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Patent Portfolio Analysis a Negotiation Tool

a case study in the automotive industry

Written by Jennifer Asp

Paper category

Master Thesis

Subject

Business Administration>Management

Year

2017

Abstract

Thesis: Patent portfolio analysis The patent portfolio consists of all outstanding, published and in-use patents of an entity. You can search for patent portfolio data internally for your own patents, or you can search for patent portfolio data externally through publicly available databases. When studying the patent portfolio, it is important to pay attention to the time delay difference between internal and external information collection. The delay stems from the 18-month period between application submission and publication (Fabry et al., 2006; U.S. Patent and Trademark Office, 2015), but because patents are usually filed before development is completed, and the finished product and patent are published. The time can be shorter (Fabry et al., 2006). Patent data provides a good database of information that can be used to analyze the breadth and depth of the company's internal knowledge (Suominen, Toivanen, and Seppänen, 2017). For example, one can observe the degree of product development of a particular entity (Suominen et al., 2017). When the analysis is done correctly, it is possible to identify active participants in the market in a particular field, as well as participants with extraordinary patent status (Fabry et al., 2006). Fabry et al., (2006) further pointed out that with the improvement of computing power, the convenience of obtaining patent information has led to the trend of computer-assisted PPA. PPA can be used to evaluate the organization's R&D landscape and business opportunities, or to evaluate the entire business landscape from a larger perspective (Shen & Su, 2016). PPA is also considered to be easy to implement in an organization, mainly for senior management and external stakeholders who wish to cooperate or invest in companies for strategic planning reasons (Ernst, 2003). PPA is becoming more and more important because the competitive landscape requires a broad base of capabilities while maintaining depth in core business areas (Suominen et al., 2017). When evaluating the usefulness of patents for business development, it is important to realize that individual patents are not the focus, but the value of the entire patent information (Fabry et al., 2006). In order for PPA to be useful, it needs to select and evaluate only applicable patents to fit the analytical model based on appropriate patent data (Fabry et al., 2006). Analyzing a large number of patents for an entire industry or product category can be overwhelming and make the analysis less useful. In order to analyze the correct parameters and thus achieve a very useful analysis, Fabry et al. al (2006) claimed that the way the parameters are set allows them to receive 50 to 2000 patents for analysis. Using the international patent classification system, patents can be classified naturally. When analyzing the activity of patent portfolios, Fabry et al. (2006) claimed that both the number of patents and the quality of patents are important. He further described the quality to be measured; the ratio of patent grants to applications, the international scope of patent applications, the scope of technologies that can be assessed by looking at the classification of intellectual property rights, and the frequency of citations that should consider the age of patents. Last but not least, he claims that PPA can be used to evaluate a company or department and analyze its overall patent strength by analyzing the combination of its patent activity and patent quality. According to Fabry et al. (2006), the results of PPA are best illustrated in graphical format, using bar graphs with different characteristics, and using normalized spider web graphs when presenting the overall graph of the analysis. However, care should be taken when obtaining abnormal or unexpected values, as these situations may require special treatment or may have characteristics that affect the analysis in an unfair way (Fabry et al., 2006). However, recent machine learning can minimize the need for such reliability analysis (Suominen et al., 2017). 2.3 TAM-Technology Acceptance Model Technology Acceptance Model (TAM) was developed by Davis (1985) to improve the understanding of user acceptance, process and analyze how users might react, and accept new technologies before they are introduced into the market It. It is a popular technology acceptance model (Marangunić & Granić, 2015), which has been modified and further developed many times to adapt to different purposes and businesses. It is considered a leading model for predicting the behavior of rejecting and accepting new technologies (Marangunić and Granić, 2015) (such as PPA). The first draft of the Davis (1985) model is shown in Figure 2 below. According to the Davis (1985) model, the use attitude determines whether to use the technology, or reject the technology by establishing the user's motivation to use the technology. Attitude to use is affected by perceived usefulness and perceived ease of use (Davis, 1985). As shown in Figure 2, perceived ease of use also has an impact on perceived usefulness. Both usefulness and ease of use depend on the actual system functions and features, what they do and how they are used, Davis (1985) named them system design features. The arrows in the model represent causality, where the attitude towards use will determine the actual use of the technology represented by the actual system use. 2.4 Management strategy: four processes Organizations often establish management systems around financial measures and goals (Kaplan & Norton, 1996) such as cost KPIs. Read Less