A Concept for the Exploratory Visualization of

Patent Network Dynamics

Florian Windhager

, Albert Amor-Amorós

, Michael Smuc

, Paolo Federico

, Lukas Zenk

and

Silvia Miksch

Department for Knowledge and Communication Management, Danube University Krems,

Dr.-Karl-Dorrek-Str 30, 3500 Krems, Austria

Institute of Software Technology and Interactive Systems, Vienna University of Technology,

Favoritenstraße 9-11, 1040 Vienna, Austria

Keywords: Information Visualization, Visual Analytics, Patent Data, Emergent Technologies, Dynamic Networks.

Abstract: Patents, archived as large collections of semi-structured text documents, contain valuable information about

historical trends and current states of R&D fields, as well as performances of single inventors and

companies. Specific methods are needed to unlock this information and enable its insightful analysis by

investors, executives, funding agencies, and policy makers. In this position paper, we propose an approach

based on modelling patent repositories as multivariate temporal networks, and examining them by the

means of specific visual analytics methods. We illustrate the potential of our approach by discussing two

use-cases: the determination of emerging research fields in general and within companies, as well as the

identification of inventors characterized by different temporal paths of productivity.

1 INTRODUCTION

Together with scientific papers, patents rank among

the most common and widely used information

carriers to document newly developed knowledge

and technical procedures. While a patent’s legal

function is the temporal appropriation and protection

of its content against exploitation and infringement

by other parties, patent databases are a valuable

resource that can be exploited for collective learning

purposes, to answer the information need of various

interest groups throughout different domains. This

corresponds to the twofold function of exclusion and

diffusion (Ordover, 1991). With this position paper,

we want to contribute to the collective learning and

diffusion side from a point of view, where the field

of patinformatics (Trippe, 2003) becomes

augmented by the methods and technologies of

Visual Analytics (Koch, Bosch, Giereth, & Ertl,

2009).

Our approach is guided by the research question

of “How can Visual Analytics methods support

patent data analysts in gaining insight according to

their specific analytical tasks?” and faces the three

main challenges of time, scale, and relational

structure. Our position is that a conceptual

framework built on multivariate temporal networks

would enable the adoption of existing visual and

analytical methods and thus bring along new

possibilities to gain insights into the dynamic

behavior of individual actors, companies, as well as

whole research and technology fields.

In the following we elaborate on how to

conceptually organize different user groups and

some of their common tasks (Sec. 2), introduce the

design rationale of our approach (Sec. 3), discuss

two different Visual Analytics use cases of patent

data exploration (Sec. 4), and conclude with an

outlook on research challenges and future work

(Sec. 5).

2 USERS, DATA AND TASKS

In the following, the domain will be characterized in

terms of its data, users and their tasks, in order to

generate a set of requirements and to proceed with a

user-centric perspective in designing Visual

Analytics methods for time-oriented data (Miksch

and Aigner, 2014).

When it comes to the current state and future

development of different science and technology

268

Windhager F., Amor-Amorós A., Smuc M., Federico P., Zenk L. and Miksch S..

A Concept for the Exploratory Visualization of Patent Network Dynamics.

DOI: 10.5220/0005360002680273

In Proceedings of the 6th International Conference on Information Visualization Theory and Applications (IVAPP-2015), pages 268-273

ISBN: 978-989-758-088-8

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

Table 1: Common user groups of patent information and selected analytical tasks.

user groups researchers & inventors managers & investors policy makers & funding agencies

main focus R&D classes companies countries

exemplary

tasks

monitoring trends

within R&D classes

monitoring one’s own or

competitors performance

monitoring a countries

R&D performance

macro level

monitoring of all or selected

R&D classes >> see sec. 4.1

monitoring R&D trends

within selected companies

monitoring or evaluating the R&D

of countries or regions

micro level

monitoring inventors’

performance

identifying key actors within

companies >> see sec. 4.2

identifying leading

companies or inventors

fields, there is a variety of user groups in

knowledge-based societies, who are in constant need

of up-to-date analysis according to their specific

tasks and goals. Amongst others we highlight (1)

researchers and inventors, (2) investors and

managers of companies, as well as (3) policy makers

and funding agencies. Each of these groups have to

deal with questions ranging from the macro to the

micro levels of research and development (R&D)

performance on a regular basis. While there are

various ways to tackle these issues (e.g., experts’

assessments, surveys, and evaluations) patents

provide a rich source of evolving information to be

taken into closer consideration (Jaffe and

Traitenberg, 2002).

