IoT + AI + Big Data Integration Strategy Insights from Patents 3Q 2016

©2016 TechIPm, LLC All Rights Reserved www.techipm.com
IoT +AI + Big Data IntegrationStrategy:
Insightsfrom Patents
3Q 2016
Alex G. Lee (alexglee@techipm.com)

©2016 TechIPm, LLC All Rights Reserved www.techipm.com 2
1. IoT Innovation Insights from Patents ……………………. ………….…...…... 3
2. IoT Frontiers Insights from Patents ……………………. ….………..………... 7
3. IoT Strategy Perspectives from Patents …................……………………... 16
4. IoT Innovation Exploiting Patents …………………..…….…………………... 18
5. IoT Patent Strategy …………..…………………..……………….………………... 32
6. IoT Startup Patent Strategy …….……………..……………….………………...57
7. IoT for Business Growth Insight from Patents..………….………………... 60
8. Artificial Intelligence Innovation Insight from Patents..…...…………...63
9. Big Data Innovation Insight from Patents..…...…………………………….. 75
10. IoT + AI+ Big Data Integration Strategy …………………………………... 88
Contents

1. IoT Innovation Insights from Patents
Smart Home Patent Leadership
Reference: Smart Home Patents Data 3Q 2016

Connected Car Patent Leadership
Reference: Connected Car Patents Data 3Q 2016

Connected Health Patent Leadership
Reference: Connected Health Patents Data 2Q 2016

IoT Connectivity Patent Leadership
Reference: LTE Patents for Standards Data 2Q 2016

2. IoT Frontiers Insights from Patents
Smart Home Audio and Video Analytics
US20150364028 illustrates a smart home security system for detecting the home
premise condition using audio analytics. Sounds (e.g., sound of doorbell,
sound of breaking glass, or sound of baby crying) occurring on or near the
premise can be detected and analyzed whether the sound belongs to a
recognized class of sounds (e.g., security, health condition, property damage).
In response to the analysis, the system performs tasks that are responsive to the
detected sounds (e.g., sending messages to law enforcement or medical
personnel).
US20150194034 illustrates a system for monitoring an incapacitated person
(e.g., unable to move or respond) in the monitored area using video analytics.
The system processes captured images to detect an anomaly when a person
becomes ill and incapacitated (e.g., sharp movements representing a fall). When
such anomalies are detected, a notification can be transmitted to another location
indicating that medical assistance is needed.

Smart Home Robots
Have you ever dreamed about future that your favorite food is ready
automatically when you feel hungry? US20150290795 indicates a possibility by
combing robotics and artificial intelligence. US20150290795 illustrates the
computerized robotic food preparation system for food preparation by digitizing
the food preparation process of professional and non-professional chef dishes
and subsequently replicating a chef's cooking movements, processes and
techniques with real-time electronic adjustments.
US20140207282 illustrates a household robot that can take care of your home.
The household robot can monitor the state of a home, clean the home and turn
on/off various appliances for you automatically and transmit the surveillance
data to you in an emergency condition detected by the robot. As illustrated in
US20150224640 the household robot even can take care of your health.

Self-aware Self-healing IoT
Billions of interconnected devices that are connected to the internet should be
self-aware and self-healing: Self-Aware Self-Healing IoT is concerned with
systems that can auto-detect, analyze, and fix any issue with the IoT devices
and networks without human intervention.
Deployment of significant numbers of lighting devices with associated controllers
and/or sensors and networking in smart cities presents increasing challenges for
set-up and management of the system elements and network communication
elements of the lighting system. US20150250042 illustrates the system of
networked intelligent lighting devices that provides autonomous discovery and
set-up at installation. The system also can provide autonomous detection of
failure and fix of the failure.

Emotion-Aware IoT
An individual's mental state is important to general well-being and effective
decision making. Mental states include a wide range of emotions and
experiences from happiness to sadness, from contentedness to worry, or from
excitation to calm. US20140200463 illustrates the emotion-aware IoT
applications by determining well-being status from an analysis of facial
information and physiological information of an individual. By monitoring the
well-being status of an individual, the home lighting can adjust it while turning on
the favorites music automatically when a person stressed.
US20130054090 illustrates the emotion-aware IoT application for providing the
safe driving service system by analyzing information necessary for cognizing
emotion on the basis of bio signals sensed by in-vehicle sensors.

Autonomous IoT
The autonomous smart home system can recognize the contextual
or semantic profiling of a person or place or devices (physical environment)
based on sensed data by the IoT devices. The adaptable autonomous smart
home system determines particular interpretation instructions (define particular
IoT device control rules) that are associated with the particular physical
environment and dynamically updates the control rules for changing physical
environment.
Qualcomm patent US9413827 is the first issued patent regarding the
autonomous IoT system. The autonomous IoT system includes a variety of
heterogeneous IoT devices to be able to communicate with each other.
The IoT heterogeneous devices can recognize each other by virtue of a unique
identifier. The autonomous IoT system detects a context of the IoT devices
(e.g., space, time, location, status/events, etc.) and performs actions in response
(e.g., issues a command or trigger a specific IoT device to perform a
corresponding action or state change).

Autonomous IoT -2
For example, if the sleep monitoring device senses that a child is sleeping in a
room, the autonomous IoT system makes the lighting in the room remain off.
If a parent walks in to check on the child (context of the parent and child being
in the same room), the autonomous IoT system triggers an action of lighting up
to a 10% dimmed state.
The autonomous IoT system also can recognize the context with respect to
relationship among different users. For example, if person A (e.g., husband)
walks into the room when a music system in the room playing the genre of music
for person B (e.g., wife), the music system can switch to a music genre that both
person A and person B like.

Augmented Reality
US20150347850 illustrates an IoT AR (Augmented Reality) application in
a smart home. A smart home IoT device communicates via a local network to a
user AR device (e.g., smartphone) for providing the tracking data. The tracking
data describes the smart home IoT device. The AR devices can recognize the
smart home IoT device in the camera view based on the tracking data.
Once the smart home IoT device is identified in the camera view, the AR
application can augment the camera view with additional information and control
interface about the smart home IoT device. The user can control the smart home
IoT device using the AR device.
US20140063064 illustrates an IoT AR application in a connected car. An AR
head-up display in the connected car displays the overlaying a virtual image
regarding the surrounding environmental information on an actual image of the
external vehicle that is observed through the transparent display. The surrounding
environmental information are obtained via the V2X communication system.

Convergence
US20120120930 illustrates a vehicle network system interconnected with a
home network. When a predetermined request signal is transmitted from the
vehicle network system to the home network, the home network performs an
operation corresponding to the request signal. The home network determines
whether the vehicle network system is detected within a predetermined range.
The home network may include a RFID system to detect the presence of the
vehicle network system. When the vehicle network system is detected within
the predetermined range, the home network transmits parking information
with respect to the vehicle equipped with the vehicle network. The transmitted
parking information can then be displayed on the display unit in the vehicle.

