Abstract: Intellectual Property (IP) analytics is an area that is growing at a fast pace in view of the importance of IP in an increasingly technology dependent world. There are large number of patent applications filed every year, in several different languages; these has made the task of prior art search very challenging. The traditional search strategies using boolean & proximity operators along with select keywords seem increasingly inadequate and unreliable in identifying the relevant prior art. The gap has triggered the development of search engines with features and tools that increasingly rely on machine/artificial intelligence; some of them utilize text-mining and language processing algorithms to quickly zoom into more relevant prior art. Many of these tools uses semantics/natural text, similarity or patent/patent publication – citation based search and categorization approaches. We will share few of our observations on the emerging scenario, covering AI/machine based search options from established players like Orbit and also relatively novice but specialized players like Automatch, Ambercite etc.

1. Emerging Trends in IP Searching:
Machine/AI Based Search Strategies
Anoop Cheeran & Manish Badani
Philips IP&S/IP Analysis
September 27, 2016

2. September 27, 2016
Philips IP Analysis Team
IP&S IP Analysis
IP Search
IP Intelligence
Market Intelligence
2

3. 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

4. September 27, 2016
Patentability Search
Building 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/

5. September 27, 2016
Artificial/Machine Intelligence
Application 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

6. Semantic Search
Situations when it helps
6 IP&S IP AnalysisSeptember 27, 2016
Source: http://www.slideshare.net/fullscreen/ABLVienna/semantic-search-8159539/4

7. 7 IP&S IP AnalysisSeptember 27, 2016
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

8. Semantic Search - Orbit
Free Text
8 IP&S IP AnalysisSeptember 27, 2016
• 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

9. 9 IP&S IP AnalysisSeptember 27, 2016
Semantic Search - Orbit
Concept 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

10. Semantic Search - Relecura
10 IP&S IP AnalysisSeptember 27, 2016
• 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

11. 11 IP&S IP AnalysisSeptember 27, 2016
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

12. 12 IP&S IP AnalysisSeptember 27, 2016
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
Semantic Search - AutoMatch

13. September 27, 2016 IP&S IP Analysis13
Semantic Search - AutoMatch
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

14. September 27, 2016 IP&S IP Analysis14
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 document
http://www.ambercite.com/index.php/support/getting-started

15. Ambercite: Ranking Method
September 27, 2016 IP&S IP Analysis15
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
Citation Based Search - Ambercite

16. September 27, 2016 IP&S IP Analysis16
http://www.ambercite.com/index.php/support/getting-started
Citation Based Search - Ambercite
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

17. Summary
17 IP&S IP AnalysisSeptember 27, 2016
• 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 ☺