{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004863258,0.0002356028,0.0006090526,0.0008369226,0.0002579395,0.0003540856,0.002361362,0.0001210391,0.00007759446],"category_scores_gemma":[0.004132575,0.0001898441,0.0002271427,0.004852561,0.0004534761,0.002151538,0.0009610032,0.0004920861,0.00009746545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007039096,"about_ca_system_score_gemma":0.0002827997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002316626,"about_ca_topic_score_gemma":0.0001200263,"domain_scores_codex":[0.995661,0.0004340039,0.001965508,0.0006523473,0.0008835043,0.0004035922],"domain_scores_gemma":[0.9933942,0.002252421,0.00129046,0.001436535,0.00137554,0.0002508219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002082148,0.0007248524,0.0001822112,0.00003634643,0.0006946162,0.00003123865,0.002950334,0.003176107,0.003229581,0.02611013,0.0002844066,0.9623719],"study_design_scores_gemma":[0.00003029028,0.0007564035,0.0003938733,0.00005293683,0.001306949,0.0001039636,0.005863312,0.8855626,0.03730967,0.05653406,0.01158799,0.0004979931],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009747892,0.0003084239,0.9854304,0.003048236,0.000791538,0.0002813567,0.00003878761,0.00005719774,0.0002961571],"genre_scores_gemma":[0.9801453,0.001771042,0.01736321,0.000270372,0.000377588,0.00001046048,0.00001673942,0.00001590635,0.00002938434],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9703974,"threshold_uncertainty_score":0.7741619,"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."}}