Technology area: Computing & Software
54 pending office actions • 32 art units • 50 examiners • 8 of 54 (15%) have an AI response strategy ready • 15 patents granted in the last 365 days
DeepMind Technologies Limited has 56 pending office actions in Computing & Software. The portfolio spans 53 examiners and 34 art units, indicating extensive distribution across examination personnel and technical groups. The lower examiner count relative to pending matters shows some repeated examiner assignments, while the art-unit count reflects substantial technical breadth.
TRAN, TAN H is the busiest examiner, with 2 pending matters. That recurring assignment supports a targeted review of examiner treatment, but it represents only a limited concentration within the broader portfolio.
The 34 art units create a substantial need for structured coordination. Counsel should organize recurring claim and eligibility issues by art unit, compare treatment where examiners recur, and preserve matter-specific analysis for the many distinct examination paths.
Based on the USPTO statutory response window for each pending office action. 6 of the docket's apps have a known mailing date; the rest are excluded from the tile counts.
Every pending office action with a known statutory deadline, placed on a days-until-due axis. Dots left of Today are overdue; the further right, the more runway. Cases that share a deadline window stack vertically. 6 of the docket's apps have a known mailing date.
Difficulty is derived from the rejection statutes on the most recent pending office action. §101-driven and multi-statute cases are graded Hard; §112-only and obviousness-type double-patenting cases are graded Easy; everything else is Medium. "Unknown" means we have not yet parsed a statute for that office action.
| Bucket | Cases |
|---|---|
| §101 only | 2 (4%) |
| §101 + other | 23 (43%) |
| §103 only | 19 (35%) |
| §112 only | 2 (4%) |
| Double-patenting only | 1 (2%) |
| Multi-statute (no §101) | 7 (13%) |
How the docket's pending cases split across USPTO tech-center bands.
Manual office-action response work runs about 10 hours per case. The time-saved bands below show what IP Author's prosecution pipeline typically delivers — a conservative 20% on the low end, 35% in the middle, 50% on the high end.
| Examiner | Apps on this docket | Allow rate | Interview lift |
|---|---|---|---|
| TRAN, TAN H | 2 | 61.3% | +32.3% |
| ROY, SANCHITA | 2 | 72.3% | +47.1% |
| FONSECA LOPEZ, FRANCINI ALVARENGA | 2 | 28.6% | +30.0% |
| HAN, KYU HYUNG | 2 | 41.2% | +39.4% |
| BUKSA, CHRISTOPHER ALLEN | 1 | 73.5% | +18.5% |
| REFAI, RAMSEY | 1 | 50.7% | +12.6% |
| YI, HYUNGJUN B | 1 | 34.5% | +30.2% |
| PHAM, ANNIE | 1 | 90.0% | +11.1% |
| LIU, XIAO | 1 | 87.5% | +12.3% |
| BEZUAYEHU, SOLOMON G | 1 | 75.7% | +29.2% |
Cases in front of an examiner with an allow rate of 80%+ where the difficulty is Easy or Medium. The top 1 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18951101 | GENERATING TRAINING DATA USING A GENERATIVE NEURAL NETWORK | PHAM, ANNIE | — |
Multi-statute / §101-driven matters, or cases in front of an examiner with an allow rate under 30%. The top 8 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 18277729 | NOWCASTING USING GENERATIVE NEURAL NETWORKS | ISHIZUKA, YOSHIHISA | 38d overdue |
| 18127551 | ALLOCATING COMPUTING RESOURCES BETWEEN MODEL SIZE AND TRAINING DATA DURING TRAINING OF A MACHINE LEARNING MODEL | TAN, DAVID H | 38d overdue |
| 17959232 | NEURAL NETWORKS WITH TRANSFORMED ACTIVATION FUNCTION LAYERS | HADDAD, MAJD MAHER | 31d overdue |
| 19111985 | TRAINING POLICY NEURAL NETWORKS IN SIMULATION USING SCENE SYNTHESIS MACHINE LEARNING MODELS | BUKSA, CHRISTOPHER ALLEN | — |
| 18878496 | SIMULATING INDUSTRIAL FACILITIES FOR CONTROL | REFAI, RAMSEY | — |
| 18945375 | UNSUPERVISED LEARNING OF OBJECT KEYPOINT LOCATIONS IN IMAGES THROUGH TEMPORAL TRANSPORT OR SPATIO-TEMPORAL TRANSPORT | LIU, XIAO | — |
| 18849935 | MACHINE LEARNING SYSTEMS WITH COUNTERFACTUAL INTERVENTIONS | NYE, LOUIS CHRISTOPHER | — |
| 18846412 | REINFORCEMENT LEARNING TO EXPLORE ENVIRONMENTS | WERNER, MARSHALL L | — |
Cases in front of an examiner whose interview lift is 10 percentage points or more — i.e. interviewed cases historically resolve more favorably than non-interviewed ones. The top 8 ordered by deadline are shown.
