ISSN 0915-3160

通巻百三十一号

No.

3

Vol. 33 July 2021
SummerS pec i a l Feat u re

Promising ICT Solutions for the New Normal After COVID-19

Image Recognition and Facial Authentication Solutions as Measures for
COVID-19
Real-time Detection of the “Three Cs” with Image Analysis “COVID-19 AI
Image Analysis Solution”
Non-Desk work DX with Connected Worker: Attracting attention around theworld in the new COVID-19 era
Corona Tracer®: Preventing expansion of workplace clusters
Making SNS and other Digital Spaces more Safe and Sound

New Breeze Vol. 33 No.3 Summer

New Breeze

July 2021

C O N T E N T S

ISSN 0915-3160

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		 Special Feature — P romising ICT Solutions for the New Normal
1
5
9

Editorial Committee
Chairman: Wataru Kameyama
Members: Ministry of Internal Affairs andCommunications, Association of Radio Industries
and Businesses, Communication Line Products
Association of Japan, Fujitsu Limited, Hitachi, Ltd.,
Japan Broadcasting Corporation,
KDDI CORPORATION, Mitsubishi Electric
Corporation, National Institute of Information andCommunications Technology, NEC Corporation,
NIPPON TELEGRAPH AND TELEPHONECORPORATION, Oki Electric Industry Co., Ltd.,
Panasonic Corporation, Softbank Corp.,
Sony Group Corporation,
The Japan Commercial Broadcasters Association, and
The Telecommunication Technology Committee

15
19

After COVID-19

Image Recognition and Facial Authentication Solutions as Measures
for COVID-19
Real-time Detection of the “Three Cs” with Image Analysis“COVID-19 AI Image Analysis Solution”

Non-Desk work DX with Connected Worker: Attracting attention
around the world in the new COVID-19 era
Corona Tracer®: Preventing expansion of workplace clusters

Making SNS and other Digital Spaces more Safe and Sound

		 Digital Opportunities
23

FY2020 APT Workshop— Development of fundamental network planning skills for
E-application in regional community to bridge the digital divide —		Column
25= A Serial Introduction Part 2 =Winners of ITU-AJ Encouragement Awards 2020

Publisher: Tetsuo Yamakawa
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Junichi Kishimoto
Kaori Ohno
Naoko Ishida

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Image Recognition and Facial Authentication Solutions
as Measures for COVID-19

Koh Sungmyeong

Assistant Manager
5G & IoT Business Department, NTT DOCOMO, Inc.

1. Introduction

Since the expansion of the COVID-19 pandemic, there arefewer opportunities for human contact, and we have needed toadapt to new types of lifestyle. In many fields, this is resulting inaccelerating use of machines to automate work that was formerly
done by people.
The camera modules found in smartphones and other devices
have become both generic and inexpensive, and performance
has leapt forward in recent years. With a single smartphone, thecamera and communication modules can be used easily, together
with image recognition technologies on the cloud. As such, image
recognition technology has reached a stage where it can be usedpractically as a technical element in replacing human work withmachinery.
In March, 2020, NTT DOCOMO also began provisionof our 5 th generation mobile communications system(5G), which features high speed and capacity, low delay, andmassive connectivity. 5G will enable video transmission withhigher capacity than ever before, so solutions combining 5G
communication and image recognition technology are expected toappear.
This article introduces NTT DOCOMO’s image recognition
platform, the “EasyPass powered by SAFR” face-recognition
solution and the “AI Temperature Measurement Solution,” as apotentially effective measures against infectious disease.

2. NTT DOCOMO Image Recognition Platform

In May, 2020, NTT DOCOMO began providing our “NTTDOCOMO Image Recognition Platform”[1]. This platform is a
cloud service that makes it easy to use our image recognition AI.
Previously, application of image recognition AI solutions
required much work and specialized technology, including
annotation work creating the data used to train the image
recognition AI, generating the trained model, and building
the image recognition engine. The NTT DOCOMO Image
Recognition Platform automates these processes, from creating
the training model to building the image recognition engine, solead time for introducing solutions can be reduced significantly.
Developers can focus their efforts on developing the application orsystem that will use the image recognition AI, rather than on theimage recognition AI component (Figure 1).
One example using the NTT DOCOMO image recognition
technology was to automate inspection of mobile phone basestations for rust using aerial images taken by drones. NTT
DOCOMO has mobile base stations in approximately 50,000
locations and the work of inspecting them is time consuming
and dangerous. Automation of inspection work contributes toimproved work environments and reduced costs. An example ofusing the image recognition engine to detect rust areas is shownin Figure 2. The state of rusted areas can be checked from basestation images taken by the drones. As a result of this automation,■ Figure 1: NTT DOCOMO image recognition platform overview

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■ Figure 2: Detection of rust■ Figure 3: Comparison of recognition speed and algorithm
size

the time required to inspect each base station can be reduced
by approximately 100 minutes. The technology is to be used forinspecting base stations at approximately 1,500 locations, whichshould eliminate more than 2,000 hours of work[2].

3. COVID-19 Solution using Face
Authentication

3.1 The SAFR® Face Authentication Engine
The SAFR face authentication engine provided by Real
Networks Inc. is image recognition software that authenticatesusers quickly and accurately using a face image. NTT DOCOMO
has recognized the technical superiority of SAFR and its
applicability in mobile solutions, and has used it as the facialrecognition engine in various face authentication solutions. It has
also obtained good results for recognition accuracy and speed in
the Face Recognition Vendor Tests (FRVT) organized by the
USA National Institute of Standards and Technology (NIST)[3]. According to SAFR developer, Real Networks Inc., it was
the fastest and most light-weight among algorithms achieving
recognition accuracy of 96% or greater in FRVT 1:1 recognition
tests conducted in FY2019 [4]. Compared with other highly
accurate algorithms, SAFR was twice as fast as the averageand 35% smaller than the next-smallest algorithm (Figure 3).
Thus, SAFR achieves a good balance in the tradeoff between
recognition accuracy and computational cost, and does not require
expensive computation devices, so it is very practical. In the
FRVT Wild category, which focuses on recognition of subjects
that are unaware of the camera including profile images, SAFR
was shown to be extremely fast and accurate as an algorithm
for recognizing images in live video. In addition to being
highly evaluated in the FRVT Wild category, SAFR also has
development environments for smartphones, including iOS andAndroid SDKs, so it is very attractive for NTT DOCOMO’s
solutions business and we are introducing it into various solutions.
With the spread of the COVID-19 pandemic, SAFR also

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added support for recognition of users while wearing masks in
April 2020[5]. To recognize faces obscured by masks or otherobjects, the AI recognizes the parts of the face that are not
obscured. The ability to perform authentication without requiring
masks to be removed is extremely useful under conditions of the
COVID-19 pandemic. According to Real Networks internal
testing, SAFR achieved 98.85% accuracy recognizing subjects
wearing masks in the Wild category.
Figure 4 shows a case of face authentication while wearing amask.
■ Figure 4: Facial authentication while wearing a mask

■ Figure 5: EasyPass overview

3.2 EasyPass powered by SAFRIn May, 2020, NTT DOCOMO began providing the“EasyPass powered by SAFR” service (“EasyPass”)[6]. An overview
of EasyPass is shown in Figure 5.
The EasyPass solution achieves access control with simple,
fast and highly accurate face recognition by simply installing the
EasyPass application on a smartphone. It is expected to be usedby providing security personnel with a smartphone, which will
be used to check those entering and leaving a facility by simply
having them face the smartphone camera.
EasyPass is both convenient and extensible throughintegration of SAFR face authentication with Genetec ®
Security Center SynergisTM , which is a Video Management
System (VMS). It can be implemented with smartphones andmobile networks, so it requires no installation of cameras orcommunication lines or other construction, minimizing
preparation time required to introduce the system. It also hasvarious operational benefits from using smartphone devices, suchas the ease of replacing any faulty devices.
In one company that has introduced EasyPass, security staff
at entrances previously checked photo IDs of those enteringand leaving, which resulted in line-ups and congestion at the
beginning and end of the workday. These conditions also createdsecurity risks such as potential for impersonation or error by
security staff. After EasyPass was introduced, security personnel
only had to visually check the authentication result on theapplication, reducing the time required to less than one second(Figure 6).
EasyPass is extremely useful as a way to deal with COVID-19,
for enterprises enforcing this sort of access control. Since EasyPassdoes not require removal of masks for authentication, it can reduce
the risk for security personnel, of infection from those enteringand leaving. According to analysis of COVID-19[7], doubling■ Figure 6: A uthenticating a driver using EasyPass

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■ Figure 7: T emperature measurement using Seek Thermal

the distance between people halves the risk of infection. With
the earlier operating model, visual checks required distances ofapproximately 1 m, while this solution allows distances of 2 m, so
we can say the solution reduces risk by half.
3.3 AI Temperature Measurement Solution
In November, 2020, NTT DOCOMO began providing our
AI Temperature Measurement Solution[8]. By integrating the
SAFR face authentication engine, a temperature measurement
device from SeekThermal Inc., and communication tools such as
Microsoft Teams, it provides a solution that includes non-contact
staff authentication and temperature measurements together with
reporting and management. Although remote work is becoming
widely established, it is still difficult to completely eliminate theneed to visit company offices. Many companies are requiring
temperature measurements of those visiting their offices, which isburdensome for both those coming to work and for administration.
For commercial facilities with many visitors, one COVID-19
counter measure is to screen visitors by taking their temperaturewhen they enter. According to a September 2020 report fromthe USA Centers for Disease Control and Prevention, trial
calculations indicate that approximately 40% of patients are
asymptomatic, so taking temperatures when people arrive is
not a perfect measure, but from a psychological perspective forthose visiting a commercial facility, it does raise awareness thatthe facility is screening for people with a fever. This type ofsolution that takes consumer psychology into consideration is also
necessary for enterprises as they continue economic activity in our
new way of life.
Note that the temperature measuring device used for thissolution is the Seek Scan from Seek Thermal Inc. It is used
together with a black-body standard to calibrate the thermosensor, giving it excellent characteristics able to take temperatureswith only 0.3 degrees of measurement error. A SeekScan screen isshown in Figure 7. The red square appearing in the thermal image
on the right is the black-body standard, showing how the standard
temperature is constantly being measured.

