Machine Learning Patents (Class 706/12)
  • Patent number: 11983920
    Abstract: A method including: receiving, as input, an image; providing a neural network structure including a plurality of multilayer multi-scale neural networks, wherein the plurality of multilayer multi-scale neural networks are arranged sequentially, by laterally connecting corresponding scale-level layers between each two adjoining multilayer multi-scale neural networks in the sequence; and at a training stage, training the neural network structure on a training dataset, to obtain a trained machine learning model configured to perform a computer vision task which includes outputting at least one of: (i) a classification of the image into one class of a set of two or more classes, (ii) a segmentation of a least one object in the image, and (iii) a detection of at least one object in the image.
    Type: Grant
    Filed: December 20, 2021
    Date of Patent: May 14, 2024
    Assignee: International Business Machines Corporation
    Inventors: Vadim Ratner, Yoel Shoshan, Flora Gilboa-Solomon
  • Patent number: 11985102
    Abstract: A message suggestion service may use clusters of pre-approved messages to improve the quality of messages suggested to users. During a conversation, messages of the conversation may be processed with a neural network to compute a conversation encoding vector. The neural network may also be used to compute pre-approved message encoding vectors of the pre-approved messages. Distances between the conversation encoding vector and the pre-approved message encoding vectors may be used to select one or more clusters. Distances between the conversation encoding vector and the pre-approved message encoding vectors may then be used to select one or more pre-approved messages from the selected clusters. The selected pre-approved messages may then be presented as suggested messages to a user.
    Type: Grant
    Filed: April 30, 2021
    Date of Patent: May 14, 2024
    Assignee: ASAPP, INC.
    Inventors: William Abraham Wolf, Melanie Sclar, Clemens Georg Benedict Rosenbaum, Christopher David Fox, Kilian Quirin Weinberger
  • Patent number: 11983653
    Abstract: A computing platform is configured to: (i) at a first time, input data values for a first set of data variables associated with a given construction project into a first machine-learning model that functions to output a prediction of a first set of reference projects that are similar to the given construction project, (ii) based on historical data for the first set of reference projects, determine a predicted value for a parameter of the given construction project, (iii) at a second time, input data values for a second set of data variables associated with the given construction project into a second machine-learning model that functions to output a prediction of a second set of reference projects that are similar to the given construction project, and (iv) based on historical data for the second set of reference projects, determine an updated predicted value for the parameter of the given construction project.
    Type: Grant
    Filed: November 8, 2021
    Date of Patent: May 14, 2024
    Assignee: Procore Technologies, Inc.
    Inventors: James Adam Pita, Catherine Knuff
  • Patent number: 11985043
    Abstract: A method and system for aggregating into a unique aggregated group (AG), protection groups (PGs) that are possible classifications with at least a threshold probability for a same unique combination of IP addresses. The PGs and the unique combination of IP addresses are included in the AG. Each of the IP addresses of the unique combination of IP addresses have respective associated probabilities for each PG included in the AG. The method further includes selecting and providing for display AGs based on the probabilities associated with the respective IP addresses included in the AGs, and providing for display at least one interactive graphical element in association with each AG selected for display. User activation of one of the interactive graphical element accepts assignment of one or more selected IP addresses included in the AG to a selected one of the one or more PGs included in the AG.
    Type: Grant
    Filed: July 10, 2020
    Date of Patent: May 14, 2024
    Assignee: Arbor Networks, Inc.
    Inventors: Sean O'Hara, Kyle Barkmeier, Alan Saqui, Brantleigh Bunting, Bryan Beecher
  • Patent number: 11983919
    Abstract: The disclosure relates to a video anomaly detection method based on human-machine cooperation, in which video frames and traditional descriptors of optical stream of an image are utilized as an input for auto-encoder neural network coding, and converted into a representation content of a hidden layer, and then the representation content of the hidden layer is decoded, reconstructed and output. The auto-encoder network is trained with normal samples. In a test stage, if an input is a normal sample, a final reconstructed error keeps high similarity with an input sample; on the contrary, if the input is an abnormal sample, the final reconstructed error deviates greatly from the input sample.
