Centralized QR Trust Repository (CQTR) with Merchant Name Uniqueness for Fraud Prevention in QR Code Payments

The disclosed systems and methods use a Centralized QR Trust Repository (CQTR) for securing and streamlining merchant onboarding and transaction validation, and to address critical challenges in QR-based payments such as cross-acquirer fraud and counterfeit QR codes system performs transaction validation. At the time of onboarding, merchant details such as merchant or business name, account information, and KYC are collected by client servers and CQTR conducts uniqueness validation to ensure name uniqueness across all acquirers. CQTR performs similarity checks, including fuzzy matching and phonetic analysis and prevents duplicate registrations. After successful validation, CQTR generates and registers unique QR codes static or dynamic linked to the merchant’s identity. For transaction processing, the system verifies QR authenticity by matching QR hashes or templates against CQTR’s secure database before payment authorization. Merchant identity is confirmed to the customers in real-time thus the risks of fraudulent or counterfeit QR codes are mitigated

SYSTEM AND METHOD FOR DYNAMIC GROUPING AND RESOLUTION OF CORRELATED TROUBLE TICKETS IN A NETWORK SERVICE ENVIRONMENT

Proposed herein is a system that enables an operations platform to automatically detect, group, and resolve multiple trouble tickets that stem from a single underlying network or service issue. For each new ticket, the system builds a composite representation that combines semantic content with structured attributes and topology context, then compares it against recent tickets within sliding time windows to identify correlated incident groups. When such a group is identified with sufficient confidence, the platform runs a single, group-level troubleshooting and remediation workflow targeting the shared dependency and propagates the resulting root cause, actions, and resolution status back to all member tickets, closing them efficiently while continuously refining correlation behavior over time

Feasibility Study: Frugal Production of Au@C60 (Gold Endohedral Fullerene)

This document, produced with the assistance of ChatGPT 5.2 Thinking and Gemini 3 Raisonnement, is released under the Apache 2.0 licence. 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 PC 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 frugal, industrializable routes to synthesize, purify, characterize, formulate, and deploy Au@C60 (helium arc discharge, composite anodes, arc control, gas recirculation, quench/soot collection, solvent extraction, antisolvent-driven colloid formation, density gradients, batch/continuous ultracentrifugation, solvent recycling, gold recovery). It further discloses theranostic uses (CT contrast, imaging-guided dosing), devices (redox/ROS sensors, photoactivable wound dressings, implant coatings), and data/AI building blocks (interoperability, federated learning, secure MLOps, biomarker closed-loop control). Original Zenodo url: https://zenodo.org/records/1811580

Research Report and Business Case: “VCP-V1” Device for Detecting Non-Coherent Photonic Projections

This document, produced with the assistance of GPT-5.2 Thinking and Gemini 3 Reasoning, 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). This report discloses an optronic + software system (VCP-V1) designed to discriminate real objects from synthetic wavefronts/photonic projections using second-order intensity correlations g(2), speckle analysis, polarimetric imaging (Stokes), topological signatures (Berry phase, OAM), spectral filtering (silica Bragg gratings, tunable filters), optional active probes (ToF/LiDAR), and edge+cloud AI fusion, including calibration/QA, traceability, and operational workflows. Original Zenodo url: https://zenodo.org/records/1811897

Comprehensive Study of the Topological Phase Resonance Filter (TPRF)

This document, produced with the assistance of GPT-5.2 Thinking and Gemini 3 Reasoning, 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 chiral micro-helix metamaterial architectures (Berry phase) operating as a passive phase-stabilization filter, a high spin-orbit Cu–Au–Bi alloy, nano-fabrication routes (3D printing + ALD + sacrificial removal, GLAD variant, ion implantation), qualification protocols (reference coupon, end-of-line metrology, 3D AI-imaging inspection), and operational methods (closed-loop tuning, security, traceability, certification). Original Zenodo url: https://zenodo.org/records/1812020

LLM Fine-Tuning Using a Multimodal Reward Model Trained with Ground Truth

This 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

System for Validating Medical Software Algorithms With Closed-Loop Adversarial Persona Generation

Validating conversational artificial intelligence (AI) for regulated medical software applications may present challenges, as static test datasets and manual review may be limited in identifying emergent, conversational anomalies. A multi-agent AI system may be configured in a closed-loop for automated validation. The system can, for example, utilize an end user persona simulator agent to generate prompts for a target model and a domain /regulatory expert adjudicator agent to evaluate the target model’s responses against a configurable rubric. A meta-analysis agent can analyze anomalies to identify underlying vulnerabilities, which may then be used to programmatically synthesize new adversarial personas. This adaptive process can generate evidence to support regulatory compliance and continuous performance monitoring for medical software algorithms systems

Chrono-Synchronous Resonance Anchor (ARCS) – Technical and Strategic Report

This document, produced with the assistance of GPT-5.2 Thinking and Gemini 3 Reasoning, 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). Description: this report discloses systems, materials, methods, and software for clock phase stabilization and jitter reduction (jitter cleaning), based on photorefractive phase memory (bismuth-doped Bi12SiO20) coupled to a 432 Hz quartz reference (and harmonic locking), including calibration/QA workflows, EMI-shielded packaging, time-security primitives (attestation, anti-rollback), interoperability, federated learning, timing-integrity certification, and variants for 6G, edge, data centers, automotive, circadian wellness, imaging, and supply chain.Original Zenodo url: https://zenodo.org/records/1811981

System for Context-Aware Payment Recommendations Using Retrieval-Augmented Generation

A system can generate context-aware payment recommendations to assist users in selecting a suitable payment method, which may be beneficial given the complexity of financial product benefits. The system may employ a multi-stage processing pipeline where, for example, an intent classifier can first infer a user\u27s need from a transactional context. A request may then be processed by a handler that can query a retrieval-augmented generation system to obtain relevant data from a knowledge base. A large language model may synthesize this data to generate a structured output, such as a JSON payload containing both human-readable text and machine-readable user interface directives. The structured JSON output is designed to bridge the gap between unstructured natural language and platform-specific interactive UI components. This process can enable a client application, such as one on a smartphone or wearable device, to dynamically present payment guidance to a user, for example, within a checkout experience

Automated Generation of Image Alternative Text Attribute Text

Web 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