8 research outputs found

    Identifying patent conflicts: TRIZ-Led patent mapping

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    This paper presents a novel method of patent mapping for visualising conflicts between patent claims that incorporates the Theory of Inventive Problem Solving (TRIZ). The method uses TRIZ engineering parameters as the criteria for evaluating dissimilarities between patent claims, producing a visualisation based on Multi-Dimensional Scaling (MDS) that can be compared with legal judgments. The advantages of the method are that it (a) reduces evaluation complexity by transforming claim-to-claim comparisons into claim-to-criteria comparisons, and (b) provides a means of comparing judgment standards between different legal authorities in mechanical engineering terms. Reliability and validity of the method are tested through focus groups using a case study on aircraft seats. The scope of the method is limited to the field of mechanical inventions

    Coverage & cooperation: Completing complex tasks as quickly as possible using teams of robots

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    As the robotics industry grows and robots enter our homes and public spaces, they are increasingly expected to work in cooperation with each other. My thesis focuses on multirobot planning, specifically in the context of coverage robots, such as robotic lawnmowers and vacuum cleaners. Two problems unique to multirobot teams are task allocation and search. I present a task allocation algorithm which balances the workload amongst all robots in the team with the objective of minimizing the overall mission time. I also present a search algorithm which robots can use to find lost teammates. It uses a probabilistic belief of a target robotโ€™s position to create a planning tree and then searches by following the best path in the tree. For robust multirobot coverage, I use both the task allocation and search algorithms. First the coverage region is divided into a set of small coverage tasks which minimize the number of turns the robots will need to take. These tasks are then allocated to individual robots. During the mission, robots replan with nearby robots to rebalance the workload and, once a robot has finished its tasks, it searches for teammates to help them finish their tasks faster

