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AI, Blockchain, IoT Convergence Insights from Patents1
Alex G. Lee2
Executive Summary3
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.
To find AI, Blockchain, IoT technology innovation status, patent applications in the
USPTO, EPO, IPO during the period of January 1, 2010 - September 30, 2020 in priority date
that specifically describe the major AI, Blockchain, IoT technologies are searched and reviewed.
48,000, 17,000, 32,000 published patent applications that are related to the key AI, Blockchain,
IoT technology innovation respectively are selected for detail analysis.
Top 100 AI, Blockchain, IoT technology innovation entities are selected based on their
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
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.
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. AI, Blockchain, IoT patenting activities
with respect to the technology innovation date (priority year) are analyzed.
1
This research was supported in part by the WEF Global Partnership Program for the Fourth Industrial Revolution
through KAIST KPC4IR funded by the Ministry of Science and ICT of S. Korea.
2
Alex G. Lee, Ph.D/Patent Attorney, is a principal consultant at TechIPm, LLC.
3
Final Report-11/26/2020

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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 rapid growth stage already before 2010. The IoT patent application activity
chart indicates that the IoT technology innovation activity became matured in 2018.
A patent counting for can be a good measuring tool for technology innovation status of a specific
country. Landscape of the AI, Blockchain, IoT technology innovation entities with respect to
country of H.Q. location are presented. Top 10 AI technology innovation countries are USA,
Japan, S. Korea, China, Germany, UK, Canada, Ireland, Netherlands, and India. Top 10
Blockchain technology innovation countries are USA, China, Japan, UK, Germany, Canada,
Antigua and Barbuda, S. Korea, Switzerland, and Ireland. Top 10 IoT technology innovation
countries are USA, S. Korea, Japan, Sweden, China, Germany, France, UK, Israel, and Canada.
Patents are a good information resource for obtaining the state of the art of technology
innovation insights in a specific technology filed. Technology innovation status in deep
reinforcement learning, deep learning for autonomous vehicle, deep learning applications for 5G,
deep learning applications for cybersecurity, blockchain privacy, blockchain interoperability,
blockchain decentralized identifier, and blockchain tokenization are presented.
To find AI, Blockchain, IoT convergence technology innovation status, patent applications in the
USPTO, EPO, IPO during the period of January 1, 2010 - September 30, 2020 in priority date
that specifically describe the major AI, Blockchain, IoT convergence technologies are searched
and reviewed. 1,288 published patent applications that are related to the key AI, Blockchain, IoT
convergence technology innovation respectively are selected for detail analysis. Top 100 AI,

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Blockchain, IoT convergence technology innovation entities are selected based on their number
of published patent applications.
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, 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.
Top 10 AI technology innovation countries are USA, India, S. Korea, Germany, Canada,
UK, Ireland, Japan, Singapore, and Israel.
The major AI, Blockchain, IoT convergence patent applications with respect to the field
of applications are Digital Infrastructure, Cybersecurity/Fraud Detection, Consumer Electronics,
Banking/Financial Transaction, Entertainment/Game, Automotive/Transportation,
Industrial/Manufacturing, Supply Chain Management, Environment, Government/Public Service,
Healthcare, Retail, Agriculture, Augmented Reality, Business Intelligence, International Trade,
Office Equipment, Real Estate Marketplace, and Telecommunications.
Patent information can provide many valuable insights that can be exploited for developing and
implementing new technologies. Patents can also be exploited to identify new product/service
development opportunities. Patents regarding various AI, Blockchain, IoT convergence
technology implementations/products/services are presented: Privacy-preserving Blockchain-
based AI Data/Model Marketplace; Blockchain-based Decentralized Machine Learning Platform;
Blockchain-based Secure Telehealth-diagnostic System; Peer-to-Peer Micro-Loan Transaction
System; Predictive Maintenance Platform for Industrial Machine using Industrial IoT;
Autonomous Vehicle V2V Communication Management to Facilitate Operational Safety;
Blockchain Augmented IoT System for Dynamic Supply Chain Tracking; Decentralized Energy
Management Utilizing Blockchain Technology; Provisioning Edge Devices in Mobile Carrier
Network as Compute Nodes in Blockchain Network; Distributed Handoff-related Processing for
Wireless Networks.

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I. AI, Blockchain, IoT Technology Innovation Status
1. Technology Innovation Status in Innovation Entity
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.
To find AI, Blockchain, IoT technology innovation status, patent applications in the USPTO,
EPO, IPO during the period of January 1, 2010 - September 30, 2020 in priority date that
specifically describe the major AI, Blockchain, IoT technologies are searched and reviewed.
48,000, 17,000, 32,000 published patent applications that are related to the key AI, Blockchain,
IoT technology innovation respectively are selected for detail analysis. Top 100 AI, Blockchain,
IoT technology innovation entities are selected based on their number of published patent
applications.
A. AI
Following figure shows the landscape of the top 100 AI technology innovation entities with
respect to number of published patent applications. The top 100 AI technology innovation
entities represent 23,593 patent applications. The top 10 AI innovation leaders (IBM, Google,
Microsoft, Samsung Electronics, Intel, Siemens, Facebook, Philips, GE, Accenture) account for
11,217 patent applications. The size of bubble chat for IBM represents 2,542 patent applications.

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B. Blockchain
Following figure shows the landscape of the top 100 Blockchain technology innovation entities
with respect to number of published patent applications. The top 100 Blockchain technology
innovation entities represent 7,809 patent applications. The top 10 Blockchain innovation leaders
(Alibaba Group, IBM, nChain, Mastercard, Walmart, Bank of America, Visa, Microsoft, Intel
Accenture) account for 3,791 patent applications. The size of bubble chat for Alibaba Group
represents 1,182 patent applications.

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C. IoT
Following figure shows the landscape of the top 100 IoT technology innovation entities with
respect to number of published patent applications. The top 100 IoT technology innovation
entities represent 18,786 patent applications. The top 10 IoT innovation leaders (Qualcomm,
Ericsson, LG Electronics, Samsung Electronics, Intel, Ford, IBM, Huawei, GM, Toyota) account
for 9,326 patent applications. The size of bubble chat for Qualcomm represents 1,851 patent
applications.

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2. Technology Innovation Evolution
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. AI, Blockchain, IoT patenting activities
with respect to the technology innovation date (priority year) are analyzed.
A. AI
Following patent application activity chart shows the AI technology innovation growth trends.
The patent application activity chart indicates that the AI technology innovation activity started

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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 patent
application activity chart indicates that the AI technology innovation activity is still in a growth
stage.
B. Blockchain
Following patent application activity chart shows the Blockchain technology innovation growth
trends. The patent application activity chart indicates that the Blockchain 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 patent
application activity chart indicates that the Blockchain technology innovation activity is still in a
growth stage.
C. IoT

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Following patent application activity chart shows the IoT technology innovation growth trends.
The patent application activity chart indicates that the IoT technology innovation activity started
in a rapid growth stage already before 2010. Since there is usually a time lag between the initial
application date (priority year) and the publication date by around two years, the patent
application activity chart indicates that the IoT technology innovation activity became matured in
2018.
3. Technology Innovation Status in Innovation Country
A patent counting for can be a good measuring tool for technology innovation status of a specific
country. Following figures show the landscape of the AI, Blockchain, IoT technology innovation
entities with respect to country of H.Q. location respectively.
A. AI
As can be seen in the following figure, top 10 AI technology innovation countries are USA,
Japan, S. Korea, China, Germany, UK, Canada, Ireland, Netherlands, and India.

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B. Blockchain
As can be seen in the following figure, top 10 Blockchain technology innovation countries are
USA, China, Japan, UK, Germany, Canada, Antigua and Barbuda, S. Korea, Switzerland, and
Ireland.
C. IoT
As can be seen in the following figure, top 10 IoT technology innovation countries are USA, S.
Korea, Japan, Sweden, China, Germany, France, UK, Israel, and Canada.