| App # | Title | Examiner | Due in |
|---|---|---|---|
| 19111985 | TRAINING POLICY NEURAL NETWORKS IN SIMULATION USING SCENE SYNTHESIS MACHINE LEARNING MODELS | BUKSA, CHRISTOPHER ALLEN | — |
| 18878496 | SIMULATING INDUSTRIAL FACILITIES FOR CONTROL | REFAI, RAMSEY | — |
| 18872883 | NEURAL NETWORKS WITH REGULARIZED ATTENTION LAYERS | YI, HYUNGJUN B | — |
| 18951101 | GENERATING TRAINING DATA USING A GENERATIVE NEURAL NETWORK | PHAM, ANNIE | — |
| 18945375 | UNSUPERVISED LEARNING OF OBJECT KEYPOINT LOCATIONS IN IMAGES THROUGH TEMPORAL TRANSPORT OR SPATIO-TEMPORAL TRANSPORT | LIU, XIAO | — |
| 18935365 | TRAINING IMAGE REPRESENTATION NEURAL NETWORKS USING CROSS-MODAL INTERFACES | BEZUAYEHU, SOLOMON G | — |
| 18668080 | CONTROLLING AGENTS BY TRANSFERRING SUCCESSOR FEATURES TO NEW TASKS | TRAN, TAN H | — |
| 18443285 | CONTROLLING AGENTS USING SUB-GOALS GENERATED BY LANGUAGE MODEL NEURAL NETWORKS | ROY, SANCHITA | — |
| Art Unit | Apps |
|---|---|
| 2146 | 5 |
| 1685 | 5 |
| 2141 | 3 |
| 2145 | 3 |
| 2143 | 3 |
| 2124 | 2 |
| 2148 | 2 |
| 2126 | 2 |
| 2147 | 2 |
| 2125 | 2 |
| App # | Title | Examiner | Art Unit | Statutes | Status | Due in | AI | Filed |
|---|---|---|---|---|---|---|---|---|
| 19111985 | TRAINING POLICY NEURAL NETWORKS IN SIMULATION USING SCENE SYNTHESIS MACHINE LEARNING MODELS | BUKSA, CHRISTOPHER ALLEN | 3658 | §102§103 | Non-Final OA | — | Pending | Mar 14, 2025 |
| 18878496 | SIMULATING INDUSTRIAL FACILITIES FOR CONTROL | REFAI, RAMSEY | 3664 | §101§102 | Non-Final OA | — | Pending | Dec 23, 2024 |
| 18872883 | NEURAL NETWORKS WITH REGULARIZED ATTENTION LAYERS | YI, HYUNGJUN B | 2146 | §103 | Non-Final OA | — | Pending | Dec 09, 2024 |
| 18951101 | GENERATING TRAINING DATA USING A GENERATIVE NEURAL NETWORK | PHAM, ANNIE | 2662 | §103 | Non-Final OA | — | Pending | Nov 18, 2024 |
| 18945375 | UNSUPERVISED LEARNING OF OBJECT KEYPOINT LOCATIONS IN IMAGES THROUGH TEMPORAL TRANSPORT OR SPATIO-TEMPORAL TRANSPORT | LIU, XIAO | 2664 | §102§103Other | Non-Final OA | — | Pending | Nov 12, 2024 |
| 18935365 | TRAINING IMAGE REPRESENTATION NEURAL NETWORKS USING CROSS-MODAL INTERFACES | BEZUAYEHU, SOLOMON G | 2674 | §103 | Non-Final OA | — | Pending | Nov 01, 2024 |
| 18849935 | MACHINE LEARNING SYSTEMS WITH COUNTERFACTUAL INTERVENTIONS | NYE, LOUIS CHRISTOPHER | 2141 | §101§103 | Non-Final OA | — | Pending | Sep 23, 2024 |
| 18885422 | GENERATING TRAINING DATA USING AN AUDIO GENERATION MODEL | SMITH, SEAN THOMAS | 2659 | §103 | Final Rejection | — | Pending | Sep 13, 2024 |
