Abstract: An advanced system and method for conducting automated patent infringement analysis and prior art review are disclosed. The system utilizes artificial intelligence (AI) technologies, including machine learning, natural language processing, and computer vision, to evaluate target products against existing patents and prior art references. The invention features a data ingestion module capable of processing patent documents, including design patent drawings and utility claim texts, and a machine learning model trained to identify patterns indicative of patent infringement. Additionally, the system includes a novelty detection module that benchmarks product attributes against prior art to assess innovation. Users interact with the system through a secure interface to input product data and receive comprehensive analysis reports, which detail infringement risks, similarity scores, and novelty assessments.

Abstract: A data processing system is configured to identify treatment responsive to a health risk determined from feature data provided by one or more networked data sources. A classification engine generates a feature vector based on a natural language processing (NLP) of input data representing words provided by a user. Features of the feature vector represent health risk factors. Machine learning logic classifies the features to generate a classification metric indicating whether the features are indicative of health risks or not indicative of health risks. A prediction value is generated indicating a likelihood of each health risk factor for the patient. The patient can be diagnosed with a health condition or disease based on the identified health risks.

Abstract: The present invention relates to an AI-driven cybersecurity system that provides adaptive protection by identifying, mitigating, and preventing cyber threats in real-time. The system leverages advanced machine learning algorithms to detect anomalies in network traffic, enabling the identification of potential threats, which are then neutralized through automatic adjustments to firewall rules and network configurations. The system includes a tracing module capable of locating the source of cyberattacks by analyzing IP addresses and other network metadata, allowing for comprehensive incident reporting. Additionally, the invention logs and monitors all devices connecting to the network, both via Wi-Fi and hardline access, ensuring security compliance and detecting unauthorized activity. The system's adaptive approach ensures continuous protection by automatically updating security measures and reporting detailed findings to security personnel for future prevention.

Abstract: The present invention relates to an AI-driven system for optimizing the energy capture of renewable energy farms, including solar farms, wind farms, and ocean current farms. The system utilizes artificial intelligence (AI) to monitor real-time environmental data, such as solar irradiance, wind speed, ocean current speed, temperature, and humidity. The AI dynamically adjusts the operational parameters of energy capture devices—solar panels, wind turbines, and ocean current turbines—to optimize energy output in varying conditions. The system integrates predictive environmental data, such as weather forecasts and tidal patterns, to proactively adjust energy capture settings in advance of changes in environmental conditions.

Abstract: The present invention relates to an AI-based system and method for generating enhanced radiology reports. The system comprises a database for storing multimodal patient data, a natural language processing (NLP) module for extracting clinical information, and a machine learning module for correlating the clinical information with radiology images to identify diagnostic insights. An AI-based report generation module analyzes the images and clinical information to generate a preliminary report, which is refined based on radiologist input. The generated report is then integrated into the patient's electronic health record. The system employs techniques such as multimodal deep learning, active learning, explainable AI, and federated learning to enhance diagnostic accuracy, capture expert feedback, provide transparency, and enable multi-institutional collaboration. The invention aims to improve the accuracy, efficiency, and value of radiology reporting in patient care.

Abstract: The present invention relates to a system and method for the automated categorization, management, and storage of financial transaction data using artificial intelligence (AI) and machine learning (ML) algorithms. The system processes large volumes of financial data, such as tax records, transaction histories, and receipts, by assigning a significance score to each data item based on its relevance, legal obligations, and usage frequency. Data is categorized into hot storage for critical, frequently accessed data, cold storage for infrequently accessed but legally significant data, or flagged for deletion when deemed redundant or obsolete. The system includes a user interface allowing customization of significance thresholds and storage preferences, ensuring compliance with retention policies and auditing standards. By automating the categorization process, the invention optimizes storage resources, reduces costs, enhances data accessibility, and provides robust security measures, including encryption.

Abstract: A method and system for optimizing a semiconductor manufacturing process using an artificial intelligence (AI) system comprising machine learning (ML) models trained on mask work datasets. The AI system generates optimized process parameters for a multi-step semiconductor manufacturing process based on an input mask work. The manufacturing process includes photolithography, etching, ion implantation, chemical vapor deposition (CVD), physical vapor deposition (PVD), atomic layer deposition (ALD), thermal oxidation, and/or chemical-mechanical polishing (CMP). During manufacturing, metrology data is collected and input into the ML models to predict end-of-line electrical performance parameters. If the predicted parameters deviate from target values, the AI system adjusts process parameters to optimize performance. The ML models are retrained using the collected metrology data to improve AI system performance over time.

