Patents by Inventor Yuan-Ming Fleming Lure
Yuan-Ming Fleming Lure 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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Patent number: 11688264Abstract: A system and method for patient movement detection and fall monitoring to address the need to proactively monitor patients to detect abnormal movements, in-room activity, and other movements associated with providing in-room care. The system comprises an environmental model which can be used to track the position and movement of a patient and a classifier network configured to receive movement data and classify a patient's movement as normal or abnormal movement. In addition to monitoring in-room activity, the system and method create safe zones within the room to ensure patients are proactively monitor in the event of a seizure, fall, or other unintended activity. The system will record and store in-room video in a secure environment. Videos and notifications are automatically sent to designated staff as events occur.Type: GrantFiled: January 11, 2023Date of Patent: June 27, 2023Assignee: MS TECHNOLOGIESInventors: Shuchuan Jack Cheng, Yuan-Ming Fleming Lure
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Patent number: 11651672Abstract: A system and method for quantifying Alzheimer's disease (AD) risk using one or more interferometric micro-Doppler radars (IMDRs) and deep learning artificial intelligence to distinguish between cognitively unimpaired individuals and persons with AD based on gait analysis. The system utilizes IMDR to capture signals from both radial and transversal movement in three-dimensional space to further increase the accuracy for human gait estimation. New deep learning technologies are designed to complement traditional machine learning involving separate feature extraction followed-up with classification to process radar signature from different views including side, front, depth, limbs, and whole body where some motion patterns are not easily describable. The disclosed cross-talk deep model is the first to apply deep learning to learn IMDR signatures from two perpendicular directions jointly from both healthy and unhealthy individuals.Type: GrantFiled: November 22, 2022Date of Patent: May 16, 2023Assignee: MS TECHNOLOGIESInventors: Shuchuan Jack Cheng, Yuan-Ming Fleming Lure
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Patent number: 11651862Abstract: A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing deep learning. More specifically, the system and method produce predictions of MCI conversions to Alzheimer's/dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is a deep learned model trained using transfer learning. An MCI-DAP server may then receive a request from a clinician to process predictions related to a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.Type: GrantFiled: July 15, 2022Date of Patent: May 16, 2023Assignee: MS TECHNOLOGIESInventors: Yuan-Ming Fleming Lure, Jing Li, Teresa Wu, David Weidman, Kewei Chen, Xiaonan Liu, Yi Su
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Publication number: 20230140093Abstract: A system and method for patient movement detection and fall monitoring to address the need to proactively monitor patients to detect abnormal movements, in-room activity, and other movements associated with providing in-room care. The system comprises an environmental model which can be used to track the position and movement of a patient and a classifier network configured to receive movement data and classify a patient's movement as normal or abnormal movement. In addition to monitoring in-room activity, the system and method create safe zones within the room to ensure patients are proactively monitor in the event of a seizure, fall, or other unintended activity. The system will record and store in-room video in a secure environment. Videos and notifications are automatically sent to designated staff as events occur.Type: ApplicationFiled: January 11, 2023Publication date: May 4, 2023Inventors: Shuchuan Jack Cheng, Yuan-Ming Fleming Lure
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Publication number: 20230078905Abstract: A system and method for quantifying Alzheimer's disease (AD) risk using one or more interferometric micro-Doppler radars (IMDRs) and deep learning artificial intelligence to distinguish between cognitively unimpaired individuals and persons with AD based on gait analysis. The system utilizes IMDR to capture signals from both radial and transversal movement in three-dimensional space to further increase the accuracy for human gait estimation. New deep learning technologies are designed to complement traditional machine learning involving separate feature extraction followed-up with classification to process radar signature from different views including side, front, depth, limbs, and whole body where some motion patterns are not easily describable. The disclosed cross-talk deep model is the first to apply deep learning to learn IMDR signatures from two perpendicular directions jointly from both healthy and unhealthy individuals.Type: ApplicationFiled: November 22, 2022Publication date: March 16, 2023Inventors: Shuchuan Jack Cheng, Yuan-Ming Fleming Lure
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Publication number: 20230042243Abstract: A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing hybrid machine learning. More specifically, the system and method produce predictions of MCI conversions to dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is trained using transfer learning. A platform may then receive a request from a clinician for a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.Type: ApplicationFiled: October 20, 2022Publication date: February 9, 2023Inventors: Yuan-Ming Fleming Lure, Jing Li, Teresa Wu, David Weidman, Kewei Chen, Xiaonan Liu, Yi Su
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Publication number: 20220367056Abstract: A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing deep learning. More specifically, the system and method produce predictions of MCI conversions to Alzheimer's/dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is a deep learned model trained using transfer learning. An MCI-DAP server may then receive a request from a clinician to process predictions related to a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.Type: ApplicationFiled: July 15, 2022Publication date: November 17, 2022Inventors: Yuan-Ming Fleming Lure, Jing Li, Teresa Wu, David Weidman, Kewei Chen, Xiaonan Liu, Yi Su
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Publication number: 20220344051Abstract: A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing deep learning. More specifically, the system and method produce predictions of MCI conversions to Alzheimer's/dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is a deep learned model trained using transfer learning. An MCI-DAP server may then receive a request from a clinician to process predictions related to a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.Type: ApplicationFiled: July 5, 2022Publication date: October 27, 2022Inventors: Yuan-Ming Fleming Lure, Jing Li, Teresa Wu, David Weidman, Kewei Chen, Xiaonan Liu, Yi Su
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Publication number: 20220262514Abstract: A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis. More specifically, the system and method produce predictions of MCI conversions to dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is trained using transfer learning. A server may then receive a request from a clinician for a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.Type: ApplicationFiled: December 22, 2021Publication date: August 18, 2022Inventors: Yuan-Ming Fleming Lure, Jing Li, Teresa Wu, David Weidman, Kewei Chen, Xiaonan Liu
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Patent number: 11380181Abstract: A system for passively predicting and detecting falls using one or more dual-polarized Doppler radars and machine learning algorithms. The system is typically implemented for use in predicting or detecting falls in older adults and may be connected with various systems that can alert emergency services or hospice personnel in the event of a fallen individual. Furthermore, the system overcomes conventional radar systems by integrating vertical and horizontal micro-Doppler signatures into a combined signature which is analyzed by machine learning algorithms to correctly and expeditiously predict and detect a variety of human movements. The system also finds applications wherever micro-Doppler signals may be generated such as predicting or detecting behaviors or movements over time to detect and predict the onset of diseases and other disabilities.Type: GrantFiled: December 9, 2020Date of Patent: July 5, 2022Assignee: MS TECHNOLOGIESInventors: Shuchuan Jack Cheng, Yuan-Ming Fleming Lure
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Publication number: 20220180723Abstract: A system for passively predicting and detecting falls using one or more dual-polarized Doppler radars and machine learning algorithms. The system is typically implemented for use in predicting or detecting falls in older adults and may be connected with various systems that can alert emergency services or hospice personnel in the event of a fallen individual. Furthermore, the system overcomes conventional radar systems by integrating vertical and horizontal micro-Doppler signatures into a combined signature which is analyzed by machine learning algorithms to correctly and expeditiously predict and detect a variety of human movements. The system also finds applications wherever micro-Doppler signals may be generated such as predicting or detecting behaviors or movements over time to detect and predict the onset of diseases and other disabilities.Type: ApplicationFiled: December 9, 2020Publication date: June 9, 2022Inventors: Shuchuan Jack Cheng, Yuan-Ming Fleming Lure
