Independent Design Engineering Project II (ADV9382): Final Presentation

Proposal for A New Ideation Process
Harvard University, Independent Design Engineering Project
Togo Kida

Independent Design Engineering Project
Harvard University Master of Design Engineering“Proposal for A New Ideation Process”
Togo Kida
2
Problem Domain
Creative teams working in
advertising industries.
How can I design something
that would solve their
problem in their job?
Togo Kida

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3
Why Creative Matters: Consulting Firms Buying Creative Agencies
Oct. 2019
Aug. 2019
Apr. 2019
Apr. 2019
Mar. 2019
Mar. 2019
Nov. 2018
Oct. 2019
Jul. 2017
Jul. 2017
Oct. 2016
May. 2015
Nov. 2016
Source: Press Release Information

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Preliminary Research: Interviewing Stakeholders
T. K.
Dentsu Inc.
Creative Strategist
A. S.
Bytedance Japan
Creative Director
R. N.
Dentsu Inc.
Creative Director
A. S
Dentsu Inc.
Copywriter, Strategist
Y. K.
Dentsu Inc.
Creative Strategist
K. W.
Dentsu Inc.
Copywriter, Planner
Y. K.
Dentsu Inc.
Creative Planner
K. Y.
Dentsu Inc.
Creative Technologist
Advertising Agency Broader Creative (non-advertising) Domain Experts
M. S.
Dentsu Inc.
O.K.
Dentsu Inc.
Creative Director
Y. N.
Dentsu Creative X Inc.
Creative Techologist
N. W.
Dentsu Inc.
Comm. Planner
I. C.
Cash Studios
Creative Director
S. H.
Certain Measures
Computational Designer
P. R.
Pablo Rochat Studio
Creative / Art Director
R. C
Ovo Energy
Senior UX Designer
M. K.
Fahrenheit 212
Strategist
T. A.
Harvard BS
Professor
L. G.
Uber
Executive Cr. Director
R. K.
Drive Inc.
Art Director
S. O.
Harvard GSD
Researcher
M. W.
Normal Inc.
Creative Coder
M. C.
Deep Mind
Senior Visual Designer
S. A.
Harvard GSD
Researcher
Domain
Interviewee

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Preliminary Research: Stakeholder Analysis
Creative Director
Art Director Copywriter/ 
TV Spot Planner
Creative
Strategist
Creative
Technologist
PR Planner Media Planner
Programmer /
Developper
Designer
Media
Representative
Account
Executives
Client
Client Domain Agency Domain Partners Domain
Client
Other Dept.
Client
Other Dept.
Client
Other Dept.
Client
Other Dept.
Videographer
Other partners
based on projects
…
=
communication
and/or
interaction

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Preliminary Research: Work Flow Analysis
Brieﬁng Ideation Pitch Building Pitch Revision Decision Execution Reporting
Planning Phase Pitching Phase Execution Phase
• Receive orientation /
brief from account team
• Understand what needs
to be pitched.
• Conduct necessary
research
• Study precedents
• Come up with ideas
individually
• Meet as a team to
discuss ideas.
• Conduct the above
multiple times to
converge on the basic
storyline for the pitch.
• Breakdown what needs
to be in the pitch deck,
and assign task
members to work on the
component.
• Report on progress,
converge as a team if
necessary.
• Talk with account
executive team members
to align on overall
direction
• Conduct the above
multiple times if
necessary.
Process
Detail
Players
• Present the idea to the
client.
• Seek feedback from the
client and make
revisions if necessary.
• Present again.
• Agree with the client
what idea is going to be
executed.
• Agree upon how to
measure success.
• Work with partnering
companies, entities to
execute the idea.
• Upon completion of the
project, return to the
client to report the result
of the campaign.
• Share key learnings
from the project with the
client.
Creative
Director
Account
Executives
Creative
Director
Art Director
Copywriter/
Planner
Strategist
Creative
Technologist
PR Planner
Media Planner
Creative Director
Client
Account
Executive
Creative Director
Client
Account
Executive
Creative Director
Client
Account
Executive
All Creative Team
All Creative Team
Client
Partners
Responsi-
bility
Creative
Director
Account
Executive
Duration 7 - 10 days 1day 7 - 10 days 7 - 10 days 90 - 180days 14 - 30days7 - 30 days 7 - 30 days