    Type: Grant
    Filed: April 23, 2022
    Date of Patent: May 14, 2024
    Assignee: Northwestern Polytechnical University
    Inventors: Zhiwen Yu, Fan Yang, Qingyang Li, Bin Guo
  • Patent number: 11983645
    Abstract: In one or more embodiments, one or more methods, processes, and/or systems may receive data associated with completion of tasks by agents. Each task corresponds to a category of tasks and is associated with an outcome relative to satisfaction of a specification of performance by a work distributor. An aptitude prediction model is trained to map, for each category of task, a correlation between an outcome corresponding to satisfaction of the specification of performance and one or more aspects of each task and one or more attributes of each agent that has completed the task. An aptitude of each agent towards a category of tasks is determined. A probability that a first agent will complete a first task in a manner specified by the work distributor is predicted using the trained aptitude prediction model. Identification information of the first agent is provided for display in association with the determined probability.
    Type: Grant
    Filed: July 30, 2021
    Date of Patent: May 14, 2024
    Assignee: Workfusion, Inc.
    Inventors: Andrii Volkov, Maxim Yankelevich, Mikhail Abramchik, Abby Levenberg
  • Patent number: 11983909
    Abstract: A method includes receiving, with a computing device, a first client request from a first client that identifies a machine learning model and a sensor. The method includes sending, with the computing device, a call to a server to apply the identified machine learning model to a set of data from the identified sensor, in response to the first client request. The method includes receiving, with the computing device, a second client request from a second client that identifies a same machine learning model and sensor as the first client request. The method includes sending, with the computing device, response data from the identified machine learning model to both the first client and the second client without sending an additional call to the server in response to the second client request.
    Type: Grant
    Filed: March 14, 2019
    Date of Patent: May 14, 2024
    Assignee: Hewlett-Packard Development Company, L.P.
    Inventors: David Murphy, Thomas da Silva Paula, Wagston Tassoni Staehler, Joao Edurado Carrion, Alexandre Santos da Silva, Jr., Juliano Cardoso Vacaro, Gabriel Rodrigo De Lima Paz
  • Patent number: 11983720
    Abstract: A computer-implemented system, platform, method and computer program product for optimizing a data analytics fraud prediction/detection pipeline that includes a combination of a classical machine learned classifier model with a quantum machine learned model to optimize the performance of the fraud prevention model. The feature selection uses different feature maps: one determined by the classic classifier and the other determined by the quantum model implementation that exploits the entanglement quantum property. The quantum method can include a quantum support vector machine implementing a built feature forward algorithm that uses a quantum kernel estimate for feature mapping. This quantum model can be run on a quantum computer or quantum simulator that can run a quantum algorithm built for extracting feature importance.
    Type: Grant
    Filed: October 21, 2021
    Date of Patent: May 14, 2024
    Assignee: International Business Machines Corporation
    Inventors: Noel R Ibrahim, Voica Ana Maria Radescu, Michele Grossi, Constantin Harald Peter von Altrock, Kirsten Muentner
  • Patent number: 11983735
    Abstract: Described are systems and methods for generating recommendation campaigns that optimize for both a desired short-term user behavior and a desired long-term user behavior. In comparison to existing techniques that focus on targeting advertisements or recommendations to specific individuals with a single goal of receiving an interaction with the advertisement from that individual (i.e., a desired short-term behavior), the disclosed implementations consider the long-term user behavior, such as increased visits to a website during a long-term rage, and generate a recommendation campaign that also optimizes for that desired long-term user behavior.
    Type: Grant
    Filed: June 2, 2017
    Date of Patent: May 14, 2024
    Assignee: Pinterest, Inc.
