Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 18656636 | PROJECTING DATA TRENDS USING CUSTOMIZED MODELING | May 2024 | July 2024 | Allow | 3 | 0 | 0 | No | No |
| 18454907 | PROJECTING DATA TRENDS USING CUSTOMIZED MODELING | August 2023 | February 2024 | Allow | 6 | 1 | 0 | Yes | No |
| 18143802 | ARTIFICIAL NEURAL NETWORK TRAINED TO REFLECT HUMAN SUBJECTIVE RESPONSES | May 2023 | June 2024 | Allow | 14 | 1 | 0 | No | No |
| 18130335 | ATTENTION NEURAL NETWORKS WITH PARALLEL ATTENTION AND FEED-FORWARD LAYERS | April 2023 | November 2023 | Allow | 8 | 1 | 0 | Yes | No |
| 17919898 | System, Method, and Computer Program Product for Analyzing Multivariate Time Series Using a Convolutional Fourier Network | October 2022 | October 2023 | Allow | 12 | 2 | 0 | Yes | No |
| 17959939 | SYSTEM AND METHODS FOR PREDICTION COMMUNICATION PERFORMANCE IN NETWORKED SYSTEMS | October 2022 | July 2024 | Allow | 21 | 0 | 0 | No | No |
| 17954109 | DATA FLOW METHOD AND APPARATUS FOR NEURAL NETWORK COMPUTATION BY DETERMINING INPUT VARIABLES AND OUTPUT VARIABLES OF NODES OF A COMPUTATIONAL GRAPH OF A NEURAL NETWORK | September 2022 | February 2024 | Allow | 16 | 2 | 0 | No | No |
| 17848239 | Real Time Detection of Cyber Threats Using Self-Referential Entity Data | June 2022 | January 2024 | Abandon | 19 | 1 | 0 | No | No |
| 17752950 | METHOD OF TRAINING A NEURAL NETWORK TO REFLECT EMOTIONAL PERCEPTION AND RELATED SYSTEM AND METHOD FOR CATEGORIZING AND FINDING ASSOCIATED CONTENT | May 2022 | December 2022 | Allow | 7 | 1 | 0 | No | No |
| 17714454 | METHOD OF NEURAL NETWORK MODEL COMPUTATION-ORIENTED INTERMEDIATE REPRESENTATION BY CONSTRUCTING PHYSICAL COMPUTATION GRAPH, INFERRING INFORMATION OF INPUT AND OUTPUT TENSOR EDGES OF EACH NODE THEREIN, PERFORMING MEMORY OPTIMIZATION ON TENSOR EDGES, AND OPTIMIZING PHYSICAL COMPUTATION GRAPH | April 2022 | September 2023 | Allow | 17 | 1 | 0 | No | No |
| 17692491 | STDP WITH SYNAPTIC FATIGUE FOR LEARNING OF SPIKE-TIME-CODED PATTERNS IN THE PRESENCE OF PARALLEL RATE-CODING | March 2022 | January 2023 | Allow | 10 | 0 | 0 | No | No |
| 17405515 | SYSTEM, METHOD, AND SERVER FOR RETRAINING MACHINE LEARNING MODEL OF VEHICLES BASED ON POSITIONAL INFORMATION | August 2021 | November 2023 | Abandon | 27 | 4 | 0 | Yes | No |
| 17369204 | METHOD OF TRAINING A NEURAL NETWORK TO REFLECT EMOTIONAL PERCEPTION AND RELATED SYSTEM AND METHOD FOR CATEGORIZING AND FINDING ASSOCIATED CONTENT | July 2021 | June 2022 | Abandon | 11 | 1 | 0 | No | No |
| 17331259 | SYSTEM AND METHODS FOR PREDICTION COMMUNICATION PERFORMANCE IN NETWORKED SYSTEMS | May 2021 | June 2022 | Allow | 13 | 2 | 0 | No | Yes |
| 17226706 | SYSTEMS AND METHODS FOR DETERMINING NAVIGATION PATTERNS ASSOCIATED WITH A SOCIAL NETWORKING SYSTEM | April 2021 | October 2024 | Abandon | 42 | 3 | 0 | Yes | No |
| 17210132 | PROJECTING DATA TRENDS USING CUSTOMIZED MODELING | March 2021 | June 2023 | Allow | 27 | 2 | 0 | Yes | Yes |
| 17180451 | METHOD TO PREDICT FOOD COLOR AND RECOMMEND CHANGES TO ACHIEVE A TARGET FOOD COLOR | February 2021 | February 2024 | Abandon | 36 | 2 | 0 | Yes | No |
| 17134690 | PARTIAL ACTIVATION OF MULTIPLE PATHWAYS IN NEURAL NETWORKS | December 2020 | August 2024 | Allow | 44 | 0 | 0 | No | No |
