IoT + Big Data + Cloud + AI Integration Strategy Insights from Patents

©2016 TechIPm, LLC All Rights Reserved www.techipm.com
IoT + Big Data + Cloud +AI
IntegrationStrategy :
Patent Insights
2Q 2016
Alex G. Lee (alexglee@techipm.com)

©2016 TechIPm, LLC All Rights Reserved www.techipm.com 2
1. IoT Business Reality ………………………………………………………………... 3
2. IoT Business Model ………..………………………………………………………... 5
3. IoT Business Ecosystem …..…………………………………………………….... 8
4. IoT Frontiers Insights from Patents ……………………. ………….………... 10
5. IoT Strategy Perspectives from Patents …................……………………... 22
6. IoT Innovation Exploiting Patents …………………..…….…………………... 26
7. IoT Patent Strategy …………..…………………..……………….………………... 41
8. IoT Startup Patent Strategy …….……………..……………….………………... 57
9. IoT + Big Data + Cloud + AI Integration Strategy ………………………... 65
Contents

1. IoT Business Reality
Goldman Sachs defines IoT (Internet of Things) as the third wave of internet
revolution: by connecting to the internet billions of devices, IoT opens up a
host of new business opportunities and challenges.
According to McKinsey, IoT has the potential to create up to $6 trillion
economic value annually by 2025.
IoT has various applications including, smart home, connected car, connected
healthcare, industry 4.0, and smart city.
Many players across diverse industries including semiconductor, consumer
electronics, IT, telecommunication, healthcare, retail, industrial & manufacturing
and transportation are participated in the IoT business.

1. IoT Business Reality -2
According to Research and Markets, there are more than 2000 companies
that are selling IoT enabled products, playing a vital part in the IoT
technology infrastructure, or act as an enabler to the IoT development.
A few major companies will not dominate the growth of the IoT market.
Rather, virtually all of business ecosystem players will involved in creating and
developing the IoT market.
The IoT business will be interdisciplinary across many industries.
The IoT business will have synergies by the convergence of many different
technologies or business attributes. The IoT business can create entirely new
industries providing dramatically different values to the customers.
Convergence

2. IoT Business Model
A business model is the way in which a company makes money through its
business. The key building blocks of a business model are the value
propositions, profit model, and business resources.
The key building blocks of business models for the IoT applications can be
summarized as follows.
A. Business Resources
• IoT devices: Devices that can sense/recognize their surrounding
environments (including position/movement/identification) and communicate
with other devices
• Networks: Connecting/communicating medium/infrastructure that can
interconnect devices and connect devices to the internet
• IT systems: Back-end or embedded 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

4. IoT Business Model -2
B. Value Propositions
• Automation: Providing various automation services for the customers’ job
to be done (e.g., order the out of stock product automatically; self-driving car)
• Care: Caring for customers (e.g., monitor person’ s body status and provide
various health care services; control customer’s home cooling and heating/
lighting system for energy savings)
• Business/Industry Intelligence: Providing better working/production
tools/environments (e.g., analyze customers’ shopping behavior for proactive
promotion/advertisement; industrial assets management)
• Entertainment: Providing fun to customers (e.g., augmented reality games)

4. IoT Business Model -3
C. Profit Model
• Device/IT system sales with value added services
• Subscription fees
• After-market sales (e.g. part sales)
• Third-party sales (e.g. advertisement)
Combing the key building blocks, various business models for the IoT
applications can be developed.

3. IoT Business Ecosystem
A business ecosystem is the community of business entities that is formed by
the competitive and collaborative interactions among business entities for
new innovations. A business ecosystem evolves to form a new value network,
and thus, to create a new market.
The key building blocks of a business ecosystem are the platform providers
and value creators. A platform provider develops a platform, which is a
collection of core technologies/solutions with open interfaces for interaction
with the value creators. A value creator creates values by exploiting the
platform.
Recently, many global leaders in IoT business provide the IoT platforms to
create diverse platform-based business models and form the IoT business
Ecosystem (e.g., Samsung Artic platform).

