Jianxi LuoEngineering Product Development Pillar and

SUTD-MIT International Design Centre,

Singapore University of Technology and Design,

8 Somapah Road,

Singapore 487372, Singapore

e-mail: luo@sutd.edu.sg

Bowen Yan1

Engineering Product Development Pillar and

SUTD-MIT International Design Centre,

Singapore University of Technology and Design,

8 Somapah Road,

Singapore 487372, Singapore

e-mail: bowen_yan@sutd.edu.sg

Kristin WoodEngineering Product Development Pillar and

SUTD-MIT International Design Centre,

Singapore University of Technology and Design,

8 Somapah Road,

Singapore 487372, Singapore

InnoGPS for Data-DrivenExploration of DesignOpportunities and Directions:The Case of Google DriverlessCar ProjectEngineers and technology firms must continually explore new design opportunities anddirections to sustain or thrive in technology competition. However, the related decisionsare normally based on personal gut feeling or experiences. Although the analysis of userpreferences and market trends may shed light on some design opportunities from ademand perspective, design opportunities are always conditioned or enabled by the tech-nological capabilities of designers. Herein, we present a data-driven methodology fordesigners to analyze and identify what technologies they can design for the next, basedon the principle—what a designer can currently design condition or enable what it candesign next. The methodology is centered on an empirically built network map of allknown technologies, whose distances are quantified using more than 5 million patentrecords, and various network analytics to position a designer according to the technolo-gies that they can design, navigate technologies in the neighborhood, and identify feasi-ble paths to far fields for novel opportunities. Furthermore, we have integrated thetechnology space map, and various map-based functions for designer positioning, neigh-borhood search, path finding, and knowledge discovery and learning, into a data-drivenvisual analytic system named InnoGPS. InnoGPS is a global position system (GPS) forfinding innovation positions and directions in the technology space, and conceived byanalogy from the GPS that we use for positioning, neighborhood search, and directionfinding in the physical space. [DOI: 10.1115/1.4037680]

1 Introduction

“What technologies shall we design next?” This is a lastingquestion that designers often ask themselves. Likewise, technol-ogy firms need to constantly explore new design opportunities anddirections departing away from their prior capabilities, technolo-gies, and products, in order to sustain or thrive in the competitionin technologies. Some companies, such as R.I.M. and Motorola,succeeded in one generation of products, but later lost the direc-tion to continue innovation. Meanwhile, Google and Amazonhave been leading the innovation of driverless cars and robotics,departing away from the technological specialization that theystarted with. Designers and firms are faced with high uncertaintyin the search for design opportunities and directions. However,the related activities and decisions are normally based on personalgut feeling, intuition, or experiences of the designers or decisionmakers of firms.

Our research aims to develop a data-driven methodology andtool for designers to conduct more grounded and informedsearches regarding what technologies they should design for thenext, i.e., the next design opportunities and directions. Thus, ourfocus on the data-driven design decision support is the fuzzy startof a general creative design process. The growingly available bigdata about users, designers, firms, and technologies offer newopportunities for the design and development of data-drivendesign decision support methods and tools. However, the data-driven methods and tools to aid in the identification of design

opportunities are still underdeveloped, whereas many structuredmethods and tools have been developed to aid in the later phasesof the design process, such as design representation, prototyping,and redesign.

We take a different perspective from the user-centeredapproaches to identify design opportunities suggested by user pref-erences, market demands, and institutional factors [1–7]. Mean-while, future design opportunities for a designer can also beidentified according to the technologies that the designer can cur-rently design. Our research is motivated by the fundamental princi-ple that engineering design is the creative transformation (e.g.,recombination, synthesis, or analogy) of existing technologies andknowledge into new ones [8–11]. Following that, the prior techno-logical knowledge or capabilities of a designer influence what itcan design next.2 Our research focuses on analyzing the technolo-gies that a designer has mastered, relative to those technologies thatthey cannot currently design, to explore new design opportunities.

The technologies that a designer has mastered might be distantor proximate to other unexplored technologies to different degreesin terms of how similar the knowledge and skills required todesign them are. The set of all proximate or distant technologiesconstitutes the total technology space [12], in which two technolo-gies are proximate or proximate if similar or distinct knowledgeor capabilities are required to design them. It will be relativelyeasy for the designer to comprehend, learn, and synthesizethe technologies in the proximity than the distant ones. Therefore,the structure of the total technology space, together with the

1Corresponding author.Contributed by the Design Automation Committee of ASME for publication in

the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 4, 2017; finalmanuscript received August 5, 2017; published online October 2, 2017. Assoc.Editor: Yan Wang.

2This principle applies to not only a person (e.g., Thomas Edison) but also anorganization (e.g., Google) and even a region (e.g., Singapore). Hereafter, the notionof “designer,” if not especially noted, refers generally to a design engineer,technology firm, or R&D organization.

Journal of Mechanical Design NOVEMBER 2017, Vol. 139 / 111416-1Copyright VC 2017 by ASME

Downloaded From: http://mechanicaldesign.asmedigitalcollection.asme.org/ on 10/05/2017 Terms of Use: http://www.asme.org/about-asme/terms-of-use

specialized technology positions of a designer in the space, willcondition or enable future design opportunities of the designer.

We operationalize the foregoing conceptual idea of the technol-ogy space as a network map of patent technology classes accord-ing to their knowledge distances to each other. The map isexpected to support design direction finding in the technologyspace, in a way similar to that we use a geographical map to finddirections in the physical space. In this paper, we will present var-ious network analytics to locate the technology position of adesigner according to the technologies that it can currently design,identify near-field technologies for feasible near-term designopportunities, and explore indirect paths to far-field technologiesfor long-term design directions.

The construction of the technology space map, together withthe network analyses of a given designer’s positions, neighbor-hoods, and paths on the map, constitutes our methodology (asillustrated in Fig. 1) to aid in the data-driven exploration of nextdesign opportunities and directions. In particular, we have imple-mented the methodology into the data-driven computational-visual software system called InnoGPS, which stands for “GPSfor Innovation.” After reviewing related work, we will introducethe key elements of the data-driven methodology as implementedin the InnoGPS system and demonstrate it via the case of Googlewith a focus on its driverless car project.

Google is an ideal example of continually exploring new designopportunities. Over time, it has ventured incrementally away fromits starting technology, i.e., the page-ranking algorithm for webpage searches, to many other technological domains, such as tele-communication (e.g., Android platform and the devices based onit), robotics, and motor vehicles. Google’s driverless car project isparticularly useful for demonstrating how to explore design direc-tions and paths using the technology space map, because thedesign of driverless cars requires the synthesis of computing, com-munication, signal processing, and vehicle technologies, whichare distant to different degrees in the technology space.

2 Related Work

Our research leverages the prior works on patent data mining tobuild large-scale technology networks for the interests and uses ofengineering designers to explore design opportunities and direc-tions. In this section, we review these relevant literatures to shedlight on the value of bringing them together.

2.1 Fundamental Views on the Design Process. The to-be-proposed methodology and tool for design opportunity explorationare grounded on the fundamental understandings of the designprocess. Prior design creativity studies have explained the inventionof new technologies as the analogical transfer [8], recombination

[13], or synthesis [9] of prior technologies and knowledge. Theconcept–knowledge theory generalized the design processes asthe iterative creative transformations of prior knowledge and con-cepts into new ones [10,14,15]. Luo [16] argued that the designprocess requires knowledge as inputs, and such knowledge inputsinclude existing solutions, methods, tools, and practices, as wellas the scientific understanding of related natural phenomena. Theconcept–knowledge theorists further suggested that the priorknowledge structure of designers has major influences on the nextdesign process [10,14,15]. Designers are normally specialized incertain technology domains because expertise requires specializa-tion and is crucial for design success [17–20]. Meanwhile, design-ers need to go beyond their specialization to explore newtechnologies and knowledge for design-by-analogy [21,22] ornovel combinations of prior technologies [23–25].

Thus, it comes to the question on what additional technologiesto explore and learn to perceive a new analogy or synthesis, sincevast technologies have been created to date. Some technologiesare “proximate” to each other as they share common knowledgeinputs required designing them, whereas some technologies are“distant” because distinct knowledge inputs are required to designthem [26]. All the technologies which are proximate or distant toeach other to different degrees constitute a heterogeneous technol-ogy space [12]. Therefore, engineering design can be viewed as aprocess of search, identification, learning, and transformation(e.g., analogy, combination, and synthesis) of either proximate ordistant technologies in the heterogeneous technology space.

Design researchers have proposed methodologies or principlesto facilitate such a process across technical fields or disciplines.For instance, the infused design uses metalevel design problemrepresentation to facilitate the discovery and use of knowledge,methods, and solutions across technological fields [27]. Reich andShai [28] proposed the interdisciplinary engineering knowledgegenome to aid in the retrieval of common knowledge and methodstructures in different technological fields. The design-by-analogyleverages existing technical solutions from source fields to solvedesign problems in target fields [21,22]. Altshuller [29] estimatedthat approximately 95% of design problems have already beensolved in other fields.

