PATENT SEARCH USING IPC
CLASSIFICATION VECTORS
MANISHA VERMA, VASUDEVA VARMA
OUTLINE
• Motivation
• Proposed Approach
• Vector Generation
• Vector Propagation
• Evaluation
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Dataset
Cosine similarity (CS)
Graded Cosine Similarity (GSC)
Text Score + Similarity Score
Results
• Future Work
SEARCH PATENTS, BUT HOW ?
• Input : A patent application
• Process :
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Study the patent
Find key words related to the invention
Formulate query with help of several operators
Query the patent database
Refine the query depending on the results
• Problem : Search results depend on the query, query
depends on keywords selection.
Quality of search results depends heavily on the
choice of words and their weight in the query
MOTIVATION
• Selection of keywords requires domain expertise,
restricts the number and areas of examiner’s patent
searches.
• Manual and tedious process of query creation. So
many words and so many operators, optimal
combination needs expertise.
• What claims to focus on, which ones to leave. Some
patent applications have over 100 claims.
WHAT MAY HELP
• Automatic query creation and
refinement.
• Exploiting the meta-data present
in the application to improve
results.
WHAT'S IN PATENTS
• Inventor information
• IPC (International Patent Classification) class
code information.
• Date of filling
• Citations
• Images
• And of course the patent text – Title,
Abstract, Description and Claims
OUR APPROACH
1. Use Category and citation
information in patents to filter
results.
2. Use text search to improve
precision.
IPC VECTORS
Category information at each level is represented
as a Vector. Combination of vectors of all the
levels results in IPC Vector of a patent.
Level 1 : Section + Class + Subclass
Level 2 : Section + Class + Subclass + Main Group
Level 3 : Entire classification code
VECTOR PROPAGATION
• Citation graph of patents is used to enrich the
vector. It is a directed graph which has a link from
Node A to Node B if patent A cites patent B.
• Inlinks (incoming edges) of a node are used to add
information to its vector.
• Propagation ensures that if Node A is retrieved then
its neighbors are also present in the solution set, this
improves the recall of the system
VECTOR PROPAGATION CONTD..
• For a given node Pi, let In(Pi) be subset of the set of
nodes that point to it (predecessors) and k be the
current iteration. The vector of node Pi for k +1th
iteration is defined as follows :
• 1/2k is used to dampen the effect of adjacent
vectors as the iterations increase. The above
formula simply adds the average of vectors of all
nodes that point to Pi.
EVALUATION
• We use the CLEF-IP 2011 collection of Prior Art
Search (PAC) task that has 2.6 million patents
pertaining to 1.3 million patents European Patent
Office (EPO) with content in English, German and
French, and extended by documents from WIPO.
• There are 300 sample patent applications as
queries with the dataset.
• Both English and original patents are used for
making queries.
EVALUATION
• Base: Simple Text Retrieval, 20 words, from the
query patent, with high tf-idf values are used to
form a weighted query. The weight of each word is
its tf-idf score.
• COS: Cosine Similarity, IPC information present in
the patent is used to make the vector. Entire vector
is used to calculate cosine similarity between a
patent and query.
• GCS: Graded Cosine Similarity, calculating similarity
at each level and linearly combining them to get
final score.
GRADED COSINE SIMILARITY
If Pq is the query patent vector and Pi is a
vector of ith patent in the corpus, we use
following to calculate graded similarity:
where aj represent importance of similarity
score at level j and simlevelj is cosine similarity
between vectors of levelj .
RE-RANKING TOP PATENTS
• We re-rank top 1000 documents by using following
methods:
• COS + Base: top 1000 documents obtained from
COS are re-ranked using λBase + (1 − λ)COS.
• GCS + Base: top 1000 documents from GCS are reranked using λBase + (1 − λ)GCS.
RESULTS
CONCLUSION AND FUTURE WORK
• Both IPC based representation and re-ranking on
sample queries of CLEF-IP 2011 dataset perform
better than the baseline i.e. text based retrieval in
terms of precision and recall.
• An extension to this work would be to use a
learning-to-rank approach to re-rank top
documents. It would be interesting to observe
effects of combining both vector representation
with patent text to avoid re-ranking.
QUESTIONS ?????
• THANK YOU