This is an AAO appeal decision — a case denied once and appealed. It shows how the framework is applied; it is not the filing population.
The Petitioner, an AI/ML research software engineer, proposed to continue research on autonomously updating ML models and privacy-enhancing techniques to incorporate machine learning across sectors such as automotive and healthcare.
Field: artificial intelligence / machine learning · Read the decision (uscis.gov)
EB-2 threshold — addressed
Director found Petitioner qualified as a member of the professions holding an advanced degree; not disputed on appeal.
Prong 1 — national importanceestablished
Prong 2 — well positionedestablished
Prong 3 — balance of factorsestablished
How the evidence was treated
- recommendation letter · credited
- citations publications · credited
- patents · credited
- degree · credited
- resume experience · credited
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Notable
AAO fully withdrew the Director's negative findings on all three Dhanasar prongs and sustained the appeal, an outcome favorable to the petitioner throughout.
Authorities this decision leans on
From the doctrinal survivor set — 187 authority tests across 47 distinct authorities cleared the differential-lift gates. Only those appear here.
- Flores v. Garland
- INA 203(b)(2)
What this case teaches
Analyst reading of the decision text.
AAO found Director erred on all three prongs; strong citation record, expert letters, and broad ML/AI applicability sufficed to reverse denial.
Pair independent citation evidence and expert letters linking research to broad-sector applications (e.g., automotive, healthcare) across all three Dhanasar prongs.
moderate
critical_emerging_tech · field_advancement · us_competitiveness
mixed
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