This presentation is from the Environmental Futures & Big Data Impact Lab's (Impact Lab) Artificial Intelligence Mythbusters Workshop, on 25 July 2019 in Exeter. The event was run in collaboration with the European Space Agency (ESA). The slide pack provides an overview of the Impact Lab itself, as well as presentations on: - ESA Business Applications Overview & AI Kickstarters: (Donna Lyndsay, ESA) - Machine Learning & Earth Observation (Dr. Kavitha Muthu, ESA) - Building the Foundations of an Automated Property Market (Kris Clark, Landmark) - Business Outcoms, Accelerated (William Eccles, Dell Boomi) - AI in the Yachting Industry (Conrad Humphreys) - Machine Learning for Spatial Incident Risk Intelligence (Bola Adegbulu, Predina) The Impact Lab offers free support to businesses looking to create new products and services by capitalising on Big Data and Environmental opportunities. It also helps academics and scientists to commercialise their expertise. The Impact Lab is part-funded by the European Regional Development Fund. It is a 3-year collaborative project between: University of Exeter, Exeter City Futures, Met Office, University of Plymouth, Plymouth College of Art, Plymouth Marine Laboratory and Rothamsted Research. For further information and general enquiries, please contact: info@impactlab.org.uk

1. AI MYTH BUSTER WORKSHOP
THURSDAY, 25 JULY 2019
#AIExeter
@efdb_impactlab

2. 2
AGENDA
• 12:30 – Welcome: Donna Lyndsay/Kathryn White
• 12:35 – Impact Lab Overview: Kathryn White
• 12:40 – ESA Business Applications Overview & AI Kickstarters: Donna Lyndsay
• 12:50 – Machine Learning & Earth Observation – Kavitha Muthu
• 13:10 – Business Case Study: Predina – Bola Adegbulu
• 13:25 – Business Case Study: Landmark – Kris Clark
• 13:40 – Business Case Study: Dell Boomi – William Eccles
• 13:55 – AI in the Yachting Industry: Conrad Humphreys
• 14:10 – Q & A
• 14:20 – Coffee Break
• 14:45 – Workshops: Tom Torkar & Kavitha Muthu
• 15:30 – Wrap Up & Close : Kathryn White / Donna Lyndsay

3. 3
KATHRYN WHITE DONNA LYNDSAY
AI MYTH BUSTER WORKSHOP
#AIExeter
WELCOME
Innovation Manager
IMPACT LAB
UNIVERSITY OF EXETER
k.j.white2@exeter.ac.uk
UK Business Applications
Regional Ambassador (SW & S. Wales)
EUROPEAN SPACE AGENCY
d.lyndsay@exeter.ac.uk

4. 4
KATHRYN WHITE
Innovation Manager
IMPACT LAB
AI MYTH BUSTER WORKSHOP
#AIExeter

5. • Free specialist technical support and grants for Devon SMES and entrepreneurs
• Boosting entrepreneurial growth by supporting new products, services and processes
• A gateway to Devon’s leading scientific research centres

6. 6

7. 7
DONNA LYNDSAY
UK Regional Ambassador
EUROPEAN SPACE AGENCY
AI MYTH BUSTER WORKSHOP
#AIExeter

8. European Space Agencybusiness.esa.int
Donna Lyndsay
ESA Business Applications Regional Ambassador for
South West and South Wales
July 2019
Why Space?

9. Space Companies.
“Upstream”
Also Space Companies
“Downstream”

10. 10
Space Makes us Smart

11. Science and
Exploration
Enabling and
Support
Safety and Security
EVERYDAY LIFE AT ESA
Applications

12. 12
Kavitha Muthu
UK Regional Ambassador Platform
– Midlands & NE England
Kavitha.muthu@Leicester.ac.uk
University of Leicester
→ UK AMBASSADOR PLATFORM NETWORK
Donna Lyndsay
UK Regional Ambassador Platform
– SW England & S Wales
D.Lyndsay@exeter.ac.uk
University of Exeter
Alan Cross
UK Regional Ambassador Platform
– NW England & N Wales
Alan.cross@stfc.ac.uk
STFC
Pamela Anderson
UK Regional Ambassador Platform
– Scotland & Northern Ireland
Pamela.anderson@strath.ac.uk
University of Strathclyde
Tom Greenwood
UK Regional Ambassador Platform
– London & SE England
tom.greenwood@port.ac.uk
University of Portsmouth
Reproduced by kind permission of Ordnance Survey
© Crown Copyright NC/2005/32578
Ian Downey
Business Applications UK Ambassador
Platform Network Coordinator
Ian.downey@esa.int +44 (0)1235 444319
Victoria Christmas
Business Developer
Victoria.Christmas@esa.int
+44 (0)1235 444313

