USPTO Examiner WERNER MARSHALL L - Art Unit 2125

Dashboard Recent Applications Appeals Analysis Summary Report

Examiner Information

Name: WERNER, MARSHALL L

Art Unit: 2125

Total Cases: 173

Last Active: August 2024

Recent Applications

Detailed information about the 100 most recent patent applications.

Application Number	Title	Filing Date	Disposal Date	Disposition	Time (months)	Office Actions	Interview	Appeal
18815286	METHOD AND APPARATUS FOR FEW-SHOT RELATION CLASSIFICATION AND FILTERING, AND DEVICE	August 2024	April 2025	Allow	7	1	Yes	No
18769001	AA2CDS:Pre-trained Amino Acid-to-Codon Sequence Mapping Enabling Efficient Expression and Yield Optimization	July 2024	April 2025	Allow	9	2	Yes	No
18523482	Method and System for Assessing Drug Efficacy Using Multiple Graph Kernel Fusion	November 2023	September 2024	Allow	10	1	No	No
18140633	ARTIFICIAL INTELLIGENCE CHARACTER MODELS WITH MODIFIABLE BEHAVIORAL CHARACTERISTICS	April 2023	May 2024	Allow	13	3	Yes	No
18122641	UNCERTAINTY QUANTIFICATION FOR MACHINE LEARNING CLASSIFICATION MODELLING	March 2023	December 2024	Allow	21	4	Yes	No
18050694	SYSTEMS AND METHODS FOR SYNTHETIC DATABASE QUERY GENERATION	October 2022	May 2025	Allow	30	3	Yes	No
17892542	SYSTEMS AND METHODS FOR CORRELATING CUTANEOUS ACTIVITY WITH HUMAN PERFORMANCE	August 2022	February 2024	Allow	18	5	Yes	No
17852521	Method and System for Assessing Drug Efficacy Using Multiple Graph Kernel Fusion	June 2022	August 2023	Allow	14	2	Yes	No
17841252	MACHINE LEARNING MODELS FOR AUTOMATED SUSTAINABILITY DATA SOURCE INGESTION AND PROCESSING	June 2022	August 2023	Abandon	14	1	No	No
17825774	Classical and Quantum Algorithms for Orthogonal Neural Networks	May 2022	August 2023	Allow	14	1	No	No
17515115	SYSTEMS AND METHODS FOR MACHINE LEARNING-BASED SITE-SPECIFIC THREAT MODELING AND THREAT DETECTION	October 2021	May 2025	Allow	42	3	Yes	No
17498618	Convolutional Neural Network Hardware Configuration	October 2021	September 2024	Allow	35	2	No	No
17479708	CONTROLLABLE FORMULA GENERATION	September 2021	April 2024	Abandon	30	1	No	No
17464278	VERIFIED QUANTUM PHASE ESTIMATION	September 2021	May 2025	Allow	45	0	Yes	No
17459036	VARIABLE PARAMETER PROBABILITY FOR MACHINE-LEARNING MODEL GENERATION AND TRAINING	August 2021	October 2024	Abandon	38	3	Yes	No
17244362	METHODS FOR EFFICIENT IMPLEMENTATION OF UNITARY OPERATORS IN THE CLIFFORD ALGEBRA AS QUANTUM CIRCUITS AND APPLICATIONS TO LINEAR ALGEBRA AND MACHINE LEARNING	April 2021	July 2023	Allow	26	1	No	No
17191698	USER TARGETED CONTENT GENERATION USING MULTIMODAL EMBEDDINGS	March 2021	March 2025	Allow	49	1	Yes	No
17184132	SYSTEM, METHOD, AND MODEL STRUCTURE FOR USING MACHINE LEARNING TO PREDICT FUTURE SPORT OUTCOMES BASED ON MATCH STATE TRANSITIONS	February 2021	January 2025	Allow	46	2	Yes	No
17180550	NEURAL NETWORK METHOD OF GENERATING FOOD FORMULAS	February 2021	December 2023	Abandon	34	3	Yes	No
17169849	SYSTEM AND METHOD FOR NEURAL NETWORK RADIOSITY CALCULATIONS	February 2021	June 2021	Allow	4	1	Yes	No
17167981	CONTROLLABLE FORMULA GENERATION	February 2021	August 2021	Allow	6	1	Yes	No
