Patents by Inventor Luo LI
Luo LI has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240099920Abstract: A method for controlling a device for automatically adjusting an airway opening body position is provided. The device includes a horizontal base plate, a head support block, a back support plate, a neck support apparatus, a head cover assembly, and a programmable logic controller (PLC). The neck support apparatus is positioned between the head support block and the back support plate. The PLC is configured to controls a stroke of an electric cylinder according to the following equations: ?=1.235?+?, and ?=KX+B+C, where ? is a body position angle, the body position angle is an angle between a positive projection line of a connecting line from a mandibular angle to an external acoustic meatus on a symmetrical surface of a human body and the back support plate, and ? is a preset value ranging from 90° to 100°.Type: ApplicationFiled: November 14, 2022Publication date: March 28, 2024Inventors: XIANG-MEI YANG, MIN-YUE SUN, HONG-MEI CHEN, YAN LUO, JUN WU, JUAN HUANG, DONG-MEI LI, QING ZENG, JING ZHOU, JING WEN, JIN-JIN GUO
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Publication number: 20240094420Abstract: The embodiment of the present disclosure provides an imaging method for a dispersion energy spectrum of surface waves, an electronic device, and a storage medium. The imaging method includes: obtaining first surface wave data, and the first surface wave data corresponding to a space-time domain representation; processing the first surface wave data to obtain the second surface wave data, and the second surface wave data corresponding to a space-frequency domain representation; and processing the second surface wave data based on a preset algorithm to obtain a first imaging result, the first imaging result corresponding to a slowness-frequency domain representation.Type: ApplicationFiled: June 27, 2023Publication date: March 21, 2024Applicant: SOUTHWEST PETROLEUM UNIVERSITYInventors: Weiping CAO, Luo LI, Xuri HUANG, Hai YAO, Yungui XU, Yezheng HU, Jing TANG, Ran YANG, Mengcheng LI
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Publication number: 20240045093Abstract: The present disclosure discloses a denoising method and a system for preserving amplitude variation with offset (AVO) features of pre-stack seismic data. The method includes: acquiring seismic wave data at at least one receiving point through a seismic signal acquisition device, and storing the seismic wave data in a memory; in response to ending of an acquisition operation of the seismic signal with the seismic signal acquisition device, determining, based on the seismic wave data in the memory, pre-denoising angle gather data through a processing device; and obtaining and outputting a denoised angle gather signal through inputting the pre-denoising angle gather data to a denoising device and performing denoising processing on the pre-denoising angle gather data based on the denoising device.Type: ApplicationFiled: June 5, 2023Publication date: February 8, 2024Applicant: SOUTHWEST PETROLEUM UNIVERSITYInventors: Weiping CAO, Ran YANG, Xuri HUANG, Xiaoqing CUI, Mengcheng LI, Luo LI, Haoyuan LI, Mengyu REN, Sheng Yang, Moyan LI, Xinwang LI
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Publication number: 20230409311Abstract: The present disclosure is directed to automated generation and management of update estimates relative to application of an update to a computing device. One or more updated to be applied to a computing device are identified. A trained artificial intelligence (AI) model is applied that is adapted to generate an update estimate predicting an amount of time that is required to apply an update to the computing device. An update estimate is generated based on a contextual analysis that evaluates one or more of: parameters associated with the update; device characteristics of the computing device to be updated; a state of current user activity on the computing device; historical predictions relating to prior update estimates for one or more computing devices (e.g., that comprise the computing device); or a combination thereof. A notification of the update estimate is then automatically generated and caused to be rendered.Type: ApplicationFiled: August 4, 2023Publication date: December 21, 2023Inventors: Yutong LIAO, Cheng WU, Nicolas Justin LAVIGNE, Frederick Douglass CAMPBELL, Chan CHAIYOCHLARB, Raymond Duane PARSONS, Alexander OOT, Paul Luo LI, Minsuk KANG, Abhinav MISHRA
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Patent number: 11762649Abstract: The present disclosure is directed to automated generation and management of update estimates relative to application of an update to a computing device. One or more updates to be applied to a computing device are identified. A trained artificial intelligence (AI) model is applied that is adapted to generate an update estimate predicting an amount of time that is required to apply an update to the computing device. An update estimate is generated based on a contextual analysis that evaluates one or more of: parameters associated with the update; device characteristics of the computing device to be updated; a state of current user activity on the computing device; historical predictions relating to prior update estimates for one or more computing devices (e.g., that comprise the computing device); or a combination thereof. A notification of the update estimate is then automatically generated and caused to be rendered.Type: GrantFiled: June 30, 2021Date of Patent: September 19, 2023Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Yutong Liao, Cheng Wu, Nicolas Justin Lavigne, Frederick Douglass Campbell, Chan Chaiyochlarb, Raymond Duane Parsons, Alexander Oot, Paul Luo Li, Minsuk Kang, Abhinav Mishra
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Publication number: 20230137131Abstract: A server computing device generates training data based upon an identifier for a device, a timestamp, and a label received from a developer computing device. The server computing device trains a computer-implemented machine learning (ML) model based upon the training data. The server computing device also generates client configuration data for the ML model that specifies transformations that are to be applied to values in order to generate input values for the ML model. The server computing device deploys ML assets to client computing devices, the ML assets comprising the ML model and the client configuration data. The client computing devices execute the ML model using input values derived via transformations of (local) values produced by the client computing devices and transmit telemetry data to the server computing device. The server computing device updates the ML assets based upon the telemetry data.Type: ApplicationFiled: December 29, 2022Publication date: May 4, 2023Applicant: Microsoft Technology Licensing, LLCInventors: Paul Luo LI, Ho Jeannie CHUNG, Xiaoyu CHAI, Irina Ioana NICULESCU, Minsuk KANG, Brandon H. PADDOCK, Jilong LIAO, Neeraja ABBURU, James Henry DOOLEY, IV, Frederick Douglass CAMPBELL