Fog Computing
US20150261876 illustrates the network environment includes multiple fog
computing devices each connected with a communication network.
Fog computing pushes applications, data, and computing power
(including computing services) away from centralized points in a network
to logical extremes or edges of the network. Fog computing covers a wide
range of technologies including wireless sensor and actuator networks, mobile
data acquisition, mobile signature analysis, cooperative distributed peer-to-
peer ad hoc networking and processing.
US9098344 illustrates the cloud-edge hybrid system that an application
instance runs on the fog computing device and another application
instance runs on the cloud-based computing resources.

3. Strategy Perspectives from Patents
Cisco
Cisco announced a strategic partnership with and Ericsson to create the
networks of the future. Followings show the insights regarding Cisco strategy
perspectives for developing the IoT networks of the future to make Cisco as the
IoT networks innovation leader.
Predictive Analytics for IoT networks
Predictive analytics analyzes current and historical data to make predictions
about future events and trends. US20150333992 illustrates the application of
predictive analytics for managing the IoT Networks.
Machine Learning for IoT networks
Cisco is developing the intelligent autonomous IoT networks exploiting the
machine learning (ML). US20150195192 illustrates the use of the ML in order to
predict whether a network element failure is relatively likely to occur.

3. Strategy Perspectives from Patents
IBM
IBM announced a plan to invest more than $3 billion for four years to build the
IoT business unit. Followings show the insights regarding IBM strategy
perspectives for developing the IoT analytics to make IBM as the IoT business
leader.
Big Data Analytics
IoT big data analytics are becoming important to process unimaginably large
amounts of information and data that are obtained by the sensor embedded
interconnected IoT devices. US20140068180 illustrates a system capable of
efficiently analyzing big data.
Predictive Analytics
US20140236650 illustrates the cost effective end-to-end analytics driven asset
management by managing maintenance operations (e.g., scheduling, preventive
maintenance, operating parameter control).

4. IoT Innovation Exploiting Patents
White Space Analysis
Patent information can provide many valuable insights that can be exploited
for developing new IoT products/services.
One can identify the potential R&D areas (“white space”) that can lead to new
product/service development through the patent landscape analysis.

Cross-competitor Analysis
Patents can provide insights regarding the competitive advantage innovation
strategy in alignment with the strategic move of a specific company for the IoT
business leadership through the cross-competitor analysis.

Scenarios Analysis
Patents can be exploited to identify new IoT product/service development
opportunity from scenarios analysis. The scenarios analysis can show potential
interactions between the future user and the IoT product/service via the specific
usage of the product/service and behavior of the user under environments
provided by the product functionality.
The scenarios analysis exploiting patent information can provide the new
IoT product concept (e.g., specific benefits to the user, product design, product
functionality and the technology for the product).

Smart Home Scenarios Development
• The first step in the scenario development is to do patent search and
review for the state of the art of IoT innovation for smart home applications.
The searched patents that are related to the smart home applications should
be analyzed to find the measurable user benefits provided by the value
propositions (automation, energy management, care, safety, entertainment)
and method/device/system to offer the value propositions.
• Then, develop a story of the future implementations of the state of the art
of IoT innovation for smart home applications exploiting 5W1H –
Who, When, Where, What, Why, How.

Smart Home Scenarios Development -2
• The stakeholders (users) in the scenario for the IoT smart home are usually
the member of home who can be specified by age, gender, profession.
The time can be the morning, afternoon, evening.
The place can be the bedroom, living room, bathroom, kitchen and outside.
• Then, describe how the specific product (including system) that is
implemented based on the patent disclosures performs (interacts with a user)
to provide each value proposition under the circumstances for a specific
purpose (expectations of the product’s future user or solutions for product’s
future user’s problems). Base on the developed scenarios, potential
improvements over the implemented product can be devised by listing new
product features, functionality, UI/UX, integration method etc.

• Followings summarize the key elements of the IoT smart home scenarios
for each value proposition.
• Home Automation
The smart home product should determine how to solve user’s problem or
perform for a specific situation without constant guidance from the user.
The smart home product should adapt to changing situation/environment.
The smart home product should react properly to the changing
situation/environment in accordance with the performance objectives.
The smart home product should join the home networks without user’s
manual configuration.

• Home Energy Management
The smart home product should adapt the overall energy efficiency of the
home to the comfort and quality of life.
The smart home product should control the home heating for reducing
energy consumption while ensuring comfort.
The smart home product should control lighting based on user’s presence
and environment.
• Home Care
The smart home product should proactively act for caring the user under
the environment/situation on behalf of the user.
The smart home product should provide UI/UX depend on the user context.

• Home Safety
The smart home product should provide private space protection and
control against hazard home environment.
The smart home product should measure of gas, carbon monoxide, fire,
smoke, water and weather conditions.
The smart home product should provide remote access to home
environment.
• Home Entertainment
The smart home product should provide personalize entertainment to
the user.
The smart home product should exploit semantic information regarding
the home entertainment devices.

2020 IoT Scenarios
Following future scenarios are developed based on disclosures in the IoT
patents. The scenarios describe possible situations in 2020 that a person
will experience in his/her everyday life: home, highway, shopping mall,
business meeting and medical emergency situation. Scenarios can provide
several IoT business insights including the IoT products/services and
application business models development.
In the morning, at home
Alex, married with Janet and has two daughters Alice and Jenny, wakes up at 6
am with the morning call music Feels So Good by Chuck Mangione, one of his
favorites. Alex’s smart home system selects the Feels So Good for gloomy Friday
morning due to heavy clouds in the sky of Alex’s Boston home. The wake up time
and music were selected based on Alex’s personal profile for his favorite music,
his yesterday’s busy schedule and current weather condition sensed from his
front yard.

2020 IoT Scenarios -2
As Alex stands up, his closet doors are opened and LED lighting from the bottom
shins the sox that is match in color with his black coat for an important business
meeting in the afternoon. The smart home system operates the closet doors and
lightings depend on Alex’s position and movement. As Alex moves out from his
bed room and moves into the living room, smart TV is turned on and
automatically changes the TV channel for the traffic information. At that time,
Kitchen lights flick to signal to Alex that his breakfast is ready in the rice cooker.
As Alex finishes his breakfast, Roombo, a home serving robot, approaches and
asks him whether he had his subscription pills. When Alex has his medicine, he
takes the image of the pill and sends the image to his primary care doctor using
his smartphone.

On the way to shopping mall
On her way to nearby shopping mall, Janet hears a voice massage that she
needs to exit at the next to buy milk. The onboard screen in her car shows a map
for nearby shops with milk price if a shop is on sale. When Janet dozes off at the
wheel, the automated vehicle monitoring system changes her car to a self-driving
mode. As Janet drives her car at 75 mph at 55 mph speed limit highway, her car
smart control system automatically slows down. About 5 miles before the
shopping mall, her car slows down to a complete stop because her car receives
an accident notice from a car drives in the opposite direction. About 3 miles
before the shopping mall, Janet hears a traffic congestion voice massage near
the shopping mall exit.

