USPTO Examiner WERNER MARSHALL L - Art Unit 2125

Recent Applications

Detailed information about the 100 most recent patent applications.

Application NumberTitleFiling DateDisposal DateDispositionTime (months)Office ActionsRestrictionsInterviewAppeal
18986400Method and System for Assessing Drug Efficacy Using Multiple Graph Kernel FusionDecember 2024July 2025Allow710NoNo
18815286METHOD AND APPARATUS FOR FEW-SHOT RELATION CLASSIFICATION AND FILTERING, AND DEVICEAugust 2024April 2025Allow710YesNo
18769001AA2CDS:Pre-trained Amino Acid-to-Codon Sequence Mapping Enabling Efficient Expression and Yield OptimizationJuly 2024April 2025Allow920YesNo
18523482Method and System for Assessing Drug Efficacy Using Multiple Graph Kernel FusionNovember 2023September 2024Allow1010NoNo
18338744Stochastic Control with a Quantum ComputerJune 2023October 2025Allow2810NoNo
18140633ARTIFICIAL INTELLIGENCE CHARACTER MODELS WITH MODIFIABLE BEHAVIORAL CHARACTERISTICSApril 2023May 2024Allow1330YesNo
18245829Apparatus and Method for Controlling a Growth Environment of a PlantMarch 2023November 2025Allow3221YesNo
18122641UNCERTAINTY QUANTIFICATION FOR MACHINE LEARNING CLASSIFICATION MODELLINGMarch 2023December 2024Allow2140YesNo
18145417LOCAL INTERPRETABILITY ARCHITECTURE FOR A NEURAL NETWORKDecember 2022March 2026Allow3810YesNo
18050694SYSTEMS AND METHODS FOR SYNTHETIC DATABASE QUERY GENERATIONOctober 2022May 2025Allow3030YesNo
17892542SYSTEMS AND METHODS FOR CORRELATING CUTANEOUS ACTIVITY WITH HUMAN PERFORMANCEAugust 2022February 2024Allow1850YesNo
17904377UNIVERSAL FAST-FLUX CONTROL OF LOW-FREQUENCY QUBITSAugust 2022March 2026Allow4320YesNo
17815551PREDICTING DISCRETE OUTCOMES IN COMPUTER APPLICATIONS USING MACHINE LEARNING MODELS ON TIME SERIES DATA INSTANCESJuly 2022October 2025Abandon3940YesYes
17852521Method and System for Assessing Drug Efficacy Using Multiple Graph Kernel FusionJune 2022August 2023Allow1420YesNo
17841252MACHINE LEARNING MODELS FOR AUTOMATED SUSTAINABILITY DATA SOURCE INGESTION AND PROCESSINGJune 2022August 2023Abandon1410NoNo
17825774Classical and Quantum Algorithms for Orthogonal Neural NetworksMay 2022August 2023Allow1410NoNo
17662296SYSTEMS AND METHODS FOR CANONICAL CONSTRUCTION OF QUANTUM ORACLESMay 2022January 2026Abandon4410NoNo
17592796Machine-Learned Attention Models Featuring Omnidirectional ProcessingFebruary 2022November 2025Allow4510YesNo
17527875TECHNOLOGIES FOR SIGNAL CONDITIONING OF SIGNALS FOR QUBITSNovember 2021March 2026Allow5120YesNo
17515115SYSTEMS AND METHODS FOR MACHINE LEARNING-BASED SITE-SPECIFIC THREAT MODELING AND THREAT DETECTIONOctober 2021May 2025Allow4230YesNo
17498618Convolutional Neural Network Hardware ConfigurationOctober 2021September 2024Allow3520NoNo
17491305METHOD AND APPARATUS FOR PROCESSING INFORMATIONSeptember 2021November 2025Abandon5020NoNo
17479708CONTROLLABLE FORMULA GENERATIONSeptember 2021April 2024Abandon3010NoNo
17464278VERIFIED QUANTUM PHASE ESTIMATIONSeptember 2021May 2025Allow4500YesNo
