Due the nature of their work, an in-house legal team for a large multi-national company, received and needed to process court letters to capture court and submission dates, and then communicate them to a list of relevant staff via email.
This mundane activity was being conducted by a busy paralegal, who was struggling to keep up with the volume of letters.
The manual process included
- Scanning court document on printer (and a pdf copy sent to Paralegal via email)
- Upload document to document management system
- Paralegal reviews document, and adds relevant dates to calendar, for the following types of litigation:
- Hearing dates
- Questionnaire due dates
- Bundle share dates
- Final hearing dates
- Send Outlook invites to set list of relevant staff, so they are informed of the deadlines
- Add details to the Litigation list on Excel: by Case, item, due date etc
The main challenge with this automation was reading unstructured text to extract keywords/dates.
The capability was proven by using a Python NLP library called SpaCy.
- Microsoft Outlook
- Document Management System (DMS)
- Microsoft Excel
- SpaCy (NLP)
Process Flow Map
RPA Bot Steps
Triggered automatically twice a day:
- Connect to email server
- Search email and extract attachments with subject line ‘courtDates’
- Determine type of court document
- Extract deadline dates, case number, and case title
- Create directory named with case title
- Save PDF to DMS with syntax (letterDate, courtDoc Type, Case#)
- Create Outlook invite for each date and Send to stakeholder group
- Add case details to Excel litigation list
- Move email to ‘actioned’ folder
Process as % of Employees Time
Estimated Payback (RAAS)
Redeployment of Paralegal onto more challenging legal work, currently being conducted by external legal firm.