2.2 Patent Mining for Design. These fundamental views anddesign theories have implied the crucial value of design reposito-ries, which contain and organize vast information of prior designsand technologies, to enable relevant design methodologies forconcept generation and design opportunity identification in actualdesign practices [30,31]. In contrast to many design repositoriesthat need to be manually constructed, the patent database is a nat-ural one that has a systematic catalog of extant technologies andaccumulates over time as inventors file patents for new

Fig. 1 Flow diagram summarizing the overall methodology

111416-2 / Vol. 139, NOVEMBER 2017 Transactions of the ASME

Downloaded From: http://mechanicaldesign.asmedigitalcollection.asme.org/ on 10/05/2017 Terms of Use: http://www.asme.org/about-asme/terms-of-use

technologies. Patent documents contain rich information on thetechnologies, systems, or processes from various domains in thedescriptions, their relations to prior designs in the form of cita-tions, and to other designs in the form of classification andcoclassifications.

Such information has been mined for various interests andapplications, such as intellectual property protection, patent valid-ity examination, assessment of mergers and acquisitions, markettrend analysis, inventor or company search, etc. [32–37]. Indeed,patent mining itself has been widely studied field with variousapplications, and interested readers may refer to Bonino et al. [37]and Zhang et al. [36] for general reviews. Hereafter, our reviewprimarily focuses on patent mining for design informationretrieval and aiding in different design methodologies.

In particular, a major strand of research has used text mining,semantics, and natural language processing techniques, in con-junction with the theory of inventive problem solving (TRIZ), toidentify innovative solutions to design problems from the patentdocuments in the patent database [38–42]. For instance, Casciniand Russo developed a software system called PAT-ANALYZER thatanalyzes patent texts to automatically identify the design contra-dictions underlying a patented invention for use in TRIZ [38]. Liet al. proposed a method that combines natural language process-ing and machine learning techniques to classify patents based onthe level of invention as defined in the TRIZ [40]. Fantoni et al.proposed a method to extract the information of the functions, thephysical behaviors, and the states of the system from patent texts[41]. Souili et al. proposed an approach that uses linguisticmarkers to automatically retrieve design problems, solutions, andparameters from patent documents, based on the inventive designmethod, an extension of TRIZ [42].

In addition to the use in TRIZ, the biomedical patent semanticweb by Mukherjea et al. [43] can recommend patents based on thesemantic associations between biological terms within theabstracts of biomedical patents. Fu et al. [44] developed a methodto identify functional analogies between patents, applying latentsemantic analysis to the function terms in patent documents. Fuet al. [45] further created a Bayesian network of patents based onfunction vector similarity and considered the length of pathbetween a design problem and patent stimuli in the network as“analogical distance.” Engineers can use their network of patentsto make more informed choices of patents as design stimuli.

A few recent studies have investigated the varied effects of thedifferent patent stimuli with different distances to the design prob-lem on the ideation outcome. For example, Chan et al. [46], usingpatent stimuli, observed that far-field analogies led to concepts ofhigher novelty, higher quality variability, and greater solutiontransfer, but fewer concepts than near-field analogies. Chan et al.[47] further observed that the most creative solutions arise fromnear distance stimuli, owing to easier perception and moreobvious relevance to the design problem. Fu et al. [48] tested theeffects of patent stimuli of near versus far analogies to designproblems, according to their Bayesian network of patents, andobserved that it is significantly difficult for designers to conceivethe relevance of far patent stimuli, “near-field” patents only stimu-lated new design concepts with limited novelty but higher quality.

Fu et al. [48] further posited that stimuli across a moderate dis-tance between the source and target domains are most favorable.By measuring the novelty of a patented invention as the frequencythat the patent’ referenced technologies had been combined in his-torical patents, He and Luo [25] observed that the patented inven-tions with medium novelty present the highest invention value,thus showing the existence of such a favorable middle ground fordesign outcome. Furthermore, Srinivasan et al. [49] alloweddesigners themselves to search and choose patents for inspirationto generate rolling robot design concepts and found that designersmost frequently obtained inspiration from patents in a mediumknowledge distance to the design problem. As such a distanceincreases, the novelty of generated concepts increases but theirquality decreases.

Taken together, these empirical studies on patents are theoreti-cally aligned with the conceptual leap hypothesis that design stim-uli from “far” sources contribute to novelty and are the best forcreative breakthroughs [50–53], despite the cognitive difficultyfor designers to conceive an analogy between distant domains[8,47,54]. This strand of studies with a focus on the effect of ana-logical distance on concept generation has implied the strategicvalue for designers to have the macropicture of the relative distan-ces or proximities between different technologies, and use suchknowledge distance information together with the awareness ofpotential outcomes to guide the search for design stimuli fromeither distant or proximate domains.

In particular, the macropicture of technologies and their prox-imity or distance relationships have been operationalized in astrand of prior studies that use the patent classification system andmassive patent data to build large-scale maps of technologies,which we will review in Sec. 2.3.

2.3 Patent Mining for Technology Space Mapping. A fewrecent studies have represented the total space of technologies asa network map [12,55,56], empirically constructed by mining thepatent database. In such a network, the nodes represent technologydomain and are operationalized as all patent classes defined in apatent classification system, such as the International Patent Clas-sification (IPC), to represent technology design domains, e.g.,combustion engines. They represent different technologydomains. The links are weighted according to the knowledgeproximity between technology domains, measured using massivepatent data [26,57]. Such network maps cover all technologyclasses in the patenting system and utilize the entire patent data-base to compute knowledge proximity deriving statistical signifi-cance, in order to provide an as complete and accurate as possiblepicture of all known technologies and their distance or proximityrelationships in the total technology space. In contrast, the priorpatent mining works in the engineering design literature havebeen limited to small samples of patents and focused on solvingspecific predefined design problems.

In particular, a fruitful set of patent data-based measures ofknowledge proximity between technology classes have been devel-oped and capture the intuition that the knowledge and capabilities(e.g., human skills and physical equipment) required to design tech-nologies in one class can also be easily used for designing technolo-gies in the other. One group of measures mines patent referenceinformation. For instance, the coreference measure takes the formof Jaccard index [58] to calculate the count of shared references ofa pair of classes normalized by the total count of all unique referen-ces of patents in either class [59,60]. Leydesdorff et al. [55] andKay et al. [56] used the cosine similarity index, i.e., the cosine ofthe vectors of patent references made from a pair of classes to allother classes, respectively (i.e., class-to-class reference vectors).For a higher granularity, Yan and Luo [26] extended the cosinesimilarity measure to class-to-patent vectors, concerning referencesto specific patents instead of aggregated classes.

Another group of measures mines the “coclassification” infor-mation, i.e., how often two classes are coassigned to the same pat-ents [57]. For instance, Breschi et al. [61] measured theknowledge proximity between technological domains as thecosine of respective patent classes’ vectors of occurrences with allother classes in patents. Nesta and coworker [62] measured thedeviation of the actual observed cooccurrences of class pairs inpatents from random expectations. Interested readers may refer toa recent review and comparison of the most popular knowledgeproximity measures used in patent mapping [26]. However, thisstrand of research on patent data mining to measure the proximitybetween different technologies has not been previously engagedin the engineering design literature, which appears to be one ofthe contributions of the present paper.

Furthermore, prior studies [26,63] have shown that the net-works of patent technology classes are consistent over time,

Journal of Mechanical Design NOVEMBER 2017, Vol. 139 / 111416-3

Downloaded From: http://mechanicaldesign.asmedigitalcollection.asme.org/ on 10/05/2017 Terms of Use: http://www.asme.org/about-asme/terms-of-use

regardless of the choices of proximity measures. For example,Yan and Luo’s [26] longitudinal analysis showed that the changesin all links’ weights and their relative rankings by weights overdifferent years and decades are small and insignificant. Such sta-bility of the measurements may be the result of the innate butlatent proximity or distance between different physical technolo-gies, e.g., computing and combustion engine. That is, the proxim-ity or distance between physical technologies has an innatephysical nature; the technology space is also a latent physicalexistence. Therefore, the approximations of the technology spaceusing data from different time periods are not supposed to vary, ifsufficient data are computed in a chosen time period and statisticalsignificance is ensured.

In turn, the stability of the patent technology networks hasallowed them for the analysis of over-time patent portfolio diver-sification of firms [61,62] and regions [64,65], showing statisticalevidence that firms or regions tend to first diversify into new tech-nology domains near their prior domains due to the relative easeto master new but proximate technologies. Our prior analysis of 2million inventors and 4 million patents granted from 1976 to 2010in the U.S. Patent and Trademark Office (USPTO) database alsostatistically shows that inventors are far less likely to succeed inobtaining a new patent in new technology classes that are moredistant to the classes of their prior patents [66]. In another analysisof the historical patents for hybrid electrical vehicles (HEVs), wefound the HEV patents granted in new classes are more likely tobe in those more proximate to the classes containing prior HEVpatents [67].

