AI Blockchain IoT Convergence for ESG Digital Transformation Insights from Patents

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©2021 TechIPm, LLC All Rights Reserved http://www.techipm.com/

AI, Blockchain, IoT Convergence for ESG Digital Transformation Insights from Patents
Alex G. Lee1
Contents
I. AI, Blockchain, IoT, and Their Convergence Technology Innovation Status
II. ESG Digital Transformation Innovation Status
III. AI, Blockchain, IoT, and Their Convergence for ESG Digital Innovation Use Case Examples
1. Use Case for Reducing Carbon Footprints (AI + IoT)
2. Use Case for Increasing Renewable Energy Use (AI + Blockchain)
3. Use Case for Waste Management (AI +Blockchain+IoT)
4. Use Case for Workplace/Workforce Management in Social-Human Care (AI+IoT)
5. Use Case for Cybersecurity & Privacy in Social-Business Relationship Management (AI +Blockchain+IoT)
6. Use Case for Corporate Governance (Blockchain)

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Alex G. Lee, Ph.D/Patent Attorney, is a principal consultant at TechIPm, LLC.

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Patents are a good information resource for obtaining the state of the art of technology innovation insights. Patents
that specifically describe the major technology filed of a specific technology innovation are a good indicator of the
technology innovation status in a specific innovation entity. A patent counting for growth in patenting over a
period of times can be a good measuring tool for monitoring the evolution of technology innovation.
To find AI, blockchain, IoT, and their convergence technology innovation status, patent applications in the USPTO,
EPO, IPO during the period of January 1, 2010 - September 30, 2020 in priority date (technology innovation date)
that specifically describe the major AI, blockchain, IoT, and their convergence technologies are searched and
reviewed. Total of 48,000, 17,000, 32,000, 1,288 published patent applications that are related to the key AI,
blockchain, IoT, and their convergence technology innovation respectively are selected for detail analysis. Figure 1
summarizes our patent analysis results.
Figure 1 (1) shows the top 100 AI, blockchain, and IoT technology innovation entities respectively selected by the
total number of published patent applications. The top 100 AI technology innovation entities represent 23,593
patent applications. The top 10 AI innovation leaders are IBM, Google, Microsoft, Samsung Electronics, Intel,
Siemens, Facebook, Philips, GE, and Accenture. The top 100 blockchain technology innovation entities represent
7,809 patent applications. The top 10 blockchain innovation leaders are Alibaba Group, IBM, nChain, Mastercard,
Walmart, Bank of America, Visa, Microsoft, Intel, and Accenture. The top 100 IoT technology innovation entities

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represent 18,786 patent applications. The top 10 IoT innovation leaders are Qualcomm, Ericsson, LG Electronics,
Samsung Electronics, Intel, Ford, IBM, Huawei, GM, and Toyota.
Figure 2 (2) shows the AI, blockchain, and IoT patenting activities with respect to the technology innovation date
(priority year) respectively. The AI patent application activity chart indicates that the AI technology innovation
activity started in a rapid growth stage from 2016. Since there is usually a time lag between the initial application
date (priority year) and the publication date by around two years, the AI patent application activity chart indicates
that the AI technology innovation activity is still in a growth stage. The blockchain patent application activity chart
indicates that the Blockchain technology innovation activity started in a rapid growth stage from 2016. The
Blockchain patent application activity chart indicates that the Blockchain technology innovation activity is still in a
growth stage. The IoT patent application activity chart indicates that the IoT technology innovation activity started
in a growth stage already before 2010. The IoT patent application activity chart indicates that the IoT technology
innovation activity became matured in 2018.
Figure 1 (3) shows the convergence status among AI, blockchain, and IoT, the top 10 AI, blockchain, and IoT
convergence technology innovation entities, and the AI, blockchain, IoT convergence patenting activities with
respect to the technology innovation date (priority year) respectively. The key AI, Blockchain, IoT convergence
innovation entities are IBM, Strong Force IP, Intel, Accenture , Microsoft , Bank of America , Bao Tran, Capital
One Services, LG Electronics, Cisco, Ericsson, Samsung Electronics, HP, nChain. Nokia, Inmentis, LLC,

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Salesforce, Tata Consultancy Services, Siemens, and T-Mobile. AI+IoT convergence is the most innovated AI,
Blockchain, IoT convergence technology followed by AI+Blockchain, Blockchain+IoT, and AI+Blockchain+IoT.
Patent application activity chart indicates that the AI, Blockchain, IoT convergence technology innovation activity
is in a rapid growth stage.