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4. Technology Innovation Status in CPC Classification
The Cooperative Patent Classification (CPC) is a patent classification system, which has been
jointly developed by the EPO and the USPTO. Each CPC classification4
term consists of a
symbol such as "A01B33/00". The first letter is the "section symbol" consisting of a letter from
A: Human Necessities, B: Operations and Transport, C: Chemistry and Metallurgy, D: Textiles,
E: Fixed Constructions, F: Mechanical Engineering, G: Physics, H: Electricity, Y: Emerging
Cross-Sectional Technologies. This is followed by a two-digit number to give a "class symbol"
("A01" represents "Agriculture; forestry; animal husbandry; trapping; fishing"). The final letter
makes up the "subclass" (A01B represents "Soil working in agriculture or forestry, parts, details,
or accessories of agricultural machines or implements, in general"). The subclass is then
followed by a 1- to 3-digit "group" number, an oblique stroke and a number of at least two digits
representing a "main group" ("00") or "subgroup".
Following figures show the landscape of the AI, Blockchain, IoT technology innovation status
with respect to CPC classification respectively.
4
https://www.uspto.gov/web/patents/classification/

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A. AI
As can be seen in the following figure, top 20 AI technology innovation CPC classifications are
G06N-0020/00 (Machine learning)
G06N-0003/08 (AI for automation)
G06N-0003/0454 (AI using combination of multiple neural networks)
G06T-0007/0012 (Image analysis using segmentation)
G06N-0003/063 (Computer systems based on biological models using electronic means)
G16H-0050/20 (AI for medical diagnosis)
G06N-0003/084 (Back-propagation for deep learning)
G06N-0003/04 (Artificial life, i.e. computers simulating lif)
G06N-0005/04 (Inference methods using knowledge-based models)
G06F-0040/30 (NLP for semantic analysis)
G06N-0003/0445 (Feedback networks for AI)
G06K-0009/6256 (Recognizing patterns by training AI system)
H04L-0063/1425 (AI for network anomaly detection)
G10L-0015/22 (AI method for a speech recognition process)
G06F-0016/9535 (AI for search customization based on user profiles and personalization)
H04L-0063/1416 (AI for network attack signature detection)
G06N-0007/005 (Probabilistic networks based on specific mathematical models)
G06K-0009/6262 (Active pattern learning technique)
G06K-0009/66 (Pattern recognition using adaptive learning)
G16H-0010/60 (AI for processing electronic patient records)

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B. Blockchain
As can be seen in the following figure, top 20 Blockchain technology innovation CPC
classifications are
H04L-0009/3239 (Cryptography involving non-keyed hash functions)
G06Q-0040/04 (Data processing for cryptocurrency exchange)
G06Q-0020/065 (Cryptocurrency payment architectures, schemes or protocols)
H04L-0009/0637 (Cryptography for blockchain)
G06F-0021/64 (Protecting data integrity, e.g. using checksums, certificates or signatures)
H04L-0009/3247 (Cryptography involving digital signatures)
G06Q-0020/3829 (Payment protocols involving key management)
G06Q-0020/401 (Payment transaction verification)
H04L-0009/3236 (Cryptography using hash functions)
G06Q-0020/02 (Payment protocols involving certification authority)
G06F-0016/2379 (Database updates)

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G06Q-0020/389 (Keeping log of payment transactions for guaranteeing non-repudiation of a
transaction)
G06F-0021/602 (Providing cryptographic facilities or services for data protection)
G06F-0021/6245 (Protecting personal data, e.g. for financial or medical purposes)
G06F-0016/2365 (Process for ensuring data consistency and integrity)
G06F-0021/6218 (Distributed database system)
G06Q-0020/10 (Payment transaction for electronic funds transfer)
G06Q-0020/0658 (Cryptocurrency payment management)
G06Q-0040/025 (Financial credit processing or loan processing)
G06F-0021/10 (Security arrangements for protecting programs or data)
C. IoT
As can be seen in the following figure, top 20 IoT technology innovation CPC classifications are
H04L-0067/12 (Network arrangements or communication protocols for IoT applications)
H04W-0004/40 (Vehicular wireless communications)
G05B-0015/02 (Electric systems controlled by a computer)

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H04W-0004/70 (Services for M2M or MTC)
H04W-0012/06 (Authentication, e.g. verifying user identity or authorisation)
G07C-0005/008 (communicating information to a remotely located station for vehicles)
H04W-0048/18 (Selecting a wireless network or a communication service)
H04W-0072/02 (Selection of wireless resources by user or terminal)
H04W-0072/042 (Downlink wireless resource allocation)
H04L-0067/125 (Network arrangements or communication protocols involving the control of
IoT device applications)
H04W-0074/0833 (Wireless channel access using a random access procedure)
H04W-0048/16 (Discovering, processing wireless access restriction)
G16H-0010/60 (Processing data for electronic patient records)
G08C-0017/02 (Arrangements for transmitting signals using a radio link)
H04L-0005/0053 (Allocation of signaling for multiful transmission)
G06Q-0040/08 (Insurance/Finance risk analysis)
H04W-0004/80 (Services using short range wireless communications)
H04L-0012/282 (Home networking based on user interaction)
H04W-0024/10 (Scheduling measurement reports for wireless communications)
H04W-0072/0446 (Allocation of wireless resources, the resource being a slot, sub-slot or frame)

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5. Technology Innovation Status in Specific Technology
Patents are a good information resource for obtaining the state of the art of technology
innovation insights in a specific technology filed.
A. Deep RL Technology Innovation Status
Patents that specifically describe the major deep reinforcement learning (Deep RL) technologies
are a good indicator of the Deep RL innovations in a specific innovation entity. To find the deep
learning technology innovation status of Deep RL, 260 published patent applications in the
USPTO that are related to the key Deep RL technology innovation are selected for detail analysis.
Following figure shows the Deep RL patent application landscape with respect to the innovation
entity. As shown in the figure, Google/Deepmind is the leader in Deep RL technology innovation
followed by IBM, Fanuc Corp., Siemens Healthcare, GM, Huawei, Honda, Baidu, Intel, Royal
Bank of Canada, LG Electronics, and Microsoft.

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Following figure shows the Deep RL patent application landscape with respect to the key
technology innovation field. As shown in the figure, Deep Q-Learning/SARSA/DRQN is the
most innovated Deep RL followed by Neural Network Training Technique/Procedure, Actor
Critic System/A2C/ A3C/DDPG, Policy Gradient Algorithm/ REINFORCE/PPO/TRPO , Action
Selection System/Policy, Multi-agent Algorithm, Imitation Learning, Exploration, Hierarchical
RL, Inverse RL Algorithm, Model-based RL, Multi-armed Bandit Algorithm, Partially
Observable MDP, Distributed Training, MCTS, RL Processor Architecture, Adaptive Tree-
Search, Algorithm, DCTD Algorithm, Dynamic Agent Grouping, MDP, Memory-enhanced RL,
Reward System, and Task Partitioning.

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Patent information can provide many valuable insights that can be exploited for developing and
implementing new technologies. Patents can also be exploited to identify new product/service
development opportunities.
FinTech/Deep Q-Network (DQN) Learning
US20190392314 (Alibaba Group) illustrates a Deep RL application in FinTech. This is an
example application of DQN Learning regarding the cash return fraud recognition for consumer
credit products. "Cash return" refers to using a financial transaction to obtain cash or other
benefits. For example, a buyer purchases a product with a credit card and then returns the
purchased product for a cash refund, where the purchase transaction can be a fake transaction.
For another example, a buyer and a merchant may perform fake transactions to get bonus points
from payment platforms or credit card companies.
As shown in Figure, the fraud recognition system 100 includes the first DQN 11,
a policy unit 12, a sample preparation unit 13, a sample queue 14, the second DQN 15, and an
evaluation unit 16. The first DQN 11 recognizes a cash-return transaction, and the second DQN

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15 trains model. In a DQN, a neural network is used to non-linearly approximate a Q-value
function. A value of the Q-value function is referred to as a Q-value. The DQN outputs a two-
dimensional Q-value vector according to an input feature vector of transaction information, the
two Q-values in the Q-value vector correspond to two actions respectively: the transaction being
a cash-return transaction and the transaction being a non-cash-return transaction. Transaction
amounts can be obtained after the recognition system 100 selects an action, and used as returns
for the recognition system 100.
Deep RL system training procedures are as follows:
(1) The first transaction information s1 and corresponding cash-return label value b1 are
randomly acquired from a batch of transaction data concerning payment using a historical
database which stores data of transactions. The first transaction information s1 can include an
attribute feature of the transaction, a buyer's feature, a seller's feature, a logistic feature, etc.
Similarly, s2 and b2 can also be used to represent the second transaction. The information s1 and
s2 correspond to states in the DQN 11 and DQN 15, and the cash-return label values b1 and b2
correspond to label values of actions in the DQNs. The batch of transaction data can include data
of hundreds or thousands of transactions.
(2) The information s1 and s2 are input to the first DQN 11 in sequence to output corresponding
two-dimensional Q-value vectors q(s1) and q(s2) respectively. The vector q(s1) can be a two-

