Bank of America was granted a patent for detecting suspicious network activity.

{
  "signal_id": "178abe47-dbf5-5e26-a71b-8e6e118fb07b",
  "signal_type": "patents-company",
  "signal_subtype": "patentGrant",
  "detected_at": "2026-09-09T08:20:43+00:00",
  "company": {
    "name": "Bank of America",
    "domain": "bankofamerica.com"
  },
  "data": {
    "cpc": [
      {
        "code": "H04L 63/1425",
        "label": "Transmission of digital information"
      },
      {
        "code": "H04L 63/1416",
        "label": "Transmission of digital information"
      },
      {
        "code": "H04L 63/1441",
        "label": "Transmission of digital information"
      }
    ],
    "tags": [
      "Intellectual Property",
      "Research & Development",
      "Cyber Security",
      "Information Security"
    ],
    "detail": "In September 2026 Bank of America patented behavior-monitoring technology for financial-services networks, helping identify and stop unusual account and data-access activity.",
    "summary": "Bank of America was granted a patent for detecting suspicious network activity.",
    "event_at": "2026-09-01",
    "inventors": [
      {
        "name": "Steven Sinks",
        "status": "resolved",
        "is_primary": true,
        "linkedin_url": "https://www.linkedin.com/in/steven-sinks-1a95305"
      },
      {
        "name": "Joshua Abraham",
        "status": "resolved",
        "is_primary": false,
        "linkedin_url": "https://www.linkedin.com/in/josh-abraham-152a00"
      }
    ],
    "tech_area": "networking / data transmission",
    "event_kind": "grant_date",
    "grant_date": "2026-09-01",
    "source_url": "https://patents.google.com/patent/US12726502",
    "cpc_primary": {
      "code": "H04L 63/1425",
      "label": "Transmission of digital information"
    },
    "patent_title": "Event to action and activity correlation system",
    "patent_number": "US12726502",
    "uspto_assignee": "Bank of America Corporation",
    "patent_abstract": "Methods may detect, terminate and prevent nefarious actions within a computing network. Methods may mine, track and store a data set comprising patterns of normal user activity. Normal user activity may include login times, data access requests per unit time and self-generated network traffic volume per unit time. Methods may create a frequency baseline corresponding to a pattern of normal activity for each user. Methods may monitor the system for deviations in access frequency within the established baseline. Upon identification of deviations, methods may create a security incident and/or electronically relocate a user associated with the security incident within a secured sandbox environment.",
    "precision_class": "structural",
    "invention_explanation": "A system learns each user’s normal login and data-use patterns, then flags major changes that could indicate harmful activity.",
    "technologies_mentioned": [
      "behavior analytics",
      "network security",
      "user activity monitoring",
      "anomaly detection"
    ]
  }
}

curl https://signals.autobound.ai/v1/signals/178abe47-dbf5-5e26-a71b-8e6e118fb07b \
  -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":"bankofamerica.com","limit":20}'