Why Explainable AI is the Most Important Aspect in Automation of IP Analysis?

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, explainability is one of the most important concerns.

There are many databases and analytical tools that are available; however, the explainability of artificial intelligence is the most important aspect.

How XLSCOUT Solved the Explainability Issue?

The XLSCOUT corpus is a large lexical database of the technology. Nouns, verbs, adjectives, and adverbs are grouped into sets of cognitive synonyms, each expressing a distinct concept. Cognitive synonyms are interlinked by means of conceptual-semantic and lexical relations. The resulting network of meaningfully related words and concepts can be retrieved using the XLSCOUT corpus weblink. The XLSCOUT corpus structure makes it a useful tool for computational linguistics and natural language processing.

The XLSCOUT corpus superficially resembles a thesaurus in that it groups words together based on their meanings. However, there are a few important distinctions:

First, the XLSCOUT corpus interlinks not just word forms—strings of letters—but specific senses of words. As a result, words that are found in close proximity to one another in the network are semantically disambiguated. 
Second, the XLSCOUT corpus labels the semantic relations among words, whereas the groupings of words in a thesaurus do not follow any explicit pattern other than meaning similarity.

Custom Training Option

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 overwhelming.

To make it more focused and precise, the 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 interrelationships.

For Example

Use Cases

Explainable Taxonomy (Corpus Assisted)

Corpus assists in creating a comprehensive taxonomy for technology breakdown into clusters.

Explainable Categorization

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

Context Capturing in Novelty & Invalidation Searches

Better semantic variations capturing to perform better prior art searches.

Conclusion

Manual Boolean searches: The conventional approach is widely accepted practice, as traditional databases were only rule-based engines. With the rapid development of AI technologies, we have seen a lot of NLP and ML algorithms to compare, categorize, and summarize text documents. However, experts feel that “if you are betting your job on AI, it is better to be explainable.”

XLSCOUT Corpus is a step taken forward to make AI more explainable, and it is one of the core technologies that we combined with multiple NLP and ML technology layers to develop various R&D and IP assistance apps on our platform, such as NLP-enriched landscapes, Techscaper, Company Explorer, etc. Our R&D team has done a deep dive to find solutions where XLSCOUT Corpus can achieve the desired output and is further explainable to make the life of the customer easy.