Patents by Inventor Cristhian Mauricio Potes Blandon
Cristhian Mauricio Potes Blandon 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: 20240071110Abstract: A method (100) for generating a textual description of a medical image, comprising: receiving (130) a medical image of an anatomical region, the image comprising one or more abnormalities; segmenting (140) the anatomical region in the received medical image from a remainder of the image; identifying (150) at least one of the one or more abnormalities in the segmented anatomical region; extracting (160) one or more features from the identified abnormality; generating (170), using the extracted features and a trained text generation model, a textual description of the identified abnormality; and reporting (180), via a user interface of the system, the generated textual description of the identified abnormality.Type: ApplicationFiled: November 6, 2023Publication date: February 29, 2024Inventors: Christine Menking SWISHER, Sheikh Sadid AL HASAN, Jonathan RUBIN, Cristhian Mauricio POTES BLANDON, Yuan LING, Oladimeji Feyisetan FARRI, Rithesh SREENIVASAN
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Patent number: 11875277Abstract: Techniques disclosed herein relate to learning and applying contextual patient similarities. Multiple template similarity functions (118) may be provided (602). Each template similarity function may compare a respective subset of features of a query entity feature vector with a corresponding subset of features of a candidate entity feature vector. A composite similarity function (120) may be provided (604) as a weighted combination of respective outputs of the template similarity functions. A plurality of labeled entity vectors may be provided (606) as context training data. An approximation function may be applied (608) to approximate a first context label for each respective labeled entity vector. A first context specific composite similarity function may be trained (610) based on the composite similarity function by learning first context weights for the template similarity functions using a first loss function based on output of application of the approximation function to the first context training data.Type: GrantFiled: September 17, 2021Date of Patent: January 16, 2024Assignee: KONINKLIJKE PHILIPS N.V.Inventors: Bryan Conroy, Minnan Xu, Asif Rahman, Cristhian Mauricio Potes Blandon
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Patent number: 11836997Abstract: A method (100) for generating a textual description of a medical image, comprising: receiving (130) a medical image of an anatomical region, the image comprising one or more abnormalities; segmenting (140) the anatomical region in the received medical image from a remainder of the image; identifying (150) at least one of the one or more abnormalities in the segmented anatomical region; extracting (160) one or more features from the identified abnormality; generating (170), using the extracted features and a trained text generation model, a textual description of the identified abnormality; and reporting (180), via a user interface of the system, the generated textual description of the identified abnormality.Type: GrantFiled: May 7, 2019Date of Patent: December 5, 2023Assignee: KONINKLIJKE PHILIPS N.V.Inventors: Christine Menking Swisher, Sheikh Sadid Al Hasan, Jonathan Rubin, Cristhian Mauricio Potes Blandon, Yuan Ling, Oladimeji Feyisetan Farri, Rithesh Sreenivasan
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Patent number: 11676733Abstract: Techniques disclosed herein relate to learning and applying contextual patient similarities. In various embodiments, a first value for a query entity may be displayed (702) on an interface. The first value may be related to a first context. A first trained similarity function may be selected (704) from a plurality of trained similarity functions. The first trained similarity function may be associated with the first context. The first selected trained similarity function may be applied (706) to a set of features associated with the query entity and respective sets of features associated with a plurality of candidate entities. A set of one or more similar candidate entities may be selected (708) from the plurality of candidate entities based on application of the first trained similarity function. Information associated with the first set of one or more similar candidate entities may be displayed (710) on the interface.Type: GrantFiled: December 18, 2018Date of Patent: June 13, 2023Assignee: KONINKLIJKE PHILIPS N.V.Inventors: Bryan Conroy, Minnan Xu, Asif Rahman, Cristhian Mauricio Potes Blandon
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Patent number: 11620554Abstract: An electronic clinical decision support (CDS) device (10) employs a trained CDS algorithm (30) that operates on values of a set of covariates to output a prediction of a medical condition. The CDS algorithm was trained on a training data set (22). The CDS device includes a computer (12) that is programmed to provide a user interface (62) for completing clinical survey questions using the display and the one or more user input devices. Marginal probability distributions (42) for the covariates of the set of covariates are generated from the completed clinical survey questions. The trained CDS algorithm is adjusted for covariate shift using the marginal probability distributions. A prediction of the medical condition is generated for a medical subject using the trained CDS algorithm adjusted for covariate shift (50) operating on values for the medical subject of the covariates of the set of covariates.Type: GrantFiled: August 1, 2017Date of Patent: April 4, 2023Assignee: Koninklijke Philips N.V.Inventors: Bryan Conroy, Cristhian Mauricio Potes Blandon, Minnan Xu