    ์ •๋ณด์‹œ๊ฐํ™”๋ฅผ ์ด์šฉํ•œ GTM ๊ธฐ๋ฐ˜ ์ง€๋„์˜ ๊ฐœ๋ฐœ ๋ฐ ํ™œ์šฉ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฐ์—…๊ณตํ•™๊ณผ, 2012. 8. ๋ฐ•์šฉํƒœ.์ •๋ณด์‹œ๊ฐํ™”์˜ ์ฃผ์š” ๋ชฉ์ ์€ ์–ด๋– ํ•œ ์ •๋ณด๋ฅผ ์‹œ๊ฐ์  ํ˜•์ƒ์œผ๋กœ ํ‘œํ˜„ํ•จ์œผ๋กœ์จ ๊ทธ ์ •๋ณด๋ฅผ ๋ฌ˜์‚ฌํ•˜๊ณ  ํƒ์ƒ‰ํ•˜๋Š” ํ”„๋กœ์„ธ์Šค๋ฅผ ๊ตฌ์ถ•ํ•˜๋Š” ๊ฒƒ์œผ๋กœ์จ ๋ฐฉ๋Œ€ํ•œ ์–‘์˜ ์ •๋ณด๋ฅผ ๋ถ„์„ํ•  ๋•Œ ์ •๋ณด์‹œ๊ฐํ™”์˜ ํ™œ์šฉ์€ ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ์žฅ์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์ฆ‰, ์‹œ๊ฐํ™”๋Š” ์ธ๊ฐ„์˜ ์ธ์ง€๋Šฅ๋ ฅ์„ ์ฆํญ์‹œํ‚ค๊ณ  ํŠน์ •ํ•œ ํ™œ๋™์„ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด์„œ ํ•„์š”ํ•œ ๋ณต์žกํ•œ ์ธ์ง€๊ณผ์ •์„ ์ค„์—ฌ์ค€๋‹ค. ๋˜ํ•œ, ํฐ ๊ทธ๋ฆผ์„ ๋ณด์—ฌ์คŒ์œผ๋กœ์จ ํ†ต์ฐฐ๋ ฅ์„ ์ œ๊ณตํ•˜๊ธฐ๋„ ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์€ ๊ธฐ์ˆ ๊ฒฝ์˜์˜ ๋ณ€ํ™”์— ๋”ฐ๋ผ์„œ ๊ณ„์†์ ์œผ๋กœ ๊ทธ ์ค‘์š”์„ฑ์ด ๋”ํ•ด์ง€๊ณ  ์žˆ๋Š” ๊ธฐ์ˆ  ๋ฐ ์„œ๋น„์Šค ์˜์—ญ์—์„œ์˜ ์ง€๊ธˆ๊นŒ์ง€ ๊ฐœ๋ฐœ๋˜์ง€ ์•Š์€ ๊ณต๋ฐฑ์„ ๋ฐœ๊ฒฌํ•˜๊ณ  ๊ทธ ํŠธ๋ Œ๋“œ๋ฅผ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด์„œ ์ •๋ณด์‹œ๊ฐํ™”๋ฅผ ์ด์šฉํ•œ ๊ธฐ์ˆ ์ง€๋„๋ฅผ ๊ฐœ๋ฐœํ•˜๊ณ  ํ™œ์šฉํ•œ๋‹ค. ์ด๋Š” ๊ตฐ์˜ ๋ฌด๊ธฐ์ฒด๊ณ„ ๊ฐœ๋ฐœ ๋ฐ ํš๋“์— ํ•„์š”ํ•œ ๊ธฐ์ˆ ๊ฐœ๋ฐœ์— ํ™œ์šฉํ•  ์ˆ˜ ์žˆ์–ด์„œ ๊ตญ๋ฐฉ๋ ฅ ๊ฐ•ํ™”์— ํฌ๊ฒŒ ์ด๋ฐ”์ง€ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์€ 3๊ฐœ์˜ ์—ฐ๊ตฌ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. 3๊ฐœ์˜ ์—ฐ๊ตฌ ์•ˆ์—์„œ 1) Generative topographic mapping (GTM) ๊ธฐ๋ฐ˜์˜ ํŠนํ—ˆ ๊ณต๋ฐฑ์ง€๋„๋ฅผ ์ด์šฉํ•ด์„œ ๊ธฐ์ˆ  ๊ณต๋ฐฑ์„ ๋ฐœ๊ฒฌํ•˜๊ณ  2) GTM ๊ธฐ๋ฐ˜์˜ ์„œ๋น„์Šค ๊ณต๋ฐฑ์ง€๋„๋ฅผ ์ด์šฉํ•ด์„œ ์„œ๋น„์Šค ๊ณต๋ฐฑ์„ ๋ฐœ๊ฒฌํ•˜๋ฉฐ 3) Generative topographic mapping through time (GTM-TT) ๊ธฐ๋ฐ˜์˜ ์„œ๋น„์Šค ํŠธ๋ Œ๋“œ ์ง€๋„๋ฅผ ํ™œ์šฉํ•ด์„œ ์„œ๋น„์Šค ํŠธ๋ Œ๋“œ๋ฅผ ๋ถ„์„ํ•˜๋Š” ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ๋ฐฉ๋Œ€ํ•œ ์–‘์˜ ๋ฌธ์ž ๋ฐ์ดํ„ฐ๋กœ๋ถ€ํ„ฐ ์ค‘์š”ํ•œ ์ •๋ณด๋ฅผ ์ถ”์ถœํ•˜๊ณ  ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด์„œ ํ…์ŠคํŠธ ๋งˆ์ด๋‹ ๊ธฐ๋ฒ•์„ ๋ฐ”ํƒ•์œผ๋กœ ๋ฒกํ„ฐ ๊ณต๊ฐ„ ๋ชจํ˜•์„ ํ™œ์šฉํ•˜์—ฌ ๋น„๊ตฌ์กฐํ™”๋œ ๋ฌธ์„œ๋ฅผ ๊ตฌ์กฐํ™”๋œ ์ž๋ฃŒ๋กœ ๋ณ€ํ™˜ํ•œ๋‹ค. GTM์€ ๋‹ค์ฐจ์›์˜ ๋ฐ์ดํ„ฐ ๊ณต๊ฐ„์„ ์ €์ฐจ์›์˜ ์ž ์žฌ๊ณต๊ฐ„์œผ๋กœ ๊ทธ๋ฆฌ๊ณ  ๊ทธ ์—ญ๋ฐฉํ–ฅ์œผ๋กœ ์‚ฌ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ํ™•๋ฅ ์  ๋ชจ๋ธ์ด๋ฉฐ ๋ฒ ์ด์ง€์•ˆ ์ด๋ก ์— ๊ธฐ์ดˆํ•œ ํ™•๋ฅ ์  ๋ฐฉ๋ฒ•์„ ํ™œ์šฉํ•œ๋‹ค๋Š” ์ธก๋ฉด์—์„œ self organizing map (SOM)์˜ ๋Œ€์šฉ ๋ชจ๋ธ์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. GTM-TT๋Š” ์‹œ๊ฐ„ ๊ธฐ๋ฐ˜ ๊ตฐ์ง‘ํ™”์™€ ์‹œ๊ฐํ™”๋ฅผ ๋™์‹œ์— ์ˆ˜ํ–‰ํ•จ์œผ๋กœ์จ ๋‹ค๋ณ€๋Ÿ‰ ์‹œ๊ณ„์—ด์ž๋ฃŒ์˜ ํƒ์ƒ‰์  ๋ถ„์„์„ ์œ„ํ•œ GTM์˜ ํ™•์žฅ ๋ชจ๋ธ์ด๋‹ค. ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” GTM ๊ธฐ๋ฐ˜์˜ ํŠนํ—ˆ ๊ณต๋ฐฑ์ง€๋„๋ฅผ ์ด์šฉํ•˜์—ฌ ํŠนํ—ˆ๊ณต๋ฐฑ์„ ํŒŒ์•…ํ•˜๊ณ  ์ด๋ฅผ ํ†ตํ•ด์„œ ์ƒˆ๋กœ์šด ๊ธฐ์ˆ  ๊ธฐํšŒ๋“ค์„ ๋ฐœ๊ฒฌํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃฌ๋‹ค. ํŠนํ—ˆ์ง€๋„๋Š” ์ž ์žฌ๋˜์–ด ์žˆ๋Š” ๊ธฐ์ˆ ์  ์ •๋ณด๋ฅผ ์–ป๊ธฐ ์œ„ํ•œ ์œ ์šฉํ•œ ๋„๊ตฌ๋กœ์จ ์˜ค๋žซ๋™์•ˆ ์ธ์‹๋˜์–ด ์™”๋‹ค. ๋‹ค๋ฅธ ์˜์—ญ ์ค‘์—์„œ๋„ ์ƒˆ๋กœ์šด ๊ธฐ์ˆ ์˜ ํƒ์ƒ‰๋˜์ง€ ์•Š์€ ์˜์—ญ์œผ๋กœ ์ •์˜๋˜๋Š” ํŠนํ—ˆ๊ณต๋ฐฑ์„ ๋ฐœ๊ฒฌํ•˜๊ณ ์ž ํ•˜๋Š” ์—ฐ๊ตฌ์˜์—ญ์ด ์ฃผ๋ชฉ์„ ๋ฐ›์•„์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด์ „์˜ ์—ฐ๊ตฌ๋“ค์—์„œ๋Š” ํŠนํ—ˆ์ง€๋„์—์„œ ํŠนํ—ˆ๊ณต๋ฐฑ์„ ๋ฐœ๊ฒฌํ•จ์—์„œ ์žˆ์–ด์„œ ํŠนํ—ˆ๊ณต๋ฐฑ์„ ์ฃผ๊ด€์ ์œผ๋กœ ๊ทธ๋ฆฌ๊ณ  ์ˆ˜๋™์ ์œผ๋กœ ํŒŒ์•…ํ•ด์•ผ ํ•˜๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ์–ด์™”๋‹ค. ๋”ฐ๋ผ์„œ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํŠนํ—ˆ๊ณต๋ฐฑ์„ ์ž๋™์ ์œผ๋กœ ๊ทธ๋ฆฌ๊ณ  ๊ฐ๊ด€์ ์œผ๋กœ ๋ฐœ๊ฒฌํ•˜๊ธฐ ์œ„ํ•ด์„œ GTM ๊ธฐ๋ฐ˜ ํŠนํ—ˆ ๊ณต๋ฐฑ์ง€๋„๋ผ๋Š” ๊ธฐ์ˆ ์ง€๋„๋ฅผ ์ œ์•ˆํ•œ๋‹ค. GTM์€ ๋‹ค์ฐจ์›์˜ ๋ฐ์ดํ„ฐ ๊ณต๊ฐ„์„ ์ €์ฐจ์›์˜ ์ž ์žฌ๊ณต๊ฐ„์œผ๋กœ ์‚ฌ์ƒํ•˜๊ณ  ๊ทธ ์—ญ์‚ฌ์ƒ์ด ๊ฐ€๋Šฅํ•˜๋ฏ€๋กœ ํŠนํ—ˆ๊ณต๋ฐฑ์„ ์ž๋™์œผ๋กœ ๋ฐœ๊ฒฌํ•˜๊ณ  ํ•ด์„ํ•˜๋Š”๋ฐ ๊ธฐ์—ฌํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ์ ‘๊ทผ๋ฒ•์€ ํฌ๊ฒŒ 3๊ฐ€์ง€ ๋‹จ๊ณ„๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. ์ฒซ ์งธ, ํ…์ŠคํŠธ ๋งˆ์ด๋‹์„ ์ด์šฉํ•ด์„œ ํŠนํ—ˆ๋ฌธ์„œ๋“ค์„ ๊ตฌ์กฐํ™”๋œ ๋ฐ์ดํ„ฐ์ธ ํ‚ค์›Œ๋“œ ๋ฒกํ„ฐ๋กœ ๋ณ€ํ™˜ํ•œ๋‹ค. ๋‘˜ ์งธ, GTM์„ ์ ์šฉํ•˜์—ฌ ํŠนํ—ˆ์ง€๋„๋ฅผ ๋งŒ๋“ค๊ณ  ์ง€๋„์—์„œ ๋น„์–ด์žˆ๋Š” ์˜์—ญ์œผ๋กœ ํ‘œํ˜„๋˜๋Š” ํŠนํ—ˆ๊ณต๋ฐฑ์„ ๋ฐœ๊ฒฌํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ํŠนํ—ˆ์ง€๋„๋ฅผ ํ‚ค์›Œ๋“œ ๋ฒกํ„ฐ๋กœ ์—ญ์‚ฌ์ƒํ•˜์—ฌ ํŠนํ—ˆ๊ณต๋ฐฑ์„ ์ƒˆ๋กœ์šด ๊ธฐ์ˆ  ๊ธฐํšŒ๋กœ ํ•ด์„ํ•œ๋‹ค. ์‚ฌ๋ก€์—ฐ๊ตฌ๋Š” ๋ฐ˜๋„์ฒด ๊ณต์ •์—์„œ ํ•„์š”ํ•œ ๋ฆฌ์†Œ๊ทธ๋ผํ”ผ (lithography) ๊ธฐ์ˆ  ๊ด€๋ จ๋œ ํŠนํ—ˆ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์‹ค์‹œํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํŠนํ—ˆ๊ณต๋ฐฑ์„ ๋ฐœ๊ฒฌํ•˜์—ฌ ์ƒˆ๋กœ์šด ๊ธฐ์ˆ  ๊ธฐํšŒ๋ฅผ ๋ฐœ๊ฒฌํ•˜๊ธฐ ์œ„ํ•œ ์‹œ๊ฐ„๊ณผ ๋…ธ๋ ฅ์„ ์ ˆ์•ฝํ•  ์ˆ˜ ์žˆ์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๊ฐ๊ด€์„ฑ๊ณผ ์‹ ๋ขฐ์„ฑ์„ ์ฆ์ง„์‹œํ‚ฌ ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค. ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ๊ธฐ์ˆ  ์˜์—ญ์„ ๋‹ค๋ฃจ์—ˆ๋˜ ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ์™€๋Š” ๋‹ฌ๋ฆฌ GTM ๊ธฐ๋ฐ˜ ์„œ๋น„์Šค ๊ณต๋ฐฑ์ง€๋„๋ฅผ ์ด์šฉํ•˜์—ฌ ์„œ๋น„์Šค ๊ณต๋ฐฑ์„ ๋„์ถœํ•จ์œผ๋กœ์จ ์ƒˆ๋กœ์šด ์„œ๋น„์Šค ๊ธฐํšŒ๋ฅผ ๋ฐœ๊ฒฌํ•˜๊ณ ์ž ํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃฌ๋‹ค. ์ƒˆ๋กœ์šด ์„œ๋น„์Šค ๊ธฐํšŒ์— ๋Œ€ํ•œ ์ „๋žต์  ๊ธฐ์ˆ ์  ์ค‘์š”์„ฑ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์ƒˆ๋กœ์šด ์„œ๋น„์Šค ๊ธฐํšŒ์˜ ์ง€๋Šฅ์  ํƒ์ƒ‰๊ณผ ์ฒด๊ณ„์  ๋ฐœ๊ฒฌ์— ๊ด€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ƒ๋Œ€์ ์œผ๋กœ ๋ถ€์กฑํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ƒˆ๋กœ์šด ์„œ๋น„์Šค ๊ธฐํšŒ๋ฅผ ๋ฐœ๊ฒฌํ•˜๊ธฐ ์œ„ํ•œ GTM ๊ธฐ๋ฐ˜ ์„œ๋น„์Šค ์ง€๋„๋ฅผ ๊ฐœ๋ฐœํ•˜๊ณ  ํ™œ์šฉํ•˜๋Š” ์ ‘๊ทผ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์Šค๋งˆํŠธํฐ ์‚ฌ์šฉ์ž๋“ค์—๊ฒŒ ๋ชจ๋ฐ”์ผ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜ ์„œ๋น„์Šค์— ์ ‘๊ทผํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ์›น์„œ๋น„์Šค๋ฅผ ์ œ๊ณตํ•˜๋Š” ์• ํ”Œ์˜ ์•ฑ์Šคํ† ์–ด (AppStore) ๋กœ๋ถ€ํ„ฐ์˜ ๋ชจ๋ฐ”์ผ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜ ์„œ๋น„์Šค๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์‚ฌ๋ก€์—ฐ๊ตฌ๋ฅผ ์‹ค์‹œํ•œ๋‹ค. ์„ธ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” GTM-TT ๊ธฐ๋ฐ˜์˜ ์„œ๋น„์Šค ํŠธ๋ Œ๋“œ ์ง€๋„๋ฅผ ๊ฐœ๋ฐœํ•˜๊ณ  ์ด๋ฅผ ์ด์šฉํ•˜์—ฌ ์„œ๋น„์Šค์˜ ํŠธ๋ Œ๋“œ๋ฅผ ํŒŒ์•…ํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ตœ๊ทผ, ์„œ๋น„์Šค์˜ ํญ๋ฐœ์ ์ธ ์ฆ๊ฐ€๋กœ ์ธํ•ด ๊ธฐ์—…๋“ค์€ ์ง๊ด€์ ์ด๊ณ  ๊ฐ๊ด€์ ์œผ๋กœ ์„œ๋น„์Šค์˜ ํŒจํ„ด๊ณผ ํŠธ๋ Œ๋“œ๋ฅผ ๋ถ„์„ํ•ด์•ผ ํ•˜๋Š” ๋ฌธ์ œ์— ์ง๋ฉดํ•ด ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ƒํ™ฉ์—์„œ ์„œ๋น„์Šค ํŠธ๋ Œ๋“œ ์ง€๋„๋ผ๋Š” ๊ฒƒ์ด ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ์ง€๋„๋Š” ์‚ฌ๋žŒ๋“ค์ด ๋ฐฉ๋Œ€ํ•œ ์–‘์˜ ์ •๋ณด๋ฅผ ์‹œ๊ฐํ™”ํ•  ์ˆ˜ ์žˆ๋Š” ์‹œ๊ฐํ™” ๋„๊ตฌ๋กœ์„œ์˜ ์ž ์žฌ์„ฑ ๋•Œ๋ฌธ์— ์ƒ๋‹นํ•œ ์ฃผ๋ชฉ์„ ๋ฐ›์•„์˜ค๊ณ  ์žˆ๋‹ค. ํŠนํžˆ, GTM-TT๋Š” ์‹œ๊ฐ„ ๊ธฐ๋ฐ˜ ๊ตฐ์ง‘ํ™”์™€ ๋ณ€ํ™” ๊ฒฝ๋กœ๋ฅผ ์ œ๊ณตํ•จ์œผ๋กœ์จ ๋™ํƒœ์  ๋ถ„์„์— ์ ํ•ฉํ•œ ๋ชจ๋ธ์ด๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์„œ๋น„์Šค์˜ ํŠธ๋ Œ๋“œ๋ฅผ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด์„œ ์„œ๋น„์Šค ๊ตฐ์ง‘ ์ง€๋„์™€ ์„œ๋น„์Šค ๊ฒฝ๋กœ ์ง€๋„๋กœ ๊ตฌ์„ฑ๋œ GTM-TT ๊ธฐ๋ฐ˜ ์„œ๋น„์Šค ํŠธ๋ Œ๋“œ ์ง€๋„๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํฌ๊ฒŒ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๊ตฌ์ถ•, ๋ฐ์ดํ„ฐ ์ „์ฒ˜๋ฆฌ, GTM-TT ์„œ๋น„์Šค ํŠธ๋ Œ๋“œ ์ง€๋„์˜ ๊ฐœ๋ฐœ, ํ•ด์„์˜ 4๊ฐ€์ง€ ๋‹จ๊ณ„๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ์œผ๋ฉฐ ๋‹ค๋ฅธ ์„œ๋น„์Šค ์˜์—ญ์—์„œ๋„ ๋™ํƒœ์  ์„œ๋น„์Šค ํŠธ๋ Œ๋“œ๋ฅผ ํŒŒ์•…ํ•˜๋Š”๋ฐ ๋„์›€์„ ์ค„ ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค.Implementing information visualization has various advantages when to analyze voluminous information since the primary objective of information visualization is to construct a process that describes and explores specific information through graphical representation. That is, visualization amplifies humans cognitive ability and reduces the complex cognitive work necessary to perform certain activities. As well, unexplored insights are able to be provided through showing big pictures. Accordingly, this doctoral dissertation proposes a systematic approach to development and application of GTM-based maps using information visualization to identify new opportunities and explore trends or changes in technology or service. The dissertation