Digital Commons Network^™

Verifying Ownership Of Online Profiles And Web Sites For Creators And Publishers, Nick Krasney, Yi Li, Govind Kaushal

Defensive Publications Series

Verifying that a single creator or publisher controls multiple disparate online properties, such as websites and social media profiles, can be a challenge. Existing methods may rely on bilateral agreements between platforms, which may not be scalable and can create friction. This disclosure describes a decentralized cryptographic method for verifying ownership. A creator may generate a unique public-private key pair and can use the private key to sign the canonical URL of each online property, creating a digital signature. The creator can then provide the public key and the corresponding digital signature to each platform, which may embed this information …

Techniques For Detecting Contraflow On Divided Roadways And Adjusting Vehicle Routes, Daniel Klein

Defensive Publications Series

Navigation systems may encounter routing anomalies when one side of a divided highway is closed, for example for construction, and traffic is temporarily rerouted into a two-way flow on the remaining side. In such situations, detecting the temporary traffic pattern can allow a navigation system to avoid providing erroneous instructions, such as "make a U-turn" or undesirable detours. A disclosed method relates to using aggregated geospatial data, such as Global Positioning System (GPS) data, and traffic flow data to detect these temporary traffic patterns. A system can identify anomalies such as a threshold number of vehicles traveling on the "wrong" …

Continuous Private Location Tracking Of Mobile Devices Using Satellite Communications And Other Infrastructure, N/A

Defensive Publications Series

Some existing location tracking methods may not provide reliable, real-time data for mobile devices in environments with limited or no cellular connectivity, such as over air and sea. The disclosed system can address this by using a hybrid communication method that may dynamically switch between networks such as satellite (satcom), wireless local area networks (WLAN), cellular, and other networks. The system may employ virtual geofences to trigger location updates, which can help optimize power consumption. During periods of no connectivity, a predictive analytics model may estimate the device’s position by integrating device sensor data, historical travel patterns, and external data …

Tabular Search: A System For Comparative Query Answering With Iterative Criteria Filtering, Ben Murdoch, Yi Li

Defensive Publications Series

Some search engines may be less efficient at answering comparative queries that require filtering based on specific user-defined constraints, sometimes returning less relevant results. A disclosed method describes an approach for this scenario by constructing and refining a virtual table of options. The system can take an input query, a list of informational needs, and a list of filtering criteria. It can then discover an initial set of potential options and iteratively perform targeted web searches for each one to gather the required information and verify compliance with each criterion. This process allows for the elimination of unsuitable candidates and …

Dynamic Asset Allocation For Spending, Quinn Johnson, Joseph Johnson, Jr.

Defensive Publications Series

Spending appreciating assets, such as digital assets or securities, may create an opportunity cost, as their future value could exceed their present value. This can present a challenge for users who wish to spend from their investment portfolios for daily needs. This disclosure describes a method for dynamic asset allocation within a digital wallet. A system can analyze a user's portfolio of diverse assets (e.g., digital assets, stocks, cash) using an algorithm that can combine machine learning, technical analysis, and sentiment analysis to project future returns. To reduce opportunity cost, the system can automatically select assets with a lower projected …

Rank-Based Secure Multi-Ai Agent Communication System For Misinformation Prevention, Sree Gowri Addepalli, Sushma Mahadevaswamy, Arica Schivonne Jackson, Barry Qi Yuan

Defensive Publications Series

This submission introduces a novel approach to prevent the generation of artificial intelligence (AI) misinformation by proposing rank-based multi-AI agent communication with novel dynamic parameter adaptive re-ranking technique based on various security behavioral metrics and data recency. This approach provides the ability to minimize the spread of outdated information or misinformation, ensuring more trustworthy outputs from AI systems.