The onboard screen in her car shows a map for alternative route to the shopping
mall. As Janet approaches the shopping mall, smart LED lights detects available
parking spaces, then directs her to them. When Janet enters into the shopping
mall, her smartphone receives indoor navigation guide maps, product information
and promotional advertisements. At a gift shop, Janet buys Alex’s birthday gift
and pays using her smartphone.
At the business meeting
Alex has a business meeting with John at Financial Building located in the
Boston downtown. As Alex arrives in front of Financial Building, he receives a
welcoming message of an access information service of Financial Building
(displayed on his smartphone). Alex then moves on to Conference Room 313
by being provided with a WiFi signal based location guidance through his
smartphone.

While Alex is moving to Conference Room 313, automatic authentication of all
doors located along the path may be realized, thereby allowing Alex to pass
through all doors in order to reach Conference Room 313. When Alex enters
Conference Room B, lights and lamps installed in the conference room are
automatically turned on, and operation of a ventilation system is initiated.
When John enters Conference Room 313, a projector is turned on, blinders of
the conference room windows are shut down, and the lights are turned off. Alex
discusses with John regarding the adoption of the smart inventory system that
utilizes the 3D printing system in John’s warehouse.

In the evening, at home
As Alex approaches his home the smart home system determines whether his
car network system is detected within a radius of 2 miles. When the car network
system is detected within the predetermined range, the smart home system
transmits parking information. The transmitted parking information is displayed
on the display unit in the car. Then, smart home system turns on the light in the
front door and changes the living room temperature at Alex’s most comfortable
temperature. While Alex is having a good evening time his smartphone notifies
him that his garage door is open and his electric vehicle is not charging.

5. IoT Patent strategy
Integrated IoT Patent Strategy

5. IoT Patent Strategy
Patent Development Strategy
Prior Arts Search
Even if the internet of things (IoT) gets a huge attention recently, the concept of
interconnected devices and connecting billions of devices to the internet in the
IoT is not new and has been researched for over 10 years. Thus, a large number
of non-patent prior arts including research papers and books can be existed.
Therefore, the intensive search of non-patent prior arts is needed for preparing
the IoT patent.
US20150019710 illustrates a good example case that intensive search of
non-patent prior arts is required. US20150019710 claims a method of
maintaining interoperability amongst IoT devices connected via an IoT
integration platform. The key elements of the claim 1 at issue are the
interoperable control rule based on the semantic label associated with a
user's IoT device.

Patent Development Strategy -2
Prior Arts Search -2
The USPTO rejected the claim 1 under the AIA 102(b) as being anticipated by
a non-patent prior art regarding the semantic interoperability on the web.
Semantic interoperability enables exchange of data among different web system.
The inventor argued that the semantic label in the non-patent prior associated
with a user's IoT device and amended claim to clarify the distinguished aspects
of the claim form the prior art. However, the USPTO rejected the claim again in
the final rejection based on newly founded non-patent prior art that explicitly
mentions the IoT device in the disclosure.

Claim Drafting for Maximum Quality & Value
When the IoT patent claims are drafted, several factors that can make the patent
high quality and value should be considered. The factors are (1) claim scope
and broadness, (2) value propositions of a claim’s novelty point (non-obvious
inventive departure from the prior arts), (3) claim mix (degree to which different
kind of statutory claim types (e.g. apparatus, process, manufactures,
composition) are used in a patent), (4) claim diversity (degree to which different
kind of claim formats (e.g. means-plus-function claim), terminology, arrangement
in a given statutory claim type (e.g. number or order of claim elements) are used
In a patent) and (5) claim setting (degree to which different kind of commercially
significant settings (e.g. component/system or transmitter/receiver) are used in
a patent).

Claim Drafting for Maximum Quality & Value -2
The features to be considered in the claim drafting to get the high quality and
value patents with the optimal quality to cost (efforts and time in addition to
monetary cost) ratio can be decided exploiting the insights for products/services
that is going to be covered by the patent (e.g. attributes of a product/service that
valued most by customers), the patent landscape to figure out the competitors’
patents (e.g. whether design around is required to avoid infringing competitors’
patents) and internal patenting process (including available patent budget).
For example, if a patent is going to use for excluding the competitors, the key
features to be considered in the claim drafting will be the claim diversity (to avoid
potential infringement loophole) and claim mix (to provide protection against
invalidation).

Claim Drafting for Maximum Quality & Value -3
The best practice in the IoT patent claims drafting is the Qualcomm patents
for the IoT connectivity. Total of 273 patents issued in the USPTO for the IoT
connectivity are reviewed. Following table summarizes the assessment.

Claim Drafting & Amendment under Post-Alice 101 Eligibility Test
The basic building blocks of the IoT are devices that can sense/recognize their
surrounding environments and communicate with other devices, connecting/
communicating network medium/infrastructure that can interconnect devices and
connect devices to the internet, back-end IT systems that can process
information (data) obtained by the IoT devices (e.g. cloud computing/big data
analytics) and provide the value added services exploiting the information.
Therefore, a part or whole of data aggregation, data transfer, data correlation,
data analysis and services based on the data are the essential elements of the
IoT inventions, and thus, the elements of the IoT patent claims. Consequently,
many of IoT patents can be identified as abstract ideas because they are the
certain methods of organizing human activities/mental process or fundamental
economic practices or mathematical relationships/formulas unless the IoT patent
claims are drafted carefully to pass the post-Alice 101 patent eligibility test.

Claim Drafting & Amendment under Post-Alice 101 Eligibility Test -2
For example, claim 1 in US20140012945 claimed: A method of automatically
detecting issues and facilitating the transmission of alert messages in a home
network consisting of a plurality of consumer electronic devices coupled to a
service node, the method comprising: storing information regarding home
entities in an inventory database, the entities comprising the household,
one or more users within the household, devices in the household, applications
running on the devices, and a community of which the household is included;
defining usage classes for each entity, the usage classes denoting a primary
function associated with an entity, and comprising networking, productivity,
and entertainment; assigning an index value to each usage class for each
entity, the index value comprising an integer value within a fixed range, and
reflecting a relative level of satisfaction associated with the entity for each
respective usage class; monitoring data regarding usage trends, performance
characteristics, and external variables associated with the entities;

modifying one or more of the index values based on the monitored data; and
facilitating the transmission of messages regarding services or products related
to the entities of the home from one or more parties, wherein the messages are
conformed to index values.
This claim was rejected under 101 by the USPTO because the claim is directed
towards the abstract idea: storing data regarding entities, classifying entities,
assigning index values, monitoring usage data, modifying the index values and
transmitting messages regarding services or products related to the entities
based on index values. Thus, simply transferring the data through the networks
or processed the data for providing services will not sufficient to transform the
nature of the claimed invention into a patent-eligible claim. Additional elements
that are sufficient to amount to significantly more than the abstract idea are
needed.

Potential claim elements that can overcome the reject may be the claim
elements that embed/link to a specific machine/article (not a generic purpose
computer) (e.g. medical device, thermostat, application specific
microprocessor/controller) or that either recite a specific series of steps that
resulted in a departure from a human activity or transform a particular thing to
a different thing or improve the technology.

Patent Disclosure Drafting & Prosecution in Alignment with Business
Strategy
The IoT patents should support future development of the IoT businesses and
commercial implementations of the IoT innovations. The disclosures of the IoT
patent can be considered as a system that is consist of the basic building blocks
of the IoT as a subsystem: (1) devices that can sense/recognize their
surrounding environments and communicate with other devices, (2) connecting
/communicating network medium/infrastructure that can interconnect devices and
connect devices to the internet, (3) back-end IT systems that can process
information (data) obtained by the IoT devices (e.g. cloud computing/big data
analytics) and (4) provide the value added services exploiting the information.
Then, the system as a whole and each basic building block as a subsystem can
be construed in the disclosures to support future development of IoT businesses
and commercial implementations of the IoT innovations.

Patent Disputes Risk Assessment
As we have seen in the smartphone market development, it is expected that the
super-competition to occupy the leadership in the lucrative IoT market can
lead to another round of patent wars. The post-smartphone patent wars,
however, will be more extensive because of more extensive participation of
players across several different industries. The post-smartphone patent wars will
also be more complex because of the recent rapid change in legal environment
and the learning curve from the smartphone patent wars.
• A large number of patents owned by commercially unsuccessful companies
can be the potential patent disputes risk (e.g., smart home).
• A large number of patents owned by the patent monetizing entities is a
potential patent disputes risk (e.g., connected car, IoT connectivity).
• A large number of patents owned by entities from different industries is a
potential patent disputes risk (e.g., connected car, connected health).

Patent Disputes Risk Assessment -2

IoT Patent Wars 2020 Scenarios

Patent Monetization
Even though there were so many patents that were issued up to now, however,
the number of patents that were commercially exploited and monetized
successfully was very small. This is similar to the situation that, even if there
were so many innovators and entrepreneurs, only small numbers achieved
commercial success from their ideas.
In “The Innovator's DNA”, the authors identified the code of successful
innovators that distinguishes them from others based on the case study of well
known successful innovators such as Steve Jobses and Jeff Bezoses. The key
lesson from the research of the innovator’s DNA was that the successful
innovators’ DNA is not simply genetic traits endowed, but that they can be
developed. Then, by the analogy, one can ask similar questions for patent:
What make the monetizing patents different?;
Can we find the code of the monetizing patents?;
Can we identify and develop the monetizing patents?

Patent Monetization -2
Monetizing patents protect the market for commercially successful products/
services from competitors. Monetizing patents can be exploited as financial/
investment assets or through licensing to other parties for additional cash flow.
Followings are the monetizing patent’s DNA uncovered from the case study of
carefully selected monetizing patents through the integrated technology, legal
and business analysis.
Monetizing patents solve the right technological problem
The right technological problem is a problem in great needs of improved/
innovative solution;
The right technological problem is recognized well in an industry;
The right technological problem leads to a solution that has great business
impacts;
The solution of the right technological problem follows the correct evolutionally
pass for emerging technology.

Monetizing patents practice value innovation
Monetizing patents provide unique value proposition with maximum market
protection;
Monetizing patents provide values to customers without additional cost/pain or
infrastructure/process change requirements.
Monetizing patents actively participate in the business ecosystem
Monetizing patents can be a platform or integrated into a platform;
Monetizing patents form a dominant design or industry standard;
Monetizing patents form diverse value chains;
Monetizing patents derive the value for M&A.

Monetizing patents are legal savvy
Monetizing patents are created following the case laws and statutes to obtain
maximum legal rights;
Monetizing patents are the litigation-proof patents;
Monetizing patents adapt to the dynamic changes of legal environment.

TiVo patent US6233389 is an invention regarding the digital video recorder
(DVR). Today, many digital set-top boxes include the key functionality of
US6233389. TiVo monetized US6233389 through licensing and litigation
including $500 million settlement with EchoStar and $490 million settlement
with Google and Cisco.
Monetizing
DNA
Details
solve the righ
t technologic
al problem
The viewer stores TV programs onto magnetic tape using the VCR. The
VCR can pause the program playback whenever he desires, fast forward
through unwanted program material or commercials, and to replay favorit
e scenes. However, a VCR cannot both capture and play back informatio
n at the same time. The invention provides an easily manipulated, low co
st multimedia storage and display system that allows the user to view a t
elevision broadcast program with the option of instantly reviewing previo
us scenes within the program. In addition, the invention allows the user t
o store selected television broadcast programs while the user is simultan
eously watching or reviewing another program: solve a problem in grea
t needs of improved/innovative and recognized well in an industry

Monetizing
DNA
Details
solve the righ
t technologic
al problem
An embodiment of the invention accepts television (TV) input streams in
a multitude of forms including digital forms such as Digital Satellite Syste
m (DSS), Digital Broadcast Services (DBS), or Advanced Television Stan
dards Committee (ATSC): leads to a solution that has great business
impacts (with the development of digital TV and broadband internet
access)
The invention can be used in the detection of gambling casino crime. Th
e input section of the invention is connected to the casino's video surveill
ance system. Recorded video is cached and simultaneously output to ext
ernal VCRs: follows the correct evolutionally pass for emerging tech
nology (e.g. applications in smart home/building security system)

Monetizing
DNA
Details
practice valu
e innovation
An embodiment of the invention accepts television (TV) input streams in
a multitude of forms including digital forms such as Digital Satellite Syste
m (DSS), Digital Broadcast Services (DBS), or Advanced Television Stan
dards Committee (ATSC): provide values to customers without additi
onal cost/pain or infrastructure/process change requirements by be
ing included in the current digital systems
Although a VCR is specifically mentioned, any multimedia recording devi
ce (e.g., a Digital Video Disk-Random Access Memory (DVD-RAM) recor
der) is easily substituted in its place: provide unique value proposition wit
h maximum market protection; The invention can be used in the detectio
n of gambling casino crime. The input section of the invention is connect
ed to the casino's video surveillance system. Recorded video is cached a
nd simultaneously output to external VCRs: provide unique value prop
osition with maximum market protection

Monetizing
DNA
Details
actively parti
cipate in the
business eco
system
HBS Professor Iansiti, the author of “The Keystone Advantage,” suggest
ed technological assimilation as a new engine for technological innovatio
ns in his article “Creative Construction.” In the technological assimilation
frameworks, a core innovation that once provided stand-alone products o
r services for a specific market can be the building blocks for mass mark
et generating innovations through assimilation to broader platforms that
were not existed at the time of innovations. A good example may be the
GPS technology providing LBS (location based services) applications for
smartphones and automobiles. Another good example may be the DVR t
echnology of TiVo that was integrated into digital set-top box and smart
home platforms: can be a platform or integrated into a platform

