๐๐๐๐ ๐๐๐๐๐๐๐ ๐๐๐๐ ๐๐ ๐๐๐๐๐๐๐๐๐๐ ๐๐ ๐๐ ๐ ๐๐๐๐๐๐๐๐๐ ๐๐ ๐๐๐๐๐๐๐๐๐ ๐๐ ๐๐๐ ๐๐๐ Decision T 1806/20, discussed in one of my previous posts, deals with "functional data". ย Such data is intended for controlling a technical device may be considered to have technical character because it has the potential to cause technical effects. In G1/19, reasons, point 94 the Enlarged Board of Appeal has generalized this as follow: ๐๐ฏ ๐ต๐ฉ๐ฆ ๐ค๐ฐ๐ฏ๐ต๐ฆ๐น๐ต ๐ฐ๐ง ๐ต๐ฉ๐ฆ ๐ฑ๐ณ๐ฐ๐ฃ๐ญ๐ฆ๐ฎ-๐ด๐ฐ๐ญ๐ถ๐ต๐ช๐ฐ๐ฏ ๐ข๐ฑ๐ฑ๐ณ๐ฐ๐ข๐ค๐ฉ ๐ข๐ฏ๐ฅ ๐ต๐ฉ๐ฆ ๐๐๐๐๐๐ ๐ข๐ฑ๐ฑ๐ณ๐ฐ๐ข๐ค๐ฉ, ๐ด๐ถ๐ค๐ฉ ๐ฑ๐ฐ๐ต๐ฆ๐ฏ๐ต๐ช๐ข๐ญ ๐ต๐ฆ๐ค๐ฉ๐ฏ๐ช๐ค๐ข๐ญ ๐ฆ๐ง๐ง๐ฆ๐ค๐ต๐ด ๐ฎ๐ข๐บ ๐ฃ๐ฆ ๐ค๐ฐ๐ฏ๐ด๐ช๐ฅ๐ฆ๐ณ๐ฆ๐ฅ ๐ช๐ง ๐ต๐ฉ๐ฆ ๐ฅ๐ข๐ต๐ข ๐ณ๐ฆ๐ด๐ถ๐ญ๐ต๐ช๐ฏ๐จ ๐ง๐ณ๐ฐ๐ฎ ๐ข ๐ค๐ญ๐ข๐ช๐ฎ๐ฆ๐ฅ ๐ฑ๐ณ๐ฐ๐ค๐ฆ๐ด๐ด ๐ช๐ด ๐ด๐ฑ๐ฆ๐ค๐ช๐ง๐ช๐ค๐ข๐ญ๐ญ๐บ ๐ข๐ฅ๐ข๐ฑ๐ต๐ฆ๐ฅ ๐ง๐ฐ๐ณ ๐ต๐ฉ๐ฆ ๐ฑ๐ถ๐ณ๐ฑ๐ฐ๐ด๐ฆ๐ด ๐ฐ๐ง ๐ช๐ต๐ด ๐ช๐ฏ๐ต๐ฆ๐ฏ๐ฅ๐ฆ๐ฅ ๐ต๐ฆ๐ค๐ฉ๐ฏ๐ช๐ค๐ข๐ญ ๐ถ๐ด๐ฆ. ๐๐ฏ ๐ด๐ถ๐ค๐ฉ ๐ค๐ข๐ด๐ฆ๐ด: โพ ๐ฆ๐ช๐ต๐ฉ๐ฆ๐ณ ๐ต๐ฉ๐ฆ ๐ต๐ฆ๐ค๐ฉ๐ฏ๐ช๐ค๐ข๐ญ ๐ฆ๐ง๐ง๐ฆ๐ค๐ต ๐ต๐ฉ๐ข๐ต ๐ธ๐ฐ๐ถ๐ญ๐ฅ ๐ณ๐ฆ๐ด๐ถ๐ญ๐ต ๐ง๐ณ๐ฐ๐ฎ ๐ต๐ฉ๐ฆ ๐ช๐ฏ๐ต๐ฆ๐ฏ๐ฅ๐ฆ๐ฅ ๐ถ๐ด๐ฆ ๐ฐ๐ง ๐ต๐ฉ๐ฆ ๐ฅ๐ข๐ต๐ข ๐ค๐ฐ๐ถ๐ญ๐ฅ ๐ฃ๐ฆ ๐ค๐ฐ๐ฏ๐ด๐ช๐ฅ๐ฆ๐ณ๐ฆ๐ฅ โ๐ช๐ฎ๐ฑ๐ญ๐ช๐ฆ๐ฅโ ๐ฃ๐บ ๐ต๐ฉ๐ฆ ๐ค๐ญ๐ข๐ช๐ฎ, ๐ฐ๐ณ โพ ๐ต๐ฉ๐ฆ ๐ช๐ฏ๐ต๐ฆ๐ฏ๐ฅ๐ฆ๐ฅ ๐ถ๐ด๐ฆ ๐ฐ๐ง ๐ต๐ฉ๐ฆ ๐ฅ๐ข๐ต๐ข (๐ช.๐ฆ. ๐ต๐ฉ๐ฆ ๐ถ๐ด๐ฆ ๐ช๐ฏ ๐ค๐ฐ๐ฏ๐ฏ๐ฆ๐ค๐ต๐ช๐ฐ๐ฏ ๐ธ๐ช๐ต๐ฉ ๐ข ๐ต๐ฆ๐ค๐ฉ๐ฏ๐ช๐ค๐ข๐ญ ๐ฅ๐ฆ๐ท๐ช๐ค๐ฆ) ๐ค๐ฐ๐ถ๐ญ๐ฅ ๐ฃ๐ฆ ๐ค๐ฐ๐ฏ๐ด๐ช๐ฅ๐ฆ๐ณ๐ฆ๐ฅ ๐ต๐ฐ ๐ฆ๐น๐ต๐ฆ๐ฏ๐ฅ ๐ข๐ค๐ณ๐ฐ๐ด๐ด ๐ด๐ถ๐ฃ๐ด๐ต๐ข๐ฏ๐ต๐ช๐ข๐ญ๐ญ๐บ ๐ต๐ฉ๐ฆ ๐ธ๐ฉ๐ฐ๐ญ๐ฆ ๐ด๐ค๐ฐ๐ฑ๐ฆ ๐ฐ๐ง ๐ต๐ฉ๐ฆ ๐ค๐ญ๐ข๐ช๐ฎ๐ฆ๐ฅ ๐ฅ๐ข๐ต๐ข ๐ฑ๐ณ๐ฐ๐ค๐ฆ๐ด๐ด๐ช๐ฏ๐จ ๐ฎ๐ฆ๐ต๐ฉ๐ฐ๐ฅ. T 1806/20 discusses a kind of test to find out whether data are cognitive or functional: โพ Data is considered to be functional data when its loss would impair the technical operation of the system (cf. T 1194/97, reasons, point 3.3). This means, data is considered to be functional data, when the loss of the data would make the system inoperable at the technical level. In contrast, if cognitive data is lost, the system would still work but possibly produce results that would be unintended for non-technical reasons. โพ E.g. in T 1194/97, the loss of functional data prevented the system from generating any television picture, whereas the loss of cognitive data only resulted in a meaningless television picture resembling snow. โพ E.g. in T 1806/20, the loss of water-sensitivity information would not cause the system to stop working; the vehicle would still be guided, and parcels would be delivered. However, it would result in leaving waterโsensitive parcels standing in the rain โ an unintended operation comparable to producing a television picture that resembles snow. The reasons why these outcomes are unintended are non-technical. For more information on computer-implemented inventions, follow me on LinkedIn: โก https://lnkd.in/e4jxK5PB #patent #intellectualproperty #innovation #ip #software
