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An approach to support long-term creative thinking in everyday life Hirohito Shibata and Koichi Hori Department of Advanced Interdisciplinary Studies, University of Tokyo RCAST, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan {shibata, hori}@ai.rcast.u-tokyo.ac.jp

Abstract. Most of prior creativity support systems aimed to support to generate ideas efficiently in the situation restricted in time and space. In this paper, we propose a novel method and its implementation to support long-term ideageneration in everyday life. In order to understand actual idea-generation, we have carried out a cognitive diary study. Based on the results, we have designed a system consisting of two components: a management system for problems and ideas named IdeaManager; and a personal information storage system named iBox. On the timing when information is stocked in iBox, iBox searches related problems and ideas in IdeaManager and presents the result if any. Its aim is to support non-intentional idea-generation.

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Introduction

Since the end of 1980’s, a lot of systems to support idea-generation, called creativity support systems, have been proposed [7]. However, most of them have not gained widespread use. Exceptionally, systems based on creative thinking technique TRIZ [9] are introduced in companies as tools for patent strategy, but their use is restricted in improving artifacts and we cannot adapt its technique or these systems to other fields, for example concept creation or non-technical everyday activities. A lot of fascinated systems have been proposed, but there is no system used in actual everyday life. We think the reasons are these. First, most of them support short-term thinking restricted in time. They do not support to refine problems or improve ideas repeatedly. However, analyzing our experiences and prior cases of idea-generation, we think that a person needs to think for a long time and enhance ideas. Also, in our actual activities, it is rare to be needed to generate ideas immediately. Most problems have deadlines to be resolved and we can reconsider the problems many times. Second, most of them support to think in front of the systems. Their support is restricted in space. They support creative thinking processes separated from dairy life. However, creative thinking is a cognitive process that cannot be separated from everyday life and there must be a limit to systems that support only a part of work process [5]. Third, almost all of them support only intentional idea-generation. That is, they assume that users use them with consciousness of generating ideas. As we describe

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details in Section 2, in actual activities there is more non-intentional idea-generation than intentional. Fourth, most of them support idea-generation in a group. However, especially in the cases to be important for individuals or the cases to demand technical knowledge, a person needs to think by oneself. Also, there are some reports that the best individual performance is usually superior to group’s performance while the group outperformed its average member (for example [3]). Finally, many of them support the thinking process based on creative thinking techniques, such as brainstorming or KJ-method. However, most of these methods are not confirmed whether they are effective or not. As for brainstorming which is only one technique whose effect has been discussed in psychological experiments, the results are negative in its productivity (for example [11]). Moreover, their effects depend on individual’s preference and situations. Most systems do not take this aspect into consideration. Based on these claims, we have been pursuing a system to satisfy the above concerns. Our everyday life is filled with stimuli. They might work as clues to generate ideas. They are chances to generate ideas. Based on the thought of ”A chance is not what is given. A chance is what we should get by ourselves”, we propose a system not to lose chances to generate ideas. The purpose of this paper is to propose a framework and its implementation of a long-term creativity support system used in everyday life. To begin with, we present our cognitive diary study and its results. Based on the results, we establish two design philosophies of the system as our approaches. Next, we present our system designed from these philosophies. 2

Idea-generation in everyday life: Data from a cognitive diary study

Ideas do not always come to mind only when a person is trying to generate ideas intentionally. In prior researches, such phenomena are called inspirations, illumination, insight, Aha experiment, or Eureka phenomena. In history of science, there are lots of such phenomena, for example anecdotes of Archimedes or Kekule. In this paper, in order to avoid mysterious image evoked by these terms, we dare to call them non-intentional idea-generation. In order to understand actual idea-generation, we have carried out a cognitive diary study. In this study, subjects always had diary notes including some questions on environmental or mental situations. If they generated an idea, they wrote down its details in the notes and answered these questions. Subjects were 12 researchers of software engineering and psychology, and the experimental period was 3 weeks. Using a cognitive diary study, we can acquire reliable data in comparison with introspective reports, because it decreases changing of memory and forgetting [8]. In this study, 65 cases of idea-generation were reported (see Table 1). There were 33 cases (50.8%) of non-intentional idea-generation, 22 cases of intentional ones, and 10 cases that could not be identified. This suggests that in actual activities there are more cases of non-intentional idea-generation than intentional ones. In the following of this section, we explain two interesting results related to nonintentional idea-generation.

An approach to support long-term creative thinking in everyday life

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Table 1: The number of ideas in the diary study. Pattern of idea-generation Non-intentional External clue Internal clue No clue Intentional Others

Ideas 20 33 65 10 3 22 10

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Figure 1: Relation between the number of ideas and elapsed time. Graph (A) shows the relation between the number of ideas and elapsed time from the final thought on problems to idea-generation. Graph (B) shows the relation between the number of ideas and elapsed time from recognition of problems to idea-generation.

