Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 17112329 | ULTRA-LOW POWER KEYWORD SPOTTING NEURAL NETWORK CIRCUIT | December 2020 | March 2025 | Abandon | 52 | 2 | 0 | No | No |
| 17090128 | MACHINE LEARNING DEVICE, POWER CONSUMPTION PREDICTION DEVICE, AND CONTROL DEVICE | November 2020 | June 2025 | Abandon | 55 | 4 | 0 | No | No |
| 17073602 | NEURAL NETWORK MODEL COMPRESSION WITH QUANTIZABILITY REGULARIZATION | October 2020 | March 2025 | Allow | 53 | 3 | 0 | Yes | No |
| 17072628 | SECURE DATA PROCESSING USING A FIRST SYSTEM AND A SECOND SYSTEM | October 2020 | June 2025 | Abandon | 56 | 4 | 0 | Yes | No |
| 17033132 | POWER-EFFICIENT HYBRID TRAVERSAL APPARATUS AND METHOD FOR CONVOLUTIONAL NEURAL NETWORK ACCELERATOR ARCHITECTURE | September 2020 | April 2025 | Allow | 55 | 3 | 0 | Yes | No |
| 17032248 | EFFICIENT WEIGHT CLIPPING FOR NEURAL NETWORKS | September 2020 | May 2025 | Allow | 56 | 3 | 0 | Yes | No |
| 17030299 | LEARNING WEIGHTED-AVERAGE NEIGHBOR EMBEDDINGS | September 2020 | January 2024 | Abandon | 40 | 1 | 0 | No | No |
| 17017751 | INFORMATION PROCESSING APPARATUS AND INFORMATION PROCESSING METHOD | September 2020 | June 2024 | Abandon | 45 | 3 | 0 | No | No |
| 17016184 | FORCING WEIGHTS OF TRANSFORMER MODEL LAYERS | September 2020 | April 2025 | Allow | 55 | 5 | 0 | Yes | No |
| 17014475 | Neural Network Approach for Identifying a Radar Signal in the Presence of Noise | September 2020 | August 2024 | Allow | 47 | 3 | 0 | Yes | No |
| 17011569 | ANTISYMMETRIC NEURAL NETWORKS | September 2020 | November 2024 | Allow | 50 | 3 | 0 | No | No |
| 17009483 | ACCELERATED CONVOLUTION OF NEURAL NETWORKS | September 2020 | September 2024 | Allow | 48 | 3 | 0 | Yes | No |
| 17002978 | METHOD FOR ANALYZING CLASS SIMILARITIES IN A MACHINE LEARNING MODEL | August 2020 | January 2025 | Abandon | 53 | 4 | 0 | No | No |
| 17003870 | NEURAL NETWORK BASED MASK SYNTHESIS FOR INTEGRATED CIRCUITS | August 2020 | December 2024 | Allow | 52 | 3 | 0 | Yes | No |
| 17002035 | STORAGE CONTROLLERS, STORAGE SYSTEMS, AND METHODS OF OPERATING THE SAME | August 2020 | October 2024 | Abandon | 49 | 3 | 0 | Yes | No |
| 17002518 | Compressing and Decompressing Data for Language Models | August 2020 | August 2024 | Allow | 48 | 3 | 0 | Yes | No |
| 16986273 | MULTI-FUNCTION CALCULATOR AND OPERATION METHOD THEREOF | August 2020 | November 2023 | Abandon | 39 | 1 | 0 | No | No |
| 16985415 | DEEP NEURAL NETWORK ACCELERATOR USING HETEROGENEOUS MULTIPLY-ACCUMULATE UNIT | August 2020 | September 2024 | Allow | 49 | 4 | 0 | Yes | No |
| 16984331 | CLASS-DEPENDENT MACHINE LEARNING BASED INFERENCES | August 2020 | June 2025 | Abandon | 58 | 4 | 0 | Yes | No |
| 16984648 | FEATURE EQUIVALENCE AND DOCUMENT ABNORMALITY THRESHOLD DETERMINATION | August 2020 | August 2024 | Allow | 49 | 2 | 0 | Yes | No |
| 16940199 | DROPOUT LAYER IN A NEURAL NETWORK PROCESSOR | July 2020 | July 2024 | Allow | 48 | 3 | 0 | Yes | No |
| 16933859 | CONFIGURABLE PROCESSOR FOR IMPLEMENTING CONVOLUTION NEURAL NETWORKS | July 2020 | October 2024 | Allow | 51 | 3 | 0 | Yes | No |
| 16962830 | NEURAL NETWORK HAVING REDUCED MEMORY USAGE | July 2020 | June 2025 | Abandon | 59 | 4 | 1 | No | No |
| 16931228 | SYSTEMS AND METHODS FOR PARTIALLY SUPERVISED ONLINE ACTION DETECTION IN UNTRIMMED VIDEOS | July 2020 | January 2025 | Allow | 54 | 3 | 0 | Yes | No |
