USPTO Examiner BALDWIN RANDALL KERN - Art Unit 2125

Examiner Information

Name: BALDWIN, RANDALL KERN
Art Unit: 2125
Total Cases: 187
Last Active: September 2025

Recent Applications

Detailed information about the 100 most recent patent applications.

Application NumberTitleFiling DateDisposal DateDispositionTime (months)Office ActionsRestrictionsInterviewAppeal
19315849System and Method for Enterprise Hierarchical Persistent Cognitive Machines with Organizational Hierarchy Awareness and Compliance IntegrationSeptember 2025February 2026Allow510NoNo
19294125Mobile-Optimized Multi-Stage LLM with Federated Persistent Cognitive ArchitectureAugust 2025February 2026Allow610NoNo
19260485PROCESSING HETEROGENEOUS GENERATIVE ARTIFICIAL INTELLIGENCE MODELSJuly 2025October 2025Allow410YesNo
19221862MODULAR SOC AI/ML INFERENCE ENGINE WITH DYNAMIC UPDATES USING A CENTRAL COLLECTING AND CONSOLIDATED LAYER-TO-LAYER DATA TRANSFER TOPOLOGY AT EACH NEURAL NETWORK LAYERMay 2025February 2026Allow810YesNo
19204647METHOD AND DEVICE FOR OPTIMIZING NEURAL NETWORK MODELMay 2025February 2026Allow910NoNo
19178873Mobile-Optimized Multi-Stage LLM with Autonomous ReasoningApril 2025March 2026Allow1110NoNo
19093171SCHEDULING METHOD FOR A MULTI-LAYER CONVOLUTIONAL NEURAL NETWORK, ELECTRONIC DEVICE AND STORAGE MEDIUMMarch 2025November 2025Allow710YesNo
19072823GENERATIVE ARTIFICIAL INTELLIGENCE FOR CONTENT GENERATION WITH SEARCHABLE REPOSITORYMarch 2025September 2025Allow710YesNo
19063602MODULAR OPEN SYSTEM ARCHITECTURE FOR COMMON INTELLIGENCE PICTURE GENERATIONFebruary 2025July 2025Allow510YesNo
19042006NEURAL PROCESSING UNIT OPERABLE IN MULTIPLE MODES TO APPROXIMATE ACTIVATION FUNCTIONJanuary 2025July 2025Allow510YesNo
18985282INTEGER GATE LOGIC (IGL) ARTIFICIAL NEURAL NETWORK WITH PARRALLELIZATION AND INTERNAL VISUALIZATION CAPABILITIESDecember 2024June 2025Allow610YesNo
18844254System, Method, and Computer Program Product for Saving Memory During Training of Knowledge Graph Neural NetworksSeptember 2024June 2025Allow910YesNo
18818342DATA PREFILTERING FOR LARGE SCALE DATA CLASSIFICATIONAugust 2024March 2025Allow710YesNo
18798833MODULAR SOC AI/ML INFERENCE ENGINE WITH DYNAMIC UPDATES USING A HUB-AND-SPOKE TOPOLOGY AT EACH NEURAL NETWORK LAYERAugust 2024May 2025Allow910YesNo
18792455Multimodal Generative AI Model Protection Using Sequential SidecarsAugust 2024March 2025Allow820NoNo
18781938MACHINE LEARNING ARCHITECTURES WITH SUB-QUADRATIC ITERATOR MODULESJuly 2024February 2025Allow610YesNo
18732648METHOD, DEVICE, AND MEDIUM FOR PREDICTING FLUE DUST CONCENTRATIONJune 2024April 2025Allow1110NoNo
18596535USING EMBEDDING FUNCTIONS WITH A DEEP NETWORKMarch 2024March 2026Allow2410NoNo
18418201SYSTEMS AND METHODS FOR RESPONDING TO PREDICTED EVENTS IN TIME-SERIES DATA USING SYNTHETIC PROFILES CREATED BY ARTIFICIAL INTELLIGENCE MODELS TRAINED ON NON-HOMOGENEOUS TIME-SERIES DATAJanuary 2024August 2024Allow610YesNo
18406829SYSTEMS AND METHODS FOR COHORT-BASED PREDICTIONS IN CLUSTERED TIME-SERIES DATA IN ORDER TO DETECT SIGNIFICANT RATE-OF-CHANGE EVENTSJanuary 2024February 2025Allow1310YesNo
18522075Systems and Methods of Sparsity ExploitingNovember 2023June 2025Abandon1910NoNo