17459036VARIABLE PARAMETER PROBABILITY FOR MACHINE-LEARNING MODEL GENERATION AND TRAININGAugust 2021October 2024Abandon3830YesNo
17304325DATA LOG CONTENT ASSESSMENT USING MACHINE LEARNINGJune 2021August 2025Abandon5020YesNo
17327385METHOD, SYSTEM, AND MEDIUM FOR PROCESSING SATELLITE ORBITAL INFORMATION USING A GENERATIVE ADVERSARIAL NETWORKMay 2021July 2025Abandon5020YesNo
17318336SYSTEMS AND METHODS FOR OPTIMIZED QUANTUM SEARCHINGMay 2021July 2025Allow5120YesNo
17245324DEEP LEARNING SYSTEM FOR DYNAMIC PREDICTION OF ORDER PREPARATION TIMESApril 2021September 2025Allow5220YesNo
17244362METHODS FOR EFFICIENT IMPLEMENTATION OF UNITARY OPERATORS IN THE CLIFFORD ALGEBRA AS QUANTUM CIRCUITS AND APPLICATIONS TO LINEAR ALGEBRA AND MACHINE LEARNINGApril 2021July 2023Allow2610NoNo
17226917SYSTEMS AND METHODS FOR DATA-AWARE STORAGE TIERING FOR DEEP LEARNINGApril 2021February 2026Allow5820YesNo
17227086SYSTEM AND METHOD FOR TESTING MACHINE LEARNINGApril 2021January 2026Allow5720YesNo
17226911COMPUTER-IMPLEMENTED METHOD, AND DEVICE FOR PRODUCING A KNOWLEDGE GRAPHApril 2021November 2025Abandon5520NoNo
17191698USER TARGETED CONTENT GENERATION USING MULTIMODAL EMBEDDINGSMarch 2021March 2025Allow4910YesNo
17184132SYSTEM, METHOD, AND MODEL STRUCTURE FOR USING MACHINE LEARNING TO PREDICT FUTURE SPORT OUTCOMES BASED ON MATCH STATE TRANSITIONSFebruary 2021January 2025Allow4620YesNo
17180550NEURAL NETWORK METHOD OF GENERATING FOOD FORMULASFebruary 2021December 2023Abandon3430YesNo
17169849SYSTEM AND METHOD FOR NEURAL NETWORK RADIOSITY CALCULATIONSFebruary 2021June 2021Allow410YesNo
17167981CONTROLLABLE FORMULA GENERATIONFebruary 2021August 2021Allow610YesNo
17162479ARTIFICIAL INTELLIGENCE ENGINE FOR GENERATING CANDIDATE DRUGSJanuary 2021March 2025Abandon5020NoNo
17160309REINFORCEMENT LEARNING WITH QUANTUM ORACLEJanuary 2021April 2025Allow5120YesNo
17153902DEBATER SYSTEM FOR COLLABORATIVE DISCUSSIONS BASED ON EXPLAINABLE PREDICTIONSJanuary 2021July 2025Abandon5420YesNo
17153895CHANNEL SCALING: A SCALE-AND-SELECT APPROACH FOR SELECTIVE TRANSFER LEARNINGJanuary 2021September 2024Allow4310YesNo
17151117TRAINING A NEURAL NETWORK USING STOCHASTIC WHITENING BATCH NORMALIZATIONJanuary 2021August 2024Allow4310NoNo
17151007TRAINING NEURAL NETWORKS BASED ON DUAL PIPELINE ARCHITECTURESJanuary 2021August 2025Allow5530YesNo
17139190CROSS-DOMAIN STRUCTURAL MAPPING IN MACHINE LEARNING PROCESSINGDecember 2020November 2025Allow5930YesNo
17129222METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR DEPLOYING MACHINE LEARNING MODELDecember 2020October 2025Allow5730YesNo
17126981TRAINING NEURAL NETWORKS USING A VARIATIONAL INFORMATION BOTTLENECKDecember 2020January 2023Allow2500YesNo
17123876READOUT-ERROR MITIGATION FOR QUANTUM EXPECTATIONDecember 2020August 2025Allow5630YesNo
17122041SYSTEM AND METHOD FOR REAL-TIME RADAR-BASED ACTION RECOGNITION USING SPIKING NEURAL NETWORK(SNN)December 2020May 2025Allow5320NoNo
