innovation/Clarivate Analytics
Relevance:
Relevance is determined by looking at instances of the
search query within the patent according to 3 main
components: use of the query in the title, frequency of use in
the body, and how early within the body the query term was
used
• Assignee
• Application Date
• Grant Date
• Patent Status
• Patent Numbers
• Filing Locations
• Citation Count
• Inventor
• Classification Code (IPC or CPC)
Similar:
Also know as the Discover More feature – by clicking this
button (after a first initial search), Quid will search its
database for other patents that are similar to the ones you
have selected. You can discover up to 500 other similar
patents
Within the search page, there are several ways to refine your search in order to surface the most relevant companies within a given space
Filtering:
There are a variety of ways to filter the data once a query has been run

27. 3. VISU A LIZE N ETW OR K A N D R EN A ME C LU STER S
Legend:
The legend will provide a reference for
the coloring of any visualization. This is
a dynamic panel so you can select any
of the options in the legend to highlight
the data point that the coloring applies
to. By default, the legend with reflect
cluster coloring.
Export:
Once you have a visualization that you
like or you have a curated network you
may want export the data into a
Powerpoint, CSV, or image to share with
colleagues or clients.
Information Panel:
The info panel reflects general
information on any visualization within
Quid. Depending on the dataset,
different information will be displayed.
This is a dynamic panel and will reflect
whatever is selected on the screen.
Key information that can be found here
is: filing location, assignee, citation
count, and abstract
Search In Network:
Allows you to search for specific
words, people, or entities within a
network.
Control Panel:
The control panel is the
navigation hub within Quid,
enabling the user to pivot and
analyze the data across different
views and lenses. The options
vary by dataset and view. Typical
views found in all three integrated
datasets include network,
scatterplot, bar chart, timeline,
and histogram.
Tagging:
Tags displays the network tags
that you have created within the
visualization. Tags can be a great
way to add a personal touch to an
analysis. Tags can be created
through several methods including
search in network and selection.
Filtering:
The filtering menu allows you to filter the
displayed data across available attributes
within the network. This is a great way to
focus your insights and cut the data differently
for deep dives.
Once you arrive at the visualize page, you’ll be able to rename clusters using the Cluster Legend on the right

28. QU ID META D ATA : PATEN TS
Metadata Description
Invention Title English title of patent
Status Displays if the published patent has been granted yet
Application Date Date application sent to filing agency
Original Filing Location First country (or global/regional filing agency) where invention was filed
Abstract Patent abstract (translated to English if originally in a different language)
Inventors Names of inventors credited with invention, format: Last Name, First Initial
All Current Assignees The current assignees (or “owners”) of the invention, which may be different from the original
All Original Assignees The assignees (or “owners”) of the invention at the time of its initial filing
Filing Location Count The count of countries/filing agencies in which the invention has been filed
All Filing Locations List of countries/filing agencies in which the invention has been filed
Filings Count The count of individual filings (if greater that 1, due to multiple agencies or subsequent patents added to the invention)
IPC Classification Codes The International Patent Classification (IPC) classifies patents according to the different areas of technology to which they pertain
Original Company Assignee Count The count of original assignees that are corporations, rather than individuals/academic institutions
Original English Title The total count of acquisitions, including those which were undisclosed, is shown in parenthesis.
Patent Filings List of publication numbers associated with given patent
Citations - Incoming The number of times the patent of interest has been cited by subsequent inventions – a metric for the influence of a given patent
Citations - Outgoing The number of times the patent of interest references previous work