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4. Conclusion

Technology has a large role in dealing with changes in ourlifestyles due to the spread of the COVID-19 pandemic. Solutions
using image recognition to check for the three C’s (Closed spaces,
Crowded places, and Close contact) or for masks can be expectedto spread in the future. With demand to replace human workwith machinery, image recognition AI is a technology elementthat is developing remarkably, replacing human eyes and brains,
but a simple and user friendly framework is needed to apply
these latest technology elements in solutions. NTT DOCOMO
is providing frameworks on the cloud, such as the DOCOMO
Image Recognition Platform and the SAFR face authenticationengine, that enable these advanced processing technologies tobe used easily from a smartphone or Web browser. We also
have our 5G Open Partnership Program, which is a base forpartner enterprises to collaborate with NTT DOCOMO, orwith each other, to create valuable solutions. Combining these
initiatives, NTT DOCOMO and these partner enterprises will
create solutions for societal issues in the future.
References[1] NTT DOCOMO, “NTT DOCOMO begins offering its DOCOMO Image Recognition
Platform,” 2020/5/29
https://www.nttdocomo.co.jp/info/news_release/2020/05/29_02.html[2] N TT DOCOMO Uses AWS to Build Cell Tower Inspection Tool That Cuts Costs, Improves
Safety, Amazon Webservice, 2020
https://aws.amazon.com/jp/solutions/case-studies/NTT-DOCOMO/[3] O ngoing Face Recognition Vendor Test, NIST 2019/10/16,
https://www.nist.gov/system/files/documents/2019/10/16/frvt_report_2019_10_16.pdf[4] The Fastest Facial Algorithm Just Got Faster, Realnetworks 2019/7/19,
https://safr.com/general/the-fastest-facial-algorithmjust-got-faster/[5] R eal Networks, “SAFR® AI Face Authentication adds new Functionality― Authentication while wearing a mask,” PR Times, 2020/4/2
https://prtimes.jp/main/html/rd/p/000000018.000046740.html[6] N TT DOCOMO, “Face authentication solution ‘Easy Pass powered by SAFR’ Launched,”
2020/5/29
https://www.nttdocomo.co.jp/info/news_release/2020/05/29_03.html[7] P hysical distancing, face masks, and eye protection to prevent person-to-persontransmission of SARS-CoV-2 and COVID-19: A systematic review and meta-analysis, THE
LANCET 2020/6/27
https://www.thelancet.com/journals/lancet/article/PIIS0140-6736（20）31142-9/fulltext[8] NTT DOCOMO, “AI Temperature Detection Solution Launched,”
https://www.nttdocomo.co.jp/info/news_release/2020/11/05_02.html, 2020/11/5[9] Pandemic Planning Scenarios, Centers for Disease Control and Prevention,
https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html

Real-time Detection of the “Three Cs” with Image Analysis“COVID-19 AI Image Analysis Solution”
Takashi Yanagihara

Representative Director, CEO
Ridge-i Inc.

1. Introduction

Efforts to stop the spread of COVID-19 and restore stableeconomic and social life continue. Negative impacts on the entire
market are huge, and annualized GDP is expected to drop 20%
or more. However, there are also industries that have prospered
during the pandemic, and solutions needed for the “New Normal”
are f lourishing, with demand for online meeting and SaaS
business tools for remote communication and remote work, robotic
technologies for remote operation and automation, and services
such as online shopping and delivery for at-home consumers.
With a mission to “Solve social and customer issues and create
new business and a new society using advanced technology,”
Ridge-i Co. Inc. has provided unique AI solutions in wideranging fields and has had much success* with AI image analysis,
focusing particularly on deep learning.
The majority of our members are either practitioners andresearchers in machine learning, or from strategic consulting
firms, pursuing ideas from a broad perspective (hence “Ridge”)
through knowledge synergy among specialists from various fields.
＊ One example of success in development is a technology toautomatically colorize black-and-white video, developed incollaboration with NHK Art Inc. In collaboration with Ebara
Environmental Plant Co. Ltd., we also developed a deeplearning system to recognize garbage features. It has beenin use for over one year as part of an “Automatic garbage
identifying AI crane” at a waste incinerator facility operated
by Funabashi City. We also have several initiatives using
AI to analyze satellite data, including a land-slide analysis
AI developed under contract with JAXA, and SAR image
analysis for the major oil spill that occurred near Mauritius,
as reported on Yahoo! News and other major media. We havebeen awarded the Fourth Space Development and Utilization
Grand Prize by the Minister of Economy, Trade and Industry(METI) for our utilization of advanced technology to solve
societal issues.
In this article, we discuss our “COVID-19 AI Image Analysis
Solution,” which is able to detect the three C’s: “Closed spaces,”“Crowded places” and “Close-contact settings”; from camera
images in real time. We developed and released it in only two
weeks after the state of emergency was declared in April 2020.
We discuss its features and future prospects, as well as future
requirements for AI technology and organizations.

2. Background to “COVID-19 AI Image
Analysis Solution”

On April 7, 2020, when many countries were just beginningto implement measures such as social distancing to prevent the
spread of novel coronavirus infection, seven prefectures, including
Tokyo, declared a state of emergency. On April 16, our Chief
Research Officer (CRO), Yoshitaka Ushiku and I met to discuss
for the first time whether there was anything we could do. That
was when the concept for “COVID-19 AI Image Analysis
Solution” took shape. The next day, we met with our researchengineers and decided on an architecture, and our engineers
volunteered to take on parts to be worked on in their spare time.
Development was completed on May 1st, and the whole solution
was released on that day. All work from conception to release wasdone in approximately two weeks.

3. Measuring closeness, crowdedness andcrowd size from camera video

The “Video Analysis AI Solution for COVID-19,” releasedin May 2020, is an AI solution that accurately detects the number
and location of people in network cameras in almost real-time,and analyzes the distance between people, the degree of crowding,and the flow of people in crowds. It consists of the following threeelements.
In addition, we offered a course on AI fundamentals (4) tohelp participants make effective use of their home time during the
major holidays under the emergency declaration and to deepen
their knowledge of AI and machine learning.
(1) C rowd counting, density estimation, and time-series
measurement of passers-by (for street cameras, commercial
facilities, and tourist facilities)(2) Close-contact alerts (for offices, hospitals, shops, etc.)(3) Person tracking and Re-ID(4) AI fundamentals lecture, free-of-charge on YouTube(1) is a system that analyze video from cameras in the street
or commercial areas, displaying congestion conditions as a heat
map and counting passers-by and the size of groups in the video
in near-real-time. It can also show a graph of the results, whichshows the number of passers-by over time. Currently, a demo of
the crowd-size counting module is available on YouTube, used
to analyze real-time video from areas of Hamamatsu City in
Shizuoka Prefecture (see link at the end of the article). This has

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been operating continuously since May, using AI functions thatanalyze images from cameras installed at fixed locations in thecity. It locates passers-by and areas with congestion in the video,
displaying data on the web page as a heat map or as a graph ofdaily and hourly traffic.
The camera stream is processed by an AI installed on thecloud, and the analysis is achieved with almost no delay from thelive video.
Currently, the system is accurate enough for practicalapplications in large facilities such as stadiums, commercial
facilities, or public offices with many people coming and going.
In the future, we intend to also support night-time operation, andwe are also already preparing to provide versions that run well on
Android and edge devices.
(2) assesses the separation of people in more closed
environments such as offices, hospitals, or shops; in order todetect and alert the user when people get too close, or whencongestion occurs as the density of people exceeds a certain level.
For example, in an office with a large number of staff movingaround, if the distance between them becomes less than 1.5 meters(the distance can be set freely), an alert is issued in real time.
It is also possible to notify by sound or vibration. In particular,

the distance between people is measured with high accuracy by
capturing the position of each foot using AI for posture estimationand person detection.
(3) performs person tracking and re-identification, identifying
each person by analyzing their face, bone structure, clothing,walking style, etc. without a need for prior registration of users.
The mechanism assigns a unique ID to each person when theyfirst appear on the camera image. Once this ID has been assigned,
it will be applied to that person even if they leave the image andreappear later. As such, the technology can be used to authenticatethat it is the same person (Figure 1). This person tracking function
could be used to check a person’s past movements from camera
video if, for example, they are diagnosed with COVID-19, to
identify who they were most in contact with or locations requiring
careful disinfection. This could also prove itself useful in order toperform procedures more efficiently after an infection is detected.
By using the kind of solutions that we provide, it is possible todetect and avoid “dense” situations in various environments in realtime. In particular, since they are based on camera image analysis
technology, they have advantages over mobile base station data
(Mobile Spatial Statistics), which operates on a coarse mesh, andcan cover indoor environments where GPS is weak. They can also

■ Figure 1: Person tracking and Re-Identification AI. Assigns IDs without prior registration. Assigns ID by making use offeatures such as posture, body type, etc. so that the same ID is assigned even after the subject puts on a jacket.