is composed of three studies each of which addresses each of the three subjects: 1) systematic approach to identifying new technology opportunities through the generative topographic mapping (GTM) based patent vacuum map, 2) approach to identifying new service opportunities through the GTM-based service vacuum map, and 3) approach to analyzing service trends by the generative topographic mapping through time (GTM-TT) based service trend map. Text mining techniques are employed to transform unstructured textual items into structured data by using the vector space model for extracting and analyzing valuable information from voluminous textual data. The GTM is a probabilistic model to mapping multidimensional data space onto a low-dimensional latent space and vice versa and provide to be a creditable alternative to the self organizing map (SOM) in terms of using a probabilistic method based on Bayesian theory. The GTM-TT is one such extension of GTM for the exploratory analysis of multivariate time series by performing simultaneous time series clustering and visualization. In this research, discovering patent vacuums to identify new technology opportunities using GTM-based patent vacuum map is conducted. The patent map has long been considered as a useful tool for mining latent technological information. Among others, the detection of patent vacuums, defined as unexplored areas of new technologies, deserves intensive research. However, previous studies for identifying patent vacuums on the patent map have been subjected to some limitations, stemming from the subjective and manual identification of patent vacuums. To address these limitations, this study proposes a GTM-based patent vacuum map, which aims to automatically identify a patent vacuum. Since GTM is a probabilistic approach of mapping multidimensional data space onto a low-dimensional latent space and vice versa, it contributes to the automatic detection and interpretation of patent vacuums. The proposed approach consists of three stages. Firstly, text mining is executed in order to transform patent documents into keyword vectors as structured data. Secondly, the GTM is employed to develop the patent map, subsequently leading to the discovery of patent vacuums, which are expressed as blank areas in the map. Lastly, the meaning of each patent vacuum is interpreted as new technology opportunities by the inverse mapping of patent vacuums onto the original keyword vector. The case study is conducted with lithography technology-related patents. We believe the proposed approach not only saves time and