Detailed information about the 100 most recent patent applications.
| Application Number | Title | Filing Date | Disposal Date | Disposition | Time (months) | Office Actions | Restrictions | Interview | Appeal |
|---|---|---|---|---|---|---|---|---|---|
| 17133673 | COMPUTER-IMPLEMENTED METHOD FOR TEXT CONVERSION, COMPUTER DEVICE, AND NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM | December 2020 | March 2023 | Allow | 27 | 0 | 0 | No | No |
| 17125991 | METHOD AND SYSTEM FOR IMPROVING PERFORMANCE OF TEXT SUMMARIZATION | December 2020 | March 2023 | Allow | 27 | 0 | 0 | No | No |
| 17109267 | COMFORT NOISE GENERATION | December 2020 | April 2022 | Allow | 17 | 1 | 0 | No | No |
| 17106254 | IDENTIFYING ROUTINE COMMUNICATION CONTENT | November 2020 | April 2023 | Allow | 28 | 1 | 0 | No | No |
| 16950158 | DETECTION OF TV STATE USING SUB-AUDIBLE SIGNAL | November 2020 | May 2022 | Allow | 17 | 1 | 0 | No | No |
| 17087423 | VOICE CONTROL OF A MEDIA PLAYBACK SYSTEM | November 2020 | January 2021 | Allow | 2 | 1 | 0 | No | No |
| 17069978 | MULTI-WORD PHRASE BASED ANALYSIS OF ELECTRONIC DOCUMENTS | October 2020 | April 2023 | Allow | 30 | 2 | 0 | Yes | No |
| 17038924 | SELECTIVELY GENERATING WORD VECTOR AND PARAGRAPH VECTOR REPRESENTATIONS OF FIELDS FOR MACHINE LEARNING | September 2020 | March 2022 | Allow | 18 | 1 | 0 | Yes | No |
| 17038506 | CONTEXTUAL NATURAL LANGUAGE UNDERSTANDING FOR CONVERSATIONAL AGENTS | September 2020 | September 2022 | Allow | 23 | 1 | 0 | Yes | No |
| 17037128 | SIGNAL PROCESSING METHODS AND APPARATUSES FOR ENHANCING SOUND QUALITY | September 2020 | March 2022 | Allow | 17 | 1 | 0 | No | No |
| 17036561 | METHOD, DEVICE AND STORAGE MEDIUM FOR PREDICTING PUNCTUATION IN TEXT | September 2020 | October 2021 | Allow | 13 | 0 | 0 | No | No |
| 17043433 | CREATED TEXT EVALUATION DEVICE | September 2020 | July 2023 | Allow | 34 | 1 | 0 | No | No |
| 17028743 | VECTORIZATION DEVICE AND LANGUAGE PROCESSING METHOD | September 2020 | October 2022 | Abandon | 25 | 2 | 0 | No | No |
| 17025539 | TEXT CLASSIFICATION USING MODELS WITH COMPLEMENTARY GRANULARITY AND ACCURACY | September 2020 | May 2022 | Allow | 20 | 0 | 0 | No | No |
| 17024428 | LANGUAGE AUTODETECTION FROM NON-CHARACTER SUB-TOKEN SIGNALS | September 2020 | February 2022 | Allow | 17 | 0 | 0 | No | No |
| 17021644 | METHOD AND DEVICE FOR GENERATING STATEMENT | September 2020 | February 2021 | Allow | 5 | 1 | 0 | No | No |
| 17020166 | METHOD AND APPARATUS FOR PERFORMING WORD SEGMENTATION ON TEXT, DEVICE, AND MEDIUM | September 2020 | June 2022 | Allow | 21 | 2 | 0 | No | No |
| 17013705 | MAN-MACHINE CONVERSATION METHOD, ELECTRONIC DEVICE, AND COMPUTER-READABLE MEDIUM | September 2020 | June 2023 | Allow | 33 | 3 | 0 | Yes | No |
| 17008572 | SYSTEMS AND METHODS FOR ENHANCED REVIEW COMPREHENSION USING DOMAIN-SPECIFIC KNOWLEDGEBASES | August 2020 | November 2022 | Allow | 27 | 0 | 0 | No | No |
| 17004115 | METHOD FOR PREVENTING INTELLIGIBLE VOICE RECORDINGS | August 2020 | September 2021 | Allow | 13 | 0 | 0 | No | No |