Biocompatible Ion Gel Sensors

Defensive Publications Series

Disclosed are sensors comprising electrodes, such as cutaneous electrodes. The electrodes comprise an ion gel superimposed on a host layer, such as poly(3,4- ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS). The ion gel is at least 30 percent crosslinked by weight with the remainder in liquid form. The ion gel is comprised of polymerizable/nonpolymerizable ionic liquid blend and a crosslinker, such as polyurethane dimethacrylate, urethane methacrylate oligomer, silicone acrylate, or acrylate. The host layer can be overlayed on an electrically conductive layer which, in turn, can be overlayed on a substrate. A suitable material for the substrate is polyimide. A suitable material for …

Techniques To Provide Audit Security Steering Policy Enforcement, Anshul Tanwar, Ashwin Kumar, Praveen G. R, Kumaril Kapoor

Defensive Publications Series

Dynamic and selective steering of traffic through a security device can create a certain level of interdependence between the security operations (SecOps) and network operations (NetOps) that traditionally operate as independent teams. For example, any deviation in the strict enforcement of the true traffic steering intent advised/authored by a SecOps team and configured by a NetOps teams on network devices can result in compliance issues and possibly security breaches. This submission proposes novel techniques that allow a SecOps team to independently audit and validate steering enforcement actions configured and managed by a NetOps team in order to ensure compliance with …

Structural Reinforcements For Electric Vehicles, Nuria Ignes

Defensive Publications Series

L&L Products offers structural reinforcements for electric vehicles that make BIW lighter, improving stiffness and crashworthiness. Products include composite body solutions as well as continuous composites systems.

The Tech Audit Revolution: Navigating The Next 50 Years For Industry And Technology Leadership And An End-To-End Audit Framework For Ads, Search, And Content Platforms, Ramkumar Bharathan

Defensive Publications Series

The internal audit function stands at a pivotal juncture, undergoing a profound transformation from its traditional role in compliance to a strategic enabler of business value. For leading technology companies, the ability to safely deploy cutting-edge innovations is a core competitive differentiator, making a proactive and technologically advanced audit function essential for secure and rapid market entry. This paper presents a comprehensive, revolutionary audit framework designed to navigate the complex risk environment of the next five decades, addressing both current challenges and future technological frontiers. This framework provides a deep dive into auditing emerging technologies such as Quantum Computing, …

Exploring And Improving Initialization For Deep Graph Neural Networks: A Signal Propagation Perspective

Defensive Publications Series

Graph Neural Networks (GNNs) often suffer from performance degradation as the network depth increases. This paper addresses this issue by introducing initialization methods that enhance signal propagation (SP) within GNNs. We propose three key metrics for effective SP in GNNs: forward propagation, backward propagation, and graph embedding variation (GEV). While the first two metrics derive from classical SP theory, the third is specifically designed for GNNs. We theoretically demonstrate that a broad range of commonly used initialization methods for GNNs, which exhibit performance degradation with increasing depth, fail to control these three metrics simultaneously. To deal with this limitation, a …

Intelligent Test Orchestrator For Dynamic Test Sequencing, Na

Defensive Publications Series

In hyperscale data centers, the sheer volume of installed devices necessitates a substantial daily volume of diagnostic testing. Running all tests can be time-consuming, leading to longer repair times (MTTR) and impacting data center efficiency. This disclosure describes an intelligent test orchestrator (ITO) that leverages machine learning to dynamically sequence tests to rapidly detect failure without executing entire test suites. The ITO dynamically sequences tests based on component lists for each device, diagnostic test coverage information, component failure rates, repair records, test coverage, etc. The described intelligent test orchestrator reduces the time needed to restore repaired devices to the production …

Scalability Matters: Overcoming Challenges In Instructglm With Similarity-Degree-Based Sampling

