Prosecution Insights
Prosecution Insights
Tech Center 4100 • Art Units: 1754 2126 2127 2128 2177 2179 4100
This examiner grants 49% of resolved cases
Jeremy L. Stanley is a patent examiner in Tech Center 4100 and Art Unit 1754. He has overseen 292 resolved cases. The examiner has an allowance rate of 49.3 percent, which indicates that approximately half of the applications reaching a final resolution result in a grant. This rate provides a clear expectation for practitioners regarding the difficulty level of prosecution in this unit.
A notable feature of this profile is the interview lift of 40.0 percent. This high lift suggests that interviews are exceptionally effective at moving cases toward allowance. Practitioners should consider the 40.0 percent lift as a strong incentive to engage in direct communication with the examiner to resolve outstanding issues. This metric is based on the examiner's history across 292 resolved cases.
The median time to allowance is 1190 days. This duration represents the typical timeframe for successful applications to move from filing to issuance. When combined with the allowance rate, this 1190 day median helps practitioners manage client expectations regarding both the outcome and the timeline. These statistics offer a data-grounded view of the examination process in Art Unit 1754.
| App # | Title | Status | Assignee |
|---|---|---|---|
| 17961223 | DISCOVERING DIGITAL ASSISTANT TASKS | Final Rejection | Apple Inc. |
| 18184742 | COLLABORATIVE INFERENCE METHOD AND COMMUNICATION APPARATUS | Non-Final OA | HUAWEI TECHNOLOGIES CO., LTD. |
| 18386431 | CHECKPOINT AVERAGING TO MITIGATE AND/OR ELIMINATE CATASTROPHIC FORGETTING OF MACHINE LEARNING MODEL(S) IN DECENTRALIZED LEARNING THEREOF | Non-Final OA | GOOGLE LLC |
| 18641260 | CONTINUOUS UPDATE OF A MACHINE LEARNING WORKFLOW USING PROGRAMMATIC INSTANCES | Non-Final OA | Capital One Services, LLC |
| 18337655 | DEEP NEURAL NETWORK ACCELERATOR WITH MEMORY HAVING TWO-LEVEL TOPOLOGY | Non-Final OA | Intel Corporation |
| 18529182 | FAIRNESS FEATURE IMPORTANCE: UNDERSTANDING AND MITIGATING UNJUSTIFIABLE BIAS IN MACHINE LEARNING MODELS | Non-Final OA | Oracle International Corporation |
| 18339407 | PREDICTING SYSTEM STATUS WITH TRUSTWORTHY ARTIFICIAL INTELLIGENCE | Non-Final OA | International Business Machines Corporation |
| 18331993 | LEARNING FROM AND PROVIDING MEMORIES | Non-Final OA | International Business Machines Corporation |
| 18322898 | MACHINE LEARNING FOR OPERATING A MOVABLE DEVICE | Final Rejection | Ford Global Technologies, LLC |
| 18640709 | SYSTEMS AND METHODS FOR TRAINING AN AUTONOMOUS MACHINE TO PERFORM AN OPERATION | Non-Final OA | Naver Corporation |
| 18419362 | SIMULATED ANNEALING BASED INTEGERIZATION OF HIDDEN WEIGHTS FOR AREA-EFFICIENT IOT EDGE INTELLIGENCE | Non-Final OA | UNIVERSITY OF SOUTH FLORIDA |
| 17776152 | NEURAL NETWORK EXPLANATION USING LOGIC | Final Rejection | SRI International |
| 18634510 | EFFICIENT DATA AUGMENTATION FOR MOTION SENSOR AND MICROPHONE RELATED MACHINE LEARNING APPLICATIONS IN EMBEDDED DEVICES | Non-Final OA | InvenSense, Inc. |
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