| 16969420 | DATA ENTRY FEATURE FOR INFORMATION TRACKING SYSTEM | August 2020 | May 2023 | Allow | 34 | 0 | 0 | No | No |
| 16987043 | Intent Identification for Agent Matching by Assistant Systems | August 2020 | August 2022 | Allow | 24 | 3 | 0 | Yes | No |
| 16938024 | LAYERED CODING FOR COMPRESSED SOUND OR SOUND FIELD REPRESENTATIONS | July 2020 | September 2021 | Allow | 14 | 0 | 0 | No | No |
| 16920916 | AUTOMATED DOCUMENT REVIEW SYSTEM COMBINING DETERMINISTIC AND MACHINE LEARNING ALGORITHMS FOR LEGAL DOCUMENT REVIEW | July 2020 | July 2022 | Allow | 24 | 1 | 0 | No | No |
| 16904785 | TRAINING POLICY NEURAL NETWORKS USING PATH CONSISTENCY LEARNING | June 2020 | May 2022 | Allow | 23 | 0 | 0 | No | No |
| 16899917 | INFORMATION UNIQUENESS DETERMINATION FOR NATURAL LANGUAGE DATA | June 2020 | March 2023 | Allow | 33 | 1 | 0 | No | No |
| 16896426 | VOICE INTERACTION AT A PRIMARY DEVICE TO ACCESS CALL FUNCTIONALITY OF A COMPANION DEVICE | June 2020 | November 2020 | Allow | 5 | 0 | 0 | No | No |
| 16891705 | BILINGUAL CORPORA SCREENING METHOD AND APPARATUS, AND STORAGE MEDIUM | June 2020 | March 2022 | Allow | 21 | 1 | 0 | No | No |
| 16890045 | METHOD AND APPARATUS WITH NEURAL NETWORK PARAMETER QUANTIZATION | June 2020 | August 2022 | Allow | 26 | 0 | 0 | No | No |
| 16885516 | MACHINE TRANSLATION METHOD, MACHINE TRANSLATION SYSTEM, PROGRAM, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM | May 2020 | July 2022 | Allow | 26 | 1 | 0 | No | No |
| 16883816 | SYSTEMS AND METHODS FOR USING DYNAMIC REFERENCE GRAPHS TO ACCURATELY ALIGN SEQUENCE READS | May 2020 | November 2021 | Allow | 18 | 1 | 0 | Yes | No |
| 16881783 | METHOD FOR PROVIDING CHATTING SERVICE WITH CHATBOT ASSISTED BY HUMAN COUNSELOR | May 2020 | March 2021 | Allow | 10 | 1 | 0 | No | No |
| 16874855 | ELECTRONIC APPARATUS AND CONTROLLING METHOD THEREOF | May 2020 | January 2023 | Allow | 32 | 1 | 0 | No | No |
| 15931408 | COMPUTATIONALLY EFFICIENT EXPRESSIVE OUTPUT LAYERS FOR NEURAL NETWORKS | May 2020 | June 2022 | Allow | 25 | 0 | 0 | No | No |
| 16869155 | Hybrid Language Detection Model | May 2020 | March 2022 | Allow | 22 | 0 | 0 | No | No |
| 16867032 | USING ENVIRONMENT AND USER DATA TO DELIVER ADVERTISEMENTS TARGETED TO USER INTERESTS, E.G. BASED ON A SINGLE COMMAND | May 2020 | March 2021 | Allow | 10 | 1 | 0 | Yes | No |
| 16861883 | TECHNOLOGY CONTROL EVALUATION PROGRAM | April 2020 | August 2022 | Allow | 27 | 0 | 0 | No | No |
| 16861295 | TEXT SEQUENCE SEGMENTATION METHOD, APPARATUS AND DEVICE, AND STORAGE MEDIUM THEREOF | April 2020 | June 2021 | Allow | 14 | 0 | 0 | No | No |
| 16860977 | Spatial-Temporal Reasoning Through Pretrained Language Models for Video-Grounded Dialogues | April 2020 | June 2022 | Allow | 26 | 0 | 0 | No | No |
| 16759519 | DISTRIBUTIONAL REINFORCEMENT LEARNING FOR CONTINUOUS CONTROL TASKS | April 2020 | May 2022 | Allow | 25 | 0 | 0 | No | No |
