CHOWA GIKEN Corporation
© CHOWA GIKEN Corporation 2
Catch the forefront of evolving AI
and open up the future with ideas
We are a startup company born from a university laboratory on a
mission to apply AI research into practical use for our society. For
over ten years of experience, we are now making the most of AI
knowledge and challenging to be at the forefront of the times
together with our customers in various industries.
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Company Overview
Company Name Chowa Giken Corporation
Location Sapporo, Hokkaido (Head office)
Tokyo (Branch)
Establishment 4 November 2009
Number of employees 30
Capital 24,500,000 Yen
Business AI Research and Development
Chowa-AI Technology License
AI Expert Education
Representative Director Nakamura Takuya
Outside Director Suzuki Keiji
Outside Director Kawamura Hidenori
November 2009
Established Chowa Giken Corporation
July 2017
Moved office to Hokkaido University North Campus
“Hokudai Business Spring Bldg.”
October 2017
Authorized as Hokkaido University Venture
by Hokkaido University
May 2018
Established Tokyo Office
January 2019
Established Bangladesh and
Thailand promotion division
Our Clients
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Key Members
11 doctoral degree holders
Hidetoshi Nonaka
Doctor of Engineering
Former associate professor
at Hokkaido University
Mathematical engineering,
Human interface
Outside director
Kawamura Hidenori
Doctor of engineering
Professor at Hokkaido
University
Artificial intelligence,
Tourism Information,
Multi-Agent System,
Learning / Adaptation,
Optimization, Complex System
CEO
Nakamura Takuya
Keio University
Faculty of Commerce
Yohko Konno
Doctor of Engineering
Hokkaido University Researcher
Optimization, Data mining,
Natural language processing
Kin Shofuku
Researcher
Machine learning, Image analysis,
Image recognition,
Image classification,
Framework for deep learning,
Web application vulnerability diagnosis
Outside Director
Suzuki Keiji
Doctor of engineering
Professor
at Hakodate Future University
Artificial intelligence,
Multi-agent system, Distributed
autonomous robotic systems
Yamashita Tomohisa
Doctor of Engineering
Associate Professor at
Hokkaido University
Service engineering, People Flow
analysis, Artificial Intelligence
Yokoyama Soichiro
Doctor of Engineering
Assistant Professor at
Hokkaido University
Scheduling, Optimization,
Artificial Intelligence
Yamashita Akihiro
Doctor of Engineering
Professor at National Institute of
Technology (Tokyo College),
Intelligent system, Embedded system
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Sapporo, Hokkaido
Tokyo
Where we are in Japan?
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Collaboration with academic research institutes
Advanced information
technology research Needs
Hokkaido Univ. Company/Administration
Hakodate Future University
Hokkaido Joho Univ.
Chitose Institute of Science
and Technology
Hokkai-Gakuen Univ.
Implementation
of research results
Since its establishment, We have continued transmitting “Practical AI” to meet
the needs of the times. By collaborating research and development with
academic research institutes as well as holding a regular technology sharing
meeting, we can keep updated on the latest AI research trend.
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Research Environment
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Our competitive advantages
1: Cutting-edge AI Technology
2: Ten years of experience and achievement
3: AI Expert and Intellectually curious people
By collaborating with universities and research institutes, we can keep
updated with AI logic and technology to solve any specialized challenging
task.
According to the lab-like environment, there are a large number of
intellectually curious people gathering for working with us.
After the establishment as a university based venture, we have been
experienced practical research and achievement for over ten years.
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Solutions
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AI Engines
we have been cooperating with research institutes and universities to develop and
utilize versatile AI engines. Therefore the engines are constantly updated and
adopted the latest technology. By customizing these engines according to your
business challenges, we can build the powerful AI that serves your specific needs
and delivers a superior result.
Language engine
group
Image engine
group
Numerical engine
group
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AI engines
Sentence classification engine
An engine that classifies texts into an appropriate
category based upon their context. We can extract
feature quantities and classify documents
appropriately by using N-Gram, Word2Vec, Doc2Vec,
Deep Learning, etc.
Emotion Recognition engine
An engine that detects and recognizes types of
feelings such as joy and sadness from the expression
of texts. Considering the whole sentences, RNN will
learn the context and analyze the emotion.
Sentence summarization engine
An engine that creates a text summary by
understanding and evaluating the importance of
words base on morphological analysis, syntax
analysis, LexRank, etc.
Feature word extraction engine
An engine that extracts the important phrase by
using TFIDF and AIC to evaluate how important a
word is to a document.