17120914ULTRA-HIGH SENSITIVE TARGET SIGNAL DETECTION METHOD BASED ON NOISE ANALYSIS USING DEEP LEARNING BASED ANOMALY DETECTION AND SYSTEM USING THE SAMEDecember 2020October 2024Allow4620NoNo
17118683AUTOMATIC HYBRID QUANTIZATION FOR DEEP NEURAL NETWORKDecember 2020May 2025Allow5320YesNo
17118004APPARATUS AND METHODS FOR QUANTUM COMPUTING WITH PRE-TRAININGDecember 2020November 2024Allow4830YesNo
17247237ACTIVITY RECOGNITION MODEL BALANCED BETWEEN VERSATILITY AND INDIVIDUATION AND SYSTEM THEREOFDecember 2020March 2025Allow5120NoNo
17105033ADAPTIVE ARTIFICIAL NEURAL NETWORK SELECTION TECHNIQUESNovember 2020August 2023Allow3220YesNo
16950129Method, System, and Computer Program Product for Training Distributed Machine Learning ModelsNovember 2020November 2023Allow3610YesNo
17093442ENSEMBLE OF NARROW AI AGENTSNovember 2020November 2024Abandon4830YesNo
17087935ENHANCED PRECISION MACHINE LEARNING PREDICTIONNovember 2020May 2024Allow4310YesNo
17082364APPARATUS AND METHOD FOR UNSUPERVISED DOMAIN ADAPTATIONOctober 2020January 2023Allow2710YesNo
17082366DETERMINING AT LEAST ONE NODEOctober 2020March 2025Allow5320NoNo
17080351APPARATUS AND METHOD FOR EMBEDDING SENTENCE FEATURE VECTOROctober 2020December 2023Allow3820YesNo
17073065APPARATUS FOR QUALITY MANAGEMENT OF MEDICAL IMAGE INTERPRETATION USING MACHINE LEARNING, AND METHOD THEREOFOctober 2020December 2024Abandon5060YesNo
17066530INFERENCE VERIFICATION OF MACHINE LEARNING ALGORITHMSOctober 2020November 2023Abandon3710NoNo
17061956ANALYSIS OF SYSTEM LOG DATA USING MACHINE LEARNINGOctober 2020January 2026Abandon6040YesNo
17044104ELECTRONIC DEVICE AND CONTROL METHOD THEREOFSeptember 2020July 2025Abandon5830NoNo
16981682INTELLIGENT CONTROL METHOD FOR DYNAMIC NEURAL NETWORK-BASED VARIABLE CYCLE ENGINESeptember 2020September 2023Allow3600YesNo
16979658CLASSIFIER CORRECTION DEVICE, CLASSIFIER CORRECTION METHOD, AND STORAGE MEDIUMSeptember 2020November 2024Abandon5020YesNo
17017102HYBRID QUANTUM-CLASSICAL GENERATIVE MODELS FOR LEARNING DATA DISTRIBUTIONSSeptember 2020October 2023Abandon3710NoNo
17015560METHOD AND APPARATUS FOR GENERATING SAMPLE DATA, AND NON-TRANSITORY COMPUTER-READABLE RECORDING MEDIUMSeptember 2020August 2025Abandon6030YesNo
17014435COMPUTATION REDUCTION USING A DECISION TREE CLASSIFIER FOR FASTER NEURAL TRANSITION-BASED PARSINGSeptember 2020September 2023Allow3620YesNo
17012357ROBUSTNESS ESTIMATION METHOD, DATA PROCESSING METHOD, AND INFORMATION PROCESSING APPARATUSSeptember 2020January 2026Abandon6030NoNo
17008856KNOWLEDGE INDUCTION USING CORPUS EXPANSIONSeptember 2020August 2024Abandon4740YesNo
16976805CONTINUOUS PARAMETRIZATIONS OF NEURAL NETWORK LAYER WEIGHTSAugust 2020June 2024Allow4620YesNo
16976174SYSTEMS AND METHODS FOR USING AND TRAINING A NEURAL NETWORKAugust 2020March 2024Abandon4310NoNo
16993147TRAINING A NEURAL NETWORK USING PERIODIC SAMPLING OVER MODEL WEIGHTSAugust 2020October 2023Allow3810NoNo
16989413NEURAL NETWORK METHOD OF GENERATING FOOD FORMULASAugust 2020January 2021Allow510YesNo