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determine whether masks are being worn through image analysis.
We have already had inquiries from public offices, retail chains
and automobile dealerships and we are conducting trials withsome of them.
Our fourth initiative (4) is unique as a solution. On theassumption that the number of on-line students will increase
because they are spending more time at home, we have beenoffering basic AI training lectures free-of-charge on our YouTube
channel since May. The lectures cover a curriculum conceived
by our CRO, Yoshitaka Ushiku, to provide an overall grasp of
AI. They include ten basic-lecture videos following the historyof machine-learning development, from the birth of AI to thepresent. This course is also used as a tool to build a correctcommon understanding of AI with our customers.

Our AI development for posture estimation and person
identification is at the top level in the world in academic
benchmarks, and in particular, our proprietary deep learning
method for posture estimation has achieved SOTA (State of theArt) in public benchmarks (as of June 2020).
A single deep learning is rarely enough to solve a real-world
problem, but by combining multiple deep learning models, users
can experience an end-to-end AI solution.

given limited resources and unexpected troubles, it has been
more difficult to work concretely based on this mission than we
imagined. There are other companies with similar missions, but
it seems that most of them were not able to maneuver under thedeclaration of emergency and its effects, or to adjust for remote
work and other measures. We also had planned projects just before
the declaration of emergency, which changed drastically becauseof it, and we were forced to respond quickly. However, taking
action under such conditions is the hallmark and raison d’etre of aventure company, and we managed to overcome these problems bya good margin.
In addition, in order to respond quickly to unpredictablesituations, it is important to create robust elemental modules andtechnologies such as the ones mentioned earlier, so that we can
combine them flexibly to provide an AI that can answer specificproblems. Beyond just advanced development skills, the ability
to understand real-world problems, divide them in elemental
components and translate them into technical requirements is also
crucial.
Even if we see what needs to be done technically to solve
the problem, it can be difficult to reach the decisions necessary
to execute it. In this case, our CRO, Yoshitaka Ushiku, and I
went ahead based on our own judgment, but we could not havesucceeded without our volunteers’ support. We strongly felt theimportance of our mission and our organizational culture. It
was important to decide roles quickly and pursue multiple tasks
simultaneously without waste of time, not only by top-down
decisions but also by parallel and organic decisions taken at alllevels of hierarchy.
The four solutions we provided this time are not the ultimate
solutions to problems, but they have provided an excellent
opportunity to challenge the technologies and the organizational
culture we have cultivated over four years. I think the greatest
assets we have gained are a greatly increased awareness of ourdevelopment processes, of what sort of AI technical elements
can be modularized to provide functionality quickly and withflexibility, and the real experience of creating a solution within aperiod of only two weeks.

5. New social challenges from the COVID-19
pandemic

6. AI technology requirements and company
qualities required in the future

4. AI technologies used in the solution

Our COVID-19 AI Image Analysis solution’s strengths are its
exceptional accuracy and inference speed in each of the technicalelements, as well as how they are integrated. The main technicalelements used in the solution are described below. While each ofthem alone could be considered AI, we refer to combinations ofmultiple elements as a single solution.
・Pose estimation Deep Learning
・Human Detection Deep Learning
・Human attribute classifier Deep Learning
・Human and Object Tracking Technology
・High-speed, stable video stream processing
・Crowd Counting Deep Learning

One thing we felt keenly in creating this “COVID-19 AI
Image Analysis Solution” was the importance of preparing systems
such that technologies can be provided immediately when needed.
To solve immediate needs, quality is of course necessary, but speed
is required above all else.
As mentioned in the introduction, our mission is to “Solvesocial and customer issues using advanced technology andcreate new business and a new society,” but practically speaking,After the announcement of the solution, we received not
only inquiries from commercial facilities, but also requests forinterviews from various media groups, including NHK. It wasparticularly impressive that we were asked not only about thedetails of the functions and technology, but also about why wedecided to create such a solution.
Six years have already passed since the tertiary AI market in
Japan began gaining notice in 2014. With a large amount of AI

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ventures in the market, it is difficult for a company to shine basedon its AI technology alone. The market also has a reputationthat many projects do not go beyond the PoC stage, resulting
in mixed feelings of hope and disappointment. According tomaterial published by the Ministry of Economy, Trade andIndustry (METI) in March 2020, 97% of AI related projects areabandoned before completion, which is quite surprising. We havesummarized the obstacles faced by some representative projects
below (Figure 2).
Considering the level of maturity of this market, companies
that have been developing AI thus far will need to make drasticchanges. They are already expected to provide not only promising
AI prototypes, but also successful integrations that can be used inpractice.
Social and business needs are also changing prompted by
the COVID-19 pandemic, but the New Normal has not settled
yet. Under such conditions, we must assume that there are still
no precedents for some user needs or how to address them, andventures must find agile solutions quickly, by experimenting
quickly and not being afraid of failure.
In the New Normal, the need will likely increase for solutions
characterized by keywords such as remote monitoring, remote
operation, semi-automation, and location-free. AI technology will

be a necessary element of such solutions, but many problems inrobotics, communications, edge inference, sensing and other fields
cannot be solved with AI alone, making it increasingly importantto have partnerships with enterprises in various other fields.
The qualities that a company must have in such anenvironment include (1) ability to sense and adapt flexibly to newneeds in society, (2) rapid development, deployment capabilities
and technical assets to respond quickly to urgent issues, (3) design
and technical capabilities to go beyond planning to successfully
deploy their solution and produce results, and (4) ability tocollaborate with partners in solving problems rather than only
acting alone.
At Ridge-i, we plan to maximize the AI technology assets andpersonnel that we have cultivated to provide solutions that produce
major benefits for our customers, replacing the negative impacts
brought on by COVID-19. We encourage partner companies andindividuals to contact us, to propose and implement solutions for
the New Normal together.
Related Links
Ridge-i Inc. https://ridge-i.com/
Hamamatsu City, Kajicho-dori, Live demo of counting crowd numbers(Ridge-i YouTube Channel) https://www.youtube.com/channel/UCTBaJrfibBNosxCVEjPnYWw■ Figure 2: Bottlenecks to introduction of AI. 97% of AI projects are abandoned uncompleted.

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Non-Desk work DX with Connected Worker: Attracting
attention around the world in the new COVID-19 era
Jun Kuchii

Chief Operating Officer/ Futurist
Fairy Devices Inc.

1. What is Connected Worker and how is it
advancing Non-Desk work DX?

Even though the level of skill required and workload areincreasing, most manufacturers and infrastructure industries
continue to face the challenge of workforce shortages. On theother hand, automation using robotics is not progressing fastenough. Robots are skilled at handling fixed tasks, but they cannot
work with the flexibility that people can. For some tasks, such as
bulk materials handling that people can do easily, even as a parttime job, most attempts to implement them with robots have notbeen practical.
Robots are also expensive, so it is difficult for all but thelargest companies, such as automobile manufacturers, to adopt
them. Under these conditions, digital technologies that are able
to reduce the workload for on-site workers and multiply the added
value created by several times have begun to attract attention.
An example of such technology, which uses digital devices
that on-site workers can wear to receive AI and IT support,
improve their skills and implement DX, is a solution that we call“Connected Worker.”
Connected Worker can improve the standard of on-site work
to that of an experienced technician through use of various digital
supports including: (1) remote support from an experienced
technician, (2) automatic collection of data during work (automatic
creation of reports, etc.), (3) collection of work-site data, generationof hazard alerts, (4) training and manual updates based on pastwork data, and (5) work support using data and AI.
Connected Worker is starting to be introduced to achievecomplex goals such as “improving work productivity,” “managing
worker health and safety,” and “preventing work errors andomissions,” for on-site work that is intolerant of error, such as inhigh-tech materials facilities, chemical plants, oil refineries, andelectrical power plants.

2. Non-Desk work DX achievable with
Connected Worker

Currently, particular benefits from implementing Connected
Worker are: more efficient business travel, because the onsite worker can receive remote help working together with anexperienced technician on technical tasks; and more efficientcompletion of non-core tasks during daily work, such as decisionmaking, creation of reports, and collection of evidence and otheron-site data.
I expect many readers have had the difficult experience, asI have, of introducing DX or AI, such as information in real
documents that are not converted to digital data, or huge amountsof data which require a tremendous workload to cleanse.
Introducing Connected Worker enables you to improvebusiness travel and non-core work efficiencies, while alsocollecting data.
■ Figure 2: Connected Worker use cases and effects■ Figure 1: Value provided by Connected Worker

Copyright © 2021 Fairy Devices Inc. All rights reserved.

