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+Breaking Out of the Ivory Tower:
+A Large-scale Analysis of Patent Citations to HCI Research
+Hancheng Cao
+Computer Science
+Stanford University
+California, United States
+hanchcao@stanford.edu
+Yujie Lu
+Computer Science
+University of California, Santa
+Barbara
+California, United States
+yujielu@ucsb.edu
+Yuting Deng
+School of Computer Science
+Carnegie Mellon University
+Pennsylvania, United States
+yutingde@andrew.cmu.edu
+Daniel A. McFarland
+School of Education
+Stanford University
+California, United States
+dmcfarla@stanford.edu
+Michael S. Bernstein∗
+Computer Science
+Stanford University
+California, United States
+msb@cs.stanford.edu
+ABSTRACT
+What is the impact of human-computer interaction research on
+industry? While it is impossible to track all research impact path-
+ways, the growing literature on translational research impact mea-
+surement offers patent citations as one measure of how industry
+recognizes and draws on research in its inventions. In this paper,
+we perform a large-scale measurement study primarily of 70,000
+patent citations to premier HCI research venues, tracing how HCI
+research are cited in United States patents over the last 30 years. We
+observe that 20.1% of papers from these venues, including 60–80%
+of papers at UIST and 13% of papers in a broader dataset of SIGCHI-
+sponsored venues overall, are cited by patents—far greater than
+premier venues in science overall (9.7%) and NLP (11%). However,
+the time lag between a patent and its paper citations is long (10.5
+years) and getting longer, suggesting that HCI research and practice
+may not be efficiently connected.
+CCS CONCEPTS
+• Human-centered computing → Empirical studies in HCI.
+KEYWORDS
+Industry impact, technology transfer, translational science, patent,
+citation analysis
+ACM Reference Format:
+Hancheng Cao, Yujie Lu, Yuting Deng, Daniel A. McFarland, and Michael S.
+Bernstein. 2023. Breaking Out of the Ivory Tower: A Large-scale Analysis
+of Patent Citations to HCI Research. In Proceedings of ACM Conference
+(Conference’17). ACM, New York, NY, USA, 24 pages. https://doi.org/10.
+1145/nnnnnnn.nnnnnnn
+∗Corresponding author.
+Conference’17, July 2017, Washington, DC, USA
+2023. ACM ISBN 978-x-xxxx-xxxx-x/YY/MM...$15.00
+https://doi.org/10.1145/nnnnnnn.nnnnnnn
+1
+INTRODUCTION
+What is the impact of human-computer interaction research beyond
+academia? Does HCI research diffuse into the industry1, contribut-
+ing to technological inventions and products? Are most its insights
+ignored by the industry? As an applied field of study intended to be
+closely relevant to application — where a considerable proportion
+of our research community’s contributions are functional proto-
+types and design implications for practitioners — the answers to
+these questions are critical to evaluating our translational success.
+There have been rich discussions regarding the industry impact of
+HCI research since the early years of the field, and the relationship
+between research and practice in HCI has long been a focal subject
+in both research papers [18, 19] and conference panels [9, 15, 22].
+The literature remains unclear on the field’s level of success
+in achieving this impact. One line of the literature suggests high
+barriers: that HCI research has remained distant from industry
+impact, and that “HCI researchers and HCI practitioners work in
+relatively separate spheres of influence” [22]. This line of work
+also argues there is a considerable research-practice gap, one that
+is “real and frustrating” [60] and likely the result of a long list of
+barriers [18, 75]. However, another line of literature argues that the
+field achieves considerable success, that “HCI is at the vanguard
+of innovation and has repeatedly influenced industry” [32] and
+that “there is no question that research in the area of user interface
+software tools has had an enormous impact on the current practice
+of software development” [57].
+These threads of work are not necessarily incompatible—high
+barriers do not rule out the existence of successes that overcome
+these barriers—but the field’s overall status remains unclear: how
+far have we come, and how far do we have to go? One approach to-
+ward resolving this debate is to pursue new methods for measuring
+HCI’s impact. Prior work has developed rich in-depth qualitative ev-
+idence ranging from personal technology transfer experience [22]
+to interviews with multiple stakeholders involved in the translation
+process [16]. Yet as the HCI community grows and both well-known
+1In this paper, we use ‘industry’ to refer to non-research efforts that aim at practical
+impacts, e.g. patents, products, design practices, which usually target a broad audience
+than academic researchers. Thus, in this paper, industry labs whose primary focus is
+to publish research papers are considered academia rather than industry.
+arXiv:2301.13431v1  [cs.HC]  31 Jan 2023
+
+Conference’17, July 2017, Washington, DC, USA
+Hancheng Cao et al.
+successes and painful failures become easier to point to, it becomes
+more and more urgent that we also assess broader patterns.
+To fill this gap, we draw on methods from the growing measure-
+ment literature on innovation in translational sciences [1, 7, 45, 50,
+77], where patent citations to research have been regarded as a
+valuable proxy of the impact that science has on industrial practice.
+While patent citation to research citation does not directly guar-
+antee industry impact, it reveals one potential pathway through
+which industrial inventors are aware of and recognize research ar-
+ticles: a necessary but not sufficient step towards industry impact.2
+Work using this approach has revealed the relevance of research
+and practice across science [1], mapped the translation landscape
+in bio-medicine [45, 50], and demonstrated that referencing science
+in the invention is associated with greater practical value [34].
+Leveraging the modern analysis approaches from this line of
+work [51, 52], we report the first large-scale quantitative analysis
+of how HCI research is (and is not) being cited by patents. In do-
+ing so, we focus on one possible route of industry impact through
+HCI research: patents. There are many types of contributions in
+HCI—design patterns, behavioral results and theory among many
+others—and a patent lens focuses us only on styles of contribution
+that are considered prior art for patents, often systems and inter-
+action contributions. Specifically, we draw on data from Microsoft
+Academic Graph, Semantic Scholar, the United States Patent and
+Trademark Office (USPTO), and linkages between them [51, 52].
+This dataset enables us to study research papers from four premier
+venues in HCI, including CHI, CSCW, UIST, and UbiComp, and
+then replicate across all 20 SIGCHI sponsored venues that appear in
+Microsoft Academic Graph, tracing how those research papers are
+cited in patent documents from the 1980s through 2018. We study
+the institutes involved in the process, leverage citation analysis to
+measure the number and proportion of papers cited by patents over
+time and measure the length of time it takes before a paper is recog-
+nized by patents. We further conduct textual analysis to understand
+the topics that are likely to be cited in patents, and compare how
+patent-cited research differs from its non-patent cited counterparts.
+We observe that: (1) HCI research has been cited extensively
+by patents — overall 20% of papers from CHI, CSCW, UIST and
+UbiComp, and 13.4% of SIGCHI sponsored venues, are patent-cited,
+including a surprising 60-80% of UIST papers over a twenty year
+period, higher than 1.5% of science overall and 7.7% of biomedicine;
+(2) The patent-paper time lag is long (on average 10.5 years) and
+is getting longer, such that citations from academic HCI research
+have dropped off by the time a paper receives patent attention;
+and (3) Within HCI research, there is substantial heterogeneity in
+patent citations across topics, for example, interaction and input
+techniques research are especially likely to be referenced by patents
+while theory, social and experience design research are not. This
+analysis provides the first quantitative survey of the HCI technol-
+ogy transfer landscape. While acknowledging potential limitations
+of patent citation as a method, we conclude that HCI has had a
+considerable impact on industry and is finding more relevance to
+practice than most disciplines in science. Yet, it takes a long time for
+2More discussion and reflection on the usage of patent citation to science to study
+industry impact of research in Section 3.1 and Section 5.3
+innovations in academia to be recognized and taken up by industry,
+corroborating the “long nose” theory on HCI innovation [12, 32].
+The contributions of this paper are as follows:
+• We introduce measuring patent citations to science as a novel
+method to study research-practice relationships in HCI. This
+provides quantitative evidence that complements qualitative
+evidence in existing HCI literature. We release our analyzed
+dataset to enable future analysis.3
+• We present the first large-scale, empirical study measuring
+the translational, longitudinal landscape of HCI research
+from paper to patent inventions with comparisons to other
+fields. This allows us to better understand and evaluate how
+HCI as an applied field is or is not finding connections to
+practice.
+• Our work contributes to reflections and recommendations
+for the HCI community to better foster a translational envi-
+ronment and recognize impacts beyond academia.
+2
+BACKGROUND AND RELATED WORK
+In this section, we position our work in the literature on industry
+impact, the HCI research-practice divide, and bibliometric analysis
+in HCI.
+2.1
+Industry impact
+Industry impact are often achieved through technology transfer,
+which refers to the transmission of knowledge generated by an
+individual, the university, government agencies, or any institution
+capable of generating knowledge, to another person or organiza-
+tion, in an attempt to transform inventions and scientific outcomes
+into new products and services that benefit society [55]. Govern-
+ment and funding agencies (e.g., in the United States, NSF and NIH)
+increasingly seek to nurture “translational research” to facilitate
+industry impact from basic research so as to generate greater ap-
+plied value and promote technology advances [76, 79], and prior
+research has shown inventions that refer to high-quality research
+are more likely to be great inventions of value [34, 61].
+Prior research has sought to identify when, where, and how sci-
+entific research influences industry invention [3, 7, 17, 45]. There,
+patent citations to science have been widely used as a proxy for
+studying technology transfer from research to practice despite
+noises, as it is one of the only available large-scale records of the
+knowledge flow from research to practice that demonstrate the ini-
+tial awareness and recognition of research in industrial inventions.
+For instance, Tijssen [68] revealed through patent-paper citations
+how Dutch-authored research papers influence inventions. Like-
+wise, Ahmadpoor and Jones [1] studied 4.8 million US patents and
+how they link to 32 million research articles, finding that over half
+of patents cite back to a research article and that patents and papers
+are on average 2–4 degrees separated from the other domain, pro-
+viding some insight into the interplay between patents and prior
+research. Jefferson et al. [37], Manjunath et al. [50] used patent cita-
+tions to science data, measuring and reporting statistics describing
+how research in biomedicine turns into inventions. Liaw et al. [46]
+proposed a method to rank academic journals that utilizes non-
+patent references in patent documents to evaluate their practical
+3Available dataset at: https://doi.org/10.7910/DVN/QM8S1G.
+
+A Large-scale Analysis of Patent Citations to HCI Research
+Conference’17, July 2017, Washington, DC, USA
+impact. Other works used patent citation to science to study the
+strategy of inventors (e.g. deep search vs. wider scope search) and
+how the strategy relates to technology impacts and organization
+performance [2, 25, 28, 38]. To facilitate further studies on how
+inventions rely on basic science, Marx and Fuegi [51, 52] linked
+and disambiguated patent citations to science linking the USPTO
+dataset and Microsoft Academic Graph.4
+We build off this rich social science literature by studying indus-
+try impacts of HCI research through leveraging and extending their
+methods[32].
+2.2
+From HCI research to practice
+HCI is a field that emphasizes the design and the use of computer
+technology, especially interfaces between people and computers.
