Visually Exploring Patent Collections for Events and Patterns

Visually Exploring Patent Collections for Events

and PatternsDerek X. Wang

Associate Director of the Charlotte Visualization Center

Together with: Wenwen Dou, Wlodek Zadrozny, Suraj Ankam, Debbie Strumsky, Terry Rabinowitz

BusinessesValue

BusinessesValue

• 800 patents:

• $1 billion worth of patents from AOL to Microsoft

BusinessesValue

• 800 patents:


• 1,100 patents from Kodak

• 525 Million to group license

BusinessesValue

• 800 patents:


• 1,100 patents from Kodak

• 525 Million to group license

• 17, 000 Patents

• $12.5 billion Motorola Mobility to Google

2006 2007 2008 2009 2010

Dataset: 123 Publications from VAST proceedings from 2006-2010.

Value

2006 2007 2008 2009 2010


ValueTechnology

2006 2007 2008 2009 2010

Cyan topic: variable uncertainty trend correlation linear multivariate sensitivity


ValueTechnology

2006 2007 2008 2009 2010


Blue topic: dimension quality cluster measure lda attribute reduction projection


ValueTechnology

2006 2007 2008 2009 2010




FODAVA

ValueTechnology

2006 2007 2008 2009 2010




FODAVA

ValueTechnology

**X. Wang et al., ParallelTopics: A probabilistic approach to exploring document collections, IEEE VAST 2011

Goal

Value

GoalValue

• Can we spot an emerging new technology?

GoalValue


• Text mining and visualization

GoalValue



• Can we spot novelty within a patent?

GoalValue




• How much do claims differ from class descriptions?

GoalValue





• How much do claims differ from claims in other similar patents

GoalValue





• How much do claims differ from claims in other similar patents

• Can we list “all” patents relevant for some technology? (and what does it mean)

GoalValue

A Robust and Scalable Patent Analysis Infrastructure Is Needed

GoalValue


Visual Analytics Will Play a Key Role

BalancedAnalytics

Technology

GoalValue


Visual Analytics Will Play a Key Role

BalancedAnalytics

Technology

Human

Computer+=

Value

Challenge

Goal

Value ChallengeGoal

Value ChallengeGoal

Unstructured or semi-structured

Highly heterogeneous

Leading to highly heterogeneous models

Incomplete or with holes

With intrinsic uncertainty (and in some cases deception)

Inside and outside the enterprise

Containing detailed time and space information:

GoalValue Challenge

Research

GoalValue Challenge ResearchStructuring the Unstructured:

Topic Modeling


Topic Modeling• Latent Dirichlet Allocation (LDA)



• Reveals Latent topics from large textual corpus




• Coherent sets of most likely words to describe topics





• Topics defined by keyword groups





• Topics defined by keyword groups

• Topics in text collections can effectively be inferred

GoalValue Challenge Research


Investigative Element Extraction



• Recognition of entities including people, locations, buildings, organizations.




• Recognition of times and dates.




• Recognition of times and dates.

• Construct near-real-time analysis pipeline for entity association

RealityValue Challenge Research

RealityValue Challenge ResearchStructuring the Unstructured:

Event Structuring


Event Structuring

Events: Meaningful occurrences in space and time


Event Structuring


Motivating Event

Particular Topic Stream


Event Structuring


Motivating Event

Particular Topic Stream

Narrative: a series of clustered (event-based) stories temporally-linked based on content similarity.


Results

RealityValue Challenge Research ResultsCan we spot an emerging new technology?


Data: 50,000 telecommunication patents, in past 10 years Abstract text and patent meta-information;

1.5 Gb Raw Patent Documents




Methods: Topic modeling and visualization


Results: We can see a significant change in the topic of “software and storage” in communication around 2007 (corresponding to Apple iPhone?)



Methods: Topic modeling and visualization

RealityValue Challenge ResearchCan we spot an emerging new technology?

Results

**W. Dou et al., HierarchicalTopics: Visually Exploring Large Text Collections Using Topic Hierarchies, IEEE VAST 2013

RealityValue Challenge ResearchCan we spot an emerging new technology?

Results

Model: § 100 topics § Each topic a distribution on

words § Each abstract a combination

of topics !

Note: Width of the graph proportional to the number of patents and the number of words from a particular topic (topic signal strength). Number of class 455 patents grew from 2234 in 2005 to 7647 in 2012

**W. Dou et al., HierarchicalTopics: Visually Exploring Large Text Collections Using Topic Hierarchies, IEEE VAST 2013

RealityValue Challenge Research Results


Typical Keyword: “transistor”


!

Emergent: “storage, software, …”

Typical Keyword: “transistor”

RealityValue Challenge ResearchCan we spot novelty within an existing patent?

Results

RealityValue Challenge ResearchCan we spot novelty within an existing patent?Data$$

$Ini(ally:$A"random"sample"of"40"patents"in"several"classes"with"focus"on"455"(telecom)."""

