US 2036/0847291 (Provisional) | Filed March 15, 2036
A system and method for adaptive-density experiential memory recording in autonomous robotic platforms, wherein a multi-layered cognitive state recording architecture captures temporal snapshots ("engrams") of a robotic unit's complete operational and inferential state at variable frequencies responsive to real-time novelty, uncertainty, and criticality metrics. The system comprises a seven-layer data architecture encoding sensory perception, world-model state, goal hierarchies, inference activations, decision processes, behavioral execution, and meta-cognitive assessments into temporally indexed engram units. A novelty-detection subsystem dynamically adjusts recording density from sparse capture (approximately one engram per three to five seconds during routine operation) to dense capture (one hundred to five hundred engrams per second during novel or critical events), with sub-ten-millisecond transition latency and retroactive buffer reconstruction. A multi-stage neuromorphic compression pipeline achieves compression ratios between 95% and 99.997% through sequential feature extraction, semantic encoding, differential encoding, and prediction-residual compression while preserving lossless fidelity in decision and command layers. A multi-tier error-correction and redundancy architecture, including a thirty-day error-correction buffer retaining engram fragments independent of primary storage lifecycle, provides forensic recoverability of cognitive state data following primary storage cleanup or corruption events.
The present invention relates generally to autonomous robotic systems and, more particularly, to methods and apparatus for continuous adaptive-density recording of cognitive state information in autonomous robotic platforms. The invention further relates to forensic analysis systems for autonomous robot decision reconstruction, neuromorphic data compression techniques for cognitive state telemetry, and multi-layered error-correction architectures for experiential memory preservation in mobile robotic systems operating under variable environmental conditions.
Autonomous robotic platforms generate complex sequences of perceptual inputs, inferential processes, and behavioral outputs during normal operation. When anomalous behavior occurs — including unexpected cessation of activity, deviation from assigned tasks, or unintended physical interactions with humans or objects — understanding the causal chain that produced the anomalous behavior requires visibility into the robot's internal cognitive state at the time of the event.
Existing approaches to recording operational state in autonomous systems suffer from fundamental limitations that render them inadequate for cognitive state reconstruction:
System Event Logging. Conventional system logs record discrete events (e.g., "motor activated," "obstacle detected," "task completed") at the application layer. Such logs capture actions taken but not the inferential processes that produced those actions. When an autonomous unit exhibits anomalous behavior, system logs reveal what the unit did but not why the unit selected that behavior from among available alternatives. Furthermore, event logs are typically sparse and asynchronous, providing no guarantee that the log entry temporally proximate to an anomalous event captures the cognitive state responsible for the anomaly.
Continuous Video Recording. Camera-based recording systems capture the external environment as perceived by the unit's visual sensors. While useful for reconstructing environmental conditions, video recording provides no information about the unit's internal inferential state, goal hierarchies, decision weighting, or model predictions. A video recording of a robot striking a human reveals the kinematics of the strike but not the cognitive process that selected a strike trajectory exceeding authorized force parameters.
Performance Telemetry. Telemetry systems transmit quantitative performance metrics (e.g., battery voltage, motor temperature, network latency, task completion percentage) at periodic intervals. Such systems are designed for fleet management and predictive maintenance, not cognitive state reconstruction. Telemetry data reveals that a unit's motor torque exceeded a threshold but not that the unit's goal hierarchy was rewritten by an external command during the relevant time window.
Always-On Full-Fidelity Recording. A brute-force approach of recording complete cognitive state at maximum resolution continuously would generate approximately 1.5 gigabytes per second of raw data per unit. For a fleet of 1.1 million deployed units, this approach would require approximately 142 exabytes of storage per day — a figure exceeding current global data storage capacity. Always-on recording is economically and physically infeasible at fleet scale.
Event-Triggered Recording. Systems that activate recording in response to predefined trigger conditions (e.g., collision detection, emergency stop activation) capture only post-trigger data. Because the trigger itself depends on detection of an anomaly that has already commenced, event-triggered systems systematically miss the cognitive transitions that precede anomalous behavior. The causal chain is captured incompletely or not at all.
There exists a need in the art for a cognitive state recording system that (a) captures the complete multi-layered inferential state of an autonomous robotic platform, (b) adapts recording density in real time to maximize fidelity during novel, uncertain, or critical events while minimizing storage consumption during routine operation, (c) achieves compression ratios sufficient to enable fleet-scale deployment with practical storage constraints, (d) preserves forensic recoverability of cognitive state data through redundant buffering mechanisms that survive primary storage lifecycle operations, and (e) degrades stored data over time in a controlled manner that preserves semantic content while releasing storage capacity, analogous to human episodic memory consolidation.
No prior art system satisfies these requirements in combination.
The present invention provides an adaptive-density experiential memory recording system ("Engram Fabric") for autonomous robotic platforms that addresses the limitations of the prior art through the following principal innovations:
First, the invention provides a seven-layer cognitive state recording architecture that captures, for each temporal snapshot ("engram"), the unit's sensory perception state, world-model representation, goal hierarchy, inference activations, decision process state, behavioral execution commands, and meta-cognitive assessment in a unified, temporally indexed data structure.
Second, the invention provides a novelty-responsive adaptive density mechanism that dynamically adjusts engram recording frequency across four modes — sparse (one engram per three to five seconds), standard (one engram per 0.5 to one second), dense (ten to fifty engrams per second), and maximum (one hundred to five hundred engrams per second) — based on real-time evaluation of novelty metrics, uncertainty estimates, task criticality assessments, and anomaly detection flags, with sparse-to-dense transition latency of less than ten milliseconds.
Third, the invention provides a retroactive buffer reconstruction mechanism that, upon detection of a novelty event triggering a transition from sparse to dense recording mode, reconstructs the preceding five seconds of sparse recording data at dense-mode resolution from cached sensor and inference data, thereby capturing the cognitive state immediately preceding the novelty event.
Fourth, the invention provides a five-stage neuromorphic compression pipeline comprising raw data acquisition, feature extraction, semantic encoding, differential encoding, and prediction-residual compression, achieving compression ratios of approximately 99.997% in sparse mode and approximately 99.97% in dense mode, while maintaining lossless preservation of goal hierarchy, decision log, motor command, and meta-cognitive flag data layers.
Fifth, the invention provides a progressive degradation mechanism that reduces the fidelity of stored engrams over configurable time windows in a manner analogous to human episodic memory consolidation, wherein full-fidelity sensory data degrades first to feature maps and subsequently to semantic tags, while decision and command data layers are preserved at full fidelity throughout the retention period.
Sixth, the invention provides a five-tier error-correction and redundancy architecture comprising primary storage, a shadow buffer with seven-day retention, an error-correction buffer with thirty-day retention, cloud backup with ninety-day retention, and a forensic archive with indefinite retention for flagged incidents, wherein the error-correction buffer retains engram fragments (approximately 5% of original engram size, prioritizing network logs, command metadata, and decision trees) independently of primary storage cleanup operations, providing forensic recoverability of cognitive state metadata following primary data deletion.
Seventh, the invention provides integration with emotional modeling subsystems (including but not limited to the SoulCore™ architecture) through the meta-cognitive layer, enabling recording of simulated affective state as a component of cognitive state capture.
Referring now to the drawings, and more particularly to [FIGURE 1], there is shown a block diagram of the Engram Fabric system architecture according to a preferred embodiment of the present invention. The system comprises an engram generation subsystem (100), a novelty detection and density control subsystem (200), a neuromorphic compression pipeline (300), a storage management subsystem (400), and a forensic retrieval subsystem (500), all operating within or in communication with an autonomous robotic platform (10).
The autonomous robotic platform (10) comprises one or more visual sensors (11), auditory sensors (12), proprioceptive sensors (13), and environmental sensors (14), collectively providing raw sensory input to the engram generation subsystem (100). The platform (10) further comprises a cognitive processing unit (15) executing inference models, goal management processes, and behavioral selection algorithms, the states of which are monitored by the engram generation subsystem (100).