+HCI research implement, demonstrate and test new technologies
+through prototyping and end-user feedback [47], and most HCI
+work includes ‘design implications’ sections aiming to translate
+their research insights to more practical outcomes. The applied
+nature of HCI lead to the community’s long-standing interest in
+industry impact, with many publications and panel discussions at
+conferences aimed at facilitating better technology transfer [15,
+22, 39]. One line of the literature primarily focus on the many
+barriers HCI faced in translating research insights to industrial
+practice [18, 22], while another line of literature speaks to the
+considerable impact that HCI research has had or could have on
+the industry [32, 57, 65].
+Many papers argue that despite the insights that HCI research
+can offer to practitioners, HCI research findings are rarely used in in-
+dustry [18]: that there has been an “immense” research practice gap
+in practice that is “real and frustrating” [60], that “HCI researchers
+and HCI practitioners work in relatively separate spheres of influ-
+ence” [22], and that “attendees at venues like ACM CHI often lament
+that no HCI research ever goes into product” [32]. Colusso et al.
+[18] interviewed design practitioners so as to understand why they
+do not use academic research and why and how they use other re-
+sources in their works, presenting a detailed catalog of barriers that
+inhibit academic resources usage in industry, such as the content
+being hard to read, hard to find, and not actionable. Chilana et al.
+[16] stated the distinct goals of HCI research and product may result
+in a research-practice gap, that the users who are the major focus
+of the user-centered design approach in HCI research are generally
+not the buyers of HCI products, and that to make a research-to-
+product transition one has to switch from being user-centered to
+adoption-centered. Furthermore, prior work [22, 75] suggested that
+HCI researchers usually lack the knowledge, resources, connections,
+experience, interest, or time to pursue technology transfer. Other
+work has shown similar results demonstrating a research practice
+gap in HCI [10, 27].
+Prior research has discussed potential approaches to address the
+research-practice gap. For instance, Velt et al. [69] identified two
+key dimensions of the research-practice gap – general theory vs.
+particular artifacts, and academic HCI research vs. professional UX
+design practice – and discussed the benefits of translation led by
+researchers, by practitioners, or co-produced by both as bound-
+ary objects. Colusso et al. [19] proposed a continuum translational
+4We leverage this particular dataset in our analysis.
+science model for HCI that consists of three steps: basic research, ap-
+plied research, and design practice. Shneiderman [65] wrote a book
+proposing principles to better blend science, engineering and design
+to achieve innovations and breakthroughs. Other work discusses
+the challenges and lessons learned from the specific translation of
+HCI research to practice [62, 63].
+Meanwhile, another line of work argues that HCI research could
+have considerable impact on industrial practice despite the barriers.
+Harrison argues that “HCI is at the vanguard of innovation and
+has repeatedly influenced industry [...] HCI research has a much
+greater impact in identifying opportunities in the first place, es-
+tablishing the science and methods, building a shared vision, and
+developing a pipeline of human talent” [32]. Likewise, Myers et al.
+[57] wrote “There is no question that research in the area of user
+interface software tools has had an enormous impact on the cur-
+rent practice of software development. Virtually all applications
+today are built using window managers, toolkits, and interface
+builders that have their roots in the research of the 70’s, 80’s, and
+90’s”. Shneiderman’s work [66] further stated that “The remarkably
+rapid dissemination of HCI research has brought profound changes
+that enrich people’s lives”, but also providing a tire-tracks diagram
+showing how HCI research on subjects such as hypertext, direct
+manipulation, etc. turned into product innovations by industry.
+Similarly, product innovations over the years mirror the early ideas
+of canon HCI visions [11, 74]. Other research detailed successful
+cases of tech transfer, such as the translation of the multi-touch
+interface from research into the Apple iPhone and Microsoft Sur-
+face, while highlighting a long time lag between initial research
+and commercialization, which can be 20 years or more [12, 32, 66].
+This prior work guides us to the following research questions:
+RQ1: What is the impact of HCI research on patents? How much
+HCI research is cited in patents?
+RQ2: When is the impact of HCI research on patents? How long
+does that impact take?
+RQ3: Where is the impact of HCI research on patents? Which
+topics of research are especially likely or unlikely to diffuse?
+RQ4: Who is involved in the process of recognizing HCI research
+on patents? Which institutions produce such work, and which
+consume it?
+The rich qualitative insights derived from case studies, field-
+work, interviews, and personal experience, open an opportunity
+for complementary work that engages in quantitative, longitudinal
+analysis that directly measures how HCI research gets recognized
+in industry inventions and technologies. We believe that such a
+viewpoint might systematically detail the translation landscape of
+HCI as a field.
+2.3
+Bibliometrics and HCI
+As an important area of computing and information science, HCI
+has featured several projects (e.g., [40, 49]) that quantitatively un-
+derstand the structure and evolution of the field through the study
+of writing and citation patterns, known as bibliometrics [26].
+One commonly used bibliometric method is an analysis of a large-
+scale citation network, which leverages the increasingly available
+citation data from publishers such as Web of Science and Microsoft
+Academic Graph and their associated metadata of the scientific
+
+Conference’17, July 2017, Washington, DC, USA
+Hancheng Cao et al.
+publications (e.g. institutes, authors), and even textual analysis (e.g.
+topic modeling, keyword extraction) of the scientific publications,
+so as to gain insights on patterns behind the diffusion of scientific
+ideas [26, 70], research productivity [48, 72], and identify potential
+ethical and social issues in science [35, 41]. For instance, Koumaditis
+and Hussain [42] leveraged citation data from 962 HCI publications
+and reveal that HCI research can be categorized into major themes
+of design, data management, user interaction, psychology, and
+cognition, and they identified more recent trends in HCI in the
+workplace, sensors, and wearables. Likewise, Kaye [40] reported
+“some statistical analyses of CHI”, including author counts, gender
+analysis, and representations of repeat authors so as to motivate dis-
+cussions on the preferred state of CHI. Bartneck and Hu [5] reveal
+that only a small percent of countries account for the majority of
+CHI proceedings, and present a ranking of countries and organiza-
+tions based on their H-index of CHI proceedings. Correia et al. [21]
+used 1713 CSCW publications and characterized top CSCW papers,
+citation patterns, prominent institutes as well as frequent topics,
+highlighting the fact that CSCW is influenced primarily by a few
+highly recognized scientists and papers. The authors further quanti-
+tatively explored the relationship between collaboration types and
+citations, paper frequency, etc [20]. Similar types of analysis have
+also been done on more regional HCI conferences [4, 30, 56, 59] as
+well as studying subcommunities in HCI [49, 71, 73].
+Visual analytics is another approach used to help understand
+HCI’s evolution. For instance, Lee et al. [43] proposed a system
+PaperLens to reveal trends, connections, and activity of 23 years
+of the CHI conference proceedings. Matejka et al. [54] proposed
+an interactive visualization that highlights family trees of CHI and
+UIST papers. Henry et al. [33] presented a visual exploration of
+four HCI conferences. They showed that the years when a given
+conference was most selective are not correlated with those that
+produced its most highly referenced articles and that influential
+authors have distinct patterns of collaboration.
+To the best of our knowledge, there have been no analyses lever-
+aging quantitative methods to study recognition of HCI research
+beyond academia as we present in this article. In contrast with
+prior work, we leverage large-scale patent citations to quantify the
+impact of HCI research in practice.
+3
+METHOD
+In this section, we describe the method we used to study the impact
+of HCI research papers in practice using patent citations to science.
+3.1
+Patent citations as a pathway to study
+industry impact of research papers
+We leverage patent citations to research as a proxy to study the
+influence of HCI research on industrial practice at scale. While
+patent citation to research citation does not directly mean industry
+impact, it reveals one important potential pathway from research
+to practice where industrial inventions become aware of and recog-
+nize research articles, which is often a necessary but not sufficient
+step towards producing industry impact. Alongside with studying
+other forms of influence, such as design processes (e.g., usability
+testing, heuristic evaluation), design patterns, open source software
+(e.g., d3, Vega), patent citations to science could help us piece to-
+gether the translational landscape in HCI. This method is widely
+used in the innovation literature (e.g., [1, 25, 28, 38, 50, 51]). Patent
+citations to research are considered valuable signals indicating the
+influence of research on the industry, signals that “reflect genuine
+links between science and technology.” [68], and “appear to be a
+substantive if a noisy indicator of the role of specific, prior scientific
+advances” [1]. While citations between research articles capture
+research influence [26], patent-to-research citations capture “how
+basic research influences commercialization and thus provides a
+complementary measure of impact” [50]. Such data has been used
+extensively to measure knowledge spillovers from academia and
+government to industry [1, 23, 51].
+The rationale behind the validity of this approach is that in
+patented inventions, inventors are obliged to disclose any “prior
+art” related to their invention, i.e., all information known to that
+individual to be material to patentability”,5 including materials that
+the inventors leveraged in the invention process, or other similar
+material to the focal invention in order to distinguish it. The prior
+art includes both references to prior patents, and references to non-
+patent literature, such as academic articles. Patent citation is an
+important part of a patent, as missing prior art (either prior patents
+or non-patent literature), could have potential legal issues. Apart
+from citations provided by inventors, patent examiners who review
+patents for approval or rejections also add references they think
+are of relevance to ensure the legitimacy of the patent.
+Prior work has validated this method. Nagaoka et al. [58] sur-
+veyed 843 inventors finding patent citations to science are indeed
+important linkages to science, despite possible errors of over- and
+under-inclusion. Callaert et al. [13] interviewed 36 inventors and re-
+port 44% of patent citations to science are considered as “important”
+or “very important”, and another 34% are “background” citations.
+Based on the rich literature in this space, we conclude that patent
+citation to science can be used as a reliable data source to measure
+the recognition of HCI research efforts in inventions, thus provid-
+ing a valuable proxy of HCI research impact in the industry. Of
+course, there is no perfect appoach for studying industry impact:
+we discuss and reflect on the limitations of our method in detail in
+Section 5.3, and it is especially important to bear in mind there are
+multiple translational gaps in HCI research [19], and we are only
+studying one important step in the process with regard to patent,
+where certain types of contribution such as theory are likely to be
+under evaluated through this dimension.
+Empirically, we find support for the validity of using patent
+citations to research as a proxy of impact in industry. We manu-
+ally check patent reference lists of a number of patents. As shown
+in Figure 1, the highly-cited patent by Apple Inc. “Mode-based
+graphical user interfaces for touch sensitive input devices” (cited
+1,898 times),6 cites closely related research papers in CHI on multi-
+touch, such as “A Multi-Touch Three Dimensional Touch-sensitive
+Tablet", which is the case of technology transfer discussed by Bux-
+ton [12]. The even more well-cited Apple Inc. patent (cited 4,018
+times) “Method and apparatus for integrating manual input” 7 also
+made reference to several relevant HCI papers. These cases motivate
+5https://www.uspto.gov/web/offices/pac/mpep/mpep-2000.pdf
+6https://patents.google.com/patent/US8239784B2/en
+7https://patents.google.com/patent/US6323846B1/en
+
+A Large-scale Analysis of Patent Citations to HCI Research
+Conference’17, July 2017, Washington, DC, USA
+us to leverage patent citations as a signal indicating the invention’s
+recognition of research.