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Publication number: 20170337681Abstract: A method for classifying the degree of healthiness from chest radiographs comprises inputting image data with a medical imaging acquisition system or from individual's computers or smartphones or cloud storage devices. The image data is transmitted from the medical imaging acquisition system or from the computers or storage device to a computer-aided-analysis (CAA) system via the Internet and an archive/review station. Classification results are generated by processing the image data to perform lung segmentation and generate various radiomics, perform classification of radiomics. The classification results are transmitted from the CAA system to archive/review servers or the computers/smartphones via the Internet. The classification results are used to retrieve the associated clinical, wellness, and health knowledge from the database to form composite data.Type: ApplicationFiled: February 13, 2017Publication date: November 23, 2017Inventors: Yuan-Ming Fleming Lure, Hao Zhou
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Patent number: 6760468Abstract: A method and system improve the detection of abnormalities, such as lung nodules, in radiological images using digital image processing and artificial neural network techniques. The detection method and system use a nodule phantom for matching in order to enhance the efficiency in detection. The detection method and system use spherical parameters to characterize true nodules, thus enabling detection of the nodules in the mediastinum. The detection method and system use a multi-layer back-propagation neural network architecture not only for the classification of lung nodules but also for the integration of detection results from different classifiers. In addition, this method and system improve the detection efficiency by recommending the ranking of true nodules and several false positive nodules prior to the training of the neural network classifier. The method and system use image segmentation to remove regions outside the chest in order to reduce the false positives outside the chest region.Type: GrantFiled: February 15, 2000Date of Patent: July 6, 2004Assignee: Deus Technologies, LLCInventors: Hwa-Young Michael Yeh, Yuan-Ming Fleming Lure, Jyh-Shyan Lin
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Patent number: 6654728Abstract: A fuzzy logic based classification (FLBC) method for the automated discrimination of objects and the automated identification of nodules based on their features, a computer programmed to implement the method, and a storage medium which stores a program for implementing the method, wherein nodule (or, object) features are first normalized and then automatically selected. Based on the selected features, suspect nodules (or, objects) are pre-grouped and then subjected to the corresponding trained linear classifier to remove those false positive nodules or abnormal objects that are linearly separable. Finally, the remaining suspect nodules or objects are further subjected to a trained fuzzy classifier for removing those false positive nodules or abnormal objects that are not linearly separable.Type: GrantFiled: July 25, 2000Date of Patent: November 25, 2003Assignee: Deus Technologies, LLCInventors: Ruiping Li, Hwa-Young Michael Yeh, Yuan-Ming Fleming Lure, Xin-Wei Xu, Jyh-Shyan Lin
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Patent number: 6549646Abstract: A divide-and-conquer (DAC) method and system improve the detection of abnormalities, like lung nodules, in radiological images via the use of zone-based digital image processing and artificial neural networks. The DAC method and system divide the lung zone into different zones in order to enhance the efficiency in detection. Different image enhancement techniques are used for each different zone to enhance nodule images, as are different zone-specific techniques for selecting suspected abnormalities, extracting image features corresponding to selected abnormalities, and classifying the abnormalities as either true or false abnormalities.Type: GrantFiled: February 15, 2000Date of Patent: April 15, 2003Assignee: Deus Technologies, LLCInventors: Hwa-Young Michael Yeh, Jyh-Shyan Lin, Yuan-Ming Fleming Lure, Xin-Wei Xu, Ruiping Li, Rong Feng Zhuang
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Patent number: 5857030Abstract: An automated method and system for digital imaging processing of radiologic images, wherein digital image data is acquired and subjected to multiple phases of digital imaging processing. During the Pre-Processing stage, simultaneous box-rim filtering and k-nearest neighbor processing and subsequent global thresholding are performed on the image data to enhance object-to-background contrast, merge subclusters and determine gray scale thresholds for further processing. Next, during the Preliminary Selection phase, body part segmentation, morphological erosion processing, connected component analysis and image block segmentation occurs to subtract unwanted image data preliminarily identify potentials areas including abnormalities.Type: GrantFiled: April 9, 1996Date of Patent: January 5, 1999Assignee: Eastman Kodak CompanyInventors: Roger Stephen Gaborski, Yuan-Ming Fleming Lure, Thaddeus Francis Pawlicki
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Patent number: D433507Type: GrantFiled: November 25, 1998Date of Patent: November 7, 2000Assignee: Deus Technologies, LLCInventors: Hwa-Young Michael Yeh, Yuan-Ming Fleming Lure, Jyh-Shyan Lin, Xin-Wei Xu, William Bredlow, Richard Lipscher, Matthew Thomas Freedman