Feedback Loops and Insight Reintegration

of a fictional epidemic outbreak, its means of transmission, and its cause [Bosch et al., 2011]. In combination with other coordinated views, the method turned out to be especially helpful to test a variety of hypotheses, and was significant for finding a meaningful interpretation of the given scenario. In 2012, the same technique was exploited as part of a toolkit for analyzing a large computer network [Krüger et al., 2012] and helped exclude certain events that might have caused some of the problems encountered affecting the network.

Besides having an explicit means for insight management, the mechanism for selection management also describes parts of the analytic process itself, including aspects such as invalidated hypotheses and other analytic steps that probably cannot be used further, but are still important for increasing an analyst’s trust in the validity of the analysis. Together with the changes applied to previous queries, analytic processes are formally represented in the system without any explicit recording triggered by the user. However, users can provide descriptive information to made selections and combinations of them in order to identify and remember specific analytic findings more easily.

One distinctive aspect of the approach described above is its user-directed con-structive nature. The formalized user-steered construction of an analysis and its explicit representation creates potential for exploiting a variety of synergetic effects.

It is, for example, a suitable base for representing analytic provenance, which can be exploited in later steps to support collaboration and analytic reporting. Those aspects are discussed in detail in Chapter 5.

3.3 ● Feedback Loops and Insight Reintegration 79 an individual view are restricted to the adjustment of view-dependent parameters like sorting, filtering, highlighting, zooming, and panning. The user can only gain insights by exploiting the set’s (meta)data which is related to the current view.

The first level of integration is therefore realized through brushing and linking between the views to make, for example, connections in the result set visible. By cross-highlighting, the user can answer questions about the frequent filing countries of the applicant with the highest number of patents in the set. While being a powerful method, brushing and linking can only show connections between the selection in one view and its representation in the other views, but does not take into account their combination.

The second level of integration is therefore the saving and recombining of selections.

Multiple views can now be used to define subsets and to combine them employing set operators, allowing the user to answer the same type of questions as above but with additional restrictions from other views, e.g., ’who is the applicant with the highest number of patent documents valid in Spain within my result set?’. This question could also be formulated as a new query, but this would make the combination of the answer with other subsets of the result set more complicated.

Up to this point patent analysts do not leave the phase of exploring the result set.

While this phase is important for creating insights regarding the problem domain, it interferes with the patent domain’s need for high relevance of result sets. Therefore, query widening has to come into play. The third level of integration addresses this requirement in the form of a query refinement by result set interaction. The views are aware of the type of data they are displaying and are capable of providing a search expression based on the user’s selection in the corresponding view. The selection management component, in turn, is capable of combining the selections and their attached search term description into complex queries. Finally, the visual query editor allows for the direct incorporation of (combined) selections, to find more or exclude documents of the specified kind. This aspect cannot be achieved by a single component, but only by the whole system.

It is important that the last step of integrating findings into previous query formula-tions is steerable by the analyst, since the system cannot decide automatically how the integration into previous queries should be realized. The semantics regarding the correct scope inside the query and the intended Boolean operation to be applied for the integration have to be provided by the human analyst. The feedback loop to exploit insights from the analysis for query refinement is realized in PatViz in such a steerable manner, e.g., by dragging content nodes of the selection management facility directly into the query view or by adding it via context menu. A description of a use case, exemplifying iterative patent search and analysis with the proposed set of techniques, can be found in Koch et al. [2009].

For patent search and analysis the described composition of the visual front-end also opens up new search strategies which can hardly be followed using traditional approaches. A direct and formal query approach is intelligently connected with views for explorative proceedings. This allows for a seamless combination of an analyst’s previous knowledge with berrypicking strategies [Bates, 1989; Hearst, 2009], which can be applied as a secondary means for increasing relevance. While the query approach should accommodate patent searchers in their established routines, visual berrypicking introduces new strategies alongside these familiar search patterns.

Instead of applying a search plan starting with a high-precision query as described by Alberts et al.[2011] followed by subsequent systematic broadening of the search, patent analysts can start with high-relevance approaches taking into account all factors at the very beginning. With the selection management system and filter techniques they can test and compare the different aspects against each other in order to increase precision for reducing the effort of a subsequent detailed patent inspection. Furthermore, crosschecking of patents and the (in)validation of hypotheses subsets becomes available through selection management. This establishes trust in the relevant subsets of a broad search, again without the cost of additional query formulation and the need to store intermediate results for later comparison. Such an approach can reduce the number of required iterations, as opposed to currently available systems for patent search, and is suitable for speeding up the search process.

This chapter presented an approach that covers the iterative patent search and analysis process. While the approach clearly addresses a specific domain, it is still very flexible regarding the analytic paths that can be followed by analysts.

Moreover, it can be adapted for analyzing and searching scientific literature, which is also one aspect in patent searching, but has not yet been addressed.

Domain adaption is important to optimally meet the needs of domain experts;

for larger analytic approaches as those presented here, it is therefore imperative.

Smaller analytic subtasks, which can also be supported employing visual analytics approaches, however, have the potential for broader application. The next chapter presents two of these approaches, also in context of patent analysis, but emphasizing characteristics that make it easier to generalize them.

Chapter

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