16968413GENERATING OUTPUT EXAMPLES USING RECURRENT NEURAL NETWORKS CONDITIONED ON BIT VALUESAugust 2020July 2024Allow4720YesNo
16963566Method and Apparatus of Neural Networks with Grouping for Video CodingJuly 2020June 2025Abandon5940YesNo
16924006LATENT SPACE METHOD OF GENERATING FOOD FORMULASJuly 2020November 2020Allow510YesNo
16919955MACHINE LEARNABLE SYSTEM WITH CONDITIONAL NORMALIZING FLOWJuly 2020January 2025Abandon5520NoNo
16959440SYSTEM AND METHODS TO SHARE MACHINE LEARNING FUNCTIONALITY BETWEEN CLOUD AND AN IOT NETWORKJuly 2020March 2024Abandon4410NoNo
16959508CROSS-MODAL NEURAL NETWORKS FOR PREDICTIONJuly 2020March 2025Abandon5730NoNo
16954744System and Method for Use in Training Machine Learning UtilitiesJune 2020July 2024Abandon4920NoNo
16894970USING NEGATIVE EVIDENCE TO PREDICT EVENT DATASETSJune 2020November 2025Allow6050YesNo
16893565RECURRENT ENVIRONMENT PREDICTORSJune 2020October 2021Allow1600YesNo
16875533EFFICIENT TECHNIQUES FOR DETERMINING THE BEST DATA IMPUTATION ALGORITHMSMay 2020September 2025Abandon6060YesNo
16635892HUMAN BRAIN LIKE INTELLIGENT DECISION-MAKING MACHINEJanuary 2020October 2023Abandon4420NoNo
16635900UNIVERSAL GEOMETRIC-MUSICAL LANGUAGE FOR BIG DATA PROCESSING IN AN ASSEMBLY OF CLOCKING RESONATORSJanuary 2020January 2024Abandon4720NoNo
16731085LEARNING METHOD AND LEARNING DEVICE FOR GENERATING TRAINING DATA FROM VIRTUAL DATA ON VIRTUAL WORLD BY USING GENERATIVE ADVERSARIAL NETWORK, TO THEREBY REDUCE ANNOTATION COST REQUIRED IN TRAINING PROCESSES OF NEURAL NETWORK FOR AUTONOMOUS DRIVING, AND A TESTING METHOD AND A TESTING DEVICE USING THE SAMEDecember 2019October 2020Allow1010YesNo
16707830APPARATUS FOR QUALITY MANAGEMENT OF MEDICAL IMAGE INTERPRETATION USING MACHINE LEARNING, AND METHOD THEREOFDecember 2019July 2020Allow810YesNo
16695538SYSTEMS AND METHODS FOR MACHINE LEARNING-BASED SITE-SPECIFIC THREAT MODELING AND THREAT DETECTIONNovember 2019August 2021Allow2130YesNo
16665180QUANTUM COMPUTING SYSTEM AND METHODOctober 2019December 2021Abandon2620NoNo
16590417METHODS AND SYSTEMS FOR IDENTIFYING A CAUSAL LINKOctober 2019April 2021Allow1930YesNo
16543478Hybrid Quantum-Classical Computer System and Method for Performing Function InversionAugust 2019September 2022Allow3740YesNo
16459596INTERPRETABLE NEURAL NETWORKJuly 2019March 2024Allow5740YesNo
16455294Compressed Network for Product RecognitionJune 2019July 2020Allow1220YesNo
16424840INFORMATION PROCESSING APPARATUS FOR EMBEDDING WATERMARK INFORMATION, METHOD, AND COMPUTER READABLE STORAGE MEDIUMMay 2019October 2022Allow4020NoNo
16414965ARTIFICIAL INTELLIGENCE (AI) SERVICE-BASED HANDLING OF EXCEPTIONS IN CONSUMER ELECTRONIC DEVICESMay 2019July 2023Allow5030YesNo
16413085METHOD AND APPARATUS FOR REAL-TIME FRAUD MACHINE LEARNING MODEL EXECUTION MODULEMay 2019February 2023Allow4520YesNo
16403352RECURRENT ENVIRONMENT PREDICTORSMay 2019March 2020Allow1010YesNo
16402782TRAINING NEURAL NETWORKS USING A VARIATIONAL INFORMATION BOTTLENECKMay 2019August 2020Allow1620YesNo

