Some tasks are better suited for the implementation of artificial intelligence compared to others, as AI has become a well-established business practice across innovative companies. AI reduces the average non-patent literature search time for R&D and IP teams driving their organizations through the innovation lifecycle. NPL-powered semantic search democratizes the patent search process, allowing trained IP professionals to gain access to extensive patent and non-patent literature. Regardless of society’s general suspicion of AI, these benefits—and their impact on the cost of innovation—cannot be ignored.
There are artificial intelligence use cases that explain why individuals are suspicious of AI. Accidents occur in self-driving cars. Facebook’s algorithms amplify potentially harmful cognitive biases. We have welcomed AI in applications that make our lives easier, such as Netflix and Spotify recommendations, Apple’s Instagram-ready portrait mode, and voice search, to name a few.
Explainable, tested, trained, and observed models that make life easier, such as the XLSCOUT’s Novelty Checker, are the antidote to employers’ and employees’ skepticism of AI. When a searcher enters a portion of an invention disclosure into our patent search tool’s search bar, the AI engine recognizes the most relevant existing technical literature. That searcher may be reluctant to accept those results as complete because AI’s fundamental “black box” nature makes explainability difficult for the average user. However, our commitment to training AI models in-house and testing, observing, and maintaining them over time with unstructured, up-to-date real-world data enhances the overall transparency of our AI engine.
Training AI
XLSCOUT has trained its NLP engine to understand the language structure contained within patents. It also understands the language used by people searching for these types of technical documents. The AI is continuously trained using a high-quality dataset consisting of hundreds of millions of data points from corporations, patents, litigation records, and other technical literature. This dedication to consistent, high-quality data is critical for training AI. Artificial intelligence learns from the information it is given; if the inputs are low-quality, the outputs will be as well.
Many generally used AI engines, including NLP algorithms, learn from larger datasets. In-house engine training allowed XLSCOUT to teach its AI modules using application-specific data—in this case, technical documents like patents and publications.
Testing AI
AI, like human learners, may memorize patterns during training rather than being taught to make predictions and reveal insights. The author of an AI engine would be unaware of this flaw if novel inputs were not tested. Unstructured, real-world data must be used in the testing and evaluation process to spot any inconsistencies, implicit biases, or bugs.
At XLSCOUT, testing ensures that the AI models that power workflow solutions work as expected. It should be able to return relevant patent query results in a fraction of the time that human searchers do. Users can confidently utilize our AI-based tool in their innovation workflow because our AI precisely understands what they’re looking for.
Observing AI
The AI engine is continuously evolving. With several hundred thousand patents issued each year, in addition to new litigation records and technical literature. This evolution, or the ability to keep learning based on new inputs, is a key component of AI’s power. It can also end badly if not observed. Spend some time browsing AI Weirdness, and you’ll quickly see how AI can become sidetracked when left alone. We monitor and maintain our AI engine to meet users’ current needs. While, also raising the bar for what an AI tool can deliver. This final step in the AI training process ensures that XLSCOUT consistently delivers best-in-class results.
