Docusign was granted a patent for machine-learning document templates.

{
  "signal_id": "06a606f3-f40c-5c51-972b-5b1dbe5685e1",
  "signal_type": "patents-company",
  "signal_subtype": "patentGrant",
  "detected_at": "2026-10-30T00:34:17+00:00",
  "company": {
    "name": "DocuSign",
    "domain": "docusign.com"
  },
  "data": {
    "cpc": [
      {
        "code": "G06F 40/186",
        "label": "Electric digital data processing"
      },
      {
        "code": "G06F 40/194",
        "label": "Electric digital data processing"
      },
      {
        "code": "G06F 40/197",
        "label": "Electric digital data processing"
      }
    ],
    "tags": [
      "Intellectual Property",
      "Research & Development",
      "Artificial Intelligence",
      "Machine Learning",
      "Document Management"
    ],
    "detail": "In October 2026 Docusign patented technology for creating and updating reusable electronic-document templates from existing documents in digital signing and workflow systems.",
    "summary": "Docusign was granted a patent for machine-learning document templates.",
    "event_at": "2026-10-27",
    "inventors": [
      {
        "name": "Mariam Badiei Turner",
        "status": "resolved",
        "is_primary": true,
        "linkedin_url": "https://www.linkedin.com/in/mbturner"
      }
    ],
    "tech_area": "software / computing",
    "event_kind": "grant_date",
    "grant_date": "2026-10-27",
    "source_url": "https://patents.google.com/patent/US12775784",
    "cpc_primary": {
      "code": "G06F 40/186",
      "label": "Electric digital data processing"
    },
    "patent_title": "Document template generation",
    "patent_number": "US12775784",
    "uspto_assignee": "Docusign, Inc.",
    "patent_abstract": "A method, a system, and a computer program product for generation of templates for electronic documents. A first template is generated based on a plurality of electronic documents. The first template defines a first structural arrangement of one or more portions extracted from the electronic documents and includes one or more portions. A machine learning model determines the first structural arrangement. An update to at least one portion is received. A second template is generated based on the first template and the received update. The second template defines a second structural arrangement of the portions as determined based on the first structural arrangement and the received update. An object model representative of at least one of: the first template, the second template, and a difference between the first and second templates is stored.",
    "precision_class": "structural",
    "invention_explanation": "A system that uses machine learning to recognize how content is arranged across documents, build a reusable template, and revise it when a section changes.",
    "technologies_mentioned": [
      "machine learning",
      "document automation",
      "electronic documents",
      "template generation"
    ]
  }
}

curl https://signals.autobound.ai/v1/signals/06a606f3-f40c-5c51-972b-5b1dbe5685e1 \
  -H "X-API-KEY: $AUTOBOUND_API_KEY"

curl -X POST https://signals.autobound.ai/v1/companies/enrich \
  -H "X-API-KEY: $AUTOBOUND_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"domain":"docusign.com","limit":20}'