IP protection

1 Please provide a short description of the state-of-the-art and/or current trends in the field? How does the result fit into it?
One of the application areas of data mining is the World Wide Web (WWW or Web), which serves as a huge, widely distributed, global information service for every kind of information such as news, advertisements, consumer information, financial management, education, government, e-commerce, health services, and many other information services. The Web also contains a rich and dynamic collection of hyperlink information, Web page access and usage information, providing sources for data mining. Recommendations have a rapid increase in interest in the past 15  years due to the World Wide Web expansion (from single content based filters using N. Bayers theorems to produce end results) to ALS (Alternating Least Squares) algorithm that won the Netflix Prize, that is considered the state of the art algorithm that produce the fastest variation, as it was presented in RecSys2010 in Barcelona.
2 What is the problem/need/knowledge gap that the research result is responding to?  How was it addressed before?
The two most important issues in recommendation theories that this project tries to provide solution is the accuracy and throughput. Recommender systems are a special class of personalized systems that aim at predicting a user’s interest on available products and services by relying on previously rated items or item features. Human factors associated with a user’s personality or lifestyle, although potential determinants of user behavior are rarely considered in the personalization process.
3 What is the potential for further research?
Since recommendation engines is a new research area there are still open research issues that must be addressed:

  • Trend detection
  • Dynamics of popularity in rates
  • Use of external resources
  • Preprocessing of data
  • Interconnection of various application models
  • Sparse data, cold start problem, grey sheep problem
4 What is the proposed method of IPR-protection? (patent, license, trademark etc.)
Intelligent Web 2.0 recommender is patentable because it meets specific requirements that include e.g. novelty and integration of software and hardware. Patents are handled somewhat differently in different countries and so the requirements for a patent to be granted may vary. For example, in the US, software can be quite freely patented where as in the EU, software is not patentable (though patent lawyers have found ways to get patents for software or parts of software). Digital technologies are characterized by several unusual features. The costs of reproduction are insignificant, as compared with the costs of copying physical products.. For these reasons, digital technologies make it difficult to protect innovations through IPRs. Thus USPTO patent is considered to be the suitable solution to have the most geographic coverage since the product refers to global markets.
5 What are the steps that need to be taken in order to secure the IPR-protection? What is the cost of IPR-protection?
In order to apply for USPTO patent for Intelligent Web 2.0 recommender the following steps must be taken:

  • Preparation of a written description of Intelligent Web 2.0 recommender with all necessary drawings and organizational materials
  • Preparation of prototype that will be used as a blueprint for Intelligent Web 2.0 recommender

The cost is 700 euros plus 350 euros maintenance fee every three years plus 1500 for file preparation

6 What is you overall assessment of the scientific maturity of the research result?
Intelligent Web 2.0 recommender is scientific established through publications and primary research results. Moreover this research area is emerging rapidly as multidisciplinary area including behavioral models of user demographic. The research result step by step builds upon a new research field that will consequently produce intelligent algorithms. The manipulation of huge set data and resources on the web advance a dynamic research area that continuously advancing and will be very difficult to reach maturity in the near future.

KEYWORDS QUANTITATIVE ASSESSMENT (0-5).

Please put X as appropriate. 1 2 3 4 5
Scientific maturity x
Synergies x
State-of-the-art/innovation x
IPR-potential x

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