سیستم چند خبره برای رتبه بندی ثبت اختراعات: رویکرد مبتنی بر توزیع های پرداخت فازی و چارچوب TOPSIS–AHP
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|6322||2013||11 صفحه PDF||30 صفحه WORD|
- تولید محتوا با مقالات ISI برای سایت یا وبلاگ شما
- تولید محتوا با مقالات ISI برای کتاب شما
- تولید محتوا با مقالات ISI برای نشریه یا رسانه شما
پیشنهاد می کنیم کیفیت محتوای سایت خود را با استفاده از منابع علمی، افزایش دهید.
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 40, Issue 12, 15 September 2013, Pages 4749–4759
طرح اولیه برای سیستم پژوهشی اجماع چند خبره به منظور حمایت از گزینه ثبت اختراع
ایجاد سناریوهای وجوه گردش و ایجاد توزیع پرداخت فازی
دستیابی به اجماع در طول توابع توزیع پرداخت فازی عوامل
The aim of this paper is to introduce a decision support system that ranks patents based on multiple expert evaluations. The presented approach starts with the creation of three value scenarios for each considered patent by each expert. These are used for the construction of individual fuzzy pay-off distribution functions for the patent value; a consensual fuzzy pay-off distribution is then determined starting from the individual distributions. Possibilistic moments are calculated from the consensus pay-off distribution for each patent and used in ranking them with TOPSIS. It is further showed how the analytic hierarchy process (AHP) can be used to include additional decision variables into the patent selection, thus allowing for a two-tier decision making process. The system is illustrated with a numerical example and the usability of the system and the combination of methods it includes for patent portfolio selection in the real world context is discussed.
Ranking and selection of patents is an important issue from the point of view of intellectual property (IPR) managers everywhere. It is most often a recurring task in companies that commonly have their IPR managers visit the patent and R&D portfolios once or twice per year, analyzing the composition of the portfolios and making decisions about the modification of portfolio composition. New patents may also be considered on continuing basis, emphasizing the need for tools even further. One way to quantitatively rank and to select patents is to use estimation of their future value from the point of view of the firm owning them as a measure of goodness. Value to the firm may very well be the single most important characteristic of a patent. Other issues that are important in analyzing and ranking patents are most often non-financial and have to do with strategic criteria, such as fit of the patents to the corporate portfolio and to the future plans of the firm. Generally, we can say that a good ranking is able to consider both of these types of information, financial and non-financial. Commonly there are three main approaches for the valuation of patents, these are the “cost approach”, the “market method”, and the “income approach” also known as the discounted cash-flow method (DCF) (e.g., see Reilly & Schweihs, 1998; Smith & Parr, 2000). Of these, the cost approach and the market method are meant only for market valuation of patents that is to say, for the derivation of an estimate for a sale price for a patent. The DCF method is based on the well known principles of present value (PV) and the same principles can be used also in the “in-house” valuation of patents, that is, to derive the “value to the firm” of patents. It is important to note that patent analysis is a forward-looking exercise, as patents are an enabling class of assets that is most often used to secure the future of the firms’ business. This means that methods used in the valuation and analysis of patents should be able to take into consideration the (sometimes considerable) estimation inaccuracy present in forward-looking estimation, as the estimation of future cash-flows for patents, since it is not realistic to expect anyone to be able to produce precise estimates for future (patent) cash-flows (Karsak, 2006). Using cash-flow scenarios is a widespread practice of modeling the inaccurate and uncertain future cash-flows, and it can also be applied to patent analysis (Collan, Fuller, Mezei, & Wang, 2011). Information to support the creation of cash-flow scenarios can come from systems specifically designed for supporting patent analysis, such as are presented (for example in Camus & Brancaleon, 2003; Huang, Liang, Lin, Tseng, & Chiang, 2011; Littman-Hillmer & Kuckartz, 2009; Park, Kim, Choi, & Yoon, 2013), or it can come directly from experts, most often from within the firm itself. Fuzzy logic is an established way to express imprecision precisely and as such is a usable tool also when patent cash-flows are considered. Fuzzy pay-off method, introduced in Collan et al., 2009a and Collan et al., 2009b and further presented in Collan (2012) is a tool for investment analysis and is based on using cash-flow scenarios to create an asset’s pay-off distribution that is considered as a fuzzy number. The fuzzy pay-off method can be employed in the valuation of patents (Collan & Heikkilä, 2011). As already observed above, patent analysis is a forward-looking procedure and there may be differing views about the direction that the future will take. This observation can be