emerging trends in ip searching: machine/ai based searching - anoop cheeran & manish badani

Emerging Trends in IP Searching:

Machine/AI Based Search Strategies

Anoop Cheeran & Manish Badani

Philips IP&S/IP Analysis

September 27, 2016

September 27, 2016

Philips IP Analysis Team

IP&S IP Analysis

IP Search

IP Intelligence

Market Intelligence

2

3 IP&S IP AnalysisSeptember 27, 2016

Search Tasks

• Patentability search for Invention Disclosures

• Validity/Invalidity Searches

• Product Risk Assessment Searches

• Trade Mark Searches

• Landscape Searches

• Company Analysis

• Market Intelligence

September 27, 2016

Patentability SearchBuilding the IP Assets of Philips

IP&S IP Analysis

Input

• Abstract

• Invention Disclosure

• Prior art (if any)

Output

• Search Criteria

• Prior art

Challenges

• Large number of patent applications filed

each year

• Filings in several different languages

• Traditional keyword search is increasingly

inadequate

Stringent Quality

Checks

4

Source: http://ipstats.wipo.int/ipstatv2/

September 27, 2016

Artificial/Machine IntelligenceApplication in IP Search

Artificial Intelligence

• Simulate human like intellect and/or behavior by a

machine

Natural Language Processing

� A sub-field of Artificial Intelligence

� Computers/Search Engines can interact with

human beings like human

Semantic Search

� Understands searcher’s intent and context

� Major web search engines like Google

incorporate some elements of Semantic

search

IP&S IP Analysis5

Source: http://www.slideshare.net/ecodrew/future-of-artificial-

intelligence-iftf-fujitsu-labs-2015

Semantic SearchSituations when it helps


Source: http://www.slideshare.net/fullscreen/ABLVienna/semantic-search-8159539/4


Keyword Search vs Semantic Search

Semantic search helps

• Reduce the time of running the search

• Improves RECALL and PRECISION

• Uncover relevant patents that would

otherwise be missed with keyword

search

Semantic Search - OrbitFree Text


• Find similar patents based on freely entered text

• One paragraph is required for the search to run

• If the text is entered in non-English language, it gets translated by Google


Semantic Search - OrbitConcept Selection

• Concepts are extracted and shown in the

selection window

• Additional concepts can be searched and

added to the list

• Based on the selected concepts relevant

patent families are searched and listed

Semantic Search - Relecura


• Semantic search

based on uploaded

text/file

• Suggested keywords

and concepts

• Seed Data to quickly

zoom into relevant

documents

• Search trail to track

selected concepts

and keywords


Semantic Search - AutoMatch

www.auto-match.se

• AutoMatch is a natural language text based search tool for patents and patent

applications

• Tool creates a finger print reflecting the given input textual content (for example, from

an abstract) that is adapted and matched against the tool’s IP publication repository

• Customization of technology areas (search index) possible

• Provides up to 100 hits per search

https://www.auto-match.se/Guide%20AutoMatch%20Evaluation.pdf


� Can copy paste directly from

an invention disclosure or

can provide a descriptive

summary encompassing the

keywords as the search

query

� Tool uses text in title and

the descriptive summary to

match and provides the

result set

https://www.automatch.se/Guide%20AutoMatch%20

Evaluation.pdf

Step 1: Register an idea: Concise summary

text (without truncations) about half a

page disclosing the subject area

Choose the technical index (default AM Master

covering all areas or the optimized index covering

specific technical fields) and run the query


September 27, 2016 IP&S IP Analysis13


� Tool has an option of ranking

each of the retrieved

references to ‘match, relevant,

background & noise’

� Can add annotations for each

document analyzed

� Can add new text into the idea

summary and create a new

search

� Can download matched results

to an Excel report

Summary

https://www.auto-match.se/Guide%20AutoMatch%20Evaluation.pdf


Citation Based Search - Ambercite

� Citation based tool that has 7 generations of citations; 3 earlier generations, 3 later

generations and the seed document (citations of citations of citations)

� The relevancy of a document is based on the direct and indirect connection of the

document with the seed document(s)

� The citations are based on individual records and not patent family; all members of

patent families may need to be added as a seed documenthttp://www.ambercite.com/index.php/support/getting-started

Ambercite: Ranking Method


Direct Citations

In-Direct Citations

� Higher number of

shared citations

(direct & indirect)

result in higher

similarity ranking

� Indirect citation

analysis identifies

references otherwise

not obtained through

direct citation analysis

http://www.ambercite.com/index.php/support/getting-started



http://www.ambercite.com/index.php/support/getting-started


� Searching based on relevant patent

numbers, avoiding need to make

predictions on likely keywords or

class codes

� Listing of direct patent citations,

and indirect ‘unknown’ citations in

search results

� Tool provides an ‘AmberScore’, a

similarity metric based on

citation(s) to each document

� Recent patent/patent applications

may have a lower AmberScore

� Option for ‘hiding’ known

documents from search results and

a ‘like’ button to categorize

documents

Summary

Summary


• Large number of patent applications in various languages have increased

challenges for searching prior art

• Several patent search tools are now available that utilize natural language

processing systems

• Machine intelligence based on citations is also used in identifying related patents

• The AI/MI based search tools can significantly reduce the search time and improve

the search accuracy

• At the moment, there is still room for improvement of these tools for them to

exactly mimic a human

� Until then, there will always be a need for the traditional IP Search Analyst ☺

emerging trends in ip searching: machine/ai based searching - anoop cheeran & manish badani

Law