| 16836110 | Hybrid Filter Banks for Artificial Neural Networks | March 2020 | April 2024 | Allow | 49 | 4 | 0 | Yes | No |
| 16832601 | METHODS AND APPARATUS FOR DYNAMIC BATCHING OF DATA FOR NEURAL NETWORK WORKLOADS | March 2020 | June 2024 | Allow | 51 | 3 | 0 | Yes | No |
| 16807841 | ARITHMETIC OPERATION CIRCUIT | March 2020 | April 2023 | Allow | 37 | 2 | 0 | Yes | No |
| 16796039 | MACHINE LEARNING MODEL AND ASSOCIATED METHODS THEREOF FOR PROVIDING AUTOMATED SUPPORT | February 2020 | December 2023 | Allow | 46 | 4 | 0 | Yes | No |
| 16793832 | DOMAIN-ADAPTED CLASSIFIER GENERATION | February 2020 | June 2024 | Abandon | 52 | 3 | 0 | Yes | No |
| 16720318 | Autoencoder Neural Network for Signal Integrity Analysis of Interconnect Systems | December 2019 | August 2023 | Abandon | 44 | 1 | 0 | No | No |
| 16714669 | LAYER FUSION IN NEURAL NETWORK PROCESSING | December 2019 | November 2024 | Allow | 59 | 3 | 0 | No | No |
| 16698236 | HIERARCHICAL PARTITIONING OF OPERATORS | November 2019 | August 2024 | Allow | 57 | 5 | 0 | Yes | No |
| 16681655 | MOTIF SEARCH AND PREDICTION IN TEMPORAL TRADING SYSTEMS | November 2019 | March 2025 | Abandon | 60 | 4 | 0 | Yes | Yes |
| 16660171 | THERMOSTAT AND METHOD USING A NEURAL NETWORK TO ADJUST TEMPERATURE MEASUREMENTS | October 2019 | June 2023 | Abandon | 44 | 1 | 0 | No | No |
| 16573032 | INTEGRATED CIRCUIT FOR CONVOLUTION CALCULATION IN DEEP NEURAL NETWORK AND METHOD THEREOF | September 2019 | May 2023 | Abandon | 44 | 2 | 0 | No | No |
| 16558314 | HARDWARE ARCHITECTURE AND PROCESSING METHOD FOR NEURAL NETWORK ACTIVATION FUNCTION | September 2019 | January 2023 | Abandon | 40 | 1 | 0 | No | No |
| 16555254 | COMPUTER-READABLE RECORDING MEDIUM, ABNORMALITY DETERMINATION METHOD, AND ABNORMALITY DETERMINATION DEVICE | August 2019 | March 2025 | Allow | 60 | 6 | 0 | No | No |
| 16550498 | METHOD OF ACCELERATING TRAINING PROCESS OF NEURAL NETWORK AND NEURAL NETWORK DEVICE THEREOF | August 2019 | August 2024 | Allow | 59 | 7 | 0 | Yes | No |
| 16550190 | NEURAL NETWORK GENERATOR | August 2019 | November 2024 | Abandon | 60 | 6 | 0 | Yes | Yes |
| 16516838 | INFORMATION PROCESSING APPARATUS AND INFORMATION PROCESSING METHOD | July 2019 | November 2022 | Abandon | 40 | 1 | 0 | No | No |
| 16515159 | RECURRENT AUTOENCODER FOR CHROMATIN 3D STRUCTURE PREDICTION | July 2019 | June 2022 | Allow | 35 | 1 | 0 | Yes | No |
| 16511560 | Neural Network for Performing Operations on a Portion of Data Elements | July 2019 | September 2023 | Abandon | 50 | 4 | 0 | Yes | No |
| 16506479 | SYSTEMS AND METHODS FOR DISTRIBUTING A NEURAL NETWORK ACROSS MULTIPLE COMPUTING DEVICES | July 2019 | February 2023 | Allow | 43 | 2 | 0 | Yes | No |
| 16457480 | MACHINE GENERATED CONTENT NAMING IN AN INFORMATION CENTRIC NETWORK | June 2019 | December 2022 | Allow | 42 | 3 | 0 | Yes | No |
| 16425403 | DYNAMIC PRECISION SCALING AT EPOCH GRANULARITY IN NEURAL NETWORKS | May 2019 | August 2024 | Allow | 60 | 4 | 0 | Yes | Yes |
| 16425012 | Training Method, Apparatus, and Chip for Neural Network Model | May 2019 | January 2023 | Abandon | 43 | 2 | 0 | Yes | No |
| 16424429 | Memory as a Service for Artificial Neural Network (ANN) Applications | May 2019 | May 2025 | Allow | 60 | 6 | 0 | No | No |
| 16415500 | NEURAL NETWORK INCLUDING A NEURAL NETWORK PROJECTION LAYER CONFIGURED FOR A SUMMING PARAMETER | May 2019 | March 2023 | Abandon | 46 | 2 | 0 | No | No |