13. 13
What does AI mean to us?
“Artificial intelligence (AI) is the simulation of human
intelligence processes by machines, especially computer
systems”
“Machine Learning (ML) is the scientific study of algorithms
and statistical models that computer systems use in order to
perform a specific task effectively without using explicit
instructions, relying on patterns and inference instead. It is
seen as a subset of artificial intelligence”

15. →
ESA business applications
FUEL FOR BUSINESS
Human Spaceflight
Technologies
Space Weather
Earth Observation
Satellite
Navigation
Satellite
Communication
Financial
Aviation
Education
Energy
Media
Agriculture
Environment
Transport
Healthcare
Maritime
For projects which
integrate at least one
space asset to add
value to terrestrial
assets and legacy
systems to provide
new, commercially
driven & sustainable
services
→start from market
demand – not from
technology push
→are focused on
business
development – not
technology
development

16. →
ESA business applications
OUR OFFER
Use of the
ESA Brand for
Credibility
We work together with companies to make ideas commercially viable, with:
Zero-Equity
Funding
(€60k-€2M+)
Tailored Project
Management
Support
Access to
Our Network
& Partners

17. →
Any SECTOR
Any TIME,
3 gates
Typically ~ €200K
Webinars,
Bootcamps,
Hackathons
and more..
Max €60K
KICK-START
EVENTS
INVESTORS
NETWORK
OPEN
COMPETITION
ESA business applications
OPPORTUNITIES
AI
8/04/2019
75% funding100% funding
ESA Defined Thematic Areas &
Response Time
Industry Defined
Thematic Areas
50% funding *
€ 100k-€2M+
Cyber Security
open 22/08
closes 31/10/19
200K

18. | 08/02/2018 | Slide 18
ESA Business Applications
CURRENT OPPORTUNITIES
AI Kick-Start Activities 2019
60K Euros, 75% intervention, 6-9month feasibility
• AI Social Impact (opens 25th June 2019 - closes 30th
August 2019)
• AI Infrastructure (opens 2nd September 2019 -
closes 11th October 2019)
• AI Environment and natural resources (opens 14th
October 2019 - closes 29th November)
https://business.esa.int/funding/invitation-to-tender/artificial-
intelligence-kick-start

19. | 08/02/2018 | Slide 19
ESA Business Applications
FUTURE OPPORTUNITIES
• Ports of the Future, (Rotterdam, Antwerp, Gran
Canarias, Southampton, British Ports
Association)
• Kickstarters Q4 2019:
• Environmental Crimes,
• Predictive Maintenance,
• Biodiversity,
• Future Internet
• Feasibility: Space for Clean and Safe
management of Hydrocarbons and other
resources (200K 100%)
https://business.esa.int/how-to-apply

20. European Space
Agency
→ YOUR BUSINESS POWERED BY SPACE
Donna Lyndsay
d.Lyndsay@Exeter.ac.uk

21. 21
KAVITHA MUTHU
UK Business Applications Regional Ambassador (Midland and NE)
EUROPEAN SPACE AGENCY
AI MYTH BUSTER WORKSHOP
#AIExeter

22. European Space Agency
→ Machine Learning and
Earth Observation
Dr. Kavitha Muthu
ESA Business Applications UK Regional Ambassador –
Midlands and North East

23. European Space AgencyESA UNCLASSIFIED - For Official Use ESA | 25/07/2019 | Slide 23
ESA Business Applications
MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

24. European Space AgencyESA UNCLASSIFIED - For Official Use ESA | 25/07/2019 | Slide 24
ESA Business Applications
Learning -> Intelligence
MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE
Machine Learning -> Artificial Intelligence
This Water tastes like Honey
This Honey tastes like Water