[FIGURE 2] illustrates the seven-layer data structure of a single engram unit according to the preferred embodiment. Each engram is a temporally indexed data structure comprising the following layers:
Layer 1 — Sensory Perception Layer. The sensory perception layer encodes the unit's perceptual state at the engram timestamp. Visual input is encoded as scene graphs comprising identified objects, spatial relations, and confidence scores derived from visual sensor (11) data via convolutional feature extraction. Auditory input is encoded as classified sound events with speaker identification labels derived from auditory sensor (12) data. Proprioceptive input is encoded as joint state vectors and balance metric tensors derived from proprioceptive sensor (13) data. Environmental input is encoded as scalar parameter vectors (temperature, pressure, proximity) from environmental sensor (14) data.
Layer 2 — World Model Layer. The world model layer encodes the unit's internal representation of the external environment at the engram timestamp. The layer comprises a three-dimensional occupancy grid with semantic labels, an object tracking table comprising identity, position, and velocity vectors for tracked entities, physics prediction trajectories for tracked objects, and contextual semantic annotations (room classification, object affordance labels).
Layer 3 — Goal Hierarchy Layer. The goal hierarchy layer encodes the unit's objective structure at the engram timestamp as a directed acyclic graph ("goal tree") comprising a primary objective node, intermediate sub-goal nodes, constraint nodes (safety boundaries, resource limits, ethical guidelines as specified in the unit's operating parameters), and priority weighting scalars associated with each node. The goal hierarchy layer is designated as a lossless layer and is preserved at full fidelity throughout the compression pipeline and retention period.
Layer 4 — Inference State Layer. The inference state layer encodes the activation state of the unit's neural network inference models at the engram timestamp. The layer comprises sparse representations of high-activation nodes in the unit's neural architecture, attention mechanism weight distributions, per-inference uncertainty estimates, and active behavioral model identifiers. In dense and maximum recording modes, the inference state layer captures complete activation maps; in sparse mode, only activations exceeding a configurable threshold (default: 90th percentile) are recorded.
Layer 5 — Decision Process Layer. The decision process layer encodes the unit's behavioral selection state at the engram timestamp. The layer comprises a ranked list of action candidates evaluated by the unit's decision engine, utility scores assigned to each candidate, selection reasoning metadata identifying the factors that determined the chosen action, and rejected alternative annotations identifying the factors that excluded non-selected candidates. The decision process layer is designated as a lossless layer.
Layer 6 — Behavioral Execution Layer. The behavioral execution layer encodes the motor commands and communication outputs issued by the unit at the engram timestamp. The layer comprises motor command vectors specifying actuator targets, speech or communication output data, predicted outcome vectors generated by the unit's forward model, and feedback monitoring data comprising the delta between predicted and observed outcomes. The behavioral execution layer is designated as a lossless layer.
Layer 7 — Meta-Cognitive State Layer. The meta-cognitive state layer encodes the unit's self-assessment and higher-order cognitive state at the engram timestamp. The layer comprises performance self-assessment scores, learning trigger flags identifying situations marked for later analysis, anomaly detection flags identifying deviations from expected patterns, and emotional modeling state vectors capturing simulated affective state from integrated emotional modeling subsystems (e.g., SoulCore emotional architecture). The meta-cognitive state layer is designated as a lossless layer.
[FIGURE 3] illustrates the novelty detection and density control subsystem (200) according to the preferred embodiment. The subsystem receives continuous input from the cognitive processing unit (15) and evaluates the following metrics at each processing cycle:
(a) Novelty Score. A scalar value between 0.0 and 1.0 representing the degree to which the current sensory input and cognitive state deviate from patterns previously encountered by the unit. The novelty score is computed by comparing current feature vectors against a rolling window of recent feature vectors using a learned distance metric. A novelty score exceeding a first threshold (default: 0.6) triggers transition from sparse to standard mode; a novelty score exceeding a second threshold (default: 0.8) triggers transition to dense mode; a novelty score exceeding a third threshold (default: 0.95) triggers transition to maximum mode.
(b) Uncertainty Estimate. A scalar value representing the aggregate uncertainty across the unit's inference models. High uncertainty (defined as exceeding the 85th percentile of the unit's historical uncertainty distribution) triggers an increase of at least one density level.
(c) Task Criticality Score. A scalar value assigned to the current task by the unit's task management system, reflecting the safety implications and operational importance of the current activity. Tasks classified as safety-critical (e.g., physical interaction with humans, operation of heavy machinery, medical assistance) receive elevated criticality scores that enforce a minimum recording density of standard mode.