| 18846412 | REINFORCEMENT LEARNING TO EXPLORE ENVIRONMENTS | WERNER, MARSHALL L | — | §101§103 | Non-Final OA | — | Pending | Sep 12, 2024 |
| 18844798 | TRAINING OBJECT DISCOVERY NEURAL NETWORKS AND FEATURE REPRESENTATION NEURAL NETWORKS USING SELF-SUPERVISED LEARNING | SACKALOSKY, COREY MATTHEW | 2128 | §101§103 | Non-Final OA | — | Pending | Sep 06, 2024 |
| 18843608 | AGENT CONTROL THROUGH CULTURAL TRANSMISSION | KASSIM, HAFIZ A | 3623 | §101§103 | Non-Final OA | — | Pending | Sep 03, 2024 |
| 18794786 | USING VISUAL LANGUAGE MODELS TO DETERMINE LOCATIONS OF IMAGE ELEMENTS WITHIN GRAPHICAL IMAGES | PATEL, JAYESH A | 2677 | §101 | Non-Final OA | — | Pending | Aug 05, 2024 |
| 18834208 | CONTROLLING REINFORCEMENT LEARNING AGENTS USING GEOMETRIC POLICY COMPOSITION | JANSEN II, MICHAEL J | 2626 | §101§102§103 | Non-Final OA | — | Pending | Jul 29, 2024 |
| 18754726 | DISTRIBUTIONAL REINFORCEMENT LEARNING | HUANG, YAO D | 2124 | §103Other | Final Rejection | — | Pending | Jun 26, 2024 |
| 18668080 | CONTROLLING AGENTS BY TRANSFERRING SUCCESSOR FEATURES TO NEW TASKS | TRAN, TAN H | 2141 | §103 | Non-Final OA | — | Pending | May 17, 2024 |
| 18666682 | GENERATING PREDICTIONS FOR NON-STATIONARY DATA USING DISTRIBUTIONS OVER OUTPUT HEAD WEIGHTS | RUDY, ANDREW J | 3668 | §112 | Non-Final OA | — | Pending | May 16, 2024 |
| 18665702 | VERIFICATION OF AGENT OUTPUT THROUGH ADVERSARIAL DEBATE | VO, STEVEN | 2148 | §101§103 | Non-Final OA | — | Pending | May 16, 2024 |
| 18661188 | Dynamic Controlled Decoding | TRAN, QUOC A | 2145 | §112 | Non-Final OA | — | Pending | May 10, 2024 |
| 18639686 | BLACK-BOX OPTIMIZATION USING NEURAL NETWORKS | SIPPEL, MOLLY CLARKE | 2126 | §101Other | Non-Final OA | — | Pending | Apr 18, 2024 |
| 18636971 | TRAINING A POPULATION OF ADVERSARIAL NEURAL NETWORKS TO IMPROVE A BASE NEURAL NETWORK | HARPER, ELIYAH STONE | 2166 | §102§103 | Non-Final OA | — | Pending | Apr 16, 2024 |
| 18698218 | DEMONSTRATION-DRIVEN REINFORCEMENT LEARNING | MRABI, HASSAN | 2147 | §103 | Non-Final OA | — | Pending | Apr 03, 2024 |
| 18623952 | GENERATING DISCRETE LATENT REPRESENTATIONS OF INPUT DATA ITEMS | WU, NICHOLAS S | 2148 | DP | Non-Final OA | — | Pending | Apr 01, 2024 |
| 18443285 | CONTROLLING AGENTS USING SUB-GOALS GENERATED BY LANGUAGE MODEL NEURAL NETWORKS | ROY, SANCHITA | 2146 | §103 | Non-Final OA | — | Pending | Feb 15, 2024 |
| 18424687 | GENERATING ENVIRONMENT MODELS USING IN-CONTEXT ADAPTATION AND EXPLORATION | ACOSTA, RILEY SULLIVAN | 2143 | §101§103§112 | Non-Final OA | — | Pending | Jan 26, 2024 |
