Hidden Markov Model (HMM) in Support of Intellectual Property Risk Management
Lois Onyejere Nwobodo1, Hyacinth C. Inyiama2
1Engr.(Mrs) Lois Onyejere Nwobodo, Computer Engineering Department, Enugu State, University of Science and Technology [ESUT], Enugu, Nigeria.
2Engr. Prof Hyacinth C. Inyiama, Electronics/Computer Engineering Department,, Nnamdi Azikiwe University[Unizik], Awka, Anambra State, Nigeria.
Manuscript received on May 25, 2015. | Revised Manuscript received on May 29, 2015. | Manuscript published on June 30, 2015. | PP: 1-5 | Volume-2 Issue-7, June 2015. | Retrieval Number: G0310062715
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: An important element of intellectual property (IP) risk management is valuation, forecasting and strategy. Forecasting the optimal likelihood probabilities for the risk can be an audacious exercise, but it is critical in understanding the damage that can be caused by infringement, IP rights litigations etc providing the basis for prioritizing risk management activities and allocating resources. In this paper the occurrence, interactions of risk events as it impacts intellectual property management is modeled as Hidden Markov Model (HMM). The paper presents the HMM as a tool that can be used to optimize IP risk management response. The paper developed a HMM that can be used to predict the maximum likelihood probability for IP risk. This gives substantial information for optimal planning & coordination of IP risk response activities.
Keywords: IP, risk management, HMM maximum likelihood probabilities, IP risk features.