Monetizing
DNA
Details
legally savvy 4 US Families/ Total of 16 independent claims/High in different kind of st
atutory claim types: protection against invalidation
Exploited in 9 litigations and won the cases: litigation-proof

6. IoT Startup Patent Strategy
Best Practice
Nest Labs
Company information
A smart home company headquartered in Palo Alto, California, that
manufactures smart thermostats and smoke detectors. Founded in 2010
and acquired by Google for $3.2 billion in 2014.
IP value recognition in M&A
Build up 132 US issued patents (+ pending applications + international families)
in four years
Cf. SmartThings’ acquisition by Samsung for $200 million with no patent
*notice the value difference: $3.2 billion v. $200 million

Best Practice -2
Strategic development of patent portfolios
• Patent fortress for core business
Create 33 US issued utility patents and 28 US issued design patents regarding
smart thermostat
• Strategic patent acquisition
Acquired 69 US issued patents and one US patent applications regarding
smart sensors and sensor networks.
Strategic exploitation of patent portfolios
• Strategic financing
Utilize 29 US issued patents for patent secured lending from Wells Fargo Bank
*after Series A and B funding, before Series C funding
Strategic patent licensing
Licensed patents for accessing to Intellectual Ventures’ IP for defense

IoT Innovative Startups Insights from Patents
Magic Leap (http://www.magicleap.com) is a leading augmented reality (AR)
startup company. Magic Leap had raised more than $1.3 billion of venture
funding from Alibaba, Google, Qualcomm, and other investors since its
foundation in 2010. Financial Times valued Magic Leap at $4.5 billion.
Magic Leap filed 167 US patent applications including two issued patents as of
1Q 2016. Magic Leap’s AR technology innovation strategy are as follows.
• Biomedical engineering approach to the complex human visual perception
system for producing and providing the AR contents
• AR technology that facilitates a comfortable, natural-feeling, rich presentation
of virtual image elements
• AR system that work with the visual configuration of a typical human to
address various challenges in AR applications

7. IoT for Business Growth Insights from Patents
Growth by generating revenue from new product/service offerings
US9218740 illustrate a connected car cloud service to assist drivers with
respect to improving driver safety. The cloud-based driver assistive system can
warn drivers upon impending collisions. Using mobile devices and relatively
inexpensive sensors and wireless connections to the cloud service,
the cloud-based driver assistive system can be implemented inexpensively for
enriching the driving experience without needing new roadside infrastructure
for vehicle-to-infrastructure (V2I) communications and embedding the Dedicated
Short-Range Communications (DSRC) device to every vehicle for inter-vehicle
(V2V) communications.

Growth through strengthening competitive position by adding new functionality/performance for the existing
product/service while lowering production cost and manufacturing quality control
US20150351698 illustrates a system for analyzing physiological and health data
(e.g., activity data) retrieved from wearable monitors using a smartphone to
identify emergencies or medically significant events in real-time.
The system generates medical notifications corresponding to the identified
medical event and generated health level in real-time. The generated health
level is transmitted by smartphone to an emergency response system when
the identified medical event is an emergency medical event.
US20160202692 illustrates an IoT system for efficiently and accurately
managing a multi-part product production line in a factory environment.
The factory is equipped with sensors for detecting technician’s location and
surrounding environment within a particular zone of the production line.
When the technician enters an assigned zone of the production line,
the technician receives instructions and images for the designated task.

Growth through business model innovation by creating new market or by providing fundamentally different
value propositions for the current market
US20120165617 illustrates a system for early health and preventive care using
data from wearable sensors. Data collected from the sensors is transmitted to a
mobile cloud computing platform-as-a-service (PaaS). The predictive analytics
analyzes the received data to predict diseases and other conditions to which
the patient may be predisposed.

8. Artificial Intelligent Innovation Insights from Patents
Deep Learning Innovation Insights
Deep learning is a machine learning technique that attempts to learn layered
models of inputs that mimics the human brain’s reasoning process.
Deep learning techniques, such as deep belief networks and deep
convolutional networks, are layered artificial neural networks architectures
in which the output of a first layer of neurons becomes an input to a second
layer of neurons, the output of a second layer of neurons becomes and input
to a third layer of neurons, and so on. The layers correspond to distinct levels of
concepts where higher-level concepts are derived from lower-level concepts
(hierarchy of complex concepts that are constructed out of simpler concepts).
Reference
AI Deep Learning Patents Data 3Q 2016

Deep Learning Innovation Insights -2
Since the United States is such a large market for products and services from
nearly all technology innovations, a patent counting for growth in patenting over
a period of times in the US can be a good measuring tool for monitoring the
evolution of technology innovations. Following figure shows the growth trends
for the deep learning technology innovations base on the patent research of
nearly 250 published applications and issued patents by the USPTO as of 3Q
2016.
The figure indicates that very active deep learning technology innovations
started from 2013 and keep increasing (since there is usually a time lag
between the priority date and publication date by two to three years, the
number of patents in 2015 is not fully accounted as of 3Q 2016).

Number of patents for an assignee divided by the total number of patents gives
what percentage the assignee contributes to the deep learning technology
innovations. Ranking the assignees by the number of patents is thus an
important part of visualizing the innovations landscape.
Following figure shows the top assignees for the deep learning technology
innovations. The top deep learning technology innovators include Microsoft,
NEC, IBM, Samsung Electronics, Google, and Qualcomm.

Following figures show the key applications of the deep learning and use cases
by industry. The figures indicate that Object/Image Recognition is the most
used application of the deep learning and IT solution/software industry is the
top industry that exploits the deep learning.

Artificial Intelligence for FinTech/Investment Insights from Patents
US20160196610 illustrates a system for predicting global financial crises.
The system searches state, local, federal and international government
websites and databases, including political and financial news sources, to
obtain real time financial insights. The system correlates this information to
individual stocks, bonds, commodities or currencies and processes it using
machine learning engine that predicts the price move for each stock or
exchange traded fund. The system also aggregates and analyzes predictions
from a geographical area based on country, country capitols and continents or
geographical areas and regions. Thus, system offers automated geopolitical
risk and determines the investing climate, business for individual countries.

Artificial Intelligence for Human Activities Discovery Insights from Patents
With the advent of mobile video cameras, digital video recording technology
integrates of into consumer electronic devices more and more. In computer
vision, machine learning techniques are used to detect the presence of and
recognize various elements of the visual scenes that are depicted in digital
images. Thus, the captured video by mobile video cameras can be used for
understanding human activities.
US20140316881 illustrates a system for analyzing images of human reactions
(e.g., facial expressions, head poses, and/or gestures) to external sensory and
psychological stimuli (e.g., exposure to products, smells, tastes, sounds, etc.).
The system can recognize such reactions automatically using machine learning
techniques. For example, if an advertisement is displayed to a user’s
smartphone, the smartphone simultaneously record the user's reaction
obtained using the camera. The smartphone analyzes the reactions of the user.
Based on the analysis, new advertisement or incentive can be displayed or
delivered to the user.