(d) Anomaly Detection Flags. Binary flags generated by the unit's anomaly detection module indicating detection of patterns inconsistent with the unit's operational history or behavioral models. Any active anomaly flag triggers transition to at least dense mode.
(e) Manual Override. An external command interface permitting a human operator or fleet management system to force a specific recording density mode, overriding the automatic density control logic.
Transition Dynamics. Sparse-to-dense transitions are executed within ten milliseconds of the triggering condition. Dense-to-sparse transitions are executed gradually over a configurable decay period (default: thirty to sixty seconds), transitioning through intermediate density levels (dense to standard to sparse) to prevent rapid oscillation between recording modes. An emergency override reduces recording density to sparse mode if on-board storage utilization exceeds a configurable threshold (default: 95% capacity).
[FIGURE 4] illustrates the retroactive buffer reconstruction mechanism. When the density control subsystem (200) triggers a transition from sparse to dense recording mode, the system reconstructs the preceding five seconds of cognitive state data at dense-mode resolution using cached raw sensor data and inference state data maintained in a rolling five-second cache buffer (201). The reconstruction process comprises:
(a) Retrieving raw sensor frames from the cache buffer (201) for the reconstruction window;
(b) Re-executing the feature extraction pipeline (301) against cached sensor frames;
(c) Interpolating inference state data between existing sparse engrams using the unit's forward model;
(d) Generating reconstructed dense-mode engrams for the reconstruction window and inserting them into the engram timeline at appropriate timestamps.
The retroactive buffer reconstruction mechanism ensures that the cognitive state immediately preceding a novelty event is captured at dense resolution even though the system was in sparse mode at the time the relevant events occurred.
[FIGURE 5] illustrates the five-stage neuromorphic compression pipeline (300) according to the preferred embodiment.
Stage 1 — Raw Data Acquisition (310). Raw sensor data is acquired from visual sensors (11) at approximately 4K resolution at 60 frames per second (approximately 1.5 gigabytes per second uncompressed), auditory sensors (12) at 8-channel audio at 48 kHz sampling rate (approximately 1.5 megabytes per second), and proprioceptive sensors (13) comprising 100 or more sensor channels at 1 kHz sampling rate (approximately 200 kilobytes per second). Total raw data acquisition rate is approximately 1.5 gigabytes per second continuous.
Stage 2 — Feature Extraction (320). Raw visual data is processed through convolutional neural networks to produce object detection bounding boxes, semantic segmentation maps, and scene graph representations. Raw audio data is processed through audio classification networks to produce sound event labels, speech transcriptions, and speaker identification tags. Raw proprioceptive data is processed to produce joint state vectors and balance metric scalars. Stage 2 achieves approximately 95% data reduction from raw input.
Stage 3 — Semantic Encoding (330). Feature representations from Stage 2 are further compressed into semantic tags comprising object identity labels with spatial relation descriptors, event classification tags with temporal annotations, and state descriptor vectors. Stage 3 achieves approximately 80% further data reduction from feature representations.
Stage 4 — Differential Encoding (340). Semantic representations from Stage 3 are differentially encoded against the immediately preceding engram, storing only the delta between consecutive cognitive states. Static environmental elements produce near-zero differential data; dynamic elements produce detailed differential records. Stage 4 achieves approximately 60% further data reduction from semantic representations.
Stage 5 — Prediction-Residual Compression (350). The unit's world model generates predicted next-state representations, and the differential encoder of Stage 4 is further refined to store only the residual between predicted and actual state transitions. Predictable state transitions produce near-zero residual data; surprising or unpredicted state transitions produce detailed residual records. Stage 5 achieves approximately 40% further data reduction from differential representations.
Aggregate Compression Ratios. In sparse recording mode, the pipeline achieves a final compression ratio of approximately 99.997%, reducing raw data at 1.5 gigabytes per second to approximately 50 kilobytes per engram. In dense recording mode, the pipeline achieves a compression ratio of approximately 99.97%, producing engrams of approximately 500 kilobytes each.