effort for identifying patent vacuums, but also increases objectivity and reliability. Unlike the above first study deals with technology area, second study concerned with identifying new service opportunities through derived service vacuums using GTM-based service vacuum map. Despite the strategic and technological gravity of new service opportunities, relatively little research has been devoted to the intelligent exploration and systematic identification of new service opportunities. This study proposes a unique approach for developing and utilizing GTM-based service vacuum maps to discover new service opportunities. The detailed procedure of the approach is illustrated for the case of mobile application services from Apples AppStore, which is a web service that allows smartphone users to access mobile application services. The proposed approach is expected to aid the discovery of new service opportunities from various information systems. Lastly, identifying trends of service using GTM-TT service trend map is dealt with. Recently, due to the explosive increase of services, firms have faced with challenges to analyze patterns and trends in services in an intuitive but objective ways. The notion of service map can be adapted to this end. Maps, in general, have been receiving a great deal of attention because of their potential as visualization tools that can allow people to visualize massive amounts of information. Specifically, the GTM-TT algorithm is suitable for dynamic analysis since GTM-TT provides a time-based clustering and change path. In response, this study proposes an approach for developing and using GTM-TT service trend maps consisting of a service clustering map and a service sequence map for analyzing service trends. The proposed approach, broadly, is comprised of four steps: 1) the construction of a database, 2) data preprocessing, 3) development of a GTM-TT service trend map, and 4) interpretation. The proposed approach is expected to aid in the identification of dynamic service trends for other service areas as well.Chapter 1. Introduction 1 1.1 Background and motivations 1 1.2 Purposes 5 1.3 Scope and framework 13 1.4 Dissertation outline 16 Chapter 2. Background 17 2.1 Theoretical background 17 2.1.1 Information visualization 17 2.1.2 Trend analysis 22 2.1.2.1 Concept of trend analysis 22 2.1.2.2 Methods, tools, and techniques for trend analysis 23 2.1.2.3 Application of trend analysis 24 2.2 Methodological background 26 2.2.1 Text mining 26 2.2.2 Generative topographic mapping (GTM) 27 2.2.2.1 Basic concept of the GTM 27 2.2.2.2 The