SlideShare a Scribd company logo
1 of 15
Download to read offline
+ IMWUT 2018
/ ๋ฅ˜๋ช…๊ท 
Understanding the Long-Term Use
of Smart Speaker Assistants


- FRANK BENTLEY et al.
01
02
03
04
05
06
WHY THIS PAPER
RESEARCH QUESTION
METHOD
FINDINGS
DESIGN IMPLICATION
TAKEAWAY
01 WHY THIS PAPER
์‚ฌ์šฉ์ž๋“ค์€ ์‹ค์ œ๋กœ ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค๋ฅผ ์–ด๋–ป๊ฒŒ ์‚ฌ์šฉํ•˜๊ณ  ์žˆ๋Š”์ง€โ€ฆ
๋ˆ„๊ตฌ๋‚˜ VUI๋ฅผ 

์‰ฝ๊ฒŒ ์“ธ ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉด ์ข‹๊ฒ ๋‹ค
ASR, NLP
๋ถ€์กฑํ•œ ์ปจํ…์ธ 
๋””๋ฐ”์ด์Šค์˜ ํ•œ๊ณ„
โ€ฆ
โ€ฆ
โ€ฆ
์™œ ์ง€๊ธˆ์€ ๋ชป์“ฐ๊ณ  ์žˆ์„๊นŒ?
Discoverability, Learnability, Repair
์ง€๊ธˆ๊นŒ์ง€ ์‚ดํŽด๋ดค๋˜ ๋ถ€๋ถ„
ํ˜„์žฌ ์‚ฌ์šฉ์ž๋Š” ์–ด๋–ป๊ฒŒ 

์‚ฌ์šฉํ•˜๊ณ  ์žˆ๋Š”๊ฐ€?
01 WHY THIS PAPER
๋‹ค๋ฅธ IT๊ธฐ๊ธฐ๋ณด๋‹ค ๋น ๋ฅธ ์†๋„๋กœ ๋ณด๊ธ‰๋˜๊ณ  ์žˆ์Œ
๊ฐ•์„๋ฌด, 2020, ์œ„์น˜์ •๋ณด ์‚ฐ์—… ๋™ํ–ฅ ๋ณด๊ณ ์„œ, ํ•œ๊ตญ์ธํ„ฐ๋„ท์ง€ํฅ์›
* ์ดˆ๊ธฐ ์ˆ˜์šฉ ๋‹จ๊ณ„๋Š” ๋ณด๊ธ‰์œจ ์•ฝ 16%๊นŒ์ง€, ์ดˆ๊ธฐ ๋Œ€์ค‘ํ™” ๋‹จ๊ณ„๋Š” ๋ณด๊ธ‰์œจ 16~50%๋ฅผ ๊ธฐ์ค€์œผ๋กœ ํ•œ๋‹ค
02 RESEARCH QUESTION
โ€ข ๊ธฐ๋Šฅ๊ณผ ๋„๋ฉ”์ธ์— ๋”ฐ๋ผ์„œ

- What types of commands do people make to their smart speakers (e.g. weather, music playback,
smart home control, etc.) and in what percentages do they use these different features?
โ€จ
โ€ข ํ•˜๋ฃจ, ํ‰์ผ/์ฃผ๋ง์— ๋”ฐ๋ผ์„œ

- How are these devices used at different times of day or days of the week? Are there differences in
the categories of commands?
โ€จ
โ€ข ์‹œ๊ฐ„ ๊ฒฝ๊ณผ์— ๋”ฐ๋ผ์„œ

- How do these types of commands change over time as users become more familiar with these
devices? Do the topics change? Does the length of commands change per topic over time?
โ€จ
โ€ข ๋‚˜์ด, ๊ฐ€๊ตฌ ํ˜•ํƒœ์— ๋”ฐ๋ผ์„œ

- Are there any differences in use of these devices in different age groups or household sizes?
๋‹ค์–‘ํ•œ ์ธก๋ฉด์—์„œ ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค ์žฅ๊ธฐ๊ฐ„ ์‚ฌ์šฉ ํ–‰๋™์„ ์‚ดํŽด๋ด„
03 METHOD
88๋ช…์œผ๋กœ๋ถ€ํ„ฐ 110์ผ ๋™์•ˆ์˜ ์‚ฌ์šฉ ๋กœ๊ทธ๋ฅผ ์ˆ˜์ง‘
โ€ข Amazon MTurk์„ ํ†ตํ•ด์„œ ๊ณผ๊ฑฐ ์‚ฌ์šฉ ๋ฐ์ดํ„ฐ(๋กœ๊ทธ) ์ˆ˜์ง‘ (ํ•œ๋ช… ๋‹น $5๋กœ ์ง€๊ธ‰)

โ€ข 2017๋…„ ์—ฌ๋ฆ„์— ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ–ˆ๊ณ , ์ด ๋‹น์‹œ๋Š” ๊ตฌ๊ธ€ํ™ˆ ์ถœ์‹œ 1๋…„์ด ์•ˆ๋จ

- ๊ตฌ๊ธ€ํ™ˆ ์ถœ์‹œ : 2016๋…„ 11์›” 14์ผ (๋ฏธ๊ตญ), 2018๋…„ 9์›” 18์ผ(ํ•œ๊ตญ)

โ€ข Demographics

- 53%: ๋‚จ์„ฑ / 47%: ์—ฌ์„ฑ, 18~64์„ธ

- 17%๋Š” ์•„๋งˆ์กด ์—์ฝ” ์†Œ์œ 

- 31%-1์ธ ๊ฐ€๊ตฌ, 32%-2์ธ ๊ฐ€๊ตฌ,15%-3์ธ ๊ฐ€๊ตฌ, 15%-4์ธ ๊ฐ€๊ตฌ, 6%๋Š” 4์ธ ์ด์ƒ

โ€ข 65,499 ์ปค๋งจ๋“œ ์ˆ˜์ง‘ (ํ‰๊ท  744์ปค๋งจ๋“œ/์ธ, 110์ผ ๋™์•ˆ)

โ€ข ๋ถ„์„ ๋ฐฉ๋ฒ•

- 20,000๊ฐœ๋Š” ์ˆ˜๋™ ์ฝ”๋”ฉ(์„œ๋ธŒ ๋„๋ฉ”์ธ) ํ›„์— SVM ํ›ˆ๋ จ ์‹œ์ผœ์„œ ๋‚˜๋จธ์ง€ ๋ฐ์ดํ„ฐ ์„œ๋ธŒ ๋„๋ฉ”์ธ์œผ๋กœ ์ž๋™ ๋ถ„๋ฅ˜
โ€จ
(์ •ํ™•๋„ 88%)

- ๊ฐ€๊ตฌ ํ˜•ํƒœ, ์‚ฌ์šฉ ํŒจํ„ด์œผ๋กœ MANOVA ๋ถ„์„
04 FINDINGS > Daily Use
ํ•˜๋ฃจ์— ์•ฝ 4๋ฒˆ ์ปค๋งจ๋“œ๋ฅผ ํ•˜๊ณ , 1~2๋ฒˆ ์งง์€ ์ธํ„ฐ๋ž™์…˜์ด ๋Œ€๋ถ€๋ถ„
4.1
17.7
2.5
4
Commands Words
2
5
1
2
39%
21%
์„ธ์…˜๋‹น ์ปค๋งจ๋“œ ์ˆ˜
ํ•˜๋ฃจ๋‹น ์ปค๋งจ๋“œ ์ˆ˜ ์ปค๋งจ๋“œ๋‹น ๋‹จ์–ด ์ˆ˜
์„ธ์…˜๋‹น ๋„๋ฉ”์ธ ์ˆ˜
1 48%
2 29% 35% - ๊ธฐ๊ธฐ ์ปจํŠธ๋กค

