Nicholas G Giles

Array ( [id] => 17614314 [patent_doc_number] => 20220156594 [patent_country] => US [patent_kind] => A1 [patent_issue_date] => 2022-05-19 [patent_title] => FEATURE ENHANCEMENT VIA UNSUPERVISED LEARNING OF EXTERNAL KNOWLEDGE EMBEDDING [patent_app_type] => utility [patent_app_number] => 16/950598 [patent_app_country] => US [patent_app_date] => 2020-11-17 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 0 [patent_figures_cnt] => 0 [patent_no_of_words] => 6339 [patent_no_of_claims] => 0 [patent_no_of_ind_claims] => -17 [patent_words_short_claim] => 129 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => publication [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 16950598 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 17031983 [patent_doc_number] => 11093797 [patent_country] => US [patent_kind] => B1 [patent_issue_date] => 2021-08-17 [patent_title] => Feature superposition predictor [patent_app_type] => utility [patent_app_number] => 17/098408 [patent_app_country] => US [patent_app_date] => 2020-11-15 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 1 [patent_figures_cnt] => 1 [patent_no_of_words] => 6656 [patent_no_of_claims] => 14 [patent_no_of_ind_claims] => 3 [patent_words_short_claim] => 194 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => patent [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 17098408 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 16659801 [patent_doc_number] => 20210056438 [patent_country] => US [patent_kind] => A1 [patent_issue_date] => 2021-02-25 [patent_title] => DATA DRIVEN FEATURIZATION AND MODELING [patent_app_type] => utility [patent_app_number] => 17/092082 [patent_app_country] => US [patent_app_date] => 2020-11-06 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 0 [patent_figures_cnt] => 0 [patent_no_of_words] => 10478 [patent_no_of_claims] => 0 [patent_no_of_ind_claims] => -17 [patent_words_short_claim] => 2 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => publication [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 17092082 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 16826062 [patent_doc_number] => 20210141355 [patent_country] => US [patent_kind] => A1 [patent_issue_date] => 2021-05-13 [patent_title] => SYSTEMS AND METHODS OF AUTONOMOUS LINE FLOW CONTROL IN ELECTRIC POWER SYSTEMS [patent_app_type] => utility [patent_app_number] => 17/091917 [patent_app_country] => US [patent_app_date] => 2020-11-06 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 0 [patent_figures_cnt] => 0 [patent_no_of_words] => 7035 [patent_no_of_claims] => 0 [patent_no_of_ind_claims] => -27 [patent_words_short_claim] => 99 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => publication [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 17091917 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 16745658 [patent_doc_number] => 10970652 [patent_country] => US [patent_kind] => B1 [patent_issue_date] => 2021-04-06 [patent_title] => System and method for selecting a candidate transfer apparatus [patent_app_type] => utility [patent_app_number] => 17/072653 [patent_app_country] => US [patent_app_date] => 2020-10-16 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 5 [patent_figures_cnt] => 5 [patent_no_of_words] => 10460 [patent_no_of_claims] => 20 [patent_no_of_ind_claims] => 2 [patent_words_short_claim] => 229 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => patent [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 17072653 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 17522361 [patent_doc_number] => 20220108210 [patent_country] => US [patent_kind] => A1 [patent_issue_date] => 2022-04-07 [patent_title] => METHOD FOR DEVELOPING MACHINE-LEARNING BASED TOOL [patent_app_type] => utility [patent_app_number] => 17/063923 [patent_app_country] => US [patent_app_date] => 2020-10-06 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 0 [patent_figures_cnt] => 0 [patent_no_of_words] => 7113 [patent_no_of_claims] => 0 [patent_no_of_ind_claims] => -17 [patent_words_short_claim] => 169 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => publication [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 17063923 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 18826994 [patent_doc_number] => 11842273 [patent_country] => US [patent_kind] => B2 [patent_issue_date] => 2023-12-12 [patent_title] => Neural network processing [patent_app_type] => utility [patent_app_number] => 17/030176 [patent_app_country] => US [patent_app_date] => 2020-09-23 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 21 [patent_figures_cnt] => 24 [patent_no_of_words] => 23556 [patent_no_of_claims] => 20 [patent_no_of_ind_claims] => 5 [patent_words_short_claim] => 289 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => patent [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 17030176 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 16543947 [patent_doc_number] => 20200410362 [patent_country] => US [patent_kind] => A1 [patent_issue_date] => 2020-12-31 [patent_title] => OPTIMIZING NEURAL NETWORKS FOR RISK ASSESSMENT [patent_app_type] => utility [patent_app_number] => 17/019098 [patent_app_country] => US [patent_app_date] => 2020-09-11 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 0 [patent_figures_cnt] => 0 [patent_no_of_words] => 11838 [patent_no_of_claims] => 0 [patent_no_of_ind_claims] => -17 [patent_words_short_claim] => 185 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => publication [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 17019098 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 16715020 [patent_doc_number] => 20210082167 [patent_country] => US [patent_kind] => A1 [patent_issue_date] => 2021-03-18 [patent_title] => METHODS AND SYSTEMS FOR DETERMINING AND DISPLAYING PEDIGREES [patent_app_type] => utility [patent_app_number] => 16/948311 [patent_app_country] => US [patent_app_date] => 2020-09-11 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 0 [patent_figures_cnt] => 0 [patent_no_of_words] => 24068 [patent_no_of_claims] => 0 [patent_no_of_ind_claims] => -94 [patent_words_short_claim] => 19 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => publication [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 16948311 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 19093130 [patent_doc_number] => 11954592 [patent_country] => US [patent_kind] => B2 [patent_issue_date] => 2024-04-09 [patent_title] => Collaborative deep learning methods and collaborative deep learning apparatuses [patent_app_type] => utility [patent_app_number] => 17/012494 [patent_app_country] => US [patent_app_date] => 2020-09-04 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 6 [patent_figures_cnt] => 7 [patent_no_of_words] => 6406 [patent_no_of_claims] => 18 [patent_no_of_ind_claims] => 2 [patent_words_short_claim] => 547 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => patent [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 17012494 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 17462837 [patent_doc_number] => 20220076143 [patent_country] => US [patent_kind] => A1 [patent_issue_date] => 2022-03-10 [patent_title] => AUTOMATICALLY RECOMMENDING AN EXISTING MACHINE LEARNING PROJECT AS ADAPTABLE FOR USE IN A NEW MACHINE LEARNING PROJECT [patent_app_type] => utility [patent_app_number] => 17/013136 [patent_app_country] => US [patent_app_date] => 2020-09-04 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 0 [patent_figures_cnt] => 0 [patent_no_of_words] => 8683 [patent_no_of_claims] => 0 [patent_no_of_ind_claims] => -17 [patent_words_short_claim] => 207 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => publication [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 17013136 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 16527790 [patent_doc_number] => 20200401870 [patent_country] => US [patent_kind] => A1 [patent_issue_date] => 2020-12-24 [patent_title] => SYSTEM AND METHOD FOR DESIGNING EFFICIENT SUPER RESOLUTION DEEP CONVOLUTIONAL NEURAL NETWORKS BY CASCADE NETWORK TRAINING, CASCADE NETWORK TRIMMING, AND DILATED CONVOLUTIONS [patent_app_type] => utility [patent_app_number] => 17/007739 [patent_app_country] => US [patent_app_date] => 2020-08-31 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 0 [patent_figures_cnt] => 0 [patent_no_of_words] => 8924 [patent_no_of_claims] => 0 [patent_no_of_ind_claims] => -21 [patent_words_short_claim] => 49 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => publication [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 17007739 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 16659739 [patent_doc_number] => 20210056376 [patent_country] => US [patent_kind] => A1 [patent_issue_date] => 2021-02-25 [patent_title] => TRAINING MACHINE LEARNING MODELS FOR AUTOMATED COMPOSITION GENERATION [patent_app_type] => utility [patent_app_number] => 16/998583 [patent_app_country] => US [patent_app_date] => 2020-08-20 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 0 [patent_figures_cnt] => 0 [patent_no_of_words] => 23769 [patent_no_of_claims] => 0 [patent_no_of_ind_claims] => -17 [patent_words_short_claim] => 73 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => publication [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 16998583 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 18703724 [patent_doc_number] => 11790238 [patent_country] => US [patent_kind] => B2 [patent_issue_date] => 2023-10-17 [patent_title] => Multi-task neural networks with task-specific paths [patent_app_type] => utility [patent_app_number] => 16/995655 [patent_app_country] => US [patent_app_date] => 2020-08-17 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 6 [patent_figures_cnt] => 6 [patent_no_of_words] => 10259 [patent_no_of_claims] => 20 [patent_no_of_ind_claims] => 3 [patent_words_short_claim] => 304 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => patent [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 16995655 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 16470821 [patent_doc_number] => 20200372358 [patent_country] => US [patent_kind] => A1 [patent_issue_date] => 2020-11-26 [patent_title] => ATTENTION-BASED SEQUENCE TRANSDUCTION NEURAL NETWORKS [patent_app_type] => utility [patent_app_number] => 16/988547 [patent_app_country] => US [patent_app_date] => 2020-08-07 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 0 [patent_figures_cnt] => 0 [patent_no_of_words] => 8203 [patent_no_of_claims] => 0 [patent_no_of_ind_claims] => -17 [patent_words_short_claim] => 220 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => publication [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 16988547 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 16585221 [patent_doc_number] => 20210019623 [patent_country] => US [patent_kind] => A1 [patent_issue_date] => 2021-01-21 [patent_title] => ATTENTION-BASED SEQUENCE TRANSDUCTION NEURAL NETWORKS [patent_app_type] => utility [patent_app_number] => 16/988518 [patent_app_country] => US [patent_app_date] => 2020-08-07 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 0 [patent_figures_cnt] => 0 [patent_no_of_words] => 8203 [patent_no_of_claims] => 0 [patent_no_of_ind_claims] => -17 [patent_words_short_claim] => 226 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => publication [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 16988518 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 17402717 [patent_doc_number] => 20220044808 [patent_country] => US [patent_kind] => A1 [patent_issue_date] => 2022-02-10 [patent_title] => PREDICTIVE MONITORING OF THE GLUCOSE-INSULIN ENDOCRINE METABOLIC REGULATORY SYSTEM [patent_app_type] => utility [patent_app_number] => 16/985759 [patent_app_country] => US [patent_app_date] => 2020-08-05 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 0 [patent_figures_cnt] => 0 [patent_no_of_words] => 33193 [patent_no_of_claims] => 0 [patent_no_of_ind_claims] => -17 [patent_words_short_claim] => 279 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => publication [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 16985759 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 16470820 [patent_doc_number] => 20200372357 [patent_country] => US [patent_kind] => A1 [patent_issue_date] => 2020-11-26 [patent_title] => ATTENTION-BASED SEQUENCE TRANSDUCTION NEURAL NETWORKS [patent_app_type] => utility [patent_app_number] => 16/932422 [patent_app_country] => US [patent_app_date] => 2020-07-17 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 0 [patent_figures_cnt] => 0 [patent_no_of_words] => 8179 [patent_no_of_claims] => 0 [patent_no_of_ind_claims] => -17 [patent_words_short_claim] => 2 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => publication [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 16932422 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 18839493 [patent_doc_number] => 11847567 [patent_country] => US [patent_kind] => B1 [patent_issue_date] => 2023-12-19 [patent_title] => Loss-aware replication of neural network layers [patent_app_type] => utility [patent_app_number] => 16/923003 [patent_app_country] => US [patent_app_date] => 2020-07-07 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 33 [patent_figures_cnt] => 37 [patent_no_of_words] => 44049 [patent_no_of_claims] => 21 [patent_no_of_ind_claims] => 2 [patent_words_short_claim] => 247 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => patent [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 16923003 [rel_patent_id] =>[rel_patent_doc_number] =>)

Array ( [id] => 16379258 [patent_doc_number] => 20200328101 [patent_country] => US [patent_kind] => A1 [patent_issue_date] => 2020-10-15 [patent_title] => SEARCH APPARATUS AND SEARCH METHOD [patent_app_type] => utility [patent_app_number] => 16/911669 [patent_app_country] => US [patent_app_date] => 2020-06-25 [patent_effective_date] => 0000-00-00 [patent_drawing_sheets_cnt] => 0 [patent_figures_cnt] => 0 [patent_no_of_words] => 16109 [patent_no_of_claims] => 0 [patent_no_of_ind_claims] => -16 [patent_words_short_claim] => 290 [patent_maintenance] => 1 [patent_no_of_assignments] => 0 [patent_current_assignee] =>[type] => publication [pdf_file] =>[firstpage_image] =>[orig_patent_app_number] => 16911669 [rel_patent_id] =>[rel_patent_doc_number] =>)