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dimensional vector that includes two Q-value's predictive values, one corresponding to a cash-
return action, and the other corresponding to a non-cash-return action. The vector q(s2) can also
be a two-dimensional vector that includes two Q-value's predictive values (q1, q2). The value q1
corresponds to an action that the transaction is a cash-return transaction, and q2 corresponds to
an action that the transaction is a non-cash-return transaction. The Q-value vector q(s1) for the
first transaction is sent to the policy unit 12, and the Q-value vector q(s2) for the second
transaction is sent to the sample preparation unit 13.
(3) The policy unit 12 selects an action according to a predetermined policy. For example, the
predetermined policy can be the ε-greedy policy. An action is selected from a set containing all
available actions according to the ε-greedy policy. The policy unit 12 acquires a cash-return
predictive value a1, which predicts whether s1 is a cash-return transaction, for the first
transaction from the Q-value vector q(s1) according to the selected policy , and transfers the
cash-return predictive value a1 to the sample preparation unit 13.
(4) The sample preparation unit 13 determines the return value r for the action a1 for the first
transaction based on a1, b2, and the transaction amount. The sample preparation unit 13 obtains a
Q-value's predictive value q(s2, b2) from the Q-value vector q(s2) for the second transaction
based on the cash-return label value b2 of the second transaction, and use the Q-value's
predictive value q(s2, b2) as the maximum of the Q-values contained in the Q-value vector q(s2).
The sample preparation unit 13 compares the cash-return predictive value a1 for the first
transaction to the cash-return label value b1 to determine a return r. After r is determined, the
sample preparation unit 13 calculates the Q-value's label value Q(s1, a1) for the first transaction
based on the calculated r and q(s2, b2) according to the Deep Q Learning algorithm as expressed
in the following pseudo-code.
(5) The sample preparation unit 13 sends a1, Q(s1, a1), and s1 to the sample queue 14 as one
sample, and sends the return r to the evaluation unit 16. In the DQN 11 and DQN 15, a Q-value
corresponding to a pair of a state (transaction information) and an action (a cash-return predictive
value or a cash-return label value) represents an accumulated return of the system after
the action is performed. The Q learning algorithm is to determine an optimal action-
selection policy such that the policy maximizes the expected value of a reward over a number of
steps. The Q-value represents the value of the reward.

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(6) A batch of samples (e.g., 500 samples) can be randomly selected from the sample queue 14
for training the second DQN 15. During the training, corresponding to each sample, parameters
of the second DQN 15 can be adjusted through a stochastic gradient descent algorithm by using
s1 and a1 as inputs and Q(s1, a1) as an output label value, such that an output q(s1, a1) of the
second DQN 15 corresponding to inputs s1 and a1 is more close to the label value Q(s1, a1) than
that of the second DQN 15 before the parameters are adjusted. After the training has been
performed by using a number of batches (e.g., 100 batches) of samples (each batch including,
e.g., 500 samples), the second DQN 15 transfers and assigns its weights to the first DQN 11. In
addition, upon receiving the return r for the first transaction, the evaluation unit 16 adds the
return r to a total return value of the system to accumulate the total return value, and thus
evaluate the system's learning capacity based on the total return value. The total return value
increases with the number of iterations in the training, and stabilize around a fixed value after
second DQN 15 converges.
Google Deepmind’s Innovation for Challenging Deep RL Issues
Open questions in the application of RL systems to practical systems are:

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Sparse Reward
One of the critical problems in Deep RL is how to design an appropriate reward function. It is
easy to design a sparse reward function, which gives a positive reward such as +1 when the task
is accomplished correctly and zero otherwise. In practice, however, rarely occurring sparse
reward signal are difficult for neural networks to model, and thus, use of a sparse reward
function can make it hard to find an optimal policy. Hierarchical learning, where breaking large
tasks into smaller subtasks, may help accelerate learning on individual tasks by mitigating the
exploration challenge of sparse reward problems.
Safety Training
Safe Deep RL tries to ensure reasonable system performance and respect safety constraints
during the learning processes. Maintaining safety during training of the RL system is a critical
issue because most real-world applications require training solutions that are safe to operate
against potential catastrophic failures. Learning on a simulated environment and then transferring
the learned knowledge to the real world case can offer a partial solution. Use of human
intervention during training in order to make sure the RL system does not go into a catastrophic
state can offer another partial solution.
Generalization
Generalizing between tasks remains is a challenge for Deep RL algorithms. Although trained
agents can solve complex tasks, they struggle to transfer their experience to new environments.
In traditional Deep RL systems, a generalization is achieved by approximating the optimal value
function with a low-dimensional representation using a deep neural network. While this
approach works well in some domains, in domains where the optimal value function cannot
easily be reduced to a low-dimensional representation, learning can be very slow and unstable.
US20200143206 addresses the Safe Deep RL issue using continuous action guidance of look-
ahead search with Monte Carlo Tree Search (MCTS) for better exploration combining with
Imitation Learning. This innovation differs from AlphaGo-like innovation in multiple aspects: (i)
Enabling on-policy model-free RL methods to explore safely in hard-exploration domains where

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negative rewarding terminal states are ubiquitous, in contrast to off-policy methods which use
intensive search to fully learn a policy; (ii) Generalizing in that other demonstrators (human or
other sources) can be integrated to provide action guidance by using the auxiliary loss refinement;
and (iii) Using the demonstrator with a small look-ahead search to filter out actions leading to
immediate negative terminal states so that model-free RL can imitate those safer actions to learn
to safely explore.
US20200151562 addresses the problem of efficiently training a continuous-control
reinforcement learning system using demonstrations, and of the problem training using a sparse
reward. The critic neural network of implemented actor-critic system can trained using an
estimated n-step return. Thus return data can be derived from a combination of the reward data
and a discounted reward from a predicted succession or rollout of n-1 transitions forward from a
current state of the environment, where n can be selected between n=1 and n>1 depending upon
whether demonstration or operation transitions are selected. This allows to adapt when
demonstration trajectories are longer than operation transitions, by allowing a sparse reward to
affect more state-action pairs, in particular the values of n preceding state-action pairs.
The critic neural network receives state, action, and reward/return data and defines a
value function for a TD error signal, which is used to train both actor and critic neural networks.
The actor neural network receives the state data and has an action data output defining actions in
a continuous action space. The actor-critic system can be implemented in an asynchronous,
multi-threaded manner comprising a set of worker agents each with a separate thread, a copy of
the environment and experience, and with respective network parameters used to update a global
network.
B. Deep Learning for Autonomous Vehicle Technology Innovation Status
Patents that specifically describe the major deep learning applications in Autonomous Vehicle
are a good indicator of the deep learning for Autonomous Vehicle innovations in a specific
innovation entity. To find the deep learning for Autonomous Vehicle technology innovation
status, 201 published patent applications in the USPTO that are related to the key deep learning
for Autonomous Vehicle technology innovation are selected for detail analysis.