These statistically found patterns on the cross-field patentingbehaviors of designers (of different aggregation levels) resonatewith the studies on the effects of varied analogical distances frompatent stimuli to design problems on concept generation (reviewedin Sec. 2.2) and suggest the strong conditioning or enabling effectsof knowledge proximity on the perception and pursuant of newdesign opportunities. Taken together, these prior studies havemade it possible and provided a ground for us to utilize the patenttechnology space map to develop a methodology and tool to aid inthe search for next design opportunities and directions. In the fol-lowing, we will introduce the methodology.

3 Methodology

Our methodology integrates the network map of the total tech-nology space with a few network-based analytics functions fordesigner positioning, neighborhood analysis, and direction find-ing, to aid in the search for design opportunities and directions,grounded on the theoretical understanding of design process andstimulation distance. Traditionally, such a search at the front endof the design process was intuitive and unguided, and structureddata-driven aid was not available. Figure 1 depicts the overallframework of the methodology.

Our methodology contrasts with the prior research on patentdata mining for design (reviewed in Sec. 2.2) in a few aspects.First, our methodology aims to aid in the search for high-levelopen-ended design opportunities and directions, instead of retriev-ing a small number of patents with specific solutions to givendesign problems to support TRIZ or design-by-analogy. Second,our methodology primarily mines patent reference and classifica-tion data, instead of patent textual data (e.g., abstracts, descrip-tions, and claims) that have been the focus of the analysis in theprior research. Moreover, the large-scale network map of patenttechnology classes, which is the core of our methodology, has notbeen previously related to engineering design research andadopted to support design decisions. In the following, we willintroduce its key elements, and the core element is the total tech-nology space map.

3.1 Network Construction. We utilize the complete USPTOpatent database of 5,256,505 utility patents granted in 1976–2016to create a patent technology network that represents the total

technology space. Nodes in the network represent different tech-nology domains and are operationalized as IPC, established byWorld Intellectual Property Office. The patent classification sys-tem provides a natural means to approximate different technologydomains and categorize the patent documents belonging to differ-ent domains. For example, the node F02 categorizes the patentsfor combustion engine technology, and B82 categorizes the pat-ents for nanotechnology. Prior patent mapping research has sug-gested IPC classes are the most suitable and stable representationsof technology domains [55], and the three-digit classes providethe best resolution and ease of visual analytics of the maps[26,68].

In the network, 122 three-digit IPC classes are connected andpositioned according to the knowledge proximity (or distance)among them. In Sec. 2, we have reviewed various measures thatcan be computed using patent data to statistically approximateknowledge proximity between technologies. Given the variety ofmeasures, one needs to select the one for their specific purpose orinterest of analysis. For our upcoming case study of Google (Sec.4), the cosine class-to-patent similarity measure that was proposedin our prior work [26] was chosen based on a comparison withalternative measures reviewed in Sec. 2.3, in terms of theirexplanatory power on Google’s historical entrances into newdomains given its prior domains at the times of entrances.

The measure is the cosine of the angle of the two vectors repre-senting two patent classes’ distributions of references to specificunique patents, formulated as

uij ¼

Xk

CikCjk

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXk

C2ik

r ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXk

C2jk

r (1)

where Cij denotes the number of citations of all patents in patentclass i to the specific patent j; k belongs to all patents. The proxim-ity value is between [0, 1] and indicates the similarity of theknowledge bases of two domains. This measure appears as one ofthe most correlated with and representative of other measures andhighly correlated with the likelihoods for the inventors to havepatents classified in respective classes [26].

We statistically investigated the likelihood that Google wouldexpand into a new domain (i.e., having the first patent in a newpatent class) according to its proximity to Google’s prior positionsin the technology space. We measured the percentile of the prox-imity of an unexplored domain (at a certain point in time) to thealready entered domains, among such proximity values of allunexplored domains. Based on Google’s total 72 historical eventsof entering new domains from 1997 to 2016, Fig. 2 reports thecumulative probability of the proximity percentiles of technologydomains when Google entered them for the first time.

Fig. 2 Accumulative probability to enter a new domain givenits proximity to prior technology positions of Google

111416-4 / Vol. 139, NOVEMBER 2017 Transactions of the ASME

Downloaded From: http://mechanicaldesign.asmedigitalcollection.asme.org/ on 10/05/2017 Terms of Use: http://www.asme.org/about-asme/terms-of-use

The dashed line represents the probability given by the nullhypothesis: New domains are explored randomly regardless oftheir proximities to the previously established technology posi-tions of Google. The gap between an empirical probability curveand the dashed line indicates the extent to which Google’s histori-cal exploration of new domains was conditioned by their proxim-ity to Google’s prior positions. The cosine class-to-patentsimilarity yields the farthest curve from the dashed line, and thus,the map based on it will provide relatively the highest explanatorypower on Google’s historical exploration of new technologies. Infact, the top 10% of domains in terms of proximity to its prior posi-tions explain about 90% of all domains next entered by Google.

It is noteworthy that, in Fig. 2, the empirical probability curvesbased on different measures are in fact close to each other and allstiff, and thus suggest that Google preferentially entered newdomains that have a higher proximity to its prior positions thanthose with lower proximity in the technology space, regardless ofthe choice of proximity measures. This statistical pattern also sug-gests that the results of our case study will not vary much whendifferent knowledge proximity measures are used, and thusensures the general utility of alternative technology space mapsfor the analysis of design opportunities and directions of Google.For a different designer and context, such a process of comparativealternatives to select the proximity measure will be needed again.

To compute the cosine similarity of class-to-patent referencevectors for each pair of the 122 patent classes, we used the richestpossible historical patent data (i.e., the complete USPTO utilitypatent records and their citation information from 1976 to 2016),for the most accurate empirical approximation of the latent tech-nology space. In fact, the measurements using the data in each ofthe past few decades are highly stable and consistent, as suggestedby the prior studies [26,63] reviewed in Sec. 2.3. Such stability ofthe network map approximation of the latent technology spaceensures its feasibility as a background map for either historical orpredictive cross-time analyses of a designer within the innatetechnology space.

3.2 Network Visualization. The technology network isalmost fully connected and indicates the technology space is highdimensional by nature. Its two-dimensional visualization can beviewed as a projection from a high-dimensional space to a two-dimensional plane. However, projecting from different angles inthe high-dimensional space may lead to different two-dimensionalnetwork layouts, some of which are more informative than others.Force-directed algorithms are widely used to generate aestheti-cally pleasing network layouts via minimizing graph energy [69].The force-directed algorithms often produce locally optimal lay-outs for high-dimensional systems, but can easily reach a globallyoptimal layout for a low-dimensional system.

In the technology network we created, most of the pairs of pat-ent classes are only weakly related, that is, the proximity values ofthe links among them are extremely low. The distribution of linksby their weights (i.e., cosine similarity values) is highly screwedtoward the value of 0. Over 72.6 percent of the links have weightslower than 0.01, 97% of them have weights below 0.1, and themaximum weight is 0.70. Meanwhile, out of the total 7381 theo-retically possible links among 122 patent classes, only 0.09% ofthem are equal to 0. In other words, although the network isalmost fully connected, most links are extremely weak, notinformative, and can be filtered for more effective visualization.Filtering can be done to various levels, and the extreme version ismaximum spanning tree (MST) [68]. The MST of the technologynetwork only includes the strongest 121 links that connect the 122technology domains into a tree structure (Fig. 3), i.e., the back-bones of the technology space, and significantly reduces thedimensions of the system. Thus, we ran a force-directed algorithmon the MST (instead of the full network) to reach a stable layoutwithout link crossing. Then, we can add the original network linksback to the maximum spanning tree in the decreasing order of linkweights. The network in Fig. 4 adds another strongest 1000 linksback to MST. Note that, regardless visualizations, the networkanalyses (to be introduced in Sec. 4) utilize the full information ofthe original technology network.

Fig. 3 Maximum spanning tree of the patent technology network and the front-end interface of InnoGPS

Journal of Mechanical Design NOVEMBER 2017, Vol. 139 / 111416-5

Downloaded From: http://mechanicaldesign.asmedigitalcollection.asme.org/ on 10/05/2017 Terms of Use: http://www.asme.org/about-asme/terms-of-use

3.3 Positioning the Technology Portfolio of a Designer onthe Map. A designer might be present in one or multiple technol-ogy positions (i.e., domains) in the total technology space. With thenetwork map, we can identify the nodal positions of a designeraccording to the classification of its patents or nonpatent data aboutthe designer’s technical capabilities. Such network positions of adesigner can be further evaluated visually and quantitatively toindicate its next innovation prospects from different perspectives.

First of all, one can overlay the map with such positions to visu-ally compare and reveal the differences of the network positionsof different designers in the technology space, or the evolution ofthe same designer over time. Second, in order to quantitativelyanalyze the technology positions of a designer, we consider twomeasures, i.e., entropy and coherence, from the literature on theanalysis of technological coherence and diversification [70–72],and also propose a new measure (i.e., expandability) to access thefurther expanding capacity of a designer given its prior positionsembedded in the total technology space.

