Prosecution Insights
Last updated: October 01, 2026

Examiner: SAMARA, HUSAM TURKI

Tech Center 2100 • Art Units: 2161 2163

This examiner grants 56% of resolved cases

Analysis

Husam Turki Samara has resolved 175 cases in Tech Center 2100. The allowance rate is 55.4 percent. This rate suggests a moderate level of difficulty for applicants in Art Unit 2161. Practitioners should be prepared for a balanced prosecution where success is likely but requires careful navigation of the examiner's requirements, as indicated by the 55.4 percent baseline.

The interview lift is 17.3 percent. This positive lift indicates that interviews are a helpful tool for advancing prosecution and clarifying technical points. While not as high as some other examiners, a 17.3 percent increase in allowance probability is a meaningful advantage that should be utilized during the examination process.

The median days to allowance is 1364. This pendency is relatively long, suggesting a deliberate and thorough examination process. Applicants should plan for a longer prosecution cycle and use the 17.3 percent interview lift to potentially expedite the 1364 day timeline to grant.

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

Performance Statistics

55.7%
Allow Rate
+0.7% vs TC avg
192
Total Applications
+17.6%
Interview Lift
1364
Avg Prosecution Days
Based on 176 resolved cases, 2023–2026

Rejection Statute Breakdown

16.7%
§101 Eligibility
15.9%
§102 Novelty
58.4%
§103 Obviousness
6.4%
§112 Clarity

Currently Pending Office Actions

App #TitleStatusAssignee
18691416 INTELLIGENT GRAPH FOR REPRESENTING DATA OBJECTS Final Rejection OneTrust, LLC
18161204 PREEMPTIVE PROCESSING TO AVOID DATA ROT Final Rejection INTERNATIONAL BUSINESS MACHINES CORPORATION
17650636 PROVIDING A STATE-OF-THE-ART SUMMARIZER Non-Final OA INTERNATIONAL BUSINESS MACHINES CORPORATION
19105061 QUERYABLE ASSET MODEL ASSOCIATED WITH OPC UA AND GRAPH Final Rejection Siemens Corporation
19067354 METHODS AND SYSTEMS FOR OPERATING LARGE LANGUAGE MODELS USING SINGLE-INSTRUCTION-MULTIPLE-DATA REGISTERS Non-Final OA William Marsh Rice University

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