{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9853878","patent":{"patent_number":"US-9853878","title":"Limiting data output from windowing operations","assignee":null,"inventors":[],"filing_date":"2015-06-09T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["H04L","G06F","H04L","H04L","H04L","H04L"],"num_claims":17,"abstract":"A method for regulating output from stream operators performing a windowing operation may include receiving stream of tuples to be processed by a plurality of processing elements, each processing element having one or more stream operators. The method may also include receiving a first stream of tuples at a first stream operator, the first stream operator being configured to generate a stream of accumulated tuples according to a set of windowing conditions. The method may then include processing the stream of accumulated tuples in response to a window trigger, where the processing generates a quantity of output. At least one processor may then monitor the quantity of output to determine whether the quantity of output exceeds a data output threshold. The processing may then be adjusted, in response to determining that the quantity of output exceeds the data output threshold, to reduce the output to approach the data output threshold."},"analysis":{"summary":"The Limiting Data Output from Windowing Operations patent introduces a method for dynamically regulating data output from stream operators performing windowing operations. The core innovation lies in monitoring the quantity of output generated during these operations and adjusting processing parameters to maintain output within a predefined data output threshold. This approach addresses the problem of excessive data output, which can overwhelm processing elements and lead to bottlenecks in real-time data analytics.\n\nThe technology involves receiving a stream of tuples, processing them through stream operators, and generating accumulated tuples based on windowing conditions. A key aspect is the monitoring of output quantity, with adjustments made when the output exceeds a threshold. These adjustments can include modifying windowing conditions, aggregating data more effectively, or prioritizing specific data subsets.\n\nThe business value of this innovation lies in its ability to enhance the scalability, efficiency, and responsiveness of data stream processing systems. By preventing resource overload and minimizing processing delays, it enables organizations to extract timely and actionable insights from their data streams. This has significant implications for industries such as finance, IoT, and fraud detection, where real-time data analysis is critical.\n\nThe market opportunity for this technology is substantial, driven by the ever-increasing volume of data and the growing demand for real-time analytics. Organizations are seeking solutions to manage and process their data more efficiently, and this patent offers a compelling approach to address this need. The ability to regulate data output and maintain system performance is crucial for real-time decision-making and gaining a competitive edge.","layman_explanation":"The Limiting Data Output from Windowing Operations patent addresses a common problem in today's data-rich environment: how to efficiently process large streams of information in real-time. Many modern applications, such as financial trading platforms, IoT sensor networks, and fraud detection systems, rely on the continuous analysis of vast data streams. However, the sheer volume and velocity of this data can overwhelm traditional processing methods, leading to delays and inefficiencies.\n\n**What Problem Does This Solve?**\nImagine a factory assembly line where products are constantly moving past inspection stations. Each station needs to analyze the product and decide whether it meets quality standards. If the products move too quickly, the inspectors can become overwhelmed and make mistakes. Similarly, in data processing, if the data stream is too fast, the processing elements can become overloaded and fail to keep up. This patent solves this problem by intelligently regulating the flow of data to prevent overload and maintain optimal performance. Existing solutions often fall short because they either lack the ability to dynamically adjust to changing data volumes or they introduce significant delays in the processing pipeline.\n\n**How Does It Work?**\nThis patent works by continuously monitoring the amount of data being processed and comparing it to a predefined threshold. If the data volume exceeds the threshold, the system automatically adjusts the processing parameters to reduce the output. This adjustment can involve various techniques, such as selectively aggregating data, prioritizing specific data subsets, or modifying the way data is grouped into windows for analysis. Think of it like a smart thermostat that automatically adjusts the temperature in your house to maintain a comfortable level. The system constantly monitors the temperature and adjusts the heating or cooling system accordingly. Similarly, this patent constantly monitors the data volume and adjusts the processing parameters to maintain optimal performance. The key is that it does this dynamically, in real-time, without requiring manual intervention.\n\n**Why Does This Matter?**\nThe ability to efficiently process large data streams has significant implications for businesses across various industries. In the financial sector, it enables faster trade execution and more accurate risk management. In the IoT sector, it enables more responsive smart city infrastructure and more efficient energy management. In the fraud detection sector, it enables faster identification of fraudulent transactions and reduced financial losses. The market impact is substantial, as organizations increasingly rely on real-time data analytics to gain a competitive edge. This technology offers a competitive advantage by enabling organizations to process data more efficiently and make better decisions faster. The potential ROI is high, as it can lead to significant cost savings and revenue generation.