Another failure of artificial intelligence when it comes to processing legal documents is its inability to discern the “posture” of a motion, meaning the way a motion is teed up to the court. Is the motion contested? Stipulated? Sua sponte? Renewed? The posture of a motion has a great impact on the court’s willingness to grant or deny it.
The posture of a motion has a great impact on the court’s willingness to grant or deny it.
For example, contested motions to stay pending an inter partes review are granted only 43% of the time. However, stipulated motions of the same type are granted a whopping 94% of the time. When advising clients on a judge’s track record for a specific type of motion, it only makes sense to compare apples to apples. If your motion is contested, it would not make sense to rely on historical data based on stipulated motions. The ability to exclude or include specific postures in search results is vital when building a strategy.
In the Docket Navigator platform, court rulings are diagrammed with several different components, but the four main components are (i) the posture (how the motion was presented), (ii) the type of motion (the procedural tool used to request relief), (iii) the result (how the court ruled on the motion), and (iv) legal issues (the legal concepts addressed in the court’s ruling). Because those exist as four different data points, users can filter by just one of them, or use any combination of the four. This model is called a “Document Filter Group,” and this is how it appears in the user interface, using the contested motion to stay pending IPR as an example:
By designing the data in this way, questions such as “How many rulings on original, contested motions to stay pending IPR have there been?” take only a few seconds to answer, comprehensively, in the Docket Navigator platform.