3. IoT Business Ecosystem -2
The cluster of a platform will be complex because of participation of diverse
ecosystem players across several different industries. Following figure
illustrates the clustering of a platform with value creators across the smart
home and connected car industry.
Smart Home
Utility
Platform Platform
Connected Car
Lighting
Appliance
Environment
Security
Infortainment
Safty
Control
Automation
IoT Ecosystem

4. IoT Frontiers Insights from Patents
Smart Home Security
US20150120598 illustrates the smart home system for tracking delivery of a
package by a delivery service-provider entity. The smart home system detects
delivery data associated with the delivery of the package to the smart home. The
detected delivery data comprises information indicative of the identity of the
deliverer and the package at a particular time, and an action of the deliverer and
the particular receiver of the package.
The smart home system also can receive and retain package in a secure location
that is trusted by both deliverer and a system user. For example, a certain secure
area of environment can be temporarily made accessibly by the system to
deliverer such that deliverer can deposit package within the secure area for later
retrieval by the intended recipient. The secure area can be a lobby secured by a
smart doorknob. The system unlocks the lobby door for granting deliverer
temporary access to that lobby for depositing package.

4. IoT Frontiers Insights from Patents -2
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.

Ambient Intelligence for Smart Living
Ambient intelligence (AmI) is the smart environments that the IoT devices
support people in carrying out their everyday life activities.
US20150057808 illustrates AmI for smart home applications. The smart home
AmI discovers and recognizes patterns using sensors in resident behavior and
generates adaptive automation rules for controlling the IoT devices based on
the patterns and exploiting the machine learning (machine learning refers to
intelligent systems that can learn from data, rather than merely follow explicit
programmed instructions).
US20140108307 illustrates AmI for the connected car applications. Base on the
profile and contextual information, AmI provides suggestions to the driver
exploiting the machine learning. For example, AmI obtains contextual information
indicating that a contact of the driver is only a few minutes in driving time from
the driver’s route.

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.

AI: Deep Learning
Deep learning is a machine learning method that attempts to learn layered
models of inputs that mimics the human brain’s reasoning process. 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).
US20150294422 illustrates the deep learning application for dynamic risk
management in the IoT connected cars. The deep learning correlates the
driver's usage patterns, driving styles, weather conditions, traffic or interaction
with the vehicle based on a priori knowledge of how normalized driver data sets
behave to predict potential driving risk dynamically. Real time data are provided
using the vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V)
communications.

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.

Adaptable Autonomous IoT
US20150019714 illustrates the adaptable autonomous smart home system.
The adaptable 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.

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.

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.

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.

5. 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.

5. Strategy Perspectives from Patents -2
Toyota
Toyota announced its plans to make self-driving cars by around 2020. Toyota
also creates new department to unify its connected car technologies. Followings
show the insights regarding Toyota strategy perspectives for developing
self-driving connected car of the future to make Toyota as self-driving connected
car innovation leader.
Driver Assistance/Collision Avoidance
US20140005906 illustrates the system for assisting the human or electronic
driver of a host vehicle through 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.
Autonomous Navigation
US9031779 illustrates the navigation system with the hazard avoidance feature.

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).

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.

6. 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.

6. IoT Innovation Exploiting Patents -2
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.

Blue Ocean Differentiation
The basic principle in the Blue Ocean Patent Strategy is to exploit existing
patents to achieve the value innovation, and thus, to serve the customers
in fundamentally different ways.

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.

• 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
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.

