Tech Center 2100 • Art Units: 2142 2147
This examiner grants 24% of resolved cases
Alexandria Josephine Miller is an examiner in Tech Center 2100 and Art Unit 2142, with 33 resolved cases. Her allowance rate is 21.2%, which suggests a challenging prosecution environment for applicants. The median days to allowance is 1438, indicating a lengthy process for those who eventually secure a patent grant under her review.
However, the data reveals a significant interview lift of 70.0%, which is a critical metric for strategy development. This lift suggests that while the baseline allowance rate is 21.2%, the probability of success increases substantially following an interview. For practitioners, this makes interviewing a primary strategic necessity to overcome the low general allowance rate in Art Unit 2142.
The 1438-day median pendency underscores the need for efficient strategies when working with Miller. Given the 33 resolved cases, the 70.0% interview lift stands out as a highly actionable piece of data. Applicants should prioritize interviews early in the process to shift the odds toward a more favorable outcome and navigate the tech center's requirements.
| App # | Title | Status | Assignee |
|---|---|---|---|
| 18148779 | DATA PROCESSING METHOD AND ELECTRONIC DEVICE | Final Rejection | NEC CORPORATION |
| 17487497 | METHOD AND SYSTEM FOR PROBABLY ROBUST CLASSIFICATION WITH MULTICLASS ENABLED DETECTION OF ADVERSARIAL EXAMPLES | Final Rejection | Robert Bosch GmbH |
| 17375556 | METHOD AND DEVICE FOR THE FUSION OF SENSOR SIGNALS USING A NEURAL NETWORK | Non-Final OA | Robert Bosch GmbH |
| 17899728 | Hardware-Aware Progressive Training Of Machine Learning Models | Non-Final OA | Google LLC |
| 18527487 | ENSEMBLE CLASSIFIER FOR IMPUTATION OF MOBILITY DATA OF UNKNOWN SUBJECT | Non-Final OA | Tata Consultancy Services Limited |
| 18060705 | DISTRIBUTED TRAINING METHOD BASED ON END-TO-END ADAPTION, AND DEVICE | Final Rejection | BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD. |
| 17704866 | SELF INSTANTIATING ALPHA NETWORK | Non-Final OA | RED HAT, INC. |
| 18067503 | COMPRESSION OF MODEL WEIGHTS FOR DISTRIBUTED AND FEDERATED LEARNING | Final Rejection | VMware LLC |
| 18762333 | TRAINING NEURAL NETWORKS ON ARBITRARILY LARGE DATA FILES | Non-Final OA | SRI International |
| 17494176 | TRAINING A MACHINE LEARNING MODEL USING INCREMENTAL LEARNING WITHOUT FORGETTING | Final Rejection | Actimize Ltd. |
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