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II. ESG Digital Transformation Innovation Status
Two key words for the post COVID-19 pandemic economic recovery will be the ESG (Environmental, Social, and
Governance) management and digital transformation acceleration. It is also expected that three core technologies in
the data-centric digital economy - AI, Blockchain, IoT and their convergence - will support a long-term sustainable
economic system development.
To find AI, blockchain, IoT, AI, Blockchain, IoT and their convergence innovation status that enable the ESG
digital transformation, patent applications in the USPTO/EPO/IPO during the period of January 1, 2010 -
September 30, 2020 in priority date are searched and reviewed, which that specifically describe the ESG related AI,
Blockchain, IoT technologies - the most important 10 sub-fields of ESG digital transformation: E: environmental
sustainability, natural resources, climate change, pollution, waste; S: social responsibility, human care, business
relationship; G: corporate governance, code of conduct. 625 published patent applications that are related to the
ESG digital transformation innovation are selected for detail analysis.
Figure 2 shows the distribution of AI, Blockchain, IoT patent applications that are related to the ESG digital
transformation innovation. E-climate change and S-business relationship (includes cybersecurity & privacy) are the
most innovated sub sub-fields of ESG digital transformation.

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Figure 2. ESG Digital Transformation related AI, blockchain, IoT patent analysis results

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III. AI, Blockchain, IoT, and Their Convergence for ESG Digital Innovation Use Case Examples
1. Use Case for Reducing Carbon Footprints (AI + IoT)

An excellent way to reduce emissions of GHGs (green house gas) is to reduce of energy consumption. Buildings
and their construction together account for 36 percent of global energy use and 39 percent of energy-related carbon
dioxide emissions annually, according to the United Nations Environment Program. Especially, HVAC (heating,
ventilating, and air conditioning) systems account for more than half the energy consumed in buildings. AI can
help to reduce energy consumption in HVAC systems by forecasting temperatures needed across the systems,
improved control with the aim of achieving those temperatures, and fault detection.
US10094586 illustrates a building control system for optimizing energy use and thermal comfort for a building.
Heating and cooling account for most of a building's energy consumption and typically are the most expensive
items with respect to building energy costs as electrical power for heating and cooling is often purchased at peak
rates. While the cost of heating and cooling buildings is increasing, the thermal comfort of building occupants
remains an important concern as such comfort supports occupants' productivity, health, and is related to optimal
operating conditions for buildings.

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The building control system includes IoT sensors to measure temperatures in a thermal zone within a building. The
building control system determines a forecast ambient temperature value for an external surface of the building
proximate the thermal zone. The building control system sets a desired temperature range for the thermal zone. The
desired temperature range and the forecast ambient temperature value are used as inputs to a AI predictive model
that is trained using respective historical measured value data. Using the predictive model, the building control
system determines set points for the HVAC system associated with the thermal zone that minimize energy use by
the building. Finally, the building control system controlls the HVAC system with the set points to maintain an
actual temperature value of the thermal zone within the desired temperature range for the thermal zone. Actually,
DeepMind reduced energy used for cooling in Google data centers by 40% using developed AI-powered energy
saving recommendation system.