Conversation engine
An interactive response engine that understands the
conversation content and makes a response
appropriately. RNN will learn conversation data and
make a response by considering the context.
Object recognition engine
An engine that detects the position of various objects
in an image and recognizes what the image contains,
such as things and faces. The combination of YOLO
algorithm and classifiers such as R-CNN and SVM
enables high-speed object recognition.
Image classification engine
An engine that classifies images into appropriate
category base on their features. With CNN
incorporating the latest research such as ResNet and
Inception, we can analyze the image with high
accuracy .
Prediction engine
By using Bayesian estimation, neural networks, and
random forests, this engine can analyze previous and
current situation to predict the likelihood in the
future, such as the number of sales and visitors.
Optimization engine
Among the conditions, this engine searches for the
best way and creates efficient schedule proposals by
using optimization algorithms such as genetic
algorithms and metaheuristics.
Recommendation engine
Based on user behavior, this engine can determine
hobbies and thoughts and make a recommendation
such as a coupon or event information. This
recommendation engine is performed by using a
memory-based method or a model-based method
such as Bayesian networks.
An engine that detects anomalies based on
accumulated data in the past and sends us the
notification. This engine performs a density ratio
estimation and One-Class SVM to detect values
outside the distribution of data.
Abnormality detection engine
An engine that converts an ordinary photo into a
masterpiece as well as generates a real-image like
from a drawing. Generator algorithm typified by VAE
and GAN is applied.
Image generation engine
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Case study Case Study
概要 ( 顧客・背景・課題・目的 )
使用したエンジン
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背景技術
AILL: AI Love Navigator
Conversation Navigation Favorable Navigation
We have been experimenting on how "love navigation” works by learning how
love develops. By navigating the progress of the relationship between men and
women, the result lowers the psychological hurdles of confusing love and
creates an environment where love can be grown without being hurt.
https://aill-navi.jp/
Topic analysis
Word hierarchy relation analysis
Logistic regression
Deep Learning
AI will assist you and the
other party chat by advising
on a topic to talk and the
right timing to invite him/her
to a date. Therefore, you
can know the other party
better before you meet.
AI will support you by
visualizing his/her
preference for you.
According to that, you can
decide how to approach
him/her efficiently.
概要 ( 顧客・背景・課題・目的 )
使用したエンジン
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背景技術
Store Development by Demand Forecasting
By identifying factors, such as weather or campaign, that affect store sales,
we can predict the sales tendency and use the result for inventory
optimization and human resources management. A big retailing company in
Japan now testing this demand forecasting.
Predict sales and visitor
numbers from POS data
using autoregressive models,
random forests, neural
networks, and deep learning.
Store section
Factor
Quantification
POS DATA
Event Calendar
(Event ・ Ad)
External
environment
Store Section
Sales forecast
Store product
Sales forecast
Inventory / delivery
schedule management /
order number calculation
Store section
Customer
number forecast
Task shift management /
Human allocation
Model
evaluation
feedback
Store ordering support
Human resources
support
Latest forecast: For
inventory
Interannual prediction:
for people and time
Model
evaluation
feedback
Business style
and sales floor
strategy
Advertising
strategy
Product
combination
analysis
Store · EC
Customer
analysis
Store
development
life cycle
Household
information
Demand
forecast
Investigate
customer needs
Store
development
Evaluation
feedback
概要 ( 顧客・背景・課題・目的 )
使用したエンジン
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背景技術
Dynamic pricing by Demand Forecasting
Current recommended
price
240[Yen/60mins]
Revenue simulation
Revenue increased
by 22% compared
to fixed price
Implementation example of dynamic pricing for coin parking
It is a service that predicts future demand based on factors, such as weather,
day of the week and time of the day, then calculates and show the price that
maximizes KPI.
Forecast demand by neural
network. We train the machine
in advance that “to earn a profit,
how much should be set for the
price according to the demand.”
Usage data
chronological
order
the weather
event
Step1
Predict future
"demand"
Step2
Calculate "price"
suitable for
demand
概要 ( 顧客・背景・課題・目的 )
使用したエンジン
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背景技術
Automatic Shift work Creating for the Bus driver
This is a demonstration experiment for automatic shift work creating for a bus
driver. By considering complex constraints and conditions, this will help human
to work more effectively. Currently, we are experimenting and developing this
service with our partner company to make it available for bus companies
throughout Japan.