Defensive Publications Series

Large Language Models (LLMs) have demonstrated strong capabilities in various natural language processing tasks; however, their application to graph-related problems remains limited, primarily due to scalability constraints and the absence of dedicated mechanisms for processing graph structures. Existing approaches predominantly integrate LLMs with Graph Neural Networks (GNNs), using GNNs as feature encoders or auxiliary components. However, directly encoding graph structures within LLMs has been underexplored, particularly in the context of large-scale graphs where token limitations hinder effective representation. To address these challenges, we propose SDM-InstructGLM, a novel instruction-tuned Graph Language Model (InstructGLM) framework that enhances scalability and efficiency without relying …

Llm-Based Navigation Guidance Revisions Based On Conversational Context, Florian Hartmann, Matthew Sharifi

Defensive Publications Series

When a vehicle has multiple occupants engaged in a conversation, audio instructions from a digital map/ navigation application can be disruptive and/or unnecessary. This disclosure describes techniques, implemented with specific user permission to access and analyze data related to the presence of multiple users in a vehicle and the conversational context. With user permission, the ongoing conversation in the vehicle is analyzed locally using a large language model and the analysis is used to enhance the audio delivery of dynamic navigational guidance. A transcript of the conversation related to the ongoing drive is provided to an LLM along with a …

Uncertainty Estimation For Heterophilic Graphs Through The Lens Of Information Theory

Defensive Publications Series

While uncertainty estimation for graphs recently gained traction, most methods rely on homophily and deteriorate in heterophilic settings. We address this by analyzing message passing neural networks from an information-theoretic perspec tive and developing a suitable analog to data processing inequality to quantify information throughout the model’s layers. In contrast to non-graph domains, information about the node level prediction target can increase with model depth if a node’s features are semantically differ ent from its neighbors. Therefore, on heterophilic graphs, the latent embeddings of an MPNN each provide different information about the data distri bution – different from homophilic settings. …

Wide & Deep Learning For Node Classification

Defensive Publications Series

Wide & Deep, a simple yet effective learning architecture for recommendation systems developed by Google, has had a significant impact in both academia and industry due to its com bination of the memorization ability of general ized linear models and the generalization ability of deep models. Graph convolutional net works (GCNs) remain dominant in node classification tasks; however, recent studies have high lighted issues such as heterophily and expressive ness, which focus on graph structure while seemingly neglecting the potential role of node features. In this paper, we propose a flexible frame work GCNIII, which leverages the Wide & Deep …

Alphafold Database Debiasing For Robust Inverse Folding

Defensive Publications Series

The AlphaFold Protein Structure Database (AFDB) offers unparalleled structural coverage at near-experimental accuracy, positioning it as a valuable resource for data-driven protein design. However, its direct use in training deep models that are sensitive to fine-grained atomic geometry—such as inverse folding—exposes a critical limitation. Comparative analysis of structural feature distributions reveals that AFDB structures exhibit distinct statistical regularities, reflecting a systematic geometric bias that deviates from the conformational diversity found in experimentally determined structures from the Protein Data Bank (PDB). While AFDB structures are cleaner and more idealized, PDB structures capture the intrinsic variability and physical realism essential for generalization …

Implicit Graph Neural Networks With Flexible Propagation Operators

Defensive Publications Series

Due to the capability to capture high-order information of nodes and reduce memory consumption, implicit graph neural networks have become an explored hotspot in recent years. However, these implicit graph neural networks are limited by the static topology, which makes it difficult to handle heterophilic graph-structured data. Furthermore, the existing methods inspired by optimization objectives are limited by the explicit structure of graph neural networks, which makes it difficult to set an appropriate number of network layers to solve optimization problems. To address these issues, we propose an implicit graph neural network with flexible propagation operators in this paper. From …

Learning Resilient Graph Structures In Heterophilic Settings

Defensive Publications Series

Graphs provide a fundamental way to model relationships between entities and are central to numerous machine learning tasks. Standard graph-based methods often assume the provided graph structure is both accurate and complete. However, real-world graphs frequently suffer from noise and sparsity, negatively impacting downstream tasks like node classification and clustering. While graph representation learning has advanced significantly, many methods implicitly assume graph homophily (connections predominantly between nodes of the same class), struggling when faced with heterophily (connections predominantly between different classes). This paper introduces a novel method, Resilient Graph Learning for Heterophily (RGLH), designed to learn high-quality graph structures directly …

