{"id":"W4389510882","doi":"10.23977/jaip.2023.060801","title":"Patentability Analysis of Artificial Intelligence and Big Data Patent Applications","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Law, AI, and Intellectual Property","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Patentability; Patent law; Big data; Flourishing; Legislation; Business; Computer science; Engineering; Artificial intelligence; Intellectual property; Law; Political science; Data mining; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03228426,0.0004540105,0.001102089,0.03217968,0.002822651,0.01092069,0.001899166,0.002662675,0.009100337],"category_scores_gemma":[0.1797294,0.000312387,0.002148434,0.0237787,0.007033955,0.01002091,0.003053322,0.002982683,0.0008747927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005109057,"about_ca_system_score_gemma":0.005110036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003846517,"about_ca_topic_score_gemma":0.001610821,"domain_scores_codex":[0.9425704,0.01011674,0.005085018,0.00371592,0.03530256,0.003209302],"domain_scores_gemma":[0.6848021,0.2272139,0.04030637,0.01291219,0.03180455,0.002960899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002202681,0.0002400898,0.04958453,0.0006052683,0.0002422546,0.0007359205,0.001251291,0.004521793,0.001092546,0.8643292,0.005229328,0.07194748],"study_design_scores_gemma":[0.0001488506,0.0005896646,0.217296,0.0009557524,0.0006477354,0.001898444,0.003112177,0.04400736,0.004968721,0.6523717,0.07378108,0.0002226397],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4765775,0.01053225,0.0900209,0.008266698,0.0003440529,0.001846897,0.004369927,0.0002873571,0.4077544],"genre_scores_gemma":[0.9814728,0.001497204,0.008536324,0.0003995158,0.0003070434,0.0005358273,0.001209677,0.00005183088,0.005989706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03228426,"threshold_uncertainty_score":0.1707375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.347622916190066,"score_gpt":0.3741755355060469,"score_spread":0.02655261931598091,"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."}}