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Patent number: 11529101Abstract: When evaluating the quality of photoplethysmography (PPG) signal (52) measured from a patient monitor (e.g., a finger sensor or the like), multiple features of the PPG signal are extracted and analyzed to facilitate assigning a score to the PPG signal or portions (e.g., heartbeats) thereof. Heartbeats in the PPG signal are segmented out using concurrently captured electrocardiograph (ECG) signal (50), and for each heartbeat, a plurality of extracted features are analyzed. If all extracted features satisfy one or more predetermined criteria for each feature, then the heartbeat waveform is compared to a predefined heartbeat template. If the waveform matches the template (e.g., within a predetermined match percentage or the like), then the heartbeat is classified as “clean.” If the heartbeat does not patch the template, or if one or more of the extracted features fails to satisfy its one or more pre-determined criteria, the heartbeat is classified as “noisy.Type: GrantFiled: November 10, 2016Date of Patent: December 20, 2022Assignee: KONINKLIJKE PHILIPS N.V.Inventors: Erina Ghosh, Cristhian Mauricio Potes Blandon
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Patent number: 11406323Abstract: A system (400) for monitoring an individual's sleep includes: (i) a patient monitor (410) configured to obtain a patient waveform, the patient waveform comprising information representative of a vital statistic of the patient; a processor (420) in communication with the patient monitor and configured to: (i) process the patient waveform to generate a segmented waveform; (ii) extract at least one feature from a segment of the waveform in a time domain and/or at least one feature from the segment of the waveform in the frequency domain; (iii) classify, using the at least one extracted feature, a sleep stage of the patient for the segment of the waveform; and (iv) generate, from classified sleep stages for a plurality of segments of the waveform, a sleep quality measurement; and a user interface (480) configured to report the generated sleep quality measurement.Type: GrantFiled: July 10, 2018Date of Patent: August 9, 2022Assignee: KONINKLIJKE PHILIPS N.V.Inventors: Gary Nelson Garcia Molina, Cristhian Mauricio Potes Blandon, Pedro Miguel Ferreira Dos Santos Da Fonseca, Bryan Conroy, Minnan Xu
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Publication number: 20220004906Abstract: Techniques disclosed herein relate to learning and applying contextual patient similarities. Multiple template similarity functions (118) may be provided (602). Each template similarity function may compare a respective subset of features of a query entity feature vector with a corresponding subset of features of a candidate entity feature vector. A composite similarity function (120) may be provided (604) as a weighted combination of respective outputs of the template similarity functions. A plurality of labeled entity vectors may be provided (606) as context training data. An approximation function may be applied (608) to approximate a first context label for each respective labeled entity vector. A first context specific composite similarity function may be trained (610) based on the composite similarity function by learning first context weights for the template similarity functions using a first loss function based on output of application of the approximation function to the first context training data.Type: ApplicationFiled: September 17, 2021Publication date: January 6, 2022Inventors: BRYAN CONROY, MINNAN XU, ASIF RAHMAN, CRISTHIAN MAURICIO POTES BLANDON
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Patent number: 11126921Abstract: Techniques disclosed herein relate to learning and applying contextual patient similarities. Multiple template similarity functions may be provided. Each template similarity function may compare a respective subset of features of a query entity feature vector with a corresponding subset of features of a candidate entity feature vector. A composite similarity function may be provided as a weighted combination of respective outputs of the template similarity functions. A plurality of labeled entity vectors may be provided as context training data. An approximation function may be applied to approximate a first context label for each respective labeled entity vector. A first context specific composite similarity function may be trained based on the composite similarity function by learning first context weights for the template similarity functions using a first loss function based on output of application of the approximation function to the first context training data.Type: GrantFiled: April 19, 2018Date of Patent: September 21, 2021Assignee: KONINKLIJKE PHILIPS N.V.Inventors: Bryan Conroy, Minnan Xu, Asif Rahman, Cristhian Mauricio Potes Blandon
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Publication number: 20210241884Abstract: A method (100) for generating a textual description of a medical image, comprising: receiving (130) a medical image of an anatomical region, the image comprising one or more abnormalities; segmenting (140) the anatomical region in the received medical image from a remainder of the image; identifying (150) at least one of the one or more abnormalities in the segmented anatomical region; extracting (160) one or more features from the identified abnormality; generating (170), using the extracted features and a trained text generation model, a textual description of the identified abnormality; and reporting (180), via a user interface of the system, the generated textual description of the identified abnormality.Type: ApplicationFiled: May 7, 2019Publication date: August 5, 2021Inventors: Christine Menking Swisher, Sheikh Sadid Al Hasan, Jonathan Rubin, Cristhian Mauricio Potes Blandon, Yuan Ling, Oladimeji Feyisetan Farri, Rithesh Sreenivasan
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Publication number: 20200388398Abstract: Techniques disclosed herein relate to learning and applying contextual patient similarities. In various embodiments, a first value for a query entity may be displayed (702) on an interface. The first value may be related to a first context. A first trained similarity function may be selected (704) from a plurality of trained similarity functions. The first trained similarity function may be associated with the first context. The first selected trained similarity function may be applied (706) to a set of features associated with the query entity and respective sets of features associated with a plurality of candidate entities. A set of one or more similar candidate entities may be selected (708) from the plurality of candidate entities based on application of the first trained similarity function. Information associated with the first set of one or more similar candidate entities may be displayed (710) on the interface.Type: ApplicationFiled: December 18, 2018Publication date: December 10, 2020Inventors: BRYON CONROY, MINNAN XU, ASIF RAHMAN, CRISTHIAN MAURICIO POTES BLANDON
