[toggle title=”عنوان انگلیسی”]
A multi-expert system for ranking patents: An approach based on fuzzy pay-off distributions and a TOPSIS–AHP framework
[/toggle]
[toggle title=”فهرست مطالب”]
چکیده
مقدمه
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
نتیجه گیری
[/toggle]
[toggle title=”ترجمه چکیده”]
هدف از این مقاله معرفی سیستم پشتیبانی تصمیم گیری است که ثبت اختراعات را بر اساس ارزیابی های چند خبره رتبه بندی می کند. رویکرد ارائه شده با ایجاد سه سناریو با ارزش و با توجه به هر حق ثبت اختراع توسط هر خبره آغاز می شود. این موارد برای ایجاد توابع توزیع پرداخت فازی فردی به خاطر ارزش ثبت اختراعات بکار برده شده است. سپس توزیع پرداخت فازی مبنی بر رضایت طرفین مشخص شده که از بخش توزیع های فردی شروع می شود. گشتاور مبتنی بر امکان حاصل از اجماع توزیع پرداخت برای هر ثبت اختراع محاسبه شده است و در بخش رتبه بندی آنها با TOPSIS مورد استفاده قرار گرفته است. پیشتر نشان داده شد که چگونه فرآیند تحلیل سلسله مراتبی (AHP) را می توان برای گنجاندن متغیرهای تصمیمات اضافی در گزینه حق ثبت اختراع بکار برد، در نتیجه امکان فرآیند تصمیم گیری دو لایه فراهم می شود. این سیستم با نمونه عددی و قابلیت استفاده از سیستم نشان داده شده است و و ترکیبی از روش های که شامل گزینه ای از نمونه کارهای ثبت اختراع در دنیای واقعی می باشد مورد بحث قرار گرفته است.
[/toggle]
[toggle title=”ترجمه مقدمه”]
رتبه بندی و انتخاب ثبت اختراعات از دیدگاه مدیران مالکیت معنوی (IPR) در همه جا مسئله مهمی است. این موضوع اغلب یک کار تکرار شونده در شرکت هایی است که معمولا مدیران IPR خاص خودشان را برای بازدید نمونه کارهای ثبت اختراع و R & D یک بار یا دو بار در سال، تجزیه وتحلیل ترکیب نمونه کارها و تصمیم گیری در مورد تغییر ترکیب بندی نمونه کارها دارند. ثبت اختراعات جدید نیز ممکن است بر پایه استمرار در نظر گرفته شود، که بر ضرورت وجود ابزارها و حتی بیشتر تاکید می ورزد. یکی از راه های رتبه بندی از دیدگاه کمیتی و انتخاب ثبت اختراعات این است که از برآورد مقادیر آینده شان از دیدگاه شرکتی که دارای این موارد به عنوان معیار سنجش خوبی است، استفاده کنیم . این ارزش برای شرکت ممکن است مهمترین ویژگی حق ثبت اختراع باشد. دیگر مسائلی که در تجزیه و تحلیل و رتبه بندی ثبت اختراعات مهم هستند اغلب غیر مالی می باشند و باید با معیارهای استراتژیکی، مانند متناسب با ثبت اختراع برای نمونه کارهای شرکت های بزرگ و برنامه های آینده شرکت انجام گیرد. به طور کلی، می توانیم بگوییم که رتبه توانایی در نظر گرفتن این نوع از اطلاعات مالی و غیر مالی دارد.
[/toggle]
[toggle title=”مقدمه انگلیسی”]
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.
[/toggle]
[toggle title=”منبع”]
Journal : Expert Systems with Applications, Volume 40, Issue 12, 15 September 2013, Pages 4749–4759
Publisher : Science Direct (Elsevier)
[/toggle]
[aio_button align=”none” animation=”none” color=”red” size=”small” icon=”none” text=”انجام مقاله علمی پژوهشی و ISI در این زمینه” target=”_blank” relationship=”dofollow” url=”http://payannameha.ir/?p=796″]
[aio_button align=”none” animation=”none” color=”orange” size=”small” icon=”none” text=”دریافت سایر مقالات در این زمینه” target=”_blank” relationship=”dofollow” url=”http://payannameha.ir/?page_id=297″]
[aio_button align=”none” animation=”none” color=”blue” size=”small” icon=”none” text=”انجام پایان نامه در این حوزه” relationship=”dofollow” url=”http://payannameha.ir/?page_id=3206″]
[aio_button align=”none” animation=”none” color=”pink” size=”small” icon=”none” text=”انجام پروپوزال در این حوزه” target=”_blank” relationship=”dofollow” url=”http://payannameha.ir/?page_id=3206″]
فایل مقاله : 11 صفحه PDF
فایل ترجمه : 30 صفحه WORD
سال انتشار : 2013
جهت خرید فایل مقاله و ترجمه فارسی آن بر روی دکمه زیر کلیک نمایید: