{"id":"W7024221712","doi":"","title":"The Rise of Machines: Patenting Inventions Generated by Artificial Intelligence in Canada","year":2022,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Law, AI, and Intellectual Property","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Patentability; Context (archaeology); Statutory law; Incentive; Patentable subject matter; Invention; Database transaction; Patent law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005787294,0.0001245338,0.0001572691,0.0000403255,0.0007036033,0.0001258282,0.001204216,0.00002209395,0.000328206],"category_scores_gemma":[0.000141998,0.00009442295,0.00005819128,0.0007315153,0.0001062788,0.000220034,0.0004901542,0.0003516453,0.00001805275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002800845,"about_ca_system_score_gemma":0.0006416136,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7330915,"about_ca_topic_score_gemma":0.7892824,"domain_scores_codex":[0.9980972,0.0003193516,0.0005075422,0.0003168974,0.0004139517,0.0003450694],"domain_scores_gemma":[0.9990907,0.000136123,0.0001296567,0.000460991,0.00008380425,0.00009872792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001424943,0.0007826206,0.007814486,0.00007556257,0.0001637998,0.0001133496,0.002906327,0.02374382,0.02813519,0.5875053,0.1086561,0.2399609],"study_design_scores_gemma":[0.0002054484,0.0001617495,0.000147471,0.00002783315,0.000009611876,0.00001511397,0.0008283192,0.8308461,0.02441829,0.01916883,0.1237068,0.0004644612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8641137,0.006403628,0.1032447,0.004655325,0.005009789,0.001212644,0.0001718996,0.0002373798,0.0149509],"genre_scores_gemma":[0.9983015,0.00003763471,0.0004779426,0.0004428468,0.00003809056,0.00004705436,0.000007520089,0.00001027271,0.0006371465],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8071023,"threshold_uncertainty_score":0.5411619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02424339005482008,"score_gpt":0.2275341017449953,"score_spread":0.2032907116901752,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}