Generic AI tools like AI chatbots, image and video generators, coding agents and more have recently emerged out of nowhere and seem to have surprised everyone with their extremely advanced features. However, the underlying technologies for these AI applications, including various data classification and regression algorithms, artificial neural networks, machine learning models, and natural language processing technologies, have been developed for over half a century. These technologies have been successfully implemented by companies in various science, technology and industrial sectors to analyze images and data, recognize patterns, create predictions, process optimization and automation, machine translation and speech integration. In fact, hundreds of thousands of patents have been filed and recognized by companies for inventions in a variety of technical fields that incorporate the innovative use of AI.
Cybersecurity
US Patent No. 12,333,009 describes a system for using AI to detect abnormalities in computer processes, particularly a machine learning system combined with the Markov chain. The system monitors the execution of processes, compares events with behavioral models, calculates probability, and identifies anomalies. Machine learning models analyze event data to assess anomalous behavior and increase detection accuracy and efficiency. This approach allows for real-time identification of vulnerabilities and threats, and overcomes the limitations of traditional methods by leveraging AI to adapt to evolving cyber threats.
Cloud Computing
US Patent No. 12,033,002 discloses a system for scheduling program operations on cloud-based services using machine learning technology. This involves breaking down program operations into sub-operations and matching them with the appropriate cloud service components, taking into account user constraints such as budgets and deadlines. The system uses machine learning to identify the best combination of service components that meet user constraints while considering processing constraints. The scheduler generates cost-effective schedules to perform operations, ensuring efficient use of cloud resources.
Fintech
US Patent No. 11,562,298 describes an AI-based predictive marketing platform that uses first-party data from long-term conversion entities to utilize predictive analytics to optimize media content direction. Patented predictive analytics techniques increase conversion frequency and speed by analyzing online behavioral data using machine learning algorithms. The platform integrates online behavior with customer relationship management (CRM) data to predict transformation possibilities and promote more efficient ad targeting and campaign management.
Data Backup
US Patent No. 12,314,219 discloses a machine learning-based system for data archiving. The system effectively trains statistics and event data to classify data, predict access requests, and archive and extract data as needed. By identifying access patterns, the system adjusts file access thresholds, assigns access categories, and allows for file migration between hot and cold data regions based on these categories. This approach optimizes data storage and search by leveraging machine learning to efficiently manage file access.
networking
US Patent No. 11,966,500 describes a system for separating personal information in streamed data using machine learning technology. Machine learning algorithms are used to automatically identify and extract personal information such as face images and license plate numbers based on usage-specific rules. This extracted data is stored separately from the modified stream. This will ensure privacy even if unauthorized access occurs, as personal information has been deleted. The system's machine learning capabilities enable efficient and automated data management, adapting to a variety of data types and usage scenarios to protect your personal information.
car
US Patent No. 10,579,883 describes how vehicle detection is done in intelligent drive systems using AI technology. Computer vision algorithms are used to process images from monocular cameras, identify lane lines, and determine effective areas for vehicle detection based on these lines and the speed of the vehicle. Machine learning, particularly weak classifiers, are used to detect vehicles within this effective area, focusing only on relevant image sections to optimize processing efficiency. This approach reduces computational load and increases the speed and accuracy of vehicle detection in driver assistance systems.
health care
US Patent No. 11,849,792 discloses head-mount devices that utilize AI and machine learning to increase the accuracy of monitoring wearer physical conditions for thermal stroke risk assessment. The device integrates sensors for salt, humidity, temperature and heart rate, and uses AI to process data and predict potential thermal stroke scenarios. Machine learning algorithms Analyze collected data to calculate sweating and salt loss, providing real-time insights and alerts. This intelligent system allows for proactive management of heat-related risks and ensures timely intervention to prevent serious health problems.
communication
US Patent No. 11,616,879 describes a system that uses machine learning to handle calls such as unwanted calls that contain fraud and spam. The system intercepts and records calls and uses an automated speech recognizer to convert audio into digital information. Machine Learning Algorithms Analyze this data to classify calls as unnecessary or authentic based on content. Classification models are continuously trained and improved using user feedback, improving information security, increasing the ability to accurately identify and manage unnecessary calls.
Computer Vision
US Patent No. 12,080,054 discloses a method for improving small object detection in images using machine learning, particularly neural networks. Although traditional neural networks struggle with detecting small objects, the proposed method reconstructs the network by shifting the detection layer to a stage before the resolution increases, increasing the network's ability to detect small objects without increasing the input image size. This approach allows real-time detection in applications such as sports broadcasts where small objects are quickly identified.
Biotechnology
US Patent No. 11,259,721 describes a non-invasive detection of total hemoglobin concentration in the blood using AI technology. This involves determining the differential path coefficients based on physiological parameters and photoplethysmographic (PPG) signals at two different wavelengths. Machine learning, in particular neural networks, are used to establish the relationship between physiological parameters and differential path factors, increasing the accuracy of hemoglobin concentration measurements. This approach allows for accurate, real-time monitoring of hemoglobin levels without the need for invasive blood sampling.
industry
US Patent No. 12,182,700 discloses how to combine deep neural networks with decision trees to improve blast furnace failure diagnosis. This approach utilizes the high accuracy of deep neural networks to model historical failure data and translates this knowledge into operator-understandable rules. This method addresses the challenges of traditional professional systems and data-driven models by providing a more reliable and interpretable solution. The system enhances the automation and intelligence of iron manufacturing processes, enables effective Hue Machine collaboration, and improves the accuracy of fault diagnosis in industrial applications.
Drone
US Patent No. 11,618,562 utilizes AI in the context of unmanned aerial vehicles (UAVs) to conquer targeted individuals. The system employs AI algorithms to analyze real-time data from UAV sensors, allowing target individuals to autonomously identify, track and engage them. For example, machine learning is used to detect offensive individuals based on gesture analysis. By leveraging machine learning and computer vision technologies, UAVs can make informed decisions about the most effective ways to suppress targets, ensure accuracy, and minimize the risk of collateral damage.
Robotics
US Patent No. 11,297,755 describes how AI technology can be used, particularly convolutional neural networks (CNNS) to control soil adversaries such as lawn mowers and harvesters. These networks process images to create synthetic soil descriptors, allowing the machine to operate autonomously by recognizing soil properties and obstacles. AI-driven systems eliminate the need for external infrastructure such as GPS and beacons and provide robust performance in dynamic environments in a variety of conditions. This approach increases the machine's ability to adapt to unexpected changes in the environment and efficiently navigate and execute tasks.
Resource Management
US Patent No. 12,242,996 discloses a system for managing schedule data by interpreting, detecting and correcting schedule anomalies based on historical data. It includes components that interpret schedule data, identify violations of schedule norms, and generate corrective actions to coordinate schedules. Machine learning, in particular neural networks, are used to identify trends in historical data and help establish norms of schedules. The system issues alerts, adjusts parameters, and addresses issues such as excessive overtime and adverse shift patterns to ensure compliance with these norms.