17162479	ARTIFICIAL INTELLIGENCE ENGINE FOR GENERATING CANDIDATE DRUGS	January 2021	March 2025	Abandon	50	2	No	No
17160309	REINFORCEMENT LEARNING WITH QUANTUM ORACLE	January 2021	April 2025	Allow	51	2	Yes	No
17153895	CHANNEL SCALING: A SCALE-AND-SELECT APPROACH FOR SELECTIVE TRANSFER LEARNING	January 2021	September 2024	Allow	43	1	Yes	No
17151117	TRAINING A NEURAL NETWORK USING STOCHASTIC WHITENING BATCH NORMALIZATION	January 2021	August 2024	Allow	43	1	No	No
17126981	TRAINING NEURAL NETWORKS USING A VARIATIONAL INFORMATION BOTTLENECK	December 2020	January 2023	Allow	25	0	Yes	No
17122041	SYSTEM AND METHOD FOR REAL-TIME RADAR-BASED ACTION RECOGNITION USING SPIKING NEURAL NETWORK(SNN)	December 2020	May 2025	Allow	53	2	No	No
17120914	ULTRA-HIGH SENSITIVE TARGET SIGNAL DETECTION METHOD BASED ON NOISE ANALYSIS USING DEEP LEARNING BASED ANOMALY DETECTION AND SYSTEM USING THE SAME	December 2020	October 2024	Allow	46	2	No	No
17118683	AUTOMATIC HYBRID QUANTIZATION FOR DEEP NEURAL NETWORK	December 2020	May 2025	Allow	53	2	Yes	No
17118004	APPARATUS AND METHODS FOR QUANTUM COMPUTING WITH PRE-TRAINING	December 2020	November 2024	Allow	48	3	Yes	No
17247237	ACTIVITY RECOGNITION MODEL BALANCED BETWEEN VERSATILITY AND INDIVIDUATION AND SYSTEM THEREOF	December 2020	March 2025	Allow	51	2	No	No
17105033	ADAPTIVE ARTIFICIAL NEURAL NETWORK SELECTION TECHNIQUES	November 2020	August 2023	Allow	32	2	Yes	No
16950129	Method, System, and Computer Program Product for Training Distributed Machine Learning Models	November 2020	November 2023	Allow	36	1	Yes	No
17093442	ENSEMBLE OF NARROW AI AGENTS	November 2020	November 2024	Abandon	48	3	Yes	No
17087935	ENHANCED PRECISION MACHINE LEARNING PREDICTION	November 2020	May 2024	Allow	43	1	Yes	No
17082366	DETERMINING AT LEAST ONE NODE	October 2020	March 2025	Allow	53	2	No	No
17082364	APPARATUS AND METHOD FOR UNSUPERVISED DOMAIN ADAPTATION	October 2020	January 2023	Allow	27	1	Yes	No
17080351	APPARATUS AND METHOD FOR EMBEDDING SENTENCE FEATURE VECTOR	October 2020	December 2023	Allow	38	2	Yes	No
17073065	APPARATUS FOR QUALITY MANAGEMENT OF MEDICAL IMAGE INTERPRETATION USING MACHINE LEARNING, AND METHOD THEREOF	October 2020	December 2024	Abandon	50	6	Yes	No
17066530	INFERENCE VERIFICATION OF MACHINE LEARNING ALGORITHMS	October 2020	November 2023	Abandon	37	1	No	No
16981682	INTELLIGENT CONTROL METHOD FOR DYNAMIC NEURAL NETWORK-BASED VARIABLE CYCLE ENGINE	September 2020	September 2023	Allow	36	0	Yes	No
17017102	HYBRID QUANTUM-CLASSICAL GENERATIVE MODELS FOR LEARNING DATA DISTRIBUTIONS	September 2020	October 2023	Abandon	37	1	No	No
16979658	CLASSIFIER CORRECTION DEVICE, CLASSIFIER CORRECTION METHOD, AND STORAGE MEDIUM	September 2020	November 2024	Abandon	50	2	Yes	No
17014435	COMPUTATION REDUCTION USING A DECISION TREE CLASSIFIER FOR FASTER NEURAL TRANSITION-BASED PARSING	September 2020	September 2023	Allow	36	2	Yes	No
17008856	KNOWLEDGE INDUCTION USING CORPUS EXPANSION	September 2020	August 2024	Abandon	47	4	Yes	No
16976805	CONTINUOUS PARAMETRIZATIONS OF NEURAL NETWORK LAYER WEIGHTS	August 2020	June 2024	Allow	46	2	Yes	No
16976174	SYSTEMS AND METHODS FOR USING AND TRAINING A NEURAL NETWORK	August 2020	March 2024	Abandon	43	1	No	No