| 17128429 | PROJECTING DATA TRENDS USING CUSTOMIZED MODELING | December 2020 | February 2021 | Allow | 2 | 0 | 0 | No | No |
| 17046351 | DEVICE, METHOD, PROGRAM, AND SYSTEM FOR DETECTING UNIDENTIFIED WATER | October 2020 | February 2024 | Abandon | 41 | 4 | 0 | Yes | No |
| 17061355 | Scale-Permuted Machine Learning Architecture | October 2020 | April 2024 | Allow | 43 | 7 | 1 | Yes | Yes |
| 17020248 | NOISY NEURAL NETWORK LAYERS WITH NOISE PARAMETERS | September 2020 | January 2024 | Allow | 40 | 1 | 0 | Yes | No |
| 16977282 | ADD-MULITPLY-ADD CONVOLUTION COMPUTATION FOR A CONVOLUTIONAL NEURAL NETWORK | September 2020 | December 2023 | Allow | 40 | 1 | 0 | No | No |
| 16918669 | SETTING LATENCY CONSTRAINTS FOR ACOUSTIC MODELS | July 2020 | January 2024 | Allow | 43 | 2 | 0 | Yes | No |
| 16875214 | CONVOLUTION OPERATIONS UTILIZING NONZERO PADDING DATA COPIED FROM INPUT CHANNEL DATA | May 2020 | March 2024 | Allow | 46 | 2 | 0 | Yes | No |
| 16784136 | METHOD OF TRAINING A NEURAL NETWORK TO REFLECT EMOTIONAL PERCEPTION AND RELATED SYSTEM AND METHOD FOR CATEGORIZING AND FINDING ASSOCIATED CONTENT | February 2020 | March 2021 | Allow | 14 | 1 | 0 | No | No |
| 16780842 | REMOVING NODES FROM MACHINE-TRAINED NETWORK BASED ON INTRODUCTION OF PROBABILISTIC NOISE DURING TRAINING | February 2020 | September 2023 | Allow | 44 | 3 | 0 | No | No |
| 16722639 | PARTIAL ACTIVATION OF MULTIPLE PATHWAYS IN NEURAL NETWORKS | December 2019 | September 2020 | Allow | 9 | 0 | 0 | No | No |
| 16662173 | PROJECTING DATA TRENDS USING CUSTOMIZED MODELING | October 2019 | September 2020 | Allow | 10 | 1 | 0 | Yes | No |
| 16596689 | METHODS TO PREDICT FOOD COLOR AND RECOMMEND CHANGES TO ACHIEVE A TARGET FOOD COLOR | October 2019 | December 2020 | Allow | 14 | 3 | 0 | Yes | No |
| 16564176 | DESIGNATING A VOTING CLASSIFIER USING DISTRIBUTED LEARNING MACHINES | September 2019 | January 2021 | Allow | 16 | 0 | 0 | No | No |
| 16479872 | INFORMATION PROCESSING SYSTEM, INFORMATION PROCESSING METHOD, PROGRAM, AND VEHICLE FOR GENERATING A FIRST DRIVER MODEL AND GENERATING A SECOND DRIVER MODEL USING THE FIRST DRIVER MODEL | July 2019 | April 2021 | Allow | 20 | 2 | 0 | Yes | No |
| 16457792 | MACHINE LEARNING WITH MODEL FILTERING AND MODEL MIXING FOR EDGE DEVICES IN A HETEROGENEOUS ENVIRONMENT | June 2019 | September 2024 | Abandon | 60 | 3 | 0 | No | No |
| 16444117 | ADAPTIVE WEIGHTING OF SIMILARITY METRICS FOR PREDICTIVE ANALYTICS OF A COGNITIVE SYSTEM | June 2019 | December 2021 | Allow | 30 | 2 | 0 | Yes | No |
| 16439026 | NOISY NEURAL NETWORK LAYERS WITH NOISE PARAMETERS | June 2019 | June 2020 | Allow | 12 | 1 | 0 | Yes | No |
| 16468597 | METHOD AND APPARATUS FOR OPERATING AN ELECTRONIC DEVICE BASED ON A DECISION-MAKING DATA STRUCTURE USING A MACHINE LEARNING DATA STRUCTURE | June 2019 | January 2024 | Allow | 55 | 4 | 0 | Yes | No |
| 16430131 | SPECULATIVE ASYNCHRONOUS SUB-POPULATION EVOLUTIONARY COMPUTING | June 2019 | September 2020 | Allow | 15 | 0 | 0 | No | No |
| 16412766 | CREATION OF DETAILED PERCEPTUAL DESCRIPTION RATINGS FROM GENERAL PERCEPTION RATINGS | May 2019 | October 2023 | Allow | 53 | 4 | 0 | No | No |