Appeals Overview

This analysis examines appeal outcomes and the strategic value of filing appeals for examiner WERNER, MARSHALL L.

Patent Trial and Appeal Board (PTAB) Decisions

Total PTAB Decisions
3
Examiner Affirmed
1
(33.3%)
Examiner Reversed
2
(66.7%)
Reversal Percentile
86.0%
Higher than average

What This Means

With a 66.7% reversal rate, the PTAB has reversed the examiner's rejections more often than affirming them. This reversal rate is in the top 25% across the USPTO, indicating that appeals are more successful here than in most other areas.

Strategic Value of Filing an Appeal

Total Appeal Filings
6
Allowed After Appeal Filing
4
(66.7%)
Not Allowed After Appeal Filing
2
(33.3%)
Filing Benefit Percentile
91.4%
Higher than average

Understanding Appeal Filing Strategy

Filing a Notice of Appeal can sometimes lead to allowance even before the appeal is fully briefed or decided by the PTAB. This occurs when the examiner or their supervisor reconsiders the rejection during the mandatory appeal conference (MPEP § 1207.01) after the appeal is filed.

In this dataset, 66.7% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is in the top 25% across the USPTO, indicating that filing appeals is particularly effective here. The act of filing often prompts favorable reconsideration during the mandatory appeal conference.

Strategic Recommendations

Appeals to PTAB show good success rates. If you have a strong case on the merits, consider fully prosecuting the appeal to a Board decision.

Filing a Notice of Appeal is strategically valuable. The act of filing often prompts favorable reconsideration during the mandatory appeal conference.

Examiner WERNER, MARSHALL L - Prosecution Strategy Guide

Executive Summary

Examiner WERNER, MARSHALL L works in Art Unit 2125 and has examined 185 patent applications in our dataset. With an allowance rate of 68.1%, this examiner has a below-average tendency to allow applications. Applications typically reach final disposition in approximately 49 months.

Allowance Patterns

Examiner WERNER, MARSHALL L's allowance rate of 68.1% places them in the 30% percentile among all USPTO examiners. This examiner has a below-average tendency to allow applications.

Office Action Patterns

On average, applications examined by WERNER, MARSHALL L receive 2.62 office actions before reaching final disposition. This places the examiner in the 77% percentile for office actions issued. This examiner issues more office actions than most examiners, which may indicate thorough examination or difficulty in reaching agreement with applicants.

Prosecution Timeline

The median time to disposition (half-life) for applications examined by WERNER, MARSHALL L is 49 months. This places the examiner in the 6% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.

Interview Effectiveness

Conducting an examiner interview provides a +48.5% benefit to allowance rate for applications examined by WERNER, MARSHALL L. This interview benefit is in the 92% percentile among all examiners. Recommendation: Interviews are highly effective with this examiner and should be strongly considered as a prosecution strategy. Per MPEP § 713.10, interviews are available at any time before the Notice of Allowance is mailed or jurisdiction transfers to the PTAB.

Request for Continued Examination (RCE) Effectiveness

When applicants file an RCE with this examiner, 26.3% of applications are subsequently allowed. This success rate is in the 43% percentile among all examiners. Strategic Insight: RCEs show below-average effectiveness with this examiner. Carefully evaluate whether an RCE or continuation is the better strategy.