(1) Entropy:This index reveals the spread of technology positions of adesigner in the technology space or the extent of technolog-ical diversification of the designer [70,71]. It is calculatedusing the form of information entropy as follows:

entropy ¼ � 1

C

Xi

xi � ln xið Þ (2)

where xi is the portion of the designer’s patents in technol-ogy class i, and C is the maximum entropy attainable whenthe patents of the designer are equally distributed across alltechnology domains i.

(2) Coherence:It is defined as the extent to which the various technologiesin the present portfolio of the designer are similar or cohe-sive [70,72]. In turn, such cohesiveness of different tech-nologies implies the potential or the ease to combine/recombine or synthesizes them for new designs. Herein,coherence is computed as the weighted average proximitybetween all pairs of technology domains of a designer

coherence ¼P

i

Pj xixjuijP

i

Pj xixj

(3)

where uij is the proximity between domain i and j, and xi isthe portion of the designer’s patents in technology class i.

(3) Expandability:It indicates the capacity of the designer’s present technol-ogy positions to be further expanded into unexplored tech-nology domains, given their prior coverage and relativelocations in the heterogeneous total technology space. Itimplies the potential to synthesize unexplored technologieswith the ones that designer has already mastered for newdesign opportunities. Herein, expandability is calculated asthe weighted sum of the values of proximity between thedesigner’s current technology positions and other unex-plored domains in the technology space

expandability ¼

Xi

Xj 6¼ið Þ

xiuij

A(4)

where uij is the knowledge proximity between domain iand j. xi is the portion of the designer’s patents in domain i.j is the set of domains that designer has no patents. A is thelargest possible weighted sum (i.e., the numerator) andattainable when the all the patents of the designer are posi-tioned in the most connected node of the network. A is usedto normalize the metric value into the range [0, 1].

The definition and formulation of “expandability” suggest thata designer can possibly improve its expandability by entering gen-erally more connected technology domains than its current tech-nology positions in the total technology space. If the designer hasalready entered the most connected domains in the entire space,its expandability is set to decline because the remaining unex-plored domains are generally less connected. To the best of ourknowledge, the expandability concept and metric are proposed forthe first time in the present paper, while the coherence and entropymetrics appeared in the literature.

Taken together, these three metrics provide a systemic assess-ment of the technology positions of a designer from different butcomplementary perspectives. Entropy measures the distribution oftechnologies of a designer positioned in different domains, regard-less of the relationships of these domains. Coherence looks insidethe portfolio of the designer and concerns the relationshipsbetween the technologies it has mastered. Expandability looksbeyond the portfolio and considers how the portfolio as a whole is

Fig. 4 The original technology space position of Google, from 1997 to 1999

111416-6 / Vol. 139, NOVEMBER 2017 Transactions of the ASME

Downloaded From: http://mechanicaldesign.asmedigitalcollection.asme.org/ on 10/05/2017 Terms of Use: http://www.asme.org/about-asme/terms-of-use

connected with other unexplored domains and is embedded in thetotal technology space.

3.4 Exploring Technological Neighborhoods for FeasibleDesign Opportunities. One can further identify the unexploreddomains that are most proximate to the designer’s positions,according to the network structures around the positions of thedesigner. Such nearby domains host the new technologies that thedesigner can most easily learn and comprehend for conceivingdesign opportunities, because they are based on similar knowledgewith its current ones. In turn, the most feasible design opportuni-ties for a designer are in such proximate neighborhoods of its cur-rent positions. The most proximate domains nearby a designer’spositions can be identified in alternative ways. For instance, wecan focus on the most proximate domain to each of the designer’sprior positions or the top N unexplored domains based on one’sweighted average proximity with all the prior positions where thedesigner has patents

Weighted average proximity ¼

Xi 6¼j

uijxi

Xi6¼j

xi

(5)

where i belongs to all the current technology positions; j repre-sents each of the unexplored domains in the network; xi is thenumber of patents of the designer in domain i; and uij is theknowledge proximity between domains i and j.

3.5 Exploring Technology Paths for Design Directions. Adesigner may also perceive design opportunities that require tech-nologies and knowledge in far domains from its technology posi-tions. For example, Google pursues the driverless car designopportunity that requires the automobile design knowledge farfrom Google’s original expertise in web search engines. Suchdesign opportunities are unlikely to be motivated for the ease andfeasibility, but other reasons, such as perceived user needs, hugemarket demands, significant social-economic impacts, designer’sintrinsic interests, etc. In such cases, the designer needs to traversea path of learning and building relevant capabilities from its origi-nal domain to the target domain in the technology space. The liter-ature has suggested that the technical capabilities of an innovatorare often built up incrementally through a path-dependent learningprocess shaped by its prior capability positions [73,74].

The network map can suggest indirect paths for incrementallearning or capability building from the current technology posi-tion of a designer to technologies in its neighborhoods, and thenfar-field technologies. Network path analysis is a long-standingtopic in graph theory. A variety of algorithms exist and can beapplied to the technology network. In our case analysis on Google,we will compare alternative network paths from the current posi-tion of a designer to a distant domain. One might be able to iden-tify a direct link between the home and target domains. The link isdirect, but might be weak. At another extreme, one may identify aroute that connects two domains through multiple links on theMST. It removes weak links and keeps only the strongest links ofthe network, i.e., the backbones of the technology space, and mayserve as the “broad ways” for designers to effectively travel fromone domain to another.

Taken together, technology space network mapping and theanalyses of positions, neighborhood, and paths are the key ele-ments of and constitute our overall methodology for data-drivenexploration of design opportunities and directions (Fig. 1). Fur-thermore, we have implemented and integrated the previouslydescribed network position, neighborhood, and path analyses,together with the network map and the access to USPTO patentdatabase, into a web-based computational and visualization sys-tem named InnoGPS. Figure 3 is a screenshot of the current inter-face of InnoGPS. InnoGPS is named by analogy from the GPS

that we use for positioning, neighborhood exploration, and direc-tion finding in the physical space. Likewise, InnoGPS is fordesigner positioning, neighborhood exploration, and directionfinding in the technology space.

In the following, we will demonstrate the methodology, asimplemented in InnoGPS, using Google as the case with a focuson its driverless car project. Before that, it is noteworthy that themethodology framework depicted in Fig. 1 is general, and not lim-ited to the specific patent database, visualizations, metrics, andalgorithms introduced previously and demonstrated in the casestudy of Google; additional and alternative ones can be also devel-oped and implemented to fulfill the key elements of the overallmethodology according to specific needs, purposes, and contexts.

4 Case Study—Google and Its Driverless Car Project

Google is arguably a leader in designing and developing driver-less cars today. It started as a specialized web search engine com-pany in 1997, but over time has developed technologies that areincreasingly synthesized with the design, development, and manu-facturing of motor vehicles, for which Google had no relevantdesign capabilities initially. Google’s self-driving technology useslasers, radars, and cameras to map out the surroundings of the carand detect objects in all directions. Its software processes the datafrom the sensors to predict movements of the objects around thecar and adjusts the speed and directions accordingly. Googleworks with other companies, e.g., Toyota, Audi, and Chrysler, totransform their existing car models into driverless ones and hasalso developed its own custom driverless vehicle model called“Koala.” Google’s test fleet has completed more than 1.7� 106

miles of autonomous driving.

4.1 Data-Driven Analysis and Visualization of DesignOpportunities and Directions

4.1.1 Positioning Google (1997–1999) in the TechnologySpace. Larry Page and Sergey Brin started Google in 1997 andfiled a U.S. patent for their PageRank algorithm in the same year.The patent was classified in one single class “G06-Computing,”which covers electric digital data processing technologies forinformation retrieval and database structures. From 1997 to 1999,Google had only three patents, and all of which were in G06-Computing. The overlay technology map in Fig. 4 highlightsG06-Computing as the technology position of Google from 1997to 1999. At the time, Google was a highly specialized start-upcompany. Indeed, G06 is still the largest technology domain ofGoogle and classifies 69% of Google’s patents by end of 2016.

As the start-up grows and continues to explore new designopportunities, it needs to decide the directions for exploration.The question is: Which technologies present the most feasibleopportunities to explore for the next? With knowing its position(i.e., G06) in the technology space, the designer may navigate thedomains throughout the space map, from the ones nearby thehome domain “G06” to those in the far fields. Such map naviga-tion may inspire the designer to conceive new design opportuni-ties through at least two basic cognitive processes:

(1) Applying the design expertise or technologies from G06-Computing to new applications in other domains. For exam-ple, computing technologies can be used for new applica-tions in the domains of “H04-Electric Communication” and“B60-Vehicles in General.”

(2) Leveraging technologies from other fields to G06 for newdesigns of the designer’s current technologies or products.For example, certain computing techniques initially devel-oped for H04-Electric Communication can inspire thedesign of new general-purpose computing technologies inG06-Computing.

