Patents by Inventor Richard Chen
Richard Chen 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).
-
Publication number: 20250134084Abstract: A pest trap includes a trap module and a connector module. The trap module is disposed on a columnar standing structure. The trap module includes a trap body that cooperates with an outer surrounding surface of the columnar standing structure to define an inside path therebetween for a pest to move therein. The connector module is connected to the trap module. The connector module defines a tunnel that has a first opening through which the tunnel is in spatial communication with the inside path, and that is for the pest to move therethrough. An inner surface of the trap body is at least partially slippery for the pest.Type: ApplicationFiled: October 23, 2024Publication date: May 1, 2025Applicant: BARN OWL TECHNOLOGIES CORP.Inventors: Richard CHEN, Behrooz AZIMZADEH ISMALI KANDI, Scott SMITH
-
Publication number: 20250139428Abstract: In some aspects, the techniques described herein relate to a method including: providing a machine unlearning algorithm, wherein the machine unlearning algorithm is configured to: approximate a final training state of model parameters trained with an unfiltered dataset; approximate a final training state of model parameters trained with a retain dataset; and compute a vector for shifting parameter weights from the final training state of model parameters trained with the unfiltered dataset to the final training state of model parameters trained with the retain dataset; tuning a batch normalization layer of a convolutional neural network included in a machine learning model with the machine unlearning algorithm, wherein parameters of a convolution layer of the convolutional neural network remain fixed; and tuning prompt parameters of a transformer model included in the machine learning model with the machine unlearning algorithm, wherein other parameters of the transformer model remain fixed.Type: ApplicationFiled: October 25, 2023Publication date: May 1, 2025Inventors: Guihong LI, Hsiang HSU, Richard CHEN
-
Publication number: 20250127157Abstract: A modular pest scouting system includes a trapping module and a pest redirect module. The trapping module is for catching pests. The pest redirect module is detachably attached to the trapping module. The pest redirect module is for luring the pests to move toward the trapping module so as to be caught by the trapping module.Type: ApplicationFiled: October 18, 2024Publication date: April 24, 2025Applicant: BARN OWL TECHNOLOGIES CORP.Inventors: Richard CHEN, Behrooz AZIMZADEH ISMALI KANDI, Scott SMITH
-
Publication number: 20250103878Abstract: In some aspects, the techniques described herein relate to a method including: providing a first datum to a target model, wherein the first datum is retrieved from a forget dataset; providing a sample drawn from Gaussian noise to an original model; computing a first loss, wherein the first loss is based on target model output from processing the first datum and original model output from processing the sample drawn from Gaussian noise; providing a second datum to the target model, wherein the second datum is retrieved from a retain dataset; providing the second datum to the original model as input to the original model; computing a second loss, wherein the second loss is based on target model output from processing the second datum and original model output from processing the second datum; and combining the first loss and the second loss with an alpha weighting to generate a weighted combination.Type: ApplicationFiled: September 26, 2023Publication date: March 27, 2025Inventors: Guihong LI, Hsiang HSU, Richard CHEN
-
Publication number: 20250103914Abstract: In some aspects, the techniques described herein relate to a method including: determining a first cross-entropy loss, wherein the first cross-entropy loss is determined based on a set of predictions, and wherein the set of predictions are based on a classifier head of a machine learning model generating the set of predictions based on a set of feature vectors; updating the classifier head and a prompt of the machine learning model with the first cross-entropy loss; generating outlier samples based on the set of feature vectors; providing, as input to the classifier head, the set of feature vectors and the outlier samples, wherein a second cross-entropy loss and an outlier regularization loss are computed by the classifier head based on the set of feature vectors and the outlier samples; and updating the classifier head with the second cross-entropy loss and the outlier regularization loss.Type: ApplicationFiled: September 27, 2023Publication date: March 27, 2025Inventors: Wei-Cheng HUANG, Richard CHEN, Hsiang HSU
-