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Following figure shows the deep learning for Autonomous Vehicle patent application landscape
with respect to the innovation entity. As shown in the figure, the key deep learning for
Autonomous Vehicle innovation entities are GM, Ford, Toyota, Baidu, Honda, Zoox, Inc.,
Nvidia, Uber, Huawei, Intel, LG Electronics, Metawave Corporation, Micron Technology, NEC
Laboratories America, and Samsung Electronics.
Following figure shows the deep learning for Autonomous Vehicle patent application landscape
with respect to the key technology innovation field. As shown in the figure,
Navigation/Operation/Motion Control (End-to-End/Collision-Avoidance/Parking) and
Obstacle/Object Detection/Identification are the most innovated deep learning for Autonomous
Vehicle technology followed by Behavior/Motion/Trajectory/Path Prediction/Planning,
Surrounding Traffic/Vehicle/Road/Lighting Situation/Safety Recognition , Surrounding Scene
Recognition , Localization (Odometry), Surrounding Object Behavior/Movement Prediction
(Scene Flow), Lane Detection/Identification, Performing Vehicle Maneuvers (Turn/Lane

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Change), Navigation Map Generation/Update/Correction, and Occupant Emotion/Behavior
Recognition.
Patent information can provide many valuable insights that can be exploited for developing and
implementing new technologies. Patents can also be exploited to identify new product/service
development opportunities.
Objects Detection in 3D Point Clouds/ US20200019794
3D point cloud of Light Detection And Ranging (LiDAR) sensor can be used for a detailed
understanding of the environment surrounding the autonomous vehicle. Due to the computational
burden introduced by the third spatial dimension, however, renders a naive transfer of
Convolutional Neural Networks (CNNs) from 2D computer vision applications to native 3D
perception in point clouds infeasible for large-scale applications. As a result, previous

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approaches tend to convert the data into a 2D representation first, where spatially adjacent
features are not necessarily close to each other in the physical 3D space, requiring models to
recover these geometric relationships leading to poorer performance t. Thus, computationally
efficient approach to detecting objects in 3D point clouds using CNNs natively in 3D is
developed.
Following figure shows a flow-chart outlining a method of detecting objects within a 3D
environment. A LiDAR sensor that is mounted on a vehicle monitors its locale and generates
data on a sensed scene around the vehicle that provides a representation of the 3D-evironment in
step 800. Next step is to convert a point-cloud captured by the LiDAR sensor to a discrete 3D
grid (step 802), such that for each cell that contains a non-zero number of points, a feature vector
is extracted based on the statistics of the points in the cell (step 804). The feature vector holds a
binary occupancy value, the mean and variance of the reflectance values and three shape factors.
Step 804 performs a sparse convolution across this native 3D representation followed by a ReLU
(Rectified Linear Unit) non-linearity using a Convolutional Neural Network (CNN), which
returns a new sparse 3D representation.

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In step 810, as shown in the following figure, each non-zero input feature vector casts a
set of votes, weighted by filter weights within units of the CNN 136, to its surrounding cells in
the output layer 200, as defined by the receptive field of the filter. This voting/convolution, using
the weights, moves the data between layers (401, 402, 404, 200) of the CNNs. The voting output
is passed through (step 814) a ReLU (Rectified Linear Unit) nonlinearity which discards non-
positive features as described in the next section. In step 818, the predicted confidence scores in
the output data layer indicates the confidence score as to whether an object exists within the cells
of the n-dimensional grid data input to the network.
Lane Change/US20200139973
Performing safe and efficient lane-changes is a crucial feature for creating fully autonomous
vehicles. With a Deep Reinforcement Learning (RL), the autonomous agent can obtain driving
skills by learning from trial and error without any human supervision. A method for learning

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lane-change policies via the Deep RL actor-critic algorithm5
is developed, wherein each lane-
change policy describes actions selected to be taken by an autonomous vehicle.
The Deep RL system implements an actor-critic neural network (NN) architecture. For
better training stability, the actor-critic NN architecture is implemented with two neural NNs: an
actor network and a critic network. This architecture decouples the action evaluation and the
action selection process into two separate deep NNs. The actor network learns lane-change
policies as a set of hierarchical actions. Each lane-change policy is a distribution over
hierarchical actions. For example, the actor network processes the image data received from the
environment to learn the lane-change policies as the set of hierarchical actions that are
represented as a vector of a probability of action choices and a first set of parameters coupled to
each discrete hierarchical action. The actor network collects transitions each comprising an
observation (i.e., frame of image data), a hierarchical action, reward, a next observation (i.e.,
next frame of image data), and stores them in a replay memory.
The critic network predicts action values via an action value function, evaluates a lane-
change policy, and calculates loss and gradients to drive learning and update the critic network.
For example, the critic network draws a mini-batch of the transitions each comprising an
observation, hierarchical action, reward, and a next observation, collected by the actor network,
from the replay memory and uses the mini-batch of transitions to improve the critic
network prediction of the state/action/advantage value.
Following figure shows a block diagram of an example implementation of the actor-critic
network architecture. The actor-critic network architecture 102 is based on a Deep Recurrent
Deterministic Policy Gradient (DRDPG) algorithm, which is a recurrent version of DDPG
algorithm6
. The actor-critic network architecture 102 includes three sub-systems (dotted-line
boxes), where each sub-system includes an instance of common elements: an input of image data
(s) image data 129, CNN module 130, a set of region vectors 132, hidden state vector 133,
attention network 134, spatial context vector 135 and a LSTM network 150. Each sub-system
represents the actor-critic network architecture 102 being continuously applied to updated
5
Actor-critic RL algorithm combines the stability of on-policy training (e.g., Policy Gradient) with the data
efficiency of off-policy approaches (e.g., Deep Q-Network (DQN) learning) where the data efficiency of policy
gradient is improved by training an off-policy critic.
6
DDPG algorithm combines Q-learning and Policy Gradients algorithm. The actor is a policy network that takes the
state as input and outputs the exact (continuous) action, instead of a probability distribution over actions. The critic
is a Q-value network that takes in state and action as input and outputs the Q-value. US20190004518 (Baidu)
illustrates a Deep RL application in Unmanned Aerial Vehicle (Drones) using DDPG.

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information at different steps within a time series (t−1, t, t+1 . . . T). In other words, each dotted-
line box represents processing by the actor-critic network architecture 102 at different instances
in time.
The actor-critic network architecture 102 includes both temporal attention module 160
that learns to weigh the importance of previous frames of the image data at any given image data
129, and spatial attention module 140 that learns the importance of different locations in the
image at any given image data 129. The outputs generated by the actor-critic network
architecture 102 are combined over a temporal dimension (via the 160) and over a spatial
dimension (via the 140) to generate the hierarchical actions 172. The bracket associated with the
spatial attention module 140 (spatial attention) represents repetitions of the actor-critic network
architecture 102 along the spatial dimension. The spatial attention module 140 will detect the

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most important and relevant regions in the image for driving, and add a measure of importance to
different regions in the image. The bracket associated with the temporal attention module 160
(temporal attention) represents repetitions of the actor-critic network architecture 102 along the
temporal dimension. The temporal attention module 160 weighs the past few frames with respect
to importance to decide the current driving policy.
Processing of image data 129 begins when CNN module 130 receives and
processes the image data 129 captured by sensors to extract features and generate a feature
map/tensors (represented by the top big cuboid inside 130). The CNN module 130 can evaluate
lane-change policies of the hierarchical actions 172 to generate an action-value function (Q). A
set of region vectors 132 that collectively make up the feature map are extracted. Each region
vector corresponds to features extracted from a different region/location in the convolutional
feature maps/tensors (represented by the top big cuboid inside 130) that were extracted from the
image data 129. The region vectors 132 are used by the spatial attention network 134 in
calculating the context feature vectors 135, which are then processed by the LSTM 150 based
temporal attention block 160.
The LSTM outputs 152-1, 152-2 . . . 152-3 are then combined to generate a combined
context vector (CT) 162. The combined context vector (CT) 162 is a lower dimensional tensor
representation that is a weighted sum of all the LSTM outputs through T time steps (e.g., a
weighted sum of all the region vectors). The combined context vector (CT) 162 is then passed
through the fully connected (FC) layers 170 before computing the hierarchical actions 172 of the
actor network.
The hierarchical action space of hierarchical actions 172 designed for lane-change
behaviors, which can includes various lane-change policies. To deal with lane-change behaviors
in driving policies, it is necessary to make both high-level decisions on whether to have lane-
changes as well as do low-level planning regarding how to make lane-changes. For example,
there can be three mutually exclusive, discrete, high-level actions: left lane-change, lane
following, and right lane-change. At each time step, a driver agent must choose one of the three
high-level actions to execute. During training, the system will learn to choose from the three
high-level action decisions and apply proper parameters specific to that action.