+3.2
+Dataset
+To study how HCI papers are recognized by patents, we required
+a citation graph from patent to research, and the metadata (e.g.,
+author name, affiliation, publication year, title, venue) from both
+the paper side and patent side. The data preparation pipeline is
+composed of three steps: 1) Prepare metadata of papers and patents,
+and the citation graph from patents to research, 2) Select papers
+from the venues of interest and clean the data, and 3) Link the clean
+metadata based on the citation graph. This pipeline could be applied
+to other research communities, or other venues within SIGCHI, by
+selecting other venues of interest.
+Patent citation to science that connects USPTO to Microsoft
+Academic Graph. To capture references from patents to HCI re-
+search papers, we drew on a public dataset [52, 53]. This dataset
+is a state-of-the-art approach to connect each patent reference in
+USPTO (1947-2020) to academic papers (1800-2020) from Microsoft
+Academic Graph through matching unstructured front-page and
+in-text references in patents to published papers using a disam-
+biguation matching method, resulting in 22 million patent citations
+to research papers (known as Patent Citation Science dataset).8
+In their papers, the dataset creators verified the quality of their
+datasets through manual checking and error analysis. We captured
+the reference type (e.g., from applicant, from examiner, unknown),
+whether the reference appears in-text or on front page, the time
+between paper publication and the citing patent application, and
+whether a patent citation is a self-citation to a research paper by
+one of the patent authors. A paper to patent pair is considered
+self-cited when there is an overlap between the inventors of the
+patent and the authors of the cited scientific papers.
+Microsoft Academic Graph Metadata. The Microsoft Academic
+Graph is a heterogeneous graph that provides scientific publication
+records, citation relationships, the information of authors, insti-
+tutions, journals, conferences, and fields of study. We leveraged
+the public Microsoft Academic Graph dataset provided at Zenodo
+Reliance on Science project site9 so as to extract information with
+regard to academic publications, e.g., title, author, author affiliation,
+and year.
+USPTO metadata. We leveraged US patent data from the United
+States Patent and Trademark Office (USPTO)10 to represent tech-
+nological inventions. Patents have similar fields as academic publi-
+cations, e.g., title, abstract, inventor, assignee, and year.
+Semantic Scholar (abstract, citation). The abstract informa-
+tion of the paper and their academic influence (e.g., number of
+published papers, citation count) are missing or hard to process in
+the original Microsoft Academic Graph metadata.11 To further ex-
+pand data information about authors, papers, citations, and venues,
+8Specifically, we used the patent-to-article citations of Version v37 (Jul 19, 2022) at
+Zenodo: http://relianceonscience.org
+9http://relianceonscience.org
+10https://patentsview.org/download/data-download-tables
+11https://docs.microsoft.com/en-us/academic-services/graph/resources-faq
+we utilize the Semantic Scholar Academic Graph API,12 which fills
+in this data.
+The details of the data we utilize can be found in Appendix A.
+3.3
+Data Preprocessing
+Venue selection. In our analysis, we primarily considered four
+impactful Human-Computer Interaction (HCI) venues: the ACM
+CHI Conference on Human Factors in Computing Systems (CHI),
+ACM Conference On Computer-Supported Cooperative Work And
+Social Computing (CSCW), ACM Symposium on User Interface Soft-
+ware and Technology (UIST), and International Joint Conference on
+Pervasive and Ubiquitous Computing (UbiComp).13 For a broader
+footprint of HCI research, we created a second dataset of SIGCHI
+sponsored venues14 — a total of all 20 SIGCHI sponsored venues15
+that appear in the Microsoft Academic Graph, which covers not
+only large, premier venues such as CHI, but also smaller, more
+specialized venues such as MobileHCI and CHI PLAY. We used this
+second set as more representative of the overall field of HCI, to
+further validate our findings and compare with overall patterns
+reported in other fields of science in a fairer way16.
+Data Cleaning. We further conducted data cleaning on the four
+chosen venues by looking up papers in Semantic Scholar rather
+than Microsoft Academic Graph. We found that Microsoft Aca-
+demic Graph metadata sometimes wrongly classify venues such as
+“Brazilian Symposium on Human Factors in Computing Systems”
+as “CHI”. To solve this issue, we filtered out irrelevant papers by
+manually checking the full name of the venue column from Seman-
+tic Scholar, which proves to be of better quality. We then applied
+this filtering process to all the paper and patent citations to science
+files by joining over the paper id.
+Data Linking. In order to better combine the paper and patent
+information for analysis, we linked patent data, Microsoft Academic
+Graph data and Semantic scholar data via the Patent Citation Sci-
+ence dataset.17 The joined data after 2019 has incomplete or little
+coverage, thus we focus our analysis on HCI research papers and
+patents that cite HCI papers before 2019.
+Final Data Statistics. Our final data for analysis includes 23,432
+papers from the four chosen venues, with 16,014 from CHI, 3,084
+from CSCW, 1,746 from UIST, and 2,588 from UbiComp across 1980
+to 2018. Within these papers, we captured 69,900 citation records
+from patent to science, with 42,676 from CHI, 5,900 from CSCW,
+17,040 from UIST, and 4,284 from UbiComp, which are associated
+with 30,660 patents. The broader SIGCHI sponsored venue data
+include 57,385 papers in total (41% are papers from the four premier
+12https://www.semanticscholar.org/product/api
+13Starting 2017, the UbiComp conference main technical tracks consist of papers
+published in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous
+Technologies (IMWUT), which we captured in our data.
+14https://sigchi.org/conferences/upcoming-conferences/
+15Details of the venues in Appendix B
+16Note that in this paper we primarily report findings on the four chose venues rather
+than SIGCHI sponsored venues overall. We elect to focus on these four venues as a
+practical matter, as we have spent considerable manual efforts in cleaning data related
+to the four chosen venues to ensure data quality, as indicated in “Data Cleaning"
+section, which makes our analysis more likely to reflect actual trends in these venues.
+17Confusingly to HCI researchers, this is known as the “Patent Citation Science”
+(PCS) dataset. We joined information from the patent side using the field patentid to
+information from the paper side using the field magid.
+
+Conference’17, July 2017, Washington, DC, USA
+Hancheng Cao et al.
+(a) Patent US8239784 frontpage with abstract, inventors, assignee etc.
+(b) Part of the citation list of Patent US8239784.
+Figure 1: Patents are obliged to cite prior art, including prior patents and non-patent literature (e.g. research articles). Here, a
+patent by Apple Inc., “Mode-based Graphical User Interfaces for Touch Sensitive Input Devices” [36], has citation to relevant
+HCI papers, including “ActiveClick: Tactile Feedback for Touch Panels”, “A Multi-Touch Three Dimensional Touch-sensitive
+Tablet”, a mis-named citation to Ken Hinckley (“Kinkley et al.”), and many other references to HCI research.
+
+(12) United States Patent
+(10) Patent No.:
+US 8.239.784 B2
+Hotelling et al.
+(45) Date of Patent:
+Aug. 7, 2012
+(54)
+MODE-BASEDGRAPHICALUSER
+(56)
+References Cited
+INTERFACES FOR TOUCH SENSITIVE
+INPUT DEVICES
+U.S.PATENT DOCUMENTS
+3,333,160A
+7/1967
+Gorski
+(75)
+Inventors: Steve Hotelling, San Jose, CA (US);
+3,541,541A
+11/1970
+Englebart
+Brian Q.Huppi, San Francisco, CA
+3,609,695A
+9/1971
+Pirkle
+3,662,105A
+5/1972
+Hurst et al.
+178/18
+(US):JoshuaA.Strickon.SanJose,CA
+3,748.751 A
+7/1973
+Breglia et al.
+(US):DuncanRobertKerr.San
+3,757,322A
+9/1973
+Barkan et al.
+Francisco,CA(US):BasOrding.San
+3,798,370 A
+3/1974
+Hurst
+178/18
+Francsico, CA (US); Imran Chaudhri.
+3,825.730A
+7/1974 Worthington, Jr. et al.
+San Francisco, CA (US); Greg Christie,
+3,846,826 A
+11/1974 Mueller
+4,014,000A
+3/1977 Uno et al.
+SanJose.CA(US):JonathanP.Ive.San
+4,017,848A
+4/1977
+Francisco, CA (US)
+Tannas, Jr.
+4,146,924 A
+3/1979 Birk et al.
+(Continued)
+(73)
+Assignee: Apple Inc., Cupertino, CA (US)
+(*)
+Notice:
+Subjecttoanydisclaimer,thetermofthis
+FOREIGNPATENTDOCUMENTS
+patent is extended or adjusted under 35
+CA
+1243096
+10/1988
+U.S.C. 154(b) by 936 days.
+(Continued)
+(57)
+ABSTRACT
+A user interface method is disclosed. The method includes
+detecting a touch and then determining a user interface mode
+when a touch is detected. The method further includes acti-
+vating one or more GUI elements based on the user interface
+mode and in response to the detected touch.EVB Elektronik TSOP6238 IR Receiver Modules for Infrared
+Remote Control Systems dated Jan. 2004 1-pg.
+Fisher et al., Repetitive Motion Disorders: The Design of Optimal
+Rate-Rest Profiles," Human Factors, 35(2):283-304 (Jun. 1993)
+Fukumoto, et al., "ActiveClick: Tactile Feedback for Touch Panels,
+in CHI 2001 Summary, pp. 121-122, 2001.
+Fukumoto and Yoshinobu Tonomura, "Body Coupled Fingering:
+Wireless Wearable Keyboard,' CHI 97, pp. 147-154 (Mar. 1997).
+Hardy, Fingerworks" Mar. 7, 20o02; BBC World on Line.
+Hillier and Gerald J. Lieberman, Introduction to Operations
+Research (1986).
+International Search Rep0rt dated Mar. 3, 2006 (PCT/US 05/03325)
+Jacob et al., "Integrality and Separability of Input Devices," ACM
+Transactions on Computer-Human Interaction, 1:3-26 (Mar. 1994)
+ings, pp. 223-230, 1999.
+Kionx "KXP84 Series Summary Data Sheet" copyright 2005,dated
+Oct. 21, 2005, 4-pgs.
+Lee et al., A Multi-Touch Three Dimensional Touch-Sensitive Tab-
+let, in CHI '85 Proceedings, pp. 121-128, 2000.
+Lee, “A Fast Multiple-Touch-Sensitive Input Device," Master's The.
+sis, University of Toronto (1984).
+Matsushita et al., "HoloWall: Designing a Finger, Hand, Body and
+Object Sensitive Wal1,’ in Proceedings of UIST '97, Oct. 1997A Large-scale Analysis of Patent Citations to HCI Research
+Conference’17, July 2017, Washington, DC, USA
+venues), 83,793 citation records (51% are citations made to the four
+premier venues), and are associated with 36,024 patents in total
+(85% patents cited papers from the four premier venues).