25. European Space AgencyESA UNCLASSIFIED - For Official Use ESA | 25/07/2019 | Slide 25
ESA Business Applications
EVOLUTION OF MACHINE LEARNING
https://sefiks.com/2017/10/14/evolution-of-neural-networks/

26. European Space AgencyESA UNCLASSIFIED - For Official Use ESA | 25/07/2019 | Slide 26
ESA Business Applications
EVOLUTION OF EARTH OBSERVATION
August 1959 - Explorer 6 August 1960 - Discoverer 14 1 April 1960 - TIROS 11914 – WWI - aerial photograph1903 - Use of pigeons
1889 --Arthur
Batut takes the
first aerial
photograph
using a kite of
Labruguiere
France
1960-1970: First use of term “remote sensing”, start TIROS weather satellite, Skylab

27. European Space AgencyESA UNCLASSIFIED - For Official Use ESA | 25/07/2019 | Slide 27
ESA Business Applications
EVOLUTION OF EARTH OBSERVATION - SATELLITES
https://svs.gsfc.nasa.gov/4600

28. European Space AgencyESA UNCLASSIFIED - For Official Use ESA | 25/07/2019 | Slide 28
ESA Business Applications
EVOLUTION OF EARTH OBSERVATION

29. European Space AgencyESA UNCLASSIFIED - For Official Use ESA | 25/07/2019 | Slide 29
ESA Business Applications
EVOLUTION OF EARTH OBSERVATION - SPECTRAL

30. European Space AgencyESA UNCLASSIFIED - For Official Use ESA | 25/07/2019 | Slide 30
ESA Business Applications
EVOLUTION OF EARTH OBSERVATION - SPATIAL
https://www.sciencedirect.com/science/article/pii/S0303243415001300?via%3Dihub

31. European Space AgencyESA UNCLASSIFIED - For Official Use ESA | 25/07/2019 | Slide 31
ESA Business Applications
EVOLUTION OF EARTH OBSERVATION - TEMPORAL
https://www.researchgate.net/publication/332666408_Sub-annual_to_multi-
decadal_shoreline_variability_from_publicly_available_satellite_imagery

32. European Space AgencyESA UNCLASSIFIED - For Official Use ESA | 25/07/2019 | Slide 32
EVOLUTION OF EARTH OBSERVATION - ANALYSIS

33. European Space AgencyESA UNCLASSIFIED - For Official Use ESA | 25/07/2019 | Slide 33
ESA Business Applications
EVOLUTION OF EARTH OBSERVATION – AUTOMATED CLASSIFICATION

34. European Space AgencyESA UNCLASSIFIED - For Official Use ESA | 25/07/2019 | Slide 34
ESA Business Applications
EARTH OBSERVATION – DATA PROCESSING AND ANALYSIS
Pixel Based Analysis Object Based Analysis
https://gisgeography.com/image-classification-techniques-remote-sensing/

35. European Space AgencyESA UNCLASSIFIED - For Official Use ESA | 25/07/2019 | Slide 35
ESA Business Applications
EARTH OBSERVATION – DATA PROCESSING AND ANALYSIS
Data ClassificationData Pre-processing
https://www.mdpi.com/2072-4292/10/3/394
https://www.mdpi.com/2072-4292/11/12/1461

36. European Space AgencyESA UNCLASSIFIED - For Official Use ESA | 25/07/2019 | Slide 36
ESA Business Applications
EARTH OBSERVATION – DATA PROCESSING AND ANALYSIS
Data Monitoring Assessment/Predictions
https://radar.community.uaf.edu/lab-7-change-detection-using-convolutional-recurrent-neural-
networks-crnns/

37. European Space AgencyESA UNCLASSIFIED - For Official Use ESA | 25/07/2019 | Slide 37
ESA Business Applications

38. European Space AgencyESA UNCLASSIFIED - For Official Use ESA | 25/07/2019 | Slide 38
OPPORTUNITIES IN SPACE TECHNOLOGIES
Data Platforms
and cloud
Augmented
Reality and
Virtual Reality
Data standards,
Policies and
interoperability
Pre and Post
Processing
Algorithms &
Automation
Algorithms and
Modelling
(including ML)
New and Existing
Satellite and other
data sources
Space Technologies
Earth Observation
Communication
Navigation
Data
Gathering
and
Cataloguing
Data
Preparation
and
Processing
Integrated
data
analysis –
Value
Added
Data Quality
Data
Visualisation
Products and
Service
Dissemination
Stakeholders
1) Data Providers – Satellite, Ordnance survey, Met Office, BGS
etc
2) Software and Hardware Providers
3) Academics – Algorithms and Models
4) Government – Policies and Strategies (Industrial, AI,
Geospatial etc)
5) Strategic – UKSA, Innovate UK, SGP, Catapults, Research
Institutes and Centres of Excellence.
6) Funders and Venture Capitalists
New Satellite build
and launch