18501455TECHNOLOGY FOR LOWERING PEAK POWER OF NEURAL PROCESSING UNIT USING VARIABLE FREQUENCYNovember 2023August 2024Allow920NoNo
18478763SPLICING SITE CLASSIFICATION USING NEURAL NETWORKSSeptember 2023April 2024Allow710YesNo
18455026DEEP NEURAL NETWORK ARCHITECTURE USING PIECEWISE LINEAR APPROXIMATIONAugust 2023September 2024Allow1300YesNo
18354569SYSTEMS AND METHODS FOR AGGREGATING TIME-SERIES DATA STREAMS BASED ON POTENTIAL STATE CHARACTERISTICS FOLLOWING AGGREGATIONJuly 2023August 2024Allow1320YesNo
18327850SYSTEMS AND METHODS FOR RESPONDING TO PREDICTED EVENTS IN TIME-SERIES DATA USING SYNTHETIC PROFILES CREATED BY ARTIFICIAL INTELLIGENCE MODELS TRAINED ON NON-HOMOGENEOUS TIME-SERIES DATAJune 2023February 2024Allow810NoNo
18299717NEURAL NETWORK HARDWARE ACCELERATOR DATA PARALLELISMApril 2023May 2024Allow1310NoNo
18028566NEURAL NETWORK RETRAINING METHOD BASED ON AGING SENSING OF MEMRISTORSMarch 2023June 2024Allow1510YesNo
18120137TWO-DIMENSIONAL ARRAY-BASED NEUROMORPHIC PROCESSOR AND IMPLEMENTING METHODMarch 2023October 2023Allow810NoNo
18174498SYSTEMS AND METHODS FOR COHORT-BASED PREDICTIONS IN CLUSTERED TIME-SERIES DATA IN ORDER TO DETECT SIGNIFICANT RATE-OF-CHANGE EVENTSFebruary 2023November 2023Allow810YesNo
18099904SELECTION OF MACHINE LEARNING ALGORITHMSJanuary 2023August 2024Allow1820NoNo
18065441SYSTEMS AND METHODS FOR RESPONDING TO PREDICTED EVENTS IN TIME-SERIES DATA USING SYNTHETIC PROFILES CREATED BY ARTIFICIAL INTELLIGENCE MODELS TRAINED ON NON-HOMOGENOUS TIME SERIES-DATADecember 2022May 2023Allow510YesNo
17993463SYSTEM AND METHOD OF DETERMINING AND EXECUTING DEEP TENSOR COLUMNS IN NEURAL NETWORKSNovember 2022February 2024Allow1510YesNo
17992814OPTIMIZATION METHOD AND APPARATUS FOR COMPILING COMPUTATION GRAPHNovember 2022December 2024Abandon3520YesYes
17992822MEMORY OPTIMIZATION METHOD AND APPARATUS FOR NEURAL NETWORK COMPILATIONNovember 2022December 2024Abandon3520YesYes
17972466USING EMBEDDING FUNCTIONS WITH A DEEP NETWORKOctober 2022December 2023Allow1310YesNo
17959827DATA COLLECTION SYSTEM, DATA COLLECTION DEVICE, DATA ACQUISITION DEVICE, AND DATA COLLECTION METHODOctober 2022November 2023Abandon1320YesNo
17953909HARDWARE-BASED ARTIFICIAL NEURAL NETWORK DEVICESeptember 2022February 2026Allow4110NoNo
17874876TOOL FOR FACILITATING EFFICIENCY IN MACHINE LEARNINGJuly 2022January 2024Allow1810NoNo
17875223ELECTRICAL NETWORKS USING ANALYTIC LOSS GRADIENTS FOR DESIGN, ANALYSIS AND MACHINE LEARNINGJuly 2022November 2023Allow1620YesNo
17872626ANALOG NEUROMOPRHIC CIRCUIT WITH STACKS OF RESISTIVE MEMORY CROSSBAR CONFIGURATIONSJuly 2022March 2026Allow4410YesNo
17809044BLOCKWISE FACTORIZATION OF HYPERVECTORSJune 2022December 2025Allow4210YesNo
17849292SYSTEMS AND METHODS TO IDENTIFY DOCUMENT TRANSITIONS BETWEEN ADJACENT DOCUMENTS WITHIN DOCUMENT BUNDLESJune 2022August 2023Allow1410NoNo
17846837ACCELERATOR FOR DEEP NEURAL NETWORKSJune 2022December 2025Allow4110YesNo
17806143LOW POWER HARDWARE ARCHITECTURE FOR HANDLING ACCUMULATION OVERFLOWS IN A CONVOLUTION OPERATIONJune 2022August 2024Allow2660YesNo
17738436SYSTEM, METHOD, AND COMPUTER DEVICE FOR TRANSISTOR-BASED NEURAL NETWORKSMay 2022December 2022Allow710YesNo
17705129NEURAL NETWORK LEARNING FOR THE PREVENTION OF FALSE POSITIVE AUTHORIZATIONSMarch 2022June 2024Allow2710YesNo