After-Final Amendment Practice

This examiner enters after-final amendments leading to allowance in 16.3% of cases where such amendments are filed. This entry rate is in the 18% percentile among all examiners. Strategic Recommendation: This examiner rarely enters after-final amendments compared to other examiners. You should generally plan to file an RCE or appeal rather than relying on after-final amendment entry. Per MPEP § 714.12, primary examiners have discretion in entering after-final amendments, and this examiner exercises that discretion conservatively.

Pre-Appeal Conference Effectiveness

When applicants request a pre-appeal conference (PAC) with this examiner, 0.0% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 5% percentile among all examiners. Note: Pre-appeal conferences show limited success with this examiner compared to others. While still worth considering, be prepared to proceed with a full appeal brief if the PAC does not result in favorable action.

Appeal Withdrawal and Reconsideration

This examiner withdraws rejections or reopens prosecution in 57.1% of appeals filed. This is in the 30% percentile among all examiners. Strategic Insight: This examiner shows below-average willingness to reconsider rejections during appeals. Be prepared to fully prosecute appeals if filed.

Petition Practice

When applicants file petitions regarding this examiner's actions, 34.3% are granted (fully or in part). This grant rate is in the 22% percentile among all examiners. Strategic Note: Petitions are rarely granted regarding this examiner's actions compared to other examiners. Ensure you have a strong procedural basis before filing a petition, as the Technology Center Director typically upholds this examiner's decisions.

Examiner Cooperation and Flexibility

Examiner's Amendments: This examiner makes examiner's amendments in 0.0% of allowed cases (in the 9% percentile). This examiner rarely makes examiner's amendments compared to other examiners. You should expect to make all necessary claim amendments yourself through formal amendment practice.

Quayle Actions: This examiner issues Ex Parte Quayle actions in 0.0% of allowed cases (in the 10% percentile). This examiner rarely issues Quayle actions compared to other examiners. Allowances typically come directly without a separate action for formal matters.

Prosecution Strategy Recommendations

Based on the statistical analysis of this examiner's prosecution patterns, here are tailored strategic recommendations:

  • Expect multiple rounds of prosecution: This examiner issues more office actions than average. Address potential issues proactively in your initial response and consider requesting an interview early in prosecution.
  • Prioritize examiner interviews: Interviews are highly effective with this examiner. Request an interview after the first office action to clarify issues and potentially expedite allowance.
  • Plan for RCE after final rejection: This examiner rarely enters after-final amendments. Budget for an RCE in your prosecution strategy if you receive a final rejection.
  • Plan for extended prosecution: Applications take longer than average with this examiner. Factor this into your continuation strategy and client communications.

Relevant MPEP Sections for Prosecution Strategy

  • MPEP § 713.10: Examiner interviews - available before Notice of Allowance or transfer to PTAB
  • MPEP § 714.12: After-final amendments - may be entered "under justifiable circumstances"
  • MPEP § 1002.02(c): Petitionable matters to Technology Center Director
  • MPEP § 1004: Actions requiring primary examiner signature (allowances, final rejections, examiner's answers)
  • MPEP § 1207.01: Appeal conferences - mandatory for all appeals
  • MPEP § 1214.07: Reopening prosecution after appeal

Important Disclaimer

Not Legal Advice: The information provided in this report is for informational purposes only and does not constitute legal advice. You should consult with a qualified patent attorney or agent for advice specific to your situation.

No Guarantees: We do not provide any guarantees as to the accuracy, completeness, or timeliness of the statistics presented above. Patent prosecution statistics are derived from publicly available USPTO data and are subject to data quality limitations, processing errors, and changes in USPTO practices over time.

Limitation of Liability: Under no circumstances will IronCrow AI be liable for any outcome, decision, or action resulting from your reliance on the statistics, analysis, or recommendations presented in this report. Past prosecution patterns do not guarantee future results.

Use at Your Own Risk: While we strive to provide accurate and useful prosecution statistics, you should independently verify any information that is material to your prosecution strategy and use your professional judgment in all patent prosecution matters.