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Preliminary Research: Ideation Phase Analysis
Brieﬁng Ideation Pitch Building Pitch
Planning Phase Pitching Phase
Creative
Director
Art Director
Copywriter
Creative
Technologist
Strategist
Media Planner
PR Planner
Creative
Director
Art Director
Copywriter
Creative
Technologist
Strategist
Media Planner
PR Planner
Creative
Director
Art Director
Copywriter
Creative
Technologist
Strategist
Media Planner
PR Planner
Creative
Director
Creative
Director
Round 1 Round 2 Round 3
…
= 1 idea
Ideation Mode Individual Group Individual Group Individual Group
Diverge Converge Diverge Converge Diverge ConvergeIdeation Phase
Ideation Round

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“
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Preliminary Research: Expressed Pain Point
An interviewee in the advertising agency
*multiple other agency interviewees responded similarly
In certain circumstances, I need  
to formulate 20 - 30 ideas weekly.
My job would be so much easier,  
if there is something that could
help me.
”

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Ideation Among the Creatives: Text-based (Digital / Non-digital Format)
Digital Non-Digital
An example of
ideating strategy
An example of
ideating tag lines
& TVC scripts
An example of
ideating campaign
An example of
ideating campaign

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Insight and Problem Statement:
Insight
Problem Statement
Creative teams working in advertising industries  
are under strong time pressure to generate many
creative output in short time.
The teams lack the right tool to ideate more,  
better quality creative output.

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Solution Structure: Before
Staff in Creative
Agency
Creative
Creative Writing
Output
Solitary Process

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Solution Structure: After
Staff in Creative
Agency
Application
+
Machine Learning
Model Backend
Text Input
Suggestion
Creative
Creative Writing
Output
Collaborative Process

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Design Principle
• More output (quantity)
• In agency context
• Fun to use
A tool that enables the user to ideate more ideas through its usage.
A tool that that fits in the workflow of advertising agency staﬀs.
A tool that is genuinely fun to interact with to foster ideation.
*quantity eventually becomes quality
*

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Solution Design: AI-powered Text Editor “Phantom”
Phantom is a text editor powered
by an AI backend.
There is a text editing section  
which the user can enter text,
pieces of storyline.
Phantom can suggest additional
text which would be source of
inspiration for additional ideas.
Powered by:

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Powered by:
The AI is created with an LSTM-RNN. Phantom is equipped with three different LSTM models by
default. These models are capable of generating sequence of text by giving a seed text.
Neural Network Architecture of LSTM-RNN
Models
Model 1
Model 2
Model 3{ }

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Powered by:
AI
Models
Model 1
Model 2
Model 3{ }“Love is…”
Generated Text
“an inﬁnite amount of deepness…”
“something unknown…”
“is the toughest thing in this world”
Seed Text
Input Output
The AI is created with an LSTM-RNN. Phantom is equipped with three different LSTM models by
default. These models are capable of generating sequence of text by giving a seed text.

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Main Text
Entry Area
Generate Button
Suggestion Text Area
Model
Selection
Model
Settings
Powered by:

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Powered by:
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The user can easily
specify which models
to use.

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Powered by:
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Harvard University Master of Design Engineering
20
“Proposal for A New Ideation Process”
Togo Kida
The user can also
specify the parameters
of the LSTM-RNN
models.

