Impact of Patent Infringement Compensation Rules on Patent Quality Problems

Compensation rules for patent infringement greatly affect patent quality, which is closely related to R & D investments. In this study, A duopoly game model was developed to analyze innovative factories’ R&D investment and patent licensing behavior, as well as the strategic choices of potential infringers under different compensation rules for patent infringement. Furthermore, a comparative analysis was conducted to analyze the patent quality under different scenarios, ultimately finalizing an optimal sequence rule for patent infringement compensation. The results show that patent quality is influenced by the invention height of patent and R&D efficiency, while the amount of patent infringement compensation has a great effect on potential infringement behavior. Patent quality can be effectively improved if the court adopts a proper sequence rule for patent infringement compensation according to the market circumstances.

Hierarchia i relacje miast w województwie lubuskim

In socio-economic geography any regional settlement system makes one of its majorfactors of development. It is towns that play a specific role in this system, the meaning of which canbe approached from different perspectives. This paper focuses on the hierarchies and relationshipsbetween towns in the Province of Lubuskie seen from the spatial point of view. The volume and sequence rule of Zipf ’s law as well as the gravitation model have been used as the statistical tools. Thedata valid as in 2015 were accounted for in the analysis. Results from the empirical studies show thatdespite a similar urban density in Lubuskie Province and generally across Poland, the town system in theregion is visibly specific. Apart from two largest cities playing a regional and cross-regional function,i.e. Zielona Góra and Gorzów Wlkp., the issue of how small and middle-sized towns function in theprovince comes to the fore.

A survey for acquiring frequent and sequential items in E-commerce sites

Large amounts of data has made available because of the increase in e-commerce industry. Data has high significance and also important for everyone. Hundreds of websites are being deployed and each site offers millions of products. In addition to this there are several types of input forms. Different sites have different input item collection. This means that there is a substantial amount of information being provided resulting in information overload and in turn results in reduced customer satisfaction and interest. This huge amount of data needs to get processed so that we can able to extract the useful information. From this useful information we can able to increase customer interest, satisfaction along with sales of e-commerce sites. Presenting frequent and sequential patterns in e-commerce sites results in increase of sales of products without delay. Different association rule mining techniques and sequential rule mining techniques can be used for different sets of input forms in order to generate frequent and sequential patterns. This paper discusses various algorithms using techniques such as association rule mining, sequence rule mining proposed for mining frequent and sequential items.

Competitiveness analysis through comparative relation mining

Purpose Comparative opinions widely exist in online reviews as a common way of expressing consumers’ ideas or preferences toward certain products. Such opinion-rich texts are key proxies for detecting product competitiveness. The purpose of this paper is to set up a model for competitiveness analysis by identifying comparative relations from online reviews for restaurants based on both pattern matching and machine learning. Design/methodology/approach The authors define the sub-category of comparative sentences according to Chinese linguistics. Classification rules are set up for each type of comparative relations through class sequence rule. To improve the accuracy of classification, a comparative entity dictionary is then introduced for further identifying comparative sentences. Finally, the authors collect reviews for restaurants from Dianping.com to conduct experiments for testing the proposed model. Findings The experiments show that the proposed method outperforms the baseline methods in terms of precision in identifying comparative sentences. On the basis of such comparison-rich sentences, product features and comparative relations are extracted for sentiment analysis, and sentimental score is assigned to each comparative relation to facilitate competitiveness analysis. Research limitations/implications Only the explicit comparative relations are discussed, neglecting the implicit ones. Besides that, the study is grounded in the assumption that all features are homogeneous. In some cases, however, the weights to different aspects are not of the same importance to market. Practical implications On the basis of comparative relation mining, product features and comparative opinions are extracted for competitiveness analysis, which is of interest to businesses for finding weakness or strength of products, as well as to consumers for making better purchase decisions. Social implications Comparative relation mining could be possibly applied in social media for identifying relations among users or products, and ranking users or products, as well as helping companies target and track competitors to enhance competitiveness. Originality/value The authors propose a research framework for restaurant competitiveness analysis by mining comparative relations from online consumer reviews. The results would be able to differentiate one restaurant from another in some aspects of interest to consumers, and reveal the changes in these differences over time.

Descriptive Analytics

Data analytics has becoming one of the challenging platforms across various domains such as telecom, health care, social media and so on. The challenging and most promising task in analytics is the understanding of various patterns in the data. The mechanism of data retrieval and analysis seems to be the promising one in which the algorithms, techniques, way of processing data are in need with the ability to target upon large volumes of data. There are various types of analytical methods such as predictive analytics, descriptive analytics, text analytics, social media analytics and survival analytics. This chapter mainly focuses towards the mechanism of descriptive analytics its types, algorithms and applications. There are various forms of tools and techniques such as association rule mining, sequence rule mining, and data categorization such as hierarchical and non-hierarchical clustering methods with its variants.

Frontal Cortex Supports the Early Structuring of Multiple Solution Steps in Symbolic Problem-solving

problem-solving relies on a sequence of cognitive steps involving phases of task encoding, the structuring of solution steps, and their execution. On the neural level, metabolic neuroimaging studies have associated a frontal-parietal network with various aspects of executive control during numerical and nonnumerical problem-solving. We used EEG–MEG to assess whether frontal cortex contributes specifically to the early structuring of multiple solution steps. Basic multiplication (“3 × 4” vs. “3 × 24”) was compared with an arithmetic sequence rule (“first add the two digits, then multiply the sum with the smaller digit”) on two complexity levels. This allowed dissociating demands of early solution step structuring from early task encoding demands. Structuring demands were high for conditions that required multiple steps, that is, complex multiplication and the two arithmetic sequence conditions, but low for easy multiplication that mostly relied on direct memory retrieval. Increased right frontal activation in time windows between 300 and 450 msec was observed only for conditions that required multiple solution steps. General task encoding demands, operationalized by problem size (one-digit vs. two-digit numbers), did not predict these early frontal effects. In contrast, parietal effects occurred as a function of problem size irrespectively of structuring demands in early phases of task encoding between 100 and 300 msec. We here propose that frontal cortex subserves domain-general processes of problem-solving, such as the structuring of multiple solution steps, whereas parietal cortex supports number-specific early encoding processes that vary as a function of problem size.

A Probabilistic Method for Mining Sequential Rules from Sequences of LBS Cloaking Regions

Analyzing large-scale spatial-temporal anonymity sets can benefit many LBS applications. However, traditional spatial-temporal data mining algorithms cannot be used for anonymity datasets because the uncertainty of anonymity datasets renders those algorithms ineffective. In this paper, the authors adopt the uncertainty of anonymity datasets and propose a probabilistic method for mining sequence rules (PMSR) from sequences of LBS cloaking regions generated from a series of LBS continuous queries. The main concept of the method is that it designs a probabilistic measurement of a support value of a sequence rule, and the implementation principle of the method is to iteratively achieve sequence rules. Finally, the authors conduct extensive experiments, and the results show that, compared to the non-probabilistic method, their proposed method has a significant matching ratio when the mined sequence rules are used as predictors, while the average accuracy of the sequence rules is comparable and computing performance is only slightly decreased.