Patent number: 12258628Abstract: This disclosure provides systems and methods for sample processing and data analysis. Sample processing may include nucleic acid sample processing and subsequent sequencing. Some or all of a nucleic acid sample may be sequenced to provide sequence information, which may be stored or otherwise maintained in an electronic storage location. The sequence information may be analyzed with the aid of a computer processor, and the analyzed sequence information may be stored in an electronic storage location that may include a pool or collection of sequence information and analyzed sequence information generated from the nucleic acid sample. Methods and systems of the present disclosure can be used, for example, for the analysis of a nucleic acid sample, for producing one or more libraries, and for producing biomedical reports. Methods and systems of the disclosure can aid in the diagnosis, monitoring, treatment, and prevention of one or more diseases and conditions.Type: GrantFiled: March 1, 2024Date of Patent: March 25, 2025Assignee: Personalis, Inc.Inventors: John West, Christian Haudenschild, Richard Chen
-
Publication number: 20250094803Abstract: Systems and methods for efficient test-time prediction of model arbitrariness are disclosed. According to an embodiment, a method for efficient test-time estimation of predictive multiplicity may include: (1) receiving, by arbitrariness prediction computer program, a trained machine learning model, wherein the trained machine learning model comprises a plurality of nodes, and each node has a weight; (2) determining, by the arbitrariness prediction computer program, a number of dropout models for the trained machine learning model to generate; (3) creating, by the arbitrariness prediction computer program, the number of dropout models; (4) providing, by the arbitrariness prediction computer program, sample data to each of the dropout models; (5) receiving, by the arbitrariness prediction computer program, an output from each of the dropout models; and (6) determining, by the arbitrariness prediction computer program, an arbitrariness for the trained machine learning model based on the outputs.Type: ApplicationFiled: September 19, 2023Publication date: March 20, 2025Inventors: Hsiang HSU, Richard CHEN, Guihong LI
-
Patent number: 12249147Abstract: One embodiment of the invention provides a method for video recognition. The method comprises receiving an input video comprising a sequence of video segments over a plurality of data modalities. The method further comprises, for a video segment of the sequence, selecting one or more data modalities based on data representing the video segment. Each data modality selected is optimal for video recognition of the video segment. The method further comprises, for each data modality selected, providing at least one data input representing the video segment over the data modality selected to a machine learning model corresponding to the data modality selected, and generating a first type of prediction representative of the video segment via the machine learning model. The method further comprises determining a second type of prediction representative of the entire input video by aggregating all first type of predictions generated.Type: GrantFiled: March 11, 2021Date of Patent: March 11, 2025Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Rameswar Panda, Richard Chen, Quanfu Fan, Rogerio Schmidt Feris
-
Publication number: 20250061335Abstract: In some aspects, the techniques described herein relate to a method including: executing a machine learning model; providing a data transformation module of the machine learning model that outputs a transformed dataset; providing a sensitive attribute suppression module of the machine learning model that outputs a sensitive attribute suppression loss; providing an annotated useful attribute preservation module of the machine learning model that outputs an annotated useful attribute preservation loss; providing an unannotated useful attribute preservation module of the machine learning model that outputs an unannotated useful attribute preservation loss; combining the sensitive attribute suppression loss, the annotated useful attribute preservation loss, and the unannotated useful attribute preservation loss into a total loss; and training a neural network of the data transformation module and a neural network of the unannotated useful attribute preservation module using the total loss.Type: ApplicationFiled: August 14, 2023Publication date: February 20, 2025Inventors: Yizhuo CHEN, Richard CHEN, Hsiang HSU, Shaohan HU, Marco PISTOIA
-
Patent number: 12230399Abstract: Systems and methods can quantify the tumor microenvironment for diagnosis, prognosis and therapeutic response prediction by fusing different data types (e.g., morphological information from histology and molecular information from omics) using an algorithm that harnesses deep learning. The algorithm employs tensor fusion to provide end-to-end multimodal fusion to model the pairwise interactions of features across multiple modalities (e.g., histology and molecular features) and deep learning. The systems and methods improve upon traditional methods for quantifying the tumor microenvironment that rely on concatenation of extracted features.Type: GrantFiled: September 28, 2020Date of Patent: February 18, 2025Assignee: THE BRIGHAM AND WOMEN'S HOSPITAL, INC.Inventors: Faisal Mahmood, Richard Chen
-