34% - ์Œ์•… ๊ด€๋ จ
04 FINDINGS > Daily Use
์‚ฌ์šฉ์ž์˜ ์ƒํ™œ ๋ฆฌ๋“ฌ๊ณผ ์œ ์‚ฌํ•˜๊ฒŒ ์‚ฌ์šฉ ํŒจํ„ด์ด ๋ฐœ์ƒํ•จ
์Œ์•… ๊ด€๋ จํ•˜์—ฌ 

๊ฐ€์žฅ ๋งŽ์ด ์‚ฌ์šฉ
์ €๋…9์‹œ~์ƒˆ๋ฒฝ2์‹œ, 

์Šค๋งˆํŠธํ™ˆ ์กฐ์ž‘์„ ๋งŽ์ด ํ•จ
์ƒˆ๋ฒฝ์— ์‹œ๊ฐ„ ํ™•์ธ์„ ๋งŽ์ด ํ•จ. 

์–ผ๋งˆ๋‚˜ ๋” ์ž˜ ์ˆ˜ ์žˆ๋Š”์ง€ ์•Œ๊ธฐ ์œ„ํ•ด์„œ
์ผ์–ด๋‚˜์ž๋งˆ์ž ์ ์‹ฌ์‹œ๊ฐ„ ์ „
์ผ์—์„œ ๋Œ์•„์™€์„œ

(ํ•˜๋ฃจ ์ค‘ ๊ฐ€์žฅ ๋งŽ์ด
์‚ฌ์šฉํ•˜๋Š” ์‹œ๊ฐ„๋Œ€)
โ€ข ํ‰์ผ์— ๋น„ํ•ด ์ฃผ๋ง์— ์‚ฌ์šฉ๋Ÿ‰์ด ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜๋ฏธํ•˜๊ฒŒ ๋งŽ์Œ (p=0.04)

โ€ข ์‚ฌ์šฉํ•˜๋Š” ๋„๋ฉ”์ธ์˜ ๋น„์œจ์€ ๋น„์Šทํ•จ
04 FINDINGS > Daily Use
์ง‘์— ์žˆ๋Š” ์‹œ๊ฐ„๊ณผ ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค ์‚ฌ์šฉ๋Ÿ‰์€ ๊ด€๊ณ„๊ฐ€ ์žˆ์Œ
โ€ข ์‹œ๊ฐ„์ด ํ˜๋Ÿฌ๋„, ํ•˜๋‚˜์˜ ์ปค๋งจ๋“œ์— ํฌํ•จ๋œ ๋‹จ์–ด์˜ ๊ฐฏ์ˆ˜๋Š” ๋ณ€ํ•˜์ง€ ์•Š์Œ

- ์ดˆ๋ฐ˜์˜ ๋ฉฐ์น ์„ ์ œ์™ธํ•˜๊ณ , ์ธํ„ฐ๋ž™์…˜์˜ ํ˜•ํƒœ๋„ ๋ณ€ํ•˜์ง€ ์•Š์Œ

โ€ข ์‹œ๊ฐ„ ๊ด€๋ จ, ์ •๋ณด ๊ฒ€์ƒ‰(๋‚ ์”จ), ์—”ํ„ฐํ…Œ์ธ๋จผํŠธ ๋„๋ฉ”์ธ์—์„œ๋Š” ๋‹จ์–ด์˜ ์ˆ˜ ์ฆ๊ฐ€

- ๋‚ ์”จ๋ฅผ ๋ณด๋ฉด, ์ ์  ๋” ํŠน์ • ์š”์†Œ์— ๋Œ€ํ•ด ์งˆ๋ฌธํ•จ (์˜ค๋Š˜์˜ ์ตœ๊ณ  ์˜จ๋„๋Š”?, ๋‹ค๋ฅธ ์ง€์—ญ์˜ ๋‚ ์”จ)

โ€ข ๋ฐ˜๋ฉด, ์Šค๋ชฐํ† ํฌ์—์„œ๋Š” ๋‹จ์–ด์˜ ์ˆ˜ ๊ฐ์†Œ
04 FINDINGS > Daily Use
์ดˆ๋ฐ˜ ์‚ฌ์šฉ ๋ฐฉ๋ฒ•์ด ์œ ์ง€๋˜๋ฉฐ, ๋„๋ฉ”์ธ์— ๋”ฐ๋ผ ์งˆ๋ฌธ์ด ๊ตฌ์ฒดํ™”๋จ
โ€ข Commands per day - Number of domains used (r=.590) 

โ€ข Commands per day - Sessions per day (r=.545) 

โ€ข Commands per day - Commands per session (r=.378) 

โ€ข Percent Midday - Percent Small Talk (r = -.390) 

โ€ข Percent Midday - Percent Night (r = -.367) 

โ€ข Percent Evening - Percent Weekend (r = .398) 

โ€ข Percent Information - Average Command Length (r = .371) 

โ€ข Percent Information - Percent Music (r = -.478) 

โ€ข Percent Information - Percent Small Talk (r = .387)
04 FINDINGS > Usage Patterns
์‚ฌ์šฉ์ž๋งˆ๋‹ค ์ง€๋ฐฐ์ ์ธ ๋„๋ฉ”์ธ์ด ์žˆ์Œ (์ •๋ณด vs. ์Œ์•…)
โ€ข ์Œ์•…์€ ๊ตฌ๋… ์„œ๋น„์Šค๋ฅผ ๊ฐ€์ž…ํ•ด์•ผ ํ•ด์„œ 18-24 ๋‚˜์ด๋Œ€์—์„œ ์‚ฌ์šฉ๋ฅ ์ด ์ ์–ด ๋ณด์ž„

โ€ข MANOVA ๋ถ„์„ ๊ฒฐ๊ณผ, ๋‚˜์ด๋Š” ์‚ฌ์šฉ ๋„๋ฉ”์ธ ๋น„์œจ์— ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š์Œ
04 FINDINGS > Demographic Differences
์ƒ๋Œ€์ ์œผ๋กœ ์ Š์€ ์‚ฌ์šฉ์ž๊ฐ€ ๋งŽ์„ ๋ฟ, ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์€ ์—†์Œ
04 FINDINGS > Demographic Differences
๊ฐ€๊ตฌ์— ๋”ฐ๋ผ์„œ ์กฐ๊ธˆ์”ฉ ์‚ฌ์šฉ ๋„๋ฉ”์ธ ๋น„์œจ์ด ๋‹ค๋ฆ„
โ€ข ๊ฐ€๊ตฌ์˜ ์ˆ˜๊ฐ€ ๋งŽ์„์ˆ˜๋ก ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค๋ฅผ ๋” ์ ๊ฒŒ ์‚ฌ์šฉํ•จ

โ€ข 4์ธ ์ด์ƒ ๊ฐ€๊ตฌ์—์„œ ์Šค๋งˆํŠธํ™ˆ ์ปจํŠธ๋กค ์‚ฌ์šฉ ๋น„์œจ์ด ์ƒ๋Œ€์ ์œผ๋กœ ๋‚ฎ์Œ
05 DESIGN IMPLICATION
โ€ข ์‚ฌ์šฉ์ž์—๊ฒŒ ์ƒˆ๋กœ์šด ๋„๋ฉ”์ธ์„ ์†Œ๊ฐœํ•˜๋Š” ๊ฒƒ