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Step 1: Write, and highlight.
Step 2: Hit generate.
Step 3: See suggestion text.
Powered by:

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Powered by:
The user will start writing in the
main text area but the user can
also beneﬁt from leveraging the
AI models to complete the
writing task.
Proposed Ideation Process with Phantom:

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Underlying Research Question
Staff in Creative
Agency
Application
+
Machine Learning
Model Backend
Text Input
Suggestion
Creative
Would this collaborative model
between a creative agent and the
user be eﬀective?
“
”
Collaborative Model

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Experiment Structure & Measure of Success
A Prompt
A Prompt
Participants Input Intervention Output
15 min. Experiment
Creative Staff
Group 1
Creative Staff 
Group 2
No Tool
With AI Agent Writing
Writing
Evaluation
Quantitative Qualitative
Analytics upon using the tool. Comments from the participants.
Outcome  
Evaluation
Conducted series of 15 minutes online experiments with three groups and evaluate the outcomes.
A Prompt
Creative Staff 
Group 3
With Dummy Agent
*random phrase Writing

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Group Design Detail and it’s Diﬀerence
Group 1
Group 2
Group 3
Task Detail Duration
Writing a
poem of
COVID19
Tool Intervention
15 minutes
15 minutes
15 minutes
Tool Latency Task Category
3 AI models
trained in
JPOP lyrics
(LSTM-RNNs)
None
Dummy
System
(Picks random
JPOP lyrics
phrase)
5 - 10 sec.
N/A
0.1< sec.
Collaborative
Process
Solitary
Process
Collaborative
Process
Data Source
20MB lyrics
Text data
20MB lyrics
Text data
N/A
Tool Relevance
The tool will
try to generate
text related to
user input.
It has nothing
to do with
user input.
N/A
Writing a
poem of
COVID19
Writing a
poem of
COVID19
With AI
No AI
With Dummy

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AI Dataset Design: Why JPOP Lyrics?
I’ve built other models with diﬀerent dataset, but considering the prompt, I settled down to using JPOP lyrics as dataset for AI.
Dataset Used to Train AI(LSTM-RNN) Generated Output Analysis Is it Suitable for Experiment? Selected?
JPOP Lyrics
Fan Written Japanese Fantasy Novel
Contemporary Classic Japanese Lit.
Text generated from this model is
suggestive and seems wide applicability.
too speciﬁc. A lot about magic spells
and swords and knights.
too archaic.
Yes.
No.
No.
✅
👎
👎

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Quantitative Metrics Collection
Quantitative metrics are recorded as .json ﬁle while each of the subjects interact with Phantom.
Participant .json file
Collected Metrics
• The amount of text input over time.
• What was the input text used to
generate suggestions from the AI/
dummy system.
• Parameter settings when user
asked for text suggestion.
Interaction During Experiment Analytics Data Recorded

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Board of Judges
Asked user feedback after experiment. Also, had an independent group of judges to
judge the writing outputs and see if there is a noticeable diﬀerence in scores.
Creative
Writing
Score
Qualitative Metrics Collection
Participants
Score/Reﬂection
• Your self score on output?
• Was the generated text helpful?
• Rate the experience itself?
Post-experiment Questionnaire

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Group Design to Answer Research Questions
Number of research questions can be answered through this experiment.
Question to be Answered
Would this collaborative model between a
creative agent and the user be effective?
Is the ML model better in some way than
simply randomly showing text as
inspiration?
How it Could be Answered?
Compare group 2 and group 1 + 3.
Rationale
Compare group 1 and group 3.
Group 2 is a solitary task.
Group 1 + 3 is a collaborative task
with the tool.
Group 1 uses AI(LSTM-RNN) and
Group 2 is a dummy system that
simply returns text randomly, not
relevant to the user input.
Is the quality of output different among
groups?
Scores from the external judges.
3 independent judges were
assigned to score the output. This
will give objective means to
evaluate the output.
29