2020 IoT Scenarios
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.

2020 IoT Scenarios
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.

2020 IoT Scenarios
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.

2020 IoT Scenarios
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.

7. IoT Patent strategy
Integrated IoT Patent Strategy

7. IoT Patent Strategy -2
Patent Development Exploiting Existing Patents
Well-tailored value creation methodology combined with patent prosecution
strategy can develop new patent portfolios for maximum ROI from existing
patents. Many patent applications and newly issued patents can be further
developed for creating new values by amending and creating claims and
generating family patents to encompass emerging technology and
products/services trends, market demands, and strategic exploitation goals
(e.g., cross-licensing, monetization etc.).
Even if the 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, there may be a large number
of patents (that were not intended for the specific IoT applications at the time of
invention) that can be exploited for developing new IoT patents supporting
current and future IoT business goals.

Patent Monetization Exploiting Existing Patents
A strategically packaged patent portfolio is the collection of patents such that the
integrated value propositions of each patent of the portfolio is the value
propositions that are provided by new products/services. The strategically
packaged patent portfolio can be used for monetization through patent sale,
patent licensing, commercialization, spin-off, patent banking, and financing.
Strategically packaged patent portfolio development in alignment with the
specific IoT business interests (e.g., smart home automation) requires deep
understating of the IoT technologies, insights for the emerging
IoT products/services/business models, and extensive experiences in
patent analysis and development.

Patent Monetization New Method
Zetta Research Development LLC is an intellectual property management
company that acquires, develops and sells patent portfolios from patent holders
(startups, inventors, investors and lenders). Zetta finds patent holders that have
potentially valuable patents and helps patent holders to develop and sell the
patent portfolios that will be valuable for a specific emerging business.
Patents are assigned into a special purpose holding entity. Zetta’s equity-based
investment exploits the fund it manages. Rather than relying on the litigation
enforcement, Zetta seeks to sell the portfolios to the strategic buyers at the
time when market conditions are most advantageous.

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).

IoT Patent Wars 2020 Scenarios

Preparation & Prosecution 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.

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.

8. 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

8. IoT Startup Patent Strategy -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
Neura (http://www.theneura.com/) provides the intelligence IoT platform and
human-centered smart home application solutions. Neura patent application
US20150019714 illustrates the adaptable autonomous smart home system.
Allure Energy (https://www.allure-energy.com/) is a smart home energy
management company based in Austin, Texas. Allure Energy develops leading-
edge smart energy management solutions for many IoT applications.
Allure Energy holds 22 issued US patents. Significant number of forward
citations shows the high quality of the family patents. Especially, many patents
of most key industry players including Google (Nest Lab), GE and Honeywell in
the smart energy management heavily cited Allure Energy patents.

Ecofactor (http://www.ecofactor.com/) provides automated energy savings,
comfort and control through energy efficiency, demand response and HVAC
performance monitoring services. Ecofactor holds 26 issued US patents
including one design patent regarding thermostat. In assessment of the
commercial importance (e.g., potential commercial implementation or licensing
potential), Ecofactor patents ranked second following Samsung Electronics
among top 10 innovators in the smart home patent landscape research.
Pie Digital (http://piedigital.com/) provides cloud-based services for smart home
applications. Pie Digital patent application US20140201340 illustrates the smart
home system that can dramatically simplify and automate the configuration,
maintenance and integration of the IoT devices in the home and office.

Bobo Analytics (http://www.mybobo.com) is a connected healthcare technology
and data analytics company for performance optimization. Bobo Analytics patent
application US20140073486 illustrates the physiological measurement systems
for continuous health and fitness monitoring. A lightweight wearable system is
provided to collect various physiological data continuously from a wearer without
the need for a chest strap. The system also enables monitoring of the
physiological parameters in addition to heart rate including body temperature,
heart rate variability, motion, sleep, stress, fitness level, recovery level, effect of
a workout routine on health, caloric expenditure. The system enables automatic
interpretation of the physiological parameters to assess the cardiovascular
intensity experienced by a user.