- ์‚ฌ์šฉ์ž๋Š” ์‹œ๊ฐ„์ด ํ˜๋Ÿฌ๋„ ์‚ฌ์šฉ ํ–‰๋™์ด ๋ณ€ํ•˜์ง€ ์•Š์Œ

- ๋ณดํ†ต 3๊ฐœ์˜ ๋„๋ฉ”์ธ ์ •๋„์— ์ •์ฐฉํ•จ

- ๊ตฌ๊ธ€์—์„œ ์ด๋ฉ”์ผ์„ ๋ณด๋‚ด์ง€๋งŒ ์‚ฌ์šฉ์ž๊ฐ€ ์ƒˆ๋กœ์šด ๋„๋ฉ”์ธ์„ ์ฐพ๋„๋ก ์œ ๋„ํ•˜์ง€๋Š” ๋ชปํ•˜๊ณ  ์žˆ์Œ

- ๋”ฐ๋ผ์„œ, ์–ด์‹œ์Šคํ„ดํŠธ ์ž์ฒด๊ฐ€ ์ƒˆ๋กœ์šด ๋„๋ฉ”์ธ์œผ๋กœ ์ด๋Œ์–ด์•ผ ํ•จ

โ€ข ๊ด€๋ จ๋œ, ์งง์€ ์ปค๋งจ๋“œ ์ œ์•ˆ

- ์ˆ์ปท์„ ๋งŒ๋“ค์–ด์คŒ (์ผ์ข…์˜ ๋ฃจํ‹ด)

โ€ฃ Pasta timer : 12-minute timer

- ์‹œ๊ฐ„์ด ํ˜๋Ÿฌ๋„ ์ปค๋งจ๋“œ๋Š” ๊ธธ์–ด์ง€์ง€ ์•Š์Œ. ๋”ฐ๋ผ์„œ ์–ด์‹œ์Šคํ„ดํŠธ๊ฐ€ ๋ณต์žกํ•˜๊ฒŒ ๋ฌผ์–ด๋ณด๋Š” ๋ฐฉ๋ฒ•์„ ๊ฐ€๋ฅด์ณ ์ค„ ํ•„์š”๊ฐ€ ์žˆ์Œ

- ํ˜„์žฌ๋Š” ์ผ๋ฐฉํ–ฅ์ ์ธ ์†Œํ†ต์ด ๋งŽ์Œ.

โ€ข ์—์ด์ „ํŠธ ๊ธฐ๋ฐ˜ ๊นŠ์€ ์ธํ„ฐ๋ž™์…˜ ์ง€์›

- ํ˜„์žฌ์˜ ๋””๋ฐ”์ด์Šค๋Š” ๋‹จ์ผ ์ปค๋งจ๋“œ๋งŒ ์ˆ˜ํ–‰ํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์Œ

- ์Šค๋งˆํŠธ ์–ด์‹œ์Šคํ„ดํŠธ๋Š” ์‹œ๊ฐ„์— ๋”ฐ๋ผ ๋งŽ์€ ์–‘์˜ ์ปจํ…์ŠคํŠธ๋ฅผ ๋ฐฐ์šฐ๊ณ  ๊ธฐ์–ตํ•ด์•ผ ํ•จ
06 TAKEAWAY
โ€ข ์•ฝ๊ฐ„ ๋‡Œํ”ผ์…œ(?)๋กœ ์žˆ๋˜ ์ƒ๊ฐ๋“ค์„ ์—ฐ๊ตฌ๋กœ ๋ฐํ˜€๋‚ด์„œ ์ธ์šฉํ•˜๊ธฐ์—๋Š” ์ข‹์•„ ๋ณด์ž„

- ํ•˜์ง€๋งŒ ์ƒ๊ฐ๋ณด๋‹ค ์ƒˆ๋กœ์šด ๋‚ด์šฉ์€ ์—†์Œ

- ๋กœ๊ทธ๋กœ๋งŒ ์ ‘๊ทผํ•ด์„œ ๋‹ค์†Œ ํ”ผ์ƒ์ ์ธ ๋‚ด์šฉ์ด ๋งŽ์€ ๋“ฏ (๋„๋ฉ”์ธ ๋‹จ์œ„์˜ ํ†ต๊ณ„ ๋ถ„์„ ์œ„์ฃผ)

- ๊ทธ๋ž˜๋„ VUI๋Š” ๋กœ๊ทธ ์ ‘๊ทผ์ด ์‰ฝ๋‹ค๋Š” ์žฅ์ ์„ ๋ณผ ์ˆ˜ ์žˆ์—ˆ์Œ

- ํ™•์‹คํžˆ ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค๊ฐ€ ์ž˜๋จนํžˆ๋Š” ์‚ฌ์šฉ์ž ์ง‘๋‹จ์ด ์žˆ์„ ๊ฒƒ ๊ฐ™์€๋ฐโ€ฆ

โ€ข ๊ฒฐ๊ตญ ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค ์‚ฌ์šฉ์ž๋Š” ์ดˆ๋ฐ˜์˜ ๋ช‡ ๊ฐ€์ง€ ๊ธฐ๋Šฅ๋งŒ ๊ณ„์†ํ•ด์„œ ์‚ฌ์šฉ

- ์–ด๋–ป๊ฒŒ ์ƒˆ๋กœ์šด ๊ธฐ๋Šฅ(์•ฑ)์„ ์‚ฌ์šฉํ•˜๊ฒŒ ํ•  ์ˆ˜ ์žˆ์„๊นŒ

โ€ข ์‚ฌ์šฉ์ž ์ž…์žฅ์—์„œ๋Š” ์ƒˆ๋กœ์šด ๊ธฐ๋Šฅ์„ ์‚ฌ์šฉํ•  ์ƒ๊ฐ์„ ํ•˜์ง€ ์•Š์Œ

- ์ด๊ฒƒ์ด ๋‹ˆ์ฆˆ๊ฐ€ ์—†๋Š” ๊ฑด์ง€, ์‚ฌ์šฉ์ด ์–ด๋ ค์šด ๊ฑด์ง€๋Š” ๋ชจ๋ฅด๊ฒ ์Œ

โ€ข 2018๋…„ ๋…ผ๋ฌธ์ธ๋งŒํผ ์ตœ๊ทผ ๋…ผ๋ฌธ๋„ ์‚ดํŽด๋ณด๋ฉด ์ข‹์„ ๋“ฏ - ํŠนํžˆ ์„œ๋“œํŒŒํ‹ฐ ๋ณด์ด์Šค ์•ฑ ๊ด€๋ จํ•ด์„œ

- ํ˜„์žฌ ์ธ์šฉ์ˆ˜ 183โ€ฆ!