    Inventors: Bo Zhao, John William Gupta Egan, Burkay Birant Orten, Koichiro Narita, Samuel Seth Weisfeld-Filson
  • Patent number: 11983639
    Abstract: The present disclosure relates to identifying process flows from log sources (e.g., log files), and generating visual representations (e.g., flow diagrams, Sankey diagrams, etc.) of the identified process flows. In addition, the present disclosure relates to clustering of tree structures based on the shape of the tree structure using one or more hashing algorithms. The present disclosure also relates to a user interface that presents a query builder for efficiently querying a log analytics system for tree structures that satisfy a user-defined range.
    Type: Grant
    Filed: May 31, 2017
    Date of Patent: May 14, 2024
    Assignee: Oracle International Corporation
    Inventors: Sreeji Das, Jae Young Yoon, Dhileeban Kumaresan, Venktesh Alvenkar, Harish Akali, Hari Krishna Galla
  • Patent number: 11978017
    Abstract: A system for managing a client request is described herein, which may have at least one processor and a non-transitory computer-readable medium containing a set of instructions executable by the at least one processor. Execution of these instructions may cause the processor to perform steps of: validating a client request received from a remote client device, the client request including request data; transmitting, based on the validating, a response to the remote client device; based on the request data, determining a queue for the client request; asynchronously enqueuing the client request in the queue, the queue being configured to analyze the client request according to a model; analyzing the client request; and based on analyzing the client request, performing a responsive action.
    Type: Grant
    Filed: April 13, 2021
    Date of Patent: May 7, 2024
    Assignee: Coupang Corp.
    Inventor: PyongAn Byon
  • Patent number: 11977513
    Abstract: Techniques of data flow control are disclosed herein. One example technique includes upon receiving a notification indicating a change to a content item in a source shard, parsing the content item to extract data representing attributes of the content item and identifying a partition of the system-wide index based on the extracted data representing the attributes of the content item. The example technique can also include transmitting, to a token issuer, a request for a token that represents a permission to write to the identified partition of the system-wide index and upon receiving a token issued by the token issuer in response to the transmitted request, transmitting the extracted data representing the attributes of the content item along with the received token to write the extracted data in the partition of the system-wide index.
    Type: Grant
    Filed: January 26, 2022
    Date of Patent: May 7, 2024
    Assignee: Microsoft Technology Licensing, LLC.
    Inventors: Suyang Jiang, Serguei Vasilyevich Martchenko, Yuva Priya Arunkumar, Aigerim Shintemirova, Ariane Belle Tsai, Varadarajan Thiruvillamalai, Kidus Yohanes Sendeke
  • Patent number: 11977957
    Abstract: A quantum computing service may store, in a cache, one or more compiled files of respective quantum functions included in one or more quantum computing programs received one or more customers. When the quantum computing service receives another quantum computing program, from the same or a different customer, the quantum computing service may determine whether the quantum computing program may include one or more of the quantum functions corresponding to the compiled files in the cache. If so, the quantum computing service may use the compiled files in the cache to compile the quantum computing program.
    Type: Grant
    Filed: August 3, 2021
    Date of Patent: May 7, 2024
    Assignee: Amazon Technologies, Inc.
    Inventors: Saravanakumar Shanmugam Sakthivadivel, Jeffrey Paul Heckey, Derek Bolt, Yunong Shi, Jon-Mychael Allen Best
  • Patent number: 11977978
    Abstract: Certain aspects of the present disclosure provide techniques for performing finite rank deep kernel learning. In one example, a method for performing finite rank deep kernel learning includes receiving a training dataset; forming a set of embeddings by subjecting the training dataset to a deep neural network; forming, from the set of embeddings, a plurality of dot kernels; linearly combining the plurality of dot kernels to form a composite kernel for a Gaussian process; receiving live data from an application; and predicting a plurality of values and a plurality of uncertainties associated with the plurality of values simultaneously using the composite kernel.
    Type: Grant
    Filed: July 30, 2020
    Date of Patent: May 7, 2024
    Assignee: Intuit Inc.