+Note that for all chosen venues, our data includes not only main
+conference papers but also extended abstracts, posters and other
+forms of publications. We did not attempt to filter and focus our
+analysis only on main conference papers, given the difficulty to
+classify and challenge fuzzy matching based on venue name (e.g.
+in our dataset, many posters are not explicitly labeled as poster
+publications and are hard to differentiate from main conference
+papers).
+We release our dataset at: https://doi.org/10.7910/DVN/QM8S1G.
+4
+RESULTS
+4.1
+RQ1: What is the impact of HCI research
+on patents?
+We first study the quantity of HCI papers that are later recognized
+by patents and present a table of top papers cited by patents.
+Proportion of papers that get cited by patents. To assess the
+extent of HCI research being recognized in patents, we first cal-
+culated the aggregated proportion of the number of HCI papers
+at our four premier HCI venues, and SIGCHI sponsored venues
+overall, that were cited by patents. We found 20.1% of papers in the
+four venues, and 13.4% of papers from SIGCHI sponsored venues
+overall, are recognized by patents. This rate is much higher than
+the proportion of science cited by patents overall (approximately
+1.5% [51]), and the prominent journal paper patent rate (9.7% across
+multiple scientific fields [8]). The rate is also much higher than that
+of bio-medicine in general, a field that has a rich tradition empha-
+sizing translational science, which is at 7.7% [50]. We replicated our
+analysis on premier venues in other areas of Computer Science by
+comparing the premier HCI venue patent rate (20.1%) with premier
+venue patent rate of other subfields, finding that AI patent rates
+(as measured through AAAI and IJCAI, two of the largest and pre-
+mier AI conferences) are 5%, Natural Language Processing patent
+rates (as measured through ACL, EMNLP, and NAACL, three of the
+largest and premier NLP conferences) are 11%, and Computer Vision
+patent rates (as measured through CVPR, ECCV, and ICCV, three of
+the largest and premier computer vision conferences) are 25%. Two-
+proportion z tests further confirm the significance of the difference
+in percentages with 𝑧 = 51.1, 23.9, -13.1, (𝑝 < .001) when compar-
+ing premier HCI venue patent rate with patent rates of premier
+venues in AI, Natural Language Processing and Computer Vision.
+Taken together, these results suggest that HCI’s impact through
+patent citations is higher than science overall, biomedicine, AI, and
+NLP, and roughly at par with Computer Vision, an area of intense
+industry interest.
+Are research citations in patents truly central to the patents, or
+are they thrown in just to satistfy a patent examiner? To answer
+this question, we leverage a distinction between in-text citations
+and front page citations in patents. This distinction allows us to
+more directly measure the impact of HCI research in patents. In-
+text patent citation to science, as suggested by prior work [8, 52],
+are more likely to “capture the scientific articles upon which the
+scientists truly relied upon for inspiration” and “have the potential
+to more accurately represent the sources of scientific inspiration
+upon which the inventors actually drew in the invention process"
+since they “tend to be supplied by the inventors themselves”, in
+contrast to “legally binding” front page citations which “tend to be
+carefully reviewed (and sometimes added) by patent attorneys” [52].
+We find 4.1% papers in our chosen four venues have been cited in-
+text by patents, whereas the proportion of patent in-text citation to
+science is 2.3% for SIGCHI sponsored venues and 1.4% for science
+overall. This result further replicates our finding that HCI research
+appears to have real impact, surprisingly even moreso than many
+other fields.
+Investigating temporal patterns, we plot the total number of HCI
+research papers in each of the four venue published over years,
+shown in red in Figure 2. HCI research has grown rapidly over the
+past 38 years for all four venues, especially at CHI: from 74 papers
+in 1982 to 1200 in 2018. This growth is particularly pronounced
+within the last 10 years. We then counted the total number of HCI
+papers cited by patents by the publication year of the paper and
+calculated the ratio between the number of HCI papers cited and
+the total number of HCI papers accepted in a particular year by
+each venue (blue line in Figure 2). The citation ratios start climbing
+especially starting around 1990 and persist since then (Figure 2),18
+with several conferences observing a third to a half of their papers
+cited by patents. At UIST in particular, the patent citation ratio
+reaches 60% - 80% from 1990 - 2010.
+The citation ratio decreased after 2015. One possible explanation
+is the time lag between patent and paper is long, e.g., it might take
+a decade for a paper to start gathering patent citations, and papers
+since 2015 are still too young by this metric. This time lag will be
+further discussed in Section 4.2. In other words, the data are right
+censored, i.e., more recent papers have not been fully recognized
+by patents captured in our dataset. As such, we expect a higher
+proportion of HCI papers overall will be referenced by patents
+eventually.
+Increasing citations to HCI research in patents. A total of
+30,660 patents cite research in the four chosen venue, and 36024
+patents cite research from SIGCHI sponsored venues overall. This
+raw volume began increasing after 2000 (Figure 3, and has more
+than quintupled since 2000 at CHI from around 175 patents per
+year in 2000 to over 1000 per year in 2014). However, the number
+of patents plateaus and even decreases a bit in more recent years,
+e.g. patents begin citing less and less CSCW research starting in
+2014. This could be a result of changes on the demand side, e.g., the
+industry is less interested in novel social computing applications,
+or on the supply side, e.g., HCI publishing more papers that are not
+intended to be as industry-relevant. More evidence is needed to
+derive the mechanisms behind this result, beyond the scope of our
+current work.
+Top cited papers by patents in HCI. We further examined the
+HCI papers that were cited the most by patents by each venue
+(Table 1). Papers highly cited by patents also tend to be highly cited
+by research. The papers most highly cited by patents are primarily
+18We removed years where conferences did not meet from our analysis and smoothed
+the curve, e.g. CSCW was only held every other year until 2010.
+
+Conference’17, July 2017, Washington, DC, USA
+Hancheng Cao et al.
+Figure 2: Left: the number of papers published by each conference per year (red) and the number of papers published in that
+year that were later cited by at least one patent (blue), at ACM CHI, CSCW, UbiComp, and UIST. Right: a substantial proportion
+of HCI papers are recognized by patents, e.g. 60% - 80% UIST papers are recognized by patents 1990 - 2010.
+systems work, e.g., building a new system or proposing a new de-
+sign. This result parallels with the earlier observation that UIST has
+the highest rate of papers cited by patents since UIST is particularly
+targeted at new interfaces, software, and technologies. Most papers
+in this list were published prior to 2005; however, the majority of
+the patents that cited HCI papers come after 2005, indicating again
+the potential long time lag between paper publication and patent
+reference in Section 4.2.
+Highly-cited papers in academia are more likely to be recog-
+nized by patents. Moreover, we investigated how academic impact
+
+CHI
+CHI
+100
+1200
+Published
+ Percent of published papers later cited by patents
+Published and later cited by patents
+80
+S1000
+per
+(%)
+800
+60
+Percent (
+of
+600
+ber
+40
+400
+wnN
+20
+200
+0
+0-
+2010
+1980
+1985
+1990
+1995
+2000
+2005
+2015
+1985
+1990
+1995
+2000
+2005
+2010
+2015
+1980
+Year
+Year
+CSCW
+CSCW
+100
+Published
+Percent of published papers later cited by patents
+300
+Published and later cited by patents
+80
+(%)
+60
+Percent (
+40
+20
+0
+0°1980
+1995
+2015
+1995
+1990
+2000
+2005
+2010
+1985
+1990
+2000
+2005
+2010
+2015
+1980
+1985
+Year
+Year
+UIST
+UIST
+100
+150
+Percent of published papers later cited by patents
+Published
+Published and later cited by patents
+80
+(%)
+100
+60
+Percent
+75
+Number
+40
+50
+20
+25
+0
+0·1980
+1980
+1985
+1990
+1995
+2000
+2005
+2010
+2015
+1990
+2000
+2005
+2010
+2015
+1985
+1995
+Year
+Year
+UbiComp
+UbiComp
+100
+500
+Published
+Percent of published papers later cited by patents
+Published and later cited by patents
+80
+"400
+(%)
+60
+Percent
+of
+40
+20
+0
+1985
+1980
+1990
+1995
+2000
+2005
+2010
+2015
+1985
+1995
+2005
+2010
+2015
+1990
+2000
+Year
+YearA Large-scale Analysis of Patent Citations to HCI Research
+Conference’17, July 2017, Washington, DC, USA
+Figure 3: Left: over 1000 patents are citing CHI paper each year after 2014. The number of patents citing HCI research began
+rising after 2000 and more than quintupled since then. Right: the number of patents citing SIGCHI sponsored venues follow
+similar trend, as a large proportion (85%) made references to the four premier venues.
+relates to patent impacts, measured by the paper’s number of cita-
+tions from other academic papers (academic citation count) and the
+number of citations from patents (patent citation count). Figure 4
+shows the academic citation count for both papers recognized by
+patents and papers not recognized by patents over time. Patent-
+cited papers have higher paper citations (average academic citation
+count 117.1) than non-patent-cited papers (average academic ci-
+tation count 27.9), a difference that is significant via an unpaired
+t-test (𝑝 < .001), Cohen’s D=0.58.
+We further conducted zero-inflated negative binomial regres-
+sion19 over patent citation and paper citation count in CHI, CSCW,
+UIST, and UbiComp and get regression coefficient of 0.0233, 0.0172,
+0.0316, and 0.0175 respectively (𝑝 < .001). The coefficient indicates
+that highly-cited papers in academia are indeed more likely to be
+cited by patents. Such a relationship is especially salient at UIST.
+4.2
+RQ2: When is the impact of HCI research
+on patents?
+How long does it take for patents to recognize papers? To examine
+this question, we investigated the time lag between patent and
+paper.
+The time lag between patent and paper is long and getting
+longer. To measure how long it takes for an HCI paper to be rec-
+ognized by patents, for each patent, we investigated the time lag
+between the issue date of the patent and the publication date of
+all papers it cited from our four chosen venues. We measured the
+lag from the patent backward rather than from the paper forward
+because we cannot know whether a paper will receive a citation
+but has not yet—but we can know how far back a patent’s citations
+reach.
+19Zero-inflated negative binomial regression is ideal for modelling count-based de-
+pendent variables with zeroes, which corresponds to our data where a significant
+proportion of HCI papers get no patent citation.
+In the four premier HCI venues, the average patent-paper lag is
+10.5 years (𝜎 = 6.8 years), indicating that patents on average refer-
+ence HCI research papers published 10.5 years before the patent
+filing date but there is significant variance over the time lag.
+We then studied how the time lag varies over time by aggregat-
+ing the patent-paper time lag at the individual patent levels. As
+Figure 5a) shows, the median difference between the time the cited
+paper is published and the time the paper is cited by the patent, is
+becoming larger from 1989 to 2014 for all the venues from about
+around 5 years to around 10 − 15 years. However, since 2014, this
+trend bifurcates among different venues: the time lag for CSCW in-
+creases to over 15 years and Ubicomp decreases to about 10 years in
+2017. We also noticed that all venues have nearly indistinguishable
+trends except Ubicomp, which has about 3 years of time lag lower
+than other venues. In recent years, CSCW takes the longest time to
+be recognized by patents, while UIST and UbiComp take a shorter
+time, which could be explained by the fact that more system-driven
+works are likely to diffuse more quickly into practice.