| 16402204 | NEAR-INFRARED SPECTROSCOPY (NIR) BASED GLUCOSE PREDICTION USING DEEP LEARNING | May 2019 | October 2022 | Abandon | 42 | 2 | 0 | No | No |
| 16345551 | LEARNING ALGORITHMS FOR OSCILLATORY MEMRISTIVE NEUROMORPHIC CIRCUITS | April 2019 | July 2024 | Allow | 60 | 6 | 0 | Yes | Yes |
| 16362236 | IDENTIFYING BIOSYNTHETIC GENE CLUSTERS | March 2019 | February 2024 | Allow | 58 | 2 | 0 | Yes | Yes |
| 16357139 | MIXED PRECISION TRAINING OF AN ARTIFICIAL NEURAL NETWORK | March 2019 | April 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16299828 | Generative Adversarial Network Based Audio Restoration | March 2019 | April 2024 | Allow | 60 | 2 | 0 | Yes | Yes |
| 16286652 | LEARNING METHOD, LEARNING DEVICE, AND IMAGE RECOGNITION SYSTEM | February 2019 | January 2025 | Abandon | 60 | 4 | 0 | Yes | Yes |
| 16284322 | ANSWERING COGNITIVE QUERIES FROM SENSOR INPUT SIGNALS | February 2019 | January 2025 | Allow | 60 | 5 | 0 | Yes | Yes |
| 16280065 | ARTIFICIAL NEURAL NETWORK | February 2019 | June 2023 | Abandon | 52 | 4 | 0 | Yes | No |
| 16275813 | Systems and Methods for Improved Generalization, Reproducibility, and Stabilization of Neural Networks via Error Control Code Constraints | February 2019 | August 2023 | Abandon | 54 | 4 | 0 | Yes | No |
| 16325259 | ARRAY DEVICE INCLUDING NEUROMORPHIC ELEMENT AND NEURAL NETWORK SYSTEM | February 2019 | August 2023 | Abandon | 54 | 2 | 0 | Yes | Yes |
| 16270697 | TRAINING OPTIMIZATION FOR NEURAL NETWORKS WITH BATCH NORM LAYERS | February 2019 | February 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16265906 | METHOD AND SYSTEM FOR INCORPORATING REGRESSION INTO STACKED AUTO ENCODER (SAE) | February 2019 | December 2022 | Allow | 47 | 3 | 0 | Yes | No |
| 15929093 | DEEP NEURAL NETWORK ACCELERATOR WITH FINE-GRAINED PARALLELISM DISCOVERY | January 2019 | March 2024 | Allow | 60 | 4 | 0 | No | Yes |
| 16244208 | Grading And Unlearning Implementations For Neural Network Based Course Of Action Selection | January 2019 | November 2022 | Abandon | 46 | 1 | 0 | No | No |
| 16239046 | NEURAL NETWORK PROCESSING UNIT INCLUDING APPROXIMATE MULTIPLIER AND SYSTEM ON CHIP INCLUDING THE SAME | January 2019 | October 2024 | Allow | 60 | 5 | 0 | Yes | No |
| 16234617 | METHOD AND SYSTEM FOR DISTRIBUTED DEEP LEARNING | December 2018 | October 2022 | Abandon | 46 | 1 | 0 | No | No |
| 16225034 | META-LEARNING SYSTEM | December 2018 | August 2023 | Allow | 56 | 3 | 0 | Yes | No |
| 16217731 | ADAPTATION OF MEMORY CELL STRUCTURE AND FABRICATION PROCESS TO BINARY DATA ASYMMETRY AND BIT-INVERSION TOLERANCE ASYMMETRY IN DEEP LEARNING MODELS | December 2018 | June 2022 | Abandon | 42 | 1 | 0 | No | No |
| 16216425 | ENVIRONMENT CONTROLLER AND METHOD FOR IMPROVING PREDICTIVE MODELS USED FOR CONTROLLING A TEMPERATURE IN AN AREA | December 2018 | January 2023 | Abandon | 49 | 3 | 0 | No | No |
| 16212586 | NON-VOLATILE MEMORY DIE WITH DEEP LEARNING NEURAL NETWORK | December 2018 | April 2025 | Allow | 60 | 5 | 0 | Yes | Yes |
| 16204599 | APPARATUS FOR PROCESSING CONVOLUTIONAL NEURAL NETWORK USING SYSTOLIC ARRAY AND METHOD THEREOF | November 2018 | January 2023 | Abandon | 50 | 1 | 0 | No | No |
| 16201062 | ESTIMATION METHOD AND APPARATUS | November 2018 | April 2023 | Abandon | 53 | 4 | 0 | No | No |