    Inventors: Sambarta Dasgupta, Sricharan Kumar, Ji Chen, Debasish Das
  • Patent number: 11977989
    Abstract: A copy of a model comprising a plurality of trees is received, as is a copy of training set data comprising a plurality of training set examples. For each tree included in the plurality of trees, the training set data is used to determine which training set examples are classified as a given leaf. A blame forest is generated at least in part by mapping each training set item to the respective leaves at which it arrives.
    Type: Grant
    Filed: August 6, 2022
    Date of Patent: May 7, 2024
    Assignee: Palo Alto Networks, Inc.
    Inventors: William Redington Hewlett, II, Seokkyung Chung, Lin Xu
  • Patent number: 11978060
    Abstract: An embodiment for dynamic categorization of information technology (IT) service tickets is provided. The embodiment may include logging an IT service ticket when text is entered into a description field. The embodiment may also include creating a filtered description field by processing the text entered into the description field. The embodiment may further include computing a set of exponential weights and assigning the set of exponential weights to each word in the filtered description field. The embodiment may also include multiplying the set of exponential weights by the word's TF-IDF score to determine an IT service ticket category for placement of the IT service ticket into the IT service ticket category. The embodiment may further include generating features for machine learning, utilizing the generated features to build a supervised machine learning model, and evaluating the supervised machine learning model through analyzation of data from historical IT service tickets.
    Type: Grant
    Filed: August 6, 2020
    Date of Patent: May 7, 2024
    Assignee: KYNDRYL, INC.
    Inventors: Archana Dixit, Kumar Saurabh
  • Patent number: 11977990
    Abstract: A first set of features associated with a neural network are parameterized. A decision tree is generated from the first set of features. One or more adjustments for the neural network are received at the decision tree. A second set of features associated with the adjustments at the decision tree are parameterized. The parameterized first and second set of features are combined into a plurality of parameters. From the plurality, an adjusted neural network is generated.
    Type: Grant
    Filed: April 28, 2020
    Date of Patent: May 7, 2024
    Assignee: International Business Machines Corporation
    Inventors: Zhong Fang Yuan, De Shuo Kong, Yun He Gao, Tong Liu, Peng Yun Sun, Ya Dong Li
  • Patent number: 11979421
    Abstract: In some examples, a system for decorating network traffic flows with outlier scores includes a processor and a memory device to store traffic flows received from a network. The processor is configured to receive a set of traffic flows from the memory device and generate a tree model to split the traffic flows into clusters of traffic flows. Each cluster corresponds with a leaf of the tree model. The processor is further configured to generate machine learning models for each of the clusters of traffic flows separately. For a new traffic flow, the processor is configured to identify a specific one of the machine learning models that corresponds with the new traffic flow, compute an outlier score for the new traffic flow using the identified specific one of the machine learning models, and decorate the new traffic flow with the outlier score.
    Type: Grant
    Filed: December 31, 2021
    Date of Patent: May 7, 2024
    Assignee: International Business Machines Corporation
    Inventors: Yair Allouche, Aviad Cohen, Ravid Sagy, Ofer Haim Biller, Eitan Daniel Farchi
  • Patent number: 11979795
    Abstract: Systems and methods for tracking velocity information. One system includes an application execution server providing an application layer. The application execution server is configured to receive a request including metadata. The application execution server is also configured to generate and transmit a response to the request. The application execution server is also configured to enrich the metadata by structuring the metadata for further processing by a data processing layer, where the further processing includes determining velocity information associated with the metadata, and by supplementing the metadata with available historical velocity information. The application execution server is also configured to transmit the enriched metadata for further processing by the data processing layer.
    Type: Grant
    Filed: December 8, 2022
    Date of Patent: May 7, 2024
    Assignee: MASTERCARD TECHNOLOGIES CANADA ULC
    Inventors: Justine Celeste Fox, Marc Grimson
  • Patent number: 11971294
    Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that employ a distributed fiber optic sensor placement procedure that advantageously provides a desirable sensor coverage over a network at minimal cost.
