1. Document fields used for index creation are:
	- abstract
	- address
	- agents
	- applicant
	- application-reference
	- claim
	- claim-text
	- claims
	- classification-ecla
	- classification-ipc
	- classification-ipcr
	- classification-symbol
	- classifications-ipcr
	- colspec
	- copyright
	- country
	- date
	- dates-of-public-availability
	- description
	- designated-states
	- doc-number
	- doc-page
	- document-id
	- invention-title
	- inventor
	- inventors
	- patent-citations
	- patent-document
	- priority-claim
	- priority-claims

2. Number of created indexes are one (1), with English as the index language
3. Document fields used for query generation are:
	- invention-title
	- description

4. Pre-processing of the documents/topics are none
5. The indexing is done using Lemur Indri. 
   It is done directly without applying any stemmer. 
   As for the retrieving systems, the following steps are done:
	- Extraction of words from the contents of documents fields (invention-title + description) in topics. 
	  We apply the standard weighting algorithm of TF-IDF and top ten words with high TF-IDF are taken as one query.
	- We run queries using Lemur Indri and retrieve top 1000 files for each query.

6. The ranking model we used are from Lemur Indri which is Indri automatically ranks all of the retrieved documents for each query.
7. In this run, we do not use any cross language therefore nothing language specific methods are used. We simply run all of documents without paying attention to the specific languages.