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Measuring textual patent similarity on the basis of combined concepts: design decisions and their consequences

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Abstract

For certain tasks in patent management it makes sense to apply a quantitative measure of textual similarity between patents and/or parts thereof: be it the analysis of freedom to operate, the analysis of technology convergence, or the mapping of patents for strategic purposes. In this paper we intend to outline the process of measuring textual patent similarity on the basis of elements referred to as ‘combined concepts’. We are going to use this process in various operations leading to design decisions, and shall also provide guidance regarding these decisions. By way of two applications from patent management, namely the prioritization of patents and the analysis of convergence between two technological fields, we mean to demonstrate the crucial importance of design decisions in terms of patent analysis results.

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Notes

  1. On the levels of root forms of words and simple words the first possible tag that can be applied refers to syntactical class. Syntactical classes can be subdivided into lexical classes and phrasal classes. Verbs, nouns, prepositions and adjectives belong to the lexical classes. Verb phrases, noun phrases and prepositional phrases are part of the phrasal classes, to mention but a few (Collins and Hollo 2010).

  2. On this level or on the level of simple words syntactical function is a second possible tag that can be applied. Syntactical functions point to the grammatical role of a concept within a clause (Collins and Hollo 2010). They represent clause elements such as subject, predicate and object.

  3. Additional information about the selected patents can be found in “Application for prioritization ” section.

  4. If the size of the window exceeds that of the combined concepts, it makes sense to avoid building more combined concepts than necessary. For this reason, the algorithm should initially build all possible combined concepts in the first window. After moving the window to the next position, the algorithm should build only combined concepts between the new solitary concept in the window and the remaining old solitary concepts in the window. This pattern should be adhered to throughout the concept building process.

  5. The basic model was already introduced by different authors, sometimes using dissent variable names (Sepkoski 1974; Batagelj and Bren 1995).

  6. Corpora-based similarity calculation (e.g. Point-wise Mutual Information and Latent Semantic Analysis) have already been applied for the calculation of textual similarity between words, sentences, paragraphs or whole texts (e.g. Landauer et al. 1998; Foltz et al. 1998; Turney 2001).

  7. The abovementioned coefficients have already been adopted for different fields of application. For example, Qin (2000) shows the adaption of the cosine coefficient and the Jaccard coefficient for the comparison of documents. Quite early on, Braam et al. (1988) used these coefficients for co-citation cluster analysis. And Rip and Courtial (1984) described their application in the construction of co-word maps.

  8. For detailed information about the FVA: http://www.fva-net.de/.

  9. For example, n-grams can be used for the prediction of the next word in a word chain (see Manning and Schütze 2005), text categorization (see Cavanar and Trenkle 1994), malicious code detection (see Abou-Assaleh et al. 2004) and spam e-mail filtering (see Çıltık and Güngör 2008). They have also been applied in indexing, information retrieval, error correction, text compression, language identification, subject classification and speech recognition (Egghe 2000).

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Acknowledgments

The authors wish to give credit to Dr. Peter Roosen, g.o.e.the GbR, Aachen, for his constructive input regarding this paper. One of the included applications is based on the results of a joint project with the Forschungsvereinigung Antriebstechnik (FVA). We would like to thank the FVA and all industrial members, especially Dipl.-Ing. Thomas Bayer, for their contributions and their support.

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Authors and Affiliations

  1. IPMI – Institute of Project Management and Innovation, University of Bremen, Wilhelm-Herbst-Str. 12, 28359, Bremen, Germany

    Martin G. Moehrle & Jan M. Gerken

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  1. Martin G. Moehrle
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Correspondence to Martin G. Moehrle.

Appendices

Appendix 1: Calculating the quantity of combined concepts without regard to the window size

The count of combined concepts within a document is related to the concept size, more precisely; it decreases in proportion to the concept size. The relationship between the size of the text in patent i, measured as the count of solitary concepts (ci1), the size of a combined concept (n) and the maximum quantity of extracted combined concepts with the size n (c wn ), can be calculated by means of formula 1. The count of combined concepts can be even smaller, whether any combined concepts occur identically in the text.

$$ c_{in} = c_{i1} + 1 - n $$
(1)

Appendix 2: Calculating the quantity of combined concepts with regard to the window size

The maximum quantity of combined concepts is influenced by the size of the combined concepts as well as by the count of windows ciw in which a patent i can be divided, the count of combined concepts with a size n that can be extracted from a window (cwn) and the overlap of identical combined concepts in different windows co. (formula 2)

$$ c_{in} = c_{iw} \cdot c_{wn} - c_{o} $$
(2)

The counts of windows in patent i (ciw) depend on the window size m and the count of solitary concepts ci1. The total of windows within a patent can be calculated by means of formula 3, which is quite similar to formula 1:

$$ c_{iw} = c_{i1} + 1 - m $$
(3)

Calculating the number of combined concepts inside a window is reminiscent of a typical problem from the field of combinatorics. Within the window size, the solitary concepts are connected to combined concepts without variation of their sequence and without repetition. Accordingly, the quantity of combined concepts with a concept size of n c wn can be calculated by means of formula 4:

$$ c_{wn} = \frac{m!}{n! \cdot (m - n)!} $$
(4)

The overlap of identical combined concepts in the windows depends on the counts of identical solitary concepts, the combined concept size, the window size and the window offset wo.

$$ c_{o} = \frac{{c_{w1!} }}{{n! \cdot (c_{w1} - n)!}} \cdot \left( {c_{iw} - 1} \right) $$
(5)

with

$$ c_{w1} = m - w_{o} $$
(6)

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Moehrle, M.G., Gerken, J.M. Measuring textual patent similarity on the basis of combined concepts: design decisions and their consequences. Scientometrics 91, 805–826 (2012). https://doi.org/10.1007/s11192-012-0682-0

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