Patent number: 12189662Abstract: A method may include: receiving an original dataset and a sensitive attribute; initializing a distilled dataset from the original dataset; initializing a classification model; sampling original dataset data and an original dataset label from the original dataset, and distilled dataset data and a distilled dataset label from the distilled dataset; providing the sampled original dataset data from the original dataset to the classification model, resulting in an original dataset prediction probability; providing the sampled distilled dataset data from the distilled dataset to the classification model, resulting in a distilled dataset prediction probability; calculating a distilled dataset cross-entropy loss for the distilled dataset prediction probability and the distilled dataset label; calculating distilled dataset gradients for the distilled dataset cross-entropy loss; calculating a distance between the gradients as a matching loss; and updating the distilled dataset with the matching loss.Type: GrantFiled: November 21, 2023Date of Patent: January 7, 2025Assignee: JPMORGAN CHASE BANK, N.A.Inventors: Zhonghao Shi, Hsiang Hsu, Richard Chen, Wei-Cheng Huang
-
Publication number: 20240425920Abstract: This disclosure provides systems and methods for sample processing and data analysis. Sample processing may include nucleic acid sample processing and subsequent sequencing. Some or all of a nucleic acid sample may be sequenced to provide sequence information, which may be stored or otherwise maintained in an electronic storage location. The sequence information may be analyzed with the aid of a computer processor, and the analyzed sequence information may be stored in an electronic storage location that may include a pool or collection of sequence information and analyzed sequence information generated from the nucleic acid sample. Methods and systems of the present disclosure can be used, for example, for the analysis of a nucleic acid sample, for producing one or more libraries, and for producing biomedical reports. Methods and systems of the disclosure can aid in the diagnosis, monitoring, treatment, and prevention of one or more diseases and conditions.Type: ApplicationFiled: September 4, 2024Publication date: December 26, 2024Inventors: Gabor T. Bartha, Gemma Chandratillake, Richard Chen, Sarah Garcia, Hugo Yu Kor Lam, Mark R. Pratt, John West
-
Publication number: 20240392368Abstract: This disclosure provides systems and methods for sample processing and data analysis. Sample processing may include nucleic acid sample processing and subsequent sequencing. Some or all of a nucleic acid sample may be sequenced to provide sequence information, which may be stored or otherwise maintained in an electronic storage location. The sequence information may be analyzed with the aid of a computer processor, and the analyzed sequence information may be stored in an electronic storage location that may include a pool or collection of sequence information and analyzed sequence information generated from the nucleic acid sample. Methods and systems of the present disclosure can be used, for example, for the analysis of a nucleic acid sample, for producing one or more libraries, and for producing biomedical reports. Methods and systems of the disclosure can aid in the diagnosis, monitoring, treatment, and prevention of one or more diseases and conditions.Type: ApplicationFiled: April 4, 2024Publication date: November 28, 2024Inventors: Gabor T. Bartha, Gemma Chandratillake, Richard Chen, Sarah Garcia, Hugo Yu Kor Lam, Mark R. Pratt, John West
-
Patent number: 12153554Abstract: A method for removing uninterested attributes from multi-modality data may include: receiving, by a multi-modality attribute removal computer program executed by an electronic device, multi-modality data comprising a plurality of modalities from a data source, wherein data in each modality are related; receiving, by the multi-modality attribute removal computer program, an uninterested attribute in the multi-modality data to remove; training, by the multi-modality attribute removal computer program, a modality-focused encoder for each modality of the multi-modality data to remove the uninterested attribute using a removal loss and a retention loss for the respective modality; receiving, by the multi-modality attribute removal computer program, a multi-modality data set for processing; and processing, by the multi-modality attribute removal computer program, the multi-modality data set using the modality-focused encoders, wherein the processing results in a processed multi-modality data set with the uninterestType: GrantFiled: September 23, 2022Date of Patent: November 26, 2024Assignee: JPMORGAN CHASE BANK, N.A.Inventors: Zhonghao Shi, Richard Chen, Shaohan Hu, William Moriarty, Marco Pistoia
-
Patent number: 12084717Abstract: This disclosure provides systems and methods for sample processing and data analysis. Sample processing may include nucleic acid sample processing and subsequent sequencing. Some or all of a nucleic acid sample may be sequenced to provide sequence information, which may be stored or otherwise maintained in an electronic storage location. The sequence information may be analyzed with the aid of a computer processor, and the analyzed sequence information may be stored in an electronic storage location that may include a pool or collection of sequence information and analyzed sequence information generated from the nucleic acid sample. Methods and systems of the present disclosure can be used, for example, for the analysis of a nucleic acid sample, for producing one or more libraries, and for producing biomedical reports. Methods and systems of the disclosure can aid in the diagnosis, monitoring, treatment, and prevention of one or more diseases and conditions.Type: GrantFiled: May 5, 2023Date of Patent: September 10, 2024Assignee: PERSONALIS, INC.Inventors: Gabor T. Bartha, Gemma Chandratillake, Richard Chen, Sarah Garcia, Hugo Yu Kor Lam, Shujun Luo, Mark R. Pratt, John West