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Vehicle Trajectory Planning/ US20200174490
Autonomous vehicles include the environmental sensing systems that monitor the environment
of a vehicle. The sensing systems generate sensor data from the sweeps that characterize aspects
of the current environment of the vehicle. In order to make effective driving decisions, the
computing system of an autonomous vehicle processes information about a vehicle's
environment along with navigation data and information about previous locations of the vehicle,
to determine a planned trajectory of the vehicle. The planned trajectory can indicate a series of
waypoints that each represent a proposed location for the vehicle to maneuver to at a time in the
near future. The computing system selects waypoints taking into account an intended route or
destination of the vehicle, safety (e.g., collision avoidance), and ride comfort for passengers in
the vehicle.
Following figure depicts a block diagram of an example computing environment 100 of
an autonomous vehicle. The environment 100 includes a neural network system 102, a trajectory
management system 114, an external memory 120, a navigation planning system 126, and a
vehicle control system 128. The neural network system 102 processes input data components 108,
110, and 112, to score a set of locations in a vicinity of a vehicle. The score for a given location
can represent a likelihood that the location is an optimal location for inclusion as a waypoint in a
planned trajectory for the vehicle. The trajectory management system 114 processes the scores
from the neural network system 102 to select one of the locations as a waypoint for a given time
step in the planned trajectory. The trajectory management system 114 also interfaces with
external memory 120, which stores information about previous locations of the vehicle and
locations previously selected for a planned trajectory.

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The neural network system 102 and the trajectory management system 114 interact with a
navigation planning system 126, which generates navigation data 112 representing a planned
navigation route of the vehicle. The planned navigation route can indicate the goal locations of
the vehicle's travel and, optionally, a targeted path to arrive at the goal locations. The neural
network system 102, in coordination with the trajectory management system 114, generates a
planned trajectory for a vehicle by determining, at each of a series of time steps, a waypoint for
the planned trajectory at the time step. At each time step, the system 102 processes neural
network inputs that include waypoint data 108, environmental data 110, and navigation data 112

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to generate a set of scores that each correspond to a different location in a vicinity of the vehicle.
The waypoint selector 116 of the trajectory management system 114 can then select one of the
scored locations in the vicinity of the vehicle as a waypoint for the planned trajectory at the
current time step based on the set of scores generated by the neural network system 102 by
selecting the location corresponding to the highest score in the set of scores.
The planning system 126 controls the operational mode of a vehicle. For example, during
a first mode for normal autonomous driving, the planning system 126 activates the trajectory
management system 114 to use planned trajectories determined using the neural network system
102 as the basis for maneuvering the vehicle. In order for a vehicle to maneuver along a planned
trajectory, a sequence of control actions can be performed in the vehicle control system 128 to
cause the vehicle to follow the trajectory. The control actions can include steering, braking, and
accelerating.

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C. Deep Learning for 5G Technology Innovation Status
Patents that specifically describe the major deep learning applications in 5G are a good indicator
of the deep learning for 5G innovations in a specific innovation entity. To find the deep learning
for 5G technology innovation status, 24 published patent applications in the USPTO that are
related to the key deep learning for 5G technology innovation are selected for detail analysis.
Following figure shows the deep learning for 5G patent application landscape with respect to the
innovation entity. As shown in the figure, deep learning for 5G innovation entities are Samsung
Electronics, Verizon. Virginia Tech, Ball Aerospace & Technologies, Beijing University of Posts
and Telecommunications, Bluecom Systems and Consulting, CableLabs, DeepSig Inc.,
Futurewei Technologies, Google, Incelligent P.C., Korea Advanced Institute of Science and
Technology, Micron Technology, Parallel Wireless, Inc., QoScience, Inc., Qualcomm, and Sony.

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Following figure shows the deep learning for 5G patent application landscape with respect to the
key technology innovation field. As shown in the figure, MIMO is the most innovated deep
learning for 5G technology followed by Cell Coverage Planning, Network Performance
Optimization (QoS), Radio Resource Management, Radio Access Network (RAN), RF system,
Adaptive Traffic Scheduler, Cognitive Radio (SDR), Communication Channel Modeling,
Controlling Spectral Regrowth, Dynamic Software Defined Networking (DSDN), Network,
Traffic Optimization, Predicting Received Signal Strength, Proactive Network Management,
Transceiver Beam Management, and Wireless Scene Identification.
Patent information can provide many valuable insights that can be exploited for developing and
implementing new technologies. Patents can also be exploited to identify new product/service
development opportunities.

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MIMO Adaptive Antenna/ US20190372644
For next generation cellular systems such as 5G wireless communications system, efficient and
unified radio resource management is desirable. To decrease propagation loss of the radio waves
and increase the transmission coverage the 5G system implements the base station antenna that is
the massive multiple-input multiple-output (MIMO) beam forming array antenna.
US20190372644 illustrates a method for controlling and optimizing the broadcast beam for the
base stations (jointly tune the antenna beam width and e-tilt angle).
In a dynamic scenario where user equipments (UEs) are assumed to be moving according
to some mobility pattern in a given cell of base station (BS) of 5G system, the antenna beam
selection problem using deep reinforcement learning (Deep RL) includes two parts such as
offline training and online deployment. The offline training part is to learn the UE distribution
pattern from history data and to teach the neural network on the UE distribution pattern. After
obtaining typical UE distribution patterns, these patterns together with ray-tracing data can be
used to train the Deep RL network. After the neural network is trained, it can be deployed to
provide beam guidance for the network online.
In the Deep RL terminology, selecting the beam parameters (beam shape, tilt angles) can
be regarded as the action. The measurements from the UEs in the network can be regarded as the
observations. Based on the observations, several reward values can be calculated.
The reward can be the total number of connected UEs in the network based on the state
and action taken. In DQN-based Deep RL, the Q-values are predicted using deep neural network.
Input to the neural network is the UEs' state of the Deep RL environment, and output is the Q-
values corresponding to the possible actions. Two identical neural networks are used in
predicting the Q-values. One is used for computing the running Q-values--this neural network is
called the evaluation network. The other neural network, called the target neural network is held
fixed for some training duration, say for M episodes, and every M episode the weights of the
evaluation neural network is transferred to the target neural network.
Following table shows the detailed algorithm of the BS antenna beams are selected in
dynamic scenarios for multiple sector case, where each sector can independently select own
beam parameters to maximize the overall network coverage.

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MIMO Communication Channel/ US20190274108
A large-scale MIMO system with high spectrum and energy efficiency is a very promising key
technology for 5G wireless communications. For the large-scale MIMO system, accurate channel
state information (CSI) acquisition is a challenging problem, especially when each user has to

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distinguish and estimate numerous channels coming from a large number of transmit antennas
from BSs in the downlink as the overhead for channel estimation is linearly proportional to the
number of antennas. US20190274108 illustrates a cost effective and reliable MIMO
communication channel estimation method using deep learning technology.
In communication channel estimation, superimposed pilot subcarriers are used due to the
fact that there is no need to increase the number of subcarriers used even as the number of
antennas used increases. Especially for FDD channel estimation, using superimposed pilot
subcarriers, instead of traditional orthogonal subcarriers, can reduce the number of subcarriers
required down from M*Np to Np; (M is the number of transmitter antennas; Np is the number of
subcarriers required per Tx antenna). Exploiting deep learning to learn DL (downlink) channel
estimation, a cost effective and reliable MIMO communication channel estimation can be
possible:
Offline training the first deep learning network to learn DL channel estimation is
performed. The user equipment (UE) can use this process to obtain actual DL channel estimation
during real-time operation (online) based on received inputs from the base-station side.
Subsequently, via feeding these inputs to the offline trained deep-learning network, the UE can
feed the learned DL channel estimation and obtain the compressed encoded feedback. Then, the
compressed encoded feedback is sent back by the UE to the BS to minimize the UL feedback
cost. The BS feeds the received compressed encoded feedback to the second half of trained deep
learning network to recover the DL channel estimation as obtained by the UE. Once the DL
channel estimation is recovered, the BS can design the optimal precoding matrix accordingly.
Via this procedure, both DL and UL transmission cost can be significantly reduced in the
massive-MIMO context.
Following figure shows an example deep learning network using the Convolutional
Neural Network (CNN) model for DL (DL-CNN) channel estimation. The DL-CNN channel
estimation is running per time slot per subcarrier. Assuming the training duration is T, and the
number of training vectors (including input & channel vectors) is Y. The CNN will be trained
offline with T*Y samples. The offline training process will be repeated to adapt to channel
change. The interval is based on running performance. Channel multi-path parameters are
acquired via channel sounding.