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Experiment Structure: Recruitment
Recruited 30 participants from the advertising industry to test the solution design in 3 groups.
Experiment Group
Total of
30 Participants
Group 1 = 10 Group 2 = 10 Group 3 = 10
Creative Strategist
Copywriter
Creative Tech Director
TVC Planner
Copywriter
Creative Manager
Creative Director
TVC Planner
Copywriter
Creative Strategist
Copywriter
Creative Tech Director
TVC Planner
Strategist
Creative Manager
Planner
Creative Director
TVC Planner
Copywriter
Creative Strategist
Copywriter
Executive Creative Director
TVC Planner
Copywriter
Creative Manager
Creative Director
TVC Planner
Copywriter
With AI No AI With Dummy

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Experiment Result: From the Analytics
Collaborative task group (gr. 1 & 3) seem to yield more output than solitary task group (gr. 2).
0
200
400
600
0 250 500 750
Elapsed Seconds
TotalAmountofText
colour
01
02
03
04
05
06
07
08
09
10
With the assistance of AI models(group 1)
Elapsed Seconds vs Total Amount of Text Entered
n = 10
0
200
400
600
0 250 500 750
Elapsed Seconds
TotalAmountofText
colour
01
02
03
04
05
06
07
08
09
10
Without any form of assistance(group 2)
n = 10
0
200
400
600
0 250 500 750
Elapsed Seconds
TotalAmountofText
colour
01
02
03
04
05
06
07
08
09
10
With the assistance of randomly selected phrases(group 3)
n = 10
Group 1 (With AI) Group 2 (No AI) Group 3 (With Dummy)
Participants Participants Participants

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While some participants are copying generated text, not all participants are doing so necessarily (simply viewing).
0
200
400
600
0 250 500 750
Elapsed Seconds
TotalAmountofText
colour
01
02
03
04
05
06
07
08
09
10
With the assistance of AI models(group 1)
n = 10
0
200
400
600
0 250 500 750
Elapsed Seconds
TotalAmountofText
colour
01
02
03
04
05
06
07
08
09
10
Without any form of assistance(group 2)
n = 10
0
200
400
600
0 250 500 750
Elapsed Seconds
TotalAmountofText
colour
01
02
03
04
05
06
07
08
09
10
n = 10
Suggestion Copied
Suggestion Copied
Participants Participants Participants

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Collaborative task group (gr. 1 & 3) seem to yield more output than solitary task group (gr. 2).
745.4
626.4
825.3
0
200
400
600
800
group 1 group 2 group 3
Groups
TotalInputTime
group group 1 group 2 group 3
With AI(group 1) vs With No AI(group 2) vs With Dummy(group 3)
Average Input Time Among Groups
301
215
371
0
100
200
300
Groups
TotalTextAmount
Average Text Amount Among Groups
0.4
0.34
0.45
0.0
0.1
0.2
0.3
0.4
Groups
TextInputRateperSecond
Average Rate of Text Input per Second
With AI No AI With Dummy With AI No AI With Dummy With AI No AI With Dummy

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Looking at the data, some evidence to say that the more you generate, the more output quantity is achieved.
●
●
●
●
●
●
●
●
●
●
0
200
400
600
0 5 10 15 20 25
Number of Times Suggestion Generated
TotalAmountofText
With the assistance of AI models(group1)
Number of Times Suggestion Generated vs Total Amount of Text Entered
n = 10
●
●
●
●
●
●
●
●
●
0
200
400
600
0 5 10 15 20 25
Number of Times Suggestion Generated
TotalAmountofText
Number of Times Suggestion Generated vs Total Amount of Text Entered
n = 10
Group 1 (With AI) Group 3 (With Dummy)

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Group 1 which is trying to generate on user context scored higher.
mean:mean:mean:mean:mean:mean:mean:mean:mean:
6.566.566.566.566.566.566.566.566.56
0
1
2
3
4
5
0 3 6 9
Score
Count
With AI(Group 1)
How Helpful Were the Generated Results?
4.444.444.444.444.444.444.444.444.44
0
1
2
3
4
5
0 3 6 9
Score
Count
With Dummy System(Group 3)
How Helpful Were the Generated Results?

