AI 법원 기록 및 마감일 일정

Docketing has two separate jobs. The first is intake: knowing that something happened, such as a complaint served, an order entered or a hearing set. The second is calculation: turning that event into deadlines using the applicable rules. Traditional rules-based systems, such as CompuLaw and tools built on CalendarRules, handle calculation with rule sets that attorney editors maintain for each court. They often connect to Outlook or practice management software. A docketing clerk picks the court, the trigger event and its date, and the system generates the chain of deadlines. AI changes intake more than calculation. Models can read electronic court notices, PDF orders and emails. They classify the event, extract dates, case numbers and parties, and propose the matching trigger. That removes retyping, which is a major source of errors. The calculation itself should remain deterministic. Counting rules involve calendar days versus court days, weekends and legal holidays, what happens when a deadline falls on a day the clerk's office is closed, and adjustments for how a paper was served. For example, under the Federal Rules of Civil Procedure, three days are added after service by mail but not after electronic service. A notice of appeal in most federal civil cases is due 30 days after entry of judgment. State and local rules differ, and individual judges' orders often override the defaults. The stakes are high. Malpractice insurers and ABA studies have repeatedly listed missed deadlines and calendaring errors among the most frequent causes of claims. Some deadlines, such as appeal periods, can be jurisdictional, which means the parties cannot extend them by agreement. A common misconception is that a general-purpose chatbot can calculate deadlines. Language models are unreliable at date arithmetic. They may also apply the wrong jurisdiction's rules, or outdated rules, without any warning. The safer pattern is AI for reading, a rules engine for counting and a person for confirmation.