-
Publication number: 20240257913Abstract: A computer-implemented method for processing and/or analyzing nucleic acid sequencing data comprises receiving a first data input and a second data input. The first data input comprises untargeted sequencing data generated from a first nucleic acid sample obtained from a subject. The second data input comprises target-specific sequencing data generated from a second nucleic acid sample obtained from the subject. Next, with the aid of a computer processor, the first data input and the second data input are combined to produce a combined data set. Next, an output derived from the combined data set is generated. The output is indicative of the presence or absence of one or more polymorphisms of the first nucleic acid sample and/or the second nucleic acid sample.Type: ApplicationFiled: February 2, 2024Publication date: August 1, 2024Inventors: Jason Harris, Mark R. Pratt, John West, Richard Chen, Ming Li
-
Patent number: 12040305Abstract: An electronic component package includes a substrate and an electronic component mounted to the substrate, the electronic component including a bond pad. A first antenna terminal is electrically connected to the bond pad, the first antenna terminal being electrically connected to a second antenna terminal of the substrate. A package body encloses the electronic component, the package body having a principal surface. An antenna is formed on the principal surface by applying an electrically conductive coating. An embedded interconnect extends through the package body between the substrate and the principal surface and electrically connects the second antenna terminal to the antenna. Applying an electrically conductive coating to form the antenna is relatively simple thus minimizing the overall package manufacturing cost. Further, the antenna is relatively thin thus minimizing the overall package size.Type: GrantFiled: May 8, 2023Date of Patent: July 16, 2024Assignee: Amkor Technology Singapore Holding Pte. Ltd.Inventors: Jong Ok Chun, Nozad Karim, Richard Chen, Giuseppe Selli, Michael Kelly
-
Publication number: 20240200136Abstract: This disclosure provides systems and methods for sample processing and data analysis. Sample processing may include nucleic acid sample processing and subsequent sequencing. Some or all of a nucleic acid sample may be sequenced to provide sequence information, which may be stored or otherwise maintained in an electronic storage location. The sequence information may be analyzed with the aid of a computer processor, and the analyzed sequence information may be stored in an electronic storage location that may include a pool or collection of sequence information and analyzed sequence information generated from the nucleic acid sample. Methods and systems of the present disclosure can be used, for example, for the analysis of a nucleic acid sample, for producing one or more libraries, and for producing biomedical reports. Methods and systems of the disclosure can aid in the diagnosis, monitoring, treatment, and prevention of one or more diseases and conditions.Type: ApplicationFiled: March 1, 2024Publication date: June 20, 2024Inventors: John West, Christian Haudenschild, Richard Chen
-
Patent number: 12008434Abstract: A hybrid classical-quantum computing device to execute a quantum circuit corresponding to a variational problem, is configured. The configuring further comprises causing the hybrid classical-quantum computing device to execute the quantum circuit by performing an adiabatic progression operation, wherein the adiabatic progression operation comprises increasing the difficulty of the variational problem from a simplified version of the problem to the variational problem.Type: GrantFiled: April 26, 2022Date of Patent: June 11, 2024Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Don Greenberg, Marco Pistoia, Richard Chen, Giacomo Nannicini
-
Publication number: 20240185952Abstract: A method of detecting loss of heterozygosity in HLA alleles is provided. The method can include accessing a trained machine-learning model, which was trained using a training data set that included at least a training data set that includes an adjusted B allele frequency that represents a ratio between a first B allele frequency of heterozygous alleles in the tumor sample that correspond to the genomic region and a second B allele frequency of heterozygous alleles in the genomic region and associated with one or more control samples. The method can also include using the machine-learning model to generate a result corresponding to a probability of whether a loss of heterozygosity exists in an HLA allele identified in the biological sample of the particular subject by processing the sequence data using the machine-learning model.Type: ApplicationFiled: April 21, 2022Publication date: June 6, 2024Inventors: Rachel Marty PYKE, Dattatreya MELLACHERUVU, Steven DEA, Charles Wilbur ABBOTT, Simo V. ZHANG, Eric LEVY, John WEST, Richard CHEN, Sean Michael BOYLE