+We also examined the time lag between the patent and its most
+recent cited paper (Figure 5b), testing how recent the freshest re-
+search is that patents draw on. These general trends are consistent
+with the median time lag. Again, the difference between the time its
+most recent cited paper was published and the time it is patented
+also becomes larger from 1989 to 2011 for all the venues, from less
+than 5 years to around 10 years. This increase gradually slowed
+down, leading to a slight decrease in more recent years.
+The patent citation also involves different sources, some are
+added by the applicants/inventors, while others are added by patent
+examiners. The dataset we used also provides a breakdown of refer-
+ence types, including applicant/inventor added, the examiner added,
+other, and unknown types. References added by patent examiners
+are generally more recent (average time lag: 6 years) than what the
+inventor added (average 11.8 years), although similar trends of long
+time lags and increasing time lags are still observed.
+
+Patents Citing HCl Research
+CHI
+Number of patents
+1000
+CSCW
+UIST
+800
+UbiComp
+600
+400
+200
+0
+1990
+1995
+2000
+2005
+2010
+2015
+YearPatents Citing HCl Research
+2000
+SIGCHI
+patents
+1500
+of
+1000
+Number
+500
+0
+1990
+1995
+2000
+2005
+2010
+2015
+YearConference’17, July 2017, Washington, DC, USA
+Hancheng Cao et al.
+Title
+Patent Citations
+Paper Citations
+Year Published
+CHI
+A multi-touch three dimensional touch-sensitive tablet
+708
+231
+1985
+PaperLink: a technique for hyperlinking from real paper to electronic content
+200
+134
+1997
+Bringing order to the Web: automatically categorizing search results
+196
+486
+2000
+A study in two-handed input
+175
+544
+1986
+Generalized fisheye views
+175
+2180
+1986
+SmartSkin: an infrastructure for freehand manipulation on interactive surfaces
+166
+770
+2002
+AppLens and launchTile: two designs for one-handed thumb use on small devices
+159
+133
+2005
+Active click: tactile feedback for touch panels
+156
+195
+2001
+Finding others online: reputation systems for social online spaces
+153
+100
+2002
+Applying electric field sensing to human-computer interfaces
+142
+272
+1995
+CSCW
+GroupLens: an open architecture for collaborative filtering of netnews
+185
+5771
+1994
+WebSplitter: a unified XML framework for multi-device collaborative Web browsing
+166
+186
+2000
+Blogging as a social activity, or, would you let 900 million people read your diary?
+121
+584
+2004
+MMConf: an infrastructure for building shared multimedia applications
+106
+313
+1990
+An experiment in integrated multimedia conferencing
+103
+157
+1986
+Collaboration using multiple PDAs connected to a PC
+94
+391
+1998
+Interaction and outeraction: instant messaging in action
+90
+1225
+2000
+Providing presence cues to telephone users
+83
+177
+2000
+Design of a multi-media vehicle for social browsing
+72
+331
+1988
+Distributed multiparty desktop conferencing system: MERMAID
+69
+153
+1990
+UIST
+Sensing techniques for mobile interaction
+254
+592
+2000
+The world through the computer: computer augmented interaction with real-world environments
+227
+487
+1995
+HoloWall: designing a finger, hand, body, and object sensitive wall
+197
+243
+1997
+A survey of design issues in spatial input
+166
+417
+1994
+Tilting operations for small screen interfaces
+158
+412
+1996
+Multi-finger and whole hand gestural interaction techniques for multi-user tabletop displays
+156
+527
+2003
+DiamondTouch: a multi-user touch technology
+153
+1336
+2001
+The document lens
+135
+416
+1993
+The DigitalDesk calculator: tangible manipulation on a desk top display
+132
+324
+1991
+Pad++: a zooming graphical interface for exploring alternate interface physics
+131
+754
+1994
+UbiComp
+Validated caloric expenditure estimation using a single body-worn sensor
+113
+83
+2009
+InfoScope: Link from Real World to Digital Information Space
+67
+34
+2001
+Self-Mapping in 802.11 Location Systems
+63
+130
+2005
+The NearMe Wireless Proximity Server
+62
+162
+2004
+Predestination: Inferring Destinations from Partial Trajectories
+51
+498
+2006
+UbiTable: Impromptu Face-to-Face Collaboration on Horizontal Interactive Surfaces
+40
+261
+2003
+Accurate GSM Indoor Localization
+37
+537
+2005
+Very Low-Cost Sensing and Communication Using Bidirectional LEDs
+34
+157
+2003
+Particle Filters for Location Estimation in Ubiquitous Computing: A Case Study
+33
+254
+2004
+PowerLine Positioning: A Practical Sub-Room-Level Indoor Location System for Domestic Use
+31
+152
+2006
+Table 1: Top CHI, CSCW, UIST, and UbiComp papers cited by patents. The majority of them are highly-cited papers in academia
+whose major contribution is a system.
+All results here indicate that patents mostly cite old research,
+and are citing increasingly older research, which holds true across
+venues and reference types. This conclusion is largely identical to
+what is found in science in general [52]. We replicated our analysis
+on other areas of Computer Science in a similar way as in Sec
+4.1, finding that the time lag between patent and their referenced
+papers for AI, Natural Language Processing, and Computer Vision
+are 17 years, 13 years, and 10 years respectively, suggesting similar
+patterns across subfields in Computer Science.
+HCI research has moved on by the time a paper receives
+patent attention. Has the HCI community left an idea behind
+by the time industry gets interested? Concerns circulate that HCI
+has a reputation for trend following and jumping to new shiny ar-
+eas every few years [12, 32]. Are patent-cited papers still receiving
+academic interest by the time it starts receiving patent citations?
+To answer this question, for all papers from the four chosen venues
+that eventually get cited by patents in our dataset, we compare
+(a) the time lag between the publication year of the paper and the
+issue year of the first patent that cites the research paper (first
+
+A Large-scale Analysis of Patent Citations to HCI Research
+Conference’17, July 2017, Washington, DC, USA
+Figure 4: Papers cited by patents receive more academic citations in HCI.
+Figure 5: The time lag between patent and paper is long and getting longer across venues.
+patent citation lag), and (b) the time lag between the publication
+year of the paper and the paper’s “peak citation year” when the
+research paper gets the most academic citations (peak citation lag).
+Peak citation lag averages 5.74 years in our dataset, compared
+with 7.48 years for first patent citation lag.20 A paired t-test confirms
+20The first patent citation lag is lower than patent backward citation lag reported
+earlier (10.5 years) due to right censoring, i.e. recent patent-cited papers are biased
+towards short lags since those with long lags have not yet been observed in the dataset.
+Peak citation lag have similar issues. If we allow paper enough time to accrue patent
+citations, e.g. focus the analysis on papers published before 2000 (cutoff year), we get
+an average first patent citation lag of 10.4 years (thus replicating the prior results)
+that the difference between these two lags are significant 𝑡(3740) =
+18.3 (𝑝 < .001), Cohen’s D=0.38. This result supports the concern
+that HCI’s focus shifts to other topics by the time industry take up
+an idea.
+Self-cite tends to be faster. One exception to this temporal
+pattern is that self-citation patents have a shorter patent-paper time
+lag. Since 2008, the time lag for the non-self-cite patents increased
+and peak citation lag of 7.5 years. We varied the cutoff year, and found on average
+first patent citation lag is always longer than peak citation lag which suggests the
+robustness of our finding.
+
+Non patent-cited
+Patent-citedThe median time lag of the paper
+cited by patents in year X
+20
+CHI
+(Year)
+CSCW
+15
+UIST
+Time difference (
+UbiComp
+10
+5
+0
+1990
+1995
+2000
+2005
+2010
+2015
+YearThe time lag of the most recent paper
+cited by patents in year X
+20
+CHI
+(Year)
+CSCW
+15
+UIST
+Time difference (
+UbiComp
+10
+5
+0
+1990
+1995
+2000
+2005
+2010
+2015
+YearConference’17, July 2017, Washington, DC, USA
+Hancheng Cao et al.
+rapidly and was above 14.6 years in 2018, while the self-cite patents
+remain below 6.3 years, which suggests that papers transferred
+faster by authors themselves into patents compared with those
+transferred by others.
+4.3
+RQ3: Where is the impact of HCI research
+on patents?
+Which HCI research topics are the focus of industry activity? To
+answer this question, we compare non-patent-cited HCI papers
+to patent-cited HCI papers in the four chosen venues via Latent
+Dirichlet Allocation (LDA), a classic method of topic modeling [6].
+LDA automatically discovers topics within documents, where each
+topic is represented as a probability distribution of words. Each
+document can also be represented as a probability distribution over
+different topics.
+We concatenated each paper title with its abstract (if available) to
+represent its contents. Similarly, we concatenated each patent title
+with its abstract (if available) to represent the patent’s contents.
+We then tokenized the text corpora into unigrams and bigrams,
+filtered out terms that appear fewer than 5 times in the corpus,
+removed stop words in English, and then ran LDA modeling. We
+varied the number of topics and align on seven topics resulting
+in the highest quality topics. Figure 6 reports the result. Through
+checking representative documents and word clusters with HCI
+experts, we titled each topic: topic 0 is related to patent terms, the
+topic is 1 on modalities, topic 2 is system interaction, topic 3 is on
+evaluations, topic 4 is on theory, topic 5 is on social and experience
+design, and topic 6 is on input techniques.
+We then computed the topic distributions for each document
+(paper or patent) in our corpus, then aggregated topic distributions
+of all documents within a specific year that belong to a certain doc-
+ument category (patents, patent cited papers, or non-patent cited
+papers) so as to get an estimated number of documents that belong
+to a particular topic for that document category for a particular year.
+In the first row of Figure 7, we plotted the topic distribution for
+patent-cited HCI papers (left), non-patent cited HCI papers (middle),
+and patents (right), i.e., how many papers belong to topic X in year
+Y. The second row of Figure 7 normalizes this topic distribution, i.e.,
+what is the proportion of topic X in year Y for a specific document
+category, to better illustrates the distribution pattern.
+As can be observed from Figure 7, system interaction has domi-
+nated the patent-cited HCI papers over time, indicating that system-
+oriented research has been of considerable importance in patent-
+cited HCI research. From 1980 to 2000, about 40% patent-cited HCI
+paper are system interaction related. After 2000, the percentage
+of system interaction decreased to about 20% but began expand-
+ing again in 2015. We also observed that input techniques have
+expanded significantly over time and reached nearly 20% after 2015.
+Evaluations have also grown in general and contributed about 20%
+of all patent-cited HCI papers.