interpreted in the way that it makes sense to include more than one expert opinion when patent analysis is done. This is true for both, for cash-flow information, as well as, for non-cash-flow information. As budgets for patent portfolios are tight, the firm can afford to keep only the best patents. This calls for the ranking of the patents as a basis of selection. It is a fair assumption that the value to the firm is a key driver in the selection of patents and can be used as a first basis for patent selection into portfolios. Important other (secondary) considerations may include different non-financial strategic selection criteria. This means that one plausible approach to go about with patent selection is to first rank the patents that are competing for a place in the firm’s portfolio based on their value to the firm, second do a pre-selection of a sub group of the best patents, and third do a complementary analysis to narrow down the number of patents to fit the budget, based on the non-financial strategic criteria. In this paper, an approach that enables both the financial and non-financial merits to be included in ranking of patents is proposed, while taking into consideration the estimation imprecision, and the differing estimates of multiple experts. This combination is a new contribution that allows a more holistic analysis on patents to be performed. The way in which the methods used are combined is new and new to the field of application. Furthermore, we use possibilistic moments in characterizing fuzzy financial information and rank patents according to the moments, to the best of our knowledge the first proposed approach of its kind. The remainder of the paper is organized as follows. In Section 2 the general framework of the system for performing ranking and selection of patents is presented. In Section 3 we continue by presenting the construction of pay-off distributions from cash-flow scenarios by each expert for each patent takes place. In Section 4 the consensus modeling mechanism to be used to build consensus pay-off distributions from each expert’s pay-off distributions is introduced. Section 5 starts with the definition of possibilistic moments of fuzzy pay-off distributions and continues with the description of the main steps of TOPSIS, used then for producing a preliminary ranking of patents. In Section 6, after a short presentation of AHP we show how it can be used to include strategic criteria in ranking the patents. In Section 7 the two-tier process is illustrated with a numerical example that includes the selection of four patents out of twenty candidate patents. Finally, the paper is closed with a discussion and some conclusions.
نتیجه گیری انگلیسی
In this paper, a system for supporting the selection of patents to be included in a portfolio has been introduced. The system uses a two-tier decision making process, where two different teams of agents (experts, decision makers) are involved. The first team is composed by experts who are asked to create, independently of one another, a financial analysis of each patent’s value for the firm by using three value scenarios. These scenarios are then used to create individual fuzzy pay-off distribution functions that are represented as triangular fuzzy numbers. Then, a dynamic consensus reaching mechanism is introduced to determine, for each patent, the group fuzzy pay-off distribution that is, a consensual triangular fuzzy number. Three possibilistic moments are calculated from the consensual pay-off distributions for each patent. These are used in a first ranking of patents that is obtained by using the TOPSIS method. The second team of “elected” decision-makers performs an AHP process, based on relevant strategic criteria, to create the final ranking of the patents that can be used in supporting the patent portfolio selection. In Section 7, a numerical example was introduced to show how the system works in practice, and how the introduction of additional analysis with AHP based on six additional criteria has changed the ranking order of the patents. The numerical analysis has been done with Matlab. The most remarkable novelty of our approach consists in proposing a mixed procedure which permits to combine technical valuation and ranking of patents, as carried out by a team of experts focused on more accounting-based attributes, with a strategic evaluation that takes care of the scenario factors characterizing the global competitive market including, for example scope and coverage, product marketplace value, and defensive and offensive potential. The usability of the system proposed here is by no means limited to the valuation of patents. It can be used for any similar problems where the ranking of competing assets benefit from performing both financial and “strategic” analysis, such as for example ranking of R&D projects. In the future, we will extend the model introducing linguistically-based descriptions of attributes’ values in the TOPSIS ranking and in the AHP process as well and a fuzzy rule-based framework to make more effective the representation of experts’ knowledge. We aim to look also at the relationship between some financial measures such as the real option value and the possibilistic moments.