Request
from staffs
Staffing constraints
・ Legal restrictions
・ Restrictions on service rules
・ Expertise and technical level
・ Vacation and business trip / training
・ Constraint by personal convenience
Use genetic algorithm (GA)
to solve the problem of shift
creation that meets all
constraints
Shift work optimization
engine
Timetable
information
It requires time for people to consider every constraints.
Automatic shift work compared between skilled driver
and inferior.
概要 ( 顧客・背景・課題・目的 )
使用したエンジン
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背景技術
Customer Inquiry Search Engine
Usually, to respond to inquiries from a customer about products , we have to
contact many departments to get the answer. This system is a game changer
that helps us find the answer instantly. We are currently collaborating this
project with a major manufacturer.
Search for similar cases : TF -
IDF 、 Word2Vec 、 Doc2Vec 、
SCDV 、 FastText
Case summary : LexRank 、
Word weighted summarization
Messaging app
Web Chat
Q.
A.
Front end Server side
Azure
Bot
Framework
Response generation
Scenario Analysis
Existing service Individual development Existing service
1. Model
2. Phenomenon
3. etc.
API Natural language
analysis
Similar question search
Skype for Business,
etc.
Azure Bot Service Countermeasure content
search model
Case Study
DB
Dialogue
history
User
Model
improvement
by feedback
Search for appropriate measures based
on the information revealed in the
dialog with the user
Interact with the user according to the scenario
and collect the information necessary to search for
countermeasure contents
概要 ( 顧客・背景・課題・目的 )
使用したエンジン
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背景技術
Automatic target data extraction from receipts
It automatically reads the accounting information from the image of the receipt and sorts it
into structured data. An specially designed character recognition (OCR) system extracts
transaction contents and accounts information. It is free from the trouble of manual entry, and
it can be expected to save labor by improving efficiency and reducing typing errors. We are
considering to implement it for more complex tax calculation so that it can automatically
distinguish appropriate tax type that needs to be applied without any human interaction.
Word2Vec
SCDV
(Sparse Composite Document
Vectors)
Original receipt
Scan
Identification
engine
image data
Read the necessary
information from the image
Structured data
Issuer 丸善 丸の内
Date 2018/07/08
Total 4860
Consum
p. Tax
360
Title Food
概要 ( 顧客・背景・課題・目的 )
使用したエンジン
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背景技術
Bone area recognition in fresh food processing
machine
In bone removal process, there are several difficulties that can not be solved using
only parameter adjustment in traditional image recognition. Specially, low
recognition accuracy is a great problem. But, using deep learning we solved the the
biggest difficulty in extraction process of the bone area, and improved the accuracy
dramatically from about 70% to 92-96%.
Hybrid of semantic
segmentation and
traditional image
processing
Image of bone extraction area from X-ray image
In actual image, the tip of the bone area is blurred,
and has many unexpected conditions, for example,
a fracture are occasionally seen.
Performing bone extraction process
from information of rib area
概要 ( 顧客・背景・課題・目的 )
使用したエンジン
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背景技術
Transportation route optimization (VRP)
Truck transportation route planning takes a lot of time with conventional
methods. We simulated efficient transportation routes to determine which
vehicles would be most efficient when traveling in which order. By
optimizing the transportation route of the truck, we can expect not only the
cost reductions such as labor cost and gasoline cost, but also the
environmental impact reduction such as CO2 emissions.
Use guided local search
(GLS) to find low cost
routes while avoiding
local minima.
Geographic data (such
as your location and
route information)
Optimized transportation route
概要 ( 顧客・背景・課題・目的 )
使用したエンジン
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背景技術
Operation route optimization in the warehouse
Constrained optimization
problem
Meta-Heuristics
Genetic algorithm
In the logistics industry, the growing demand due to the spread of the Internet mail
order has become an issue how to efficiently load luggage in the warehouse as the
base. Upon request from the manufacturer of the load carrying vehicle in the
warehouse, we have developed an algorithm to minimize the patrol route while
meeting the requirements such as the maximum loading capacity of the vehicle and
the loading order of the safe load.
Product 2
Until YY Time
Shelf: DD
Productn
Shelf:PP
Product 1
Large size
Shelf: XX
Shipping list and constraints
Optimization
algorithm
Visit order in the warehouse
Vehicle 1
Depart
9:00
AA → XX … Shipping
Vehicle 2 DD → PP …
Vehicle 3 XX → ZZ …
概要 ( 顧客・背景・課題・目的 )
使用したエンジン
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背景技術
B
U
R
A
N
D
O
B
U
R
A
N
D
O
B
U
R
A
N
D
O
Estimation engine for branded product information
A major brand purchaser is developing an engine that automatically estimates product
information in real time from photos to improve work efficiency. The branded product
has a lot of information such as brand name, model name and model number. The time
to investigate was a big cost by human assessment. Therefore, by combining
hierarchical learning methods, product information is estimated successfully and work
becomes more efficient.
