Exploring Patent Networks Using U.S. Patent and Trademark Office Patent Records

Project Summary

This data expedition explores the local (ego) patent citation networks of three hybrid vehicle-related patents. The concept of patent citations and technological development is a core theme in innovation and entrepreneurship, and the purpose of these network explorations is to both quantitatively and visually assess how innovations are connected and what these connections mean for the focal innovations and the technologies that draw on those patents in the future. The expedition was incorporated as part of the Sociology of Entrepreneurship class, where students are thinking about the emergence and diffusion of innovations.

Themes and Categories

Graduate Students: Josh Bruce (joshua.bruce@duke.edu) and Molly Copeland (molly.copeland@duke.edu)

Faculty: Dr. Martin Ruef

Course: Sociology of Entrepreneurship (SOCIOL/MMS 359)

Guiding Questions

The main research question students answered with this expedition was: What are the characteristics of innovation networks? To address this, how can we conceptualize and visualize patent citations as evidence of innovation network structures? Then, how can we characterize features of innovation ego-networks in patents with common ego-network measures (such as size or density)?

To answer these questions, groups of students worked with different patent ego-networks in the data that are relevant to broader topics they cover in the class and class projects. Students adapted visualizations to reflect characteristics and attributes determined to be of interest within the data (e.g., patent technology classes), chose the descriptive measures that provide insight into broader questions in other innovation projects in the class, and groups of students compared descriptive measures across networks using common network measures. Additionally, after constructing the ego-networks, students brainstormed potential pitfalls of conducting and reporting descriptive network analysis (e.g., consistency in visualization, comparing across networks, interpreting network measures, coding and R-specific issues that commonly arise).

The specific techniques and teaching goals include:

  • Introduction to R and RStudio (with a focus on the network-specific package igraph)
  • Introduction to RStudio workflow components (e.g., installing and loading packages, directories and workflow, what is coding and why do we do it)
  • Introduction to network basics
    • Conceptually, we discuss:
      • What is a network?
      • How can citations make a network (i.e., ego-networks of citation) and what other data could be conceptualized as networks?
      • What do these networks tell us?
    • Practically:
      • Identifying network components (e.g., nodes, edges, attributes)
      • Constructing network objects
      • Visualizing networks
      • Calculating typical network descriptive measures
  • Meaningful interpretation of networks related to innovation

Below is an example of the network students learned to create, based on a single focal patent in the center of the network. Nodes are sized according to their number of technological claims and shaded by their primary technology class.

Example of network

The Dataset

The data for this Expedition consist of patent records from the US Patent and Trademark Office (USPTO). The raw data are individual patents granted by the USPTO, which include information on the nature of the technology being patented, the year the patent is granted, the patents each focal patent cites as “prior art,” and numerous other data points. The version of the data used for this Expedition is publicly available from the PatentsView project (www.patentsview.org), a joint effort between the USPTO, American Institutes of Research, NYU, UC Berkeley, and other stakeholders. PatentsView.org exists to make the bulk USPTO data files accessible for research and practitioner use.

The dataset used in the Expedition is a small subset of the total patent database. It was collected by identifying three focal patents (the egos in the example networks used by students) and then all patents that cited those three focal patents up to 2017. The citations among these patents were also collected, creating three distinct patent ego-networks. For both focal and citing patents, we have metadata on the primary technology class, patent title, abstract, number of claims, and year granted by the USPTO.

Course Materials

Networks of Innovation (Powerpoint presentation)

Patent Nets Code.R







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Learning objectives

  • Learn basics of exploratory data analysis (descriptive statistics, generating plots) in R
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  • Increase understanding of association between hormones and mating behavior
  • Increase science literary skills (e.g. generating predictions, interpreting results)

Course Materials


The lesson started with a brief Powerpoint presentation to introduce the class to basic information on baboon sociality and reproduction. At the end of the Powerpoint, students were introduced to 2 key hypotheses about baboon reproduction that they then explored using R. The class was divided into small groups where students worked together to propose possible predictions to test these hypotheses (and filled out the first section of the provided worksheet) before seeing the provided predictions.

Students then worked through the provided R script and accompanying dataset to test these predictions. Students with prior experience with R were able to skip ahead by following instructions on the R script, while most of the class worked through the script step-by-step with guidance from instructors. The course instructors walked the students through most of the script, then let students work independently to complete Data Visualization Part 2. Students filled out the worksheet as they went along.

At the end of the R script, students ultimately replicated Figure 1 from Gesquiere et al. 2007. This figure is included as the final slide of the Powerpoint presentation. The end of the class session was used to interpret the figure and discuss how it relates to the 2 project hypotheses.

Student level

This lesson is designed for undergraduate students who have little to no exposure to R or other programming software. It could be easily adjusted for students who are familiar with R or other programming software. This lesson takes about 75 minutes to complete.

The dataset and the Amboseli Baboon Research Project

The dataset for this expedition is a subset of the long-term database of the Amboseli Baboon Research Project, a project co-directed by Drs. Jeanne Altmann, Susan Alberts, Beth Archie, and Jenny Tung. The Amboseli Baboon Research Project has collected demographic, behavioral, genetic, and endocrinological data on a population of wild baboons since 1971 in order to study questions related to animal behavior, life history, behavioral ecology, genetics, and physiology. The project’s database is managed by Jake Gordon at Duke University and Niki Learn at Princeton University.

The unit of analysis for this dataset is a fecal estrogen sample from a cycling1 female. For each fecal sample (n = 843), 6 variables are recorded:

  1. female - identity of the female baboon
  2. cyle_day - day of her reproductive cycle
  3. estrogen - fecal estrogen concentration
  4. swelling_size - sexual swelling size2
  5. alpha_consort - whether or not the female consorted3 with an alpha male4 on that day
  6. nonalpha_consort - whether or not the female consorted with a non-alpha male on that day

This dataset includes data from 93 female baboons, with approximately 10 fecal estrogen samples per female. Minor differences between this dataset and the the dataset used in Gesquiere et al. 2007 are due to small, incremental changes in the database over time.


1 cycling: sexually mature but not pregnant or lactating
2 female yellow baboons exhibit exaggerated sexual swellings (an enlargement/engorgement of the genital and perineal skin) around ovulation
3 consortship: a period in which a male mate-guards a female. Virtually all matings and conceptions occur during consorts

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