โ€ข ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค ์ดˆ๊ธฐ์˜ ํฌ์ง€์…”๋‹๋„ ์ค‘์š”ํ•œ ๋“ฏ

- ์Œ์•…์„ ์ฃผ๋กœ ์‚ฌ์šฉํ•˜๋Š” ์‚ฌ๋žŒ์€ ์ •๋ณด๋ฅผ ์ž˜์•ˆ์“ฐ๊ณ , ์ •๋ณด ๊ฒ€์ƒ‰์„ ์ฃผ๋กœํ•˜๋Š” ์‚ฌ๋žŒ์€ ์Œ์•…์„ ์ž˜์•ˆ์“ฐ๊ณ โ€ฆ

- ์‚ฌ๋žŒ๋งˆ๋‹ค ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค๋ฅผ ์ƒ๊ฐํ•˜๋Š”๊ฒŒ ๋‹ค๋ฅธ ๋“ฏ

More Related Content

Similar to Summary : Understanding the long term use of smart speaker assistants

GNMTแ„…แ…ฉ แ„‹แ…กแ†ฏแ„‹แ…กแ„‡แ…ฉแ„‚แ…ณแ†ซ แ„‰แ…ตแ†ซแ„€แ…งแ†ผแ„†แ…กแ†ผ แ„€แ…ตแ„‡แ…กแ†ซ แ„€แ…ตแ„€แ…จแ„‡แ…ฅแ†ซแ„‹แ…งแ†จ
GNMTแ„…แ…ฉ แ„‹แ…กแ†ฏแ„‹แ…กแ„‡แ…ฉแ„‚แ…ณแ†ซ แ„‰แ…ตแ†ซแ„€แ…งแ†ผแ„†แ…กแ†ผ แ„€แ…ตแ„‡แ…กแ†ซ แ„€แ…ตแ„€แ…จแ„‡แ…ฅแ†ซแ„‹แ…งแ†จGNMTแ„…แ…ฉ แ„‹แ…กแ†ฏแ„‹แ…กแ„‡แ…ฉแ„‚แ…ณแ†ซ แ„‰แ…ตแ†ซแ„€แ…งแ†ผแ„†แ…กแ†ผ แ„€แ…ตแ„‡แ…กแ†ซ แ„€แ…ตแ„€แ…จแ„‡แ…ฅแ†ซแ„‹แ…งแ†จ
GNMTแ„…แ…ฉ แ„‹แ…กแ†ฏแ„‹แ…กแ„‡แ…ฉแ„‚แ…ณแ†ซ แ„‰แ…ตแ†ซแ„€แ…งแ†ผแ„†แ…กแ†ผ แ„€แ…ตแ„‡แ…กแ†ซ แ„€แ…ตแ„€แ…จแ„‡แ…ฅแ†ซแ„‹แ…งแ†จ
Byeong il Ko
ย 
Using Behavioral Data to Identify Interviewer Fabrication in Surveys + CHI 20...
Using Behavioral Data to Identify Interviewer Fabrication in Surveys + CHI 20...Using Behavioral Data to Identify Interviewer Fabrication in Surveys + CHI 20...
Using Behavioral Data to Identify Interviewer Fabrication in Surveys + CHI 20...
Hyunjin Ahn
ย 
์Šค๋งˆํŠธ์›Œํฌ
์Šค๋งˆํŠธ์›Œํฌ์Šค๋งˆํŠธ์›Œํฌ
์Šค๋งˆํŠธ์›Œํฌ
Kim jeehyun
ย 

Similar to Summary : Understanding the long term use of smart speaker assistants (20)

