intellectual property analytics turning unstructured information into value

Almaden Services Research

© 2008 IBM Corporation

Intellectual Property Analytics

Turning Unstructured Information Into Value

Jeffrey T. Kreulen, [email protected]

W. Scott [email protected]

mailto:[email protected]

mailto:[email protected]



Innovation is About More Than Building a Better Mouse Trap …



It’s About Beating The Competition …



From Transaction To Interaction Business Intelligence

Classic BI focuses on transactions at the boundary of the enterprise

Transactions are evolving to include more of the enterprise eco-system and a richer set of the life-cycle of interactions

Contextual information and its derived intelligence has to be integrated into the overall picture

Incorporation of unstructured information into individual and collection level analytics

Exe

cute

Con

trol

Dire

ct

Business Competencies

The Enterprise

Exe

cute

Con

trolD

irect Business Competencies

Suppliers

Exe

cute

Con

trolD


Customers

Exe

cute

Con

trolD


Business Partners

Context



Leveraging Interactions in the Enterprise Eco-System

Business partners

Customers

Consultants

Competitors

Associations, trade groups, conference boards

Academia Internet, blogs, bulletin boards

Think tanks

Other

R&D (internal)

Sales or service units

Employees (general population)

IBM Institute for Business Value, CEO Study 2006

5% 15% 25% 35% 45%45% 35% 25% 15% 5%

CEO Survey: Sources of New Ideas and Innovation

“We have...today a lot more capability and innovation in the [competitive] marketplace...than we [could] try to create on our own.”

External Internal

Procter & Gamble has set itself a goal of getting half its new product ideas from outside the company by 2010.

Classic BI

JamsCRM / Call Centers

IP, Web, …



Explore

The Analytical Approach

Understand Analyze

- Multiple data source collections- On-Topic data search- Nearest neighbor search-Intermediate analytics results

- Taxonomy Generation- Clustering- Classification- Dictionary- Synonyms- Editing- Refinement

- Trending- Network analysis- Co-occurrence- Scatter plot- Graphing/Reporting- Visualizations- . . .

Understand the Business Objective

Identify Information Sources



Business Analytical Solutions

Business Insights Workbench (BIW)A comprehensive platform for structured and unstructured information analytics

Business Information Services On the Network (BISON)An SOA implementation of information analytical capabilities used to enable solution developement

AlmadenPatent

AnalyticalWorkbench(SIMPLE)

CorporateBrand andReputationAnalysis

(COBRA)

ServiceDeliveryInsight(SDI)

Call Center Analysis



Business Objectives of Patent Analytics

Prior Art Search

Strategic Analysis– Competitive Landscape

– Technology Landscape

– People Landscape

– Mergers & Acquisitions

Portfolio Management– Partnering and Licensing

– Defensive

– Valuation



IBM Patent Portfolio Since 1994

A simple taxonomy of IBM’s patent portfolio since 1994 with counts and sorted by recency. The portfolio is migrating to the categories at the top (see summary trend lines).



Johnson & Johnson

Novartis

Bristol Myers Squibb

Merck

By comparing the most relevant concepts in patent data, we observed patterns emerging.

Genentech is staking out white space in the areas not covered by the other major pharmaceuticals.

Using patent data from last 18 years, we compared the most relevant concepts to identify emerging patterns

AstraZeneca

Amgen

Total # of patents

Pfizer

Merck

BMS

Novartis

J&J

AstraZeneca

Amgen

Total # of patents

Pfizer

Merck

BMS

Novartis

J&J

Looking at US patent data for the last 18 years shows how pharmaceutical companies are positioning themselves in the market.

Leading companies like Pfizer, AstraZeneca, and Amgen are increasing their patent activity while other companies are decreasing.



SIMPLEStrategic Information Mining Platform

Full text search on all US patents and applications

Nearest Neighbor search from a list of input patents or document text

Claims originality Analysis Divestiture Impact Analysis Chemical Structure Searches Patent Clustering View IBM patent status

information from Dossier Create and save patent

“projects” and export reports



BISON - Component Architecture for SIMPLE

AXIS 1.x based Web Service Bus

Indices

User InterfacesWebSphere 6.1 J2EE Container

Analytics Services

Data Sources

GETL

Master IP Database

Schema

Patent XML DataFeeds

Solr Applications

index

DOSSIERDatabase

Nearest NeighborDivestiture

BIW analytics

application UI

DAS (Data access Services)

Dossier Search

Patent Search BIW

Analytics Framework

AXIS Web Services Client

Tapestry Discovery Frameworkssc Discovery

Framework

Nearest Neighbor

UI

Claims Originality

UI

DivestitureUI

Patent Clustering

UI

Chemical Search

UI

Dossier Search

UI

Patent Search

UI

Citation analysis

UI

SSC Commons

Chemical Search Citation

analysis

Patent Clustering

Claims Origniality

AXIS Web Services

Client

WEB Services BUS

Analytics Services Layer

Indexing Layer

Data Storage Layer

Extraction transformation and Load

Data Sources

User Interface layer

Solr Grants index

Solr Dossier index

Published IP Database

Schema

Customer Hosted

Databases

Data Replication

AnnotationProcess

BlueGene L



SIMPLE Patent Searching

Full-text and fielded searching for US, EP and WIPO patents.



Nearest Neighbor Searching

Novel collection based prior art searching and similarity metrics.



Claims Originality

Identification of novel language in claims. Useful in identifying emergent topics and seminal patents.



Divestiture

A portfolio management tool useful in determining relative value.



Patent Clustering

The ability to automatically group a collection of patents into semantically similar groups.



Chemical Search

We have identified occurrences of chemical compounds, disambiguated by structure (using InChi and SMILES) and have a chemical similarity search engine.

Demo at http://chemsearch.almaden.ibm.com



And of course, this doesn’t just work for patents…



Themes are configured for brands, competitors, people and other topics of interest.

The system filters blogs, boards, and online news to identify the snippets which contains the information of

interest

Using orthogonal filtering techniques we can get an accuracy rate of 95%. Even with this, the users still need to

look at the document for context

User define folders provide the user to organized the alerts into their own

taxonomy. • We use multiple techniques to identify documents of interest for alerting and analysis. • We are creating industry templates.

COBRA – Corporate Brand and Reputation Analysis



Sentiment Analysis is combined with Themes

There were 20 blog postings which were identified with

negative sentiment.

Just know that a number of posting are negative tell only part

of the story. Trends help determine if this is something to

be concerned about or “old news”

COBRA – Corporate Brand and Reputation Analysis



Interactive Analytical DashboardCOBRA – Corporate Brand and Reputation Analysis



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2

3

4

5

We look for interesting correlationsCOBRA – Corporate Brand and Reputation Analysis



How do Jams work?



Service Delivery Insight

Call Center mining and analysis



http://www.miningthetalk.com

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intellectual property analytics turning unstructured information into value

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