2.1 Clue-dependency of non-intentional idea-generation As for non-intentional idea-generation, in 30 cases (90.9% of all non-intentional ideageneration), subjects perceived the existence of clues to generate ideas (see Table 1). Moreover, in 20 cases (60.6%), these clues were external ones, such as books or conversation. This suggests that non-intentional idea-generation is often driven by cognitive perception of external event. As a similar phenomenon, Suwa [10] claims that functional thoughts follow perception of visual or non-visual clues in design process. In creative activities, clues play an important role. 2.2 Recency effect in idea-generation In 32 cases (49.2% of all cases), the ideas were for problems that had been recognized before idea-generation (including both intentional and non-intentional cases). Figure 1 shows the relation between the number of ideas and elapsed time both from recognition of problems and from final thought on problems for these 32 cases.

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In 23 cases (71.9% of those 32 cases), subjects remembered and tried to resolve problems during the last one week to generate ideas. Moreover, in 18 cases (56.3%), subjects remembered and tried to resolve problems during the last three days to generate ideas. In Figure 1, the number of idea-generation decreases according to the elapsed time from the final thought on problems to idea-generation. This suggests that a person is likely to generate ideas if he or she remembers the problems recently. This phenomenon is similar to ’recency effect’ in the research of human memory. As for elapsed time from recognition of problems to idea-generation, most ideageneration (12 cases) took place between one and three months after problems are recognized. Also, in 29 cases (90.6%), idea-generation was for problem recognized more than one week before. This shows that it takes long-term, at least a few weeks, to generate ideas and then we must consider long-term support for idea-generation. 3

Design philosophies of the system

We summarize the results of the diary study. (1) There are more cases of non-intentional idea-generation than that of intentional. (2) It takes long-term to generate ideas. (3) A person is likely to generate ideas if he or she remembers the corresponding problem recently (recency effect in idea-generation). (4) Most of non-intentional idea-generation is driven by an external clue (clue-dependency of non-intentional idea-generation). The results (1) and (2) support our claims described before. Considering the results (3) and (4), we propose that the following two design philosophies are important. 3.1 Design philosophy 1: Managing problems and ideas If a problem is important and its deadline is not so immediate, generally a person tries to seek solutions and improve them repeatedly until he or she acquires satisfactory ones. We show our model of long-term problem solving process in Figure 2. In this model, at first a person recognizes a problem, and then he or she tries to generate ideas repeatedly. If a person cannot resolve the problem or cannot be satisfied with the idea of the first trial, he or she puts aside and stores the problem. Then he or she should try it again in the later. Now, for the next trial, a person must recall the problem. However, in the situation that a person has a lot of problems, he or she is likely to concentrate on only a few important problems and forget existence of others. This is so-called ’failure of prospective remembering’ in psychology, and this phenomenon occurs frequently in everyday life [4]. In order to prevent from such failures, it would be effective to manage problems and ideas and enable to refer to them easily. With high accessibility for problems, a person can increase opportunities of idea-generation.

An approach to support long-term creative thinking in everyday life

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Trial of idea-generation Recognition

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of problem

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Figure 2: Model of long-term problem solving.

Furthermore, the generated idea might not perfectly satisfy its problem and needs to be improved. Then, it would be effective to manage such an idea with its corresponding problem. 3.2 Design philosophy 2: Popping up problems on proper timing In Figure 2, a recall process is driven by a perceived clue event. Failure of this recall means a lost chance to generate ideas. Besides managing problems and ideas, it would be effective to pop up and present problems on proper timing. This approach aims to support non-intentional idea-generation. A technical problem of this approach is when to pop up. It is technically difficult to monitor person’s perception or thinking, but it is possible to construct a system based on this approach. Let us consider a personal information storage system used constantly in dairy life and a management system for problems and ideas. Information stocked in such a storage system reflects users’ interest. Therefore, such information may have something to do with users’ current problems. Such information can be regarded as a clue event. On the timing when information is stocked in the storage system, the system searches related problems and ideas and presents the results if any. If some problems popped up, users try to generate ideas for these problems or improve existing ideas using the information stocked earlier as a hint. The stocked information must have novelty, effectiveness, or emergence which Finke et al. [2] call ’preinventive properties’, because a person felt it interesting. Therefore, information registered in the storage system has the possibility to work as a hint of ideageneration effectively. Furthermore, when a person registers information in a storage system, his or her mind must be filled with thought on the information. Then, to pop up problems or ideas on this timing makes him or her try to resolve the problems or improve ideas in the context of the registered information. Therefore, we can expect that pop-up on this timing is effective. Even if stocked information does not effectively work as a clue, we can increase the accessibility for searched problems by trying to generate ideas for these problems. This would increase the opportunities of later idea-generation.