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Publication number: 20200146619Abstract: A system (400) for monitoring an individual's sleep includes: (i) a patient monitor (410) configured to obtain a patient waveform, the patient waveform comprising information representative of a vital statistic of the patient; a processor (420) in communication with the patient monitor and configured to: (i) process the patient waveform to generate a segmented waveform; (ii) extract at least one feature from a segment of the waveform in a time domain and/or at least one feature from the segment of the waveform in the frequency domain; (iii) classify, using the at least one extracted feature, a sleep stage of the patient for the segment of the waveform; and (iv) generate, from classified sleep stages for a plurality of segments of the waveform, a sleep quality measurement; and a user interface (480) configured to report the generated sleep quality measurement.Type: ApplicationFiled: July 10, 2018Publication date: May 14, 2020Inventors: Gary Nelson Garcia MOLINA, Cristhian Mauricio POTES BLANDON, Pedro Miguel FERREIRA DOS SANTOS DA FONSECA, Bryan CONROY, Minnan XU
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Publication number: 20190192110Abstract: Various embodiments of the inventions of the present disclosure provide a combination of feature-based approach and deep learning approach for distinguishing between normal heart sounds and abnormal heart sounds. A feature-based classifier (60) is applied to a phonocardiogram (PCG) signal to obtain a feature-based abnormality classification of the heart sounds represented by the PCG signal and a deep learning classifier (70) is also applied to the PCG signal to obtain a deep learning abnormality classification of the heart sounds represented by the PCG signal. A final decision analyzer (80) is applied to the feature-based abnormality classification and the deep learning abnormality classification of the heart sounds represented by the PCG signal to determine a final abnormality classification decision of the PCG signal.Type: ApplicationFiled: September 7, 2017Publication date: June 27, 2019Inventors: Saman Parvaneh, Cristhian Mauricio Potes Blandon, Asif Rahman, Bryan Conroy
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Publication number: 20190164648Abstract: An electronic clinical decision support (CDS) device (10) employs a trained CDS algorithm (30) that operates on values of a set of covariates to output a prediction of a medical condition. The CDS algorithm was trained on a training data set (22). The CDS device includes a computer (12) that is programmed to provide a user interface (62) for completing clinical survey questions using the display and the one or more user input devices. Marginal probability distributions (42) for the covariates of the set of covariates are generated from the completed clinical survey questions. The trained CDS algorithm is adjusted for covariate shift using the marginal probability distributions. A prediction of the medical condition is generated for a medical subject using the trained CDS algorithm adjusted for covariate shift (50) operating on values for the medical subject of the covariates of the set of covariates.Type: ApplicationFiled: August 1, 2017Publication date: May 30, 2019Inventors: Bryan Conroy, Cristhian Mauricio Potes Blandon, Minnan Xu
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Publication number: 20180325457Abstract: When evaluating the quality of photoplethysmography (PPG) signal (52) measured from a patient monitor (e.g., a finger sensor or the like), multiple features of the PPG signal are extracted and analyzed to facilitate assigning a score to the PPG signal or portions (e.g., heartbeats) thereof. Heartbeats in the PPG signal are segmented out using concurrently captured electrocardiograph (ECG) signal (50), and for each heartbeat, a plurality of extracted features are analyzed. If all extracted features satisfy one or more predetermined criteria for each feature, then the heartbeat waveform is compared to a predefined heartbeat template. If the waveform matches the template (e.g., within a predetermined match percentage or the like), then the heartbeat is classified as “clean.” If the heartbeat does not patch the template, or if one or more of the extracted features fails to satisfy its one or more pre-determined criteria, the heartbeat is classified as “noisy.Type: ApplicationFiled: November 10, 2016Publication date: November 15, 2018Inventors: Erina GHOSH, Cristhian Mauricio POTES BLANDON
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Publication number: 20180307995Abstract: Techniques disclosed herein relate to learning and applying contextual patient similarities. Multiple template similarity functions may be provided. Each template similarity function may compare a respective subset of features of a query entity feature vector with a corresponding subset of features of a candidate entity feature vector. A composite similarity function may be provided as a weighted combination of respective outputs of the template similarity functions. A plurality of labeled entity vectors may be provided as context training data. An approximation function may be applied to approximate a first context label for each respective labeled entity vector. A first context specific composite similarity function may be trained based on the composite similarity function by learning first context weights for the template similarity functions using a first loss function based on output of application of the approximation function to the first context training data.Type: ApplicationFiled: April 19, 2018Publication date: October 25, 2018Inventors: Bryan Conroy, Minnan Xu, Asif Rahman, Cristhian Mauricio Potes Blandon