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CAUSAL ECHO BINDING FOR EXPLAINABLE AI
No full textCausal Echo Binding, as proposed herein, is a next-generation explainable AI technique that traces echoes of causal influence backward through a model’s decision process, binding them to evolving, human-understandable concepts and validating each link with built-in counterfactual tests. Unlike static feature attribution methods, the technique produces a verifiable, time-aware narrative of why and when an AI system acted, enabling deeper trust and auditability. For a network equipment provider, this capability can enhance AI-driven networking, security, and observability products by providing transparent, regulator-ready explanations for automated decisions - from pinpointing the root cause of a network change to justifying a security alert
Xenobot-Inspired Diatom Analogues for Synthetic Kinematic Renewal of Regenerative Nanobot Payloads
No full textA system and method are presented for a time-bounded, non-genetic regeneration framework in which synthetic, diatom-shaped functional organelles are continuously produced, deployed, and exhausted within a confined system. The approach relies on geometry-driven kinematic assembly, droplet and gel physics, and swarm-level population homeostasis rather than cellular replication or organism growth. A critical design invariant enforces the release of exactly two functional organelles per release event, ensuring bounded output, predictable dynamics, and safety. The disclosure provides laboratory-recognizable implementation pathways suitable for materials science, soft-matter physics, and bioengineering groups and is released for public use to establish prior art
Change Detection in an Electronic Device Using Resonant Frequency Analysis
No full textDetecting internal physical changes in an electronic device (e.g., a smartphone, smart watch, wearable device) may be difficult with some conventional methods, and is often limited to chemical or electrical monitoring that fails to detect structural changes. A system can perform a mechanical resonant frequency analysis using existing hardware components, such as a haptic actuator and one or more sensors (e.g., an accelerometer, a microphone). The haptic actuator can be configured to generate a vibrational stimulus across a range of frequencies, while the sensors can measure the device\u27s corresponding response. This measured response data can be processed to generate a frequency response profile, which can then be compared against a stored baseline profile representing a nominal structural state. Deviations between the current and baseline profiles, such as shifts in resonant frequencies or changes in damping, may provide a non-invasive technique for early detection of structural changes within the device. These changes can include battery swelling, adhesive failure, component loosening, or liquid ingress
Integrated Road Usage Fee Management Within Digital Navigation Systems
No full textDigital navigation systems may identify routes with fee-based road segments, such as tolls or vignettes, but may not provide an integrated method for payment. This can result in a fragmented and manual process for drivers with varied and unfamiliar payment systems. A system is described for integrating road usage fee management within a digital navigation service. The system can utilize a route analysis engine to identify fees, a client-side geofencing module on a computing device (e.g., a smartphone, smart watch, wearable device, in-vehicle infotainment system, augmented reality glasses, etc.) To detect a vehicle\u27s entry into a fee-based zone, and a transactional orchestration service to execute payments with tolling authorities. Automating the identification, calculation, and payment of road usage fees within the navigation workflow can reduce manual driver intervention during transit
Continuous Ambient Microphone Calibration via Opportunistic Broadband Auditory Icons
No full textPerformance drift in microphone arrays is often caused by environmental factors such as dust, moisture, and grime accumulating over time. This spectral mismatch degrades the performance of spatial audio algorithms that rely on precise array calibration. To address this limitation, a continuous calibration method is disclosed wherein broadband earcons are opportunistically played through device speakers. Audio responses are recorded by on-device microphones during defined user scenarios. A background model is generated to filter out environmental noise and room reverberation from these recordings. Subsequently, the filtered in-field measurement is compared against a reference factory template to estimate spectral deviation. A calibration filter is then calculated and applied to correct the detected drift. High-fidelity spatial audio performance is thereby maintained without requiring manual intervention or specific user actions. In this way, the microphone calibration method is continuously and ambiently recalibrated, ensuring consistent performance despite changing operating conditions
Strategic report: Ir@C60 in regenerative medicine and longevity