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Director of Technology & Solutions at Mekitec Group | Leadership & Strategy for Products, Projects, People | AI Cloud Enterprise SW | PMP SAFe Agile Lean
Digital Twins and Machine Learning for IT ๐๐ง๐๐ซ๐๐ฌ๐ญ๐ซ๐ฎ๐๐ญ๐ฎ๐ซ๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ๐ฌ ๐๐ง๐ ๐ข๐ง๐๐๐ซ๐ฌ will love this. They always want to enhance the management and performance of IT systems. The Digital Twin & Machine Learning combo offers: ๐๐ซ๐จ๐๐๐ญ๐ข๐ฏ๐ ๐๐๐ซ๐๐จ๐ซ๐ฆ๐๐ง๐๐ ๐๐จ๐ง๐ข๐ญ๐จ๐ซ๐ข๐ง๐ ยทย ย ย ย ย Digital twins create virtual replicas of IT infrastructure components. ยทย ย ย ย ย Real-time data from the physical infrastructure is fed into the digital twin. ยทย ย ย ย ย Machine learning algorithms analyze the data to identify performance patterns and anomalies. ยทย ย ย ย ย Engineers receive proactive alerts for potential performance issues, allowing them to address them before they impact operations. ๐๐ซ๐๐๐ข๐๐ญ๐ข๐ฏ๐ ๐๐๐ข๐ง๐ญ๐๐ง๐๐ง๐๐ ๐๐ง๐ ๐ ๐๐ฎ๐ฅ๐ญ ๐๐๐ญ๐๐๐ญ๐ข๐จ๐ง ยทย ย ย ย ย Digital twins provide a detailed representation of IT infrastructure and its interconnected components. ยทย ย ย ย ย Machine learning models trained on historical data can predict maintenance needs and potential faults. ยทย ย ย ย ย Engineers can schedule maintenance activities proactively and avoid system downtime. ยทย ย ย ย ย Early fault detection enables timely troubleshooting and prevents major disruptions. ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง ๐จ๐ ๐๐๐ฌ๐จ๐ฎ๐ซ๐๐ ๐๐ฅ๐ฅ๐จ๐๐๐ญ๐ข๐จ๐ง ยทย ย ย ย ย Digital twins offer a holistic view of the IT infrastructure, including hardware, software & network components. ยทย ย ย ย ย Machine learning algorithms analyze resource utilization patterns and identify optimization opportunities. ยทย ย ย ย ย Engineers can optimize resource allocation, ensuring efficient use of computing power, storage & network bandwidth. ยทย ย ย ย ย This leads to improved performance, cost savings & better scalability. ๐๐ข๐ฆ๐ฎ๐ฅ๐๐ญ๐ข๐จ๐ง ๐๐ง๐ ๐๐๐ฌ๐ญ๐ข๐ง๐ ๐๐ง๐ฏ๐ข๐ซ๐จ๐ง๐ฆ๐๐ง๐ญ๐ฌ ยทย ย ย ย ย Digital twins provide a virtual environment for simulating and testing changes in the IT infrastructure. ยทย ย ย ย ย Machine learning algorithms can predict the impact of proposed changes based on historical data. ยทย ย ย ย ย Engineers can experiment with different configurations, software updates, or workload changes in a risk-free environment. ยทย ย ย ย ย This enables better decision-making and minimizes