Figures 3, 4 and 5 show use cases of DX in on-site operation ofa factory, a construction site and for maintenance, respectively. We
have found that it can also be effective in logistics, warehousing
and transport domains. For example, by assigning a speech
recognition digital device to long-distance truck drivers, they caninteract with the fleet management system while driving, within
the scope of what is permitted legally.
Copyright © 2021 Fairy Devices Inc. All rights reserved.

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9

■ Figure 3: Connected Worker use case in a factory

Copyright © 2021 Fairy Devices Inc. All rights reserved.

■ Figure 4: Connected Worker use case on a construction
site

Copyright © 2021 Fairy Devices Inc. All rights reserved.

■ Figure 5: Connected Worker use case for maintenanceshortages. In the 2020s in particular, more skilled technicians are
retiring each year, and the shortage of personnel is expected tomake it increasingly difficult to maintain operations in factories
and other infrastructure. This is one of the major reasons why DX
is needed in the workplace.
A shortage of workers is emerging in over 90% of enterprises
in manufacturing industries and for more than 30%, this has
already affected their business＊1. In infrastructure industries
requiring qualified technicians, such as electrical power, a shortageof electrical safety workforce numbering in the thousands is
expected by around 2030＊2.
On the other hand, factories and infrastructure are getting
more complex and advanced, so it is expected that technologists
working in the field will require even more advanced judgment
and knowledge in the future than they do now.
In other words, to maintain its GDP and highly developed
infrastructure, Japan will need to increase both the quality and thenumber of technologists in the workforce.
The government has advocated a “Connected Industries,”
approach to address this situation, promoting DX in all industries.
IoT and Digitalization have been promoted for several years,
and Connected Industries expands these ideas, using digitaltechnologies to create better working environments, to pass ontechniques, skills and knowledge, to increase productivity andeven to try to solve societal issues＊3.
■ Figure 6: E xamples of the benefits of Connected
Industries(*3 from P19 of the METI document)

Copyright © 2021 Fairy Devices Inc. All rights reserved.

* Refer to the following for use case details.
https://fairydevices.jp/cws

3. Why is Digital Transformation (DX) neededin the first place?

I admit that for most readers I am preaching to the choir,
but in recent years, the declining population has resulted in labor＊1 METI, “The shortage of personnel in manufacturing industries and the utilization of foreign personnel,” (July 12, 2018).
https://www.meti.go.jp/press/2018/07/20180712005/20180712005-2.pdf＊2 METI, “Electrical Safety Personnel/Technology WG,” (November 25, 2019).
https://www.meti.go.jp/shingikai/sankoshin/hoan_shohi/denryoku_anzen/hoan_jinzai/pdf/20191125_report.pdf (in Japanese)＊3 METI, “Kanto Bureau of Economy, Trade and Industry IoT and Robot Project and FY2019 Policies,” (February 2019)http://kantou.mof.go.jp/content/000226631.pdf (in Japanese)10

New Breeze Summer 2021

Copyright © 2021 Fairy Devices Inc. All rights reserved.

4. Why is Connected Worker attracting
attention for implementation of DX?

To implement DX requires building a “digital twin,”
optimizing in a digital virtual space, and then feeding it back into
execution in real space.
However, this path to implementation is not an easy one.
To achieve valuable DX with a digital twin requires a longprocess involving the following activities: (1) “Collecting” data,(2) “Curating” data into a shape that can be used, (3) “Cognition”
analyzing data and deriving inferences, (4) “Consultation” toderive measures to optimize for efficiency from the obtained
inferences, and (5) “Cropping,” to finally implement the measuresand reap the benefits of more efficient work.
To reach the final execution phase requires the worksite tobear the burden of collecting data, and costs for the enterprisecontinue. For ICT vendors like us, who are handling the effort,
maintaining the system infrastructure is a 24-hour-a-day task,
which amounts to rebuilding a society.
We have conducted earlier DX initiatives using IoT andother devices and found that there was not enough data createdby human activity to achieve efficiencies through digitalization.
Achieving such efficiencies requires DX of the whole business
and without doing so, it will not be possible to reap much value
for management. For this reason, we have now begun to focus on
using Connected Worker in various places, to digitalize the people
involved as well.

such operation requires many skilled technicians. Originally,skilled technicians would be dispatched from Japan to the location
to provide technical support, but this has become difficult due
to entry restrictions and measures to maintain safety. As such,methods to achieve DX while providing support in the developing
country from the developed country have become necessary.
The demand for digitalization measures using the“1.(1) “Remote support from an experienced technician” with
Connected Worker have increased due to COVID-19. This is theidea of enabling skilled technicians to telework.
■ Figure 8: Digitalization of devices and of people

Copyright © 2021 Fairy Devices Inc. All rights reserved.

■ Figure 9: Three workplace issues caused by COVID-19■ Figure 7: The 5 C’s of DX implementation

Copyright © 2021 Fairy Devices Inc. All rights reserved.

6. Our initiatives related to Connected Worker

Copyright © 2021 Fairy Devices Inc. All rights reserved.

5. Rapidly increasing demand for Connected
Worker due to COVID-19

In the midst of the great confusion caused by COVID-19
around the world, Connected Worker has begun to attract global
attention. A significant reason is that, due to COVID-19, it has
become more difficult to send skilled technicians for operation or
maintenance of advanced machinery that requires hands-on work.
In particular, companies in developed countries often operate
special facilities or factories in developing countries overseas, andAt Fairy Devices, where I am COO, we had requests toimplement speech recognition in a factory, which prompted us tofocus on this type of issue in on-site work, and continue testing on
work-sites.
However, most wearable devices are made based on consumer
oriented use cases, for entertainment such as games and otherindoor uses, so it has been very difficult to introduce them into
real work sites.
In particular, we often encountered issues with head-mounted
devices such as fatigue or physical strain due to the weight, or thescreen not functioning as required outdoors.
For these reasons, we decided to commit to developing ourown hardware. The “THINKLET®” device that we developed
is designed to be worn on the shoulders which is different from

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prior Connected Worker devices that were in a glasses format.
We selected this design because wearing a glasses-type digital
device weighing several hundred grams on one’s head can result inheadaches and other physical strain, while even a small person can
carry weight on their shoulders easily, even up to 5 kg. Humans
feel less bodily strain when they carry objects on their pelvis or
trunk through their shoulders.
We also designed the device with special attention to handsfree operation and a simple UX, comparable to operating a TV
remote control.
As a result, we were able to realize a shape that enables
digitalization of work without significant burden on on-site
workers, either physically or from changes in work practices.
■ Figure 10: I ssues with head-mounted devices

Copyright © 2021 Fairy Devices Inc. All rights reserved.

7. “THINKLET®” features that accelerate
Connected Worker adoption“THINKLET®” has four particular features.

■ Figure 11: Introducing the Fairy Devices Ltd.
“THINKLET®”(2) 	It uses high-performance microphones, so even in a factory it
can be used with speech recognition and AI, or for creating
work records.
	In fact, devices such as transceivers and pagers have beenin sites such as factories before. However, for most sites it
has not been possible to use speech recognition because thesound quality was poor, and it was difficult to hear what was
said, even for a human. THINKLET® uses several highperformance microphones, onboard edge-AI processing andbeam forming. This enables speech to be heard clearly in theworkplace. We have also implemented speech recognition
for noisy environments, which was difficult in the past. This
speech recognition works in environments with noise of 80 db
and greater, so AI can be used in factory environments.
By enabling speech recognition with noise, work records thatwere previously only done after returning to the office can becreated, and detailed evidence can be collected and delivered
while still on site.
(3) 	Use of an ultra-wide-angle camera enables the experienced
technician to provide remote support, as if they were on-site.
In the past, when the on-site worker needed help, experienced
technicians used a two-way radio or similar device to provide
support, but this had difficulties. If the on-site person said
“The green lamp is flashing,” for example, there could be morethan one green light in many cases. The THINKLET® has
an ultra-wide angle camera mounted on the front, which cancapture the on-site worker’s field of view and working area.
With this camera, the remote expert can evaluate the situation,
as if they were there, and give appropriate instructions.
T he images captured by this camera also can be used for
FairySLAM technology, which creates a 3D map from thevideo taken by the camera. This enables it to obtain location
data even inside a facility where other location informationsuch as GPS cannot be used.
(4)	Speech recognition and gesture sensors enable it to be operated
with both hands free.
■ Figure 12: THINKLET® Solution screen concept

Copyright © 2021 Fairy Devices Inc. All rights reserved.

(1) 	THINKLET® is basically an ordinary Android smartphone
inside, but it has no display and is in a form to be worn on theshoulders. Since it has no display, it is extremely lightweight
and we were able to keep power consumption very low. As
we mentioned earlier, since it is worn on the shoulders, thephysical strain on the neck is greatly reduced compared withhead-mounted and glasses-type devices.

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New Breeze Summer 2021

Copyright © 2021 Fairy Devices Inc. All rights reserved.