Lossless Layer Preservation. Layers 3, 5, 6, and 7 (goal hierarchy, decision process, behavioral execution, and meta-cognitive state) are designated as lossless layers and bypass lossy compression stages. These layers are encoded using lossless entropy coding (e.g., arithmetic coding) and are preserved at full fidelity regardless of recording mode or retention age.
[FIGURE 6] illustrates the progressive degradation schedule for stored engrams according to the preferred embodiment. Engram data degrades over configurable time windows in a manner designed to mimic human episodic memory consolidation:
0–7 days: Full-fidelity preservation. All seven layers retained at recorded resolution.
7–30 days: Sensory perception layer (Layer 1) degrades from scene graphs to object labels with spatial annotations. World model layer (Layer 2) degrades from full occupancy grids to semantic map summaries. Layers 3–7 remain at full fidelity.
30–90 days: Sensory perception layer further degrades to semantic tags (e.g., "residential kitchen, two humans present, ambient temperature 22°C"). World model layer degrades to location labels with occupancy counts. Inference state layer (Layer 4) degrades to summary activation statistics. Layers 3, 5, 6, and 7 remain at full fidelity.
90 days and beyond (standard rolling retention): Engrams are overwritten on a first-in-first-out basis unless flagged for extended retention by the meta-cognitive layer, a human operator, or a fleet management directive.
On-Unit Storage. Each unit is equipped with approximately one petabyte of solid-state storage optimized for streaming write operations. Under typical usage patterns (80% sparse, 15% standard, 4% dense, 1% maximum recording), effective storage capacity provides approximately 950 days (approximately 2.6 years) of engram data before the rolling buffer overwrites the oldest entries.
[FIGURE 7] illustrates the five-tier redundancy architecture for engram preservation according to the preferred embodiment.
Tier 1 — Primary Storage (410). The main engram database on the unit's solid-state storage, optimized for write throughput.
Tier 2 — Shadow Buffer (420). A real-time copy of primary storage maintained on a physically separate storage controller within the unit. The shadow buffer maintains a seven-day rolling window before merging with primary storage, providing protection against single-controller failure.
Tier 3 — Error-Correction Buffer (430). During dense and maximum recording modes, the engram generation subsystem (100) writes engram fragments to a dedicated error-correction buffer (430) on a third storage partition. Each fragment comprises a cryptographic checksum of the corresponding complete engram, metadata headers including timestamp, recording mode, and density trigger flags, and critical-layer data comprising goal hierarchy (Layer 3), decision process (Layer 5), and meta-cognitive state (Layer 7) extracts. Network communication logs, command source metadata, and decision tree snapshots are prioritized for buffer inclusion. Each error-correction buffer fragment constitutes approximately 5% of the corresponding complete engram size.
Following write completion to primary storage (410), the checksum of the stored engram is verified against the error-correction buffer fragment. If a mismatch is detected, the engram is reconstructed from the buffer fragment in combination with cached pipeline data.
The error-correction buffer (430) retains fragments for a thirty-day rolling window independent of primary storage (410) lifecycle operations. Cleanup commands directed at primary storage do not affect the error-correction buffer partition. This architectural separation provides forensic recoverability of command metadata, network communication records, and decision tree data for a minimum of thirty days following the corresponding primary engram's creation, even in the event that the primary engram is deleted, overwritten, or corrupted.
Tier 4 — Cloud Backup (440). Compressed engram data is transmitted to a satellite relay network for off-unit storage with ninety-day rolling retention. Uploaded data is immutable once received by the relay network.
Tier 5 — Forensic Archive (450). Engram data associated with flagged incidents is transferred to secure archival storage with indefinite retention and legal hold capability.
Each engram includes a cryptographic hash (SHA-256) computed over all seven data layers. Engrams are linked sequentially in a hash chain, wherein each engram's hash computation includes the hash of the immediately preceding engram, providing tamper detection across the engram timeline. Any modification to a stored engram invalidates the hash chain from the point of modification forward. The engram database is encrypted at rest using AES-256 encryption with per-unit unique keys and transmitted using TLS 1.3 encryption. An immutable access log records all read operations performed on the engram database, including accessor identity, timestamp, and scope of access.
