The importance of patents is increasing in global society. In preparing a patent application, it is essential to search for related patents that may invalidate the invention. However, it is time-consuming to identify them among the millions of patents. This article proposes a patent-retrieval method that considers a claim structure for a more accurate search for invalidity. This method uses a claim text as input; it consists of two retrieval stages. In stage 1, general text analysis and retrieval methods are applied to improve recall. In stage 2, the top N documents retrieved in stage 1 are rearranged to improve precision by applying text analysis and retrieval methods using the claim structure. Our two-stage retrieval introduces five precision-oriented analysis and retrieval methods: query-term extraction from a portion of a claim text that describes the characteristics of a claim; query term-weighting without term frequency; query term-weighting with "measurement terms"; text retrieval using only claims as a target; and calculating the relevant score by "partially" adding scores in stage 2 to those in stage 1. Evaluation results using test sets of the NTCIR4 Patent Retrieval Task show that our methods are effective, though the degree of the effectiveness varies depending on the test sets.

This paper describes the Patent Retrieval Task in the Fourth NTCIR Workshop, and the test collections produced in this task. We perform the invalidity search task, in which each participant group searches a patent collection for the patents that can invalidate the demand in an existing claim. We also perform the automatic patent map generation task, in which the patents associated with a specific topic are organized in a multi-dimensional matrix.

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We introduce an argument generation system in debating, one that is based on sentence retrieval. Users can specify a motion such as This house should ban gambling, and a stance on whether the system agrees or disagrees with the motion. Then the system outputs three argument paragraphs based on "values" automatically decided by the system. The "value" indicates a topic that is considered as a positive or negative for people or communities, such as health and education. Each paragraph is related to one value and composed of about seven sentences. An evaluation over 50 motions from a popular debate website showed that the generated arguments are understandable in 64 paragraphs out of 150.