53,907 research outputs found
PRIME: A System for Multi-lingual Patent Retrieval
Given the growing number of patents filed in multiple countries, users are
interested in retrieving patents across languages. We propose a multi-lingual
patent retrieval system, which translates a user query into the target
language, searches a multilingual database for patents relevant to the query,
and improves the browsing efficiency by way of machine translation and
clustering. Our system also extracts new translations from patent families
consisting of comparable patents, to enhance the translation dictionary
Community Detection and Growth Potential Prediction Using the Stochastic Block Model and the Long Short-term Memory from Patent Citation Networks
Scoring patent documents is very useful for technology management. However,
conventional methods are based on static models and, thus, do not reflect the
growth potential of the technology cluster of the patent. Because even if the
cluster of a patent has no hope of growing, we recognize the patent is
important if PageRank or other ranking score is high. Therefore, there arises a
necessity of developing citation network clustering and prediction of future
citations. In our research, clustering of patent citation networks by
Stochastic Block Model was done with the aim of enabling corporate managers and
investors to evaluate the scale and life cycle of technology. As a result, we
confirmed nested SBM is appropriate for graph clustering of patent citation
networks. Also, a high MAPE value was obtained and the direction accuracy
achieved a value greater than 50% when predicting growth potential for each
cluster by using LSTM.Comment: arXiv admin note: substantial text overlap with arXiv:1904.1204
Simple vs. sophisticated approaches for patent prior-art search
Patent prior-art search is concerned with finding all filed patents relevant to a given patent application. We report a comparison between two search approaches representing the state-of-the-art in patent prior-art search. The first approach uses simple and straightforward information retrieval (IR) techniques, while the second uses much more sophisticated techniques which try to model the steps taken by a patent examiner in patent search. Experiments show that the retrieval effectiveness using both techniques is statistically indistinguishable when patent applications contain some initial citations. However, the advanced search technique is statistically better when no initial citations are provided. Our findings suggest that less time and effort can be exerted by applying simple IR approaches when initial citations are provided
Causal relationship between eWOM topics and profit of rural tourism at Japanese Roadside Stations "MICHINOEKI"
Affected by urbanization, centralization and the decrease of overall
population, Japan has been making efforts to revitalize the rural areas across
the country. One particular effort is to increase tourism to these rural areas
via regional branding, using local farm products as tourist attractions across
Japan. Particularly, a program subsidized by the government called Michinoeki,
which stands for 'roadside station', was created 20 years ago and it strives to
provide a safe and comfortable space for cultural interaction between road
travelers and the local community, as well as offering refreshment, and
relevant information to travelers. However, despite its importance in the
revitalization of the Japanese economy, studies with newer technologies and
methodologies are lacking. Using sales data from establishments in the Kyushu
area of Japan, we used Support Vector to classify content from Twitter into
relevant topics and studied their causal relationship to the sales for each
establishment using LiNGAM, a linear non-gaussian acyclic model built for
causal structure analysis, to perform an improved market analysis considering
more than just correlation. Under the hypotheses stated by the LiNGAM model, we
discovered a positive causal relationship between the number of tweets
mentioning those establishments, specially mentioning deserts, a need for
better access and traf^ic options, and a potentially untapped customer base in
motorcycle biker groups
Analysis of free analyte fractions by rapid affinity chromatography
The invention is generally directed toward an analytical method to determine the concentration of the free analyte fraction in a sample. More particularly, the method encompasses applying a sample comprising a free and bound analyte fraction to an affinity column capable of selectively extracting the free fraction in the millisecond time domain. The signal generated by the free fraction is then quantified by standard analytical detection techniques. The concentration of the free fraction may then be determined by comparison of its signal with that of a calibration curve depicting the signal of known concentration of the same analyte
Multiple Retrieval Models and Regression Models for Prior Art Search
This paper presents the system called PATATRAS (PATent and Article Tracking,
Retrieval and AnalysiS) realized for the IP track of CLEF 2009. Our approach
presents three main characteristics: 1. The usage of multiple retrieval models
(KL, Okapi) and term index definitions (lemma, phrase, concept) for the three
languages considered in the present track (English, French, German) producing
ten different sets of ranked results. 2. The merging of the different results
based on multiple regression models using an additional validation set created
from the patent collection. 3. The exploitation of patent metadata and of the
citation structures for creating restricted initial working sets of patents and
for producing a final re-ranking regression model. As we exploit specific
metadata of the patent documents and the citation relations only at the
creation of initial working sets and during the final post ranking step, our
architecture remains generic and easy to extend
Community Detection and Growth Potential Prediction from Patent Citation Networks
The scoring of patents is useful for technology management analysis.
Therefore, a necessity of developing citation network clustering and prediction
of future citations for practical patent scoring arises. In this paper, we
propose a community detection method using the Node2vec. And in order to
analyze growth potential we compare three ''time series analysis methods'', the
Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of
our experiments, we could find common technical points from those clusters by
Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model
was higher than that of other models.Comment: arXiv admin note: text overlap with arXiv:1607.00653 by other author
Applying the KISS principle for the CLEF-IP 2010 prior art candidate patent search task
We present our experiments and results for the DCU CNGL
participation in the CLEF-IP 2010 Candidate Patent Search Task. Our work applied standard information retrieval (IR) techniques to patent search. In addition, a very simple citation extraction method was applied to improve the
results. This was our second consecutive participation in the CLEF-IP tasks. Our experiments in 2009 showed that many sophisticated approach to IR do not improve the retrieval effectiveness for this task. For this reason of we decided
to apply only simple methods in 2010. These were demonstrated to be highly competitive with other participants. DCU submitted three runs for the Prior Art
Candidate Search Task, two of these runs achieved the second and third ranks among the 25 runs submitted by nine different participants. Our best run achieved MAP of 0.203, recall of 0.618, and PRES of 0.523
A study of query expansion methods for patent retrieval
Patent retrieval is a recall-oriented search task where the objective is to find all possible relevant documents. Queries in patent retrieval are typically very long since they take the form of a patent claim or even a full patent application in the case of priorart patent search. Nevertheless, there is generally a significant mismatch between the query and the relevant documents, often leading to low retrieval effectiveness. Some previous work has
tried to address this mismatch through the application of query expansion (QE) techniques which have generally showed
effectiveness for many other retrieval tasks. However, results of QE on patent search have been found to be very disappointing. We present a review of previous investigations of QE in patent retrieval, and explore some of these techniques on a prior-art patent search task. In addition, a novel method for QE using automatically generated synonyms set is presented. While previous QE techniques fail to improve over baseline retrieval, our new approach show statistically better retrieval precision over
the baseline, although not for recall. In addition, it proves to be significantly more efficient than existing techniques. An extensive analysis to the results is presented which seeks to better understand situations where these QE techniques succeed or fail
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