{
  "abstract": "The present technology relates to artificial intelligence assisted device troubleshooting. In an implementation, an interface service of a human machine interface application trains a machine learning model on the content of an embeddings database. The interface service then receives an input comprising a context of an automation system design. The interface service generates a prompt that includes an instruction for the ML model to identify an anomaly type associated with the context of the automation system design and to generate a solution that addresses the anomaly type. The interface service transmits the prompt to the ML model and receives a response from the ML model that includes the anomaly type and the requested solution. After receiving a response, the interface service may modify the automation system design based on the content of the response and surface a graphical user interface that includes the modified design.",
  "canonical_url": "https://opentechnical.org/p/OT-2026-000000354",
  "categories": [
    "sensors-controls-machine-vision/industrial-control"
  ],
  "extent": {
    "figures": 0,
    "paragraphs": 0,
    "words": 145
  },
  "figures": [],
  "files": [
    {
      "bytes": 10152,
      "media_type": "text/html; charset=utf-8",
      "name": "index.html",
      "sha256": "6fb8490095f78a3897e70652f0a3daec9aef187b7a8e502efca18929d9ce0c90"
    },
    {
      "bytes": 1440,
      "media_type": "text/markdown; charset=utf-8",
      "name": "publication.md",
      "sha256": "dd0211263b8e1638fab38c389095d9af4238b6b3c9e5af7efe6fc20287ee0c70"
    },
    {
      "bytes": 1546,
      "media_type": "text/plain; charset=utf-8",
      "name": "publication.txt",
      "sha256": "a167533740fa578b9ad15d019e705393159048aabd216df45ac0f0fb5a59d935"
    }
  ],
  "integrity": {
    "hash_algorithm": "sha256",
    "keys_url": "https://opentechnical.org/integrity/keys",
    "record_url": "https://opentechnical.org/p/OT-2026-000000354/integrity",
    "signature_algorithm": "Ed25519",
    "signature_file": "manifest.sig",
    "verify_url": "https://opentechnical.org/integrity/verify"
  },
  "keywords": [
    "g05b19/4184",
    "g05b2219/31449"
  ],
  "language": "en",
  "license": {
    "name": "Creative Commons Attribution 4.0 International",
    "url": "https://creativecommons.org/licenses/by/4.0/"
  },
  "publication_id": "OT-2026-000000354",
  "published_at": "2026-09-15T19:02:47.081969Z",
  "publisher": "Nebula Innovations Limited",
  "record_type": "record",
  "schema": "opentechnical-publication-manifest/1.0",
  "source_date": "2026-04-21",
  "source_id": "US-12607987-B2",
  "source_name": "the United States Patent and Trademark Office",
  "source_url": "https://patents.google.com/patent/US12607987B2",
  "supersedes": null,
  "title": "Prompt engineering for artificial intelligence assisted industrial automation device troubleshooting",
  "uuid": "01a0a673-4429-77f8-a25e-190d4996d77c"
}