Inventor Networks with Neo4j and IP Street

In today’s world, the importance of innovation and intellectual property (IP) cannot be overstated. They drive progress, create competitive advantages, and foster economic growth. Within this context, understanding the dynamics of inventor networks and the impact of IP is crucial. This article explores the use of Neo4j, a leading graph database, and IP Street, a sophisticated IP analytics platform, to analyze inventor networks.

The Role of Inventor Networks

Inventor networks are complex webs of relationships among individuals and entities involved in the creation and development of new inventions. These networks can reveal patterns of collaboration, the flow of knowledge, and the distribution of expertise. Understanding these networks can provide valuable insights into innovation processes, highlight key inventors, and identify potential opportunities for collaboration.

Introduction to Neo4j

Neo4j is a powerful graph database that excels at handling complex, connected data. Unlike traditional relational databases, Neo4j uses graph structures with nodes, edges, and properties to represent and store data. This model is particularly suited for applications where relationships are key, such as social networks, fraud detection, and, in our case, inventor networks.

  • Nodes: Represent entities (e.g., inventors, patents, companies).
  • Edges: Represent relationships between entities (e.g., collaboration, citation).
  • Properties: Attributes or metadata about nodes and edges (e.g., name, date, patent number).

Introduction to IP Street

IP Street is a comprehensive IP analytics platform that provides tools and data to help users understand and leverage intellectual property. It offers services like patent searching, IP valuation, and landscape analysis. With access to vast patent databases and advanced analytical tools, IP Street enables users to gain insights into the competitive landscape, identify trends, and make informed IP-related decisions.

Combining Neo4j and IP Street

By integrating Neo4j with IP Street, we can create a robust system for analyzing inventor networks and their impact on innovation. The process involves several steps:

  1. Data Collection: Gather data from IP Street, including patent records, inventor information, and citation networks.
  2. Data Modeling: Structure this data in Neo4j, defining nodes, relationships, and properties.
  3. Analysis: Use Neo4j’s querying capabilities to explore the inventor networks, identify key players, and understand collaboration patterns.
  4. Visualization: Present the results in a clear and insightful manner using visualization tools compatible with Neo4j.

Data Collection

The first step is to collect relevant data from IP Street. This includes information about patents, inventors, companies, and citation relationships. The data can be obtained through IP Street’s APIs or bulk data services. Key data points include:

  • Patent Information: Patent number, title, abstract, filing date, issue date, and IPC/CPC classifications.
  • Inventor Information: Names, affiliations, and locations.
  • Company Information: Assignees, subsidiaries, and corporate relationships.
  • Citation Networks: Patents cited by a given patent and patents citing it.

Data Modeling in Neo4j

Once the data is collected, the next step is to model it in Neo4j. The following schema can be used:

  • Patent Node: Represents a patent with properties like patent_number, title, abstract, filing_date, issue_date, and classification.
  • Inventor Node: Represents an inventor with properties like name, affiliation, and location.
  • Company Node: Represents a company with properties like name and location.
  • Cites Relationship: Represents a citation relationship between two patents.
  • Invents Relationship: Represents the relationship between an inventor and a patent.
  • Assigns Relationship: Represents the relationship between a company and a patent.

This schema allows us to represent the key elements of inventor networks and their relationships.

Importing Data into Neo4j

With the schema defined, the next step is to import the data into Neo4j. This can be done using Neo4j’s import tools or via Cypher, Neo4j’s query language. Here is an example of how to import data using Cypher:


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// Create Patent nodes

LOAD CSV WITH HEADERS FROM ‘file:///patents.csv’ AS row

CREATE (:Patent {

  patent_number: row.patent_number,

  title: row.title,

  abstract: row.abstract,

  filing_date: row.filing_date,

  issue_date: row.issue_date,

  classification: row.classification


// Create Inventor nodes

LOAD CSV WITH HEADERS FROM ‘file:///inventors.csv’ AS row

CREATE (:Inventor {


  affiliation: row.affiliation,

  location: row.location


// Create Company nodes

LOAD CSV WITH HEADERS FROM ‘file:///companies.csv’ AS row

CREATE (:Company {


  location: row.location


// Create Cites relationships

LOAD CSV WITH HEADERS FROM ‘file:///citations.csv’ AS row

MATCH (p1:Patent {patent_number: row.citing_patent}),

      (p2:Patent {patent_number: row.cited_patent})

CREATE (p1)-[:CITES]->(p2);

// Create Invents relationships

LOAD CSV WITH HEADERS FROM ‘file:///inventorships.csv’ AS row

MATCH (i:Inventor {name: row.inventor}),

      (p:Patent {patent_number: row.patent})

CREATE (i)-[:INVENTS]->(p);

// Create Assigns relationships

LOAD CSV WITH HEADERS FROM ‘file:///assignments.csv’ AS row

MATCH (c:Company {name:}),

      (p:Patent {patent_number: row.patent})

CREATE (c)-[:ASSIGNS]->(p);

Analyzing Inventor Networks

With the data imported, we can begin analyzing the inventor networks. Neo4j’s querying capabilities allow us to explore various aspects of these networks.