17656625NEURAL PROCESSING DEVICE AND METHOD FOR PRUNING THEREOFMarch 2022December 2023Allow2140YesNo
17701809HARDWARE IMPLEMENTATION OF ACTIVATION FUNCTIONSMarch 2022October 2025Allow4310YesNo
17655838SYSTEMS AND METHODS FOR REDUCING PROBLEMATIC CORRELATIONS BETWEEN FEATURES FROM MACHINE LEARNING MODEL DATAMarch 2022June 2025Allow3810YesNo
17642266NEURAL NETWORK MAPPING METHOD AND APPARATUSMarch 2022July 2023Allow1720YesNo
17689185NEURAL BREGMAN DIVERGENCES FOR DISTANCE LEARNINGMarch 2022April 2023Allow1430YesNo
17689755Conductance Mapping Technique for Neural NetworksMarch 2022February 2026Allow4720YesNo
17639609METHOD FOR INCREASING CERTAINTY IN PARAMETERIZED MODEL PREDICTIONSMarch 2022January 2026Allow4610YesNo
17633186SEPARATION OF STATES OF MECHANICAL PRESSES BY ANALYZING TRAINED PATTERNS IN A NEURAL NETWORKFebruary 2022July 2024Allow2950NoNo
17592174Machine-Learned Attention Models Featuring Echo-Attention LayersFebruary 2022August 2025Allow4310YesNo
17581453Systems and Methods for Sparsity ExploitingJanuary 2022August 2023Allow1910NoNo
17548692SYSTEM AND METHOD OF USING FRACTIONAL ADAPTIVE LINEAR UNIT AS ACTIVATION IN ARTIFICIAL NEURAL NETWORKSDecember 2021March 2026Allow5120YesNo
17547458SYSTEM AND METHOD OF EXECUTING DEEP TENSOR COLUMNS IN NEURAL NETWORKSDecember 2021August 2022Allow820YesNo
17455181METHOD AND SYSTEM FOR OVER-PREDICTION IN NEURAL NETWORKSNovember 2021August 2025Allow4510YesNo
17510397NEURAL NETWORK HARDWARE ACCELERATOR DATA PARALLELISMOctober 2021January 2023Allow1530YesNo
17451260MACHINE UNLEARNING AND RETRAINING OF A MACHINE LEARNING MODEL BASED ON A MODIFIED TRAINING DATASETOctober 2021August 2025Allow4600YesNo
17492222SYSTEMS AND METHODS FOR TAGGING DATASETS USING MODELS ARRANGED IN A SERIES OF NODESOctober 2021January 2025Allow3920YesNo
17490287UPDATING MACHINE LEARNING MODELSSeptember 2021May 2025Allow4310NoNo
17441316LEARNING SYSTEM, DATA GENERATION APPARATUS, DATA GENERATION METHOD, AND COMPUTER-READABLE STORAGE MEDIUM STORING A DATA GENERATION PROGRAM FOR TRAINING NEURAL NETWORKS THROUGH SUPERVISED LEARNING AND DATASET GENERATED FROM ANSWER DATASeptember 2021September 2025Abandon4820NoNo
17465439SECURE, ACCURATE AND FAST NEURAL NETWORK INFERENCE BY REPLACING AT LEAST ONE NON-LINEAR ACTIVATION CHANNELSeptember 2021July 2025Allow4720YesNo
17460919GRAPH MODEL BUILD AND SCORING ENGINEAugust 2021August 2023Allow2310NoNo
17405342MACHINE LEARNING DEVICEAugust 2021December 2022Allow1620YesNo
17372921METHOD AND DEVICE FOR TRAINING TREE MODELJuly 2021October 2025Abandon5110NoNo
17368636DYNAMIC WEB PAGE CLASSIFICATION IN WEB DATA COLLECTIONJuly 2021January 2025Abandon4260YesNo
17420521ELECTRONIC DEVICE AND CONTROLLING METHOD THEREOFJuly 2021April 2025Allow4530YesNo
17362887CONTENT RECOMMENDATION METHOD AND APPARATUS, DEVICE, AND STORAGE MEDIUMJune 2021February 2025Allow4420YesNo
17361798SYSTEMS AND METHODS TO IDENTIFY DOCUMENT TRANSITIONS BETWEEN ADJACENT DOCUMENTS WITHIN DOCUMENT BUNDLESJune 2021April 2022Allow1010NoNo
17345996METHOD AND SYSTEM FOR CREATING AN ENSEMBLE OF MACHINE LEARNING MODELS TO DEFEND AGAINST ADVERSARIAL EXAMPLESJune 2021November 2024Allow4110YesNo