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Radio Access Network (RAN)/ US20200178093
A flexible and dynamic RAN can perform the end-to-end self-configuration of the RAN,
sense the quality of the user's experiences and network performance in real time, and
perform predictive analysis and intelligent optimization. According to application scenarios
supported 5G wireless communication system, the RAN slices can be classified into four
typical types, including a wide-area seamless coverage slice, a hot-spot high-capacity slice,
a low-power large-connection slice and a low-delay high-reliability slice. In practical
network deployment, a new slice type can be added according to the network requirements.
US20200178093 illustrates the RAN performance evaluation of the existing slices7
using
deep learning technology.
The RAN performance evaluation is performed for the existing slices in the network to
reduce the complexity of network operations and the instability of network performance that are
caused by the frequent change of the global network configuration. The evaluation module
evaluates the network in the current network according to historical wireless transmission data of
the network and the terminal measurement report that are collected by the data collection
processor, performs a reserving, adding or deleting operation for the existing slices in the
network, so as to determine the global slice setting. The evaluation process includes: according
7
The network performance includes a set of network spectrum efficiency, network transmission delay, network
connection number, network power consumption efficiency, network delay jitter, and extreme transmission speed.

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to real-time wireless transmission data of the network and the terminal measurement data that are
collected by the data collection processor from the RAN, and by using a Convolutional Neural
Network (CNN) and a Recurrent Neural Network (RNN), extracting a spatial feature and
temporal feature of the data respectively as show in the following figure, obtaining a network
performance status level corresponding to the current slice, and reporting it to the centralized
evaluation module. For different types of slices, the weight values of different network
performance status levels in the set are different.
The CNN and the RNN can provide a mapping relationship between wireless
transmission data, terminal measurement data and network performance statuses through a
training process of historical measurement data of the network. According to the real-time
wireless transmission data and terminal measurement data, a network performance status level
can be obtained: According to the historical wireless transmission data and the terminal
measurement data obtained in the RAN and by using the CNN and the RNN, the spatial feature
and temporal feature of the data are extracted respectively. Then, the network performance status
level corresponding to the historical wireless transmission data and the terminal measurement
data is determined. The determined network performance status is compared with the actual
network performance status, and the values of the weight parameters of the CNN and the RNN
are adjusted according to a comparison result.
Based on the RAN performance evaluation, RAN self-optimization within the slice can
be done through Deep Reinforcement Learning (DRL). The current networking mode and

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resource allocation of the slice are used as status variables of the DRL and are input to a node
supporting the training of the deep learning model. By using the service level and resources
utilization efficiency obtained based on the historical resource configuration scheme in the data
collection processor, an intra-slice resource allocation adjustment strategy is output to realize the
highest network resource utilization efficiency and the best service performance level. The to-be-
adjusted resource allocation ratio of each slice is used as an action variable of the DRL. After the
adjustment of the resource allocation, the network resource utilization efficiency collected by the
data collection processor and the performance indicators of each slice are used as DRL reward
variables. Then, the online learning is performed through a Deep Q Network (DQN). Following
figure shows a flowchart illustrating a process of deriving a resource adjustment strategy within a
slice by using a DRL training model.

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D. Deep Learning for Cybersecurity Technology Innovation Status
Patents that specifically describe the major deep learning applications in cybersecurity are a good
indicator of the deep learning for cybersecurity innovations in a specific innovation entity. To
find the deep learning for cybersecurity technology innovation status, 31 published patent
applications in the USPTO that are related to the key deep learning for cybersecurity technology
innovation are selected for detail analysis.
Following figure shows the deep learning for cybersecurity patent application landscape with
respect to the innovation entity. As shown in the figure, deep learning for cybersecurity
innovation entities are Oracle, IBM, Intel, Microsoft, NEC, Accenture, BAE Systems, Bank of
America, Boeing, Cisco, Deep Instinct Ltd, Electronics and Telecommunications Research
Institute, ESTsecurity Corp., Fortinet, Inc., F-Secure Corporation, General Electric, Infoblox Inc.,
Royal Bank of Canada, Samsung Electronics, Saudi Arabian Oil Company, Shape Security,
Sophos Limited, Trend Micro Incorporated, UT-Battelle, LLC, and ZingBox, Inc.

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Following figure shows the deep learning for cybersecurity patent application landscape with
respect to the key technology innovation field. As shown in the figure, Malware Dection is the
most innovated deep learning for cybersecurity technology followed by Network Security,
Attack Activity/Event Detection, Data Security, False Tenant Model, IoT Device Security,
Malicious Webpage Detection, Security Alerts, Security Information Analysis, Threat Detection,
and Threat/Vulnerability Assessment.
Patent information can provide many valuable insights that can be exploited for developing and
implementing new technologies. Patents can also be exploited to identify new product/service
development opportunities.
Industrial IoT Cyber-Attack Detection/ US20180159879
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

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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 attack8
. 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.
Following figure shows a high-level architecture of the cyber-threats protection system
100. The system includes a "normal space" data source 110 and an abnormal or "threatened
space" data source 120. The normal space data source 110 stores, for each of a plurality of
"monitoring nodes" 130, a series of normal values over time that represent normal operation of
an industrial asset. The threatened space data source stores, for each of the monitoring nodes 130,
a series of threatened values that represent a threatened operation of the industrial asset (e.g.,
when the system is experiencing a cyber-attack).
8
https://www.mcafee.com/enterprise/en-us/security-awareness/ransomware/what-is-stuxnet.html

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Information from the normal space data source 110 and the threatened space data source 120 are
provided to a threat detection model creation computer 140 that uses these data to create a
decision boundary that separates normal behavior from threatened behavior. Then, the decision
boundary is used by a threat detection computer 150 executing a threat detection model 155. The
threat detection model 155 monitors streams of data from the monitoring nodes 130 comprising
data from sensor nodes, actuator nodes, and/or any other critical monitoring nodes. Then, the
threat detection model 155 calculates the deep learning features for each monitoring node based
on the received data, and "automatically" output a threat alert signal to a remote monitoring
devices 150, and then to a customer.
Following figure shows an autoencoder 1400 that is used for the deep learning features.
An encode process turns raw inputs 1410 into hidden layer 1420 values. A decode process turns
the hidden layer 1420 values into output 1430. Note that the number of hidden nodes can be
specified and correspond to number of features to be learned. The autoencoder estimates values
for W-matrix (weight matrix) and b-vector (affine terms) from a first group of training data. The
W-matrix and b-vector can then be used to calculate the features from a second group of training

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data. A non-linear decision boundary can be computed to the feature vectors. Then, the threat
detection computer 150 uses the decision boundary to classify whether a feature vector of new
data corresponds to "normal" or "attack" data. For high speed detection, a De-noised Auto-
Encoder ("DAE") can be used. For example, the DAE learns features from normal and abnormal
data sets. Through the input of many examples of normal data, the DAE algorithm learns a
representation of the data set in reduced dimensions exploiting the PCA (Principal Component
Analysis). This representation can be a non-linear transform of the raw data that encapsulates the
information of the original data.
Malicious Code Detection/ US20180285740
US20180285740 provides 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.
Following figure shows deep learning system 10 for detection of malicious code.

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Following figure shows blockchain privacy patent application landscape with respect to the key
technology innovation field. As shown in the figure, Data Privacy is the most innovated
blockchain privacy technology followed by Zero-Knowledge Proof, Transaction Privacy, Smart
Contract Privacy , User Profile Sharing (KYC), IoT Privacy, Multi-Chain Privacy, Lightweight
Blockchain Client Privacy, Privacy-Preserving Machine Learning, Data Sharing, Privacy-
Preserving Shared Distributed Computing.