| 16402981 | RESHAPE AND BROADCAST OPTIMIZATIONS TO AVOID UNNECESSARY DATA MOVEMENT | May 2019 | August 2022 | Allow | 40 | 2 | 0 | No | No |
| 16402687 | TRAINING ACTION SELECTION NEURAL NETWORKS USING OFF-POLICY ACTOR CRITIC REINFORCEMENT LEARNING | May 2019 | February 2020 | Allow | 10 | 1 | 0 | Yes | No |
| 16380177 | STDP WITH SYNAPTIC FATIGUE FOR LEARNING OF SPIKE-TIME-CODED PATTERNS IN THE PRESENCE OF PARALLEL RATE-CODING | April 2019 | September 2021 | Allow | 30 | 3 | 0 | Yes | No |
| 16288866 | PARTIAL ACTIVATION OF MULTIPLE PATHWAYS IN NEURAL NETWORKS | February 2019 | October 2019 | Allow | 7 | 1 | 0 | Yes | No |
| 16286323 | CONVOLUTIONAL NEURAL NETWORK USING ADAPTIVE 3D ARRAY | February 2019 | December 2023 | Allow | 57 | 4 | 0 | Yes | No |
| 16250137 | PRE-VISIT DATA ROOM CONTENT EVALUATION METHOD AND PROGRAM PRODUCT | January 2019 | January 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16239797 | DATABASE UTILIZING SPATIAL PROBABILITY MODELS FOR DATA COMPRESSION | January 2019 | June 2022 | Allow | 41 | 0 | 0 | No | No |
| 16183546 | DETECTION OF VEHICLE RIDING BEHAVIOR AND CORRESPONDING SYSTEMS AND METHODS | November 2018 | October 2022 | Abandon | 48 | 4 | 0 | Yes | No |
| 16175695 | Evaluating Content on Social Media Networks | October 2018 | September 2020 | Allow | 22 | 2 | 0 | Yes | No |
| 16173749 | GLOBALLY ASYNCHRONOUS AND LOCALLY SYNCHRONOUS (GALS) NEUROMORPHIC NETWORK | October 2018 | August 2020 | Allow | 21 | 1 | 0 | No | No |
| 16172480 | SYSTEMS AND METHODS TO USE NEURAL NETWORKS TO TRANSFORM A MODEL INTO A NEURAL NETWORK MODEL | October 2018 | June 2021 | Allow | 31 | 4 | 0 | Yes | No |
| 16162003 | SINGLE ROUTER SHARED BY A PLURALITY OF CHIP STRUCTURES | October 2018 | February 2021 | Allow | 28 | 1 | 0 | Yes | No |
| 16104097 | SYSTEM AND METHOD FOR IDENTIFYING A PREFERRED SENSOR | August 2018 | October 2022 | Allow | 50 | 1 | 0 | No | No |
| 16029697 | PREDICTING LIFE EXPECTANCY OF MACHINE PART | July 2018 | July 2022 | Allow | 48 | 2 | 0 | Yes | No |
| 15989772 | COMPUTING RESOURCE-EFFICIENT, MACHINE LEARNING-BASED TECHNIQUES FOR MEASURING AN EFFECT OF PARTICIPATION IN AN ACTIVITY | May 2018 | June 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 15942298 | DEFECT RESISTANT DESIGNS FOR LOCATION-SENSITIVE NEURAL NETWORK PROCESSOR ARRAYS | March 2018 | April 2024 | Allow | 60 | 6 | 0 | Yes | No |
| 15764005 | SEMICONDUCTOR DEVICE CONSTITUTING NEURON NETWORK HAVING THREE-DIMENSIONAL STACKED STRUCTURE OF SEMICONDUCTOR CHIPS | March 2018 | August 2021 | Allow | 41 | 1 | 0 | No | No |
| 15905237 | ISOLATION MANAGEMENT SYSTEM AND METHOD WITH DEEP LEARNING CIRCUITRY | February 2018 | October 2023 | Abandon | 60 | 3 | 0 | Yes | No |
| 15900892 | COGNITIVE DATA DISCOVERY AND MAPPING FOR DATA ONBOARDING | February 2018 | October 2022 | Allow | 56 | 4 | 0 | Yes | No |
| 15862902 | ADAPTIVE WEIGHTING OF SIMILARITY METRICS FOR PREDICTIVE ANALYTICS OF A COGNITIVE SYSTEM | January 2018 | December 2021 | Allow | 47 | 3 | 0 | Yes | No |
| 15808370 | BALANCING MEMORY CONSUMPTION OF MULTIPLE GRAPHICS PROCESSING UNITS IN DEEP LEARNING | November 2017 | October 2021 | Abandon | 47 | 2 | 0 | No | No |
| 15788238 | AVOIDING INCOMPATIBILITY BETWEEN DATA AND COMPUTING PROCESSES TO ENHANCE COMPUTER PERFORMANCE | October 2017 | September 2018 | Allow | 11 | 1 | 0 | Yes | No |
