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 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.