โ€œThey Like to Hear My Voiceโ€: Exploring Usage Behavior in Speech-Based Mobile...
โ€œThey Like to Hear My Voiceโ€: Exploring Usage Behavior in Speech-Based Mobile...โ€œThey Like to Hear My Voiceโ€: Exploring Usage Behavior in Speech-Based Mobile...
โ€œThey Like to Hear My Voiceโ€: Exploring Usage Behavior in Speech-Based Mobile...
ย 
[๋…ผ๋ฌธ๋ฐœํ‘œ] 20160404 Supporting Serendipitous Social Interaction Using Human Mobil...
[๋…ผ๋ฌธ๋ฐœํ‘œ] 20160404 Supporting Serendipitous Social Interaction Using Human Mobil...[๋…ผ๋ฌธ๋ฐœํ‘œ] 20160404 Supporting Serendipitous Social Interaction Using Human Mobil...
[๋…ผ๋ฌธ๋ฐœํ‘œ] 20160404 Supporting Serendipitous Social Interaction Using Human Mobil...
ย 
Understanding Affective Experiences With Conversational Agents.pdf
Understanding Affective Experiences With Conversational Agents.pdfUnderstanding Affective Experiences With Conversational Agents.pdf
Understanding Affective Experiences With Conversational Agents.pdf
ย 
Things Data Scientists Should Keep in Mind
Things Data Scientists Should Keep in MindThings Data Scientists Should Keep in Mind
Things Data Scientists Should Keep in Mind
ย 
Understanding My Data Myself [Ubicomp 2011]
Understanding My Data Myself [Ubicomp 2011]Understanding My Data Myself [Ubicomp 2011]
Understanding My Data Myself [Ubicomp 2011]
ย 
GNMTแ„…แ…ฉ แ„‹แ…กแ†ฏแ„‹แ…กแ„‡แ…ฉแ„‚แ…ณแ†ซ แ„‰แ…ตแ†ซแ„€แ…งแ†ผแ„†แ…กแ†ผ แ„€แ…ตแ„‡แ…กแ†ซ แ„€แ…ตแ„€แ…จแ„‡แ…ฅแ†ซแ„‹แ…งแ†จ
GNMTแ„…แ…ฉ แ„‹แ…กแ†ฏแ„‹แ…กแ„‡แ…ฉแ„‚แ…ณแ†ซ แ„‰แ…ตแ†ซแ„€แ…งแ†ผแ„†แ…กแ†ผ แ„€แ…ตแ„‡แ…กแ†ซ แ„€แ…ตแ„€แ…จแ„‡แ…ฅแ†ซแ„‹แ…งแ†จGNMTแ„…แ…ฉ แ„‹แ…กแ†ฏแ„‹แ…กแ„‡แ…ฉแ„‚แ…ณแ†ซ แ„‰แ…ตแ†ซแ„€แ…งแ†ผแ„†แ…กแ†ผ แ„€แ…ตแ„‡แ…กแ†ซ แ„€แ…ตแ„€แ…จแ„‡แ…ฅแ†ซแ„‹แ…งแ†จ
GNMTแ„…แ…ฉ แ„‹แ…กแ†ฏแ„‹แ…กแ„‡แ…ฉแ„‚แ…ณแ†ซ แ„‰แ…ตแ†ซแ„€แ…งแ†ผแ„†แ…กแ†ผ แ„€แ…ตแ„‡แ…กแ†ซ แ„€แ…ตแ„€แ…จแ„‡แ…ฅแ†ซแ„‹แ…งแ†จ
ย 
Eliciting Conversation in Robot Vehicle Interactions
Eliciting Conversation in Robot Vehicle InteractionsEliciting Conversation in Robot Vehicle Interactions
Eliciting Conversation in Robot Vehicle Interactions
ย 
Using Behavioral Data to Identify Interviewer Fabrication in Surveys + CHI 20...
Using Behavioral Data to Identify Interviewer Fabrication in Surveys + CHI 20...Using Behavioral Data to Identify Interviewer Fabrication in Surveys + CHI 20...
Using Behavioral Data to Identify Interviewer Fabrication in Surveys + CHI 20...
ย 
"Hi! I am the Crowd Tasker" Crowdsourcing through Digital Voice Assistants
"Hi! I am the Crowd Tasker" Crowdsourcing through Digital Voice Assistants "Hi! I am the Crowd Tasker" Crowdsourcing through Digital Voice Assistants
"Hi! I am the Crowd Tasker" Crowdsourcing through Digital Voice Assistants
ย 
How to Create Value from Data, and Its Difficulty
How to Create Value from Data, and Its DifficultyHow to Create Value from Data, and Its Difficulty
How to Create Value from Data, and Its Difficulty
ย 
แ„‰แ…กแ„‹แ…ญแ†ผแ„Œแ…กแ„‡แ…ฎแ†ซแ„‰แ…ฅแ†จ @์ฝ”๋”์Šคํ•˜์ด์„ธ๋ฏธ๋‚˜
แ„‰แ…กแ„‹แ…ญแ†ผแ„Œแ…กแ„‡แ…ฎแ†ซแ„‰แ…ฅแ†จ @์ฝ”๋”์Šคํ•˜์ด์„ธ๋ฏธ๋‚˜แ„‰แ…กแ„‹แ…ญแ†ผแ„Œแ…กแ„‡แ…ฎแ†ซแ„‰แ…ฅแ†จ @์ฝ”๋”์Šคํ•˜์ด์„ธ๋ฏธ๋‚˜
แ„‰แ…กแ„‹แ…ญแ†ผแ„Œแ…กแ„‡แ…ฎแ†ซแ„‰แ…ฅแ†จ @์ฝ”๋”์Šคํ•˜์ด์„ธ๋ฏธ๋‚˜
ย 
20150331 msr outreach media_roundtable_deck_์—ฐ์„ธ๋Œ€๊ฐ•ํ™๊ตฌ๊ต์ˆ˜_์Œ์„ฑํ•ฉ์„ฑ
20150331 msr outreach media_roundtable_deck_์—ฐ์„ธ๋Œ€๊ฐ•ํ™๊ตฌ๊ต์ˆ˜_์Œ์„ฑํ•ฉ์„ฑ20150331 msr outreach media_roundtable_deck_์—ฐ์„ธ๋Œ€๊ฐ•ํ™๊ตฌ๊ต์ˆ˜_์Œ์„ฑํ•ฉ์„ฑ
20150331 msr outreach media_roundtable_deck_์—ฐ์„ธ๋Œ€๊ฐ•ํ™๊ตฌ๊ต์ˆ˜_์Œ์„ฑํ•ฉ์„ฑ
ย 
์—ฐ๊ตฌ๋ฐ์ดํ„ฐ ๊ด€๋ฆฌ์™€ ๋ฐ์ดํ„ฐ ๊ด€๋ฆฌ ๊ณ„ํš์„œ (DMP) - part04
์—ฐ๊ตฌ๋ฐ์ดํ„ฐ ๊ด€๋ฆฌ์™€ ๋ฐ์ดํ„ฐ ๊ด€๋ฆฌ ๊ณ„ํš์„œ (DMP) - part04์—ฐ๊ตฌ๋ฐ์ดํ„ฐ ๊ด€๋ฆฌ์™€ ๋ฐ์ดํ„ฐ ๊ด€๋ฆฌ ๊ณ„ํš์„œ (DMP) - part04
์—ฐ๊ตฌ๋ฐ์ดํ„ฐ ๊ด€๋ฆฌ์™€ ๋ฐ์ดํ„ฐ ๊ด€๋ฆฌ ๊ณ„ํš์„œ (DMP) - part04
ย 
Poscat seminar 1
Poscat seminar 1Poscat seminar 1
Poscat seminar 1
ย 
2017 ์ฃผ์š” ๊ธฐ์ˆ  ํ๋ฆ„ ๋ฐ ๊ฐœ์š”
2017 ์ฃผ์š” ๊ธฐ์ˆ  ํ๋ฆ„ ๋ฐ ๊ฐœ์š”2017 ์ฃผ์š” ๊ธฐ์ˆ  ํ๋ฆ„ ๋ฐ ๊ฐœ์š”
2017 ์ฃผ์š” ๊ธฐ์ˆ  ํ๋ฆ„ ๋ฐ ๊ฐœ์š”
ย 
[ํ™์ˆœ์„ฑ]์—๋ฒ„๋…ธํŠธ A to Z, ํ™œ์šฉํ•˜๊ธฐ
[ํ™์ˆœ์„ฑ]์—๋ฒ„๋…ธํŠธ A to Z, ํ™œ์šฉํ•˜๊ธฐ[ํ™์ˆœ์„ฑ]์—๋ฒ„๋…ธํŠธ A to Z, ํ™œ์šฉํ•˜๊ธฐ
[ํ™์ˆœ์„ฑ]์—๋ฒ„๋…ธํŠธ A to Z, ํ™œ์šฉํ•˜๊ธฐ
ย 
์‚ฌ์—…๊ณ„ํš์„œ
์‚ฌ์—…๊ณ„ํš์„œ์‚ฌ์—…๊ณ„ํš์„œ
์‚ฌ์—…๊ณ„ํš์„œ
ย 
100% Serverless big data scale production Deep Learning System
100% Serverless big data scale production Deep Learning System100% Serverless big data scale production Deep Learning System
100% Serverless big data scale production Deep Learning System
ย 
Big Data Overview
Big Data OverviewBig Data Overview
Big Data Overview
ย 
์Šค๋งˆํŠธ์›Œํฌ
์Šค๋งˆํŠธ์›Œํฌ์Šค๋งˆํŠธ์›Œํฌ
์Šค๋งˆํŠธ์›Œํฌ
ย 

More from Myeonggyun Ryu

More from Myeonggyun Ryu (8)

resilient chatbots: repair strategy preferences for conversational breakdowns
resilient chatbots: repair strategy preferences for conversational breakdownsresilient chatbots: repair strategy preferences for conversational breakdowns
resilient chatbots: repair strategy preferences for conversational breakdowns
ย 
What Can I say? Effects of Discoverability in VUIs on Task Performance and Us...
What Can I say? Effects of Discoverability in VUIs on Task Performance and Us...What Can I say? Effects of Discoverability in VUIs on Task Performance and Us...
What Can I say? Effects of Discoverability in VUIs on Task Performance and Us...
ย 
Guidelines for Human-AI โ€จInteraction / CHI2019
Guidelines for Human-AI โ€จInteraction / CHI2019Guidelines for Human-AI โ€จInteraction / CHI2019
Guidelines for Human-AI โ€จInteraction / CHI2019
ย 
Understanding Usersโ€™ Perception Towards Automated Personality Detection with ...
Understanding Usersโ€™ Perception Towards Automated Personality Detection with ...Understanding Usersโ€™ Perception Towards Automated Personality Detection with ...
Understanding Usersโ€™ Perception Towards Automated Personality Detection with ...
ย 
Book 'Ghost Work' summary
Book 'Ghost Work' summaryBook 'Ghost Work' summary
Book 'Ghost Work' summary
ย 
Data-driven personas: constructing archetypal users with clickstreams and use...
Data-driven personas: constructing archetypal users with clickstreams and use...Data-driven personas: constructing archetypal users with clickstreams and use...
Data-driven personas: constructing archetypal users with clickstreams and use...
ย 
A framework for interaction driven user modeling of mobile news reading behav...
A framework for interaction driven user modeling of mobile news reading behav...A framework for interaction driven user modeling of mobile news reading behav...
A framework for interaction driven user modeling of mobile news reading behav...
ย 
Understanding self reflection: how people refelct on personal data through vi...
Understanding self reflection: how people refelct on personal data through vi...Understanding self reflection: how people refelct on personal data through vi...
Understanding self reflection: how people refelct on personal data through vi...
ย 