| 16859429 | SIGNAL ENCODING METHOD AND APPARATUS, AND SIGNAL DECODING METHOD AND APPARATUS | April 2020 | September 2020 | Allow | 5 | 0 | 0 | No | No |
| 16854833 | Labeled Knowledge Graph Based Priming Of A Natural Language Model Providing User Access To Programmatic Functionality Through Natural Language Input | April 2020 | May 2021 | Allow | 13 | 0 | 0 | No | No |
| 16852791 | CONVERSATION CONTEXTUAL LEARNING-BASED AUTOMATIC TRANSLATION DEVICE AND METHOD | April 2020 | March 2023 | Abandon | 35 | 1 | 0 | No | No |
| 16848748 | TOKEN-POSITION HANDLING FOR SEQUENCE BASED NEURAL NETWORKS | April 2020 | September 2022 | Allow | 29 | 1 | 0 | Yes | No |
| 16844362 | METHOD AND APPARATUS FOR PROCESSING SEQUENCE | April 2020 | October 2021 | Allow | 18 | 0 | 0 | No | No |
| 16839950 | INTELLIGENT AUGMENTATION OF WORD REPRESENTATION VIA CHARACTER SHAPE EMBEDDINGS IN A NEURAL NETWORK | April 2020 | October 2021 | Allow | 19 | 0 | 0 | No | No |
| 16838474 | COGNITIVE ANALYSIS TO GENERATE AND EVALUATE IMPLEMENTATION PLANS | April 2020 | July 2022 | Allow | 27 | 0 | 0 | No | No |
| 16832597 | AUDIO ENCODING/DECODING BASED ON AN EFFICIENT REPRESENTATION OF AUTO-REGRESSIVE COEFFICIENTS | March 2020 | January 2021 | Allow | 10 | 1 | 0 | No | No |
| 16825650 | DATA AUGMENTATION BY DYNAMIC WORD REPLACEMENT | March 2020 | January 2023 | Allow | 34 | 2 | 0 | Yes | No |
| 16824902 | MODEL LOCALIZATION FOR DATA ANALYTICS AND BUSINESS INTELLIGENCE | March 2020 | February 2022 | Allow | 23 | 0 | 0 | No | No |
| 16815747 | APPARATUS, SYSTEM, AND METHOD FOR GENERATING VOICE RECOGNITION GUIDE BY TRANSMITTING VOICE SIGNAL DATA TO A VOICE RECOGNITION SERVER WHICH CONTAINS VOICE RECOGNITION GUIDE INFORMATION TO SEND BACK TO THE VOICE RECOGNITION APPARATUS | March 2020 | January 2021 | Allow | 10 | 1 | 0 | No | No |
| 16803910 | Converting Text-Based Requirements to A Live Prototype | February 2020 | November 2021 | Allow | 21 | 0 | 0 | No | No |
| 16798711 | INTERACTIVE CONTROL SYSTEM, INTERACTIVE CONTROL METHOD, AND COMPUTER PROGRAM PRODUCT | February 2020 | July 2023 | Allow | 40 | 2 | 0 | Yes | No |
| 16798277 | METHOD AND SYSTEM OF CREATING AND SUMMARIZING UNSTRUCTURED NATURAL LANGUAGE SENTENCE CLUSTERS FOR EFFICIENT TAGGING | February 2020 | February 2022 | Allow | 24 | 0 | 0 | No | No |
| 16797416 | ENCODER-DECODER ARCHITECTURE FOR GENERATING NATURAL LANGUAGE DATA BASED ON TELEMETRY DATA | February 2020 | June 2022 | Allow | 27 | 0 | 0 | No | No |
| 16795443 | FINANCIAL DOCUMENT TEXT CONVERSION TO COMPUTER READABLE OPERATIONS | February 2020 | July 2023 | Allow | 40 | 3 | 0 | No | No |
| 16640104 | UTTERANCE SENTENCE GENERATION SYSTEM AND UTTERANCE SENTENCE GENERATION PROGRAM | February 2020 | April 2021 | Allow | 14 | 0 | 0 | No | No |
| 16786327 | CUSTOMIZING ACTIONS BASED ON CONTEXTUAL DATA AND VOICE-BASED INPUTS | February 2020 | November 2020 | Allow | 9 | 0 | 0 | No | No |
| 16634656 | RECOMMENDER AND REMEDIATION SYSTEM FOR ENTERPRISE SERVICE MANAGEMENT | January 2020 | June 2022 | Allow | 29 | 1 | 0 | No | No |