Artificial Intelligence for Autonomous Medical Diagnostics Insights from Patents
US20160203263 illustrates a system for automatically analyzing a patient's
medical images and provides quantitative metrics related to the patient's
current health status and their risks for future health outcomes exploiting
machine learning technique. The system compares a patient’s image data from
medical imaging apparatus to images of the same or similar tissue regions from
other individuals for whom the corresponding health status and/or outcomes
are known.
An unsupervised machine-learning algorithm is used to calculate metrics
related to the degree of similarity between the patient's medical images and
the comparison images. The system identifies a corresponding cohort of
individuals represented by the comparison images whose images are the most
similar to the patient's images based on these similarity metrics. The system
calculates the quantitative status and/or risk metrics for the patient based on
the known health status and/or outcomes for the corresponding cohort.

Amazon Artificial Intelligence Strategy Insights from Patents
Autonomous Drones
US20160196755 illustrates a system for operating the autonomous drones.
The system exploits machine learning techniques to avoid collisions with the
ground or other objects. The system uses various types of sensors
(e.g., imaging, sonar, radar, lidar, infrared or laser) to detect a distance to an
object. Machine learning techniques are used to indicate at what levels
operating parameters (effective thrust of a propeller, speed of the airflow of the
propeller, etc.) are expected to be for a given distance of a drone from an
obstacle. Then, potential proximities to an obstacle can be determined based
on monitored parameter levels during flight.

Amazon Artificial Intelligence Strategy Insights from Patents -2
Artificial Intelligence as a Service
US20150379424 illustrate a system for a customizable, easy-to-use artificial
intelligence as a service (AIaaS) that designed to support large numbers of
users and a wide variety of problem. The AIaaS implements programmatic
interfaces (e.g., APIs) for a variety of operations on several entity types, such
as data sources, statistics, feature processing recipes, models, and aliases.
The programmatic interfaces guide non-expert users to start using machine
learning best practices relatively quickly, without the users having to expend a
lot of time and effort on tuning models, or on learning artificial intelligence
techniques. At the same time, expert users can customize the parameters or
settings they wish to use for various types of machine learning tasks, such as
input record handling, feature processing, model building, execution and
evaluation.

Amazon Artificial Intelligence Strategy Insights from Patents -3
Cyber Security
US20160180078 illustrates a system for providing enhanced information
security. The system authenticate a user using the biometric sensing devices
(e.g., fingerprint authentication). After successful authentication, the system
uses a machine learning classification algorithm within the trusted execution
environment to the user biometric sensor data to identify whether an elevated
risk of malicious attack exists (e.g., whether it is likely that the user
authenticated under duress). The training data for the classification machine
learning algorithm can be based on attack patterns that have already been
observed by many other cyber security systems. If elevated risk exists,
the system triggers a security response (e.g., increasing authentication
requirements).

9. Big Data Innovation Insights from Patents
Big Data Innovation Insights
Since the United States is such a large market for products and services from
nearly all technology innovations, a patent counting for growth in patenting over
a period of times in the US can be a good measuring tool for monitoring the
evolution of technology innovations. Following figure shows the growth trends
for the deep learning technology innovations base on the patent research of
nearly 200 published applications and issued patents by the USPTO as of 3Q
2016.
The figure indicates that active big data innovations started from 2012 and
peaked in 2013. Big data innovations, however, look like decreasing started
from 2014 (since there is usually a time lag between the priority date and
publication date by two to three years, the number of patents in 2015 is not
fully accounted as of 3Q 2016).
Reference
Big Patents Data 3Q 2016

Big Data Innovation Insights -2

Number of patents for an assignee divided by the total number of patents gives
what percentage the assignee contributes to the deep learning technology
innovations. Ranking the assignees by the number of patents is thus an
important part of visualizing the innovations landscape.
Following figure shows the top assignees for the big data innovations.
The top big data innovators include IBM, Fisher-Rosemount Systems, ETRI,
Aerospike, Hitachi, NEC, and Cloudera.

Following figure shows the key big data ecosystem components. The figure
shows that Big Data Analytics/AI Application is the most innovated big data
ecosystem component followed by Distributed Computing/File System and
NoSQL Database.

Big Data Aggregation
US20140297826 illustrates a system for big data aggregation in a sensor
network. The most important part of the big data analytics is collecting data
before storing the data. The Hadoop big data platform supports collecting data
in Hadoop Distributed File System (HDFS).
HDFS is an open source for storing big data dispersedly, that is, a technology
for storing collected data reliably.
The big data aggregation system includes a sensor network which comprises
many sensor nodes connected to each other over a wired/wireless network and
is configured to transfer sensor data generated by each of sensor nodes to a
big data management unit by setting a destination address in the sensor data
as an address of a big data management unit. The big data management unit
configured to distribute and dispersedly store the sensor data based on the
destination address of the sensor data.

Big Data Aggregation -2

Big Data Platform
The Hadoop big data platform is based on the MapReduce framework.
US7650331 describes the MapReduce framework. US20110313973 illustrates
the MapReduce framework including the shuffle function using the DFS.

Big Data Cloud
US20130227569 illustrates the system that can gather data from thousands of
the IoT sensors/devices and analyze the data in the cloud without the massive
amount of investment in the server and big data analytics infrastructure.
The cloud based IoT big data system provides a virtual IoT sensors/devices
cloud as an Infrastructure as a Service (IaaS) and a service cloud as a
Software as a Service (SaaS), to provide a flexible and scalable system.
The IaaS provides flexibility by handling heterogeneous IoT sensors/devices.
The SaaS provides scalability by relieving end users of computational
overheads, and enabling on-demand sharing of IoT sensors/devices data to
requesting end users. The SaaS also relieves end users from specifying IoT
sensors/devices characteristics, locating physical IoT sensors/devices, and
provisioning for the physical IoT sensors/devices. The end user, via a device
(e.g., smartphone), requests and receives services provided by the system.

Big Data Analytics
US20150179079 illustrates a system and for real time monitoring a patient's
cognitive and motor response to a stimulus. The big data analysis of massive
data obtained by the IoT healthcare/medical devices can provide many value-
added healthcare services. US20150186972 illustrate a big data analytics
system for the business IoT applications. The business IoT devices can collect
a large amount of data regarding products, product attributes, prices, and price
attributes. To be understood by a person, this large amount of data and analytic
output must be summarized, personalized, and organized in relevant terms.
The summarization and personalization of such a large and complex set of data
presents challenges in the selection and refinement of information as well as
with respect to identification of patterns and arrangement of information in a
user interface. The big data analytics system provides a user interface to
summarize and personalize a large amount of price and product information, to
identify patterns therein, and to generate recommendations in relation to the
information.