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Patent number: 11544625Abstract: A server computing device generates training data based upon an identifier for a device, a timestamp, and a label received from a developer computing device. The server computing device trains a computer-implemented machine learning (ML) model based upon the training data. The server computing device also generates client configuration data for the ML model that specifies transformations that are to be applied to values in order to generate input values for the ML model. The server computing device deploys ML assets to client computing devices, the ML assets comprising the ML model and the client configuration data. The client computing devices execute the ML model using input values derived via transformations of (local) values produced by the client computing devices and transmit telemetry data to the server computing device. The server computing device updates the ML assets based upon the telemetry data.Type: GrantFiled: February 3, 2020Date of Patent: January 3, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Paul Luo Li, Ho Jeannie Chung, Xiaoyu Chai, Irina Ioana Niculescu, Minsuk Kang, Brandon H. Paddock, Jilong Liao, Neeraja Abburu, James Henry Dooley, IV, Frederick Douglass Campbell
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Publication number: 20220350588Abstract: The present disclosure is directed to automated generation and management of update estimates relative to application of an update to a computing device. One or more updated to be applied to a computing device are identified. A trained artificial intelligence (AI) model is applied that is adapted to generate an update estimate predicting an amount of time that is required to apply an update to the computing device. An update estimate is generated based on a contextual analysis that evaluates one or more of: parameters associated with the update; device characteristics of the computing device to be updated; a state of current user activity on the computing device; historical predictions relating to prior update estimates for one or more computing devices (e.g., that comprise the computing device); or a combination thereof. A notification of the update estimate is then automatically generated and caused to be rendered.Type: ApplicationFiled: June 30, 2021Publication date: November 3, 2022Inventors: Yutong LIAO, Cheng WU, Nicolas Justin LAVIGNE, Frederick Douglass CAMPBELL, Chan CHAIYOCHLARB, Raymond Duane PARSONS, Alexander OOT, Paul Luo LI, Minsuk KANG, Abhinav MISHRA
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Publication number: 20210241167Abstract: A server computing device generates training data based upon an identifier for a device, a timestamp, and a label received from a developer computing device. The server computing device trains a computer-implemented machine learning (ML) model based upon the training data. The server computing device also generates client configuration data for the ML model that specifies transformations that are to be applied to values in order to generate input values for the ML model. The server computing device deploys ML assets to client computing devices, the ML assets comprising the ML model and the client configuration data. The client computing devices execute the ML model using input values derived via transformations of (local) values produced by the client computing devices and transmit telemetry data to the server computing device. The server computing device updates the ML assets based upon the telemetry data.Type: ApplicationFiled: February 3, 2020Publication date: August 5, 2021Inventors: Paul Luo LI, Ho Jeannie CHUNG, Xiaoyu CHAI, Irina Ioana NICULESCU, Minsuk KANG, Brandon H. PADDOCK, Jilong LIAO, Neeraja ABBURU, James Henry DOOLEY, IV, Frederick Douglass CAMPBELL
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Patent number: 10902149Abstract: Methods, systems, apparatuses, and computer-readable storage medium are described herein for remotely analyzing testing results based on LDP-based data obtained from client devices in order to determine an effect of a software application with respect to its features and/or the population in which the application is tested. The analysis is based on a series of statistical computations for conducting hypothesis tests to compare population means, while ensuring LDP for each user. For example, an LDP scheme is used on the client-side that privatizes a measured value corresponding to a usage of a resource of the client. A data collector receives the privatized data from two sets of populations. Each population's clients have a software application that may differ in terms of features or user group. The privatized data received from each population is analyzed to determine an effect of the difference between the software applications of the different populations.Type: GrantFiled: March 22, 2018Date of Patent: January 26, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Bolin Ding, Harsha Prasad Nori, Paul Luo Li, Joshua Stanley Allen
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Publication number: 20190236306Abstract: Methods, systems, apparatuses, and computer-readable storage medium are described herein for remotely analyzing testing results based on LDP-based data obtained from client devices in order to determine an effect of a software application with respect to its features and/or the population in which the application is tested. The analysis is based on a series of statistical computations for conducting hypothesis tests to compare population means, while ensuring LDP for each user. For example, an LDP scheme is used on the client-side that privatizes a measured value corresponding to a usage of a resource of the client. A data collector receives the privatized data from two sets of populations. Each population's clients have a software application that may differ in terms of features or user group. The privatized data received from each population is analyzed to determine an effect of the difference between the software applications of the different populations.Type: ApplicationFiled: March 22, 2018Publication date: August 1, 2019Inventors: Bolin Ding, Harsha Prasad Nori, Paul Luo Li, Joshua Stanley Allen