| 16184180 | WARPING SEQUENCE DATA FOR LEARNING IN NEURAL NETWORKS | November 2018 | June 2024 | Abandon | 60 | 7 | 0 | Yes | No |
| 16180250 | Hierarchical Mantissa Bit Length Selection for Hardware Implementation of Deep Neural Network | November 2018 | August 2024 | Allow | 60 | 2 | 0 | No | Yes |
| 16181168 | METHODS AND SYSTEMS FOR AUTOMATICALLY CREATING STATISTICALLY ACCURATE ERGONOMICS DATA | November 2018 | December 2024 | Allow | 60 | 8 | 0 | Yes | No |
| 16176961 | NEURAL QUESTION ANSWERING SYSTEM | October 2018 | April 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 16177017 | Method to Map Convolutional Layers of Deep Neural Network on a Plurality of Processing Elements with SIMD Execution Units, Private Memories, and Connected as a 2D Systolic Processor Array | October 2018 | August 2024 | Allow | 60 | 6 | 0 | Yes | No |
| 16176091 | Signal and/or spectrum analyzer device and method of signal matching | October 2018 | July 2024 | Allow | 60 | 6 | 0 | Yes | No |
| 16175525 | SYSTEMS AND METHODS FOR IDENTIFYING DOCUMENTS WITH TOPIC VECTORS | October 2018 | November 2024 | Abandon | 60 | 7 | 0 | Yes | No |
| 16172758 | ASSURANCE OF POLICY BASED ALERTING | October 2018 | April 2025 | Allow | 60 | 8 | 0 | Yes | No |
| 16051034 | SCHEDULER FOR MAPPING NEURAL NETWORKS ONTO AN ARRAY OF NEURAL CORES IN AN INFERENCE PROCESSING UNIT | July 2018 | April 2025 | Allow | 60 | 8 | 0 | Yes | No |
| 16071402 | NEURAL NETWORK COMPUTING METHOD, SYSTEM AND DEVICE THEREFOR | July 2018 | June 2023 | Abandon | 59 | 4 | 0 | Yes | No |
| 16008949 | PARALLEL COMPUTATIONAL ARCHITECTURE WITH RECONFIGURABLE CORE-LEVEL AND VECTOR-LEVEL PARALLELISM | June 2018 | August 2023 | Allow | 60 | 4 | 0 | Yes | No |
| 15985463 | FEEDFORWARD GENERATIVE NEURAL NETWORKS | May 2018 | November 2023 | Allow | 60 | 4 | 1 | Yes | No |
| 15972048 | QUANTIZATION FOR DNN ACCELERATORS | May 2018 | May 2023 | Abandon | 60 | 4 | 0 | Yes | No |
| 15943872 | Neural Network Processor Incorporating Inter-Device Connectivity | April 2018 | April 2023 | Allow | 60 | 3 | 0 | Yes | No |
| 15934341 | ELECTRONIC APPARATUS FOR OPERATING MACHINE LEARNING AND METHOD FOR OPERATING MACHINE LEARNING | March 2018 | September 2023 | Allow | 60 | 5 | 0 | Yes | No |
| 15933037 | INTELLIGENT VISUAL OBJECT MANAGEMENT SYSTEM | March 2018 | December 2022 | Abandon | 57 | 1 | 0 | No | No |
| 15921634 | TRAINING NETWORK TO MAXIMIZE TRUE POSITIVE RATE AT LOW FALSE POSITIVE RATE | March 2018 | July 2024 | Allow | 60 | 4 | 0 | Yes | Yes |
| 15906096 | ADJUSTING A CLASSIFICATION MODEL BASED ON ADVERSARIAL PREDICTIONS | February 2018 | January 2024 | Abandon | 60 | 4 | 0 | Yes | No |
| 15898566 | Highly Efficient Convolutional Neural Networks | February 2018 | March 2023 | Allow | 60 | 2 | 0 | Yes | No |
| 15892246 | PRIVATIZED MACHINE LEARNING USING GENERATIVE ADVERSARIAL NETWORKS | February 2018 | August 2023 | Abandon | 60 | 3 | 0 | Yes | No |
| 15888800 | METHODS FOR ADAPTIVE INFORMATION EXTRACTION THROUGH ADAPTIVE LEARNING OF HUMAN ANNOTATORS AND DEVICES THEREOF | February 2018 | July 2024 | Abandon | 60 | 5 | 0 | No | No |
| 15869364 | INTERACTION ANALYSIS AND PREDICTION BASED NEURAL NETWORKING | January 2018 | October 2022 | Abandon | 57 | 2 | 0 | Yes | No |
| 15869502 | FINE-GRAIN COMPUTE COMMUNICATION EXECUTION FOR DEEP LEARNING FRAMEWORKS VIA HARDWARE ACCELERATED POINT-TO-POINT PRIMITIVES | January 2018 | August 2024 | Allow | 60 | 6 | 1 | Yes | No |