Big Data Monetization
Big data monetization is to generate revenue from available big data products
for use by third parties by instituting the discovery, capture, storage, analysis,
dissemination, and use of the big data.
US 20150089626 illustrate a system for providing a marketplace for big data
applications. The system includes a cloud infrastructure to deploy big data
applications to the target cloud nodes connected to the cloud infrastructure.
The system includes a central control interface to provide a cloud user with
control over the cloud infrastructure. The central control interface deploys,
changes, configures, manipulates, controls, secures, sells, and rents the
applications, data and infrastructure resources to the requesting cloud user.
The system provides big data applications on demand from the cloud user and
installs the demanded application on to the dedicated big data platform for
an online big data processing. The system further includes a web interface to
provide an access to the users.

Big Data Monetization -2
US 20160050279 illustrate a system for operating the IoT (internet of Things)
big data analysis service. The system analyzes the stored data from the IoT
devices in the cloud and produces a big data analysis report. A client can
purchase or sell information on the analyzed big data analysis report through
the virtual big data marketplace. The big data analysis report can include
information on a preliminary analysis report whose results vary according to
analysis time or period. When a third party analyzes big data using the
information on preliminary analysis report, a writer who uploaded the
information on preliminary analysis report to the analysis report marketplace
charges fees for the information.

Big Data Monetization -3
Blockchain technology enables a unified decentralized peer-to-peer trusted
financial network. Trading of big data products through blockchain enables
secure transaction of big data products.
For example, US20140344015 illustrate a big data trading platform for enabling
consumers to monetize their personal data while managing their online privacy.
The platform uses cryptocurrency through blockchain as the means for
monetizing the value of personal data.
US20150379510 illustrate another big data trading platform to monetize big
data using the blockchain infrastructure. The platform matches a producer's
data product with a data buyer's specifications. The platform enables
micropayments and the maintenance of privacy of personal information. The
platform enables fair and transparent marketplace for data producers and data
buyers using the redundant distributed ledgers of transactions on peer to
peer networks.

10. IoT + AI + Big Data Integration Strategy
Smart Home Integration
US20150227118 illustrates the IoT Cloud Big Data AI system for facilitating
automatic control of the smart home devices based on past device behavior,
current device events, sensor data, and server-sourced data. Cloud-based big
data analytics is accessible via a server system for analyzing data associated
with persons or buildings in a geographic region about the building, such as
local news and weather information and data pertaining to appliances within
the geographic region, such as a neighborhood, zip code, and so on.
The analyzed data is used to develop the control rules to control smart home
devices automatically.

Smart Home Integration -2

The automatic control of the smart home devices enable various benefits,
such as triggering lights to automatically turn on when a user enters a particular
room at a particular time; activating a sprinkler system when server-side data
indicates that a fire is nearby; automatically turning on a heater in advance of
a home owner's return at a particular time when the home temperature is below
a predetermined level; turning off a sound system and lights in various rooms
after data indicates that a user is preparing to sleep; turning off lower priority
devices that may conflict with higher priority devices, and so on.

Cloud-based big data analytics can be used to make a prediction about
the future device usage and/or device behavior and/or user behavior exploiting
AI. The device usage and/or device behavior and/or user behavior predictions
can be used to generate control rules. The prediction can be derived by
comparing collected data with a sample table of data to determine whether a
correlation exists between the collected data and data in the sample table of
data. The prediction can be generated based on a correlation between the
collected data and data in the sample table of data. The prediction also can be
based on a frequency of occurrence of an instance of data in the collected data
(and timing information associated with occurrences of the instances of data) to
generate a probability estimate. The probability estimate is employed to
determine the prediction.

Google Self-driving Artificial Intelligence Connected Car
US9275308 illustrates a system that can be installed in an autonomous vehicle
to detect objects. The system obtains the images of surrounding environments
using an integrated camera on board the vehicle. The system uses the deep
learning (neural networks) to detect objects in images.
US20150248131 illustrates a system that enables an autonomous vehicle to
request assistance from a remote operator in certain predetermined situations.
During the operation of the autonomous vehicle, the vehicle senses the
environment of the vehicle. Based on the sensed environmental data,
the autonomous vehicle identifies a situation (e.g., a vehicle or other obstacle
is blocking a lane within a road) from a set of predetermined situations for which
it will request assistance. The vehicle then receives a response back from the
remote assistor through wireless vehicular communications indicating how to
proceed.

Toyota Self-driving Artificial Intelligence Connected Car
Toyota announced its plans to make self-driving cars by around 2020. Toyota
also creates new department to unify its connected car technologies.
US20140005906 illustrates the system predicting the future position and velocity
trajectory of a preceding vehicle. The dynamic state of the preceding vehicle can
be predicted based on data received from surrounding vehicles using the V2V
Communications using AI.
US9031779 illustrates the navigation system with the hazard avoidance feature.
The system navigation allows for vehicles and other entities to collaborate and
share information via vehicular networks regarding hazards, defects, obstacles,
flaws, and other abnormalities that exist in any environment. The system
navigation automatically detects and catalog environmental hazards and/or
obstacles for route planning. Routes can be planned that avoid these hazards
reducing lost time or frustration.

Allstate Connected Car Big Data Monetization
Allstate recently announced its launch of an independent telematics business
Arity. Allstate patent US9269202 provides details regarding the connected car
telematics business that provides data analytics products to third parties.
The Arity telematics system collects vehicle operation data at regular time
intervals. Thus, the collected data is time marked. The data can also be location
marked using the GPS. The vehicle operation data includes data from on board
diagnostic systems and car area network systems (e.g., speed, rate of
acceleration, activation of brakes, degree and duration of steering direction, etc.)
and other monitoring systems (e.g., presence and distance of objects behind or
ahead of the vehicle, driver physiological status, etc.). The collected data
transferred to the remote data processing system. The remote data processing
system creates databases for evaluating driver behavior or operation
characteristics (e.g., aggressiveness, recklessness, compliance with laws, etc.).

Allstate Connected Car Big Data Monetization -2
The database can be correlated or evaluated with other databases for a wide
variety of uses. For example, the data can be cross-correlated to another
information database that is also time or location specific. The time or location
specific data includes applicable local speed laws, weather events, and
traffic signals (e.g., operation of the vehicle without headlights in a rainy day,
driving 80 mph in a 55 mph speed limit zone, etc.).
The processed data can be provided to third parties (e.g., insurance companies, l
ending institutions, car rental companies, product and service marketing
companies, potential employers, etc.) to evaluate an individual's driving
behavioral characteristics in a real life.

Hyundai 5G Connected Car
5G (the fifth generation mobile communications) is scheduled to launch in around
early 2020s. Even if it is not determined yet regarding the standard technology
details, many researchers expect that 5G will transfer 1000 times more data,
and thus, can connect billions of IoT (Internet of Things) devices at the same
time. Key candidate technologies that enable 5G to support IoT devices
connection are millimeter wave communication, massive MIMO
(multiple input and multiple output) technology, cloud RAN/network function
virtualization (NFV)/ software defined network (SDN), ultra dense network (UDN)
and low latency network (a maximum response rate of about 1 ms or less).
5G connected car enables real-time processing of sensor information from
surrounding vehicles for proving autonomous driving. Mass transmission
supported by the 5G can provide the big data services to the vehicle. 5G enables
direct vehicle to vehicle communication using the Device-to-Device (D2D)
connectivity. 5G can support the multihop mesh networks among many vehicles.