In contrast with office work, on-site work often requires
holding tools with both hands or wearing gloves for protection,
making it difficult to operate a keyboard or touch panel withthe finger tips. This is another reason it has been difficult topopularize digital devices for work sites.
	THINKLET® has been designed to be operated while using
both hands for work, and is equipped with speech recognition
and gesture sensors. A worker can operate a drill with both
hands and ask the AI to read out from a manual, take a pictureor double check the work.
(5)	Hardware configuration is customizable for the usage andrequirements of the site.
	The description above is of the basic conf iguration.
THINKLET® has also been designed to be customizable forthe desired use and requirements, with optional features suchas converting to a stereo camera by substituting the gesturesensor for a second camera, or adding lights or MR goggles
that are linked to the main unit (which we developed inhouse). This allows it to support various on-site needs.

8. Implementing “Second Brain,” for realizing
workplace DX

As mentioned earlier, in the past DX proceeded based on datafrom on-site facilities and equipment. However, factories andother worksites do not operate with just machines, and generally
there was a shortage of data regarding the non-regular work doneby people.

THINKLET® is a wearable AI that stays close to the user on
their shoulders, with accurate speech recognition and first-personview video data recording through an ultra-wide-angle camera.
Thus, it is able to collect “human Big Data” to an extent which is
not possible with fixed microphones and cameras.
It can also use LTE and other high-speed communication, so
beyond providing remote work support with video, it can be used
to implement “on-site support solutions based on analyzed data,”and “skills transfer by an AI expert technician for worker support.”
We have defined four levels of DX that can be implemented
for such solutions.
DX Level 1: BPR and data acquisition through work
digitalization
The costs incurred are not only for collecting data, the digital
devices can also be used for useful work support functions suchas providing remote support with video, and automating creationof work reports using speech recognition, by which work can bedigitalized to reduce the on-site workload.
Thus, while performing regular work as usual, work
efficiencies are increased and at the same time non-routine data
created by workers at the site will be collected automatically andnaturally.
DX Level 2: Digital work support (using data)
Using the human-created non-routine data collected in DX
Level 1, including detailed work evidence with the conditions and■ Figure 13: THINKLET® customization options

Copyright © 2021 Fairy Devices Inc. All rights reserved.

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13

judgments or tacit knowledge that has been revealed, processes
that were difficult previously become possible, such as skills
transfer, improved training, evidence-based status evaluation, andknowledge management.
Examples include proposing possible causes in a situation
where the pressure in a pump is lower than normal, or deriving
techniques that skilled workers do subconsciously by comparing
videos of their work with that of average workers and then using
them to update work manuals.
DX Level 3: Digital work operation reform (using AI)
To build excellent AI, it is essential to train it with anappropriate amount of fresh, precise and accurate training data,but generally it is difficult to obtain enough such data.
It has previously been difficult to gather work data from
regular day-to-day work, surrounding conditions, judgments byskilled technicians, and so on, but if we could make this possible
and use the data for training, it should be possible to build an AI
suitable for that work.
The result would enable some of the work to be automatedor optimized by the AI, such as checking completion of work or
recommending responses to inquiries from on-site and personnel
could concentrate on work that can only be done by a person.
DX Level 4: D igitalization and work integration(collaboration with AI and machinery)This is the level that most players in the AI market aspire to in
the future, and would create a world with people getting help fromAIs through digital devices, like having your own personal fairy;

like Tinkerbell in Peter Pan or Vivien in the Knights of the Round
Table.
We recommend trying not to reach Level 4 or Level 3 in one
leap, but to achieve DX in small areas with Level 1 or Level 2.

9. Creating a bright future for Japan with DX
of workplace knowledge (OT)

Even with the remarkable progress in technology, it is still
people who are making the decisions in the workplace.
Japan has many excellent people with much workplaceknowledge and this is the basis for quality. I originally worked
at an American enterprise, and we often had inquiries about
exporting the advanced social infrastructure operations in Japan.
There were some cases for which we realized the export to foreigncountries, but the operational knowledge was usually confined to
the Japanese language and not digitalized, so in most cases it could
not be exported.
If an “expert technician AI” can be built using such worksite
knowledge, it would clearly be useful for increasing added value
for Japanese enterprises, facilitating efforts such as rapid setup
of overseas factories and acquiring foreign currency by creating
solutions from in-house technologies or maintenance practices.
We believe, with such knowledge confined to Japan which
is helpful for similar workplaces in foreign countries, that by
pursuing DX in areas that are difficult to access by major foreign
players, we can build a future in which even Japan, with its
declining population, can make earnings in other countries. Fairy
Devices is working continuously to develop technologies andrealize workplace support AI for on-site workers■ Figure 14: A reas that Japanese venture companies can target using AI¯
Copyright © 2021 Fairy Devices Inc. All rights reserved.

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New Breeze Summer 2021

Corona Tracer®: Preventing expansionof workplace clusters
Jun Matsumura

Representative Director
CTO, IoT-EX Inc.

1. Introduction

The SARS-CoV-2 coronavirus that causes COVID-19 isreported to be spread mainly through contact with infected air
droplets, and the spread can be controlled effectively by severing
the paths of such infection. Formation of clusters can be prevented
by “contact tracing,” which involves quickly identifying any close
contacts and notifying them when an infected person is found.
Conventionally, contact tracing has been done mainly by
telephone, asking questions, checking and giving instructions.
This method takes time and effort to identify patients andbecomes more difficult as the number of patients increases. With
the recent spread of smartphones, efforts to accelerate contact
tracing using mobile applications have begun.
In South Korea, a mobile application was developed using
the location services smartphone feature (a standard feature that
integrates data from GPS, base station locations, Wi-Fi, thecompass, etc.), which linked with several systems that collectpersonal information to track the movement history of infected
people. Currently published information includes age, sex,
workplace, approximate home address, convenience stores used,
and modes of transport used. Names are not published, but
associates in the same cluster are, and there have been reports
of individuals being identified through SNS and attacked. Suchincidents have been used to show the difficulty of maintaining
privacy while dealing with infection risk.
Another mobile application called “Trace Together” wasdeveloped in Singapore using a short-distance communication
technology called Bluetooth Low Energy (BLE). The application
enables rapid contact tracing and notifications by keeping a 21day log of people who were in close proximity and also had theapplication installed on their smartphones. This application uses
the phone number for notifications, so it is considered to use
individual-identifying information.
Since late March, 2020, our group and several others in Japan(such as Code for Japan) have also developed contact tracing
applications. At the time, “Trace Together” in Singapore had
about a 10% usage rate, was initially only available for Android
devices, and had to be run in the foreground. Apple severely limits
background applications over concern for privacy and battery life,
so dealing with these restrictions caused difficulty in Japan, where
iPhones have a large market share.
Amid these efforts, on April 10, 2020, Apple and Google
jointly announced their support for government and insurance
agency efforts to prevent the spread of infection, including acontact tracing framework that uses Bluetooth Low Energy (BLE)and not location services features. This enabled iOS devices,not only Android devices, to also have applications that run
continuously in the background.

2. Issues identified during development andresolutions

We initially analyzed conventional contact tracing methods
using telephones and identified the following five necessary
tasks: (1) Collection of contact information, (2) Storage ofcontact information, (3) Reporting from infected persons, (4)Identification of close contacts, and (5) Notification of closelycontacted persons. We then studied how these could be
implemented and determined the scope of manual work that could
be systematized effectively.
We first conducted tests to verify collection of contact
information using BLE, which indicated that there was muchnoise and attenuation due to pillars, walls, clothing, and people’sbodies, so that it was not possible to estimate distances (such as“within 1.5 m”) from the strength of signals. Thus, we determinedthat if the signal was relatively strong, those persons would beconsidered under close contact.
We then decided that contact information would be stored in
the application. Mobile applications use a sandbox architecture,so that a third-party application cannot see data stored in anotherapplication. Only identification numbers, which cannot be
associated with an individual, would be sent to the cloud. If alldata were sent to the cloud immediately, real-time monitoring
would be possible. Even if data from a given application isprocessed to not identify individuals, the data could be linked
with information collected by other applications from the samedeveloper on the same smart phone, enabling them to identify
individuals. How to handle the risk of identifying individuals
when linking to the cloud was a concern when developing aservice.
Apple and Google published a mechanism for contact
tracing with sample code and specified that it would support
one application per country. This mechanism was then built
into the OS itself, so that the logic for collecting and storing
contact information could not be changed. The mechanism has an
Exposure Notification function to indicate when the user has been
exposed to risk of infection.
From the start, our group has intended to provide a contact
tracing service for corporate administration (general affairs
administrators in most cases) rather than for national or insurance
agencies. Enterprises must take measures to prevent the spread of