ResNet
Hierarchical learning method
Object recognition
Text extraction
Image feature similarity
extraction
Extract target branded
product area and printed
branded product name from
input image
Model number
estimation
engine
Model number
similar image
search engine
Extracted
brand name
Image feature
B
U
R
A
N
D
O
・ brand name
・ Model name
・ Ref number
information
・ brand name
・ Model name
・ Ref number
information
・ brand name
・ Model name
・ Ref number
information
Similarity
Degree
98%
Return the same
model image as the
input photo and its
certainty factor
Brand product
information
estimation
engine Brand product
area extracted
Similar image
candidate list
Similarity
Degree
93%
Similarity
Degree
87%
B
U
R
A
N
D
O
概要 ( 顧客・背景・課題・目的 )
使用したエンジン
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背景技術
Plant fault detection
Abnormality detection,
change point detection
We also perform fault detection using the sensor system of the plant. In the
past, if the system was shut down unexpectedly, human had to wait several
hours for cooling. The machine learning based method has made it possible to
make an anomaly judgment during startup or immediately after stopping,
greatly reducing the time required for fault detection.
Abnormality detection
engine
Sensor data
・ Measurement data All 286 items
・ ON / OFF data All 2038 items
System anomaly
degree
From all items
Analyze the main
cause
system
Running
Phase analysis
The system operation status is
divided into phases, and an
appropriate machine learning
anomaly detection model is
designed for each phase
概要 ( 顧客・背景・課題・目的 )
使用したエンジン
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背景技術
Ukiyo-e style conversion
Image generation engine that converts human face photos into ukiyo-e (a
genre of Japanese art). The conventional algorithm has not been able to
extract the features of the Ukiyo-e as human figures. In our approach, by
training the data set of Ukiyo-e and human face photos, you can transform
your photo into a Ukiyo-e character.
Generation system
framework
GAN
CycleGAN
など
調和技研
Logo
×
Ukiyo-e Face photo Ukiyo-e
×
Conventional
method approach
概要 ( 顧客・背景・課題・目的 )
使用したエンジン
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背景技術
Quality and value collateral application of goods by block chain
It is an application that enables security of quality and value by
storing information such as the origin of goods, holder's history
and authenticity guarantee by block chain technology. By reading
the QR code, it is possible to register and check information easily.
Blockchain
(Hyperledger Fabric)
* Commodity code, buyer, seller, amount, status, quality etc
*Trading History
Anti-tamper seal prevents label replacement and falsification
Product
QR
Block
Chain
その他
AI Expertise
Employment
and Education
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How we hire talented people
・ Many skilled person would like to work with us
・ Most important thing in business is Human Resource
➡ Organization that help employee spontaneously looking
forward to enhancing their skill.
Let’s do what you love!
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Flexibility Policy of employment
■ Work environment that fits diversity
・ Female Researcher
➡ 3 out of 4 female employee are the holder of a Doctorate
・ Synergy from Senior Researcher
➡ Professor at University 、 Researcher of Sony Corporation
・ High skill Global Human Resource Employment
➡ Bangladesh 、 Thailand 、 Germany 、 China 、 Brazil
・ Reemployment ➡ Graduate student from Laboratory
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There is only diversity to correspond to market change.
Currently, we have members from 7 countries, couples/parent and child who
work together, employees from age 67 to high school student ➡ Bangladesh
based office opening plan
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■ Start from what you love
We believe that “Doing what you love” is one of the most important things
to encourage you to take action since it requires strong desire and
commitment. Because after all, if you don’t act on it, you won’t succeed.
■ Environment
To achieve doing what you love, the work environment is important. The
work environment that we can talk about dream, discuss and sharing
valuable knowledge as well as having communities both inside and outside
company that connect and support each other’s growth.
■ Freedom to act and decide
When we share the same mindset and company’s core value, we are free to
take action without rules. We can decide what is best for the team to
achieve the goal. So we believe that this kind of working style will help us
make a better performance and enjoy turning a vision into a reality.
We provide the best environment for employee
Thank you
Let's design an exciting future society together
ワクワクする未来社会を一緒にデザインしよう!