Prosecution Insights
Last updated: October 02, 2026

Examiner: ILES, TYLER EDWARD

Tech Center 2600 • Art Units: 2122 2123 2672

This examiner grants 56% of resolved cases

Analysis

Tyler Edward Iles examines applications in Tech Center 2600 and Art Unit 2122, with a total of 9 resolved cases. His current allowance rate is 55.6 percent, reflecting the outcome of his completed prosecutions. The median days to allowance for these cases is 1328, which is the duration applicants can expect for a granted patent. This 1328 median days to allowance indicates the prosecution period for successful outcomes in this unit.

The interview lift is recorded at 66.7 percent, suggesting that interviews have an impact on the 55.6 percent allowance rate. This 66.7 percent lift is a factor for practitioners to consider when deciding whether to request an interview in Art Unit 2122. Because there are only 9 resolved cases, these metrics are based on a small sample size. The 9 resolved cases mean that future outcomes may shift these percentages. However, the 66.7 percent lift currently points toward a value for personal interaction.

Written from this page's own data; every figure is computed from USPTO records and verified before publication.

Performance Statistics

55.6%
Allow Rate
-6.4% vs TC avg
28
Total Applications
+66.7%
Interview Lift
1328
Avg Prosecution Days
Based on 9 resolved cases, 2023–2026

Rejection Statute Breakdown

27.7%
§101 Eligibility
12.8%
§102 Novelty
52.0%
§103 Obviousness
7.4%
§112 Clarity

Currently Pending Office Actions

App #TitleStatusAssignee
18208157 BLOCK-WISE NEURAL ARCHITECTURE SEARCH USING GUIDED SEARCH ALGORITHM Final Rejection NXP B.V.
18363487 SINGLE SEARCH FOR ARCHITECTURES ON EMBEDDED DEVICES Final Rejection QUALCOMM Incorporated
17565305 SYSTEMS AND METHODS FOR KNOWLEDGE BASE QUESTION ANSWERING USING GENERATION AUGMENTED RANKING Non-Final OA salesforce.com, inc.
18018269 LEARNING SYSTEM, LEARNING METHOD, AND PROGRAM Final Rejection RAKUTEN GROUP, INC.
18639195 METHOD AND STORAGE MEDIUM FOR QUANTIZING GRAPH-BASED NEURAL NETWORK MODEL WITH OPTIMIZED PARAMETERS Non-Final OA DEEPX CO., LTD.

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