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Very very positive result on the collaborative task model.
8.568.568.568.568.568.568.568.568.56
0
1
2
3
4
5
0 3 6 9
Score
Count
With AI(Group 1)
How Fun was it to Collaborate?
8.228.228.228.228.228.228.228.228.22
0
1
2
3
4
5
0 3 6 9
Score
Count
How Fun was it to Collaborate?

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There is not that much diﬀerence among groups.
666666666
0
1
2
3
4
5
0 3 6 9
Score
Count
With AI(Group 1)
Self Reported Scores of the Creative Output
mean:mean:mean:mean:mean:mean:mean:mean:mean:mean:
6666666666
0
1
2
3
4
5
0 3 6 9
Score
Count
With No AI(Group 2)
6.226.226.226.226.226.226.226.226.22
0
1
2
3
4
5
0 3 6 9
Score
Count

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Group 3, with dummy system, got the highest judged score.
5.1
5.47
6.87
0
2
4
6
Groups
Score
Final text output scored by 3 judges

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Experiment Summary
Group 1
Group 2
Group 3
Total Text Output
745.4
Avg. Time Used Text per Sec.
626.4
825.3
301
215
371
0.4
0.34
0.45
Long
Long
Short
Long
Long
Short
Fast
Fast
Slow
# of Gen. vs. Text Amount
Strong Positive
Relation
Weak Postive
Relation
N/A
Self Score
6
6
6.22
Generation Relevancy Scr.
6.56
4.44
N/A
Highest
Lowest
Judge Score
5.1
5.47
6.87
Highest
With AI
No AI
With Dummy
Neutral
Neutral
Neutral
Lowest
Group 1 & 3 outperformed Group 2 with output metrics, and Group 3 got the highest score from the judges.

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Revisiting the Original Research Question
Question
Would this collaborative model between a
creative agent and the user be effective?
Is the ML model better in some way than
simply randomly showing inspiration?
Is the quality of output different among
groups?
Findings
Yes. Both groups (1 & 3) with collaborative models yield higher score on
output quantity and gives fun experience throughout the creative process.
Yes, in the sense that, user testing revealed that generated text from the AI
models were regarded more relevant to the prompt. However, No in the
sense that it did not translate by receiving the highest score from the
judges.
Yes. One of the groups with collaborative process yielded the highest score
from the judges.

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5.1
5.47
6.87
0
2
4
6
GroupsScore
Final text output scored by 3 judges
Why did the Group with AI Model Scored the Lowest?
Possible Reasons:
Latency Matters?
Bad Examples Help?
Due to it’s computationally heavy nature,
the AI model had a disadvantage in
comparison to the Dummy model (10 sec. vs.
0.1 sec). User being instantaneously shown
random phrases worked better in this case?
Dummy group had more complaints on the
suggested text not being relevant to the task
itself. This worked in favor for the user to
“eliminate” the bad examples from their mind?

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Other Questions that Arise from the Current Results
Quality vs Quantity?
Broader Applicability?
Generalizability?
We saw collaborative models enable users to
be more proliﬁc. How could this be translated
into quality?
Would the ﬁndings be consistent among age
groups, ethnicities, and industries? Or would
there be a different trend? If so, is there
something that can be generalized or not?

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Further Ahead: Designers as Curators of Data when Using AI?
• Potential need for users to design their own dataset for the
models.
• There is no “universal model” that will help the ideation task
in general.
• Rather, better results could be achieved by speciﬁcally
designing a model for that particular task, with a particular
set of dataset.
• In that sense, the designer’s role will become more like an
“editor” who curates dataset to build the collaborative
agents.
Staff in Creative
Agency
Application
+
Machine Learning
Model Backend
Text Input
Suggestion
Creative
“Curate Data”
and Design the AI.
Curated Dataset

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No tool is neutral, each is embedded
with the beliefs and biases of its creators.“
”Lauren McCarthy
(artist, creator of p5.js)
Thank you!

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Current Prototype

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Preliminary Testing
Had 2 professional writers / 2
non-writers interact with the
prototype and asked about their
ideation process and the
experience.