algorithm of the GTM 30 2.2.3 Generative topographic mapping through time (GTM-TT) 32 Chapter 3. Identifying vacuums: The GTM-based vacuum map 34 3.1 The GTM-based patent vacuum map for identifying technology vacuums 34 3.1.1 Overall research framework 34 3.1.2 Detailed processes 35 3.1.2.1 Data preprocessing 35 3.1.2.2 Development of GTM-based patent vacuum map 37 3.1.2.3 Detection of patent vacuums 39 3.1.2.4 Interpretation of patent vacuums 40 3.1.3 Case study: lithography technology 41 3.1.3.1 Data collection 42 3.1.3.2 Data preprocessing 43 3.1.3.3 Development of GTM-based patent vacuum map 44 3.1.3.4 Detection of patent vacuums 45 3.1.3.5 Interpretation of patent vacuums 46 3.1.4 Discussions 50 3.2 The GTM-based service vacuum map for identifying service vacuums 57 3.2.1 Overall research framework 57 3.2.2 Detailed processes 58 3.2.2.1 Step 1: Construction of the database 58 3.2.2.2 Step 2: Preprocessing 59 3.2.2.3 Step 3: Development of a GTM-based service vacuum map 63 3.2.2.4 Step 4: Exploration of new service opportunities 65 3.2.3. Case study: navigation mobile application service 70 3.2.3.1 Data collection 70 3.2.3.2 Data preprocessing 73 3.2.3.3 Developing a GTM-based service vacuum map 76 3.2.3.4 Exploring new service opportunities 77 3.2.3.2 Evaluation of new service opportunities 86 3.2.4 Discussions 88 Chapter 4. Identifying trends: GTM-TT-based trend map 91 4.1 The GTM-TT service trend map for identifying trends of service 91 4.1.1 Overall process 91 4.1.2 Detailed procedures 92 4.1.2.1 Construction of the database 92 4.1.2.2 Data preprocessing 93 4.1.2.3 Development of a GTM-TT service trend map 95 4.1.2.4 Interpretation 97 4.1.3 Advantage of the Proposed Approach 98 4.1.4 Case Study: Camera Technology-Based Mobile Application Service 98 4.1.4.1 Construction of database 99 4.1.4.2 Data preprocessing 102 4.1.4.3 Development of a GTM-TT service trend map 102 4.1.4.4 Interpretation for the service cluster map 104 4.1.4.5 Interpretation for the service sequence map 107 4.1.5 Discussion 107 4.1.5.1 Dynamic analysis 107 4.1.5.2 Period determination of GTM-TT service trend map 115 Chapter 5. Conclusions 119 5.1 Summary and contributions 119 5.2 Limitations and future research 122 Bibliography 124 Appendix 138 Appendix A. Generative topographic mapping: GTM 138 Appendix B. Generative topographic mapping through time: GTM-TT 141 Appendix C. Keyword list about navigation mobile application services 144 Appendix D. Keyword list about camera technology-based mobile application service 147Docto

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