| 15868392 | SYNAPSE SYSTEM AND SYNAPSE METHOD TO REALIZE STDP OPERATION | January 2018 | January 2023 | Allow | 60 | 4 | 0 | No | No |
| 15858014 | COMPUTE OPTIMIZATION MECHANISM FOR DEEP NEURAL NETWORKS | December 2017 | May 2024 | Allow | 60 | 5 | 1 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner BOSTWICK, SIDNEY VINCENT.
With a 85.7% 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.
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, 46.7% 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.
✓ 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 BOSTWICK, SIDNEY VINCENT works in Art Unit 2124 and has examined 95 patent applications in our dataset. With an allowance rate of 53.7%, this examiner allows applications at a lower rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 56 months.
Examiner BOSTWICK, SIDNEY VINCENT's allowance rate of 53.7% places them in the 16% percentile among all USPTO examiners. This examiner is less likely to allow applications than most examiners at the USPTO.
On average, applications examined by BOSTWICK, SIDNEY VINCENT receive 3.55 office actions before reaching final disposition. This places the examiner in the 92% 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 BOSTWICK, SIDNEY VINCENT is 56 months. This places the examiner in the 3% percentile for prosecution speed. Applications take longer to reach final disposition with this examiner compared to most others.
Conducting an examiner interview provides a +42.5% benefit to allowance rate for applications examined by BOSTWICK, SIDNEY VINCENT. This interview benefit is in the 88% 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.
When applicants file an RCE with this examiner, 19.0% of applications are subsequently allowed. This success rate is in the 21% 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 4.3% of cases where such amendments are filed. This entry rate is in the 6% 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, 25.0% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 29% percentile among all examiners. Note: Pre-appeal conferences show below-average success with this examiner. Consider whether your arguments are strong enough to warrant a PAC request.
This examiner withdraws rejections or reopens prosecution in 58.8% of appeals filed. This is in the 33% percentile among all examiners. Of these withdrawals, 10.0% occur early in the appeal process (after Notice of Appeal but before Appeal Brief). Strategic Insight: This examiner shows below-average willingness to reconsider rejections during appeals. Be prepared to fully prosecute appeals if filed.
When applicants file petitions regarding this examiner's actions, 85.7% are granted (fully or in part). This grant rate is in the 86% percentile among all examiners. Strategic Note: Petitions are frequently granted regarding this examiner's actions compared to other examiners. Per MPEP § 1002.02(c), various examiner actions are petitionable to the Technology Center Director, including prematureness of final rejection, refusal to enter amendments, and requirement for information. If you believe an examiner action is improper, consider filing a petition.
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.
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.