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Preliminary Testing Responses
I can’t say I’m going to use the text, but it’s an
interesting prompt. It might aﬀect the phrasing,
imagery, and the atmosphere of the writing.
Nothing similar to what I had in my mind. 
I liked it because it was random.
Fun!
Response 1: Response 2:
Response 3:
It shows signs that the collaborative
model as a new process of ideation is
valid.

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How This Could Scale
•Further build an extension / plugin to connect with other existing writing
tools. (i.e. Google Document, Grammarly)
•Build analytic features to further quantify the tendency of the ideation
process that could be utilized for other means. (i.e. better team structures)

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Measure of Success (Further Analysis)
Develop features to count various
types of interactions on the tool to
later on quantitatively analyze the
usage of the tool itself.
•How many times the model was
called?
•What models were used?
•What’s the amount of text written?

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Solution Design, Data Preparation
Raw Data Process 1 Process 2 Model
TV Spots that won
Cannes Lions from 2013,
2014, 2015, 2018
Cut the individual TV spots
into images. (1image / 3sec.)
Apply image captioning to the
images to generate texts.
Data Building
RNN
(Recurrent
Neural Network)
Task
Detail
Conceptual
Diagram of  
the Process
Sample TV Spot 
(~2,000 spots) …
“A guy is brushing
his teeth”
“Riding a bike on
road”
“A guy is on a TV”
}

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What creatives do in advertising agencies
State A State BObstacle
Idea
{ } { }
Ideas can be:Obstacles can be:
• A Tagline
• A Script for TV spot
• A campaign plan
• A public relations plan
• A strategy
…
• Low brand recognition rate
• Low sales
• Low presence in the market
• Low brand engagement rate
…
Using ideas ( = “creative jump”) to overcome business obstacles to reach from starting state A to a better, preferred state B.

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How do creative teams in the advertising industry operate?
What is the typical workflow?
Research Question 1:

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Critical Point in the Workflow
The ideation part is critical because, the idea that comes in
this phase defines the quality of the output until the end.
“
”One interviewee in the advertising agency
*multiple other agency interviewees responded similarly

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What does ideation process look like in the agency environment?
Research Question 2:

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Noticed Similarities and Differences in Ideation
Receptive.
Agency Creative Staff Non-agency Creative Staff
“Idea is something
I need to get.”
Ideation as a proactive process.Characteristic
Comment
Ideation as a receptive process.
Personal and CollaborativeProcess Personal and Collaborative
Pen, Paper, DigitalTools Pen, Paper, Digital
Class
“Idea is something that
will come to me.”

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Individual Process of Ideation
Input Output FormalizeSubphase
Ideation Phase Diverge
Ideation Round Round n
• Understand what is being required for
the next group meeting with the team.
• Collect relevant information from
various sources.
• Also view relevant case studies and
industry material to be up-to-date.
• Start writing out ideas.
• Keep on pushing.
• Push even further.
• Try different techniques to “squeeze”
ideas.
• Formalize the material into decks /
notes / memos so that it can be
shared with others during the next
meeting.
• Use text / images to effectively
communicate the ideas developed.
Task
Medium
Recent news
Case studies of comm. project
Content from various social media
Informal discussion with colleague
Creative brief
Look at art / design / precedents
Pen & paper
Digital entry
Idea deck

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How ideas are recorded in individual phase
Digital Non-Digital
An example of
ideating strategy
An example of
ideating tag lines
& TVC scripts
An example of
ideating campaign
An example of
ideating campaign

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How ideas are converged in group phase
Ideas formulated individually are being shared, discussed, and then converged into a direction for the next round iteration.