| 16740629 | IMAGE PROCESSING DEVICE FOR RECEIVING AN OPERATION INSTRUCTION BY A VOICE, METHOD FOR CONTROLLING IMAGE PROCESSING DEVICE, AND PROGRAM | January 2020 | October 2020 | Allow | 9 | 0 | 0 | No | No |
| 16735235 | CONVERSION OF SCRIPT WITH RULE ELEMENTS TO A NATURAL LANGUAGE FORMAT | January 2020 | July 2022 | Allow | 30 | 1 | 0 | No | No |
| 16733703 | SYSTEMS AND METHODS FOR GENERATING TEXTUAL INSTRUCTIONS FOR MANUFACTURERS FROM HYBRID TEXTUAL AND IMAGE DATA | January 2020 | October 2022 | Allow | 34 | 2 | 0 | No | No |
| 16474815 | MANAGEMENT DEVICE AND AIR-CONDITIONING SYSTEM | December 2019 | September 2021 | Allow | 27 | 1 | 0 | No | No |
| 16620450 | COLLABORATIVE TRANSLATION SYSTEMS WITH MULTIPLE ACCOUNT TYPES AND PROFILE TYPES | December 2019 | September 2021 | Allow | 22 | 0 | 0 | No | No |
| 16695741 | LAYERED CODING FOR COMPRESSED SOUND OR SOUND FIELD REPRESENTATIONS | November 2019 | January 2020 | Allow | 2 | 0 | 0 | No | No |
| 16688102 | SUMMARIZATION METHOD FOR RECORDED AUDIO | November 2019 | February 2022 | Allow | 26 | 6 | 0 | No | No |
| 16676617 | BRAIN-COMPUTER INTERFACE SYSTEM AND METHOD FOR DECODING USER'S CONVERSATION INTENTION USING THE SAME | November 2019 | November 2021 | Allow | 24 | 0 | 0 | No | No |
| 16674425 | PRIVACY-PRESERVING VISUAL RECOGNITION VIA ADVERSARIAL LEARNING | November 2019 | August 2022 | Allow | 33 | 0 | 0 | No | No |
| 16658988 | Disambiguation of Concept Classifications Using Language-Specific Clues | October 2019 | March 2021 | Allow | 17 | 0 | 0 | No | No |
| 16596282 | SELECTIVELY GENERATING WORD VECTOR AND PARAGRAPH VECTOR REPRESENTATIONS OF FIELDS FOR MACHINE LEARNING | October 2019 | June 2020 | Allow | 8 | 1 | 0 | No | No |
| 16586000 | TRANSLATION SUPPORT SYSTEM, ETC. | September 2019 | November 2021 | Allow | 26 | 1 | 0 | Yes | No |
| 16585167 | SPEECH RECOGNITION | September 2019 | September 2021 | Allow | 24 | 4 | 0 | No | No |
| 16575948 | MULTI-WORD PHRASE BASED ANALYSIS OF ELECTRONIC DOCUMENTS | September 2019 | July 2020 | Allow | 10 | 1 | 0 | No | No |
| 16556769 | DETERMINING CONTEXTUAL RELEVANCE IN MULTI-AUDITORY SCENARIOS | August 2019 | April 2021 | Allow | 20 | 3 | 0 | Yes | No |
| 16541397 | ELECTRONIC APPARATUS, METHOD FOR CONTROLLING THE SAME, AND STORAGE MEDIUM FOR THE SAME | August 2019 | July 2021 | Allow | 23 | 1 | 0 | No | No |
| 16539460 | SYSTEM AND METHOD FOR REAL TIME TRANSLATION | August 2019 | September 2019 | Allow | 1 | 0 | 0 | No | No |
| 16521104 | SIGNAL ENCODING METHOD AND APPARATUS, AND SIGNAL DECODING METHOD AND APPARATUS | July 2019 | January 2020 | Allow | 6 | 1 | 0 | No | No |
| 16506354 | AUTOMATED EVALUATION AND SELECTION OF MACHINE TRANSLATION PROTOCOLS | July 2019 | May 2022 | Allow | 34 | 2 | 0 | Yes | No |
| 16504782 | VOICE INTERACTION AT A PRIMARY DEVICE TO ACCESS CALL FUNCTIONALITY OF A COMPANION DEVICE | July 2019 | March 2020 | Allow | 8 | 2 | 0 | Yes | No |
