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    Patent Issued for System for connecting an autonomous mobile robot (USPTO 11897126)

    141-142页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Roeq ApS (Vissenbjerg, Denmark) has been issued patent number 11897126, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors. The patent’s inventors are Ejstrup Hansen, Michael (Morud, DK), Snavar Sigurgeirsson Lund, Benedikt (Aarup, DK), Sorensen, Carsten (Herlev, DK). This patent was filed on September 28, 2018 and was published online on February 13, 2024. From the background information supplied by the inventors, news correspondents obtained the following quote: “Mobile autonomous robots are used widely in industrial applications, especially for logistics and transport of goods. A typical scenario for such robots is to pick up goods at a first position, and to deliver the goods at a second position. The first and second positions are pre-programmed, and no humans are therefore needed to perform the transportation. “The goods are normally arranged on carts, which are passive equipment. The robot must therefore connect to the cart in order to be able to drive the goods, while disconnecting is required when the robot reaches the delivery position.” Supplementing the background information on this patent, NewsRx reporters also obtained the in- ventors’ summary information for this patent: “It is thus an object of the teachings herein to provide an improved system for connecting a mobile robot to associated equipment, as well as to systems for connecting equipment to stationary stations.
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    Researchers Submit Patent Application, 'Automatic FireExtinguishing System And Method For Cable Tunnel', for Approval (USPTO 20240050788)

    143-148页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – From Washington, D.C., NewsRx journalists report that a patent application by the inventors Duan, Yubing (Jinan, Shandong, CN); Gu, Chao (Jinan, Shandong, CN); Hu, Xiaoli (Jinan, Shandong, CN); Jia, Ran (Jinan, Shandong, CN); Li, Jie (Jinan, Shandong, CN); Li, Pengfei (Jinan, Shandong, CN); Liu, Hui (Jinan, Shandong, CN); Ma, Guoqing (Jinan, Shandong, CN); Ren, Jingguo (Jinan, Shandong, CN); Shi, Wei (Jinan, Shandong, CN); Sun, Jingwen (Jinan, Shandong, CN); Yao, Jinxia (Jinan, Shandong, CN); Zhang, Hao (Jinan, Shandong, CN); Zhang, Yang (Jinan, Shandong, CN); Zhou, Chao (Jinan, Shandong, CN), filed on December 27, 2021, was made available online on February 15, 2024. No assignee for this patent application has been made. News editors obtained the following quote from the background information supplied by the inventors: “This part is merely intended to provide background arts related to the present disclosure, and does not necessarily constitute the prior art. “A cable tunnel refers to a corridor or a tunnel-like structure configured to accommodate a large number of cables laid on a cable bracket. In addition to well protection of cables in tunnels, cable tunnels can facilitate an inspection and maintenance of cables. If an insulating layer of the cable in a cable tunnel is damaged due to long-term operation of the cable, the cable may be prone to catching fire. Once the cable catches fire, it will seriously affect normal operation of an electric line.
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    Patent Issued for Systems and methods for parameter optimization (USPTO 11900259)

    148-152页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Kinaxis Inc. (Ottawa, Canada) has been issued patent number 11900259, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors. The patent’s inventors are Downing, Jeffery (Ottawa, CA), Hebert, Liam (Ottawa, CA), Ouellet, Sebastien (Ottawa, CA), Stanley, Nathaniel (Ottawa, CA), Williams, Phillip (Ottawa, CA). This patent was filed on October 25, 2022 and was published online on February 13, 2024. From the background information supplied by the inventors, news correspondents obtained the following quote: “Prior to analysis of large amounts of data by machine-learning algorithms, the data must be initially configured, and must be done so intelligently in order to obtain meaningful results from the machine- learning algorithms. The data is configured by setting one or more parameters. Often, there are too many parameters in a configuration for a user to choose from. In addition, there may be hundreds of thousands of different possible values for each parameter in the configuration. Often, the user selects the parameters (that define the configuration) based on personal judgement, which turns out not to be the best choice. The user has to experiment in terms of selecting the parameters that provide an optimal configuration. This takes time and is quite inefficient.
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    Patent Issued for Method and system for mapping labels in standardized tables using machine learning (USPTO 11900272)

    152-154页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A patent by the inventors Chen, Yan (Montville, NV, US), Murthy Malladi, Dakshina (Telangana, IN), Srivastava, Agrima (Jersey City, NJ, US), filed on May 13, 2020, was published online on February 13, 2024, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents. Patent number 11900272 is assigned to Factset Research System Inc. (New York, New York, United States). The following quote was obtained by the news editors from the background information supplied by the inventors: “Optical character recognition (“OCR”) is the electronic conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document or a photo of a document. Using OCR technology, one can scan older documents and create a digital record of these documents. These digital records can include machine-encoded text which can be accessible by computer programs. “Machine learning uses statistical techniques for teaching computers with data to perform specific tasks without being explicitly programmed to do so. The goal of machine learning is to construct algorithms that can learn from and make predictions on data. These algorithms work by creating mathematical models which can classify data. The process of creating the models can involve training and fine tuning the model parameters using input data.”
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    RUDEUS, a machine learning classification system to study DNA-Binding proteins

    154-155页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from biorxiv.org: “DNA-binding proteins are essential in different biological processes, including DNA replication, tran- scription, packaging, and chromatin remodelling. Exploring their characteristics and functions has become relevant in diverse scientific domains. Computational biology and bioinformatics have assisted in studying DNA-binding proteins, complementing traditional molecular biology methods. “While recent advances in machine learning have enabled the integration of predictive systems with bioinformatic approaches, there still needs to be generalizable pipelines for identifying unknown proteins as DNA-binding and assessing the specific type of DNA strand they recognize. “In this work, we introduce RUDEUS, a Python library featuring hierarchical classification models de- signed to identify DNA-binding proteins and assess the specific interaction type, whether single-stranded or double-stranded. RUDEUS has a versatile pipeline capable of training predictive models, synergizing protein language models with supervised learning algorithms, and integrating Bayesian optimization strate- gies. The trained models have high performance, achieving a precision rate of 95% for DNA-binding identification and 89% for discerning between single-stranded and double-stranded interactions. RUDEUS includes an exploration tool for evaluating unknown protein sequences, annotating them as DNA-binding, and determining the type of DNA strand they recognize. Moreover, a structural bioinformatic pipeline has been integrated into RUDEUS for validating the identified DNA strand through DNA-protein molecular docking.
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