$Recently:$Confirmed"through"automated"analysis"of"several"subclasses"of"455.""$Method:"Compare"words"in"claims"with"words"in"class"plus"subclass"definiAon""Results:"Large"symmetric"differences

""#$%&(()*+,&)÷"#$%&(./0+1+2+#1)"""#$%&(34&2$*52&)÷"#$%&(./0+1+2+#1)"

"

Results

RealityValue Challenge Research ResultsExample

h)p://pa,t.uspto.gov/netacgi/nph-‐Parser?Sect1=PTO2&p=1&u=%2Fnetahtml%2Fsearch-‐bool.html&r=2&f=G&l=50&d=pall&s1=449%2F8.CCLS.&OS=CCL/449/8&RS=CCL/449/8

Patent Title Process for rearing bumblebee queens and process for

rearing bumblebees

Main ClassificaTon 449/1 ; 449/2; 449/8

Class 449 – Bee Culture / Subclass 1 Class 449 – Bee Culture / Subclass 8

http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&p=1&u=/netahtml/search-bool.html&r=2&f=G&l=50&d=pall&s1=449/8.CCLS.&OS=CCL/449/8&RS=CCL/449/8

RealityValue Challenge Research ResultsWe claim: 1. A process for rearing bumblebee queens (genus Bombus) comprising generaTng a colony with workers in the presence of ferTlized eggs and/or larvae from at least one colony, in a room with a controlled climate provided with food, and allowing the colony to grow unTl bumblebee queens are produced, wherein subadult and/or adult workers that originate from at least one different colony are brought together with said ferTlized eggs and/or larvae. 2. The process according to claim 1, wherein the workers that originate from said at least one different colony are brought together with a young colony in the eusocial phase, consisTng of a ferTlized queen, brood and the first born workers. 3. The process according to claim 1, wherein more than 100 workers are brought together. 4. The process according to claim 1, wherein rearing is carried out using a workers: ferTlized eggs raTo of 0.5-‐4. 5. The process according to claim 1, wherein the workers originaTng from said at least one different colony are first kept in a room without any queen and without brood for one day. 6. The process according to claim 1, wherein brood and workers from different bumblebee species are brought together. 7. A process for rearing bumblebees (genus Bombus), comprising rearing bumblebee queens by generaTng a colony with workers in the presence of ferTlized eggs and/or larvae from at least one colony, in a room with a controlled climate provided with food, and allowing the colony to grow, wherein subadult and/or adult workers that originate from at least one different colony are brought together with said ferTlized eggs and/or larvae, and using said bumblebee queens for rearing bumblebees.


Class 449 1 -> Class Definition

8 -> 7 -> 3 -> Class Definition

Subclass Nesting


Class 449 1 -> Class Definition

8 -> 7 -> 3 -> Class Definition

Subclass Nesting

Class Name: Bee Culture Class Defini;on: This class includes the methods of and structures for propagaTng, raising and caring for bees; as well as certain ancillary methods and structures.


Class 449 Subclass 1Subclass Name: Method Subclass Defini;on: This subclass is indented under the class definiTon. Process.


Class 449 Subclass 8Subclass Name: Queen Raising Subclass Defini;on: This subclass is indented under subclass 7. Structure with provision to encourage and care for the producTon of a bee larvae into a queen bee.

RealityValue Challenge Research ResultsWords in class / subclass defini;ons found in patent claim

method 0 colony 11

process 7 culture 0

queen 6 propagate 0

raise 0

encourage 0

care 0

larvae 4

producTon 1

bee 7

mulT 0

swarm 0

capture 0

house 0

hive 0

structure 0

RealityValue Challenge Research ResultsWords in claim that were not in definiTons

rearing 5

worker 10

egg 5

ferTlize 6

climate 2

food 2

different 5

control 2


Observations • Novelty is in words/relations that are not part of the definition (but appear in

patent claims or its abstract) • Some things can be left unsaid. Is there a boundary? • Happens in all patents (but degree varies)

Can we spot novelty within an existing patent?


Can we spot novelty within an existing patent?

Next • Opportunity to text mine these differences – Are they random on a time scale? – Would descriptions of emerging technologies emerge from these

patterns? – Do combination patents have more of these?

RealityValue Challenge Research ResultsCan we list “all” patents relevant for some technology?


– Data: Patents, Wikipedia

Can we list “all” patents relevant for some technology?


– Data: Patents, Wikipedia– Potential Data: Cell phone manuals or other descriptions



– Data: Patents, Wikipedia– Potential Data: Cell phone manuals or other descriptions

– Method: Text mining of patents in certain classes, text mining of filing by certain market/technology players, text mining of other patents, using Wikipedia and manuals as a guidance what to look for.



Scale

Results

RealityValue Challenge Research ScaleScalable Computing Architecture for Extracting Latent Topics and Events*

Results


Distributed Data Storage and Pre-Processing Environment

Results


**X. Wang et al., I-SI: Scalable Visual Analytics Architecture for Analyzing Latent Topical-Level Information From Social Media Data, Journal of Computer Graphics Forum, 2012


Results




MapReduce procedures for data-cleaning and pre-processing Distributed Storage Solution (MongoDB), is used for data storage,

analysis and Retrieval

Results




MapReduce-based social media crawlers for Twitter, blogs and news articles: Unstructured Contents: Textual Information, Image, Comments

Structured Contents: User Graph, Geo-tags, HashTag

Results



Results



Parallel Data Analytics Cluster

MPI-based Parallel-LDA implementation for Topic modeling with Memory Sharing Optimization

Results



Parallel Data Analytics Cluster

OpenNLP-based Parallel Implementation for Entity-Extraction Customized PBS to schedule jobs for parallel computing environment

Results

RealityValue Challenge Research Results Scale

News Briefing App

RealityValue Challenge Research Scale

Resources we’d be happy to share

• Complete US patents and applications (until 1q2013) with with a search engine (Lucene) interface • Patent Classes • Other text resources (Wikipedia, Wiktionary etc) !

We’d be happy to prepare specialized extracts or combination for those who need them.

Results

RealityValue Challenge Research Scale

Thank you!

Derek Xiaoyu Wang [email protected]

Results

News Briefing App @News_Briefing

Now FREE at App Store

mailto:[email protected]

Visually Exploring Patent Collections for Events and Patterns

Technology

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