16993147	TRAINING A NEURAL NETWORK USING PERIODIC SAMPLING OVER MODEL WEIGHTS	August 2020	October 2023	Allow	38	1	No	No
16989413	NEURAL NETWORK METHOD OF GENERATING FOOD FORMULAS	August 2020	January 2021	Allow	5	1	Yes	No
16968413	GENERATING OUTPUT EXAMPLES USING RECURRENT NEURAL NETWORKS CONDITIONED ON BIT VALUES	August 2020	July 2024	Allow	47	2	Yes	No
16963566	Method and Apparatus of Neural Networks with Grouping for Video Coding	July 2020	June 2025	Abandon	59	4	Yes	No
16924006	LATENT SPACE METHOD OF GENERATING FOOD FORMULAS	July 2020	November 2020	Allow	5	1	Yes	No
16919955	MACHINE LEARNABLE SYSTEM WITH CONDITIONAL NORMALIZING FLOW	July 2020	January 2025	Abandon	55	2	No	No
16959440	SYSTEM AND METHODS TO SHARE MACHINE LEARNING FUNCTIONALITY BETWEEN CLOUD AND AN IOT NETWORK	July 2020	March 2024	Abandon	44	1	No	No
16959508	CROSS-MODAL NEURAL NETWORKS FOR PREDICTION	July 2020	March 2025	Abandon	57	3	No	No
16954744	System and Method for Use in Training Machine Learning Utilities	June 2020	July 2024	Abandon	49	2	No	No
16893565	RECURRENT ENVIRONMENT PREDICTORS	June 2020	October 2021	Allow	16	0	Yes	No
16635892	HUMAN BRAIN LIKE INTELLIGENT DECISION-MAKING MACHINE	January 2020	October 2023	Abandon	44	2	No	No
16635900	UNIVERSAL GEOMETRIC-MUSICAL LANGUAGE FOR BIG DATA PROCESSING IN AN ASSEMBLY OF CLOCKING RESONATORS	January 2020	January 2024	Abandon	47	2	No	No
16731085	LEARNING 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 SAME	December 2019	October 2020	Allow	10	1	Yes	No
16707830	APPARATUS FOR QUALITY MANAGEMENT OF MEDICAL IMAGE INTERPRETATION USING MACHINE LEARNING, AND METHOD THEREOF	December 2019	July 2020	Allow	8	1	Yes	No
16695538	SYSTEMS AND METHODS FOR MACHINE LEARNING-BASED SITE-SPECIFIC THREAT MODELING AND THREAT DETECTION	November 2019	August 2021	Allow	21	3	Yes	No
16665180	QUANTUM COMPUTING SYSTEM AND METHOD	October 2019	December 2021	Abandon	26	2	No	No
16590417	METHODS AND SYSTEMS FOR IDENTIFYING A CAUSAL LINK	October 2019	April 2021	Allow	19	3	Yes	No
16543478	Hybrid Quantum-Classical Computer System and Method for Performing Function Inversion	August 2019	September 2022	Allow	37	4	Yes	No
16459596	INTERPRETABLE NEURAL NETWORK	July 2019	March 2024	Allow	57	4	Yes	No
16455294	Compressed Network for Product Recognition	June 2019	July 2020	Allow	12	2	Yes	No
16424840	INFORMATION PROCESSING APPARATUS FOR EMBEDDING WATERMARK INFORMATION, METHOD, AND COMPUTER READABLE STORAGE MEDIUM	May 2019	October 2022	Allow	40	2	No	No
16414965	ARTIFICIAL INTELLIGENCE (AI) SERVICE-BASED HANDLING OF EXCEPTIONS IN CONSUMER ELECTRONIC DEVICES	May 2019	July 2023	Allow	50	3	Yes	No
16413085	METHOD AND APPARATUS FOR REAL-TIME FRAUD MACHINE LEARNING MODEL EXECUTION MODULE	May 2019	February 2023	Allow	45	2	Yes	No
16403352	RECURRENT ENVIRONMENT PREDICTORS	May 2019	March 2020	Allow	10	1	Yes	No
16402782	TRAINING NEURAL NETWORKS USING A VARIATIONAL INFORMATION BOTTLENECK	May 2019	August 2020	Allow	16	2	Yes	No
16397814	METHODS AND SYSTEMS FOR CLASSIFICATION USING EXPERT DATA	April 2019	June 2021	Allow	26	4	Yes	No