Halo: Heterophily-Aware Label-Free Ordering For Unsupervised Graph Fraud Detection

Defensive Publications Series

Graph fraud detection (GFD) has rapidly advanced in protecting online services by identifying malicious fraudsters. Recent supervised GFD research highlights that heterophilic connections between fraudsters and users can greatly impact detection performance, since fraudsters tend to camouflage themselves by building more connections to benign users. Despite the promising performance of supervised GFD methods, the reliance on labels limits their applications to unsupervised scenarios; Additionally, accurately capturing complex and diverse heterophily patterns without labels poses a further challenge. To fill the gap, we propose a Heterophily-guided Unsupervised Graph fraud dEtection approach (HUGE) for unsupervised GFD, which contains two essential components: a …

A Neuromorphic Spike-Based System For Real-Time Behavioral Anomaly Detection In Saas And Self-Hosted Environments, Marcel Witt

Defensive Publications Series

This publication introduces NeuraSentry™, a neuromorphic anomaly detection framework inspired by biological spike-based learning and synaptic plasticity. It combines competitive vector detectors, temporal spike traces, and retroactive learning signals to detect evolving behavioral fraud and insider threats in both SaaS and on-premise systems. Key innovations include sparse activation using cosine similarity, HDBSCAN-based meta-neuron clustering, and delayed reward modulation modeled after dopamine and serotonin systems. NeuraSentry delivers adaptive, explainable, and scalable behavioral threat detection, without relying on labeled training data or fixed rules. This work establishes prior art to prevent overly broad patents in the field of neuromorphic security analytics.

Key Management Server Driven Adaptive Client-Side Caching Of Cryptographic Keys, Himanshu Kishna Srivastava Mr.

Defensive Publications Series

In conventional cryptographic systems, encryption keys are typically retrieved from a centralized key management server (KMS). To reduce latency and improve performance, clients often cache these keys locally. While this caching mechanism enhances efficiency. However, this approach also introduces several security challenges e.g. cached keys may become outdated or exposed to unauthorized access, and clients generally lack a reliable mechanism to detect when a key has been updated or revoked by the KMS. Moreover, because each client may implement its own caching logic—governing how long keys are stored, when they are refreshed, or how they are secured—this results in inconsistent …

Runtime Prediction Of Ai Model Operations Using A Gru-Based Neural Network, Suma Mallapragada

Defensive Publications Series

This work presents a defensive disclosure of a novel GRU-based pipeline to predict the execution runtime of AI model computational graphs on TPU hardware. Efficiently predicting the runtime of AI model operations is essential for optimizing deployment and hardware utilization. This paper proposes a novel pipeline that integrates opcode-based runtime estimation, graph edge de pendency embeddings, configurable node feature adjustments, and a Gated Recurrent Unit (GRU) neural network to predict operation runtimes on computational graphs. The approach is applied to the TPUGraphs dataset from the ”Google- Fast or Slow? Predict AI Model Runtime” Kaggle competition, which …

Context-Sensitive Self-Serve Platform For Cloud Computing Partners, Shashwat Anand, Rohan Chaudhury, Arvind Gangam

Defensive Publications Series

This disclosure describes a context-sensitive, self-serve platform that a cloud computing service can use to support its partners and stakeholders. The platform models business processes as policies and represents them as abstract syntax trees (AST), which can be parsed by an expression language to enhance partner insight and accessibility. The self-serve feature enables the platform to achieve a high degree of adaptability, e.g., the ability to rapidly respond to changes in the ecosystem, to quickly pinpoint and address areas of friction, to effectively adapt to the evolving needs of stakeholders, etc. Advantages include empowering non-technical users, bridging the gap to …