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15

■ Figure 1: Typical operations for administrators when an infected person contacts the company

infection as part of their labor health and safety efforts to begin
with, and our solution is meaningful as part of such efforts, andchecking contacts using our service can be a meaningful part ofsuch efforts for companies required to do so. It could also be used
to meet the needs of national and regional public organization as ameasure against the spread of infection.
Enterprise managers (general affairs administrators in mostcases) manage employee names and contacts as part of their workand use them as needed. We separated the system for collecting
and storing contact information from the system for identifying
close contacts and coupled them loosely to prevent real-timemonitoring and to forbid access to contact information unless aninfection occurs.
We also do not enter information related to infection into thesystem, but rather, an (infected) person’s name and (incubation)
period is specified, and a list of employees who might have had
close contact is displayed.
We have also prohibited linking of data stored in the systemwith any other system. This was made possible technically byusing IoT-HUB, which we developed in collaboration with the
University of Tokyo, Institute of Industrial Science, IoT Special
Research Committee. Since IoT-EX Co. Ltd. is a registered
telecommunications operator, which is regulated by thegovernment, we are also prohibited from monitoring data on the16

New Breeze Summer 2021

cloud and must protect data privacy and security.
Enterprises are also using feature phones instead ofsmartphones in some cases. There is also the issue of handling
visitors in addition to employees. To deal with such situations,
we also added support for beacon tags and QR codes. With
these additions, we also separated BLE operation for central andperipheral devices. Using beacon tag technology, it is also possible
to handle children and the elderly, and with QR codes, we canalso support schemes that have been used with feature phones, inwhich contacts are registered by reading a QR code and sending
an empty email message. This technology also enables us to
support situations such as event spaces.
In the process of testing various beacon tags, we found thatspecifications for beacon tags are not uniform, and there areinteroperation issues between different near-contact tracking
systems (collecting and storing contact information). This can also
be handled using IoT-HUB and applicable drivers. Specifically, byusing a driver and a particular parsing feature of the IoT-HUB,
no change to the application is required, and the devices can besupported right away, by just creating a format definition file forparsing (taking approximately 10 min.).
QR codes have also been adopted throughout Japan in recent
years, but the mechanisms used are not consistent in each region,
and we found out that there is no interoperability among them.

■ Figure 2: Overview of the Corona Tracer system■ Figure 3: Operation with Corona Tracer

New Breeze Summer 2021

17

This can also be handled using IoT-HUB and drivers.
We also had requests to be able to check work attendance rates
from companies using Corona Tracer, so we added the ability to
do so from the contact information collection and storage system.
The system provides attendance rates by department and for thewhole company, and by adding a sensor data (PM2.5 and CO2
levels) collection and storage system, we are able to determine
whether the “Three C’s” (avoidance of closed space, crowded
places and close contact) are being handled or not.
Recently, facilities are being required to operate safetymeasures for visitors in spaces where many people gather andinteract. We tested whether Corona Tracer would be effective insuch cases, and found that with multiple central devices, a roughestimate of locations of people could be determined from the BLEbase stations. This showed that we would be able to trace possible
transmission by air droplets (person-to-person), as well as throughcontact with objects that other people had touched (person-toobject-to-person). We are continuing to test this measurement.

3. Conclusion

The “COCOA” contact checking application for Japan,
which notifies people if they may have had close contact withsomeone infected with the COVID-19, was reported to have been
downloaded 18.9 million times as of the morning of October 26,
2020, which is approximately 15% of the population. We are being
required to put a new type of lifestyle into practice in our daily
lives, to protect ourselves, our families, the people around us, ourworkplaces, and our region from the spread of infection.
Our group has focused particularly on the workplace,
supporting new work styles, and we aim to provide a service thatwill be useful for enterprise administrators. There have beena variety of services offered for business continuity planning(BCP) for situations such as major earthquakes or a pandemic.
We are offering “Corona Tracer®” to contribute to stopping the
expansion of workplace clusters in enterprises. Most of the features
introduced in this article are patented or patent pending.
References
・ Privacy-Preserving Contact Tracing
https://covid19.apple.com/contacttracing
・ Exposure Notifications Frequently Asked Questions
	https://covid19-static.cdn-apple.com/applications/covid19/current/static/contact-tracing/
pdf/ExposureNotification-FAQv1.2.pdf
・ Ministry of Health, Labour and Welfare, “Novel Coronavirus Q&A (For Enterprise)”	https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/kenkou_iryou/dengue_fever_qa_00007.
html#Q6-1
・ Examples showing the need for a contact checking application in society.
https://www.nikkei.com/article/DGXMZO60262830R10C20A6MM8000/
・ Accelerate identification of cases and close contacts with Corona Tracer
https://smartiot-forum.jp/iot-val-team/iot-case/case-iot-ex
・ Planning, Development and Launch of Contact Tracing Service in Six Weeks—IoT-EX
Implementation
	https://smartiot-forum.jp/iot-val-team/iot-case/mailmagazine/mailmaga-03920200514?mailmaga=・ Contact Tracing Scheme to prevent the spread of COVID-19
https://webtan.impress.co.jp/u/2020/08/25/37244
・ How Apple and Google Are Enabling Covid-19 Contact-Tracing
https://www.wired.com/story/apple-google-bluetooth-contact-tracing-covid-19/
・ Apple and Google partner on COVID-19 contact tracing technology	https://www.apple.com/ca/newsroom/2020/04/apple-and-google-partner-on-covid-19contact-tracing-technology/
・ Why Singapore’s COVID-19 contact tracing application cannot be used in Japan as-is”
https://xtech.nikkei.com/atcl/nxt/column/18/01279/041700002/

Cover Art
Masaki Inari Shrine and Sumida River Ferry fromthe series Famous Places in the Eastern Capital(Tôto meisho)
Utagawa Hiroshige (1797~1858)

Collection of the Art Research Center (ARC)
Ritsumeikan University
Object number: BN03828992-1-12-1

18

New Breeze Summer 2021

Making SNS and other Digital Spaces more Safe and Sound

Takahiro Okumura

ManagerMarketing Communication Group, Communication Department
Eltes Co., Ltd.

1. Changes and Issues in “Human×Digital
Space” with COVID-19

The COVID-19 pandemic has resulted in accelerationof digital transformation (DX). In business, non-face-to-facecommunication has increased, conducting meetings and customer
interaction on-line, where physical distance is not an issue.
Communication is moving from real to digital space. Use of online
tools is considerable, both for B2B and B2C activities, and theimportance of reputation in digital space is expected to increase
even more in the future. As such, an understanding of how tone
governs digital space is essential for decision making in enterprisemanagement.
However, it is extremely difficult to understand tone accurately
in digital space. It is possible to understand and verify tone to someextent by just gathering information with a search engine, but on
SNS, where information can flow forcefully and energetically, it
is difficult to grasp the tone correctly. This is because distortionsof a speaker’s intentions can accumulate, and after non-factual
information has spread, it cannot be suppressed. This results ina particular risk of damage due to flare-ups or rumors on theInternet.
It is still fresh in our minds how there has also been an “infodemic” with the spread of the COVID-19 pandemic, causing fear
and uncertainty for many people. For example, in Japan there waswidespread speculation on SNS, that materials for masks would
be diverted from Japan and import of materials from China would
stop, which resulted in a shortage of toilet paper. In Iran, there wasa rumor on SNS that drinking strong alcohol would kill the virus
in one’s body. Many people who believed the rumor drank strong
alcohol containing methanol and died of methanol poisoning.
To raise awareness regarding this infodemic, the World HealthOrganization (WHO) created new content on their COVID-19
Web site, with advice regarding superstition and anxiety and how
to avoid being misled by unreliable information.
There have also been cases of widespread rumors, where
posts were made with parts of a paper or someone else’s post werequoted, cutting out and rearranged phrases to suit the biases of theposter. For example, information stating that “epigallocatechingallate (EGCG), found in green tea, has strong anti-viral effect,”degenerated into “is an effective treatment for novel coronavirus,”
and got distributed. In this case, the essence of the information
was lost by the extraction and rearranging of wording.
Communication on SNS occurs through the exchange ofshort messages, so brevity results in strong tendencies not toconvey the intended meaning, for the meaning to change when

only part of a sequence of tweets is quoted, and for information
to become distorted in the process of transmission. Readers alsoskim through the huge f low of messages over time and caninterpret messages incorrectly, without carefully scrutinizing
the information. People today are exposed to huge amounts of
information and are used to consuming information in summary,so it is not uncommon to just accept whatever information isreceived. Eltes is conducting analyses of information related
to COVID-19. In this process we have learned that incorrect
information that gets distributed on SNS often dies down due
to fact-checked articles appearing in the media, but then it
sometimes begins to spread again on bulletin boards and othermedia. In our diversifying digital space, a single fact check alone
will not correct incorrect information, and there is a possibility
that it will flare up again. Our analysis has revealed the problem
that once incorrect information has spread it transforms
successively in digital space and variations tend to increase.
The likelihood that information will be misinterpreted due topoor writing ability or poor reading comprehension is always high,so incorrect information is often repeated, moved and reissued
in digital space. For these reasons, when information starts todisperse, it is increasingly difficult to follow in real time.