Patent data usually consists of large collections

of semi-structured documents (see Fig. 1): while an

unstructured body of text and images details the

invention or procedure for which the document is

claiming protection, a standardized part of the

document is carrying metadata, which is required to

administer such documents. When handling patent

collections with millions of documents, these

categories of metadata ease and guide the

examination of the technological state of the art via

patent information databases. These databases

commonly offer public interfaces for textual queries,

but could be used for advanced studies and visual

exploration too (Markellos et al., 2002; Yoon and

Park, 2004; Bonino, Ciaramella and Corno, 2010).

With regard to specific user groups, various

patinformatics methods could be applied to answer

their specific questions or tasks (Trippe, 2003). To

organize these various points of view and interest,

Table 1 provides an overview of common tasks,

organized by their assignment to user groups

(columns) and their main focus of investigation with

tasks on the respective macro or micro level of

analysis (rows), i.e. from fields and firms down to

specific groups and individuals. While researchers

Figure 1: The structure of patent data.

and inventors are used to focus on R&D areas or

technology classes in the large (first column),

managers and inventors are bound to focus on

companies and corporate actors, whether they are

their own, their competitors’, or the ones who they

want to invest in (second column). In contrast,

policy makers and funding agencies think of

regional or national aspects as relevant (third

column). Aside from gaining overviews on key

structures and actors, we consider the detailed

investigation into temporal aspects, like emerging

technologies and companies’ or inventors’

performances as the most interesting aspects for the

visual analysis of patent data.

3 PATENT DATA AS DYNAMIC

NETWORKS

According to the core of our position, dynamic

networks constitute an expressive abstraction for

representing and manipulating patent data, due to the

fact that domain-specific relational entities, such as

citations, collaborations, or knowledge flows, can be

explicitly represented and manipulated.

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3.1 Data Model

The property graph model is one of the most flexible

and widespread graph-based data models for

representing multivariate networks (Rodriguez and

Neubauer, 2012). A property graph is a graph

structure in which elements (nodes and edges) have

types as well as attributes, edges are directed, and

parallel edges between nodes can exist. Patent data

can be easily casted into a property graph model by

introducing three main node types: i) patent

documents, as central entities containing specific

metadata and pointers to other related entities, ii)

parties, such as persons or organizations that have

been involved in the development of a particular

patent with any possible role (i.e., inventors,

applicants, assignees, or examiners), and iii)

knowledge classes, organized according to a

hierarchical structure defined by one of the many

classification systems that exist. Correspondingly,

different relationship types also exist between the

aforementioned entities, and can be explicitly

introduced in the model: patent to patent

(references), party to patent (party in), patent to

knowledge class (classified in), and knowledge class

to knowledge class (subclass of) (Fig. 2).

Time (date) is a data dimension with many

special features that require special models and

Visual Analytics methods. In order to appropriately

address temporal aspects of networks we adopt the

TimeGraph data-management framework (Amor-

Amorós, Federico, and Miksch, 2014). TimeGraph

uses the network structure to explicitly represent the

structure of temporal attributes associated to the data

items in terms of temporal primitives, such as

instants or intervals, and the hierarchy of the time

domain towards these temporal attributes map (i.e.

the calendar structure). Additionally, it extends the

graph traversal language with specific operators that

enable writing expressive temporal selection and

aggregation statements.

Once a patent repository has been modeled as a

dynamic network, it can be examined by applying an

appropriate sequence of data transformation,

analysis and visualization steps.

Figure 2: A patent repository modeled as a property graph.

3.2 Data Reduction

Networks representing patent repositories usually

contain hundreds of millions of elements. In such a

context, data reduction becomes a key prerequisite

for performing exploratory analytical tasks. The data

reduction process can be formalized as an iterative

sequence of steps that involves two types of actions:

selection and aggregation (Jankun-Kelly et al.,

2014).