Hyundai 5G Connected Car -2
US20160278153 illustrates the connected car exploiting 5G multi-antenna
beamforming technique for transmitting (receiving) sensor data into (from)
a particular region or to (from) a particular vehicle. The sensor data includes
surrounding driving condition information regarding the surrounding area of
the vehicle. The sensor data also can include position and speed information
regarding the surrounding vehicles. Using the surrounding driving condition,
position, and speed information, the dynamic route guidance system can guide
drivers along the traveling route based on the dynamic traffic information.
Using the share accident-associated information, the advanced driver assistant
system can prepare potential collision by controlling the speed of the vehicle
using the advanced smart cruise control.

Vehicle Autonomous Platooning
Vehicular convoying enables vehicles to follow closely behind each other in a
convenient and safe manner. Vehicular convoying reduces the drag caused by
the substantially turbulent airflow at the rear. This drag is responsible for the
majority of fuel used in transportation, especially for heavy trucks
(fuel is about 40% of operating expenses). Vehicular convoying provides
significant fuel savings both for the lead and the trailing vehicles.
US20160054735 illustrates a system for convoying vehicles by coordinating and
controlling vehicles to follow closely behind each other.
Platooning - how it works video: Link
https://www.youtube.com/watch?v=X7vziDnNXEY

Vehicle Autonomous Platooning -2
Vehicle Linking
A long range communication system and a central server provide a
communication link to each of vehicles. The server or an onboard computer
in each vehicle makes decisions and suggestions based on knowledge of
vehicle location, destination, load, weather, traffic conditions, vehicle type etc.
When a linking opportunity exists, the driver is notified via driver display.
The driver can manually adjust the vehicle speed to reduce the distance between
the vehicles, or the system can automatically adjust the speed
(e.g., using Adaptive Cruise Control).
Once the vehicles are sufficiently close together, a short range communications
link for communicating among the candidate vehicles causes the vehicles to
move into a linked position. The control processor in the lead vehicle
communicates its status to the control processor in the trailing vehicle,
and vice versa, to cause the vehicles to move into close proximity to one another.

Vehicle Autonomous Platooning -3
Vehicle Platooning
Vehicle control unit responsive to the short range communications link manages
vehicular functions to safely maintain a linked position among the vehicles.
During the linked, close-following mode, the system controls the engine torque
and braking, with no driver intervention required. Relative distance between the
vehicles is determined by one of RADAR, LIDAR, cameras, and precision
GPS etc. The vehicle control units, in either the lead vehicle or a following
vehicle, receive characteristics of vehicle performance in determining whether
to maintain or discontinue the linking.

Industrial IoT
US20150040051 illustrates the industrial monitoring system that provides
monitoring capabilities for various types of industrial machines and systems.
For example, the industrial monitoring system can monitor operational
parameters of the gas turbine system using a number of sensors.
US20140163763 illustrates the energy management system exploiting the
predictive analytics. Utility companies are experiencing a shortage of electrical
generating capacity due to increasing customer demand for electricity.
The energy management system predicts on-peak hours and/or off-peak hours
based on data obtained from various monitoring devices using the predictive
analytics.
*GE Predix: Cloud platform for the Industrial Internet. It is generally available for
developers, data scientists, and other IT professionals who want to build
powerful industrial-strength apps and achieve unprecedented processing mobility
between the edge and the cloud.

Industrial IoT -2
A cyber-physical system (CPS) is a system that supports efficient control and
determination by interlocking the real world and the virtual world. A CPS is a
hybrid system in which embedded systems are combined based on a network,
and has the characteristics of both continuous elements such as physical
elements and discrete elements such as software elements. CPS applications
include the smart electric grid, smart transportation, smart buildings,
smart medical technologies, air traffic management, and smart manufacturing.
CPSs are the key enabling technologies of the Industrial IoT (IIoT).

Industrial IoT -3
US20160253495 illustrates s system for detecting anomalous CPS behavior
due to cyber and/or physical attacks. Traditional analysis tools are unable to
predict adequately CPS behavior because of the complexity of CPS.
The system collects information regarding the physical system dynamics from
sensors associated with the CPS (e.g., image sensors, depth cameras,
infrared and/or thermal sensors).
The system, then, classifies system behaviors and identifies the anomalous
CPS behavior by detecting a deviation from the identified system behaviors
Using a machine learning technique.

Industrial IoT -4
US20160196527 illustrates the cyber-physical supply chain logistics
transportation system for predictive estimation of QoS across supply chains
using condition monitoring and predictive analytics. The system includes
sensors that monitor conditions associated with cyber-physical system.
Sensors monitor individual cargo units and the transportation environment
within cyber-physical system. Sensors include door latch sensors,
tire pressure sensors, oxygen-level sensors, stress sensors, chemical sensors,
reefer sensors for refrigerated trailers, temperatures sensors.
Sensors are distributed across several locations within cyber-physical system
or embedded within a product itself. Sensors generate sensor data based on
monitored conditions and transmit the sensor data to quality management
system via wireless network. The quality management system processes the
collected data to predict QoS parameter values and recommend pertinent
actions.

References
Smart Home Patents Data 3Q 2016:
http://www.techipm.com/alexdb/IoT%20Smart%20Home%20Patents%20Data%203
Q%202016_Intro.pdf
Connected Car Patents Data 3Q 2016:
http://www.techipm.com/alexdb/IoT%20Connected%20Car%20Patents%20Data%2
03Q%202016_Intro.pdf
Connected Health Patents Data 2Q 2016:
http://www.techipm.com/alexdb/IoT%20Connected%20Health%20Patents%20Data
%202Q%202016_Intro.pdf
AI Deep Learning Patents Data 3Q 2016:
http://www.techipm.com/alexdb/AI%20Deep%20Learning%20Patents%20Data%20
3Q%202016_Intro.pdf
Big Data Patents Data 3Q 2016:
http://www.techipm.com/alexdb/Big%20Data%20Patents%20Data%203Q%202016_I
ntro.pdf
LTE Patents for Standards Data 2Q 2016:
http://www.techipm.com/alexdb/LTE%20Patents%20for%20Standards%20Data%20
2Q%202016_Intro.pdf

Thank you!
If you have any questions
please contact Alex G. Lee
at alexglee@techipm.com

IoT + AI + Big Data Integration Strategy Insights from Patents 3Q 2016

More Related Content

What's hot

Viewers also liked

Similar to IoT + AI + Big Data Integration Strategy Insights from Patents 3Q 2016

More from Alex G. Lee, Ph.D. Esq. CLP

Recently uploaded

IoT + AI + Big Data Integration Strategy Insights from Patents 3Q 2016