Machine Learning in AI-based Analysis Tools is as Important as NLP

Machine Learning in AI-based Analysis Tools is as Important as NLP. When your job depends upon the explainability and validation of why a result was considered or not considered while evaluating a patent, technology, or article, Explainable AI is one of the most important concerns.

The accuracy of artificial intelligence and natural language processing increases rapidly when machine learning is added to them.

XLSCOUT implemented advanced algorithms based on NLP to enhance the accuracy and relevancy of the results. Here’s a brief overview of it.

Custom Training

Without any reference to a particular technology, we have been training the XLSCOUT Corpus using bulk technology data (generic technology data). When the system predicts synonyms, it predicts all possible synonyms and relations that customers might find overwhelming.

To make it more focused and precise, XLSCOUT Corpus provides the option of custom training the ML models by providing customer interest technology bias. This helps in verticalizing the learning of machine learning models with respect to specific technologies of interest. In turn, the system gives more focused synonyms with accurate interrelationships.

For Example

Use Cases

Explainable Taxonomy (Corpus Assisted)
Corpus assists in creating comprehensive taxonomy for technology breakdown into clusters.

Explainable Categorization
Rule-based Categorization backed by corpus with a possibility of training on expert validated data.

Context Capturing in Novelty & Invalidation Searches
Better semantic variations capturing to perform better prior art searches.