Tech Center 2600 • Art Units: 2122 2125 2146 2625
This examiner grants 51% of resolved cases
Su-Ting Chuang serves in Art Unit 2122 of Tech Center 2600 and has 113 resolved cases. The allowance rate is 51.3 percent. This percentage reflects the frequency of grants within the examiner's portfolio. The median days to allowance is 1651, which measures the time from the initial filing to the final issuance.
The interview lift is 39.4 percent. This figure indicates a significant increase in the probability of success for cases that include an interview. Practitioners can use this 39.4 percent lift to evaluate the impact of oral advocacy. The 113 cases provide the statistical grounding for these observations in Art Unit 2122.
The 1651 median days to allowance acts as a guide for prosecution timelines in Tech Center 2600. With an allowance rate of 51.3 percent, the data shows the frequency of successful outcomes. These metrics offer a clear perspective on the examiner's historical performance. The record of resolved cases helps in understanding the procedural environment.
| App # | Title | Status | Assignee |
|---|---|---|---|
| 18285307 | PREDICTION MODEL GENERATION APPARATUS, PREDICTION MODEL GENERATION METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM | Non-Final OA | NEC Corporation |
| 17944892 | Automated thresholding of binary classification ML models | Non-Final OA | Google LLC |
| 18459258 | SYSTEM AND PROCESS FOR DECONFOUNDED IMITATION LEARNING | Final Rejection | QUALCOMM Incorporated |
| 18477150 | Estimating a Gas Rate using a Generative Adversarial Network | Non-Final OA | Saudi Arabian Oil Company |
| 18103559 | DEEP LEARNING ENTITY MATCHING SYSTEM USING WEAK SUPERVISION | Final Rejection | Walmart Apollo, LLC |
| 18518925 | OPTIMIZING PLACEMENT OF FINE-TUNED MACHINE LEARNING MODELS AT HOST SYSTEMS | Non-Final OA | Amazon Technologies, Inc. |
| 18318436 | SPARSE ENCODING AND DECODING AT MIXTURE-OF-EXPERTS LAYER | Final Rejection | Microsoft Technology Licensing, LLC |
| 18393040 | SYSTEMS AND METHODS FOR AUTOMATED CORRECTION OF GIS DATA FOR LOADS AND DISTRIBUTED ENERGY RESOURCES IN SECONDARY DISTRIBUTION NETWORKS | Non-Final OA | ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY |
| 18331362 | DETERMINING VARIABLE INPUT VALUES CORRESPONDING TO A KNOWN OUTPUT VALUE USING NEURAL NETWORKS | Non-Final OA | International Business Machines Corporation |
| 18178768 | ONTOLOGY-BASED FRAMEWORK FOR INTERPRETABLE FEATURE ENGINEERING | Final Rejection | SAP SE |
| 18243348 | NEURAL NETWORK PROMPT TUNING | Final Rejection | NVIDIA Corporation |
| 17190724 | SELECTING A NEURAL NETWORK BASED ON AN AMOUNT OF MEMORY | Non-Final OA | NVIDIA Corporation |
| 17968174 | PROCESS FOR DETECTION OF EVENTS OR ELEMENTS IN PHYSICAL SIGNALS BY IMPLEMENTING AN ARTIFICIAL NEURON NETWORK | Final Rejection | STMicroelectronics (Rousset) SAS |
| 18255474 | SYSTEM AND METHOD FOR CONTROLLING MACHINE LEARNING-BASED VEHICLES | Non-Final OA | RENAULT S.A.S |
| 18573753 | Systems and Methods for Processing Data Using Interference and Analytics Engines | Non-Final OA | LOYOLA UNIVERSITY OF CHICAGO |
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