| Pub. Date | : Oct, 2020 |
|---|---|
| Product Name | : The IUP Journal of Knowledge Management |
| Product Type | : Article |
| Product Code | : IJKM21020 |
| Author Name | : Brian Haney |
| Availability | : YES |
| Subject/Domain | : Management |
| Download Format | : PDF Format |
| No. of Pages | : 32 |
Natural Language Processing (NLP) patents are one of the fastest growing niche segments in the technology patent marketplace. NLP technologies power the latest in Artificial Intelligence (AI) applications. For example, NLP technology supports Apple?s Siri, Amazon?s Alexa, and Facebook?s friend recommendation system. Yet, while the literature on software patents is visibly scaling?the literature specifically focused on NLP patents is non-existent. This paper draws on a growing body of computational linguistics, intellectual property, and technology law scholarship to provide novel NLP patent analysis and critique. Further, this paper contributes the first empirical NLP patent review, including novel software descriptions, market modeling, and legal analysis relating to patent claims. First, this paper discusses the two main technical approaches to developing NLP software. Second, this paper models an evolving NLP patent dataset, offering economic insights, legal claims analysis, and patent valuation strategies.
Natural Language Processing (NLP) sits at the intersection of computer science, artificial intelligence, and computational linguistics.1 Defined, NLP is the study of computational linguistics, which includes natural language understanding and natural language generation.2 NLP studies strive to develop machines which process, understand, and generate language representations as well as humans.3 However, language representation is a difficult task because human language interpretation depends on real world presence, commonsense, and context.4 Thus, NLP endeavors to bridge the human-machine divide, enabling computers to analyze syntax and process semantics.5