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SYSTEM AND METHOD FOR DISTRIBUTED AI MODEL PROCESSING USING COMMODITY GRAPHIC ACCELERATORS WITH INTELLIGENT WORKLOAD ORCHESTRATION
No full textThis present disclosure relates to the field of Artificial Intelligence (AI), in particular to a system and method for distributed AI model processing using commodity graphic accelerators with intelligent workload orchestration. A system and method are provided for registering and utilizing commodity hardware equipped with graphic accelerators for distributed Artificial Intelligence (AI) model inference in both batch and real-time processing environments. The system comprises a hardware registration module for onboarding GPUs and other graphic accelerators, an AI model registration module for cataloguing models and their specifications, and an AI-based orchestration agent that analyses model characteristics, including complexity, data volume, feature set, and lifecycle stage to determine optimal hardware allocation. The orchestration agent, in collaboration with other distributed agents, dynamically assigns workloads to registered hardware resources, prioritizing underutilized legacy devices. This approach enables efficient resource utilization, scalability, and promotes a decentralized, blockchain-like ecosystem for Al processing tasks
Reference-guided Generative Augmentation of Low-Quality Images
No full textMany photo archives, e.g., personal photo libraries, suffer from poor quality due to factors such as capture with older, low-resolution cameras, motion blur, etc. Such images, while possibly holding significant sentimental value, are visually unsatisfying and unsuitable for printing or for high-resolution display. This disclosure describes techniques that generatively fill in contextually accurate detail in a low-quality image. Multimodal analysis of the image is performed to detect key regions of interest (faces, objects, etc.) that are low quality (blurred, pixelated, etc.). These regions are used to retrieve relevant, high-fidelity reference images from personal and global data corpora. The resulting multimodal data, including the original image, textual descriptions, and visual references, etc., are used to condition a generative model. The generative model synthesizes an updated image with new, contextually accurate detail
Device for taking images of an aircraft engine
No full textA device for taking images of an aircraft engine, comprising a. a frame (1), in particular a mobile frame (2); b. a rotatable beam (2), in particular rotatably mounted to the frame, comprising i. a plurality of attachment elements, each attachment element configured to allow for a camera module (3) to be releasably mounted thereto
Synthetic Organismic Governance: The UTLP Architecture for Biological Time Synchronization, Liquid Aperture Synthesis, and the Epistemology of Generative Prior Art
No full textThis technical supplement extends the Universal Time Lord Protocol (UTLP) by formalizing the Mind-Body governance model required for planetary-scale distributed systems. It asserts that while application layers may utilize political consensus, the underlying timing and transport layers must operate on pre-rational biological principles to maintain stability under relativistic conditions.
This document establishes 103 Prior Art Claims, detailing the specific mechanisms that allow silicon nodes to mimic the homeostatic resilience of biological organisms. Key contributions include:
Physics (The Liquid Aperture): Defining the governance logic for a Liquid Software-Defined Aperture, where the synchronization layer utilizes Servo-Locked frequency modulation (slewing) rather than phase stepping. This allows the array to maintain the continuous waveform integrity required for volumetric beamforming even while actively adjusting to time-of-flight drift.
Governance (The Immune System): Replacing static Access Control Lists (ACLs) with Bio-Evolutionary Authentication, specifically the Missing Self protocol derived from Natural Killer (NK) cells. This mechanism treats node silence or non-compliance not as a legal violation, but as a metabolic threat, triggering automated resource starvation (anergy) without central adjudication.
Epistemology (The Method): Formalizing the Isomorphism Stress Test (The Algorithm of the Obvious ), a reproducible discovery methodology that uses adversarial AI synthesis to distinguish superficial analogies from commutative structural truths. This section documents the specific prompt architectures used to excavate these claims, effectively raising the baseline of Ordinary Skill in the Art.
Sensing (The Application): Detailing the Ground-Based Distributed InSAR implementation, which utilizes the UTLP timing mesh to perform interferometric synthetic aperture radar using consumer-grade mmWave hardware ($50), achieving micron-scale displacement sensitivity for seismology and structural health monitoring previously reserved for orbital platforms.