the risk of disruptions during actual implementation. ๐๐ง๐๐ข๐๐๐ง๐ญ ๐๐๐ฌ๐ฉ๐จ๐ง๐ฌ๐ ๐๐ง๐ ๐๐ซ๐จ๐ฎ๐๐ฅ๐๐ฌ๐ก๐จ๐จ๐ญ๐ข๐ง๐ ยทย ย ย ย ย Digital twins capture real-time data on IT infrastructure performance and user interactions. ยทย ย ย ย ย Machine learning models analyze the data to identify patterns associated with incidents or anomalies. ยทย ย ย ย ย Engineers can leverage these insights for faster incident response and troubleshooting. ยทย ย ย ย ย Root cause analysis is facilitated by identifying patterns that contribute to system failures or performance degradation. With these both, IT infrastructure engineers can enhance system reliability, optimize resource utilization while delivering efficient and resilient IT services. #digitaltwin #machinelearning #softwareengineering #technology
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Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques Abstract: Video, as a key driver in the global explosion of digital information, can create tremendous benefits for human society. Governments and enterprises are deploying innumerable cameras for a variety of applications, e.g., law enforcement, emergency management, traffic control, and security surveillance, all facilitated by video analytics (VA). This trend is spurred by the rapid advancement of deep learning (DL), which enables more precise models for object classification, detection, and tracking. Meanwhile, with the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud. Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution. The resulting new intersection, edge video analytics (EVA), begins to attract widespread attention. Nevertheless, only a few loosely-related surveys exist on this topic. The basic concepts of EVA (e.g., definition, architectures) were not fully elucidated due to the rapid development of this domain. To fill these gaps, we provide a comprehensive survey of the recent efforts on EVA. In this paper, we first review the fundamentals of edge computing, followed by an overview of VA. EVA systems and their enabling techniques are discussed next. In addition, we introduce prevalent frameworks and datasets to aid future researchers in the development of EVA systems. Finally, we discuss existing challenges and foresee future research directions. We believe this survey will help readers comprehend the relationship between VA and edge computing, and spark new ideas on EVA. Published in:ย IEEE Communications Surveys & Tutorialsย (ย Volume: 25,ย Issue: 4, Fourthquarter 2023)
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3moThank you Michael, for this update!