Patent-to-Patent Similarity: A Vector Space Model

Younge, Kenneth; Kuhn, Jeffrey M.

doi:10.2139/ssrn.2709238

Cited by 50 publications

(54 citation statements)

References 55 publications

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“…The Similarity variable measures the textual similarity between the technical specifications of two patents, where a higher value indicates a greater level of similarity between the citing and cited patent. Although textual similarity is not a perfect representation of technological relatedness-for technologies can be related in implicit ways that are not represented in the text-we show in related work that the computed similarity measure correlates strongly with both expert and lay person evaluations, and that it also predicts such characteristics as shared patent class and patent family (Younge and Kuhn, 2015).…”

Section: Lorenz Curves Of Cumulative Proportion Of Patents By Backwarmentioning

confidence: 73%

“…Such new measures could serve the same purposes as patent citations (Trajtenberg, 1990b; Harhoff et al., 1999; Hall, Jaffe, and Trajtenberg, 2005) without requiring the researcher to control for the many types of temporal trends, institutional features, and other selection effects that now undermine the use of patent citations. Early work in this direction has already begun (Nanda, Younge, and Fleming, 2015; Younge and Kuhn, 2015), and future research in this vein may identify text‐based or network‐based measures of innovation impact and novelty that correct for limitations in citation measures.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, 46.8% of references derive from less than 5% of patents with more than 100 backward citations. 1 To examine how the aforementioned changes in citation patterns affect the information content and quality of citation-based measures, we use a vector space model based on Younge and Kuhn (2015) that compares the text of every patent to every other patent granted by the United States Patent and Trademark Office (USPTO). The measure allows us to determine the technological distance between each citing/cited pair of patents based on the technical description of each patent.…”

Section: Introductionmentioning

confidence: 99%

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Patent citations reexamined

Kuhn

Younge

Marco³

2020

The RAND J of Economics

135

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Many studies rely on patent citations to measure intellectual heritage and impact. In this article, we show that the nature of patent citations has changed dramatically in recent years. Today, a small minority of patent applications are generating a large majority of patent citations, and the mean technological similarity between citing and cited patents has fallen considerably. We replicate several well‐known studies in industrial organization and innovation economics and demonstrate how generalized assumptions about the nature of patent citations have misled the field.

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Section: Lorenz Curves Of Cumulative Proportion Of Patents By Backwarmentioning

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Patent citations reexamined

Kuhn

Younge

Marco³

2020

The RAND J of Economics

135

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show abstract

“…Sreeja and Mahalakshmi (2016), for example, explored the use of VSM to automatically detect emotions in English poems. Fraser and Hirst (2016) investigated using VSM to detect language impairments among people with Alzheimer's disease, and Younge and Kuhn (2016) used VSM as a measure to detect patent similarity. Salehi, Pourzaferani, and Razavi (2013), in an attempt to provide students with a tool that can be used to cope up with the ever-increasing numbers of learning materials in the web, also developed a hybrid recommender system that locates suitable learning materials and delivers them to learners based on their specific attributes.…”

Section: Ir and Vector Space Modelmentioning

confidence: 99%

Monitoring Class Activity and Predicting Student Performance Using Moodle Action Log Data

Raga

Raga²

2017

IJCSR

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Purpose -This paper proposes a novel approach for processing course log data obtained from Moodle-based blended courses in order to visualize patterns of student activity within the online environment and to determine whether these log data can be used to predict student academic performance.Method -Logs of student activities were summarized and processed using the Vector Space Model approach. This resulted in a novel vector-based form of representation which can be used to map students' activity in a latent activity space given a set of activity dimensions. An enriched form of this representation was also generated by processing the DateTime and IP address metadata for the purpose of developing classification/predictive model of students' performance. 2Results -The activity space coupled with a one-hot vector representation for each unique activity dimension can be used to visualize the differences in level and type of activity preferences of students. Experiments using several machine learning algorithms indicate that the generated model can modestly distinguish between sets of activities that lead to High, Low, or Failed performances.Conclusion -The development of easily interpretable graphics that can depict trends in student activity is a useful tool for instructors handling blended courses. It can provide constant monitoring of course progression with minimal effort and enable instructors to determine whether and how the environment actually affects student performance.Recommendations -Further work on refining the process applied to the data is recommended. The log data should be time-sliced and processed to determine whether and how the student's level and type of activity changes over time. More powerful machine learning classification techniques shouldalso be tested to determine whether it can improve the classification accuracy.Research Implications -These types of visualizations and predictive models could be used to monitor the student or class which requires immediate and specific pedagogical adjustments.

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“…VSMs represent documents as vectors with multiple terms. There are different methods to construct VSMs, and one popular method used in recent studies is to generate a weighted vector for each patent based on the term-frequency of each term (i.e., keyword) for the patent, scaled by the inverse document-frequency of each term [18], [20]. In other words, a patent is represented by a vector of the term-frequency-inversedocument-frequency (TF-IDF) weights of its keywords.…”

Section: Introductionmentioning

confidence: 99%

Patent Analytics Based on Feature Vector Space Model: A Case of IoT

Lei

Zheng

2019

IEEE Access

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The number of approved patents worldwide increases rapidly each year, which requires new patent analytics to efficiently mine the valuable information attached to these patents. Vector space model (VSM) represents documents as high-dimensional vectors, where each dimension corresponds to a unique term. While originally proposed for information retrieval systems, VSM has also seen wide applications in patent analytics, and used as a fundamental tool to map patent documents to structured data. However, VSM method suffers from several limitations when applied to patent analysis tasks, such as loss of sentence-level semantics and curse-of-dimensionality problems. In order to address the above limitations, we propose a patent analytics based on feature vector space model (FVSM), where the FVSM is constructed by mapping patent documents to feature vectors extracted by convolutional neural networks (CNN). The applications of FVSM for three typical patent analysis tasks, i.e., patents similarity comparison, patent clustering, and patent map generation are discussed. A case study using patents related to Internet of Things (IoT) technology is illustrated to demonstrate the performance and effectiveness of FVSM. The proposed FVSM can be adopted by other patent analysis studies to replace VSM, based on which various big data learning tasks can be performed.

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Patent-to-Patent Similarity: A Vector Space Model

Cited by 50 publications

References 55 publications

Patent citations reexamined

Patent citations reexamined

Monitoring Class Activity and Predicting Student Performance Using Moodle Action Log Data

Patent Analytics Based on Feature Vector Space Model: A Case of IoT

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