Journal of Mechanical Design NOVEMBER 2017, Vol. 139 / 111416-7

Downloaded From: http://mechanicaldesign.asmedigitalcollection.asme.org/ on 10/05/2017 Terms of Use: http://www.asme.org/about-asme/terms-of-use

4.1.2 Identifying the Most Proximate Domains for Near-TermDesign Opportunities. To explore the most feasible design oppor-tunities, the designer may focus on the most proximate domains toits established technology positions, because it will be relativelyeasy to conceive analogies or synthesize technologies and knowl-edge across proximate domains [48]. Assuming we were Googlein 1997–1999 and our established technology was in G06-Computing (hereafter we call it the “home domain”), a quantita-tive network analysis of the weights of the links of G06 confirmsthat H04-Electric Communication is the most proximate domainto G06, followed by “G07-Checking Devices,” “G08-Signalling,”“G09-Infography & Display,” and “G05-Controlling & Regu-lating.” Figure 5 highlights the five most proximate domains usingcircle borders. The proximity suggests that the expertise in G06-Computing can be readily applied to these neighboring domainsand synthesized for new designs. The engineers in G06 shouldexploit and read the patents and learn technologies inside theneighboring domains for new design inspiration.

4.1.3 Identifying the Network Paths to a Far Domain forLong-Term Design Opportunity. Despite the ease and feasibilityto explore proximate domains, distant domains may offer differ-entiated opportunities with a higher novelty and a greater chanceof breakthroughs. For instance, the designer specialized in G06-Computing may also discover design interests in the technologiesin relatively far fields of the technology space, such as B60-Vehicles in General. This is indeed the case of Google’ driverlesscar project which requires the design knowledge for motorvehicles that is far from Google’s original expertise in G06. How-ever, it is naturally difficult for computer scientists to comprehendand synthesize the distant design knowledge of motor vehicles.For Google in G06 in 1997–1999, the driverless car project wasnot motivated for ease and feasibility, but possibly by the percep-tion of user needs and market demands, and significant societalimpact.

In the full network, there is a direct link from G06-Computingto B60-Vehicles in General. But this is an extremely weak linkcompared to those from G06 to H04, G07, G08, G09, etc. “B60”is not in the proximate neighborhood of G06. The design of websearch engine and the design of automobiles require distinctknowledge and capabilities. Such a long knowledge distance isindeed the fundamental challenge to Google’s pursuant of thedriverless car design opportunity, which requires the synthesis ofsuch distant technologies. Therefore, the attempts to make a direct

long leap from G06 to B60, implying that computer scientistsdirectly design cars, are likely to be ineffective and fail, as sug-gested by the literature on the effects of analogical distance[47,48,54].

Possible success in crossing the distance may require under-going a path of incremental learning and capability buildingthrough a series of domains that have relatively high knowledgeproximities in between, as suggested by the literature on learningand technology capability development [67,73,75]. Figure 6 high-lights a relatively long path from G06-Computing to H04-ElectricCommunication, G08-Signalling, and eventually B60-Vehicles inGeneral, but each link in the path is also relatively strong. Thepath G06! H04! H08! B60 is identified as the route for trav-eling from G06 to B60 in the maximum spanning tree of the tech-nology network, which contains only the strongest backbone ofthe network.

Due to the high proximities between G06 and H04, H04 andG08, and G08 and B60, the exploration and learning along thispath (Fig. 6) will be incremental, relatively easier, and more fea-sible than the direct jump from G06 to B60. In contrast to havingcomputer scientists directly learn and design automotive technol-ogies, it is relatively easier for them to learn and design commu-nication technologies, easier for electric communicationengineers to learn and design signal processing technologies, andeasier for signal processing engineers to learn and designautomobile-related technologies, many of which are about thesensors customized for the uses in motor vehicles. Accordingly,the designer specialized in G06 is suggested to first exploit tech-nologies in H04, and then in G08, before it can effectively

Fig. 5 Technology domains proximate to G06-Computing in the technology space

Fig. 6 Design capability-building paths from G06-Computingto B60-Vehicles in General on MST

111416-8 / Vol. 139, NOVEMBER 2017 Transactions of the ASME

Downloaded From: http://mechanicaldesign.asmedigitalcollection.asme.org/ on 10/05/2017 Terms of Use: http://www.asme.org/about-asme/terms-of-use

comprehend and synthesize technologies in the target distantdomain B60.

4.2 Historical Design Exploration Paths of Google. Now,we investigate the actual design exploration of Google after 1999and compare it with the foregoing data-driven and map-based rec-ommendations of its proximate domains for near-term designopportunities and the path to a distant domain for a long-term butmore impactful design opportunity, for the Google in 1997–1999.

In 2000, Google had a patent in a new field H04-Electric Com-munication for the first time. Figure 7(b) highlights the twodomains G06 and H04, where Google had totally 20 patents bythe end of 2000. Our foregoing map-based analysis had identifiedH04 as the most proximate and predicted it as the most feasibledomain for Google (in the late 1990s) to explore for near-termdesign opportunities. Google’s new position in H04 in 2000reflects its exploration of the design opportunities related to smart-phones (e.g., Android) and mobile Internet technologies in theearly 2000s.

By the end of 2004, Google had obtained 439 patents in 18 dif-ferent domains, while most of them are still classified in G06-Computing followed by H04-Electric Communication. Figure7(c) overlays the technology space map with the technology posi-tions of Google as of 2004. To this point, Google has enteredG07-Checking Devices which was identified as one of the mostproximate domains to G06 in our foregoing network analysis (Fig.5). Google also entered “G08-Signaling” which was identified onthe MST route connecting G06 to B60-Vehicles in General in ourforegoing path analysis (Fig. 6).

By the end of 2006, Google had obtained 1000 patents classi-fied in 26 domains. Figure 7(d) highlights many of its new posi-tions on the technology map, which are further distant from itshome domains G06 and not predicted by the neighborhood

analysis of G06. In particular, Google had its first patent in B60-Vehicles in General in 2006. B60 is directly related to the driver-less car design efforts of Google. It took Google 9 years and along incremental path to travel from G06-Computing to “B6-Vehicles in General” in the technology space.

By the end of 2016, Google has obtained 12,941 patents in 73domains, visually appearing as a technology conglomerate in Fig.8. For instance, Google’s patents in “G11-Information Storage”and “H01-Electric Elements” are about hardware design andrelated to its in-house efforts to design data centers. Its patents in“H02-Electric Power” and “F28-Heat Exchange in General”resulted from Google’s in-house design of power generators andthermal management system for its own data centers. Google’swide and connected positions in the technology space may allowit to conceive many design opportunities via combining near ordistant technologies or draw near or far analogies across them.

With its gradual diversification from 1997 to 2016, the coher-ence of Google’s technology positions continually decreased. Thecoherence value was 0.522 as of 2000 (Fig. 7(b)), 0.327 as of2004 (Fig. 7(c)), 0.316 as of 2006 (Fig. 7(d)), and 0.260 as of2016 (Fig. 8), suggesting Google’s continual entrances into lessand less connected technology domains over time and its preferen-ces to first enter the most proximate domains. Meanwhile, theentropy of its technology positions increased, from 0.043 as of2000 (Fig. 7(b)) to 0.141 as of 2004 (Fig. 7(c)), 0.2 as of 2006(Fig. 7(d)), and 0.366 as of 2016 (Fig. 8), in line with Google’sdiversification and expansion in the total technology space. Goo-gle’s expandability in the network has decreased from 0.524 as of2000 (Fig. 7(b)) to 0.229 as of 2004 (Fig. 7(c)), 0.148 as of 2006(Fig. 7(d)), and 0.041 as of 2016 (Fig. 8), implying its potentialfor further expansion declines with its already established widepresence in the technology space.

Figure 7 particularly reveals the incremental design explorationof Google from G06-Computing in 1997, first to its neighborhood

Fig. 7 The technology space positions of Google over time: (a) 1997–1999, (b) 1997–2000, (c)1997–2004, and (d) 1997–2006. Technology classes are highlighted with darkness correspond-ing to the numbers of Google’s patents in respective classes.

Journal of Mechanical Design NOVEMBER 2017, Vol. 139 / 111416-9

Downloaded From: http://mechanicaldesign.asmedigitalcollection.asme.org/ on 10/05/2017 Terms of Use: http://www.asme.org/about-asme/terms-of-use

and then far fields. It first entered those domains suggested in ournetwork neighborhood analysis according to their proximities toG06 (Fig. 5). Google also traversed the long path G06! H04!G08 ! B60 from computing to vehicles, suggested by the analy-sis of MST (see Fig. 6). Specifically, starting off G06-Computingin 1997, Google first entered H04-Electric Communication in2000, then G08-Signaling in 2004, and eventually B60-Vehiclesin General in 2006. Such a trajectory took Google 9 years. Adirect jump from G06 to B60 did not occur in the history. By theend of 2016, Google has been granted 121 patents in B60-Vehicles in General directly related to the design and develop-ment of driverless cars. In contrast, Google has 314, 3852, and9261 patents in G08-Signaling, H04-Electric Communication, andG06-Computing, respectively. Along the path suggested in ournetwork analysis (Fig. 6), Google has more patents in the domainsthat are closer to its home domain.