| 16504770 | SENTENCE EVALUATION APPARATUS AND SENTENCE EVALUATION METHOD | July 2019 | September 2021 | Allow | 26 | 0 | 0 | No | No |
| 16459576 | Method and System for Intelligently Suggesting Paraphrases | July 2019 | April 2022 | Allow | 33 | 1 | 0 | Yes | No |
| 16447230 | OPTIMIZING MACHINE TRANSLATIONS FOR USER ENGAGEMENT | June 2019 | March 2020 | Allow | 9 | 2 | 0 | Yes | No |
| 16441298 | IMAGE-TO-TEXT RECOGNITION FOR A SEQUENCE OF IMAGES | June 2019 | May 2022 | Allow | 35 | 1 | 0 | No | No |
| 16436704 | RECONCILIATION BETWEEN SIMULATOR AND SPEECH RECOGNITION OUTPUT USING SEQUENCE-TO-SEQUENCE MAPPING | June 2019 | September 2019 | Allow | 4 | 1 | 0 | Yes | No |
| 16430231 | MULTI-PATH CALCULATIONS FOR DEVICE ENERGY LEVELS | June 2019 | April 2023 | Allow | 47 | 4 | 1 | Yes | No |
| 16423551 | DETECTING AN EVENT FROM SIGNALS IN A LISTENING AREA | May 2019 | June 2019 | Allow | 1 | 0 | 0 | No | No |
| 16421942 | INFORMATION PROCESSING DEVICE AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING PROGRAM | May 2019 | February 2022 | Allow | 32 | 1 | 0 | Yes | No |
| 16463959 | ELECTRONIC DEVICE FOR PERFORMING TRANSLATION BY SHARING CONTEXT OF UTTERANCE AND OPERATION METHOD THEREFOR | May 2019 | January 2022 | Allow | 32 | 1 | 0 | No | No |
| 16420324 | AUTOMATIC COLLABORATION BETWEEN DISTINCT RESPONSIVE DEVICES | May 2019 | March 2023 | Allow | 45 | 5 | 0 | Yes | No |
| 16399211 | SPEECH SIGNAL PROCESSING METHOD AND SPEECH SIGNAL PROCESSING APPARATUS | April 2019 | December 2021 | Allow | 31 | 1 | 0 | No | No |
| 16399202 | SPEECH SIGNAL PROCESSING METHOD AND SPEECH SIGNAL PROCESSING APPARATUS | April 2019 | December 2019 | Allow | 7 | 1 | 0 | No | No |
| 16395028 | CUSTOMIZING ACTIONS BASED ON CONTEXTUAL DATA AND VOICE-BASED INPUTS | April 2019 | November 2019 | Allow | 7 | 1 | 0 | No | No |
| 16395001 | Device for Extracting Information from a Dialog | April 2019 | November 2019 | Allow | 7 | 1 | 0 | No | No |
| 16375197 | IDENTIFYING DIGITAL PRIVATE INFORMATION AND PREVENTING PRIVACY VIOLATIONS | April 2019 | December 2021 | Allow | 32 | 1 | 0 | No | No |
| 16365815 | DOCUMENT ANALOGUES THROUGH ONTOLOGY MATCHING | March 2019 | October 2021 | Allow | 31 | 2 | 0 | No | No |
| 16283222 | SYSTEM AND METHOD FOR NEURAL NETWORK ORCHESTRATION | February 2019 | May 2021 | Allow | 27 | 0 | 0 | No | No |
| 16272360 | IMAGE PROCESSING DEVICE, METHOD FOR CONTROLLING IMAGE PROCESSING DEVICE, AND PROGRAM | February 2019 | October 2019 | Allow | 8 | 0 | 0 | No | No |
| 16243941 | Automatic Speech Recognition (ASR) Feedback For Head Mounted Displays (HMD) | January 2019 | May 2019 | Allow | 4 | 1 | 0 | No | No |
| 16215484 | RECONCILIATION BETWEEN SIMULATOR AND SPEECH RECOGNITION OUTPUT USING SEQUENCE-TO-SEQUENCE MAPPING | December 2018 | September 2019 | Allow | 10 | 1 | 1 | Yes | No |
| 16202932 | METHOD OF PROVIDING SERVICE BASED ON LOCATION OF SOUND SOURCE AND SPEECH RECOGNITION DEVICE THEREFOR | November 2018 | January 2021 | Allow | 25 | 2 | 0 | Yes | No |
This analysis examines appeal outcomes and the strategic value of filing appeals for examiner PHAM, THIERRY L.
