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When your job depends upon explainability and validation of why a result was considered or not considered while evaluating a patent/technology/article, Explainable AI is one of the most important concerns.
The accuracy of Artificial Intelligence and Natural Language Processing rapidly increases rapidly when Machine Learning is added to it.
Xlscout implemented advanced algorithms based on NLP to enhance the accuracy and relevancy of the results. Here’s a brief overview about it.
Custom Training:
XLSCOUT Corpus is trained on bulk technology data (generic technology data) without any reference to a particular technology. When the system predicts synonyms, it predicts all possible synonyms and relations that customers might find as overwhelming information.
To make it more focused and precise XLSCOUT Corpus provides an option of custom training the ML models by providing customer interest technology bias. This helps in verticalizing the learning of ML models with respect to specific technologies of interest. In turn, the system gives more focused synonyms with accurate inter-relations.
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.