16395743	MACHINE LEARNING QUANTUM ALGORITHM VALIDATOR	April 2019	January 2023	Allow	45	1	Yes	No
16375627	Digital Experience Enhancement Using An Ensemble Deep Learning Model	April 2019	May 2023	Allow	50	3	Yes	No
16362057	SECURE DATA PROCESSING	March 2019	November 2022	Allow	44	2	Yes	No
16356991	REPRESENTATIONS OF UNITS IN NEURAL NETWORKS	March 2019	September 2023	Abandon	54	3	No	No
16354725	PARAMETER EXTRAPOLATION IN QUANTUM VARIATIONAL CIRCUITS	March 2019	August 2024	Allow	60	5	Yes	No
16298463	SYSTEMS AND METHODS FOR SYNTHETIC DATABASE QUERY GENERATION	March 2019	April 2022	Allow	37	5	Yes	No
16296380	SYSTEM FOR SECURE FEDERATED LEARNING	March 2019	August 2023	Allow	53	5	Yes	No
16264625	PRE-TRAINING NEURAL NETWORKS WITH HUMAN DEMONSTRATIONS FOR DEEP REINFORCEMENT LEARNING	January 2019	June 2025	Abandon	60	6	No	No
16263874	DEEP CONVOLUTIONAL NEURAL NETWORK ARCHITECTURE AND SYSTEM AND METHOD FOR BUILDING THE DEEP CONVOLUTIONAL NEURAL NETWORK ARCHITECTURE	January 2019	October 2022	Abandon	44	1	No	No
16262575	PLANT ABNORMALITY PREDICTION SYSTEM AND METHOD	January 2019	July 2022	Allow	42	2	Yes	No
16258116	SHARED LEARNING ACROSS SEPARATE ENTITIES WITH PRIVATE DATA FEATURES	January 2019	January 2024	Allow	60	4	Yes	No
16255219	DETERMINANTAL REINFORCED LEARNING IN ARTIFICIAL INTELLIGENCE	January 2019	July 2022	Allow	42	2	Yes	No
16254108	Method and System for Interactive, Interpretable, and Improved Match and Player Performance Predictions in Team Sports	January 2019	June 2022	Allow	40	1	Yes	No
16253457	IMPLEMENTING TRAINING OF A MACHINE LEARNING MODEL FOR EMBODIED CONVERSATIONAL AGENT	January 2019	October 2022	Abandon	44	4	Yes	No
16242045	DISTRIBUTED LEARNING USING ENSEMBLE-BASED FUSION	January 2019	May 2023	Allow	52	4	Yes	No
16232387	VOLTAGE CONTROLLED HIGHLY LINEAR RESISTIVE ELEMENTS	December 2018	March 2021	Allow	26	2	Yes	No
16204770	ASYNCHRONOUS GRADIENT WEIGHT COMPRESSION	November 2018	August 2023	Allow	56	5	Yes	No
16192649	Machine-Learning Models Based on Non-local Neural Networks	November 2018	September 2022	Allow	46	2	Yes	No
16141845	USER-CENTRIC ONTOLOGY POPULATION WITH USER REFINEMENT	September 2018	December 2022	Allow	50	3	Yes	No
16139861	STOCHASTIC CONTROL WITH A QUANTUM COMPUTER	September 2018	April 2023	Allow	55	3	Yes	No
16133971	PRODUCTION SYSTEM	September 2018	November 2021	Allow	38	3	Yes	No
16124657	Verifiable Deep Learning Training Service	September 2018	June 2023	Allow	57	3	Yes	No
16053235	CONTROLLER AND MACHINE LEARNING DEVICE	August 2018	March 2022	Abandon	43	4	No	No
16025546	METHOD AND SYSTEM OF CONTROLLING COMPUTING OPERATIONS BASED ON EARLY-STOP IN DEEP NEURAL NETWORK	July 2018	October 2022	Allow	52	2	Yes	No
16012424	VISUAL RECOGNITION VIA LIGHT WEIGHT NEURAL NETWORK	June 2018	October 2023	Abandon	60	4	Yes	No
15975280	SYSTEMS AND METHODS TO ENABLE CONTINUAL, MEMORY-BOUNDED LEARNING IN ARTIFICIAL INTELLIGENCE AND DEEP LEARNING CONTINUOUSLY OPERATING APPLICATIONS ACROSS NETWORKED COMPUTE EDGES	May 2018	September 2023	Allow	60	4	Yes	No
15926790	SEARCHING OF DATA STRUCTURES IN PRE-PROCESSING DATA FOR A MACHINE LEARNING CLASSIFIER	March 2018	October 2023	Allow	60	3	No	No