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2. Use Case for Increasing Renewable Energy Use (AI + Blockchain)
Renewable energy includes zero-carbon energy sources that their energy is derived from sources that can naturally
replenish themselves (e.g., wind and sun). US20190288513 illustrates a decentralized energy management system
utilizing blockchain technology. Renewable energy vendors and utility industries intensively are trying to explore
the field of peer-to-peer (P2P) energy, which is an idea that power generation and consumption can be fully
decentralized so that the customers with distributed generation capability could make the most use of their
distributed and renewable energy resources without any centralized control authorities.
The decentralized energy management system utilizes a smart contract in a blockchain. The decentralized energy
management system calculates power balancing at nodes. The decentralized energy management system lists a set
of strategies in the smart contract about an amount of available energy at the nodes. The decentralized energy
management system uses AI to predict a present energy demand needed by nodes. The decentralized energy
management system generates an optimal strategy from the set of strategies in the smart contract with a Markov
representation model. Finally, The decentralized energy management system transfers the available energy from
one of nodes to another nodes in need of power according the optimal strategy.

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3. Use Case for Waste Management (AI +Blockchain+IoT)
According to a study by the World Bank, 98% of the world’s waste is sent to landfills, dumped into oceans or
being incinerated. Garbage and liquid waste that end up in water bodies negatively change the chemical
composition of the water. This also causes water pollution and it affects all ecosystems existing in and around
water, including fish and other animals that drink the polluted water. Hazardous household waste items such as
batteries, computer equipment and leftover paints can be particularly dangerous for water reservoirs used for
drinking water.
AI, blockchain, and IoT can be used to help to track waste in real-time and engage users in waste recycling.
Recycling is the process of collecting and processing materials that would otherwise be thrown away as trash, and
turning them into new products. Recycling has benefits for communities and for the environment, since it reduces
the amount of waste sent to landfills and incinerators, conserves natural resources such as timber, water, and
minerals, increases economic security by tapping a domestic source of materials, prevents pollution by reducing
the need to collect new raw materials, and saves energy.
US20180243800 illustrates a material sorting system sorts materials utilizing a computer vision system with IoT
sensors using a machine learning in order to identify or classify each of the materials, which are then sorted into
separate groups based on such an identification or classification. The system enables a high throughput cost-

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effective automated sorting platform that sorts highly mixed waste and generates higher quality feedstocks (lower
levels of trace contaminants) for subsequent processing.
US20210012605 illustrates a blockchain based waste recycle system that provides compensation to participants in
recycling activities. The waste recycle system tracks using IoT sensors and analyzes human behavior using AI and
provides rewards recycling behaviors for improving their recycling habits.

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4. Use Case for Workplace/Workforce Management in Social-Human Care (AI+IoT)
Workplace accidents have been responsible for numerous injuries and fatalities. Additionally, workplace accidents
have resulted in significant property damage. Substantial losses in productivity have also occurred as a result of
worker and equipment downtime due to workplace accidents. US10204501 illustrates a system providing
predictive alerts for workplace safety. IoT sensors provide environmental measurements associated with a
workplace and physiological measurements associated with a worker. The system generates a safety score for the
worker based on the environmental measurements and the physiological measurements using machine learning.
The system generates safety-related alerts based on the safety score.
US20200160252 illustrates an AI human resource allocation adviser. The AI adviser an effective and efficient
allocation of human resources, e.g., the matching of employees' skill sets to the needs and requirements of
particular tasks and projects to be performed for business with employs who are highly skilled and/or specialized
personnel, such as engineers, technicians, or other professionals.

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5. Use Case for Cybersecurity & Privacy in Social-Business Relationship Management (AI +Blockchain+IoT)
Cyber security and customer data protection/privacy are the top ESG concern for institutional investors.
US20180285740 illustrates a deep learning application for detection of malicious code. Malicious code, like all
code, is a form of language having concepts such as grammar and context, but has its own idiosyncrasies built in.
Thus, the Natural Language Processing (NLP) of deep learning technology can be used to detect malicious code as
a language processing problem.
Industrial IoT control systems that operate the industrial asset systems (e.g., power turbines, jet engines,
locomotives, autonomous vehicles, etc.) are vulnerable to threats, such as cyber-attacks (e.g., associated with a
computer virus, malicious software, etc.). Current methods primarily consider threat detection in Information
Technology (such as, computers that store, retrieve, transmit, manipulate data) and Operation Technology (such as
direct monitoring devices and communication bus interfaces). However, cyber-threats can still penetrate through
these protection layers and reach the physical domain of the industrial asset systems as seen in 2010 with the
Stuxnet attack2
. Such attacks can diminish the performance of an industrial asset and can cause a total shut down.
US20180159879 illustrates a desirable method to protect an industrial asset from such cyber-attacks in an
automatic, rapid, and accurate manner using autoencoder deep learning application.

