Tech Center 2100 • Art Units: 1671 1685 2127 2141
This examiner grants 22% of resolved cases
Theodore Charles Striegel is an examiner in Tech Center 2100 and Art Unit 1671. He has 63 resolved cases. His allowance rate of 22.2 percent is quite low, indicating a very rigorous examination process. Applicants assigned to Striegel should be prepared for a challenging prosecution path with a high likelihood of multiple rejections. Success in this docket requires exceptionally strong technical arguments and precise claim drafting to overcome the 22.2 percent baseline.
Interviews appear to be a helpful strategy with Striegel, as shown by his 23.0 percent interview lift. Given the low baseline allowance rate, the 23.0 percent lift can significantly improve the chances of reaching a grant in some scenarios. Direct communication allows practitioners to better understand the examiner's specific concerns and potentially find a narrow path to allowance that is not apparent from written office actions.
The median days to allowance for Striegel is 1651. This is a long duration, reflecting the difficulty of securing a patent in his docket. Applicants should expect an extended struggle, as the 1651-day median suggests several rounds of prosecution. The combination of the allowance rate and the timeline means that only the most persistent and well-supported applications are likely to result in a grant.
| App # | Title | Status | Assignee |
|---|---|---|---|
| 18362382 | MEDICAL DEVICE SYSTEM FOR CLASSIFICATION AND PREDICTION OF MEDICAL EVENTS USING TIME-BETWEEN-EVENT VALUES | Non-Final OA | Boston Scientific Scimed, Inc. |
| 18264254 | RNA-PROTEIN INTERACTION PREDICTION METHOD AND APPARATUS, AND MEDIUM AND ELECTRONIC DEVICE | Non-Final OA | BOE Technology Group Co., Ltd. |
| 16951864 | PIPELINE FOR SPATIAL ANALYSIS OF ANALYTES | Final Rejection | 10X Genomics, Inc. |
| 17590095 | INTERMEDIATE RECURRENT PARENTS, AN ACCELERATED AND EFFICIENT MULTI-LAYER TRAIT DELIVERY SYSTEM | Non-Final OA | Monsanto Technology LLC |
| 18250140 | SYSTEMS AND METHODS FOR QUANTIFYING PATIENT IMPROVEMENT THROUGH ARTIFICIAL INTELLIGENCE | Non-Final OA | Northwestern University |
| 18303170 | DEVICE AND METHOD FOR DETECTING SICKLE CELL DISEASE USING DEEP TRANSFER LEARNING | Non-Final OA | Morgan State University |
IP Author analyzes examiner patterns and generates tailored response strategies with the highest chance of allowance.
Build Your Strategy