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Many Individual / Group Sessions with Numerous Ideas before Pitch
Brieﬁng Ideation Pitch Building Pitch
Planning Phase Pitching Phase
Creative
Director
Art Director
Copywriter
Creative
Technologist
Strategist
Media Planner
PR Planner
Creative
Director
Art Director
Copywriter
Creative
Technologist
Strategist
Media Planner
PR Planner
Creative
Director
Art Director
Copywriter
Creative
Technologist
Strategist
Media Planner
PR Planner
Creative
Director
Creative
Director
Round 1 Round 2 Round 3
…
= 1 idea
Ideation Mode Individual Group Individual Group Individual Group
Diverge Converge Diverge Converge Diverge ConvergeIdeation Phase
Ideation Round

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What the ideation process is in the agency environment conceptually
= 1 idea
Latent Idea Space
Round 1
Round 2
Round 3
Creative Direction
CreativeDirection
The Ideation phase is essentially creative teams
attempting to navigate possible latent idea space
to ﬁnd the best idea that would make it to the pitch.
The best ideas
for the pitch
= Pitch Idea

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Source: “The Team Creativity Model: An Exploratory Case Study ” by de Vreede et al, 2017
Reference: “Team Creativity Model”

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Quantity brings quality
Latent Idea Space 2
Round 1
Round 2
Round 3Latent Idea Space 1
Round 1
Round 2
= 1 idea
= Pitch Idea
= 1 idea
= Pitch Idea
Reaching alternative, better ideas

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Ideation methods / tools that are out there
Demo ScreenName
The Toolbox
By
Hyper Island
Overview
• Collection of numerous frameworks and methods for ideation
for individuals and groups, offered by Hyper Island (education
institution)
Source: “Toolbox” by Hyper Island
Issue
• Mainly targeted for service design, does not ﬁt into the context
of advertising agency business landscape.

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Ideation methods / tools that are out there
DemoName
Oblique Strategies
By
Brian Eno
Overview
• Cards that have instructions for the users to think differently.
Source: “Toolbox” by Hyper Island
Issue
• Maybe helpful for ideation, but the content of the card does not
relate to the current ideation task the user is working on.
• The process of ideation becomes a mere serendipity.

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Problem Statement:
Creative teams working in advertising industries
can ideate more with the right tool, but no such tool exists
designed specifically for them.
An opportunity for a solution.

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Precedent 1:
Demo ScreenProject Name
Writing Machine
Project By Project Year
Robin Sloan 2016
Source: “The Writing with the Machine” by Robin Sloan, 2016
Project Overview
Reason for Selection
There is a potential of being a new tool / interaction model which
the user leverages this as an agent to ideate.
• Recurrent Neural Network (RNN) trained using Sci-Fi novel text
data
• The trained model is connected with a text editor, and with a
tab key pressed by the user, takes the previous text as an input
to generate additional sentence.
• The generated text is in Sci-Fi theme, which the user is free to
use as a source of inspiration.

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Precedent 2:
Demo ScreenProject Name
Suggestive Drawing
Project By Project Year
Nono Martínez Alonso 2017
Project Overview
Reason for Selection
• Utilized pix2pix and designed an interface that will work in
conjunction with the user to assist / suggest drawing process.
• The user is drawing the lines, but the ﬁlling patterns are
determined by the AI backend.
Also another potential of being a new tool / interaction model
which the user leverages this as an agent for a different creative
process.
Source: “Suggestive Drawing” by Nono Martínez Alonso, 2017

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One particular user of this tool
Copywriter
TV Spot Planner
• A copywriter / TV Spot Planner in an agency.
• Needs to formulate (many) ideas for a TV Spot for client’s next
advertising campaigns.
• Usually employs digital tools writes out the ideas.

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One particular user of this tool
Sample screen of a copywriter ideating with a memo app
Copywriter
TV Spot Planner
The ideation process
is solitary, text-based.

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Data Preparation 1
January February March April May
ML Model
Const. 1
Interface  
Design/Dev 1
Workshop
(User Testing)
Data
Preparation 2
ML Model
Const. 2
Interface  
Design/Dev 2
★Completion
Month
Task
Further Plan

Independent Design Engineering Project II (ADV9382): Final Presentation

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Independent Design Engineering Project II (ADV9382): Final Presentation