39. European Space AgencyESA UNCLASSIFIED - For Official Use ESA | 25/07/2019 | Slide 39
MULTI-SOURCE DATA INTEGRATION/MODELLING
EXAMPLEOUTPUTS& OUTCOMES
Challenge: Lack of demonstrators on integration of data from various satellite (including EO and SatComms)
and other data (Ordnance Survey, Met Office, BGS etc).
Applicable Market Sectors: Across most sectors
Description: With the increasing amount of EO data and the demand on value added information from the
market sectors, there is a requirement in investigation on data modelling and process optimisation.
Data/Model Requirement: EO data and other relevant data as per user requirements, Analysis Ready Data,
Data cubes, models and algorithms (including machine learning).
Results/Outcomes: The market sectors are provided with expertise and platform for data integration using
various techniques including modelling and optimisation using AI.
Key Engagement:
• Research and Academia Alan Turing Institute, Universities and Centres of Excellence
• Government: Government Office of AI (Joint unit between BEIS and DCMS), Centre for Data Ethics and
Innovation, the AI Council, Alan Turing Institute and Geospatial Commission
• Industry: Satellite data/Information providers, Software and modelling, data hosting and processing
Strategies: Industrial Strategy (Artificial Intelligence and data), AI Strategy and UK Digital Strategy
Source of Funding: ESA Business Applications, Industrial Strategy Challenge Fund (Next Generation Services
Challenge), and GCRF
Export opportunities for
service and solutions
Increased cost and data
processing efficiency
ESA Business Applications

40. European Space
Agency
Contact Details:
Dr. Kavitha Muthu
ESA Business Applications UK Regional
Ambassador – Midlands and North East
Email: Kavitha.Muthu@le.ac.uk
Twitter: @ESABA_MNE
http://business.esa.int

41. European Space AgencyESA UNCLASSIFIED - For Official Use ESA | 25/07/2019 | Slide 41

42. 42
KRIS CLARK
Head of AI
LANDMARK INFORMATION GROUP
AI MYTH BUSTER WORKSHOP
#AIExeter

43. KRIS CLARK :: HEAD OF AI.
linkedin.com/krisclark
25TH JULY 2019.
#43.
Building the
foundations of an
automated property
market.

44. Who we are.
#44.

45. Acquisition of
Ochresoft
1994 1996 1998 2002 2004 2006 2008 20122000 2010
Landmark
Information
Group founded
Landmark
acquires
Prodat Systems
Acquisition of
Sitescope
Acquisition of Argyll
Environmental Ltd
Acquisition
of Quest
Associates
Acquisition of
Calnea Analytics Ltd
Acquisition of
Metropix
Acquisition of
Renaissance
Environmental Ltd
Acquisition of
Inframation
AG
Acquisition of
on-geo GmbH
Launch of
Landmark
Nederland
Acquisition of Decision
Insight Information
Group (Europe)
2014 2015
Acquisition of
David Kirk &
Associates
Investments in Liases
Foras & PropStack,
India
Investment in Funcent,
China
International acquisitions/investments
UK acquisitions
Acquisition of
ETSOS
Investment in
Rialto
#45.

46. #46.
550+ 200+ 3000+

47. Evolving market.
#47.

48. #48.

49. #49.
•
•
•
•

50. #50.
•
•

51. #51.
•
•
•

52. #52.
•
•
•
•

53. Use of Ai.
#53.

54. #54.

55. #55.

56. Ai @Landmark.
#56.

57. #57.

58. #58.

59. #59.

60. #60.

61. #61.

62. #62.

63. Data.
#63.

64. #64.

65. #65.

66. #66.

67. #67.

68. Summary.
#68.

69. #69.