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Patent information can provide many valuable insights that can be exploited for developing and
implementing new technologies. Patents can also be exploited to identify new product/service
development opportunities.
Anonymous Sharing of User Profile (KYC)/US20190028277
User consent-based information sharing is common. An example case is to share verified
personal information for improving compliance with know your customer (KYC) standards. If
one customer makes changes of personal information then those changes made to the customer
data by one provider should be immediately visible to others. However, service providers'
relationships with customers must not be revealed while sharing information. Additionally, when
a customer decides to revoke access to his/her data to any service provider, that provider must
not be able to view any updates to the customer's data thereafter. Creating such privacy
mechanisms can be developed by storing a user profile in a blockchain by an authorized member
of the blockchain, receiving a request by another authorized member of the blockchain to access
the user profile, identifying the request for the user profile is from the another authorized
member of the blockchain, creating a signed message that includes consent to share the user
profile with the another authorized member of the blockchain, and transmitting the signed

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message to the another authorized member of the blockchain. The blockchain members are
performed without revealing blockchain member identities of the authorized member of the
blockchain and the another authorized member of the blockchain to any of the blockchain
members.
Following figure illustrates a configuration 100 for the anonymous sharing of a user
profile.
The configuration 100 includes a customer device 112, providers ‘A’ 116 and ‘B’ 118. The
provider 116 is first provider that has an established account profile stored in a blockchain on
behalf of the customer device 112. The provider 118 is the third party provider seeking access to

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the user credentials (i.e., name, profile information, verified identity documents etc.). The
blockchain members 102-108 represent established leaders or consensus members to the
blockchain 110. The providers can also be members to the blockchain and participate in
consensus.
In operation, the customer device 112 enrolls to membership services 114 which receive
customer account information and store the information in a profile space of the blockchain. The
provider 116 receives the customer information (KYC) 124. Then, the provider 116 creates a
user profile based on a new account, updated account, etc. The information can be encrypted and
stored 126 in the blockchain 110. The provider 116 can add/update the customer information to
the blockchain after encryption using a newly generated symmetric key. A request to access the
profile can thus be seen as a request to the blockchain for the decryption key. For anonymous
key-sharing, to avoid peer-to-peer key exchange that can violate the privacy of providers, the key
is exchanged through the blockchain itself. Relying on a trusted PKI, the profile decryption key
is itself encrypted in such a way that only other authorized providers on the blockchain network
will be able to decrypt the key.
Subsequently, another provider 118 requests consent 128 and receive consent 132 from
the customer device 112. The consent is a signed message that includes an encryption key or
other information necessary to identify the customer profile and access the information. The third
party provider 118 then uses the signed credentials to request the customer information 134
directly from the blockchain 110 without communicating with the original provider 116. The
original provider 116 submits an encryption key 136 that can be used to decrypt the customer
profile. The blockchain member or other processing entity grant the access 138 based on
information stored in a smart contract stored in the blockchain. The information can then be
received, decrypted and used by the third party provider 118.
Following figure illustrates the configuration 150 for revoking the previously consented
access right to the user profile.

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The customer device 112 initiates the revocation operation 152 by notifying the blockchain of a
decision to revoke a previously granted access right. The provider 116, periodically, or via a
trigger from an access revocation operation, generates a new key 154 and encrypt the customer
profile via the new key 156. In operation, the previously privileged third party provider 118 now
attempts to read the new key and customer profile 158 and decrypt the profile unsuccessfully 162.
Signed consent and ACL-based access and revocation can include a blockchain using a
signed message representing customer's consent to authorize a provider for read/write access to
the customer profile. Access can be achieved without revealing a real identity of a provider to
other members, otherwise the constraint will be violated. Upon revocation, the signed message
on the blockchain is invalidated, and a different decryption key is generated to satisfy another
constraint. The smart contract authorization decision is decentralized and is implemented in
smart contract, and thus, ensures consensus among members. Consent signed by the customer
must not explicitly identify provider B to satisfy the anonymous constraint. The customer must
ensure that the consent request was originated by provider B and not replayed by an
unauthorized party. The consent must be explicitly bound to the profile request so that it cannot

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be misused. The profile must be readable only by a provider possessing valid consent from the
customer. An auditor must be able to unambiguously map the identity in the consent to a real
identity. Consent revocation signed by a customer must not explicitly identify the third party
provider. Future updates to customer profile should not be readable to that revoked service
provider.
Anonymous Transaction with Increasing Traceability/US20200134586
Some cryptocurrency transaction systems have higher anonymity and lower traceability, such as
Bitcoin. A user can open an account without disclosing any real world identification. Each
account is identified by a virtual wallet address. Account owners can then receive and remit
cryptocurrencies from and to others. Transactions recorded in blockchains include senders'
virtual wallet addresses, recipients' virtual wallet addresses, and remittance amounts. Such
systems have high anonymity and low traceability, since there is no connection between account
owners' real world identification and their virtual world identification. It is almost impossible to
trace the real senders and recipients of transactions. As a result, such a system can become a
money laundering facilitator. To avoid the cryptocurrency transaction system from becoming a
money laundering facilitator, the system has to provide a mechanism to trace specific
transactions when necessary while increasing anonymity.
To increase anonymity, several measures can be used: Transaction sender is not allowed
to access recipient's virtual identification so that it cannot easily trace a specific remittance
transaction; Transaction recipient is not allowed to access sender's virtual identification.
However, to construct a remittance transaction, both sender's virtual identification and recipient's
virtual identification have to be used. Thus, the sender and recipient have to work cooperatively
but at the same time avoid unnecessary information sharing to protect personal privacy. Since
sender cannot access recipient's virtual identification, recipient has to provide a token to sender
as a temporary replacement of recipient's virtual identification. Through this approach, a hacker
cannot trace specific remittance transactions even if it can access the distributed ledger of the
distributed transaction consensus network. Likewise, no party can trace specific remittance
transactions because it cannot match both sender's and recipient's physical identification and
virtual identification. To further increase anonymity, a signature on the encrypted message can
be created and send them together to prove authentication of the message.

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To avoid the cryptocurrency transaction system from becoming a money laundering
facilitator, the system has to provide a mechanism to trace specific transactions when necessary.
A remittance transaction comprises a plurality of sub-transactions (“transaction set”), including
sending transaction, inter-manager transaction, and delivery transaction. Either sender or
recipient can create a label encrypted by using a tracing key for each sub-transaction. The label
includes information to identify each sub-transaction in a transaction set and their sequences. A
tracing key can be a symmetric key or an asymmetric key. When the tracing key is a symmetric
key, each cryptocurrency transaction system creates and holds its own symmetric tracing key for
encrypting labels. The symmetric tracing key is also shared with network administrator or an
authorized/assigned node. The authorized or assigned node that performs the tracing function is
considered as network administrator. When the tracing is an asymmetric key, network
administrator or an authorized/assigned node creates a tracing key pair comprising a tracing
public key and a tracing private key. The tracing public key is used by cryptocurrency
transaction system to encrypt labels for each sub-transaction in a remittance transaction set. The
tracing private is kept confidential by the network administrator. When necessary, a network
administrator or an authorized/assigned node can decrypt the label and reconstruct the whole
remittance transaction.
Zero-Knowledge Proof for Digital Asset Transaction/US20200034834
Zero-knowledge proof is a cryptology technology such that is able to be designed to prevent an
observer of the distributed ledger from determining any information of the transaction that is
taking place. Yet, the transaction agent can use the proofs to verify access rights, correctness of
the transaction and compliance of the operation according to the defined business rules and
system state. Such proof is also used to update the system state without disclosing on the
distributed ledger information about the participants to the transactions, the identifier that is
subject of the transaction and logical links across participants, identifiers and transactions. Thus,
the system eliminates the possibility of mining the data from the ledger to extract undesired data
correlations and it protects against leak of business intelligence on the distributed ledger.
Zero-knowledge proof algorithm can be combined with a digital asset publishing
mechanism of blockchain in digital asset transfer transaction so that a node device in the

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blockchain can verify whether the asset type of an input digital asset is the same as the asset type
of the output digital asset without providing any sensitive information to the verifier.
In an unspent transaction output (UTXO) model, a digital asset transfer transaction is
published on the blockchain to trigger destroying a digital asset held by an asset transferor and
recreating a new digital asset for an asset transferee, so as to complete a transfer of the digital
asset object between different holders. When the asset transferor needs to transfer the held digital
asset in the blockchain, the asset transferor can input the output digital asset as input data to a
commitment function for calculation to obtain a commitment value, and can generate, based on
the zero-knowledge proof algorithm included in the blockchain, a proof used to perform a zero-
knowledge proof on the commitment value. Then, the asset transferor can construct the asset
transfer transaction based on the previous commitment value and the proof, and send the asset
transfer transaction in the blockchain, to complete an asset object transfer.
When receiving the asset transfer transaction, the node device in the blockchain can
obtain the previous commitment value and the proof that are included in the asset transfer
transaction, and then perform the zero-knowledge proof on the commitment value based on the
previous proof by using the previous zero-knowledge proof algorithm, to verify whether the asset
type of the input digital asset is the same as the asset type of the output digital asset in the asset
transfer transaction.
If it is determined, through verification, that the asset type of the input digital asset is the
same as the asset type of the output digital asset in the asset transfer transaction, the commitment
value corresponding to the output digital asset is published on the blockchain for deposit, to
complete transfer of the digital asset between different holders.
Because the asset transfer transaction includes only the commitment value that is
generated through calculation by inputting the asset type of the output digital asset in the asset
transfer transaction as input data to the commitment function, and the asset type of the output
digital asset is not included in the asset transfer transaction in the form of plain text, the
blockchain can hide the asset type of the digital asset transferred out by the asset transferor, and
privacy of the asset transferor can be protected.