With a 20.0% 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.
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, 13.3% 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.
⚠ 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 PHAM, THIERRY L works in Art Unit 2674 and has examined 226 patent applications in our dataset. With an allowance rate of 91.6%, this examiner allows applications at a higher rate than most examiners at the USPTO. Applications typically reach final disposition in approximately 24 months.
Examiner PHAM, THIERRY L's allowance rate of 91.6% places them in the 77% percentile among all USPTO examiners. This examiner is more likely to allow applications than most examiners at the USPTO.
On average, applications examined by PHAM, THIERRY L receive 1.57 office actions before reaching final disposition. This places the examiner in the 26% percentile for office actions issued. This examiner issues fewer office actions than average, which may indicate efficient prosecution or a more lenient examination style.
The median time to disposition (half-life) for applications examined by PHAM, THIERRY L is 24 months. This places the examiner in the 82% percentile for prosecution speed. Applications move through prosecution relatively quickly with this examiner.
Conducting an examiner interview provides a -4.4% benefit to allowance rate for applications examined by PHAM, THIERRY L. This interview benefit is in the 7% 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, 29.3% of applications are subsequently allowed. This success rate is in the 58% 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.
This examiner enters after-final amendments leading to allowance in 29.6% of cases where such amendments are filed. This entry rate is in the 44% percentile among all examiners. Strategic Recommendation: This examiner shows below-average receptiveness to after-final amendments. You may need to file an RCE or appeal rather than relying on after-final amendment entry.
When applicants request a pre-appeal conference (PAC) with this examiner, 80.0% result in withdrawal of the rejection or reopening of prosecution. This success rate is in the 64% 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 33.3% of appeals filed. This is in the 5% percentile among all examiners. Of these withdrawals, 40.0% 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, 43.5% are granted (fully or in part). This grant rate is in the 34% percentile among all examiners. Strategic Note: Petitions show below-average success regarding this examiner's actions. Ensure you have a strong procedural basis before filing.
Examiner's Amendments: This examiner makes examiner's amendments in 0.0% of allowed cases (in the 22% 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 30% percentile). This examiner issues Quayle actions less often than average. Allowances may 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.