Document Type: Technical Specification & Methodology Claim Count: 103 Keywords: Liquid Aperture, Synthetic Organismic Governance, Bio-Evolutionary Authentication, Isomorphism Stress Test, Distributed InSAR
LLM Fine-Tuning Using a Multimodal Reward Model Trained with Ground Truth
No full textThis disclosure describes techniques to improve the accuracy of large language model (LLM) responses by using a trained multimodal reward model (RM) to fine-tune the LLM. In contrast to traditional techniques that train the RM without consideration of the ground truth, the RM is trained using the prompt (including image and/or other forms of input), the response, and the ground truth. The trained RM can be applied to score the LLM response to the prompt in relation to the ground truth. The score can be used to fine-tune the LLM to improve the accuracy and the form of its responses to queries of a similar form (e.g., including image and/or other forms of input). The training data for the RM can include positive examples (examples consistent with the ground truth) and negative examples (examples that deviate from the ground truth) generated by a generative model. Training an RM over ground truth and using such a trained RM to score the LLM can improve the accuracy of LLM responses
Emergent Bias and Fairness in Multi-Agent Decision Systems
No full textThe present subject matter relates to a systematic study of emergent bias and fairness dynamics in multi-agent LLMdecision making. In particular, the present disclosure compares single-agent performance with memory-based and collective refinement multi-agent discussion paradigms across a suite of group fairness metrics on tabular datasets with sensitive attributes. Across settings, fairness changes from multi-agent collaboration relative to single-agent baselines are typically concentrated around small negative values or near zero, indicating that bias is most often only marginally reduced or remains unchanged, while a thin but long positive tail shows rare cases where multi-agent interaction can substantially worsen bias. These findings motivate systematic investigation into the conditions under which multi-agent collaboration mechanisms mitigate or amplify bias
REMOTE PROCEDURE CALL PERFORMANCE SERVICE FOR PERFORMANCE TESTING
No full textA new remote procedure call (gRPC) performance service is proposed herein, referred to as a gRPC-Perf service, which can enable a network operator to understand gRPC limitations for any gRPC service that the network operator intends to use between any two points in a network. The gRPC-Perf service may also be useful for troubleshooting gRPC performance issues (e.g., whether an issue is a client issue or a network issue based on direction of traffic)
Agentic Framework Integrating RAG for Policy Queries and Autonomous Trend Analysis
No full textTools that just pull up whole, complicated company policy documents often don\u27t help people find the specific answers they need. This new technology is a system for understanding and analyzing policies that works in two ways.
A Reactive Mode acts like a Q&A tool. It uses advanced technology (retrieval-augmented generation) to break down policies into small, searchable pieces. This allows the system to give direct answers with citations and even run simulations to show what happens in different situations based on the rules.
A Proactive Mode is an automated agent that constantly checks support data to spot trends and see how quickly certain issues are growing. Based on what it finds, this agent can take action, such as pointing out missing information in the knowledge base or flagging ongoing, serious problems.
Overall, this system provides personalized answers and helps the company improve its internal knowledge, which means employees won\u27t have to rely as much on human support teams
Automated Generation of Image Alternative Text Attribute Text
No full textWeb and document accessibility is frequently impeded by missing or low-quality alternative text (“alt text”), which is descriptive information attached to an image that screen readers use to describe the content to visually impaired users. This deficiency creates a significant barrier for users who depend on screen readers to interpret visual content on webpages. This disclosure describes a method whereby a web browser or document viewer utilizes a large language model to analyze the content of an image to be displayed, for example an image to be displayed in a document. If the image is not associated with alternative text that describes the image adequately, the web browser or document viewer generates or modifies descriptive text about the image and dynamically inserts it into an image alt text attribute such as the alt text attribute within a Document Object Model (DOM), a accessibility tree, or a tag tree where it can be accessed by a screen reader. The principal objective of this technique is to improve the experience for users of assistive technologies by automatically providing meaningful descriptions for previously difficult to access visual content
Context-Aware Adjustment of Voice Characteristics for Artificial Intelligence Agents
No full textTraditional artificial intelligence agents, such as in smart eyewear typically utilize static voice profiles that do not account for the user\u27s immediate environment or interaction history. This lack of context can result in auditory outputs that are disruptive in quiet settings or unintelligible in noisy surroundings and the overall experience. This disclosure describes a method for dynamically adjusting voice characteristics, such as volume, pitch, timbre, and directivity, based on real-time soundscape analysis and location history. Input is gathered from sensors to extract contextual cues, including environmental noise levels and the user\u27s own vocal delivery. For instance, if a user whispers, the response is delivered in a corresponding whisper. If the smart eyewear determines that one is in an environment that suggests being quiet, such as at a library or in a courtroom, the response is provided as a whisper. By tailoring the auditory interface to the specific situational context, the intelligibility of the agent is maintained, and the social appropriateness of the interaction is improved