Selection specifies a set of objects of interest out

of an input set, according to specific constraints or

characteristics. A further distinction can be made,

according to which of the graph aspects (i.e.,

topology or attributes) are involved: traversal steps

involve the connections between the elements of the

graph, propagating the selection focus through the

structure, while filtering steps specify a subset of

elements of interest out of a reference set, according

to its attributes. In general, any complex selection

step can be decomposed into a sequence of filtering

and traversal steps.

Aggregation reduces the amount of data by

introducing representative entities for specific

groups of data items. Accordingly, two stages can be

identified in an aggregation step: an optional

grouping stage, in which the items to be aggregated

are separated into groups according to a specific

rule, and a reduction stage, in which a representative

is computed for each one of the groups. Aggregation

in graphs can involve both attributes and topological

characteristics of elements in each of the two stages.

An interesting special case is projection, in which

specific paths are replaced by simple edges.

3.3 Analysis

Trend analysis constitutes one of the most common

tasks to perform on patent data (Bonino, Ciaramella

and Corno, 2010). The structural dynamics of a

network can be traced by computing a graph theory

metric on specific elements in each of the network

snapshots, and then analyzing the temporal evolution

of such metric; an exemplary result of this kind of

analysis is a time series representing the evolution of

the centrality of an inventor in the context of the

collaboration network specific to her/his company

(see section 4.2). This kind of information can

usually be further compressed by means of a

temporal abstraction computed on the time series,

i.e., "rising" instead of an increasingly growing

value. An alternative means for temporal analysis of

networks involves using the so-called temporal

network measures (Holme and Saramäki, 2012),

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which extend the concepts of static graph theory

with additional time-aware definitions (e.g.,

temporal paths). As a simple example, consider the

sequence of time intervals between subsequent

collaborations (i.e. links) between two inventors.

3.4 Visualization

Many visualization techniques are available for

relational and hierarchical data. Trees, treemaps, and

sunburst diagrams enable the visualization of the

hierarchical structure of the patent classification

(Schulz, 2011). Citation or collaboration networks

can be visualized by matrix-based techniques as well

as by node-link diagrams with different types of

layouts (Beck et al., 2014). Network dynamics can

be visualized by mapping sequenced static diagrams

into a timeline, resulting in juxtaposition,

superimposition, and 2.5-dimensional views

(Federico at al., 2011; Gleicher et al., 2011). These

views can be enriched by encoding temporal

abstractions of dynamic graph metrics into a visual

variable such as color.

4 USE CASES

In the following, two use cases – which have been

assembled from five expert interviews – will

illustrate possible applications of the described

approach to common questions, which actors in

various R&D contexts are frequently facing.

4.1 Rising and Falling Technologies

User groups like researchers and inventors are called

upon to constantly observe their central and

peripheral activity fields for recent developments

and future trends. With the resulting task rephrased

as “What are recent developments in a specific field

of interest?”, any supporting method has to delineate

relevant technology fields first, to visualize

emerging, increasing, stagnating, or decreasing

activities on that basis.

Due to the mandatory assignment of every patent

document to the specific classes of fine-grained

patent classifications (e.g., International Patent

Classification (IPC), Cooperative Patent

Classification (CPC), etc.), these hierarchical multi-

level systems could be used to deliver a background

map for any selected area, against which the activity

of focus classes could be visualized. After selecting

treemaps for further investigation, we implemented a

“global technology activity map” (see Figure 3)

Figure 3: Treemap visualization of all R&D classes,

colored by average age of patents.

Figure 4: R&D “footprints” of SIEMENS (upper left),

BOSCH (upper right), SAMSUNG (bottom left) and

APPLE (bottom right).

distinguishing the top levels of the IPC, detailing

three levels of hierarchy from 8 sections to 130

classes and 600 subclasses. These could be weighted

(e.g. for patents per class) and colored according to

data on temporal aspects (e.g., average age of

patents per class). Against this background, the

patent portfolios and activities of selected companies

can be highlighted. This allows to visually analyze

and compare the “innovation footprints” of different

corporate actors (see Fig. 4), where recent activity

areas again are highlighted in red, while older

innovation areas are shaded in blue.