Summary : Understanding the long term use of smart speaker assistants

  • 1. + IMWUT 2018 / ๋ฅ˜๋ช…๊ท  Understanding the Long-Term Use of Smart Speaker Assistants - FRANK BENTLEY et al.
  • 2. 01 02 03 04 05 06 WHY THIS PAPER RESEARCH QUESTION METHOD FINDINGS DESIGN IMPLICATION TAKEAWAY
  • 3. 01 WHY THIS PAPER ์‚ฌ์šฉ์ž๋“ค์€ ์‹ค์ œ๋กœ ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค๋ฅผ ์–ด๋–ป๊ฒŒ ์‚ฌ์šฉํ•˜๊ณ  ์žˆ๋Š”์ง€โ€ฆ ๋ˆ„๊ตฌ๋‚˜ VUI๋ฅผ ์‰ฝ๊ฒŒ ์“ธ ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉด ์ข‹๊ฒ ๋‹ค ASR, NLP ๋ถ€์กฑํ•œ ์ปจํ…์ธ  ๋””๋ฐ”์ด์Šค์˜ ํ•œ๊ณ„ โ€ฆ โ€ฆ โ€ฆ ์™œ ์ง€๊ธˆ์€ ๋ชป์“ฐ๊ณ  ์žˆ์„๊นŒ? Discoverability, Learnability, Repair ์ง€๊ธˆ๊นŒ์ง€ ์‚ดํŽด๋ดค๋˜ ๋ถ€๋ถ„ ํ˜„์žฌ ์‚ฌ์šฉ์ž๋Š” ์–ด๋–ป๊ฒŒ ์‚ฌ์šฉํ•˜๊ณ  ์žˆ๋Š”๊ฐ€?
  • 4. 01 WHY THIS PAPER ๋‹ค๋ฅธ IT๊ธฐ๊ธฐ๋ณด๋‹ค ๋น ๋ฅธ ์†๋„๋กœ ๋ณด๊ธ‰๋˜๊ณ  ์žˆ์Œ ๊ฐ•์„๋ฌด, 2020, ์œ„์น˜์ •๋ณด ์‚ฐ์—… ๋™ํ–ฅ ๋ณด๊ณ ์„œ, ํ•œ๊ตญ์ธํ„ฐ๋„ท์ง€ํฅ์› * ์ดˆ๊ธฐ ์ˆ˜์šฉ ๋‹จ๊ณ„๋Š” ๋ณด๊ธ‰์œจ ์•ฝ 16%๊นŒ์ง€, ์ดˆ๊ธฐ ๋Œ€์ค‘ํ™” ๋‹จ๊ณ„๋Š” ๋ณด๊ธ‰์œจ 16~50%๋ฅผ ๊ธฐ์ค€์œผ๋กœ ํ•œ๋‹ค
  • 5. 02 RESEARCH QUESTION โ€ข ๊ธฐ๋Šฅ๊ณผ ๋„๋ฉ”์ธ์— ๋”ฐ๋ผ์„œ - What types of commands do people make to their smart speakers (e.g. weather, music playback, smart home control, etc.) and in what percentages do they use these different features? โ€จ โ€ข ํ•˜๋ฃจ, ํ‰์ผ/์ฃผ๋ง์— ๋”ฐ๋ผ์„œ - How are these devices used at different times of day or days of the week? Are there differences in the categories of commands? โ€จ โ€ข ์‹œ๊ฐ„ ๊ฒฝ๊ณผ์— ๋”ฐ๋ผ์„œ - How do these types of commands change over time as users become more familiar with these devices? Do the topics change? Does the length of commands change per topic over time? โ€จ โ€ข ๋‚˜์ด, ๊ฐ€๊ตฌ ํ˜•ํƒœ์— ๋”ฐ๋ผ์„œ - Are there any differences in use of these devices in different age groups or household sizes? ๋‹ค์–‘ํ•œ ์ธก๋ฉด์—์„œ ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค ์žฅ๊ธฐ๊ฐ„ ์‚ฌ์šฉ ํ–‰๋™์„ ์‚ดํŽด๋ด„
  • 6. 03 METHOD 88๋ช…์œผ๋กœ๋ถ€ํ„ฐ 110์ผ ๋™์•ˆ์˜ ์‚ฌ์šฉ ๋กœ๊ทธ๋ฅผ ์ˆ˜์ง‘ โ€ข Amazon MTurk์„ ํ†ตํ•ด์„œ ๊ณผ๊ฑฐ ์‚ฌ์šฉ ๋ฐ์ดํ„ฐ(๋กœ๊ทธ) ์ˆ˜์ง‘ (ํ•œ๋ช… ๋‹น $5๋กœ ์ง€๊ธ‰) โ€ข 2017๋…„ ์—ฌ๋ฆ„์— ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ–ˆ๊ณ , ์ด ๋‹น์‹œ๋Š” ๊ตฌ๊ธ€ํ™ˆ ์ถœ์‹œ 1๋…„์ด ์•ˆ๋จ - ๊ตฌ๊ธ€ํ™ˆ ์ถœ์‹œ : 2016๋…„ 11์›” 14์ผ (๋ฏธ๊ตญ), 2018๋…„ 9์›” 18์ผ(ํ•œ๊ตญ) โ€ข Demographics - 53%: ๋‚จ์„ฑ / 47%: ์—ฌ์„ฑ, 18~64์„ธ - 17%๋Š” ์•„๋งˆ์กด ์—์ฝ” ์†Œ์œ  - 31%-1์ธ ๊ฐ€๊ตฌ, 32%-2์ธ ๊ฐ€๊ตฌ,15%-3์ธ ๊ฐ€๊ตฌ, 15%-4์ธ ๊ฐ€๊ตฌ, 6%๋Š” 4์ธ ์ด์ƒ โ€ข 65,499 ์ปค๋งจ๋“œ ์ˆ˜์ง‘ (ํ‰๊ท  744์ปค๋งจ๋“œ/์ธ, 110์ผ ๋™์•ˆ) โ€ข ๋ถ„์„ ๋ฐฉ๋ฒ• - 20,000๊ฐœ๋Š” ์ˆ˜๋™ ์ฝ”๋”ฉ(์„œ๋ธŒ ๋„๋ฉ”์ธ) ํ›„์— SVM ํ›ˆ๋ จ ์‹œ์ผœ์„œ ๋‚˜๋จธ์ง€ ๋ฐ์ดํ„ฐ ์„œ๋ธŒ ๋„๋ฉ”์ธ์œผ๋กœ ์ž๋™ ๋ถ„๋ฅ˜ โ€จ (์ •ํ™•๋„ 88%) - ๊ฐ€๊ตฌ ํ˜•ํƒœ, ์‚ฌ์šฉ ํŒจํ„ด์œผ๋กœ MANOVA ๋ถ„์„