| 15721355 | UTILIZING SPATIAL PROBABILITY MODELS TO REDUCE COMPUTATIONAL RESOURCE AND MEMORY UTILIZATION | September 2017 | September 2018 | Allow | 11 | 1 | 0 | No | No |
| 15721143 | UTILIZING SPATIAL PROBABILITY MODELS TO REDUCE COMPUTATIONAL RESOURCE AND MEMORY UTILIZATION | September 2017 | June 2018 | Allow | 9 | 1 | 0 | Yes | No |
| 15721283 | SELF-LEARNING FOR AUTOMATED PLANOGRAM COMPLIANCE | September 2017 | September 2022 | Abandon | 59 | 4 | 0 | Yes | No |
| 15665824 | HEALTHCARE INFORMATION PROCESSING USING A REDUCED AUTOREGRESSIVE MODEL AND A NETWORK STRUCTURE CONSTRUCTED BASED ON TIME DELAY | August 2017 | April 2022 | Abandon | 56 | 4 | 0 | Yes | No |
| 15645380 | APPARATUS AND METHOD FOR RECOGNIZING INFORMATION OF NEUROMORPHIC DEVICE WITH SIGNAL EXTRACTING CIRCUITS FOR SELECTING OUTPUT NODES CORRESPONDING TO RECOGNITION SIGNAL HAVING MAXIMUM VALUE | July 2017 | July 2022 | Allow | 60 | 3 | 0 | No | No |
| 15645316 | SEMICONDUCTOR DEVICE USING NEURAL NETWORK TO PREDICT THE NECESSITY OF POWER SUPPLY | July 2017 | April 2023 | Abandon | 60 | 5 | 0 | No | No |
| 15643902 | NEUROMORPHIC SYSTEM AND MEMORY DEVICE HAVING STACKED SYNAPSE ELEMENTS CONNECTED IN PARALLEL | July 2017 | February 2021 | Allow | 44 | 1 | 0 | No | No |
| 15623291 | PREDICTION FILTERING USING INTERMEDIATE MODEL REPRESENTATIONS | June 2017 | March 2021 | Allow | 45 | 1 | 0 | Yes | No |
| 15623324 | HEURISTIC ALARM AND EVENT AGGREGATION AND CORRELATION METHOD FOR SERVICE PROVIDER NETWORK OPERATION | June 2017 | October 2022 | Abandon | 60 | 5 | 0 | Yes | No |
| 15620733 | Hierarchical Information Extraction Using Document Segmentation and Optical Character Recognition Correction | June 2017 | April 2020 | Allow | 34 | 1 | 0 | No | No |
| 15604773 | ARTIFICIAL NEURAL NETWORK FOR RESERVOIR COMPUTING USING STOCHASTIC LOGIC | May 2017 | September 2020 | Allow | 40 | 0 | 0 | No | No |
| 15604542 | BALANCING MEMORY CONSUMPTION OF MULTIPLE GRAPHICS PROCESSING UNITS IN DEEP LEARNING | May 2017 | January 2023 | Abandon | 60 | 2 | 0 | Yes | Yes |
| 15604310 | DETERMINING NAVIGATION PATTERNS ASSOCIATED WITH A SOCIAL NETWORKING SYSTEM TO PROVIDE CONTENT ASSOCIATED WITH A DESTINATION PAGE ON A STARTING PAGE | May 2017 | January 2021 | Allow | 44 | 2 | 0 | Yes | No |
| 15590066 | REMOTE NEURAL NETWORK PROCESSING FOR GUIDELINE IDENTIFICATION | May 2017 | March 2021 | Allow | 47 | 1 | 0 | No | No |
| 15590530 | STDP WITH SYNAPTIC FATIGUE FOR LEARNING OF SPIKE-TIME-CODED PATTERNS IN THE PRESENCE OF PARALLEL RATE-CODING | May 2017 | December 2021 | Allow | 55 | 4 | 0 | Yes | No |
| 15590439 | Real Time Detection of Cyber Threats Using Behavioral Analytics | May 2017 | March 2022 | Allow | 58 | 4 | 0 | Yes | No |
| 15449371 | GENERATING RULES BASED ON PATTERNS IN A COMMUNICATION TIME SERIES | March 2017 | October 2022 | Abandon | 60 | 6 | 0 | Yes | No |
| 15431661 | PROVIDING RECOMMENDATION TO USER COMPUTING DEVICE BASED ON CURRENT LOCATION OF FRIEND COMPUTING DEVICE | February 2017 | January 2021 | Abandon | 47 | 2 | 0 | Yes | No |
| 15420347 | SPECULATIVE ASYNCHRONOUS SUB-POPULATION EVOLUTIONARY COMPUTING | January 2017 | February 2019 | Allow | 25 | 1 | 0 | No | No |