70. Thank you.
KRIS CLARK :: HEAD OF AI
25th July 2019
linkedin.com/krisclark
twitter.com/krisclark
#70.

71. 71
WILLIAM ECCLES
Global Head of Professional Services
DELL BOOMI
AI MYTH BUSTER WORKSHOP
#AIExeter

72. Internal Use - Confidential
Business
Outcomes,
Accelerated
WillEccles
GlobalHeadofProfessionalServices

73. Internal Use - Confidential
The pace of
business is
accelerating
• AI will replace humans
• AI is more efficient than a human
• AI is the answer
AI Myths

74. Internal Use - Confidential
A unified
ecosystem
for the
digital age

75. Internal Use - Confidential
Integrate
Synchronize and
enrich trusted data
Connect
applications and data
Manage your B2B
trading partner network
Design, secure
and scale APIsBuild customer journeys
Dell Boomi Platform

76. Internal Use - Confidential
Innovation Success
Chatbots & Enhancing Customer
Interactions
Intelligent Interactions
Deliver self service
Reduce friction
Empowering staff to
leverage their
intelligence

77. Internal Use - Confidential
Innovation Success
NPS Feedback Insights
Data Insights
• Automatic Sentiment
Recognition
• Experience ratings
• Customer Profiles
• Visibility into data
• Recommended action
suggestions

78. Internal Use - Confidential
Innovation Success
Enhanced Support Experience
First contact resolution
• Automatic device
recognition from photo
• Resolution path
suggestions
• Providing resolution on
first contact

79. Internal Use - Confidential
Grow
confidently
Scale without
limits.
Build
efficiently
Work smarter,
together.
Start
quickly
Just log in
and go.

80. Internal Use - Confidential
The faster and smarter path
to better business outcomes

81. 81
CONRAD HUMPHREYS
Founder and Managing Director
SPORT ENVIRONMENT
AI MYTH BUSTER WORKSHOP
#AIExeter

82. Beyond Smart Sound
Technology and Events that are shaping the
Competitive Sailing Industry

83. A little background…

84. RoboSail - 2002

87. Bretagne Sailing Valley
Race
Teams
14
Research
Labs
Classes
Races
7
Technopoles
160
Companies
Training
1
Detection
Centre
A Unique
Ecosystem

89. Big Data - Case Study

90. Trim Control - Case Study

91. Realtime Learning

92. Prototyping and Testing

93. Lorient - Case Study

95. Sport Environment
Conrad Humphreys
07720 287674
conrad@sportenvironment.com

96. 82
BOLA ADEGBULU
Co-Founder & CEO
PREDINA
AI MYTH BUSTER WORKSHOP
#AIExeter

97. Proprietary + Confidential
Predina: ML (Machine Learning) for spatial incident risk intelligence
Our ML platform predicts “where” and “when” incidents are likely to happen

98. Predina at a glance…
Founder
Founded & exited Telematics
startup (AutoMosys)
Managed dealer relationships
with Jaguar Land Rover, Audi,
BMW and Toyota at Enterprise
Rent-A-Car
Project engineer at General
Electric
Deep Technology company supported by leading technology, regulatory and industry stakeholders.
The Linde Group
Customer & Investor
Press/Awards
BOLA ADEGBULU -
Founder & CEO
Board
Entrepreneur First
Leading ML
Investor
MLStartups to watch - Techworld
Google's ML for Good Programme
EU Datapitch Winner - €100K
Machine Intelligence Winner
(Sponsored by Google)
Pitch at Palace Finalist
Team
Team includes:
PhDs Machine Learning
20 years of Automotive Data
15 years of Smart Cities
BMW, Imperial University, KTH,
UCL, Kapsch Trafficcom and
KPMG

99. Current risk management tools
focus on:
- The driver (Telematics &
education)
- The vehicle (Advanced Driver-
Assistance/ AV systems)
But they are no technological
solutions to intelligently manage
contextual (external factors) risks.