Abstract: A data processing system and method for generating structural engineering documents using artificial intelligence (AI) is disclosed. The system comprises a memory storing a dataset of engineered structures, including instances of structural failures, and a machine learning model trained on the dataset to perform structural analysis and generate optimized design documents. The AI system receives input data specifying design requirements, simulates the structure's behaviours under various conditions, and generates structural engineering documents, including 3D CAD models, based on an optimal combination of materials. The system may include a feedback integration module for continuous refinement of the machine learning model, an analytics engine for performance monitoring, and an electronic filing integration for streamlining the design approval process. The AI system learns from past projects to rapidly generate code-compliant, cost-optimized, and resilient structural designs.

Abstract: A data processing system is configured to identify treatment responsive to a health risk determined from feature data provided by one or more networked data sources. A classification engine generates a feature vector based on a natural language processing (NLP) of input data representing words provided by a user. Features of the feature vector represent health risk factors. Machine learning logic classifies the features to generate a classification metric indicating whether the features are indicative of health risks or not indicative of health risks. A prediction value is generated indicating a likelihood of each health risk factor for the patient. The patient can be diagnosed with a health condition or disease based on the identified health risks.

Abstract: An intelligent legal document generation and review system and associated methods are disclosed. The system comprises one or more processors coupled to memory storing instructions to provide a user interface for receiving user input, execute a natural language processing subsystem to extract relevant information, access a legal rules database, store document templates, and execute an AI algorithm. The AI algorithm retrieves a relevant template based on user input and extracted information, populates the template to generate a customized legal document, and stores the document. An attorney feedback loop allows attorneys to provide feedback that the AI algorithm learns from to improve document generation. The AI algorithm collaborates with attorneys to revise and finalize documents based on the feedback. The system streamlines document preparation, automates complex tasks, and provides expert guidance to help users adhere to legal requirements across jurisdictions.

Abstract: A system for classifying structured medical data, with each item of structured medical data, the system comprising a processing module that parses items of structured medical data to retrieve values of respective fields of the one or more items of structured medical data, the one or more retrieved values representing a set of medical attributes; a classification module that selects a classifier based at least one of the attributes in the set and applies the classifier to the set of attributes to classify one or more items of structured medical data into a particular risk profile; a user interface that renders one or more controls for input data that confirms one or more of the risk factors of the risk profile; and a transmitter to transmit to a remote medical device, an alert that specifies confirmation of the one or more of the risk factors.

Abstract: A system for classifying structured medical data, with each item of structured medical data, the system comprising a processing module that parses items of structured medical data to retrieve values of respective fields of the one or more items of structured medical data, the one or more retrieved values representing a set of medical attributes; a classification module that selects a classifier based at least one of the attributes in the set and applies the classifier to the set of attributes to classify one or more items of structured medical data into a particular risk profile; a user interface that renders one or more controls for input data that confirms one or more of the risk factors of the risk profile; and a transmitter to transmit to a remote medical device, an alert that specifies confirmation of the one or more of the risk factors.

Abstract: A data processing system is configured to identify treatment responsive to a health risk determined from feature data provided by one or more networked data sources. A classification engine generates a feature vector based on a natural language processing (NLP) of input data representing words provided by a user. Features of the feature vector represent health risk factors. Machine learning logic classifies the features to generate a classification metric indicating whether the features are indicative of health risks or not indicative of health risks. A prediction value is generated indicating a likelihood of each health risk factor for the patient. The patient can be diagnosed with a health condition or disease based on the identified health risks.

Abstract: A system for classifying structured medical data, with each item of structured medical data, the system comprising a processing module that parses items of structured medical data to retrieve values of respective fields of the one or more items of structured medical data, the one or more retrieved values representing a set of medical attributes; a classification module that selects a classifier based at least one of the attributes in the set and applies the classifier to the set of attributes to classify one or more items of structured medical data into a particular risk profile; a user interface that renders one or more controls for input data that confirms one or more of the risk factors of the risk profile; and a transmitter to transmit to a remote medical device, an alert that specifies confirmation of the one or more of the risk factors.

Abstract: Moulds are made from refractory sand (or other particulate or granular material) obtained from that sand or material. In order to control the temperature of the sand used to form the new moulds, the sand is heat exchanged with a permanent gas in liquid or solid state, or the cold vapour thereof. The temperature of the sand is sensed. The heat exchange is controlled so as to keep the sensed temperature at or below a chosen maximum.