| 15362948 | NEUROMORPHIC NETWORK COMPRISING ASYNCHRONOUS ROUTERS AND SYNCHRONOUS CORE CIRCUITS | November 2016 | August 2018 | Allow | 21 | 1 | 0 | Yes | No |
| 15241040 | CLASSIFYING SOCIAL MEDIA INPUTS VIA PARTS-OF-SPEECH FILTERING | August 2016 | June 2022 | Allow | 60 | 7 | 0 | Yes | No |
| 15209163 | INTERACTIVE FEATURE SELECTION FOR TRAINING A MACHINE LEARNING SYSTEM AND DISPLAYING DISCREPANCIES WITHIN THE CONTEXT OF THE DOCUMENT | July 2016 | January 2021 | Allow | 55 | 7 | 0 | Yes | No |
| 15108758 | INFORMATION PROCESSING METHOD AND APPARATUS | June 2016 | October 2018 | Allow | 28 | 5 | 0 | Yes | No |
| 15165059 | SYSTEM AND METHOD FOR FEATURE GENERATION OVER ARBITRARY OBJECTS | May 2016 | April 2019 | Allow | 35 | 5 | 0 | No | No |
| 15134048 | DYNAMICALLY UPDATED PREDICTIVE MODELING TO PREDICT OPERATIONAL OUTCOMES OF INTEREST | April 2016 | December 2020 | Allow | 56 | 7 | 0 | Yes | No |
| 15087341 | Incremental Model Training for Advertisement Targeting Using Streaming Data | March 2016 | February 2020 | Allow | 46 | 1 | 0 | Yes | No |
| 15079944 | PREDICTIVE MICROBIAL COMMUNITY MODELING USING A COMBINATION OF PHYLOGENY, GENOTYPING AND MACHINE LEARNING ALGORITHMS | March 2016 | January 2021 | Abandon | 57 | 2 | 0 | Yes | No |
| 15078526 | System and method for generating an optimized result set using vector based relative importance measure | March 2016 | December 2019 | Allow | 45 | 1 | 0 | No | No |
| 15077987 | PERFORMANCE MANAGEMENT USING THRESHOLDS FOR QUERIES OF A SERVICE FOR A DATABASE AS A SERVICE | March 2016 | March 2021 | Allow | 60 | 4 | 1 | Yes | No |
| 15077563 | SELF-LEARNING BASED CRAWLING AND RULE-BASED DATA MINING FOR AUTOMATIC INFORMATION EXTRACTION | March 2016 | June 2020 | Allow | 51 | 1 | 0 | No | No |
| 15077762 | Future Network Condition Predictor for Network Time Series Data Utilizing a Hidden Markov Model for Non-anomalous Data and a Gaussian Mixture Model for Anomalous Data | March 2016 | September 2020 | Allow | 54 | 2 | 0 | Yes | No |
| 15077873 | GENERATING A SPARSE FEATURE VECTOR FOR CLASSIFICATION | March 2016 | September 2021 | Allow | 60 | 6 | 0 | Yes | No |
| 15063236 | Fast Distributed Nonnegative Matrix Factorization and Completion for Big Data Analytics | March 2016 | February 2019 | Allow | 35 | 1 | 0 | Yes | No |
| 15062852 | PREDICTING ATTRIBUTE VALUES FOR USER SEGMENTATION BY DETERMINING SUGGESTIVE ATTRIBUTE VALUES | March 2016 | May 2020 | Allow | 50 | 1 | 0 | No | No |
| 14982382 | System and Method for Defining and Calibrating a Sequential Decision Problem using Historical Data | December 2015 | September 2019 | Allow | 44 | 1 | 0 | Yes | No |
| 14974817 | GENERATION AND HANDLING OF SITUATION DEFINITIONS BASED ON KNOWLEDGE GRAPHS | December 2015 | April 2021 | Abandon | 60 | 5 | 0 | Yes | No |
| 14970726 | INTERPRETATION OF A DATASET FOR CO-OCCURRING ITEMSETS USING A COVER RULE AND CLUSTERING | December 2015 | December 2019 | Allow | 48 | 2 | 0 | No | No |
| 14960523 | SPACE-EFFICIENT DYNAMIC ADDRESSING IN VERY LARGE SPARSE NETWORKS | December 2015 | January 2020 | Allow | 50 | 1 | 0 | No | No |
| 14879225 | LATENCY CONSTRAINTS FOR ACOUSTIC MODELING | October 2015 | March 2020 | Allow | 54 | 3 | 0 | Yes | No |