17345230SYSTEM AND METHOD FOR ANOMALY DETECTION IN DYNAMICALLY EVOLVING DATA USING RANDOM NEURAL NETWORK DECOMPOSITIONJune 2021September 2023Allow2710NoNo
17342197METHODS AND APPARATUS TO PREDICT SPORTS INJURIESJune 2021November 2023Abandon2910NoNo
17292783Content Classification MethodMay 2021April 2025Allow4710YesNo
17314751NEURAL NETWORK ROBUSTNESS VIA BINARY ACTIVATIONMay 2021September 2025Allow5210YesNo
17230460METHOD, ELECTRONIC DEVICE, AND COMPUTER PROGRAM PRODUCT FOR DATA PROCESSING UTILIZING SEPARABLE MULTI-STAGE DATA PROCESSING MODEL IN EDGE-CLOUD NETWORKApril 2021December 2024Allow4420YesNo
17284201TASK PROCESSING METHOD AND DEVICE BASED ON NEURAL NETWORKApril 2021June 2025Allow5010YesNo
17226619METHODS, MEDIUMS, AND SYSTEMS FOR GENERATING CAUSAL INFERENCE STRUCTURE BETWEEN CONCEPTS HAVING PREDICTIVE CAPABILITIESApril 2021June 2025Allow5010NoNo
17213167TRAINING INDIVIDUALLY FAIR MACHINE LEARNING ALGORITHMS VIA DISTRIBUTIONALLY ROBUST OPTIMIZATIONMarch 2021June 2025Allow5110YesNo
17279680PREDICTION MANAGMENT SYSTEM, PREDICTION MANAGEMENT METHOD, DATA STRUCTURE, PREDICTION MANAGEMENT DEVICE AND PREDICTION EXECUTION DEVICEMarch 2021April 2025Allow4910YesNo
17206974METHOD AND DEVICE FOR OPERATING A CLASSIFIERMarch 2021January 2026Allow5820YesNo
17195775ARITHMETIC DEVICE, COMPUTER SYSTEM, AND ARITHMETIC METHODMarch 2021April 2025Abandon5010NoNo
17188234INFORMATION PROCESSING APPARATUS, CONTROL METHODS THEREOF, AND RECORDING MEDIUM FOR NEURAL NETWORK LEARNING MODELS UTILIZING DATA MINIMIZATIONMarch 2021February 2025Allow4720YesNo
17268660BUILDING DEEP LEARNING ENSEMBLES WITH DIVERSE TARGETSFebruary 2021October 2021Allow710NoNo
17171507METHOD AND APPARATUS FOR GENERATING RECOMMENDATION MODEL, CONTENT RECOMMENDATION METHOD AND APPARATUS, DEVICE AND MEDIUMFebruary 2021September 2024Allow4300YesNo
17266624DISPATCHING DISTRIBUTIONFebruary 2021February 2025Abandon4910NoNo
17170416SYSTEMS AND METHODS FOR MODELING CONTINUOUS STOCHASTIC PROCESSES WITH DYNAMIC NORMALIZING FLOWSFebruary 2021November 2024Allow4510NoNo
17164433UTILIZING NEURAL NETWORK MODELS FOR RECOMMENDING AND ADAPTING TREATMENTS FOR USERSFebruary 2021November 2024Allow4510YesNo
17151001Systems and Methods for Training Machine-Learned Models with Deviating Intermediate RepresentationsJanuary 2021October 2024Allow4510YesNo
17139849METHODS AND SYSTEMS FOR CROSS-PLATFORM USER PROFILING BASED ON DISPARATE DATASETS USING MACHINE LEARNING MODELSDecember 2020September 2024Allow4400YesNo
17132644ANALYTICS ENGINE FOR DETECTING MEDICAL FRAUD, WASTE, AND ABUSEDecember 2020October 2023Abandon3410NoNo
17132776SYMBOLIC MODEL TRAINING WITH ACTIVE LEARNINGDecember 2020July 2024Allow4310YesNo
17115973System and method of constructing machine learning workflows through machine learning suggestionsDecember 2020August 2024Allow4420YesNo
17111097METHOD AND SYSTEM FOR RECOMMENDING TOOL CONFIGURATIONS IN MACHININGDecember 2020May 2024Allow4120YesNo
17098723SYSTEMS AND METHODS FOR TARGETING POLICYMAKER COMMUNICATIONNovember 2020April 2021Allow510NoNo
17095700AUXILIARY MODEL FOR PREDICTING NEW MODEL PARAMETERSNovember 2020May 2024Allow4310YesNo
17095603AUTOMATIC GENERATION OF TRANSFORMATIONS OF FORMATTED TEMPLATES USING DEEP LEARNING MODELINGNovember 2020September 2023Allow3510YesNo