+In comparison, the topic distribution of non-patent cited papers
+shows a very different pattern. The results mirror the methodolog-
+ical plurality of HCI, where not all contribution types have an
+industry impact. Theory work is highly visible in non-patent cited
+HCI papers over time, though the proportion is gradually decreas-
+ing from about 40% before 2000 to about 20% in 2018. Social and
+experience design has grown significantly from nearly 0 percent in
+1980 to about 20% in 2018, indicating behavior-oriented research
+has been of considerable importance in non-patent-cited HCI pa-
+pers. Evaluations and system interaction contributed to about half
+of all non-patent-cited HCI papers in 1980, but this percentage has
+decreased to about 30% in 2018. Through unpaired t-test, we further
+verify there exist statistically significant differences between topic
+distributions in patent-cited papers and non-patent cited papers:
+there is a higher proportion of theory (𝑝 < .001), social & experi-
+ence design (𝑝 < .05) work, and lower proportion of system inter-
+action (𝑝 < .001), modalities (𝑝 < .001) work in non-patent-cited
+HCI papers compared to patent-cited counterparts. We emphasize
+that this is not a negative outcome for theory, behavioral, and other
+research that does not produce artifacts, as they have an impact
+through other channels, or could influence patent in an indirect
+way [19].
+Additionally, the variation of the patents’ topic distribution over
+time is not consistent with that of papers. Since 1990, patent topics
+have been dominated by input techniques,21 which first expand
+from 1990 to 1993, then slightly shrink from 1993 to 2010 and
+expand again since 2010. In 2018, about 40% of patents that cite
+HCI research papers are input techniques. We also observed this
+growth in patent-cited HCI papers, but not this significant.
+4.4
+RQ4: Who is involved in the process of
+recognizing HCI research on patents?
+Last, we investigate through the four premier HCI venues which
+institutions are most likely to develop patents that recognize HCI
+research, and which institutions conduct HCI research that are most
+cited by patents. Such analysis is important because it identifies
+the role of different stakeholders within the technology translation
+landscape [19].
+Apple, Microsoft, IBM, but no longer Xerox: top institutes
+citing HCI research. We examined who are the top patent as-
+signees (the entity that has the property right to the patent, e.g.
+firm) that cite HCI research. The top patent assignees have been
+dominated by companies: Apple, Microsoft, and International Busi-
+ness Machines Corporation (IBM) are the top three companies
+that were granted the highest number of HCI-citing patents in the
+dataset. Other rise and fall over time. See appendix C for more
+details.
+PARC, CMU, MIT: top institutes that publish patent-cited
+research. We assessed the institutes that published the most patent-
+cited HCI papers across the years. As Figure 8 shows, contrary to
+the fact that top patent assignees have been dominated by indus-
+tries, top institutes that published patent-cited HCI papers have
+been a combination of universities and companies. Top universi-
+ties include Carnegie Mellon University, Massachusetts Institute of
+Technology, University of California, and University of Washington.
+Top companies that published patent-cited HCI papers include Xe-
+rox Palo Alto Research Center and Microsoft. The ratio of patents
+cited among all HCI papers significantly dropped from nearly half
+before 2005 to less than 30% for most institutes after 2005, due to
+21We exclude analysis of topic - ‘patent terms’ as the topic is generic language use in
+patents.
+
+A Large-scale Analysis of Patent Citations to HCI Research
+Conference’17, July 2017, Washington, DC, USA
+Figure 6: Topics were identified through a Latent Dirichlet Allocation (LDA) analysis of the combined paper-patent corpus.
+Figure 7: The first row shows the breakdowns of papers across 7 topics in HCI over time. The second row depicts the per-
+centage of each topic in terms of paper number. Three columns depict "topic distribution of patent-cited HCI papers", "topic
+distribution of non-patent cited HCI papers" and "topic distribution of patents" respectively. System Interaction dominates
+the patent-cited HCI papers while Theory dominates the non-patent cited HCI papers and Input Techniques dominate patents
+over time.
+the fact that the total number of HCI papers grew significantly and
+the right censoring issue.
+Overall, 35.5% of Microsoft’s papers, 31.0% of IBM’s, and 65.1%
+of Xerox’s were cited by patents. In comparison, universities have
+a lower rate of papers cited by patents, e.g. 25.2%, 15.3%, 26.9% of
+papers were recognized among Carnegie Mellon University, the
+University of California system, and MIT respectively. This indi-
+cates that among institutes publishing the most HCI papers, the
+
+Topic O: Patent Terms
+Topic 1: Modalities
+Topic 2: System Interaction
+datum. system
+support
+video
+environment
+tool
+use
+object
+associate
+'displaybase
+visualcharacter
+user
+design
+user
+device
+audio
+voicetext
+application
+provide
+information receive
+base
+time
+virtual
+system
+include
+first
+user
+interactive
+document
+speech
+Topic 6:Input Techniques
+interface
+display
+include
+content method
+present
+interaction
+position
+ target
+word
+surface
+user
+input
+touch
+sensor
+display
+method
+control
+gesture
+device
+Topic 3: Evaluations
+Topic 4: Theory
+Topic 5: Social & Experience Design
+first image object
+study
+time
+human
+system
+design
+base
+design
+user performance
+work
+medium
+study online
+use
+result
+group
+study
+technology
+child
+behavior method
+community
+taskactivity
+support
+social
+people
+systemdatum
+practice
+support
+paper, technology
+experience
+use
+hci
+challenge
+gameparticipant
+model
+analysis
+process
+play
+research
+playerPatent terms
+Modalities
+System Interaction
+Evaluations
+Theory
+Social&experience design
+Input techniquesPatent terms
+Modalities
+System Interaction
+Evaluations
+Theory
+Social&experience design
+Input techniquesPatent terms
+Modalities
+System Interaction
+Evaluations
+Theory
+Social&experience design
+Input techniquesConference’17, July 2017, Washington, DC, USA
+Hancheng Cao et al.
+Figure 8: Institutions publishing the most patent-cited research.
+papers from the industry have a higher proportion of papers rec-
+ognized by patents. However, the difference between industry and
+universities becomes smaller when removing self-citing patents.
+Self-citation. We also explored the degree of self-citation. We
+find that 13.9% of patents self-cite the inventor’s own research.
+Although the number of self-citing patents is growing, the percent-
+age of self-citations in all HCI patent citations is decreasing from
+around 20% to 5% in recent years. This suggests that while the HCI
+field is expanding, the number of researchers directly referring to
+their own research in patents is not growing at the same rate. Most
+of the self-citations also come from industry, with Microsoft and Xe-
+rox constituting 34.8% and 11.2% of total self-citations. Self-citation
+from academia is much less common.
+Summary of conclusions: Through our analysis, we find that
+HCI research has had a significant impact on patents, with an in-
+creasing number of patents recognizing research in CHI, CSCW,
+UIST, and UbiComp. Patents are more likely to refer to systems-
+oriented and highly-cited research in academia. However, the time
+lag between patent and paper is long (>10 years) and getting longer,
+suggesting HCI research and practice may be inefficiently con-
+nected. We further verify the robustness of our main findings
+through two additional analyses, which we report in Appendix
+D.
+5
+DISCUSSION
+In this section, we discuss the implications of our findings:
+5.1
+The patent-research relevance landscape in
+HCI
+By combining the findings from our large-scale analyses with that
+of prior qualitative evidence established by literature (e.g. case
+studies [15], personal experience, [22] and interviews [19]), we can
+now offer a more comprehensive picture of the HCI translation
+landscape.
+The impact of HCI research on patents: Our work largely
+corroborates literature arguing for the considerable impact of HCI
+research on practice [12, 32, 57]. In our analysis, among HCI re-
+search papers in CHI, CSCW, UIST, and UbiComp, 20.1% of all papers
+have been referenced by patents, and 13.4% for SIGCHI sponsored
+venues overall. This is a rate far higher than science in general
+(1.5% [51]) and prominent journals across multiple scientific fields
+(9.7% [8]). The rate is also higher than bio-medicine, a field that
+has a more systematic technology translation system and a richer
+tradition of studying technology translation, whose proportion is
+7.7% [50].22 HCI research diffuses into the industry at a similar
+rate as Computer Vision (25%) and at a higher rate than NLP (11%),
+both areas of substantial industry funding and interest. Note our
+estimate is a lower bound: given the long time lag of patent-paper
+citations, recent papers may have not fully expressed their impact
+yet (right censoring). When only considering earlier years that do
+not suffer much from right censoring issues (e.g. prior to 2005), we
+see roughly 30%-50% of papers published in those years have been
+cited by patents. For UIST, the proportion is even higher, close to
+80% for many years.
+22Bio-medicine papers from US institutes only—a filter we did not apply for our study
+of HCI—have a proportion of 23.3% [50], which is roughly the same as HCI research.
+
+Patent cited
+Non patent citedPatent cited
+Non patent citedPatent cited
+Non patent citedPatent cited
+Non patent citedA Large-scale Analysis of Patent Citations to HCI Research
+Conference’17, July 2017, Washington, DC, USA
+Issues with the current HCI translation into patents: As
+argued by Bill Buxton in ‘the long nose of innovation’ [12], the
+bulk of innovation takes place over a long period: the mouse was
+first built in 1965 by William English and Doug Engelbart, but was
+only popularized in the 1990s when Microsoft released a large-scale
+commercial mouse; multitouch was published in 1985, but took 22
+years to become a product. Our analysis further demonstrates that
+even the initial step of having research recognized in a patent, which
+may be well before there is an actual product, takes considerable
+time. In fact, the ubiquity of long time delay between research and
+practice, and thus lack of immediate impact on the industry after
+the publication of a research paper, could be one underlying reason
+why many papers on HCI translation argue that HCI lacks practical
+impact [18, 22, 63]. Furthermore, our analysis demonstrates that
+the time lag between patent and research is getting longer over
+time, indicating that the translation process in HCI may become
+more inefficient over time. This result is in line with a general
+trend across science (average over time: 14.4 years), where they
+report an average patent citation to science time lag of about 8
+years in the 1990s, rising to about 15 years in 2018 [52]. The specific
+reason for the (increasing) time lag would need further work. We
+also show that the HCI community often leaves an idea behind
+by the time industry gets interested, as a paper’s peak citation
+lag is generally shorter than the paper’s first patent citation lag.
+The result indicates that with a long time lag, HCI research has
+moved on and is exploring new emerging technologies that are
+not yet reliable enough, cheap enough, power-efficient enough, or
+accurate enough for the industry yet. The observation supports
+the observation that HCI research often plays “the time machine
+game”,23 where it fast forwards into the future by acquiring early
+versions of emerging technology (e.g., VR, AR, multi-touch, AI) and
+exploring the interactive applications of that technology. Unless
+HCI is directly working on reducing those barriers to industry entry
+for that technology, HCI research cannot directly accelerate the
+time lag: it is simply painting a compelling vision of the future
+before that future arrives.
+5.2
+How could the HCI community do better to
+facilitate technology transfer and
+industrial impact?