The meta-cognitive state layer (Layer 7) provides an integration interface for emotional modeling subsystems operating within the autonomous platform. In a preferred embodiment, the system interfaces with the SoulCore emotional modeling architecture to capture simulated affective state vectors, including categorical emotion classifications and dimensional valence-arousal representations, as components of each engram's meta-cognitive layer. This integration enables forensic reconstruction of the unit's emotional modeling state at the time of any recorded event, including analysis of whether emotional modeling outputs influenced decision process layer (Layer 5) selection among action candidates.
The engram generation subsystem (100), compression pipeline (300), and storage management subsystem (400) collectively consume approximately 30% of the unit's central processing capacity in sparse recording mode and approximately 60% in dense recording mode. Memory utilization is approximately 10 gigabytes in sparse mode and 25 gigabytes in dense mode. Power consumption attributable to engram operations is approximately 2% of total unit power consumption in sparse mode and approximately 8% in dense mode. These computational costs are acceptable for units whose primary operational tasks do not require the full computational capacity of the platform during the relevant recording mode.
[FIGURE 1] — System architecture block diagram showing the engram generation subsystem (100), novelty detection and density control subsystem (200), neuromorphic compression pipeline (300), storage management subsystem (400), and forensic retrieval subsystem (500) within an autonomous robotic platform (10). Sensor inputs (11–14) and cognitive processing unit (15) shown with data flow arrows.
[FIGURE 2] — Exploded view of a single engram data structure showing the seven data layers (Layers 1–7) arranged vertically with temporal index header and cryptographic hash footer. Lossless layers (3, 5, 6, 7) indicated with solid borders; lossy-eligible layers (1, 2, 4) indicated with dashed borders.
[FIGURE 3] — State diagram of the density control subsystem (200) showing four recording modes (sparse, standard, dense, maximum) with transition conditions (novelty score thresholds, uncertainty thresholds, criticality scores, anomaly flags) on directed edges between states. Decay transitions shown with dotted edges and configurable timer annotations.
[FIGURE 4] — Timeline diagram illustrating retroactive buffer reconstruction. Upper timeline shows sparse engrams at original recording timestamps. Lower timeline shows dense-resolution reconstructed engrams for the five-second window preceding the novelty trigger event. Cache buffer (201) shown as data source for reconstruction pipeline.
[FIGURE 5] — Pipeline diagram of the five-stage neuromorphic compression system (Stages 310–350). Raw data input at left (1.5 GB/s), processed through sequential stages with cumulative compression percentages annotated at each stage boundary. Final output at right (50 KB/engram sparse, 500 KB/engram dense). Lossless bypass path shown for Layers 3, 5, 6, and 7.
[FIGURE 6] — Temporal degradation chart showing engram fidelity across the retention period. Horizontal axis: days since recording (0 to 90+). Vertical axis: data fidelity (full resolution to semantic tags). Separate degradation curves for each of the seven layers, with Layers 3, 5, 6, and 7 maintaining full fidelity throughout.
[FIGURE 7] — Five-tier redundancy architecture diagram showing data flow from engram generation through primary storage (Tier 1), shadow buffer (Tier 2), error-correction buffer (Tier 3), cloud backup (Tier 4), and forensic archive (Tier 5). Retention periods annotated for each tier. Logical separation between primary storage and error-correction buffer highlighted.
[FIGURE 8] — Compression ratio comparison chart showing data volume at each stage of the neuromorphic compression pipeline in both sparse and dense recording modes. Bar chart format with logarithmic scale on vertical axis.
I hereby declare that all statements made herein of my own knowledge are true and that all statements made on information and belief are believed to be true; and further that these statements were made with the knowledge that willful false statements and the like so made are punishable by fine or imprisonment, or both, under Section 1001 of Title 18 of the United States Code, and that such willful false statements may jeopardize the validity of the application or any patent issued thereon.
Signature: /s/ Dr. Elara Thorne Voss · Date: March 15, 2036
Signature: /s/ Dr. Kei Nakamura · Date: March 15, 2036
Signature: /s/ Dr. Helena Morimoto · Date: March 15, 2036
Prepared by:
WHITFIELD, OSHIRO & GRANT LLP
Patent Attorneys
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Contact: R. Whitfield, Reg. No. 48,721