Prosecution Insights
Prosecution Insights
Tech Center 2100 • Art Units: 2154 2168
This examiner grants 70% of resolved cases
Mahesh H. Dwivedi serves as an examiner in Tech Center 2100 and Art Unit 2154. He has managed 766 resolved cases, maintaining an allowance rate of 69.6 percent. The median days to allowance for these cases is 1327, which outlines the expected duration for reaching a favorable conclusion. This pendency metric is derived from the 766 resolved cases currently associated with his profile in Art Unit 2154.
The interview lift for this examiner is 4.6 percent. While this lift offers a marginal improvement over the 69.6 percent baseline allowance rate, it suggests that many applications reach resolution through written prosecution. The 1327 median days to allowance indicates that prosecution is a long-term process regardless of the 4.6 percent lift. Applicants should evaluate their strategy based on these 766 resolved cases to optimize their results in Tech Center 2100.
| App # | Title | Status | Assignee |
|---|---|---|---|
| 18651418 | SYSTEMS AND METHODS FOR USING A GENERATIVE MODEL TO GENERATE AN OUTPUT BASED ON A SET OF DIVERSE INPUTS | Non-Final OA | TOYOTA RESEARCH INSTITUTE, INC. |
| 18768348 | INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND COMPUTER PROGRAM PRODUCT | Final Rejection | KABUSHIKI KAISHA TOSHIBA |
| 18611628 | CO-DISTILLATION FOR MIXING SERVER-BASED AND FEDERATED LEARNING | Final Rejection | Google LLC |
| 18615686 | METHODS AND ARRANGEMENTS FOR SIMILARITY SEARCH BASED ON MULTI-LABEL TEXT CLASSIFICATION | Final Rejection | Capital One Services, LLC |
| 19075883 | INFORMATION AGGREGATION IN A MULTI-MODAL ENTITY-FEATURE GRAPH FOR INTERVENTION PREDICTION FOR A MEDICAL PATIENT | Final Rejection | NEC Corporation |
| 19076030 | INFORMATION AGGREGATION IN A MULTI-MODAL ENTITY-FEATURE GRAPH FOR INTERVENTION PREDICTION FOR A MEDICAL PATIENT | Final Rejection | NEC Corporation |
| 19075929 | INFORMATION AGGREGATION IN A MULTI-MODAL ENTITY-FEATURE GRAPH FOR INTERVENTION PREDICTION FOR A MEDICAL PATIENT | Final Rejection | NEC Corporation |
| 17756957 | FEDERATED MIXTURE MODELS | Final Rejection | Qualcomm Technologies, Inc. |
| 18574995 | APPARATUS, METHOD, DEVICE AND MEDIUM FOR LABEL-BALANCED CALIBRATION IN POST-TRAINING QUANTIZATION OF DNN | Non-Final OA | Intel Corporation |
| 19294004 | METHOD AND APPARATUS FOR A DATA CONFIDENCE INDEX | Final Rejection | PayPal, Inc. |
| 19170106 | DOCUMENT RETRIEVAL SYSTEM | Final Rejection | Semiconductor Energy Laboratory Co., Ltd. |
| 18688844 | INFORMATION PROCESSING DEVICE | Non-Final OA | NTT DOCOMO, INC. |
| 18842034 | MODEL LEARNING APPARATUS, SECURE FEDERATED LEARNING APPARATUS, THEIR METHODS, AND PROGRAMS | Non-Final OA | NTT, Inc. |
| 18777830 | AUTOMATIC LABELING OF TEXT DATA | Final Rejection | Microsoft Technology Licensing, LLC |
| 18207755 | SPARSITY-AWARE NEURAL NETWORK PROCESSING | Non-Final OA | Microsoft Technology Licensing, LLC |
| 18537588 | MULTIMODAL UNSUPERVISED META-LEARNING METHOD AND APPARATUS | Final Rejection | ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE |
| 18463389 | METHOD, APPARATUS, DEVICE AND STORAGE MEDIUM FOR RECOMMENDING INFORMATION | Non-Final OA | Lemon Inc. |
| 18446859 | FEDERATED LEARNING METHOD USING ARTIFICIAL INTELLIGENCE | Non-Final OA | Research & Business Foundation SUNGKYUNKWAN UNIVERSITY |
IP Author analyzes examiner patterns and generates tailored response strategies with the highest chance of allowance.
Build Your Strategy