  • 7. 04 FINDINGS > Daily Use ํ•˜๋ฃจ์— ์•ฝ 4๋ฒˆ ์ปค๋งจ๋“œ๋ฅผ ํ•˜๊ณ , 1~2๋ฒˆ ์งง์€ ์ธํ„ฐ๋ž™์…˜์ด ๋Œ€๋ถ€๋ถ„ 4.1 17.7 2.5 4 Commands Words 2 5 1 2 39% 21% ์„ธ์…˜๋‹น ์ปค๋งจ๋“œ ์ˆ˜ ํ•˜๋ฃจ๋‹น ์ปค๋งจ๋“œ ์ˆ˜ ์ปค๋งจ๋“œ๋‹น ๋‹จ์–ด ์ˆ˜ ์„ธ์…˜๋‹น ๋„๋ฉ”์ธ ์ˆ˜ 1 48% 2 29% 35% - ๊ธฐ๊ธฐ ์ปจํŠธ๋กค 34% - ์Œ์•… ๊ด€๋ จ
  • 8. 04 FINDINGS > Daily Use ์‚ฌ์šฉ์ž์˜ ์ƒํ™œ ๋ฆฌ๋“ฌ๊ณผ ์œ ์‚ฌํ•˜๊ฒŒ ์‚ฌ์šฉ ํŒจํ„ด์ด ๋ฐœ์ƒํ•จ ์Œ์•… ๊ด€๋ จํ•˜์—ฌ ๊ฐ€์žฅ ๋งŽ์ด ์‚ฌ์šฉ ์ €๋…9์‹œ~์ƒˆ๋ฒฝ2์‹œ, ์Šค๋งˆํŠธํ™ˆ ์กฐ์ž‘์„ ๋งŽ์ด ํ•จ ์ƒˆ๋ฒฝ์— ์‹œ๊ฐ„ ํ™•์ธ์„ ๋งŽ์ด ํ•จ. ์–ผ๋งˆ๋‚˜ ๋” ์ž˜ ์ˆ˜ ์žˆ๋Š”์ง€ ์•Œ๊ธฐ ์œ„ํ•ด์„œ ์ผ์–ด๋‚˜์ž๋งˆ์ž ์ ์‹ฌ์‹œ๊ฐ„ ์ „ ์ผ์—์„œ ๋Œ์•„์™€์„œ (ํ•˜๋ฃจ ์ค‘ ๊ฐ€์žฅ ๋งŽ์ด ์‚ฌ์šฉํ•˜๋Š” ์‹œ๊ฐ„๋Œ€)
  • 9. โ€ข ํ‰์ผ์— ๋น„ํ•ด ์ฃผ๋ง์— ์‚ฌ์šฉ๋Ÿ‰์ด ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜๋ฏธํ•˜๊ฒŒ ๋งŽ์Œ (p=0.04) โ€ข ์‚ฌ์šฉํ•˜๋Š” ๋„๋ฉ”์ธ์˜ ๋น„์œจ์€ ๋น„์Šทํ•จ 04 FINDINGS > Daily Use ์ง‘์— ์žˆ๋Š” ์‹œ๊ฐ„๊ณผ ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค ์‚ฌ์šฉ๋Ÿ‰์€ ๊ด€๊ณ„๊ฐ€ ์žˆ์Œ
  • 10. โ€ข ์‹œ๊ฐ„์ด ํ˜๋Ÿฌ๋„, ํ•˜๋‚˜์˜ ์ปค๋งจ๋“œ์— ํฌํ•จ๋œ ๋‹จ์–ด์˜ ๊ฐฏ์ˆ˜๋Š” ๋ณ€ํ•˜์ง€ ์•Š์Œ - ์ดˆ๋ฐ˜์˜ ๋ฉฐ์น ์„ ์ œ์™ธํ•˜๊ณ , ์ธํ„ฐ๋ž™์…˜์˜ ํ˜•ํƒœ๋„ ๋ณ€ํ•˜์ง€ ์•Š์Œ โ€ข ์‹œ๊ฐ„ ๊ด€๋ จ, ์ •๋ณด ๊ฒ€์ƒ‰(๋‚ ์”จ), ์—”ํ„ฐํ…Œ์ธ๋จผํŠธ ๋„๋ฉ”์ธ์—์„œ๋Š” ๋‹จ์–ด์˜ ์ˆ˜ ์ฆ๊ฐ€ - ๋‚ ์”จ๋ฅผ ๋ณด๋ฉด, ์ ์  ๋” ํŠน์ • ์š”์†Œ์— ๋Œ€ํ•ด ์งˆ๋ฌธํ•จ (์˜ค๋Š˜์˜ ์ตœ๊ณ  ์˜จ๋„๋Š”?, ๋‹ค๋ฅธ ์ง€์—ญ์˜ ๋‚ ์”จ) โ€ข ๋ฐ˜๋ฉด, ์Šค๋ชฐํ† ํฌ์—์„œ๋Š” ๋‹จ์–ด์˜ ์ˆ˜ ๊ฐ์†Œ 04 FINDINGS > Daily Use ์ดˆ๋ฐ˜ ์‚ฌ์šฉ ๋ฐฉ๋ฒ•์ด ์œ ์ง€๋˜๋ฉฐ, ๋„๋ฉ”์ธ์— ๋”ฐ๋ผ ์งˆ๋ฌธ์ด ๊ตฌ์ฒดํ™”๋จ
  • 11. โ€ข Commands per day - Number of domains used (r=.590) โ€ข Commands per day - Sessions per day (r=.545) โ€ข Commands per day - Commands per session (r=.378) โ€ข Percent Midday - Percent Small Talk (r = -.390) โ€ข Percent Midday - Percent Night (r = -.367) โ€ข Percent Evening - Percent Weekend (r = .398) โ€ข Percent Information - Average Command Length (r = .371) โ€ข Percent Information - Percent Music (r = -.478) โ€ข Percent Information - Percent Small Talk (r = .387) 04 FINDINGS > Usage Patterns ์‚ฌ์šฉ์ž๋งˆ๋‹ค ์ง€๋ฐฐ์ ์ธ ๋„๋ฉ”์ธ์ด ์žˆ์Œ (์ •๋ณด vs. ์Œ์•…)
  • 12. โ€ข ์Œ์•…์€ ๊ตฌ๋… ์„œ๋น„์Šค๋ฅผ ๊ฐ€์ž…ํ•ด์•ผ ํ•ด์„œ 18-24 ๋‚˜์ด๋Œ€์—์„œ ์‚ฌ์šฉ๋ฅ ์ด ์ ์–ด ๋ณด์ž„ โ€ข MANOVA ๋ถ„์„ ๊ฒฐ๊ณผ, ๋‚˜์ด๋Š” ์‚ฌ์šฉ ๋„๋ฉ”์ธ ๋น„์œจ์— ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š์Œ 04 FINDINGS > Demographic Differences ์ƒ๋Œ€์ ์œผ๋กœ ์ Š์€ ์‚ฌ์šฉ์ž๊ฐ€ ๋งŽ์„ ๋ฟ, ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์€ ์—†์Œ
