Four software-implemented patent families — ML feedback-loop cache eviction, distributed-consensus ledger sharding, real-time streaming event deduplication, and federated-learning differential-privacy budget accounting — ranked and re-ranked through ClaimForge's AI retrieval pipeline. 35 U.S.C. §103 obviousness objections surfaced against Espacenet + Google Patents in 90 minutes.
Software-Implemented Invention patent families ranked by ClaimForge's AI retrieval pipeline
US 11,327,178 B2 · ML-Based Cache Eviction · Feedback-Loop Architecture
| Rejection | 35 U.S.C. §103 obviousness rejection over Belady 1966 (MIN optimal-eviction policy formulation) and Megiddo 2003 (randomized online caching with adaptive threshold); ML-based cache-eviction system claimed at the level of "predict cache utility via predictive model" without limitation to a closed-loop retraining signal coupling prediction error to eviction policy |
| Escape Logic | Restrict the ML-cache-eviction genus to a system wherein the predictive model is retrained at a defined cadence using a closed-loop signal derived from eviction-policy outcome residuals (predicted-utility-vs-actual-eviction delta), with the retraining cadence selected as a function of the rolling error statistic — closed-loop retraining cadence-control absent from Belady 1966 and Megiddo 2003 which each disclose static optimal-eviction or threshold-tuning without feedback-driven policy-outcome correction |
| ClaimForge Action | OA-parsing extracts §103 obviousness over Belady 1966 / Megiddo 2003 → embedding-based prior-art retrieval runs cosine-similarity search across ML-cache-eviction patent family → citation-graph pruning removes low-degree prior-art nodes citing only Belady or Megiddo → semantic novelty scoring flags closed-loop retraining cadence as a non-disclosed feature → amendment drafted with retraining-cadence-anchor plus rolling-residual-statistic constraint → re-search confirms clearance via Espacenet + Google Patents |
US 10,956,915 B2 · Distributed Consensus · Sharding · Throughput Optimization
| Rejection | 35 U.S.C. §103 obviousness rejection over Lamport 1998 (Paxos distributed-consensus protocol) and Zamani 2018 (blockchain sharding architecture); distributed-consensus sharding system claimed at the level of "shard assignment based on computational load" without limitation to a deterministic cross-shard transaction-commit ordering anchored to a monotonic shard-throughput ledger |
| Escape Logic | Restrict the distributed-consensus sharding system to one wherein cross-shard transaction commit ordering is derived from a monotonic shard-throughput ledger, with the throughput-ledger hash-chained across shard-boundary transactions and used to deterministically resolve cross-shard commit sequencing — monotonic-throughput-ledger-anchored cross-shard commit ordering absent from Lamport 1998 and Zamani 2018 which each disclose independent-shard consensus without coupled throughput-ledger commit ordering |
| ClaimForge Action | OA-parsing flags Lamport 1998 / Zamani 2018 cross-shard-commit anticipation → embedding prior-art retrieval scores distributed-sharding patent candidates on cross-shard-commit novelty → citation-graph pruning strips generic-sharding background art → semantic novelty scoring surfaces monotonic-throughput-ledger + hash-chain anchor as a non-disclosed commit-ordering constraint → amendment drafted with throughput-ledger-anchor and deterministic-ordering no-global-lock features → re-search validates clearance across Espacenet + Google Patents |
US 10,956,275 B1 · Streaming Event Processing · Probabilistic Deduplication · Real-Time Pipelines
| Rejection | 35 U.S.C. §103 obviousness rejection over Bloom 1970 (Bloom filter probabilistic-membership structure) and Flajolet-Martin 1985 (Flajolet-Martin sketch cardinality estimation); streaming event deduplication claimed at the level of "deduplicate events using a probabilistic data structure" without limitation to a tunable false-positive-rate-anchored decision threshold coupled to downstream-sink backpressure |
| Escape Logic | Restrict the streaming-event-deduplication system to one wherein the probabilistic-data-structure false-positive-rate parameter is dynamically adjusted as a function of the downstream consumer sink's measured backpressure, and the false-positive-rate decision threshold is updated at a sliding-window granularity derived from the sink-throughput telemetry — dynamic-FPR-backpressure coupling absent from Bloom 1970 and Flajolet-Martin 1985 which each disclose static-FPR probabilistic structures without downstream-coupled threshold adjustment |
| ClaimForge Action | OA-parsing extracts Bloom 1970 / Flajolet-Martin 1985 §103 obviousness over static-FPR probabilistic structures → embedding retrieval surfaces candidates across streaming-deduplication patent space → citation-graph pruning filters commonly-cited background art → semantic novelty scoring flags downstream-backpressure-coupled dynamic-FPR threshold adjustment → amendment grounds the genus in backpressure-signal-input + sliding-window-update features → re-search confirms clearance against Espacenet + Google Patents |
US 11,238,193 B2 · Federated Learning · Differential Privacy · Budget Accounting
| Rejection | 35 U.S.C. §103 obviousness rejection over Dwork 2006 (epsilon-differential privacy framework) and McMahan 2017 (federated-learning client-update aggregation); federated-learning DP-budget accountant claimed at the level of "track differential-privacy budget across training rounds" without limitation to a per-round budget allocation strategy coupled to a client-noise-consumption ledger with rate-adaptive allocation |
| Escape Logic | Restrict the federated-learning DP-budget accountant to one wherein the per-round DP-budget allocation is computed by a client-noise-consumption ledger that decrements the per-client epsilon budget as a function of the empirical noise-magnitude distribution observed in prior rounds, with the rate-adaptive allocation bounded by a global epsilon-cap tolerance — client-noise-consumption-ledger rate-adaptive allocation absent from Dwork 2006 and McMahan 2017 which each disclose DP budget framing without ledger-driven per-round epsilon-allocation dynamics |
| ClaimForge Action | OA-parsing identifies Dwork 2006 / McMahan 2017 §103 obviousness over generic DP-budget framing → embedding retrieval combines FL+DP patent-candidate scoring → citation-graph pruning removes foundational-ML background art → semantic novelty scoring flags client-noise-consumption-ledger + rate-adaptive allocation as coupled novelty constraints → amendment drafted with ledger-anchor + global-cap features → re-search confirms unique combination across Espacenet + Google Patents |
The autonomous ClaimForge loop covers every step from embedding-based prior-art retrieval through citation-graph pruning and amendment drafting across all four software patent families.
Upload a 35 U.S.C. §103 office action — ClaimForge ranks the most relevant prior art, prunes citation-graph noise, and drafts an amendment grounded in your software-implemented invention's specific feature novelty. 90 minutes vs. 20+ hours manually.