While the overall constellation of colored cells

allows to compare the “intellectual property shapes”

of corporate actors (e.g., all-round corporations

versus specialized niche players), temporal measures

(like average age of patents) can help to identify

recent strategic investments of relevant companies

and competitors.

With regard to the user group of R&D managers

and investors, we consider the advancement of

methods to explore the dynamics of any competitors

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Figure 5: Data transformation process for identifying rising inventors.

strategic investments as highly relevant.

Accompanying and deepening existing domain

knowledge about distributions and trends, the

outlined Visual Analytics approach can help to

support the decisions of such user groups on a real-

time basis.

Aside from identifying increasing or decreasing

R&D efforts on a global, multi- or single company

level, patent data allows to dive even further into the

activities of selected corporate actors, making the

work of single individuals visible.

4.2 Rising and Falling Inventors

In this second scenario, we demonstrate how

managers, investors, or human resource managers

can be supported in order to identify and defend key

players within their own company, or pick out rising

or falling inventors in other firms, to reassemble

them in new ventures or merge them with existing

teams. The question driving the task at hand is

“What are the inventors with increasing or

decreasing (temporal) productivity patterns inside a

specific company?”.

Figure 5 shows the specific form of the data

transformation sequence, introduced in section 3: A

company is selected as the initial entity, then the

repository is traversed to find all patents that have

the given company as assignee and all their

inventors; then the patents-inventors 2-mode

network is projected into a co-author network. This

allows not only for the investigation into individual

productivity and position within a company’s

innovation network, but also to assess the

productivity of group structures and team

environments. The resulting co-author network is

temporally partitioned and the centrality of inventors

is computed for each time-slice; then it is temporally

abstracted. In particular, with regard to two time

periods, four sorts of dynamics could be

distinguished: increasing (+/+), decreasing-

increasing (–/+), increasing-decreasing (+/–), and

decreasing (–/–).

Figure 6 shows these temporal abstractions by

the colors of red (+/+), orange (–/+), light blue (+/–)

and dark blue (–/–). In addition, the nodes’ sizes

show the total amount of patents each inventor

contributed to.

Figure 6: Co-publication network of inventors, with active

individuals in red, and inactive ones in blue.

Resulting insights can contribute to support the

human resource management within a company, as

well as to search for actors with specific skills – and

a specific pattern of temporal productivity (or

structural embeddedness) across all other companies

in the database. As such, this approach helps to

identify “rising stars” and up-and-coming R&D

departments, as well as “abandoned” inventors or

teams, who might be interested to support

innovation and development in a novel context.

5 OUTLOOK & FUTURE WORK

In this article, we presented a conceptual framework

to provide users with Visual Analytics methods to

interactively explore the dynamics of patent

databases. As illustrated by two use cases, such an

approach helps to provide a wide range of actors in

the research and development context with up-to-

date information, needed to support their decision

processes. As opposed to existing approaches, we

use a network abstraction to model the data, which

provides specific benefits when it comes to the

visual exploration of relational structures and

dynamics at both: the macro and the micro level of

individual inventor’s performance.

While we consider the development of methods

for the interactive exploration of complex datasets to

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provide a significant challenge for future work,

another challenge derives from the time-oriented

nature of patent data. Whether analysts are

investigating the dynamics of technology fields,

countries, companies, or individual publication

performances, the need to identify past, present, and

possible future trends is ranging high. As such, we

will dedicate future work to the elaboration and

refinement of time-oriented analysis methods, which

have to support the quick identification,

amplification, and comparison of trends, as well as

the detailed exploration and investigation of

behavioral patterns and flows. To allow for

transitions between different levels, focal points and

analytical tasks, we consider the development of

consistent navigation methods as a requirement,

which will be supported by feedback of user tests

and evaluations. As such, the connection of the

outlined network approach to various application

scenarios of patent data user groups will be ensured.

ACKNOWLEDGEMENTS

This research was funded by the Austrian Research

Promotion Agency (FFG) under the Project Number

835937 and builds on methods (specifically the use

case of section 4.2) developed by its company

research partner FASresearch (www.fas-research.

com).

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