2. D anger that distortion in digital space willaffect the real world“DIGITAL2020: GLOBAL DIGITAL OVERVIEW” isa report analyzing the trends and tendencies of people aroundthe world on digital, mobile and social media. According to thereport, more than 4.5 billion people used the Internet, and users ofsocial media had surpassed 3.8 billion people as of the beginningof 2020. This means nearly 60% of the global population is using
the Internet, an increase of 7% compared to 2019. The average
Internet user spends six hours and 43 minutes on line every day,so assuming they sleep eight hours a day, 40% or more of theirwaking hours are spent using the Internet.
The report also indicates that the time connected to digitalspace is increasing from year to year. The COVID-19 pandemic
has spurred this on, and with fewer opportunities to meetface-to-face, we can expect people are more active collecting
information online. Although digitization is making people’s lives
more convenient, it also brings new risks. In fact, as the amountof connection time has increased, the amount of slander anddefamation on the Internet has also tended to increase. Cases ofarrest or litigation due to malicious posts are becoming more andmore common. Cases where thoughtless, slanderous comments

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made anonymously have resulted in lost lives are also continuing tooccur. Calls for morals in digital space are increasing daily.
The idea that digital space and real society is separate isa thing of the past. A person’s personality and corresponding
reputation in digital space has become inseparable from that in
the real world, whether good or bad. Because of this, there may
be increasing danger that when distortions appear in digital space,
they will have effects in the real world as well.
This is not only a problem for individuals; it also applies toenterprises. Enterprise managers must make certain preparations
for the risk of injustices characteristic of digital space. An
important factor in doing so is the use of intelligence, the resultof information processing and analysis, as the basis of decision
making.
When slander, defamation or flare-ups occurs on the Internet,
many people have a tendency to perceive it as though it is theprevailing attitude in society. However, such issues cannot be dealt
with properly without correctly determining whether it really is aprevailing attitude or just that of a noisy few. Incidentally, during
Japan’s Warring States period, warring armies were known touse human figures to give the impression that they were stronger
militarily. In a similar way, there have been many cases when asingle person has created additional accounts on SNS and otherplatforms to spread slander and defamation, when actually it is justone or a small number of people. Rumors are like natural disasters
and impossible to prevent before they occur. However, damage
can be minimized by perceiving the matter correctly. To preventthe spread of negative effects in the real world, enterprises need
intelligence, including that gained from monitoring digital space.

3. Eltes accomplishments in the domain ofInternet flare-up and rumor damage control

Eltes has set a policy of “continuing to fight against digitalrisk,” and as an organization of digital risk management
specialists, we are developing various solutions to resolve
digital risk. We support management of digital risk emerging
as digital transformation (DX) of society progresses, including
the expansion of services such as search engines, SNS and online banking, made possible by development of information andcommunications infrastructure technology and digital devices. For
example, we provide comprehensive risk management solutions
for enterprise social media operations, which have presented realrisk as business environments have changed. We have provided
digital risk management-related services to over 1,000 companies,
including listed companies such as NTT DOCOMO, Mazda,

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and Suntory.
There are two main types of digital risk management. The
first is “Social Risk Management.” As the number of SNS users
has increased, “incidents” have occurred frequently. The numberof Internet f lare-ups has increased annually since 2011. We
develop support for social risk management to help prevent such
incidents in three phases, which are: “Survey, analysis, and systembuilding,” in which we gain an understanding of potential risks
and business improvements and decide rules; “Operations,” inwhich we perform early detection of emergent risks and rapid
initial response; and “Countermeasures,” in which we support
mitigation of such risks.
The “Survey, analysis, and system building” service involves
collection and analysis of information on the Internet regarding
the enterprise and its products and services, which we deliver as areport. We also provide a marketing analysis through comparison
with competition and information collected regarding leaks,
events and incidents occurring overseas. We first collect all
articles on the Web related to our client enterprise. These areplaced in categories such as positive or negative, according to
pre-determined conditions. We also expose potential risks andissues like the detailed reputation of products and analyze future
initiatives. These are also summarized in a report. We can also
create operating regulations and manuals necessary for introducing
a new, public SNS presence, or if social media policies werecreated several years earlier and are no longer suited to current
conditions, we can support revising them and provide otherfollow-up on the organizational structure of SNS operations.
The main activity during the “Operations” phase is Web
risk monitoring. We monitor the Internet 24 hours-a-day and
365 days-a-year for information including rumors regarding theenterprise or its products or services, risk of information leak due
to the company’s employees, and other particulars such as risk
related to malvertising, pharmaceutical products or consumer
protection. If urgent information is detected, we also consult
regarding urgent notifications and how to deal with them.
For the final phase, “Countermeasures,” we provide our
Web Risk Monitoring clients with dedicated consultants, whoseamlessly conduct a risk assessment for the matter when a risk
is detected. The content that precipitated the crisis is analyzed
for credibility and potential effect, and a profile of the source isprepared. For escalation after risk is detected, we can even provide
support for crisis management public relations as needed, createpress releases, train for press interviews and provide advice on
handling the situation going forward. If the crisis grows, we also

provide consulting services to handle public relations for crisis
management. To deal with search engine reputation, we canidentify issues with how the enterprise, its products or services, areviewed on search engines, which is an important factor in forming
reputation and users’ brand experience. We also plan strategies tosolve issues and achieve objectives, and design KPIs.
We now discuss some examples of enterprises that haveintroduced Web Risk Monitoring. Food products company, A
Inc., was concerned that they would be associated with a flareup on SNS regarding the food-products industry in 2016. They
decided to use Eltes Web Risk Monitoring based on our “24 houra-day, 365 days-a-year risk detection system,” and our “ability toprovide rapid contact with dedicated staff.” That is, the fact that
we have been able to implement effective risk monitoring, and thathuman staff use their intuition in interpreting subtleties in posts
regarding the company as they appear each day and in making
decisions. Our monitoring service can also support matters otherthan risk, and not only when negative incidents occur. An example
is collecting information regarding reactions after a commercial isbroadcast.
Web Risk Monitoring is not limited to monitoring SNS
regarding a company’s products or promotional activities.
Company B in the service industry uses it to detect potentiallyrisky posts on SNS, but also to gather positive feedback from
customers regarding service provision, which it uses internally togive recognition and commendations.
In addition to the Social Risk Management service we havebeen discussing, Eltes also has initiatives for enterprise digitalrisk counter measures, with two approaches to Internal Risk
management. These involve cross-sectional analysis of logs, andanalysis of behavior to detect internal behavioral risk.

4. Advanced flame-up/rumor damage control
using AI

Eltes is introducing AI to our Web Risk Monitoring Service
to improve quality. However, we are not depending entirely on
AI. We hope to improve both service quality and efficiency byintegrating the strengths of both AI and human operators. As
such, we first used AI to implement a mechanism that classifies
posts as either negative, neutral, or positive for our Web Risk
Monitoring service, as a measure against flare-ups and rumors
on the Internet. However, we had three main problems with thiseffort.
(1)	A large amount of correct training data was needed to obtaincorrect classifications from the AI.

(2)	Popular words and phrases come and go, so updates(maintenance) are necessary.
(3)	Interpreting the meaning correctly from the text and context isvery difficult.
To solve problem (1) we began work creating training data for
an AI to classify posts as negative, neutral, or positive, based on
the data that we had been collecting continuously since the Web
Risk Monitoring Service began in 2011. To create the training
data, we had to indicate which posts were obviously negative, or
positive, but we found that even for the same post, there are cases
when this decision would be different depending on the type ofbusiness or industry, so deciding what was “obvious” was not easy.
It was relatively easy to collect posts that are “obviously” neutral,
but it was much more difficult than we expected to prepare alarge number of posts that were negative or positive. Our longexperience providing services contributed greatly to creating
enough high-quality training data.
For problem (2), that popular words and phrases come andgo, we designed a solution involving human intervention. For
example, use of the Japanese word, “ataoka” to mean “someone
with a screw loose” became generally popular after being usedby a comedian, and won 1st prize in the Insta-Buzzword awards
announced by Petrel Inc. in the first half of 2019. Until several
years ago, “ataoka” was not a recognized word, and that might be
why the AI did not classify it as negative. Thus, there are trends inthe language used on SNS, and such changes in language must be
reflected constantly in the training data used by the AI to classify
negative and positive posts. To handle this, we are using humansensitivities to capture the meaning and sense of words, andreflecting this accurately in our training data.
Finally, problem (3), correctly interpreting meaning fromtext and context, is difficult for AIs. Morphological analysis
divides the text into small units to extract the meaning, buteven a particle can completely change the meaning, so it is very
difficult. For example, the phrase, “I like B better than A,” in apost comparing two companies’ products tends to be difficult toclassify as negative, neural, or positive. Considering that currently
AIs also have difficulty with posts that contain positive words
but are negative (such as “I always wanted to go to that store,
but unfortunately could not”), and posts that contain unusual
expressions, we have these posts classified by people.