+Encourage communications and collaborations across academia
+and industry. Through our analysis, we have found that even
+though research articles from both academia and industry are rec-
+ognized by patents, the proportion of papers in academia recognized
+by patents is much lower. While the result could be that industry
+research papers by themselves are more applied than research pa-
+pers from academia, or that industry has more internal incentives
+to have their research patented24, this could also be a sign that prac-
+titioners are not fully aware of some application-oriented advances
+in academia, and that information diffusion between academia and
+practice is inefficient [12].
+23A term attributed to Jeff Pierce, formerly a research manager at IBM Research and
+faculty member at Georgia Tech.
+24Microsoft Research, for example, would award decorative “patent cubes” to re-
+searchers for each new patent they co-authored, which researchers would often stack
+into decorative pyramids and display in their offices
+Our work thus echoes calls for a more inclusive and translation-
+friendly environment [9, 15, 18, 19]: that both academia and in-
+dustry should 1) better recognize the importance of technology
+translation rather than considering translation irrelevant, 2) estab-
+lish more communication and collaboration channels to engage
+people, e.g. SIGGRAPH-style Emerging Tech festivals where aca-
+demic researchers show their published HCI work to an applied
+audience and encourage researchers in serving as advising role
+in the industry, and 3) involve more HCI materials in Computer
+Science curriculum at universities to get ‘future practitioners’ more
+familiar with HCI research ideas, and thus prepare them as trans-
+lational developers who are more likely to bridge academia and
+industry [60]
+Encourage self-driven technology transfer. Self-driven tech-
+nology transfer (e.g. patents recognizing one’s own paper) gen-
+erally happens much faster than technology transfer in general.
+Intuitively, the self-driven transfer would not encounter many of
+the same communication and information diffusion barriers. Self-
+driven technology transfer could also potentially solve many of the
+‘recognition’ issues in the translational process as discussed in prior
+works [32]. However, as shown in our analysis, though the amount
+of self-driven technology transfer in HCI is going up over time, it
+is not on par with the rate of increase for research articles. While
+not all researchers should actively engage in technology transfer,
+there could be more steps to be taken to encourage self-driven tech-
+nology transfer from the academic side so that translation could
+happen more efficiently, e.g. through better supporting and recog-
+nizing attempts to self-translate one’s own research by providing
+legal apparatuses and funding support. Meanwhile, we want to
+emphasize while there are benefits of self-driven transfer, it may
+currently not distribute opportunities equally. For instance, in the
+life sciences [24], women faculty members patent at about 40% of
+the rate of men. It would be important to identify and mitigate these
+potential issues so as to ensure an inclusive technology transfer
+environment. Relatedly, as suggested by prior work [19], there exist
+multiple translational gaps in HCI, and basic researchers should
+also be encouraged to engage more with applied researchers and
+do more system work, which would eventually help translate HCI
+research insights into industry impacts.
+Recognizing translational work in HCI. More broadly, our
+work echoes prior work on the need of recognizing translational
+efforts in HCI. For instance, when allocating funding or considering
+researcher promotion, their impacts in the industry could be taken
+into consideration as a separate metric aside from impacts within
+academia. Our work points to a potential way to quantify one
+important pathway towards HCI research’s impacts on the industry,
+through analyzing patent-to-science citation data.
+Impact signals. Prior approaches to quantifying research im-
+pact mostly focus on impact within academia through bibliomet-
+ric analysis. However, no quantitative metric fully captures the
+complexities of our world. Could the h-index be fruitfully comple-
+mented with other information? (a “patent relevance” p-index?)
+While our analysis show impacts in academia and impacts in patents
+correlate, we also find papers with high patent citations do not nec-
+essarily have high paper citations: in one extreme case, the most
+
+Conference’17, July 2017, Washington, DC, USA
+Hancheng Cao et al.
+patent-cited paper in our dataset, “A multi-touch three-dimensional
+touch-sensitive tablet” [44], is more popular in the patent world
+than in academia. If evaluations primarily consider the academic
+impacts of such research work, the work’s value may have been
+underestimated. As one potential pathway to industry impacts that
+are relatively easy to scale, patents provide a potential signal to
+more holistically evaluate research.
+Of course, patent relevance, or practice relevance in general25,
+is not the solitary metric of scientific value, and research and re-
+searchers should not be judged based on a single metric, e.g. to
+receive funding or get a promotion. Thus, our work should not
+be interpreted as stating that non-patent cited research represents
+any sort of failure. There are many, many examples of influential
+HCI research that is not patented (or even patentable). For instance,
+our work shows that system building or application-oriented HCI
+research is more likely to find relevance in patents rather than
+design-oriented or behavioral research. The result is not an indica-
+tion that applied-oriented research is more valuable: there could
+be the indirect influence of other types of works on application-
+oriented research, e.g. applied research getting inspiration from
+behavior work, as suggested by the translational science model
+in HCI [19] – which we seek to address in future work, and 2)
+it is equally important to maintain a diversity of research ideas,
+which has proven to facilitate greater innovation for science in
+general [35]. If the measurement of this impact is desirable, we will
+require new methods, such as multi-hop influence over citation
+network [1], linguistic concept diffusion [14], from the paper to the
+public or media [77, 78].
+5.3
+Limitations and Future Work
+Patent citations to research are only a proxy signal of industry
+impact, which is a hard-to-quantify concept otherwise. It is only one,
+among many (e.g. open source software, design patterns), potential
+pathway to industry impacts. First, not all patents will turn into
+products or practices, so they may not be actual “industry impact”
+instances (false positives). There could be many other factors, such
+as assignee strategy and resources, that could influence the process.
+Even if a patent does end up as a product, most of the time the
+patent will not be valuable or impactful, with 97% of all patents
+never recouping the cost of filing them26. However, the fact that
+inventors decide to go through the long and expensive process of
+filing a patent to protect their intellectual property does indicate
+they are considering their invention having at least some potential
+to be of relevance to the practice domain, which could be regarded
+as an intended act aiming at industry impact or technology transfer.
+Second, industry impact could happen even if there is no patent-
+ing process involved (false negative), which is not uncommon in
+software [29]: startups will launch products without patents from
+time to time, which is quite different from the innovation landscape
+of more traditional fields; design processes (e.g., usability testing,
+heuristic evaluation), design patterns, and open source software
+(e.g., d3, Vega Lite) also have significant industry impact that is
+not reflected though patents. As such, our analysis of using patent
+25Though arguably it’s much harder to quantify other forms of practice relevance, e.g.
+how research influence design patterns and open source software
+26https://www.forbes.com/sites/stephenkey/2017/11/13/in-todays-market-do-
+patents-even-matter/
+citation to HCI research papers could be different from the actual
+translation landscape: the patent dataset could introduce both false
+positives and negatives, e.g., even if a patent cites a HCI paper, it
+may never be taken up in practice as product, and an actual product
+that gets influenced by HCI research that is unpatentable will not
+be observed and measured through our current approach.
+Despite all the shortcomings of patent citation to science, the
+availability and scale of the dataset make it a rare lens in the in-
+novation literature to enable conclusions on the research-practice
+relationship at scale [50–52]. In our work, in addition to building on
+these methods from the innovation literature, we tied our analysis
+to qualitative evidence discussed in prior works so as to validate
+our findings.
+In future work, we plan to 1) involve more qualitative evidence
+(e.g. interviewing inventors’ motivation behind citing HCI research)
+to further validate our findings, and 2) take more steps to quantify
+how HCI research turns into valuable inventions, e.g. by using
+patent citations to other patents as a proxy of patent value, which
+correlates well with other metrics of patent value, e.g. whether they
+are renewed to a full term, and whether they get licensed [31, 64].
+Our work also currently mostly focuses on measuring industry
+relevance at the paper level, which may not necessarily be the
+principal unit of knowledge: for example, several papers on the
+same idea can get cited by patents. While we have made preliminary
+attempts to analyze the topics prevalent to patents, patent-cited
+research papers, and non-patent cited research papers, future work
+could better study at the concept level what specific research ideas
+are transferred into research, either through keywords provided
+by the author (which is unfortunately not available in our current
+dataset), or natural language processing based approach such as
+phrase mining [14], which may help track transfer of innovations
+at a more fine-grained level.
+Other limitations include: (1) our dataset is focused on United
+States patents, which limits our cultural context and generalizability,
+though arguably a significant proportion of inventors/organizations
+using (and pushing) HCI research in practice are US-based [67];
+(2) while discussing in a descriptive way in our paper with findings
+on the role of academic impacts (section 4.1), topic (section 4.3),
+and institute/actors (section 4.4) in relating to patent impact, we do
+not have causal evidence/analysis on the causal mechanisms what
+cause some papers to have more industry relevance, which is an
+important topic we seek to address in future work, and (3) if there
+are recent trends in the last 5-10 years that have changed these
+patterns, it is still too recent to see their impact.
+6
+CONCLUSIONS
+In this work, drawing inspiration from the innovation literature, we
+quantitatively study one important pathway from HCI research to
+industry impact by conducting a large-scale analysis of how patent
+documents from USPTO refer to research articles in CHI, CSCW,
+UIST, UbiComp and other SIGCHI sponsored venues. We contribute
+to the literature by measuring to what extent HCI research has
+been featured in patent citations, with a high proportion of papers
+referenced in patents. Patents are more likely to refer to systems-
+oriented and highly-cited research in HCI. However, we also reveal
+potential translation issues: HCI research and practice may not be
+
+A Large-scale Analysis of Patent Citations to HCI Research
+Conference’17, July 2017, Washington, DC, USA
+efficiently coupled, since the time lag between paper and patent
+is long and getting longer. Our work not only demonstrates the
+potential of using patent citation data to science as a powerful tool
+to study the industry impact of HCI research, but also points to
+suggestions for the HCI community to better facilitate translation
+from research to practice.
+ACKNOWLEDGMENTS
+The authors thank Yian Yin for helpful suggestions on polishing
+the work, and Mary Czerwinski, Bongshin Lee, Lucy Lu Wang,
+James Zou, Shumin Zhai and many others for insightful discus-
+sions. Hancheng Cao was supported by Stanford Interdisciplinary
+Graduate Fellowship.
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+A Large-scale Analysis of Patent Citations to HCI Research
+Conference’17, July 2017, Washington, DC, USA
+A
+DETAILS OF DATA ACQUISITION
+Here we provide details of the data acquisition procedure that
+generate our final analyzed data.
+Patent citation to science that connects USPTO to Microsoft
+Academic Graph. To capture the information required by patent
+citation to science, we utilize a public dataset available over Zen-
+odo.27 We leverage the patent-to-article citations of Version v37 (Jul
+19, 2022), including _pcs_mag_doi_pmid.tsv and papercitations.tsv.
+For _pcs_mag_doi_pmid.tsv, we mainly focus on the fields reftype,
+diff_month, selfciteconf_avg. We focus on fields citingpaperid
+and citedpaperid in papercitations.tsv, which we used to join with
+Microsoft Academic Graph Metadata.
+Microsoft Academic Graph Metadata. Microsoft Academic
+Graph Metadata is also available over Zenodo.28 The data files we
+utilize include authoridname_normalized.tsv, conferenceidname.tsv,
+paperauthoridaffiliationname.tsv, paperauthororder.tsv, paperconfer-
+enceid.tsv and paperyear.tsv.