No full textThis document, produced with the assistance of GPT-5.2 Thinking and Gemini 3 Raisonnement, is released under the Apache License 2.0. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes : EPC Art. 54(2) (European Patent Convention), French IPC Art. L 611-11 (French Intellectual Property Code), 35 U.S.C. §102(a) (United States Patent Act), Chinese Patent Law Art. 22(5) (中华人民共和国专利法), and Japanese Patent Act Art. 29(1) (特許法). It discloses enabling innovations around Ir@C60: arc/laser synthesis, HPLC purification and density-gradient pre-enrichment, QA/QC standards, IV/oral formulations, regenerative biomaterials, bioelectronic sensors, CT imaging and AI reconstruction, closed-loop dosing protocols, cloud/federated safety analytics, and supply chain/packaging engineering, in order to pre-empt future patent claims.Original Zenodo url: https://zenodo.org/records/1811672
System and Method for Autonomous Cross-Domain Collaborative Artificial Intelligence–Driven Discovery and Innovation
No full textThe presented proposal discloses a system and method for autonomous, collaborative artificial intelligence–driven discovery and innovation. The techniques presented herein enables structured interaction among multiple specialized AI software agents, each possessing domain-specific expertise, to perform real-time cross-domain reasoning without continuous human intervention. An interaction controller governs communication, reasoning exchange, and collaboration rules among the agents, while an evaluation module assesses novelty, feasibility, and domain validity of generated outputs. Through iterative feedback and refinement, the proposed system synthesizes knowledge across disciplines to generate robust, non-obvious solutions and inventive outcomes. The proposed architecture overcomes limitations of conventional single-domain and task-parallel AI systems by enabling genuine intellectual collaboration, scalable integration of new domains, and autonomous discovery applicable to scientific research, engineering, healthcare, and other high-complexity use cases
System for Determining Impact Orientation in a Drop Test Using an Acoustic Trigger
No full textDetermining a device\u27s impact orientation during randomized drop tests can present challenges, as methods relying on internal accelerometers can be less reliable in some scenarios involving rotation and intermediate collisions. A system can utilize an external acoustic trigger to initiate data capture. For example, an optical sensor, such as an infrared gate, may be positioned in a drop apparatus to detect a falling device under test (DUT) and prompt an acoustic emitter to produce a specific audio tone. A software application on the DUT can use the device\u27s microphone to detect this tone, which can serve as a temporal marker to begin recording data from an onboard inertial measurement unit. This approach may decouple the start-of-fall detection from the DUT\u27s state of motion, which can facilitate the determination of the device\u27s three-dimensional impact orientation and velocity for product reliability testing
SMART RECURRING PAYMENT AUDITOR FOR BAIT-AND-SWITCH MITIGATION
No full textThe present disclosure relates to the field of digital payment security, in particular to a system and method for mitigating bait-and-switch practices in recurring digital payment systems. The smart recurring payment auditor disclosed herein operates at a network layer of the payment infrastructure. The smart recurring payment auditor employs a multi-modal deep learning ensemble, advanced bitmapped feature encoding and temporal pattern synthesis to analyse recurring payment behaviour. Further, the smart recurring payment auditor integrates global merchant telemetry, user behavioural feedback, and descriptor intelligence to detect and score deceptive recurring billing schemes. Furthermore, upon identification of a suspicious pattern, the smart recurring payment auditor initiates user-centric, token-based payment control actions and generates intelligent user prompts, thereby protecting consumers from unintentional financial commitments, enhancing trust in digital payments, and supporting regulatory compliance
Automated Rule Generation for Tiered Systems Using Multi-Stage Failure Learning
No full textIn multi-tiered anomaly detection systems, a challenge may exist in balancing the operational cost of accurate analytical models with the accuracy of less expensive rule engines, where manual rule creation can be slow. A described technology can address this by, for example, monitoring a multi-stage processing cascade and employing multi-stage failure learning. A system can identify instances where a low-cost analysis tier does not detect an anomaly that a higher-cost tier subsequently identifies. The system may then extract the successful detection pattern and use a generative model to formulate a new, low-cost rule that codifies this logic. This process can enable the deployment of learned intelligence to earlier processing tiers, which may reduce reliance on computationally expensive analysis for recurring patterns, help optimize operational costs, and improve adaptive capabilities