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

Examiner Affirmed

(33.3%)

Examiner Reversed

(66.7%)

Reversal Percentile

85.8%

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

Allowed After Appeal Filing

(66.7%)

Not Allowed After Appeal Filing

(33.3%)

Filing Benefit Percentile

90.9%

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 173 patent applications in our dataset. With an allowance rate of 71.1%, this examiner has a below-average tendency to allow applications. Applications typically reach final disposition in approximately 47 months.

Allowance Patterns

Examiner WERNER, MARSHALL L's allowance rate of 71.1% places them in the 26% 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 2.60 office actions before reaching final disposition. This places the examiner in the 87% 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 47 months. This places the examiner in the 1% 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 +46.5% benefit to allowance rate for applications examined by WERNER, MARSHALL L. This interview benefit is in the 93% 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, 26.4% of applications are subsequently allowed. This success rate is in the 33% 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 17.3% of cases where such amendments are filed. This entry rate is in the 13% 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 4% 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 24% percentile among all examiners. Strategic Insight: This examiner rarely withdraws rejections during the appeal process compared to other examiners. If you file an appeal, be prepared to fully prosecute it to a PTAB decision. Per MPEP § 1207, the examiner will prepare an Examiner's Answer maintaining the rejections.

Petition Practice

When applicants file petitions regarding this examiner's actions, 29.3% are granted (fully or in part). This grant rate is in the 21% 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 8% 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 9% 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.