5. E
 ltes optimized digital risk countermeasuresolution

The value of the Web Risk Monitoring Service is early

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21

detection of posts that pose a risk. Put another way, negative
posts will not go unnoticed. Although 80 to 90% of posts wereclassified as neutral even before AI was introduced, the problem
remains that it is very difficult to interpret the meaning correctlyfrom the text and context, and we found that it is difficult to leave
Web Risk Monitoring entirely to the AI. As such, we created aworkflow giving the role of screening for neutral posts to the AI,
excluding posts that could be negative and are difficult for the AI,
such as when the meaning is difficult to interpret from the text
and context, and when they contain a mix of positive and negative
words. Such posts are handled by a dedicated staff member.
As a result, our human staff concentrate on subtle posts on
the Internet that may involve our client and be related to flare-ups
or rumors. Thus, we have built an environment in which humanerror is reduced and we can detect flare-ups and issue urgent
notifications quickly, improving the quality of the service. We arealso able to use positive and negative data judged by our humanstaff to train the AI, incorporating information about language
trends and improving the accuracy of AI results.
The same post can have widely varying effect on the
reputation of an enterprise, depending on the enterprise inquestion and trends in society. In addition to sending urgent
notifications regarding posts that indicate risk to an enterprise, we
also offer dedicated consultants that can support the enterprise in
its initial response.
It is difficult to judge the effects on real society, of what issuperficially just numbers and text in digital space. Distortions
emerge in digital space due to the actions of people, and Eltes
is fighting against the resulting digital risk. We are using our
expertise to combine the strengths of both people and AI, and willcontinue the fight against digital risk as it continues to change.

 ltes Co. Ltd. “Web Risk Monitoring Service” Overview.
■ Figure: E
24 hour-a-day, 365 days-a-year monitoring of posts for various types of incident, crisis notification andconsultation to deal with them when posts including risky content are detected.

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FY2020 APT Workshop— Development of fundamental network planning skills for E-application inregional community to bridge the digital divide —International Cooperation Department
The ITU Association of Japan
As part of the human resource support
programs operated by the Asia PacificTelecommunity (APT), APT has been
holding workshops to transfer Japanesetechnologies and services to businesspeople and technologists in APT member
countries, with funding from the Japanese
government. The ITU Association ofJapan has held workshops within these
programs in an effort to reduce the digital
gap for developing countries since 2017,
such as introducing conf iguration ofmobile phone base stations.
During the last fiscal year, a workshop
was held on-line for the two weeks from
November 30 to December 11, 2020, overnine days*1. The workshop was held online
for the first time due to the Covid-19
pandemic. As such, classes were kept short,
to two or three hours per day, to allow
trainees time to concentrate efficiently
on online lectures. The trainees included
nine members from the six countries of
Bhutan, Iran, Malaysia, Mongolia, Palau
and Thailand.
The three objectives of the workshop
program were as follows.
(1)	To understand the issues in theparticipant’s own country and to■ Figure 1: Group photo 1

learn how to draft a concrete plan toovercome the digital gap in areas of
the country.
(2)	To understand the importance of
having clear government policies
regarding construction of networks.
(3)	To acquire skills for proposing
and evaluating solutions for thevarious issues in the participant’s
country, through presentations anddiscussion.
We provided support to participants inachieving these objectives.
On the first day of the workshop,
each trainee gave a presentation of current
conditions in pre-selected regions ineach of their countries. They presented
conditions resulting in the digital gap,
including population and geographicalconditions in each region and what sortof gap is occurring. Current conditions inJapan were presented by ITU-AJ Secretary
General, Kazuhiko Tanaka. After thepresentations, participants talked, asking
and answering questions. This allowed
them to gain a deeper understanding ofconditions in each of their countries.
On the second and following days,
lectures were given by Takuzou Fujii,

formerly of Hitachi Kokusai ElectricInc., on concrete network configuration
methods for resolving various issues, anddrills were given for participants to learn
through practice. Participants analyzed
geographical conditions in various regions,studied ways to design networks suited forthe regions, and ways to prepare the ICT
services and environment that would beneeded in each region.
On the fifth day, a lecture on thecurrent state of 5G technology in Japanwas given by NTT DOCOMO, one of
the top telecommunications companies inJapan and the world.
On the last day, each person proposedsolutions for issues they had presented on
the first day. Participants presented an
action plan for how the digital gap could
be filled in, applying the skills they hadlearned in the workshop, considering
the equipment and functionality to be
used, maintenance approaches and thecosts involved. After the presentations,participants discussed the proposals,
exchanging ideas.
A notable aspect of this year’s
workshop was online group discussions.
Previously, participants have come to Japan■ Figure 2: Group photo 2＊1 The weekdays, with one day off.

New Breeze Summer 2021

23and participated face-to-face, but this year
it was done fully on-line, and designed
to utilize active learning (an approach inwhich students learn proactively rather
than passively) throughout.
■ Figure 3: NTT_DOCOMO lecture

More concretely, participants studied
beforehand, reading the text and doing
exercises through e-learning, and then theon-line classes focused on question-andanswer and discussion. In this way, they
were able to approach issues proactively
and experientially.
We also utilized the breakout-roomfeature of the Zoom Web-meeting tool
to divide participants into small groups,
giving them a lot of time to share anddiscuss homework practice exerciseswith each other. Although these group
discussions were on-line*2 , there was■ Figure 4: Presentation 1 after practice■ Figure 5: Presentation 2 after practice＊2 Or possibly because they were on-line. We want to explore this issue further.

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extremely lively exchange of ideas.
Through this daily routine, although they
did not meet directly, the participants
seemed to form a tight-knit group,
accepting each others’ differences.
The post-workshop survey indicateda high level of satisfaction, with almost all
participants answering that the workshop
was successful. We expect to conduct more
APT workshops on-line in the future, so
we hope to further increase the value ofthe training, incorporating results fromthis year so that it will be even moremeaningful in the years to come.

= A Serial Introduction Part 2 =Winners of ITU-AJ Encouragement Awards 2020
In May every year, The ITU Association of Japan (ITU-AJ) proudly presents ITU-AJ Encouragement Awards to people who have madeoutstanding contributions in the field of international standardization and have helped in the ongoing development of ICT.
These Awards are also an embodiment of our sincere desire to encourage further contributions from these individuals in the future.
If you happen to run into these winners at another meeting in the future, please say hello to them.
But first, as part of the introductory series of Award Winners, allow us to introduce some of those remarkable winners.

Morihiro Sakakibara

International Cooperation Department, KDDI FOUNDATION
http://www.kddi-foundation.or.jp/english/
Fields of activity: ICT Projects in Education

International Cooperation to Improve University Education
in Myanmar
It is a great honor to be recognized with this ITU Association ofJapan Encouragement Award. I would like to offer sincere thanks to
the ITU-AJ and all those involved.
From FY2014 into FY2017, through a special contribution fromthe Japanese Government and multiple Asia-Pacific Telecommunity(APT) pilot projects operated in Myanmar to eliminate the digital
divide, education and research cloud servers were installed at the
University of Information Technology (UIT) in Yangon and a
cloud network was built, which several IT universities could use
through Internet VPN networks. As a result of participating in theseprojects, and even after they finished, I have been working withgovernment and university-related people in Myanmar to promote
the following three main policies, to expand cloud networks and build
environments to promote their use.
The first was to add four more cloud servers, distributing theHiroshi Dempoload among the major computer and technological universities,
compensating for increasing load as more universities use them inthe future. The second was to hold workshops in collaboration withAPNIC (the organization managing IP addresses in the Asia-Pacificregion) starting in FY2018, to improve networking technology
used by university professors. The third was to give students anopportunity to improve their programming and development,planning and English presentation skills, and to hold application
contests starting in FY2017 for all 46 universities under the
Education Ministry that have IT-related faculties.
I learned many things through frequent visits to the area, asking
about their needs, seeing, hearing, contacting, and understanding
again and again. The process of solving such problems takes timeand effort, but the joy of accomplishing them is very special. I hope tocontinue working with the people there in the future.

NEC Corporation

https://www.nec.com/
Fields of activity: Network Function Virtualization

Contributions to NFV Standardization Activities
It is my great pleasure to receive this Encouragement Award
from the ITU Association of Japan. I believe that it is a result of
collaborative effort with people involved and I would like to offer mysincere thanks to them.
ETSI-NFV was established in 2012 to standardize virtualisationof network functions, and is a place where many technicalorganizations share their experiences. The NFV architecture
designed at the birth of the community has frequently been referenced
by many standardization organizations, and NFV deploymentscompliant with the standards can also be seen in service providers’production environments. It has been a valuable experience to be
involved with the innovation process, from recognizing new technicalvalue to popularizing them in the market.
My main achievements can be seen in the area of network
infrastructure. I contributed to technical documents describing acollection of use cases and architecture, in which multiple NFVenvironments are interconnected over wide area networks. I also
contributed to Interoperability test reports in ETSI-NFV and Optical
Internetworking Forum, providing verification of interactions
between NFV and network infrastructure based on Transport SDN
API specifications. These experiences led me to further recognizethat devices from different vendors can be managed without vendorspecific knowledge, which clearly highlights awareness of thesignificance of technical standards. Moving on with my involvements
I became newly motivated to work on data modeling for NFVresources as an ETSI-NFV rapporteur. The achievements have beenreflected in a specification on the ETSI-NFV portal.
Encouraged by this award from the ITU Association of Japan,
I hope to continue contributing to 5G/ 6G technologies and thecomputers and communications industry.

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通巻百三十一号

定価一冊

一、六五〇円 （本体価格 一、五〇〇円、消費税 一五〇円） 年間購読料

The ITU Association of Japan六、六〇〇円 （本体価格 六、〇〇〇円、消費税 六〇〇円）