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F. Blockchain Interoperability Technology Innovation Status
To find blockchain interoperability technology innovation status, 28 published patent
applications in the USPTO that are related to the key blockchain interoperability technology
innovation are selected for detail analysis.
Following figure shows blockchain interoperability patent application landscape with respect to
the innovation entity. As shown in the figure, the key blockchain interoperability innovation
entities are Alibaba Group, Accenture, IBM, JPMorgan Chase Bank, Blockstream Co., Intel,
Salesforce, Paypal, and Infineon Technology.
Following figure shows blockchain interoperability patent application landscape with respect to
the key technology innovation field. As shown in the figure, Cross-Chain Transaction is the most
innovated blockchain interoperability technology followed by Cross-Chain Blockchain Domain
Name, Interoperability Node, Cross-Chain Data Operation/Access, Oracle (Blockchain-

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Nonblockchain Interoperability), Interoperability Smart Contract, Smart Contract Reusability ,
Cross-Chain Cryptocurrency Swap, Distributed Ledger Gateway, and Interoperable Relay-Chain.
Patent information can provide many valuable insights that can be exploited for developing and
implementing new technologies. Patents can also be exploited to identify new product/service
development opportunities.
Interoperability Smart Contract / US20200099533
Blockchain interoperability enables multiple distributed ledger networks (DLNs) to provide data
sharing, transferring, and synchronization between the DLNs. Following figure illustrates an
example of the interoperable blockchain system 100. The system includes blockchain
participants 102 that participate in a DLN 104. The blockchain participants 102 include full or
partial nodes of the DLN 104. The DLN includes the participants of the DLN that access the

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blockchain 106. The blockchain 106 includes datablocks 107 that are linked cryptographically.
The blockchain participants 102 includes a data furnisher 108 that furnishes particular
information stored in the blockchain 106 to the receivers external to the DLN 104. The data
receiver 110 includes a non-participant of the DLT network 104 or a participant of a separate
DLN. Unlike the data furnisher 108, the data receiver 110 cannot have access to the blockchain
106 for the DLN 104. The data receiver 110 receives the token data stored in the blockchain 106
from the blockchain participants 102, such as the data furnisher 108.
The blockchain participants 102 further include a membership service provider 112. The
membership service provider 112 provides access to the identities and cryptological information
associated with the blockchain participants 102 of the DLN 104. The membership service
provider 112 includes a membership service repository 114. The membership service repository
114 includes a database that stores the identities and cryptological information associated with
participants and non-participants of the DLN 104. The identities information includes IP
addresses, MAC addresses, host names, user names, and/or any other information that identifies
a participant or non-participant of the DLN 104. The cryptological information includes any
information that is used to ensure the authenticity of a digital signature such as a public key that
corresponds to a private key that is applied to generate a digital signature.
The data furnisher 108 and the data receiver 110 communicate with the membership
service provider 112 to receive the public key of the data furnisher 108. The data receiver 110
submits a message or query to the membership service provider 112. After receiving the public
key, the data receiver 110 verifies the truth of token data shared by or exported from the DLN
104. For example, the data receiver 110 receives authorization information from the data
furnisher 108. The authorization information includes a digital signature corresponding to the
token data. The digital signature includes a certification that the data furnisher 108 and data
receiver 110 consents to a particular action, such as exporting token data. The signer of the
digital signature can be confirmed based on the public key that is paired with the private key
used to sign the signature. Sharing /exporting information to/with the data receiver 110 present
technical challenges: Ability for the data receiver 110 to verify that the token data is valid and
authorized for sharing/export; Preventing double spend between the blockchain participants 102
of the DLN 104 and non-participants; and Ensuring synchronization of the token data between
participants of the DLN 104 and non-participants of the DLN 104.

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In the following figure the data furnisher 108 has access to the furnisher blockchain 210
and the data receiver 110 has access to the receiver blockchain 212. The data furnisher 108
exports token data to the data receiver 110. The exportation of token data involves token data on
the furnisher blockchain 210 and committing the token data to the receiver blockchain 212.
Before the token data is locked on the furnisher blockchain 210 (adding the data block to the
blockchain) and committed to the receiver blockchain 212, various preconditions, authorizations,
and data manipulation can occur. The data furnisher 108 includes a furnisher synchronization
controller (FSC) 302. The FSC 302 coordinates transfer and exportation of token data to a
remote blockchain. For example, the FSC 302 communicates token data stored on the furnisher
blockchain 210 to the data receiver 110 for storage on the receiver blockchain 212. The FSC 302

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can determine when a successful transfer is completed and lock the token data on the furnisher
blockchain 210.
The FSC 302 accesses an interoperability smart contract 304 to determine the criteria,
conditions, and parameters that dictate exportation of token data between DLNs. The
interoperability smart contract 304 includes an authorization to transfer data stored on the
furnisher blockchain 210 according to a protocol for asynchronous communication between the
furnisher DLN 202, the receiver DLN 204, and/or other DLNs. The interoperability smart
contract 304 includes terms, conditions, logic, and other information that the data furnisher 108
and the data receiver 110 agree to. The interoperability smart contract 304 also includes
identifiers corresponding to the token data in the furnisher blockchain 210 (data furnisher and
data receiver 110 that consent to the export). Additionally, the interoperability smart contract 304
includes a cryptologic committal 306. The cryptologic committal 306 includes commit logic
configured to cause the data receiver 110 to commit the data to the receiver blockchain 212. In
general, token data can be considered committed when the token data is appended to the receiver
blockchain 212. The commit record identifies the participants, DLNs, token data, and any other
information that records the transfer event.

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The interoperability smart contract 304 further includes a transfer logic 308. The transfer
logic 308 includes logic configured to cause the data receiver 110 to receive, generate, and
append the token data to the receiver blockchain 212. For example, the transfer logic 308 can
include instructions to require or validate information received by the data receiver 110. The
transfer logic 108 determines whether, according to predetermined rules, valid token data is
received by the data receiver. The transfer logic 308 also causes the data receiver 110 to re-create
the token data in a manner that is compliant with the receiver DLN. The data furnisher 108
recreates the token data based on the transfer logic prior to sending the data to the data receiver.
The transfer logic 308 appends the token data to the receiver blockchain 212 for compliance with
the receiver DLT.
The FSC 302 receives a pre-commit acknowledgement 310. The pre-commit
acknowledgement 310 includes a verification that the token data was successfully received and
generated by the data receiver 110. The pre-commit acknowledgement 310 indicates that the
token data was successfully appended to the receiver blockchain 212. The pre-commit
acknowledgement 310 can include digital signatures signed by the data receiver 110. For
example, the data receiver 110 can be identified in the interoperability smart contract 304. The
digital signatures can verify that the data is properly re-generated and added to the receiver
blockchain 212 in compliance with the receiver DLN and the criteria of the interoperability smart
contract 304.
To synchronize the locking and committal of the token data transferred between DLNs,
the FSC 302 encrypts the interoperability smart contract 304 such that the data receiver 110 is
initially receives the interoperability smart contract 304 without the ability to perform the
commit according to the committal logic. Additionally, the FSC 302 encrypts information that
the interoperability smart contract 304 accesses to perform the committal. For example, the FSC
302 encrypts the cryptological committal, other portions of the interoperability smart contract
304, or information provided to the interoperability smart contract 304 based on a hash function
and a committal key 312. In response to receipt of the pre-commit acknowledgement 3