| 18423239 | TRAINING A NEURAL NETWORK TO PERFORM AN ALGORITHMIC TASK USING A SELF-SUPERVISED LOSS | HICKS, AUSTIN JAMES | 2142 | §103§112 | Non-Final OA | — | Pending | Jan 25, 2024 |
| 18422620 | NEURAL POPULATION LEARNING | MAMILLAPALLI, PALLAVI | 2147 | §101§103§112 | Non-Final OA | — | Pending | Jan 25, 2024 |
| 18406995 | CONTROLLING AGENTS USING AMORTIZED Q LEARNING | CHEN, ALAN S | 2125 | §103§112 | Non-Final OA | — | Pending | Jan 08, 2024 |
| 18527211 | HIERARCHICAL TEXT GENERATION USING LANGUAGE MODEL NEURAL NETWORKS | TRACY JR., EDWARD | 2656 | §103 | Non-Final OA | — | Pending | Dec 01, 2023 |
| 18285519 | LEARNING DIVERSE SKILLS FOR TASKS USING SEQUENTIAL LATENT VARIABLES FOR ENVIRONMENT DYNAMICS | FIGUEROA, KEVIN W | 2124 | §101§103 | Non-Final OA | — | Pending | Oct 04, 2023 |
| 18374447 | DISCRETE TOKEN PROCESSING USING DIFFUSION MODELS | ROY, SANCHITA | 2146 | §101§103§112 | Non-Final OA | — | Pending | Sep 28, 2023 |
| 18283131 | TRAINING GRAPH NEURAL NETWORKS USING A DE-NOISING OBJECTIVE | TRAN, TAN H | 2141 | §103 | Final Rejection | — | Pending | Sep 20, 2023 |
| 18278473 | CONTINUAL LEARNING NEURAL NETWORK SYSTEM TRAINING FOR CLASSIFICATION TYPE TASKS | LAHAM BAUZO, ALVARO SALIM | 2146 | §103 | Final Rejection | — | AI Ready | Aug 23, 2023 |
| 18277729 | NOWCASTING USING GENERATIVE NEURAL NETWORKS | ISHIZUKA, YOSHIHISA | 2857 | §101§103§112 | Final Rejection | 38d overdue | AI Ready | Aug 17, 2023 |
| 18230056 | CONTROLLING AGENTS USING AUXILIARY PREDICTION NEURAL NETWORKS THAT GENERATE STATE VALUE ESTIMATES | CHEN, KUANG FU | 2143 | §103 | Final Rejection | 50d | AI Ready | Aug 03, 2023 |
| 18275542 | CONTROLLING AGENTS USING STATE ASSOCIATIVE LEARNING FOR LONG-TERM CREDIT ASSIGNMENT | CHUANG, SU-TING | 2146 | §103§112 | Final Rejection | — | Pending | Aug 02, 2023 |
| 18275332 | RENDERING NEW IMAGES OF SCENES USING GEOMETRY-AWARE NEURAL NETWORKS CONDITIONED ON LATENT VARIABLES | AHN, CHRISTINE YERA | 2615 | §103 | Final Rejection | 39d | AI Ready | Aug 01, 2023 |
| 18275052 | ATTENTION NEURAL NETWORKS WITH SHORT-TERM MEMORY UNITS | CADY, MATTHEW ALAN | 2127 | §103 | Final Rejection | — | Pending | Jul 31, 2023 |
| 18275145 | TEMPORAL DIFFERENCE SCALING WHEN CONTROLLING AGENTS USING REINFORCEMENT LEARNING | SPRATT, BEAU D | 2143 | §103 | Final Rejection | — | AI Ready | Jul 31, 2023 |
| 18273594 | PREDICTING COMPLETE PROTEIN REPRESENTATIONS FROM MASKED PROTEIN REPRESENTATIONS | LUO, JAMMY NMN | 1686 | §101§103 | Non-Final OA | — | Pending | Jul 21, 2023 |