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https://www.mcafee.com/enterprise/en-us/security-awareness/ransomware/what-is-stuxnet.html

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WO2019086553 illustrates a blockchain based privacy management system. The privacy management system
manages customer personal data separated from some other data silos using a smart contract implemented on a
blockchain. Personal identifiable data can comprise true identity information and/or Know Your Customer data
compliant with banking regulation. Data silos can comprise anonymous and/or anonymized and/or pseudonymized
and/or de- identified data. Data silos can be actively partitioned into datasets associated with discrete levels of
privacy breach risks. The partitioning between datasets can use mechanisms comprising in particular multi-party
computation, homomorphic encryption, k-anonymity, or differential privacy. Asymmetric encryption can be used,
along format- preserving encryption.

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6. Use Case for Corporate Governance (Blockchain)
Blockchain enables a proxy voting system which is both secure and transparent. The system blockchain based
voting system can automatically allow or disallow voting privileges for members based on what voting rights they
had within a particular organization. US20190371106 illustrates a voting system for collecting votes from multiple
voters. The voters are associated with multiple identity public keys that each identifies one voter. The system
comprises nodes to collect the votes and to combine the votes into a vote container and store the vote container on
a public data store. Each node communicates voting public keys between the nodes by using cryptography to
remove an association between the voting public keys and the identity public keys to create a list of anonymized
voting public keys. Each node, after the association between the voting public keys and the identity public keys is
removed, communicates votes, authenticated by the anonymized voting public keys, by using cryptography to
remove an association between the votes and the voting public keys to create anonymized votes and combine the
anonymized votes into the vote container.

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AI Blockchain IoT Convergence for ESG Digital Transformation Webinar
Two key words for the post COVID-19 pandemic economic recovery will be the ESG (Environmental, Social, and
Governance) management and digital transformation acceleration. It is also expected that three core technologies in
the data-centric digital economy - AI, Blockchain, IoT and their convergence - will support a long-term sustainable
economic system development. Combining the two key words and three core technologies, Alex G. Lee, Ph.D.,
Esq. (https://www.linkedin.com/in/alexgeunholee/) will present how the AI, Blockchain, IoT and their convergence
will enable the ESG digital transformation.
Alex will present the state of the art innovations of AI, Blockchain, IoT and their convergence, demos of specific
implementations of the convergence systems, and use cases for the ESG digital transformation.
Specifically, based on AI, Blockchain, IoT related patents and ESG related reports analysis, Alex will present
specific technical details about the use cases in the most important 10 sub-fields of ESG digital transformation -
E: environmental sustainability, natural resources, climate change, pollution, waste; S: social responsibility, human
care, business relationship; G: corporate governance, code of conduct.
Alex will also present the industry-wise ESG performance maps, which can be used with the ESG digital
transformation analysis in selecting best-in-class portfolio companies for ESG investment.

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Place: Online Zoom meeting
Time: May 20, 2021 7 pm – 9.30 pm Eastern Time/EDT (US and Canada)
May 20, 2021 4 pm – 6.30 pm Pacific Time/PST (US and Canada)
May 21, 2021 8 am – 10.30 am GMT+9 Time Zone (S. Korea and Japan)
Registration: https://www.eventbrite.com/e/ai-blockchain-iot-convergence-for-esg-digital-transformation-webinar-
tickets-
153378221389?utm_source=eventbrite&utm_medium=email&utm_campaign=post_publish&utm_content=shortLi
nkNewEmail

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AI Blockchain IoT Convergence for ESG Digital Transformation Insights from Patents

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