{"id":"W2035792097","doi":"10.1007/s11192-009-0008-z","title":"Discovery of factors influencing patent value based on machine learning in patents in the field of nanotechnology","year":2009,"lang":"en","type":"article","venue":"Scientometrics","topic":"Intellectual Property and Patents","field":"Business, Management and Accounting","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Selection (genetic algorithm); Field (mathematics); Patent analysis; Value (mathematics); Data science; Computer science; Feature selection; Set (abstract data type); Artificial intelligence; Machine learning; Knowledge management; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001071215,0.0001125209,0.0001883372,0.003832084,0.0000548514,0.00005994315,0.0004218513,0.00008734403,0.00001827388],"category_scores_gemma":[0.002925549,0.00007092739,0.00005821371,0.008873057,0.00003982148,0.0003373132,0.00006621038,0.0002947029,0.000007120503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003366701,"about_ca_system_score_gemma":0.0000169484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001457638,"about_ca_topic_score_gemma":0.00004496846,"domain_scores_codex":[0.9986776,0.00003309224,0.0003336042,0.0001880655,0.0005225496,0.0002450839],"domain_scores_gemma":[0.999263,0.0002824431,0.0001930813,0.0001777993,0.00007914849,0.000004548577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001444204,0.0005322554,0.9641641,0.0001161345,0.000004948662,0.00001093226,0.0006069535,0.01816598,0.003193869,0.001425616,0.00006249646,0.01157233],"study_design_scores_gemma":[0.001973884,0.0007359416,0.7907759,0.0004892279,0.00002584669,4.820376e-7,0.001064068,0.1794335,0.02273688,0.001488524,0.0008541521,0.0004215843],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974535,0.00004668967,0.0002359328,0.0003198988,0.0001499156,0.0001630698,0.000001357925,0.00001476288,0.001614951],"genre_scores_gemma":[0.9985825,0.000008567377,0.00002012057,0.001329632,0.00001487578,0.00000158567,0.000007906641,0.000004985706,0.0000297777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1733882,"threshold_uncertainty_score":0.4263209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1052886624033666,"score_gpt":0.2640623858484547,"score_spread":0.1587737234450881,"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."}}