100. Leading to avoidable
traffic collisions
and the impacts of:
safety, reputation
congestion - pollution
and cost

101. ML model uses historical accident data and spatio-temporal data
2M historical accidents data + spatio-temporal data
Weather Road Layout Area Features Public Holidays
spatialvariablesPredinaspatial
Platform
spatialRisk
Intelligence
Spatio-temporal Variables
Likelihood Severity Cause

102. Partner
Smart Cities
Our product(s)
Our core products are: API (with modules) and a white labelled mobile navigation and risk report application
Customer
ADAS/AV Navigation
Customer
Insurance Telematics

103. Case Study: The Linde Group (BOC UK)
● Largest industrial gas company in the world. Travels 1 billion km globally
● Achieved 83% Accuracy in predicting over 1000 road accidents
● Reduced road accidents by 25% - 53%
Results
Achieved: 70% - 83% accuracy
in forecasts road accidents
Reduced accidents in by 25% -
53%
Improved driver spatial risk
awareness by 100%

104. Current projects:
Highways England to provide Predictive Incident
Management
Traffic officers (and service providers) to focus on high risk areas (and times) to clear the build up
in case of a road collision and ultimately reduce the risk of road collisions.

105. Proprietary + Confidential
Predina: ML for spatial incident risk intelligence
Our ML platform predicts “where” and “when” incidents are likely to happen
Currently hiring:
VP of Engineering
bola@predina.com

106. 92
TOM TORKAR
Partner, Technology & Innovation
MICHELMORES
AI MYTH BUSTER WORKSHOP
#AIExeter

107. How to protect intellectual property inAI
solutions?
Tom Torkar, Partner Technology & Innovation

108. Agenda
• What is AI?
• What is intellectual property (IP)?
• What IP rights (IPR) may subsist in AI solutions?
• The future: will the law keep up with AI?

110. Artificial intelligence/machine learning
AI = range of advances that extend our ability to sense,
learn and understand.
• Data: Some form of data inputs (significant volumes)
• Technical solution: An innovative computing platform
(software plus algorithmic models) capable of
analysing significant amounts of data
• Output: establish new data points or patterns that
dictate some form of action.
The above may be capable of protection through IPR

111. Protecting innovation
“… the sweat of a man's brows, and the exudations of a
man's brains, are as much a man's own property, as the
breeches upon his backside.” Laurence Stern, Tristram
Shandy Volume III”
• Ownership: a negative right, stops others from using –
monopoly
• Using contracts and confidentiality to protect
innovation
• Attractive to investors!
• A potential revenue stream (licensing)

112. AI: Layers of IPR
Patents
Copyright &
database -
data inputs/
outputs
Copyright in
software
Confidentiality
& contractual
protections

113. What are patents?
• Exclusive right to use invention.
• In territory for which patent obtained.
• In UK (and EEA), last 20 years from application
(provided renewal fees are paid).

114. How do you get a patent?
• Does not arise automatically, application followed by grant.
• Owner of the invention – person entitled to apply:
• Inventor – first owner of invention
OR
• Employer – if employee makes invention as part of their normal employment.
• Inventions resulting from commissioned work will not automatically be
owned by commissioner (look at contract).
• Joint inventions

115. Requirements/thresholds for patentability
Products and processes that can be patented:
• Is the invention functional or technical?
• Is the invention new?
• Must not have made the invention public in any way (by you or anyone else.
• There must not be any prior art.
• Is there an inventive step?
• Invention must not be obvious to someone with a good knowledge of the
subject.
• Does the invention have an industrial use?
Patent Act 1977, section 1

116. Excluded subject matter
• Specific exclusions:
• Scientific or mathematical discovery, theory or method
• Literary, dramatic, musical or artistic works
• A way of performing mental acts, playing a game or doing business
• The presentation of information
• Most computer programs
• Animal or plant varieties
• Methods of treatment or diagnosis
• Anything immoral or contrary to public policy

117. Software patents: can you file them?
• Excluded subject matter: can’t get a patent if the invention relates to
excluded subject matter (computer programmes) “as such”
• Interpreted differently by the registries in different jurisdictions (UK IPO,
EPO, US).
• Also, novelty & obviousness: nature of software, easily disclosed by a
click of a button (blogs, forums etc.)

118. Applying for patents
• When to file: beat the competitor vs. sufficiently developed idea aids
application
• What territories?
• UK
• European patent: acts as a bundle of patents
• Worldwide
• Priority date: a year in which to apply for the same patent in other
countries.

