patent recommendation system

Patent RecommenderKarthik Chepudira, Jason Lott, Dhaval Bhatt, Prabhakar Gundugola

kchepudira@berkeley.edu, jason.j.lott@ischool.berkeley.edu, b.dhaval@gmail.com, prabhakar@berkeley.edu

Patent Research is Not Easy

Identifying the most pertinent patents in a list of thousands can be tedious and difficult. - IPWatchDog, Aug 2016

Why patent research matters?

WTF

Patents & Inventors

Patent Research• Information retrieval in patents• Beyond keyword search

Go Beyond Keywords

• Strategic Research tool

Improve Efficiencies

Patent Similarity

What if your keyword doesn’t get Patent 8 as a hit?

If Patent 4 & 5 are relevant, why not look at Patent 8?

Patent 1

Patent 8

Patent 4

Patent 5Patent 6

Patent 3

Patent 2

Patent 7

Patent Structure/Fields

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Similarity Measures

Latent Dirichlet Allocation (LDA):

● “The basic idea is that documents are represented as random mixtures over latent topics, where each topic is characterized by a distribution over words.” (Blei, Ng, & Jordan)

TF-IDF:

tf-idft,d = tft,d x idft

• High when term occurs often in few documents

• Lowest when it occurs in all documents (think stop words)

Similarity measure with LDA Topic modeling

Patent Text Fields Analysis

Unique Recommended Patents - TFIDF

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Key Takeaways

Similarity Metrics

Comparing Patents From Two Similarity Metrics

Green - TF-IDF BasedRed - LDAYellow - Overlap

Standard

Top 5 Results Change in Rank

LM Dirichlet

ResultsInput Data Patent Recommender

Patent

SYSTEMS AND/OR METHODS FOR STRUCTURING BIG

DATA BASED UPON USER-SUBMITTED DATA ANALYZING PROGRAMS

MODELING AND SIMULATION OF INFRASTRUCTURE

ARCHITECTURE FOR BIG DATA

REGIONAL BIG DATA IN PROCESS CONTROL SYSTEMS

METHOD FOR MANAGING IoT DEVICES AND ANALYZING SERVICE OF BIG DATA

JOINING DATA ACROSS A PARALLEL DATABASE AND A DISTRIBUTED PROCESSING SYSTEM

Systems and Methods for Auto-Scaling a Big Data System

DATA PROCESSING APPARATUS AND METHOD OF PROCESSING DATA

STREAMING DATA FOR ANALYTICS IN PROCESS CONTROL SYSTEMS

Configurable Machine Learning Method Selection and Parameter Optimization System and Method

Patent Recommender

Patents Score

1.9256938

1.6159785

1.0616564

1.0573796

0.85843694

0.85230005

0.82034045

UI Testing & Iteration

Demo

Testimonial

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Week 8-10 Week 11-13

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Week 14

Capstone DemoIn case the live demo doesn’t work out

Keyword searchSearch Phrase: Polycrystalline diamond tungsten carbide leachClick SHOW MORE => Click CREATE NEW PROFILE

Tune ProfileRemove patent #2 & #8 | Change Scores | RECOMMEND PATENTS

RecommendationsThe 3rd result is what made Trent say “Oh, that interesting!”

Patent LinkWe wanted to allow the user to review the patents we recommend.

Clicking on the link was natural and required no explanation.

EvaluationUser Rating of Recommendations

Future Work

Future Work● Future: User click on show more most of the time, start out with a larger list● “patent continuations” we want to incorporate a way to deal with these. Don’t

want to have continuations showing up in patent profiles or dominating the recommendations

● Future: Adding patents to profile from here. Would allow us to look at precision as an evaluation of the recommendations.

● Future: Click on score to see indication why (keywords, topic,..) to allow user to evaluate recommendation without reading. Allows to generate new keywords as well as filter out false positives.

The Big PictureWe see this page becoming a sub-page to a user profile. The profile could contain many collections, and when you login, you get to see prompts for looking at recommendations on established collections again. That was one of the comments we got, that he sees this as a stepping stone to that.