Appeals Overview

This analysis examines appeal outcomes and the strategic value of filing appeals for examiner BALDWIN, RANDALL KERN.

Patent Trial and Appeal Board (PTAB) Decisions

Total PTAB Decisions
7
Examiner Affirmed
6
(85.7%)
Examiner Reversed
1
(14.3%)
Reversal Percentile
25.8%
Lower than average

What This Means

With a 14.3% reversal rate, the PTAB affirms the examiner's rejections in the vast majority of cases. This reversal rate is below the USPTO average, indicating that appeals face more challenges here than typical.

Strategic Value of Filing an Appeal

Total Appeal Filings
13
Allowed After Appeal Filing
2
(15.4%)
Not Allowed After Appeal Filing
11
(84.6%)
Filing Benefit Percentile
18.1%
Lower 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, 15.4% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is in the bottom 25% across the USPTO, indicating that filing appeals is less effective here than in most other areas.

Strategic Recommendations

⚠ Appeals to PTAB face challenges. Ensure your case has strong merit before committing to full Board review.

⚠ Filing a Notice of Appeal shows limited benefit. Consider other strategies like interviews or amendments before appealing.

Examiner BALDWIN, RANDALL KERN - Prosecution Strategy Guide

Executive Summary

Examiner BALDWIN, RANDALL KERN works in Art Unit 2125 and has examined 187 patent applications in our dataset. With an allowance rate of 77.0%, this examiner has a below-average tendency to allow applications. Applications typically reach final disposition in approximately 46 months.

Allowance Patterns

Examiner BALDWIN, RANDALL KERN's allowance rate of 77.0% places them in the 44% percentile among all USPTO examiners. This examiner has a below-average tendency to allow applications.

Office Action Patterns

On average, applications examined by BALDWIN, RANDALL KERN receive 2.18 office actions before reaching final disposition. This places the examiner in the 59% percentile for office actions issued. This examiner issues a slightly above-average number of office actions.

Prosecution Timeline

The median time to disposition (half-life) for applications examined by BALDWIN, RANDALL KERN is 46 months. This places the examiner in the 11% 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 +31.7% benefit to allowance rate for applications examined by BALDWIN, RANDALL KERN. This interview benefit is in the 80% 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, 29.0% of applications are subsequently allowed. This success rate is in the 54% percentile among all examiners. Strategic Insight: RCEs show above-average effectiveness with this examiner. Consider whether your amendments or new arguments are strong enough to warrant an RCE versus filing a continuation.

After-Final Amendment Practice

This examiner enters after-final amendments leading to allowance in 18.2% of cases where such amendments are filed. This entry rate is in the 21% 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 50.0% of appeals filed. This is in the 15% percentile among all examiners. Of these withdrawals, 42.9% occur early in the appeal process (after Notice of Appeal but before Appeal Brief). 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, 29.4% are granted (fully or in part). This grant rate is in the 17% 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:

  • 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.