{"id":"W4386687028","doi":"10.54254/2754-1169/7/20230253","title":"Rapid Patent Quality Evaluation Method Based on Big Data Analysis: Chinese Invention Patentsas Sample","year":2023,"lang":"en","type":"article","venue":"Advances in Economics Management and Political Sciences","topic":"Intellectual Property and Patents","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Exploit; Intellectual property; Quality (philosophy); Sample (material); Big data; Patent visualisation; Computer science; Usability; Patent analysis; Entropy (arrow of time); Data mining; Data science; Computer security","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002360104,0.0004968411,0.0005159729,0.007283111,0.0009852664,0.001570721,0.0007821919,0.000546806,0.001875954],"category_scores_gemma":[0.007586488,0.0001756402,0.0007330774,0.005247056,0.0004509989,0.001403029,0.001039393,0.0004572782,0.0003126555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001078657,"about_ca_system_score_gemma":0.001586131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007483399,"about_ca_topic_score_gemma":0.004310243,"domain_scores_codex":[0.9978922,0.000287164,0.0003329903,0.000333252,0.0009820563,0.0001724439],"domain_scores_gemma":[0.9956858,0.001199426,0.0005547353,0.0004360126,0.001937144,0.0001869068],"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":[0.000576829,0.001114733,0.410958,0.0007624454,0.0002485076,0.001164019,0.001523925,0.01487634,0.01730232,0.008415382,0.01127201,0.5317854],"study_design_scores_gemma":[0.0002068291,0.0006137186,0.6274532,0.0001382881,0.0005099868,0.0009068755,0.003220081,0.2922045,0.04434437,0.01013499,0.02005103,0.0002160725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9321436,0.0005274927,0.05472681,0.00040538,0.00007076268,0.0007974479,0.005106536,0.0004149746,0.005807045],"genre_scores_gemma":[0.9688597,0.0002084661,0.02358414,0.00004345737,0.00006424176,0.0005953981,0.005223859,0.00001895057,0.001401858],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.007483399,"threshold_uncertainty_score":0.01487964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4263614023237378,"score_gpt":0.3803941845767052,"score_spread":0.04596721774703255,"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."}}