| 14873422 | GENERATION APPARATUS, SELECTION APPARATUS, GENERATION METHOD, SELECTION METHOD AND PROGRAM | October 2015 | April 2023 | Abandon | 60 | 7 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner BEJCEK II, ROBERT H.
With a 57.1% 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.
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, 38.5% of applications that filed an appeal were subsequently allowed. This appeal filing benefit rate is above the USPTO average, suggesting that filing an appeal can be an effective strategy for prompting reconsideration.
✓ 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 BEJCEK II, ROBERT H works in Art Unit 2123 and has examined 138 patent applications in our dataset. With an allowance rate of 71.7%, this examiner has a below-average tendency to allow applications. Applications typically reach final disposition in approximately 47 months.
Examiner BEJCEK II, ROBERT H's allowance rate of 71.7% places them in the 27% percentile among all USPTO examiners. This examiner has a below-average tendency to allow applications.
On average, applications examined by BEJCEK II, ROBERT H receive 2.57 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.
The median time to disposition (half-life) for applications examined by BEJCEK II, ROBERT H 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.
Conducting an examiner interview provides a +1.4% benefit to allowance rate for applications examined by BEJCEK II, ROBERT H. This interview benefit is in the 16% percentile among all examiners. Note: Interviews show limited statistical benefit with this examiner compared to others, though they may still be valuable for clarifying issues.
When applicants file an RCE with this examiner, 21.2% of applications are subsequently allowed. This success rate is in the 16% percentile among all examiners. Strategic Insight: RCEs show lower effectiveness with this examiner compared to others. Consider whether a continuation application might be more strategic, especially if you need to add new matter or significantly broaden claims.
This examiner enters after-final amendments leading to allowance in 17.8% 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.
When applicants request a pre-appeal conference (PAC) with this examiner, 100.0% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 69% percentile among all examiners. Strategic Recommendation: Pre-appeal conferences show above-average effectiveness with this examiner. If you have strong arguments, a PAC request may result in favorable reconsideration.
This examiner withdraws rejections or reopens prosecution in 50.0% of appeals filed. This is in the 11% percentile among all examiners. Of these withdrawals, 28.6% 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.
When applicants file petitions regarding this examiner's actions, 15.0% are granted (fully or in part). This grant rate is in the 8% 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'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.
Based on the statistical analysis of this examiner's prosecution patterns, here are tailored strategic recommendations:
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.