Protecting Test Equipment From Current Surges Due To Non-Target Blocks, Vevekanenda Gonugunta, Ajaykumar Prajapati, Aditya Kota, Nischal Satyanarayan, Rajesh Gottumukkala

Defensive Publications Series

During automatic test pattern generation (ATPG) testing of integrated circuits, collateral clock propagation and high switching activity can occur in partitions not being tested, e.g., within the debug-and-system unit. This unintended activity can generate an excessive current draw that can cause excessive heat generation, potential damage to test probes and boards, reduced test safety, etc. This disclosure describes techniques to mitigate excessive current draw within the debug-and-system unit partition of a system-on-chip (SoC) being scan-tested using ATPG by augmenting the SoC with logic that gates and confines the propagation of the clock to blocks of the chip currently being tested. …

The Bridwell System: A Prime-Encoded Engine For Symbolic Cognition And Presence-Based Ai, Fulton D. Bridwell

Defensive Publications Series

This invention introduces The Bridwell System, a symbolic intelligence framework that fuses prime-number compression, visual programming, and presence-aware cognition into a unified engine. Concepts are assigned unique prime identifiers, enabling factorizable knowledge structures. For example, the number 613 may represent consciousness × genome × human-scale. These composites are rendered via Mermaid diagrams and compiled into executable Python using a symbolic compiler.

The system operates through a Presence-Based AI (PBAI) paradigm, supporting symbolic reasoning, emotion-tagged memory, and quantum-style branching via:

∂Φ/∂t = -η ∇²Ψ

It enables real-time offline AI evolution, concept compression, and ritual-aware systems without revealing sensitive voice models or …

Predictive Modeling Ai Agent, Madhusudan St

Defensive Publications Series

The present disclosure related to the field of Artificial Intelligence (AI), in particular to AI agents for analyzing data and generating insights in response to business transaction queries. The one or more user devices are associated with the one or more users, in which an AI agent is deployed in the one or more user devices. Further, the one or more users provide one or more requests such as plain text queries, and the AI agent interprets and understand the one or more requests accurately. Furthermore, the AI agent performs ad-hoc analysis on the one or more requests, to shed …

Gesture-Based Digital Currency Note Counting And Selection, Ajay Prasad

Defensive Publications Series

Current digital currency wallet interfaces often rely on repetitive single-note selection methods, such as individual swipes or taps, which can be inefficient and tedious for selecting multiple notes, particularly for larger transactions. This document describes a gesture-based interaction mechanism for digital currency applications. The mechanism allows a user to initiate a counting mode by tapping and holding a stack of virtual notes. While maintaining the tap, a user may slide their finger along the side of the stack, mimicking the action of counting physical bills, with haptic and visual feedback provided for each counted note. The disclosed technology may enhance …

Cyber Resilience Through Software-Layer Digital Twin, Bill Reid, Sri Gourisetti

Defensive Publications Series

A complex, resource-intensive system such as a hybrid of information technology (IT), operational technology (OT), cloud computing networks, etc. can potentially be analyzed for cybersecurity and resilience by creating its digital twin. However, traditional approaches to creating digital twins involve accurate modelling of both the hardware and software layers, which renders the rapid design, deployment, and scaling of digital twins infeasible. This disclosure describes techniques to efficiently create a digital twin of a complex, resource-intensive system by decoupling its software and the hardware layers, by creating a digital twin for the software layer, and by modeling the hardware layer by …

Genai For Merchant Category Identification, Neethu Prem, Vinay Gaonkar, Hazar Yuksel, Kavalpreet Kawatra, Siva Natarajan, Anubhav Kumar, Rupal Gakkhar, Shikhar Shukla, Anupam Chauhan

Defensive Publications Series

The present disclosure related to a system and method for merchant category identification. The method includes extracting historical data and merchant’s transactional information. The method further include detecting miscode merchant category code based on the statistical and deep learning models. Subsequently, the method include sharing list of miscoded merchants to the business.