\n\n**What's Next?**\nThe future applications of this technology are vast. As data volumes continue to grow, the need for efficient data stream processing solutions will become even more critical. This patent could pave the way for more advanced data analytics techniques, such as predictive analytics and machine learning. The market adoption timeline is expected to accelerate in the coming years, as more organizations recognize the value of real-time data insights. This innovation presents significant investment implications, as it has the potential to transform the way businesses process and analyze data.","technical_analysis":"The Limiting Data Output from Windowing Operations patent provides a technical solution for managing data output in stream processing systems. The system's architecture involves multiple processing elements, each with stream operators that perform windowing operations. The key technical aspect is the monitoring mechanism that tracks the quantity of output generated by these operators.\n\nThe implementation details involve setting a data output threshold and triggering adjustments when this threshold is exceeded. The adjustment process can include several techniques, such as modifying windowing conditions, aggregating data more effectively, or prioritizing specific data subsets. The choice of adjustment technique depends on the specific application and the desired trade-off between data accuracy and processing efficiency.\n\nThe algorithm specifics involve continuously monitoring the output quantity and comparing it to the predefined threshold. When the threshold is exceeded, the system triggers an adjustment algorithm to reduce the output volume. The adjustment algorithm can be implemented using various techniques, such as adaptive windowing, data sampling, or data filtering.\n\nThe integration patterns involve incorporating this technology into existing data stream processing pipelines. This can be achieved by implementing the monitoring and adjustment mechanisms as separate modules that can be easily integrated into existing systems. The performance characteristics depend on the specific implementation and the choice of adjustment techniques. However, the overall goal is to improve the scalability, efficiency, and responsiveness of the data stream processing system.\n\nCode-level implications involve implementing the monitoring and adjustment algorithms in a programming language suitable for data stream processing, such as Java, Python, or Scala. The code should be optimized for performance and scalability to handle high data volumes and low latency requirements. The system's performance can be further optimized by leveraging hardware acceleration techniques, such as GPU processing or FPGA acceleration.","business_analysis":"The Limiting Data Output from Windowing Operations patent presents a significant business opportunity in the rapidly growing market for real-time data analytics. The increasing volume of data and the growing demand for real-time insights are driving the need for more efficient and scalable data stream processing solutions.\n\nThe market opportunity size is substantial, with the global market for real-time analytics projected to reach billions of dollars in the coming years. This growth is driven by the increasing adoption of IoT devices, the proliferation of social media, and the growing demand for data-driven decision-making.\n\nThe competitive advantages of this technology lie in its ability to dynamically regulate data output, preventing resource overload and improving scalability. This allows organizations to process larger volumes of data more efficiently, enabling them to extract timely and actionable insights.\n\nThe revenue potential for this technology is significant. It can be monetized through various business models, such as licensing, subscription, or professional services. The strategic positioning of this technology is strong, as it addresses a critical need in the market for real-time data analytics.\n\nThe ROI projections for this technology are attractive, with the potential for significant cost savings and revenue generation. By improving the efficiency and scalability of data stream processing systems, it enables organizations to reduce operational costs and increase revenue through better decision-making.\n\nOverall, the Limiting Data Output from Windowing Operations patent presents a compelling business opportunity in the market for real-time data analytics. Its ability to dynamically regulate data output, improve scalability, and enhance efficiency makes it a valuable asset for organizations seeking to unlock the full potential of their data.","faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Limiting Data Output from Windowing Operations - Patent US-9853878","description":"Discover how Limiting Data Output from Windowing Operations patent improves real-time data processing efficiency. Full analysis, claims, and applications discussed.","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9853878","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-9853878","citation_suggestion":"Patentable. \"Limiting data output from windowing operations\" (US-9853878). https://patentable.app/patents/US-9853878","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9853878","json":"https://patentable.app/api/llm-context/US-9853878","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-30T04:30:34.678Z"}