In brief, the actual history of Google exploring beyond its start-ing position G06 is characterized by first entering the proximatedomains identified in our neighborhood analysis of G06 and alsotraversing the path from G06 to B60 identified in our path analysisin Sec. 4.1. The map-based exploratory analysis of design oppor-tunities and directions in Sec. 4.1, if it were done in 1999, wouldhave pointed to a feasible “future” of Google at that time.3 There-fore, the Google case study suggests the utility of the technologyspace map, together with the network analyses of positions, neigh-borhoods, and paths, to aid designers in the search for designopportunities and directions, as demonstrated in Sec. 4.1. Mean-while, the single case of Google is not to suggest that a designer’ssearch path is deterministic and completely predictable by themap, but to demonstrate that the next design opportunities anddirections of the designer are strongly conditioned or enabled bythe innate structure of the total technology space and the design-er’s initial or prior positions.

5 InnoGPS: Current Functions and Future Extensions

By far, we have presented a new methodology that analyzes thetechnology network map to provide data-driven aids to designers

in exploring design opportunities and directions. The methodol-ogy has been implemented and automated in the data-driven ana-lytic and visualization system “InnoGPS,” whose core functionsinclude designer positioning, neighborhood search, direction find-ing, and information browsing

(1) Positioning: Visually locate the technology positions of adesigner (e.g., person, organization, city, or country) on thetechnology space map, via mining its patent records in achosen time period, and report its network position statis-tics (e.g., entropy, coherence, and expandability) and pat-enting statistics in the information panel (see Figs. 4 and 8).The quantitative and visual analytics of network positionstogether enable the comparison across different designersor the same designer over time.

(2) Nearby: Identify the most proximate unexplored domainsto the existing technology positions of a designer, whichcan be most feasibly entered next (see Fig. 5). Suchdomains present the design opportunities feasible in thenear term.

(3) Direction: Explore the learning and capability-buildingpaths between the current technology positions of adesigner to a far domain of long-term interest (see Fig. 6).Such paths present feasible long-term design directions.

(4) Browsing: Navigate the map to discover design stimuli forinspiration, technical experts for hiring, or companies forcollaboration [76], via viewing the detailed information ofdesign precedents, patents, inventors, companies, etc.,within each corresponding domain. The information in var-ious domains is organized by knowledge proximities,which enhance the heuristic search and comprehension ofnew technologies.

Indeed, these functions are analogous with those of a traditionalGPS system or “Google Maps.” The major difference lies in themaps. InnoGPS is based on a map of technologies that representsthe technology space where we design, whereas Google Maps isbased on a geographical map of the physical space where we liveand work. InnoGPS is grounded on design and creativity theories(see the literature review in Secs. 2.1 and 2.2) and enabled by thetechnology network analysis and visualization techniquesdescribed in the methodology in Sec. 3 and demonstrated in thecase study in Sec. 4.

Fig. 8 The technology space positions of Google from 1997 to 2016

3As reported in the literature review (Sec. 2.3) and methodology section (Sec.3.1), the network map in the background is a stable approximation of the innatetechnology space, and thus useful for either historical or predictive overlay mapanalyses of the exploration behaviors of designers.

111416-10 / Vol. 139, NOVEMBER 2017 Transactions of the ASME

Downloaded From: http://mechanicaldesign.asmedigitalcollection.asme.org/ on 10/05/2017 Terms of Use: http://www.asme.org/about-asme/terms-of-use

Although our case study is focused on proximity analyses andGoogle presents a case of incremental design exploration throughproximate domains, InnoGPS is not fixed on the local search innearby domains or incremental development through the indirectpath connecting pairs of highly proximate domains. As suggestedin the literature review, learning and exploration of technologiesacross more distant domains might be more difficult, but mightgive rise to the chance of novel and breakthrough design opportu-nities [8,47,54]. Some studies have suggested exploring a moder-ate distance for analogy or synthesis might provide a balanced, ifnot optimal, design outcome [23,25,48].

Therefore, InnoGPS should provide intelligent advice,grounded on the design creativity theories, regarding the pros andcons of exploring different technologies and domains with near,moderate, and far distances to the designer’s present technologypositions. InnoGPS may also recommend patents, technologies,domains, or paths to the designer, according to his/her interestsand preferences that can be either inputted by the designers ormachine-learnt from their use behaviors in InnoGPS. While it isstill up to the designers to pursue balanced or biased design searchstrategies, the exploration will be more informed and guided bythe theory-based advices provided in InnoGPS, together with thedata-driven analytics and visualization. To implement such intelli-gent recommendations would require the development of artificialintelligence algorithms based on the creativity theories, which is apromising avenue for future research.

Presently, InnoGPS only hosts and organizes the design infor-mation based on the patent data, but future research can also inte-grate other forms of design information data, such as technicalpapers and reports, technology news and videos, and organizethem into their respective technology domains in the technologynetwork map. Compare to such data, the patent documents arehighly structured and relatively easy to mine and analyze. Futureresearch is expected to develop new algorithms dedicated to themining and machine learning of design and technical informationavailable in more general and diverse forms.

InnoGPS should not be limited to the ones demonstrated in thecase study of Google and the ones discussed previously. Alterna-tive mapping methods, network metrics, and algorithms can beimplemented in the system to fulfill the key elements of the over-all methodology depicted in Fig. 1. In the future, open applicationprogram interfaces can be used to enable third party developers tocontribute additional analytics functions to the InnoGPS. InnoGPSshould also allow anyone (either user or designer) to add informa-tion and materials about the technologies, products, or services oftheir own or of his liking into certain nodes on the map, accordingto the technologies’ domains. Then, when others browse the infor-mation panel of the same domain, they will see such user-suppliedcontent, in addition to the analytics from InnoGPS and themachine-retrieved data. As a result, InnoGPS becomes a double-sided platform, where some users supply information while othersdiscover and utilize such information for design learning andinspiration. With enabling such crowd intelligence, InnoGPSbecomes a more evolvable platform in terms of its functionalitiesas well the data that it hosts and mines.

In brief, the foregoing discussions suggest our data-drivenmethodology is a flexible one and can be further evolved.InnoGPS, which implements the methodology, can be viewed as aholistic multilevel data-driven analytic and visualization system,to enhance macrolevel exploration of design opportunities anddirections, operational-level design concept conception, as well ascontinual learning of technologies, across levels.

6 Summary: Contribution, Limitations, and Future

Research

This paper contributes to the growing studies and practice ofdata-driven design, with a network-based and data-driven method-ology and tool to enhance the previously intuitive human processof exploring design opportunities and directions. Our focus for

data-driven design is the fuzzy start of a creative design process,where designers are faced with extreme uncertainty regarding“what to design.” To reduce the uncertainty, our methodologyuses a technology network map to represent the technology space,position a designer according to what it can design, and explorenext design opportunities and directions according to knowledgedistances between technologies in the total technology space.

By the Google case study, we have demonstrated our data-driven methodology and its key elements as implemented in theInnoGPS system that integrates the technology map with variousnetwork-based analytic and visual functions and access to the pat-ent database. InnoGPS is expected to change the qualitative andintuitive tradition that designers rely on to explore design opportu-nities, generate design concepts, discover and learn new technolo-gies, to a more data-driven, scientifically grounded, and visuallyinformed fashion. We have also discussed a few future directionsof research and development for InnoGPS to provide more artifi-cial intelligences to enhance the previously intuitive human pro-cess of design ideation and design opportunity exploration.

In practice, individual engineers and researchers can useInnoGPS as an ideation tool to inspire them for conceiving newdesign opportunities. InnoGPS might be particularly useful for thespecialized and inexperienced early-stage technology start-ups(e.g., Google back in 1997) to reduce the uncertainty in formulat-ing their new product lines for growth and diversification. Largecorporations, R&D organizations, or government agencies canalso use InnoGPS to assess the technology positions of their own,collaborators’, and competitors’ based on the network metrics andoverlay visualizations, and thus enhance the traditional“technology road maps” that are normally charts and diagramsbased on scattered qualitative evidence and subjective expertopinions.

The effectiveness of InnoGPS is still conditioned by a few mapand system design factors. First of all, the descriptions of domainnodes on the map may affect human perception about the relevanttechnologies and conception of design opportunities. IPC resolu-tions also matter. Four- or seven-digit classes might provide morenuanced inspiration, whereas three-digit classes may inspirehigher-level design opportunities and directions. Second, alterna-tive knowledge proximity measures and visualization techniquesexist and may lead to different network structures and layouts,which in turn affect the map navigation behaviors and conceptionoutcomes of users. Future research should explore alternative vis-ualization strategies. Other than networks, heat map or contourmap may be also useful. Furthermore, although the functions andutilities of InnoGPS are grounded by prior design creativity theo-ries, and demonstrated partly in the case study, uncertainty alwaysexists in actual uses. For instance, do designers conceive moredesign opportunities with inspirations with new nearby technolo-gies? In what occasions the designers can achieve a greater chanceto conceive novel and significant design opportunities from far-field technologies? The natural next step for future research is toconduct user tests and design experiments on different mapdesigns and uses of the functions, in order to reduce uncertaintiesand shape the further development of InnoGPS.