119. Patents and AI: Worldwide trend

120. Confidentiality – protecting the innovation
• Know-how:
• not an intellectual property right (not “owned”)
• not defined
• information which can be protected under the law of confidential information
• Trade secret: alternative name, US terminology.
• Using confidentiality as an alternative to / in conjunction with patents

121. Confidentiality – why is it important to protect
know-how
• Avoids jeopardising patentability because disclosure
• In conjunction with patents – e.g. minor improvements
• Protect information and ideas not capable of being patented
• Nature of innovation: parts of business that are not disclosed (e.g.
algorithmic models)
• Where no advantage of obtaining patent – where difficult to enforce
(e.g. algorithms– no access to competitors’ servers)
• Speed of commercialisation
• Cost

122. How do you maintain confidentiality?
• Confidential in nature, “necessary quality of confidence about it”
• Disclosed “in circumstances importing an obligation of confidence”
• Implied from special relationship between the parties
• By contract (confidentiality agreements/ “NDAs”)
• Use or disclosure must be in confidence and/or for specific limited
circumstances to maintain quality of confidence
• Rights last as long as the information remains confidential.

123. Protecting confidential information as an
employer (people talk!)
• Breach of duty under employment contracts:
• Duty of fidelity and good faith
• Fiduciary duties if management/director
• Include express confidentiality obligations, IPR assignments
• Post termination restrictive covenants (maximising protection vs.
ensuring upheld)
• Garden leave
• Database infringement

124. Practical precautions
• Mark documents “confidential”
• Use document management system
• Use NDAs
• Restrict access to confidential information
• Restricted areas (both physical and IT)
• Employment / consultancy agreements: restrictive covenants, ownership of IP,
confidentiality, garden leave.
• Ensure employees aware of duty of confidentiality (staff manual, induction)
• Plant “seeds” in databases
• Monitor activities of employees where necessary

125. Patents v confidentiality: Enforcement
• Patent does not lose validity if not enforced
• Confidential information can be rapidly and irreparably harmed if not
policed.
• Interim injunctions – pending full trial
• Final injunctions
• Damages
• Account of profits

127. Copyright –protecting the software
• Patents – protect idea rather than expression of the idea:
• Used defensively –source of licensing revenue (software corporations).
• Used offensively – protection of core product features, competitive advantage.
• Copyright - protects expression of idea, not the idea itself (Easyjet v Navitaire) – i.e.
the source code.
• Trade secret – if not detectable (e.g. new algorithm not detectable by competitors).
• Computer industry fast moving, patenting is a slow process.
• Stifles innovation? - Open source.

128. Copyright - features
• Protects among other things “literary works” e.g.
software code and database
• Must be original
• The owner has the exclusive right to copy/issue
copies
• Lasts for 70 years from death of author
• Arises automatically on creation (no registration)
• Creator is first owner; unless created in the course of
employment in which case employer owns the rights
• If hiring contractors/developers – assignment of
copyright to business is key

129. Data – protecting the valuable data inputs
• Significant effort and resource is ploughed into creating useful datasets
for AI (“big data”)
• Component parts:
• Data – possibly protected by copyright (query whether raw data has “originality”)
• Database structure – may be protected by copyright
• Database itself – protected by copyright and a sui generis database right
• Confidentiality

130. Database right
• Exclusive right to extract or re-utilise all or a substantial part of the contents of a
database, or the right systematically to extract or re-utilise insubstantial parts of its
contents
• 15 years from completion of database
• Arises on creation – if substantial investment in obtaining and verifying contents
• Compiler is first owner,; unless created in the course of employment in which case
employer owns the rights
• European law

131. The future: will the law keep up with AI?
• What happens to the output of AI/computer generated works, with little
or no human input?
• An author of copyright work is a person
• Originality under copyright law requires “sufficient skill labour and judgment”
• An inventor of a patentable invention is a person
• Should the output of AI be capable of IP protection?

132. 118
KAVITHA MUTHU
UK Business Applications Regional Ambassador (Midland and NE)
EUROPEAN SPACE AGENCY
AI MYTH BUSTER WORKSHOP
#AIExeter

133. 119
AI MYTH BUSTER WORKSHOP
#AIExeter
https://www.youtube.com/watch?v=FcETq8OWM0k
Basic Tutorial: Supervised Classification
of Land Cover using Sentinel-2 Images