  1. Identifying Key Inventors: We can identify key inventors based on their centrality in the network. Centrality measures, such as degree centrality, betweenness centrality, and closeness centrality, can highlight influential inventors.


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// Degree centrality: Number of patents an inventor is involved in

MATCH (i:Inventor)-[:INVENTS]->(p:Patent)

RETURN, COUNT(p) AS degree_centrality

ORDER BY degree_centrality DESC


  1. Collaboration Patterns: By examining the relationships between inventors, we can uncover collaboration patterns and identify clusters of inventors who frequently work together.


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// Identify collaboration clusters

MATCH (i1:Inventor)-[:INVENTS]->(p:Patent)<-[:INVENTS]-(i2:Inventor)

RETURN,, COUNT(p) AS collaborations

ORDER BY collaborations DESC


  1. Citation Networks: Analyzing citation networks can reveal the influence of specific patents and identify trends in technological development.


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// Identify influential patents based on citations

MATCH (p:Patent)<-[:CITES]-(citing:Patent)

RETURN p.patent_number, COUNT(citing) AS citation_count

ORDER BY citation_count DESC


  1. Company Influence: We can also examine the role of companies in the inventor network by analyzing the patents they hold and the inventors they collaborate with.


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// Identify companies with the most patents

MATCH (c:Company)-[:ASSIGNS]->(p:Patent)

RETURN, COUNT(p) AS patent_count

ORDER BY patent_count DESC



Visualizing the inventor networks can provide intuitive insights into the data. Tools like Neo4j Bloom, Gephi, or custom web applications using D3.js can be used to create interactive visualizations.

  1. Neo4j Bloom: Neo4j Bloom is a visualization tool that allows users to explore and visualize graph data intuitively. It provides an interactive interface for querying and visualizing data.
  2. Gephi: Gephi is an open-source network visualization tool that supports complex network analysis and visualization. Data from Neo4j can be exported and imported into Gephi for advanced visualization.
  3. Custom Web Applications: For tailored visualizations, developers can create custom web applications using libraries like D3.js, which provides powerful tools for creating interactive, web-based visualizations.

Case Study: Analyzing a Hypothetical Inventor Network

To illustrate the process, let’s consider a hypothetical case study of analyzing an inventor network using Neo4j and IP Street.

Step 1: Data Collection

We collect data from IP Street, focusing on a specific technology domain, such as renewable energy. The dataset includes patent records, inventor information, company assignments, and citation relationships.

Step 2: Data Modeling

We define the schema in Neo4j, creating nodes for patents, inventors, and companies, and relationships for citations, inventorship, and assignments.

Step 3: Data Import

We import the collected data into Neo4j using Cypher queries. This populates our graph database with the relevant information.

Step 4: Analysis

We perform various analyses to gain insights into the inventor network:

  • Key Inventors: Identify inventors with the highest degree centrality in the network, highlighting those who are most active in the domain.
  • Collaboration Patterns: Uncover clusters of inventors who frequently collaborate on patents, revealing potential research groups or partnerships.
  • Citation Networks: Analyze citation relationships to identify influential patents and track the flow of knowledge in the domain.
  • Company Influence: Determine which companies hold the most patents and have the most extensive networks of inventors.

Step 5: Visualization

Using Neo4j Bloom, we create interactive visualizations to present our findings. We can visualize the entire network, highlight key inventors, show collaboration clusters, and trace citation paths.


Integrating Neo4j and IP Street offers a powerful approach to analyzing inventor networks and understanding the dynamics of innovation. By leveraging the strengths of graph databases and comprehensive IP analytics, we can gain valuable insights into collaboration patterns, key players, and technological trends. This approach not only enhances our understanding of the innovation landscape but also provides actionable intelligence for researchers, companies, and policymakers. As technology and data continue to evolve, the integration of advanced tools like Neo4j and IP Street will become increasingly vital in driving innovation and leveraging intellectual property effectively.

Also Read: Amazon GPT-55X: The Next Evolution in Artificial Intelligence

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