| 18271611 | GENERATING NEURAL NETWORK OUTPUTS BY ENRICHING LATENT EMBEDDINGS USING SELF-ATTENTION AND CROSS-ATTENTION OPERATIONS | KIM, SEHWAN | 2129 | §103 | Final Rejection | — | Pending | Jul 10, 2023 |
| 18034989 | PREDICTING PROTEIN STRUCTURES USING PROTEIN GRAPHS | KHAN, ARSHAD HUSSAIN | 1686 | §101§103 | Non-Final OA | — | Pending | May 02, 2023 |
| 18034280 | PREDICTING PROTEIN STRUCTURES OVER MULTIPLE ITERATIONS USING RECYCLING | SMITH, JENNIFER JOY | 1685 | §101§102§103§112DP | Non-Final OA | — | Pending | Apr 27, 2023 |
| 18127551 | ALLOCATING COMPUTING RESOURCES BETWEEN MODEL SIZE AND TRAINING DATA DURING TRAINING OF A MACHINE LEARNING MODEL | TAN, DAVID H | 2145 | §102§103 | Final Rejection | 38d overdue | AI Ready | Mar 28, 2023 |
| 18027571 | PREDICTING SYMMETRICAL PROTEIN STRUCTURES USING SYMMETRICAL EXPANSION TRANSFORMATIONS | FONSECA LOPEZ, FRANCINI ALVARENGA | 1685 | §101§103§112 | Non-Final OA | — | Pending | Mar 21, 2023 |
| 18026376 | PREDICTING PROTEIN STRUCTURES BY SHARING INFORMATION BETWEEN MULTIPLE SEQUENCE ALIGNMENTS AND PAIR EMBEDDINGS | BICKHAM, DAWN MARIE | 1685 | §101§102 | Non-Final OA | — | Pending | Mar 15, 2023 |
| 18025689 | TRAINING PROTEIN STRUCTURE PREDICTION NEURAL NETWORKS USING REDUCED MULTIPLE SEQUENCE ALIGNMENTS | HAYES, JONATHAN EDWARD | 1685 | §101§103§112 | Non-Final OA | — | Pending | Mar 10, 2023 |
| 17960051 | COMPOSITIONAL GENERALIZATION FOR REINFORCEMENT LEARNING | KIM, HARRISON CHAN YOUNG | 2145 | §101§103 | Final Rejection | — | Pending | Oct 04, 2022 |
| 17959232 | NEURAL NETWORKS WITH TRANSFORMED ACTIVATION FUNCTION LAYERS | HADDAD, MAJD MAHER | 2125 | §101§103 | Final Rejection | 31d overdue | AI Ready | Oct 03, 2022 |
| 17914035 | SYSTEM AND METHOD FOR TRAINING A SPARSE NEURAL NETWORK WHILST MAINTAINING SPARSITY | HAN, KYU HYUNG | 2123 | §101§103 | Non-Final OA | — | Pending | Sep 23, 2022 |
| 17798111 | LEARNING MACHINE LEARNING INCENTIVES BY GRADIENT DESCENT FOR AGENT COOPERATION IN A DISTRIBUTED MULTI-AGENT SYSTEM | HAN, KYU HYUNG | 2123 | §103 | Non-Final OA | — | Pending | Aug 08, 2022 |
IP Author helps IP teams respond to office actions faster with AI-generated responses, examiner analytics, and prosecution intelligence.
Start Free TrialDeepMind Technologies Limited has 54 pending office actions as of October 2026, spread across 32 art units and 50 examiners.
Tan H Tran has the most, with 2 of DeepMind Technologies Limited's pending office actions.
Mostly Computing & Software (USPTO Tech Center 2100), based on where its pending office actions are examined.