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Hirohito Shibata and Koichi Hori Tree of boxes

Idea Window

Problem Window

List of names

Related Information Window

Name Content of information

List of names

Information - Name

Keywords

- Content - Keywords

Figure 3: A screen shot of IdeaManager

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Figure 4: A screen shot of iBox

System overview

In this section, we propose a system based on the design philosophies of Section 3. The system consists of two components. Based on the design philosophy 1, we have built a management system for problems and ideas named IdeaManager (see Figure 3). Based on the design philosophy 2, we have built a personal information storage system named iBox (see Figure 4). iBox can manage various kinds of information in everyday life and cooperate with IdeaManager. These systems run on Windows 95/98/NT 4.0/2000 and are implemented using the search engine of Albase1 [6]. In the following of this section, we explain IdeaManager, iBox, and their cooperation. 4.1 IdeaManager: a management system for problems and ideas IdeaManager’s data model is an extension of Albase. All information stocked in IdeaManager has its name and keywords. In the current version, only text can be stocked. That is, problems and ideas should be represented in text. Information stocked in IdeaManager is divided into following three types: problems, ideas, and related information. IdeaManager has following three child windows: Idea Window for ideas, Problem Window for problems, and Related Information Window for related information. Users can view problems, ideas, and related information, side by side. The following basic search functions are provided: search by keywords, full text search, search by date, and list of all information. Each of these functions returns a list of names (upper area of each child window). Selecting a name of the list, users can see the information with its name and keywords (lower area of each child window). As a management system for problems and ideas, problems have the following three attributes: state of whether it is resolved or not, deadline, and importance. Also, ideas have one attribute, evaluation. In addition to the basic search functions, IdeaManager has filtering functions using these attributes. If there is a problem of the day before deadline, IdeaManager warns that. Also, users can set a link between two pieces of information and they can get linked information easily. Using this link function, users can manage problems with its corre1

Now, Albase became an item for sale of Fuji Xerox Co., Ltd. as Johobako 4.0.

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Popup problems and ideas

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Figure 5: Cooperation between IdeaManager and iBox

sponding ideas and related information. 4.2 iBox: a personal information storage system Similarly to IdeaManager, iBox stocks all information with its name and keywords. In the current version, only text can be stocked. iBox provides the same basic search functions as IdeaManager. Each of these functions returns a list of names. Selecting a name of the list, a user can see the information with its name and keywords. Using iBox, users manage various types of information on the situations in both work and other everyday life. The following examples are types of information that users actually stock in iBox: research notes or memoranda, scraps or comments of books, methodologies of programming or computer setup, bugs or APIs, technical terminologies, schedules, diaries, addresses, and so on. 4.3 Cooperation between IdeaManager and iBox Based on the design philosophy 2, both IdeaManager and iBox cooperate each other. Figure 5 shows this mechanism. When information is registered in iBox, iBox searches related problems and ideas of IdeaManager. If there are some results, iBox pops up IdeaManager and the results are presented. Then users consider whether the registered information works as a hint for searched problems or ideas. Moreover, we can consider the opposite direction. When a problem or an idea is registered in IdeaManager, IdeaManager searches related information of iBox. If there are some results, IdeaManager pops up a dialog to inform the message that there is related information. When users click the button in the dialog, iBox pops up and the information of iBox is presented. Then users consider whether the searched information works as a hint for a registered problem or idea. This search direction is not mentioned in the design philosophy 2, but it is expected that users might generate ideas for the

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registered problem or refine the problem referring to related information. The reason why IdeaManager pops up a dialog and does not execute iBox directly is based on a heuristics that a person had better not use technical knowledge at first for ideageneration [2]. 5

Related work

En Passant 2 proposed by Aihara [1] supports long-term research activities. This system stocks research notes scanned by scanner. It has a function to deal with indices edited by users and to present spatial configuration of notes according to their similarity. The objective of this system is to give the triggers to recall users’ memories in contrast to current context. It supports long-term creative thinking and its concept is most similar to our aim. However, En Passant 2 cannot work information other than research notes as a hint for idea-generation and users must generate ideas with consciousness of generating ideas in front of the system. We cannot expect when and where idea-generation occurs. Considering this point, there are some systems implemented on Personal Digital Assistant (PDA). Here, we discuss GMemo proposed by Yoshino [12]. GMemo is implemented as input equipment for GUNGEN, a groupware based on KJ-method, and its input interface is handwriting. Using GMemo, users can store sudden ideas or interesting information acquired in dairy life easily. However, GMemo does not have the facilities to support idea-generation. It just supports to input generated ideas. To sum up, prior systems support only intentional idea-generation or input of ideas. Also, they do not support reconsideration and elaboration of problems or ideas. 6

Conclusions

In this paper, we carried out a cognitive diary study and analyzed actual idea-generation. Based on the results, we established design philosophies for the long-term creativity systems and proposed a system for long-term use in everyday life. Its characteristic is to cooperate with a personal information storage system. The aim of this cooperation is to work interesting information acquired in everyday life as a clue event of idea-generation. Our target users are researchers or planners. Information management plays an important role in their activities. We believe that our system is effective to support them. We are now currying out a long-term (more than half a year) user study. In the near future, we are going to enhance the system based on the result of this study. Acknowledgements Our system is implemented using the search engine of Albase implemented in Fuji Xerox Co., Ltd. Especially, we thank Yoshifumi Matsunaga for quick and warm-hearted response and Eiji Ishida for implementation and technical help of Albase. Finally, we thank Kengo Omura of Fuji Xerox Co., Ltd. for his helpful comments for this research. Without his collaboration, we could not summarize the concepts of IdeaManager.

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