29. B A SIC PATEN T VISU A LIZATION S
The visualizations in the patents dataset are the same as in the news, but are used in very different ways. Try creating “Tags” per
Company/Academic Institution, and look at Incoming Citation counts
Network Map: Shows the overall search, relationships, emerging and prominent clusters.
• Patents colored by cluster
• Patents colored by sub-cluster (more granular view)
4 Main Visualizations & Typical Views:
Scatterplot: The x axis, y axis, and nodes can all represent different variables/metadata.
• Patent influence by cluster/topic – assess the ”influence” per cluster (nodes represent clusters, x-axis is
count/volume, y-axis is count of incoming citations)
• Patent influence by competitor – use the same setup as the above scatterplot, but create “Tags” for each
company being analyzed, and have the nodes represent Tags instead of clusters
Bar Chart: Bar chart view is a standard way to quantify Quid insights – each axis is customizable
• Bars represent assignees, colored by cluster – assess top assignees and their tech focus areas
• Bars represent assignees, bar value represents ”Citations – Incoming” – assess the most “influential”
patents per top assignee
Timeline: Enables you to see how R&D focuses are changing in volume over time, and to identify emerging
technology areas to watch
• Color by cluster – assess how technology areas are changing over time
• Color by assignee – assess the filing trends for individual companies/academic institutions

30. 4. ID EN TIFY TOP A SSIGN EES
The bar chart can be used to identify top assignees
1. Use the Control Panel on the left to navigate to the Bar Chart view
2. Change Bars Represent within the Axes tab “Original Company
Assignee”
3. Add Labels by navigating to the Labels tab in the Control Panel and
selecting “Show Data Labels”
Example: AI patents top assignees*
Steps
*Based on a Quid network of 1317 patents focused on artificial intelligence
See legend in Appendix

31. 5. ID EN TIFY GEN ER A L GR OW TH A R EA S
The timeline view can be used to identify areas of growth and decline
1. Use the Control Panel on the left to navigate to the Timeline view
2. Add Labels by navigating to the Labels tab in the Control Panel and
selecting “Show Data Labels”
Example: AI patents timeline*
Steps
*Based on a Quid network of 1317 patents focused on artificial intelligence
See legend in Appendix

32. 6 . I D E NTIF Y C I T ATIONS I N C OMING/ OUTGOING
The scatterplot chart can be used to find top investors and key focus areas
1. Use the Control Panel on the left to navigate to the
Scatterplot view
2. Navigate to the Nodes tab and change Nodes Represent to
Clusters
3. Add Labels by navigating to the Labels tab in the Control
Panel and selecting “All” by “Show Node Labels”
Example: AI patents scatterplot*
Steps
*Based on a Quid network of 1317 patents focused on artificial intelligence
See legend in Appendix

33. APPENDIX

34. C O MPANY N E T WORK M A P
*Based on a Quid network of 1697 companies focused on artificial intelligence and the Internet of
Things
CLUSTERS
A HEALTHCARE 10%
B RETAIL INNOVATION 8.8%
C DIGITAL TRANSFORMATION 7.6%
D UNSTRUCTURED DATA ANALYSIS 7.0%
E INDUSTRIAL IOT 6.9%
F NLP 6.1%
G FINTECH & CRYPTO STARTUPS 6.0%
H ENERGY MANAGEMENT & SMART HOMES 5.5%
I SEMICONDUCTOR CHIPS & DEEP LEARNING 5.5%
J AUTONOMOUS VEHICLES 5.2%
K SOFTWARE DEVELOPMENT 5.2%
L CYBERSECURITY 4.9%
M AUGMENTED REALITY 4.8%
N EDUCATION 4.6%
O AGTECH 4.1%
P SUPPLY CHAIN 3.3%
Q DRONES 1.6%
R FOOD & BEVERAGE 1.2%
S SMART CONTRACTS & INSURANCE 1.1%
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
A
Q
R
S

35. P A T E NT N E T WORK M A P
*Based on a Quid network of 1317 patents focused on artificial intelligence
CLUSTERS
A DEVICES 13%
B NEURAL NETWORKS 10%
C NLP 9.4%
D MODELS 9.1%
E ROBOTS & VOICE RECOGNITION 8.6%
F CLOUD BASED PLATFORMS 8.2%
G INTELLIGENCE 5.9%
H VEHICLES 5.5%
I MEDICAL 5.3%
J CIRCUITS 4.6%
K AI ENGINES 4.1%
L ELECTRICITY 4.0%
M VIDEO GAMES 4.0%
N ALGORITHMS 2.6%
O PATTERN RECOGNITION 2.6%
P DATA MANAGEMENT 2.0%
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
A