  • 13. 04 FINDINGS > Demographic Differences ๊ฐ€๊ตฌ์— ๋”ฐ๋ผ์„œ ์กฐ๊ธˆ์”ฉ ์‚ฌ์šฉ ๋„๋ฉ”์ธ ๋น„์œจ์ด ๋‹ค๋ฆ„ โ€ข ๊ฐ€๊ตฌ์˜ ์ˆ˜๊ฐ€ ๋งŽ์„์ˆ˜๋ก ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค๋ฅผ ๋” ์ ๊ฒŒ ์‚ฌ์šฉํ•จ โ€ข 4์ธ ์ด์ƒ ๊ฐ€๊ตฌ์—์„œ ์Šค๋งˆํŠธํ™ˆ ์ปจํŠธ๋กค ์‚ฌ์šฉ ๋น„์œจ์ด ์ƒ๋Œ€์ ์œผ๋กœ ๋‚ฎ์Œ
  • 14. 05 DESIGN IMPLICATION โ€ข ์‚ฌ์šฉ์ž์—๊ฒŒ ์ƒˆ๋กœ์šด ๋„๋ฉ”์ธ์„ ์†Œ๊ฐœํ•˜๋Š” ๊ฒƒ - ์‚ฌ์šฉ์ž๋Š” ์‹œ๊ฐ„์ด ํ˜๋Ÿฌ๋„ ์‚ฌ์šฉ ํ–‰๋™์ด ๋ณ€ํ•˜์ง€ ์•Š์Œ - ๋ณดํ†ต 3๊ฐœ์˜ ๋„๋ฉ”์ธ ์ •๋„์— ์ •์ฐฉํ•จ - ๊ตฌ๊ธ€์—์„œ ์ด๋ฉ”์ผ์„ ๋ณด๋‚ด์ง€๋งŒ ์‚ฌ์šฉ์ž๊ฐ€ ์ƒˆ๋กœ์šด ๋„๋ฉ”์ธ์„ ์ฐพ๋„๋ก ์œ ๋„ํ•˜์ง€๋Š” ๋ชปํ•˜๊ณ  ์žˆ์Œ - ๋”ฐ๋ผ์„œ, ์–ด์‹œ์Šคํ„ดํŠธ ์ž์ฒด๊ฐ€ ์ƒˆ๋กœ์šด ๋„๋ฉ”์ธ์œผ๋กœ ์ด๋Œ์–ด์•ผ ํ•จ โ€ข ๊ด€๋ จ๋œ, ์งง์€ ์ปค๋งจ๋“œ ์ œ์•ˆ - ์ˆ์ปท์„ ๋งŒ๋“ค์–ด์คŒ (์ผ์ข…์˜ ๋ฃจํ‹ด) โ€ฃ Pasta timer : 12-minute timer - ์‹œ๊ฐ„์ด ํ˜๋Ÿฌ๋„ ์ปค๋งจ๋“œ๋Š” ๊ธธ์–ด์ง€์ง€ ์•Š์Œ. ๋”ฐ๋ผ์„œ ์–ด์‹œ์Šคํ„ดํŠธ๊ฐ€ ๋ณต์žกํ•˜๊ฒŒ ๋ฌผ์–ด๋ณด๋Š” ๋ฐฉ๋ฒ•์„ ๊ฐ€๋ฅด์ณ ์ค„ ํ•„์š”๊ฐ€ ์žˆ์Œ - ํ˜„์žฌ๋Š” ์ผ๋ฐฉํ–ฅ์ ์ธ ์†Œํ†ต์ด ๋งŽ์Œ. โ€ข ์—์ด์ „ํŠธ ๊ธฐ๋ฐ˜ ๊นŠ์€ ์ธํ„ฐ๋ž™์…˜ ์ง€์› - ํ˜„์žฌ์˜ ๋””๋ฐ”์ด์Šค๋Š” ๋‹จ์ผ ์ปค๋งจ๋“œ๋งŒ ์ˆ˜ํ–‰ํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์Œ - ์Šค๋งˆํŠธ ์–ด์‹œ์Šคํ„ดํŠธ๋Š” ์‹œ๊ฐ„์— ๋”ฐ๋ผ ๋งŽ์€ ์–‘์˜ ์ปจํ…์ŠคํŠธ๋ฅผ ๋ฐฐ์šฐ๊ณ  ๊ธฐ์–ตํ•ด์•ผ ํ•จ
  • 15. 06 TAKEAWAY โ€ข ์•ฝ๊ฐ„ ๋‡Œํ”ผ์…œ(?)๋กœ ์žˆ๋˜ ์ƒ๊ฐ๋“ค์„ ์—ฐ๊ตฌ๋กœ ๋ฐํ˜€๋‚ด์„œ ์ธ์šฉํ•˜๊ธฐ์—๋Š” ์ข‹์•„ ๋ณด์ž„ - ํ•˜์ง€๋งŒ ์ƒ๊ฐ๋ณด๋‹ค ์ƒˆ๋กœ์šด ๋‚ด์šฉ์€ ์—†์Œ - ๋กœ๊ทธ๋กœ๋งŒ ์ ‘๊ทผํ•ด์„œ ๋‹ค์†Œ ํ”ผ์ƒ์ ์ธ ๋‚ด์šฉ์ด ๋งŽ์€ ๋“ฏ (๋„๋ฉ”์ธ ๋‹จ์œ„์˜ ํ†ต๊ณ„ ๋ถ„์„ ์œ„์ฃผ) - ๊ทธ๋ž˜๋„ VUI๋Š” ๋กœ๊ทธ ์ ‘๊ทผ์ด ์‰ฝ๋‹ค๋Š” ์žฅ์ ์„ ๋ณผ ์ˆ˜ ์žˆ์—ˆ์Œ - ํ™•์‹คํžˆ ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค๊ฐ€ ์ž˜๋จนํžˆ๋Š” ์‚ฌ์šฉ์ž ์ง‘๋‹จ์ด ์žˆ์„ ๊ฒƒ ๊ฐ™์€๋ฐโ€ฆ โ€ข ๊ฒฐ๊ตญ ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค ์‚ฌ์šฉ์ž๋Š” ์ดˆ๋ฐ˜์˜ ๋ช‡ ๊ฐ€์ง€ ๊ธฐ๋Šฅ๋งŒ ๊ณ„์†ํ•ด์„œ ์‚ฌ์šฉ - ์–ด๋–ป๊ฒŒ ์ƒˆ๋กœ์šด ๊ธฐ๋Šฅ(์•ฑ)์„ ์‚ฌ์šฉํ•˜๊ฒŒ ํ•  ์ˆ˜ ์žˆ์„๊นŒ โ€ข ์‚ฌ์šฉ์ž ์ž…์žฅ์—์„œ๋Š” ์ƒˆ๋กœ์šด ๊ธฐ๋Šฅ์„ ์‚ฌ์šฉํ•  ์ƒ๊ฐ์„ ํ•˜์ง€ ์•Š์Œ - ์ด๊ฒƒ์ด ๋‹ˆ์ฆˆ๊ฐ€ ์—†๋Š” ๊ฑด์ง€, ์‚ฌ์šฉ์ด ์–ด๋ ค์šด ๊ฑด์ง€๋Š” ๋ชจ๋ฅด๊ฒ ์Œ โ€ข 2018๋…„ ๋…ผ๋ฌธ์ธ๋งŒํผ ์ตœ๊ทผ ๋…ผ๋ฌธ๋„ ์‚ดํŽด๋ณด๋ฉด ์ข‹์„ ๋“ฏ - ํŠนํžˆ ์„œ๋“œํŒŒํ‹ฐ ๋ณด์ด์Šค ์•ฑ ๊ด€๋ จํ•ด์„œ - ํ˜„์žฌ ์ธ์šฉ์ˆ˜ 183โ€ฆ! โ€ข ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค ์ดˆ๊ธฐ์˜ ํฌ์ง€์…”๋‹๋„ ์ค‘์š”ํ•œ ๋“ฏ - ์Œ์•…์„ ์ฃผ๋กœ ์‚ฌ์šฉํ•˜๋Š” ์‚ฌ๋žŒ์€ ์ •๋ณด๋ฅผ ์ž˜์•ˆ์“ฐ๊ณ , ์ •๋ณด ๊ฒ€์ƒ‰์„ ์ฃผ๋กœํ•˜๋Š” ์‚ฌ๋žŒ์€ ์Œ์•…์„ ์ž˜์•ˆ์“ฐ๊ณ โ€ฆ - ์‚ฌ๋žŒ๋งˆ๋‹ค ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค๋ฅผ ์ƒ๊ฐํ•˜๋Š”๊ฒŒ ๋‹ค๋ฅธ ๋“ฏ