Hello (https://hello.is) builds hardware and software to help people live better
through understanding themselves and the world around them. Founded in 2012
and based in San Francisco, California. Hello is a team of designers, engineers,
and operations staff. US20140249760 illustrates the system including wearable
or portable monitoring device that can detect user's activities, behaviors,
habit information, and health. The monitoring system determines user's melanin
level.
INRIX (http://inrix.com/) analyzes traffic and movement data from 100’s of data
sources including the most robust crowd-sourced network of 175 million
vehicles and devices. US20150035666 illustrates the system for monitoring the
user driving behavior in various driving contexts, and presenting driving
suggestions of alternative driving behaviors. The presentation of the alternative
driving behaviors to the user can facilitate changes in user driving behavior that
improve the safety, efficiency, and/or comfort of the driving experience.

Zscaler (https://www.zscaler.com/) is a global cloud-based information security
company. Zscaler patent application US20140208426 illustrates the system for
dynamic cloud-based malware behavior analysis. The cloud-based behavioral
analysis system includes nodes that communicatively coupled to the users.
The nodes perform inline monitoring and malware detection and preclusion.
EVRYTHNG (https://evrythng.com/) provides the IoT cloud platform that
connects any consumer product to the Web and manages real-time data to
drive applications. US20140181256 illustrates the system that can compute the
object identifier and contextual information based on pre-defined set of rules
and map the object identifier and contextual information to an entry point
associated with a specific computer application. The system provides access to
the specific the application to the end-user through the cloud services.

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

9. IoT + Big Data + Cloud + AI Strategy
Frontiers Insights
IoT Big Data Aggregation
US20140297826 illustrates a system for big data aggregation in a sensor
network. The most important part of the Internet of Things (IoT) 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.

9. IoT + Big Data + Cloud + AI Strategy -2
Frontiers Insights -2

IoT 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.

IoT 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.

IoT 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.

Artificial Intelligence for IoT
US20150039105 illustrates the smart home intelligence system to fulfill the
special needs of each family member exploiting AI. US20140073486
illustrates a heart rate monitoring system by providing the best type of sensor to
use at a given time determined by AI based on the level of motion (e.g., via an
accelerometer) and whether the user is asleep (e.g., based on movement input,
skin temperature and heart rate). US20140108307 illustrates the AI exploitation
in the connected car applications. Base on the profile information and/or
contextual information, AI system provides suggestions to the driver.
US20140340236 illustrates the AI application for securing the distributed power
distribution networks in the IoT smart grids.

IoT+ Big Data + Cloud + AI 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.

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 also 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.

Smart City

Industry 4.0
According to Industrial Internet Insights Research Report from GE and
Accenture, the Industrial Internet — the combination of Cloud Big Data, AI with
the Internet of Things — will produce huge opportunities for
companies in Aviation, Oil and Gas, Transportation, Power Generation and
Distribution, Manufacturing, Healthcare and Mining industries.
Cloud, Big Data, AI with the Internet of Things will help manufacturing sector to
design new business models and facilitate cost effective operation and
supply chain solutions.
*e.g., Smart Factory

IoT + Xanadu Big Data Cloud + AI: K-Strategy
IoT + Xanadu Big Data Cloud + AI Korea National Platform
Development
Proposal for new venture startups creation, new job creation (big data
application scientists and engineers), and new competitive advantage
establishment: Support K-Smart City & K-Industry 4.0 projects
• Xanadu is the most advanced big data platform technology.
• Xanadu enables the ACID-compliant NoSQL write-only (archiving) key-value
-time database.
• Xanadu provides a highly scalable, available, fault tolerant, de-duplicated,
immortal, and high performance big data store.
• Xanadu exploits the Distributed File System (DFS) technology using
commodity servers and storage devices.
• Xanadu enables database with built-in massively parallel big data
processing exploiting the DFS (e.g., Hadoop MapReduce).
• Xanadu enables Javascript-based complex integrated parallel queries.

References
Collection of IoT Articles:
https://www.linkedin.com/today/author/alexgeunholee

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

IoT + Big Data + Cloud + AI Integration Strategy Insights from Patents

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