+USPTO Metadata. We acquire USPTO metadata from PatentsView.29
+We utilize datafiles assignee, inventor, patent, patent_assignee, and
+patent_inventor.
+Semantic Scholar. We request Semantic Scholar API30 with re-
+search article IDs retrieved from Microsoft Academic Graph Meta-
+data for extra paper information. The fields we queried include
+title, abstract, venue, year, referenceCount, citationCount,
+authors, as well as name, affiliations, paperCount, and
+citationCount associated with each author.
+we retrieved all the above data in Aug 2022.
+B
+SIGCHI SPONSORED VENUES
+The 20 SIGCHI venues that we include in our analysis are: Hu-
+man Factors in Computing Systems (CHI), User Interface Software
+and Technology (UIST), Ubiquitous Computing (UbiComp), Con-
+ference on Computer Supported Cooperative Work (CSCW), Con-
+ference on Tangible and Embedded Interaction (TEI), Symposium
+on Eye Tracking Research & Application (ETRA), International
+Conference on Supporting Group Work (GROUP), Conference on
+Intelligent User Interfaces (IUI), Creativity and Cognition (C&C),
+Interaction Design and Children (IDC), International Conference
+on User Modeling, Adaptation, and Personalization (UMAP), Sym-
+posium on Engineering Interactive Computing System (EICS), Con-
+ference on Automotive User Interfaces and Interactive Vehicular
+Applications (AutomotiveUI), Conference on Human-Robot Interac-
+tion (HRI), International Conference on Computational Collective
+Intelligence (CI), Conference on Recommender Systems (RecSys),
+Annual Symposium on Computer-Human Interaction in Play (CHI
+PLAY), International Conference on Multimodal Interaction (ICMI),
+Symposium on Spatial User Interaction (SUI), Symposium on Vir-
+tual Reality Software and Technology (VRST).
+27http://relianceonscience.org
+28http://relianceonscience.org
+29https://patentsview.org/download/data-download-tables
+30https://api.semanticscholar.org/api-docs/graph#tag/Paper-Data/operation/get_
+graph_get_paper_references
+In total, there are 57,385 papers where 13.4% of them (7678 pa-
+pers) have been cited by patents in our dataset.
+C
+TOP PATENT ASSIGNEES OVER TIME
+We show top patent assignees over time in Fig 9.
+D
+ADDITIONAL ANALYSIS ON
+NON-SELF-CITING PATENTS AND
+NON-RESEARCHER PATENTS
+We provide two additional analyses using a subset of four pre-
+mier venues to further verify the robustness of our findings. To rule
+out the possibility that the impacts of HCI research on patents is a
+result of self-cite, or driven primarily by HCI researchers – thus one
+may argue the impact of HCI research in industry is actually limited
+– we run the same analysis using 1) patents that do not include
+self-cite to one’s own research papers (“non-self-citing patents”)),
+which is 26, 382 (86.04% of original patents), and 2) patents that
+are invented by people who have never published any CHI, CSCW,
+UIST or UbiComp research papers ( (“non-researcher” patents),
+which we operationalized through excluding patents where inven-
+tor last name have appeared in author lists of papers from the four
+venues we focused on.31 This results in 5, 251 (17.12% of original
+patents) of “non-researcher” patents. We find consistent patterns in
+our main analysis where a high proportion of HCI research papers
+are cited by patents, and there is a long time lag between patent
+and paper. More specific results are as follows:
+Proportion of papers that get cited by patents. The propor-
+tion of papers cited by non-self-citing patents is plotted in Figure 10
+and the ratio rises and persists since 1990 at over 30%. At UIST in
+particular, the patent citation ratio reaches 60% - 80% from 1990 -
+2010. This suggests that non-self-citing patents, similar to our main
+result, recognize a considerable number of HCI research papers.
+Identical trends can be observed for non-researcher patents, as
+shown in Figure 13.
+Increasing citations to HCI research in patents. Figure 11
+shows the number of non-self-citing patents that cite HCI research
+over time. It can be observed that non-self-citing patents first in-
+crease in 2000 and then peak around 2014, ranging from 200 to 1000
+across different venues. This agrees with the overall trend reported
+in the main paper.
+Identical trends can be observed for non-researcher patents, as
+shown in Figure 14.
+Time lag between patent and paper is long and getting longer.
+The temporal trend of the measured time lag between the issue date
+of non-self-citing patents and the publication date of HCI papers
+they cited are plotted in Figure 15a. Similar to the trend reported
+in the main results (Figure 5), the median time lag increased from
+1989 to 2014 for all the venues from about around 5 years to around
+10−15 years while since 2014, this trend bifurcates among different
+venues. The time lag between the patent and its most recent cited
+paper (Figure 15b ) is also examined, showing identical trends.
+Identical trends can be observed for non-researcher patents, as
+shown in Figure 12.
+31This set of patents is a smaller set than actual “non-researcher” patents. The primary
+objective is to ensure a set of patents with inventors who, for sure, have never published
+papers in the four academic venues we studied without tedious author disambiguation.
+
+Conference’17, July 2017, Washington, DC, USA
+Hancheng Cao et al.
+Figure 9: Top patent assignees that cite HCI research over time.
+
+A Large-scale Analysis of Patent Citations to HCI Research
+Conference’17, July 2017, Washington, DC, USA
+Figure 10: (Non-self-cite) Left: the number of papers published by each conference per year (red) and the number of papers
+published in that year that were later cited by at least one patent (blue), at ACM CHI, CSCW, UbiComp, and UIST.
+
+CHI
+CHI
+100
+1200
+Published
+Percent of published papers later cited by patents
+Published and later cited by patents
+80
+S1000
+per
+(%)
+800
+60
+Percent (
+of
+600
+ber
+40
+400
+wnN
+20
+200
+0
+0-
+2010
+1980
+1985
+1990
+1995
+2000
+2005
+2015
+1985
+1990
+1995
+2000
+2005
+2010
+2015
+1980
+Year
+Year
+CSCW
+CSCW
+100
+Published
+Percent of published papers later cited by patents
+300
+Published and later cited by patents
+80
+(%)
+60
+Percent (
+40
+20
+0
+1995
+2015
+1995
+1990
+2000
+2005
+2010
+1985
+1990
+2000
+2005
+2010
+2015
+1980
+1985
+Year
+Year
+UIST
+UIST
+100
+Percent of published papers later cited by patents
+150
+Published
+Published and later cited by patents
+80
+(%)
+100
+60
+Percent
+75
+Number
+40
+50
+20
+25
+0
+0°1980
+1980
+1985
+1990
+1995
+2000
+2005
+2010
+2015
+1990
+2000
+2005
+2010
+2015
+1985
+1995
+Year
+Year
+UbiComp
+UbiComp
+100
+500
+Published
+Percent of published papers later cited by patents
+Published and later cited by patents
+80
+"400
+(%)
+60
+Percent
+of
+40
+20
+0
+1985
+1980
+1990
+1995
+2000
+2005
+2010
+2015
+1985
+1995
+2005
+2010
+2015
+1990
+2000
+Year
+YearConference’17, July 2017, Washington, DC, USA
+Hancheng Cao et al.
+Figure 11: (Non-self-cite) The number of patents that cite HCI papers over time.
+Figure 12: (Non-self-cite) The time lag between patent and paper is long and getting longer for different types of citations and
+venues.
+
+Patents Citing HCl Research
+CHI
+1000
+Number of patents
+CSCW
+UIST
+800
+UbiComp
+600
+400
+200
+0
+1990
+1995
+2000
+2005
+2010
+2015
+YearThe time lag of the most recent paper
+cited by patents in year X
+20
+CHI
+(Year)
+CSCW
+15
+UIST
+Time difference (
+UbiComp
+10
+5
+0
+1990
+1995
+2000
+2005
+2010
+2015
+YearThe median time lag of the paper
+cited by patents in year X
+20
+CHI
+(Year)
+CSCW
+15
+UIST
+Time difference (
+UbiComp
+10
+5
+0
+1990
+1995
+2000
+2005
+2010
+2015
+YearA Large-scale Analysis of Patent Citations to HCI Research
+Conference’17, July 2017, Washington, DC, USA
+Figure 13: (Non-researcher) Left: the number of papers published by each conference per year (red) and the number of papers
+published in that year that were later cited by at least one patent (blue), at ACM CHI, CSCW, UbiComp, and UIST.
+
+CHI
+CHI
+100
+1200
+Published
+ Percent of published papers later cited by patents
+Published and later cited by patents
+80
+S1000
+per
+(%)
+800
+60
+Percent (
+of
+600
+ber
+40
+400
+wnN
+20
+200
+0
+0-
+1990
+1995
+2010
+2015
+1980
+1985
+2000
+2005
+1985
+1990
+1995
+2000
+2005
+2010
+1980
+2015
+Year
+Year
+CSCW
+CSCW
+100
+Published
+ Percent of published papers later cited by patents
+300
+Published and later cited by patents
+80
+(%)
+60
+Percent (
+Number
+40
+100
+20
+0
+0°1980
+1985
+1990
+1995
+2005
+2010
+2015
+2000
+1985
+1990
+2000
+2005
+2010
+1980
+1995
+2015
+Year
+Year
+UIST
+UIST
+100
+150
+Percent of published papers later cited by patents
+Published
+Published and later cited by patents
+80
+(%)
+100
+60
+Percent (
+75
+Number
+40
+50
+20
+25
+0
+0°1980
+1980
+1985
+1990
+1995
+2000
+2005
+2010
+2015
+1990
+2000
+2005
+2010
+2015
+1985
+1995
+Year
+Year
+UbiComp
+UbiComp
+100
+500
+Published
+Percent of published papers later cited by patents
+Published and later cited by patents
+80
+"400
+(%)
+60
+Percent
+of
+40
+20
+0
+1985
+2015
+1980
+1990
+1995
+2000
+2005
+2010
+1985
+1995
+2005
+2010
+1990
+2000
+2015
+Year
+YearConference’17, July 2017, Washington, DC, USA
+Hancheng Cao et al.
+Figure 14: (Non-researcher) The number of patents that cite HCI papers over time.
+Figure 15: (Non-researcher) The time lag between patent and paper is long and getting longer for different types of citations
+and venues.
+
+Patents Citing HCl Research
+400
+CHI
+Number of patents
+CSCW
+UIST
+300
+UbiComp
+200
+100
+0
+1990
+1995
+2000
+2005
+2010
+2015
+YearThe time lag of the most recent paper
+cited by patents in year X
+20
+CHI
+(Year)
+CSCW
+15
+UIST
+Time difference (
+UbiComp
+10
+5
+0
+1990
+1995
+2000
+2005
+2010
+2015
+YearThe median time lag of the paper
+cited by patents in year X
+20
+CHI
+(Year)
+CSCW
+15
+UIST
+Time difference (
+UbiComp
+10
+5
+0
+1990
+1995
+2000
+2005
+2010
+2015
+Year
\ No newline at end of file