Funding Data

� SUTD-MIT International Design Centre (IDC).� Singapore Ministry of Education Academic Research Fund

Tier 2 (MOE 2).

References[1] Reid, T. N., Frischknecht, B. D., and Papalambros, P. Y., 2012, “Perceptual

Attributes in Product Design: Fuel Economy and Silhouette-Based PerceivedEnvironmental Friendliness Tradeoffs in Automotive Vehicle Design,” ASMEJ. Mech. Des., 134(4), p. 041006.

[2] Chen, W., Hoyle, C., and Wassenaar, H. J., 2012, Decision-Based Design: Inte-grating Consumer Preferences Into Engineering Design, Springer Science &Business Media, London.

Journal of Mechanical Design NOVEMBER 2017, Vol. 139 / 111416-11

Downloaded From: http://mechanicaldesign.asmedigitalcollection.asme.org/ on 10/05/2017 Terms of Use: http://www.asme.org/about-asme/terms-of-use

[3] Wang, M., Chen, W., Huang, Y., Contractor, N. S., and Fu, Y., 2016,“Modeling Customer Preferences Using Multidimensional Network Analysis inEngineering Design,” Des. Sci., 2, p. e11.

[4] Burnap, A., Pan, Y., Liu, Y., Ren, Y., Lee, H., Gonzalez, R., and Papalambros,P. Y., 2016, “Improving Design Preference Prediction Accuracy Using FeatureLearning,” ASME J. Mech. Des., 138(7), p. 071404.

[5] Kang, N., Ren, Y., Feinberg, F. M., and Papalambros, P. Y., 2016, “PublicInvestment and Electric Vehicle Design: A Model-Based Market AnalysisFramework With Application to a USA–China Comparison Study,” Des. Sci.,2, p. e6.

[6] Ma, J., and Kim, H. M., 2014, “Continuous Preference Trend Mining for Opti-mal Product Design With Multiple Profit Cycles,” ASME J. Mech. Des.,136(6), p. 061002.

[7] Jin, J., Ji, P., and Liu, Y., 2014, “Prioritising Engineering Characteristics Basedon Customer Online Reviews for Quality Function Deployment,” J. Eng. Des.,25(7–9), pp. 303–324.

[8] Weisberg, R. W., 2006, Creativity: Understanding Innovation in Problem Solv-ing, Science, Invention, and the Arts, Wiley, Hoboken, NJ.

[9] Arthur, W. B., 2009, The Nature of Technology: What It Is and How It Evolves,Simon and Schuster, New York.

[10] Hatchuel, A., and Weil, B., 2009, “CK Design Theory: An Advanced For-mulation,” Res. Eng. Des., 19(4), pp. 181–192.

[11] Tang, V., and Luo, J., 2013, “Idea Matrix and Creativity Operators,” DS 75-7:19th International Conference on Engineering Design (ICED), Design for Har-monies, Seoul, South Korea, Aug. 19–22, pp. 301–316.

[12] Alstott, J., Triulzi, G., Yan, B., and Luo, J., 2017, “Mapping Technology Spaceby Normalizing Patent Networks,” Scientometrics, 110(1), pp. 443–479.

[13] Fleming, L., and Sorenson, O., 2004, “Science as a Map in TechnologicalSearch,” Strategic Manage. J., 25(8–9), pp. 909–928.

[14] Hatchuel, A., and Weil, B., 2003, “A New Approach of Innovative Design: AnIntroduction to CK Theory,” DS 31: The 14th International Conference onEngineering Design, ICED, Stockholm, Sweden, Aug. 19–21, Paper No.DS31_1794FPC.

[15] Hatchuel, A., Le Masson, P., and Weil, B., 2004, “C-K Theory in Practice: Les-sons From Industrial Applications,” DS 32: The Eighth International DesignConference, DESIGN, Dubrovnik, Croatia, May 18–21, pp. 245–258.

[16] Luo, J., 2015, “The United Innovation Process: Integrating Science, Design,and Entrepreneurship as Sub-Processes,” Des. Sci., 1, p. e2.

[17] De Groot, A. D., 1965, Thoughtand Choice in Chess, Mouton, The Hague, TheNetherlands.

[18] Chase, W. G., and Simon, H. A., 1973, “Perception in Chess,” Cognit. Psychol.,4(1), pp. 55–81.

[19] Chi, M. T., Feltovich, P. J., and Glaser, R., 1981, “Categorization and Repre-sentation of Physics Problems by Experts and Novices,” Cognit. Sci., 5(2), pp.121–152.

[20] Ericsson, K. A., 1999, “Creative Expertise as Superior Reproducible Perform-ance: Innovative and Flexible Aspects of Expert Performance,” Psychol.Inquiry, 10(4), pp. 329–333.

[21] Linsey, J. S., 2007, “Design-by-Analogy and Representation in InnovativeEngineering Concept Generation,” Ph.D. thesis, The University of Texas atAustin, Austin, TX.

[22] Linsey, J., Markman, A., and Wood, K., 2012, “Design by Analogy: A Study ofthe WordTree Method for Problem Re-Representation,” ASME J. Mech. Des.,134(4), p. 041009.

[23] Fleming, L., and Sorenson, O., 2001, “Technology as a Complex Adaptive Sys-tem: Evidence From Patent Data,” Res. Policy, 30(7), pp. 1019–1039.

[24] Uzzi, B., Mukherjee, S., Stringer, M., and Jones, B., 2013, “Atypical Combina-tions and Scientific Impact,” Science, 342(6157), pp. 468–472.

[25] He, Y., and Luo, J., 2017, “Novelty, Conventionality, and Value of Invention,”Design Computing and Cognition (DCC), Evanston, IL, June 27–29, pp. 23–38.

[26] Yan, B., and Luo, J., 2017, “Measuring Technological Distance for PatentMapping,” J. Assoc. Inf. Sci. Technol., 68(2), pp. 423–437.

[27] Shai, O., and Reich, Y., 2004, “Infused Design. I. Theory,” Res. Eng. Des.,15(2), pp. 93–107.

[28] Reich, Y., and Shai, O., 2012, “The Interdisciplinary Engineering KnowledgeGenome,” Res. Eng. Des., 23(3), pp. 251–264.

[29] Altshuller, H., 1994, The Art of Inventing (and Suddenly the InventorAppeared), Technical Innovation Center, Worcester, MA.

[30] Szykman, S., Sriram, R. D., Bochenek, C., Racz, J. W., and Senfaute, J., 2000,“Design Repositories: Engineering Design’s New Knowledge Base,” IEEEIntell. Syst. Their Appl., 15(3), pp. 48–55.

[31] Bohm, M. R., Vucovich, J. P., and Stone, R. B., 2008, “Using a Design Repositoryto Drive Concept Generation,” ASME J. Comput. Inf. Sci. Eng., 8(1), p. 014502.

[32] Chakrabarti, A. K., Dror, I., and Eakabuse, N., 1991, “InterorganizationalTransfer of Knowledge: An Analysis of Patent Citations of a Defense Firm,”Technology Management: The New International Language, Portland, OR, Oct.27–31, pp. 510–515.

[33] Indukuri, K. V., Ambekar, A. A., and Sureka, A., 2007, “Similarity Analysis ofPatent Claims Using Natural Language Processing Techniques,” InternationalConference on Computational Intelligence and Multimedia Applications(ICCIMA), Sivakasi, Tamil Nadu, Dec. 13–15, pp. 169–175.

[34] Kasravi, K., and Risov, M., 2007, “Patent Mining-Discovery of Business ValueFrom Patent Repositories,” 40th Annual Hawaii International Conference onSystem Sciences (HICSS), Big Island, Hawaii, Jan. 3–6, pp. 1–10.

[35] Moehrle, M., and Geritz, A., 2004, “Developing Acquisition Strategies Basedon Patent Maps,” 13th International Conference on Management of Technology(IAMOT), Washington, DC, May 13–15 and Apr. 3–7, pp. 19–29.

[36] Zhang, L., Li, L., and Li, T., 2015, “Patent Mining: A Survey,” ACM SIGKDDExplor. Newsl., 16(2), pp. 1–19.

[37] Bonino, D., Ciaramella, A., and Corno, F., 2010, “Review of the State-of-the-Art in Patent Information and Forthcoming Evolutions in Intelligent PatentInformatics,” World Pat. Inf., 32(1), pp. 30–38.

[38] Cascini, G., and Russo, D., 2007, “Computer-Aided Analysis of Patents andSearch for TRIZ Contradictions,” Int. J. Prod. Dev., 4(1–2), pp. 52–67.

[39] Zhang, R., Cha, J., and Lu, Y., 2007, “A Conceptual Design Model Using Axio-matic Design, Functional Basis and TRIZ,” IEEE International Conference onIndustrial Engineering and Engineering Management (IEEM), Singapore, Dec.2–4, pp. 1807–1810.

[40] Li, Z., Tate, D., Lane, C., and Adams, C., 2012, “A Framework for AutomaticTRIZ Level of Invention Estimation of Patents Using Natural Language Proc-essing, Knowledge-Transfer and Patent Citation Metrics,” Comput.-Aided Des.,44(10), pp. 987–1010.

