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    Researchers Submit Patent Application, 'Methods And Systems For Customer Accounts Association In Multilingual Environments', for Approval (USPTO 20240020711)

    124-126页
    查看更多>>摘要：From Washington, D.C., NewsRx journalists report that a patent application by the inventors Gobrial, Mark G. (Washington, IL, US); Li, Tianyi (Chicago, IL, US); Radakovic, Daniela (Chicago, IL, US); Tennent, Toby (Chicago, IL, US), filed on July 18, 2022, was made available online on January 18, 2024. No assignee for this patent application has been made. News editors obtained the following quote from the background information supplied by the inventors: “Generally, the association of all accounts belonging to the same customer is impeded by the use of incorrect, partial, and/or abbreviated relevant account information such as names, customer addresses, types of industry, and types of fleets. The format and standards of the data are often not consistent, as data is entered by a variety of people from different organizations. Software engines for automated association process have difficulty processing incorrect, partial and/or abbreviated information due to poorly or not documented descriptors. Languages such as Chinese, Japanese, and Korean, can add even more complexities. Large customers frequently use different service providers and multiple customer accounts. This results in a fragmented view of the customer for original equipment manufacturer (OEM) that supplies goods to the same customer through multiple service providers. Companies have implemented various techniques to solve this problem. For example, U.S. Patent Publication No. US20200311707A1 describes a method for mapping in-store transactions associated with traceable tenders to valid customer profiles. However, this method is only directed to associating a transaction with a customer based on attributes identified in the customer profile. Additionally, U.S. Patent Publication No. US20180191644A1 describes a method for providing interactive transaction returns within a retailer network. However, this method is only directed to identifying information for a customer from a transaction and matching it with customer profiles.”
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    Patent Application Titled 'Deep Learning-Based Fusion Techniques for High Resolution, Noise-Reduced, and High Dynamic Range Images with Motion Freezing' Published Online (USPTO 20240020807)

    135-140页
    查看更多>>摘要：According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventors Bichu, Tanmay Nitin (Mountain View, CA, US); Cheng, Tiffany J. (Cupertino, CA, US); Tico, Marius (Mountain View, CA, US), filed on July 12, 2022, was made available online on January 18, 2024. No assignee for this patent application has been made. Reporters obtained the following quote from the background information supplied by the inventors: “Fusing multiple images of the same captured scene is an effective way of increasing signal-to-noise ratio (SNR) in the resulting fused image. This is particularly important for small and/or thin form factor devices-such as mobile phones, tablets, laptops, wearables, etc.-for which the pixel size of the device’s image sensor(s) is often quite small. The smaller pixel size means that there is comparatively less light captured per pixel (i.e., as compared to a full-sized, standalone camera having larger pixel sizes), resulting in more visible noise in captured images-especially in low-light situations.
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    Patent Issued for Workpiece container system (USPTO 11874596)

    141-143页
    查看更多>>摘要：From Alexandria, Virginia, NewsRx journalists report that a patent by the inventors Chiu, Ming-Chien (New Taipei, TW), Chuang, Chia-Ho (New Taipei, TW), Hsueh, Hsin-Min (New Taipei, TW), Lin, Shu-Hung (New Taipei, TW), Wen, Hsing-Min (New Taipei, TW), filed on March 30, 2021, was published online on January 16, 2024. The patent’s assignee for patent number 11874596 is Gudeng Precision Industrial Co. Ltd. (New Taipei, Taiwan). News editors obtained the following quote from the background information supplied by the inventors: “In semiconductor industry, workpiece containers (e.g., photomask/reticle retainer) have evolved with the heightened precision requirements of the payload thereof, in order to meet the demand for increased level of workpiece protection from potential ambient hazards.
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    Patent Issued for Robot system (USPTO 11872686)

    144-145页
    查看更多>>摘要：Kabushiki Kaisha Yaskawa Denki (Fukuoka, Japan) has been issued patent number 11872686, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors. The patent’s inventors are Iida, Manabu (Fukuoka, JP), Konno, Tsuyoshi (Fukuoka, JP), Yoshino, Katsuhiko (Fukuoka, JP). This patent was filed on December 7, 2021 and was published online on January 16, 2024. From the background information supplied by the inventors, news correspondents obtained the following quote: “There have been known robots configured to operate by driving each of a plurality of joint portions. End effectors suitable for applications such as welding and gripping are attached to distal ends of such robots, and various operations such as machining and moving workpieces are performed. “Further, there have been proposed dust removal systems configured to remove dust from a workpiece by using a dust-removing robot equipped with an end effector for dust removal (for example, refer to JP 2010-89010 A).”
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    'Conditional Camera Control Via Automated Assistant Commands' in Patent Application Approval Process (USPTO 20240022809)

    145-150页
    查看更多>>摘要：A patent application by the inventors Carbotta, Domenico (Zurich, CH); Carbune, Victor (Zurich, CH); Chen, Ray (Altendorf, CH); Fu, Kevin (Taipei, TW); Garg, Neha (Palo Alto, CA, US); Kothari, Luv (Sunnyvale, CA, US); Lee, Fo (Taipei, TW); Lu, Mucun (Taipei, TW); Miao, Matthew (San Jose, CA, US); Miklos, Balint (Zurich, CH); Poblocka, Barbara (Zurich, CH); Prisacari, Bogdan (Adliswil, CH); Qian, Thomas (Santa Clara, CA, US); Sannazzaro Natta, Jacopo (Berkeley, CA, US); Seo, Jae (Palo Alto, CA, US); Sharifi, Matthew (Kilchberg, CH); Weissenberger, Felix (Zurich, CH), filed on August 8, 2023, was made available online on January 18, 2024, according to news reporting originating from Washington, D.C., by NewsRx correspondents. This patent application has not been assigned to a company or institution. The following quote was obtained by the news editors from the background information supplied by the inventors: “Humans may engage in human-to-computer dialogs with interactive software applications referred to herein as “automated assistants” (also referred to as “digital agents,” “chatbots,” “interactive personal assistants,” “intelligent personal assistants,” “conversational agents,” etc.). For example, humans (which when they interact with automated assistants may be referred to as “users”) may provide commands and/or requests using spoken natural language input (i.e., utterances) which may in some cases be converted into text and then processed, and/or by providing textual (e.g., typed) natural language input.
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    Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models

    157-157页
    查看更多>>摘要：According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from medrxiv.org: “The timely stratification of trauma injury severity can enhance the quality of trauma care but it requires intense manual annotation from certified trauma coders. “There is a need to establish an automated tool to identify the severity of trauma injuries across various body regions. “We gather trauma registry data from a Level I Trauma Center at the University of Wisconsin-Madison (UW Health) between 2015 and 2019. “Our study utilizes clinical documents and structured electronic health records (EHR) variables linked with the trauma registry data to create two machine learning models with different approaches to representing text. The first one fuses concept unique identifiers (CUIs) extracted from free text with structured EHR variables, while the second one integrates free text with structured EHR variables. Both models demonstrate impressive performance in categorizing leg injuries, achieving high accuracy with macro-F1 scores of around 0.8. Additionally, they show considerable accuracy, with macro- F1 scores exceeding 0.6, in assessing injuries in the areas of the chest and head. Temporal validation is conducted to ensure temporal generalizability.
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