[41] Fantoni, G., Apreda, R., Dell’Orletta, F., and Monge, M., 2013, “AutomaticExtraction of Function–Behaviour–State Information From Patents,” Adv. Eng.Inf., 27(3), pp. 317–334.

[42] Souili, A., Cavallucci, D., Rousselot, F., and Zanni, C., 2015, “Starting FromPatents to Find Inputs to the Problem Graph Model of IDM-TRIZ,” ProcediaEng., 131, pp. 150–161.

[43] Mukherjea, S., Bamba, B., and Kankar, P., 2005, “Information Retrieval andKnowledge Discovery Utilizing a Biomedical Patent Semantic Web,” IEEETrans. Knowl. Data Eng., 17(8), pp. 1099–1110.

[44] Fu, K., Murphy, J., Yang, M., Otto, K., Jensen, D., and Wood, K., 2015,“Design-by-Analogy: Experimental Evaluation of a Functional Analogy SearchMethodology for Concept Generation Improvement,” Res. Eng. Des., 26(1),pp. 77–95.

[45] Fu, K., Cagan, J., Kotovsky, K., and Wood, K., 2013, “Discovering Structure inDesign Databases Through Functional and Surface Based Mapping,” ASME J.Mech. Des., 135(3), p. 031006.

[46] Chan, J., Fu, K., Schunn, C., Cagan, J., Wood, K., and Kotovsky, K., 2011, “Onthe Benefits and Pitfalls of Analogies for Innovative Design: Ideation Perform-ance Based on Analogical Distance, Commonness, and Modality of Examples,”ASME J. Mech. Des., 133(8), p. 081004.

[47] Chan, J., Dow, S. P., and Schunn, C. D., 2015, “Do the Best Design Ideas(Really) Come From Conceptually Distant Sources of Inspiration?,” Des. Stud.,36, pp. 31–58.

[48] Fu, K., Chan, J., Cagan, J., Kotovsky, K., Schunn, C., and Wood, K., 2013,“The Meaning of ‘Near’ and ‘Far’: The Impact of Structuring Design Databasesand the Effect of Distance of Analogy on Design Output,” ASME J. Mech.Des., 135(2), p. 021007.

[49] Srinivansan, V., Song, B., Luo, J., Subburaj, K., Rajesh Elara, M., Blessing, L.,and Wood, K., 2017, “Investigating Effects of Analogical Distance on Perform-ance of Ideation,” ASME International Design Engineering Technical Confer-ence (IDETC), Cleveland, OH, Aug. 6–9, Paper No. DETC2017-67752.

[50] Gentner, D., and Markman, A. B., 1997, “Structure Mapping in Analogy andSimilarity,” Am. Psychol., 52(1), pp. 45–56.

[51] Ward, T. B., 1998, “Analogical Distance and Purpose in Creative Thought:Mental Leaps Versus Mental Hops,” Advances in Analogy Research: Integra-tion of Theory and Data From the Cognitive, Computational, and Neural Scien-ces, New Bulgarian University, Sofia, Bulgaria.

[52] Tseng, I., Moss, J., Cagan, J., and Kotovsky, K., 2008, “The Role of Timingand Analogical Similarity in the Stimulation of Idea Generation in Design,”Des. Stud., 29(3), pp. 203–221.

[53] Wilson, J. O., Rosen, D., Nelson, B. A., and Yen, J., 2010, “The Effects of Bio-logical Examples in Idea Generation,” Des. Stud., 31(2), pp. 169–186.

[54] Gick, M. L., and Holyoak, K. J., 1980, “Analogical Problem Solving,” Cognit.Psychol., 12(3), pp. 306–355.

[55] Leydesdorff, L., Kushnir, D., and Rafols, I., 2014, “Interactive Overlay Mapsfor U.S. Patent (USPTO) Data Based on International Patent Classification(IPC),” Scientometrics, 98(3), pp. 1583–1599.

[56] Kay, L., Newman, N., Youtie, J., Porter, A. L., and Rafols, I., 2014, “PatentOverlay Mapping: Visualizing Technological Distance,” J. Assoc. Inf. Sci.Technol., 65(12), pp. 2432–2443.

[57] Engelsman, E. C., and van Raan, A. F., 1994, “A Patent-Based Cartography ofTechnology,” Res. Policy, 23(1), pp. 1–26.

[58] Jaccard, P., 1901, “Distribution de la flore alpine dans le bassin des Dranses etdans quelques r�egions voisines,” Bull. Soc. Vaudoise Sci. Nat., 37, pp. 241–272.

[59] Von Wartburg, I., Teichert, T., and Rost, K., 2005, “Inventive Progress Meas-ured by Multi-Stage Patent Citation Analysis,” Res. Policy, 34(10),pp. 1591–1607.

[60] Leydesdorff, L., and Vaughan, L., 2006, “Co-Occurrence Matrices and TheirApplications in Information Science: Extending ACA to the Web Environ-ment,” J. Am. Soc. Inf. Sci. Technol., 57(12), pp. 1616–1628.

[61] Breschi, S., Lissoni, F., and Malerba, F., 2003, “Knowledge-Relatedness inFirm Technological Diversification,” Res. Policy, 32(1), pp. 69–87.

[62] Dibiaggio, L., and Nesta, L., 2005, “Patents Statistics, Knowledge Specialisationand the Organisation of Competencies,” Rev. d’�economie Ind., 110(1), pp. 103–126.

[63] Hinze, S., Reiss, T., and Schmoch, U., 1997, Statistical Analysis on the Dis-tance Between Fields of Technology, Fraunhofer-Inst. Systems and InnovationResearch, Karlsruhe, Germany.

[64] Boschma, R., Balland, P.-A., and Kogler, D. F., 2014, “Relatedness and Technolog-ical Change in Cities: The Rise and Fall of Technological Knowledge in U.S. Met-ropolitan Areas From 1981 to 2010,” Ind. Corporate Change, 24(1), pp. 223–250.

[65] Rigby, D. L., 2015, “Technological Relatedness and Knowledge Space: Entryand Exit of U.S. Cities From Patent Classes,” Reg. Stud., 49(11), pp. 1922–1937.

111416-12 / Vol. 139, NOVEMBER 2017 Transactions of the ASME

Downloaded From: http://mechanicaldesign.asmedigitalcollection.asme.org/ on 10/05/2017 Terms of Use: http://www.asme.org/about-asme/terms-of-use

[66] Alstott, J., Triulzi, G., Yan, B., and Luo, J., 2017, “Inventors’ ExplorationsAcross Technology Domains,” Des. Sci., epub.

[67] Song, B., Triulzi, G., Alstott, J., Yan, B., and Luo, J., 2016, “Overlay PatentNetwork to Analyze the Design Space of a Technology Domain: The Case ofHybrid Electrical Vehicles,” DS 84: The 14th International Design Conference,DESIGN, Cavtat, Croatia, May 16–19, pp. 1145–1154.

[68] Yan, B., and Luo, J., 2017, “Filtering Patent Maps for Visualization of Diversi-fication Paths of Inventors and Organizations,” J. Assoc. Inf. Sci. Technol.,68(6), pp. 1551–1563.

[69] Kobourov, S. G., 2013, “Force-Directed Drawing Algorithms,” Handbook ofGraph Drawing and Visualization, R. Tamassia, ed., CRC Press, Boca Raton,FL, pp. 383–408.

[70] Leten, B., Belderbos, R., and Van Looy, B., 2007, “Technological Diversifica-tion, Coherence, and Performance of Firms,” J. Prod. Innovation Manage.,24(6), pp. 567–579.

[71] Frenken, K., and Nuvolari, A., 2004, “Entropy Statistics as a Framework toAnalyse Technological Evolution,” Applied Evolutionary Economics and Com-plex Systems, Edward Elgar, Cheltenham, UK, pp. 95–133.

[72] Teece, D. J., Rumelt, R., Dosi, G., and Winter, S., 1994, “Understanding CorporateCoherence: Theory and Evidence,” J. Econ. Behav. Organ., 23(1), pp. 1–30.

[73] Nelson, R. R., and Winter, S. G., 1982, An Evolutionary Theory of EconomicChange, Harvard University Press, Cambridge, MA.

[74] Winter, S. G., 2000, “The Satisficing Principle in Capability Learning,” Strate-gic Manage. J., 21(10–11), pp. 981–996.

[75] Winston, P. H., 1992, Artificial Intelligence, 3rd ed., Addison-Wesley LongmanPublishing, Reading, MA.

[76] Guertler, M. R., von Saucken, C., Schneider, M., and Lindemann, U., 2015,“How to Search for Open Innovation Partners?,” DS 80-8: The 20th Interna-tional Conference on Engineering Design (ICED), Milan, Italy, July 27–30, pp.21–30.

Journal of Mechanical Design NOVEMBER 2017, Vol. 139 / 111416-13

Downloaded From: http://mechanicaldesign.asmedigitalcollection.asme.org/ on 10/05/2017 Terms of Use: http://www.asme.org/about-asme/terms-of-use