    Type: Grant
    Filed: August 18, 2021
    Date of Patent: April 30, 2024
    Assignee: NEC CORPORATION
    Inventors: Philip Ji, Ting Wang, Zilong Ye
  • Patent number: 11971936
    Abstract: Implementations are described herein for analyzing existing interactive web sites to facilitate automatic engagement with those web sites, e.g., by automated assistants or via other user interfaces, with minimal effort from the hosts of those websites. For example, in various implementations, techniques described herein may be used to extract, validate, maintain, generalize, extend and/or distribute individual actions and “traces” of actions that are useable to navigate through various interactive websites. Additionally, techniques are described herein for leveraging these actions and/or traces to automate aspects of interaction with a third party website.
    Type: Grant
    Filed: October 26, 2022
    Date of Patent: April 30, 2024
    Assignee: GOOGLE LLC
    Inventors: Gökhan Bakir, Andre Elisseeff, Torsten Marek, João Paulo Pagaime da Silva, Mathias Carlen, Dana Ritter, Lukasz Suder, Ernest Galbrun, Matthew Stokes, Marcin Nowak-Przygodzki, Mugurel-Ionut Andreica, Marius Dumitran
  • Patent number: 11972178
    Abstract: A system and methods to identify which signals are significant to an assessment of a complex machine system state in the presence of non-linearities and disjoint groupings of condition types. The system enables sub-grouping of signals corresponding to system sub-components or regions. Explanations of signal significance are derived to assist in causal analysis and operational feedback to the system is prescribed and implemented for the given condition and causality.
    Type: Grant
    Filed: February 27, 2018
    Date of Patent: April 30, 2024
    Assignee: Falkonry Inc.
    Inventors: Gregory Olsen, Dan Kearns, Peter Nicholas Pritchard, Nikunj Mehta
  • Patent number: 11973540
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned communication. One of the methods includes: receiving an RF signal at a signal processing system for training a machine-learning network; providing the RF signal through the machine-learning network; producing an output from the machine-learning network; measuring a distance metric between the signal processing model output and a reference model output; determining modifications to the machine-learning network to reduce the distance metric between the output and the reference model output; and in response to reducing the distance metric to a value that is less than or equal to a threshold value, determining a score of the trained machine-learning network using one or more other RF signals and one or more other corresponding reference model outputs, the score indicating an a performance metric of the trained machine-learning network to perform the desired RF function.
    Type: Grant
    Filed: April 10, 2023
    Date of Patent: April 30, 2024
    Assignee: DeepSig Inc.
    Inventor: Timothy James O'Shea
  • Patent number: 11972338
    Abstract: This application describes systems and methods for generating machine learning models (MLMs). An exemplary method includes obtaining a sample and user input data characterizing a product or service. A subset of the data is selected from the sample based on sampling the sample according to the user input data. An MLM is trained by applying the data subset as training input to the MLM, thereby providing a trained MLM to emulate a customer selection process unique to the product or service. A user interface (UI) configured to receive other user input data and cause the trained MLM to execute on the other user input data, thereby testing the trained MLM, is presented. A summary of results from the execution of the trained MLM is generated and presented in the UI. The summary of results indicates a contribution to the trained MLM of each of a plurality of features.
    Type: Grant
    Filed: May 2, 2023
    Date of Patent: April 30, 2024
    Assignee: ZestFinance, Inc.
    Inventors: David Sheehan, Siavash Yasini, Bingjia Wang, Yunyan Zhang, Qiumeng Yu, Ruochen Zha, Adam Kleinman, Sean Javad Kamkar, Lingzhi Du, Saar Yalov, Jerome Louis Budzik
  • Patent number: 11971955
    Abstract: Techniques are generally described for machine learning exampled-based annotation of image data. In some examples, a first machine learning model may receive a query image comprising a first depiction of an object-of-interest. In some examples, the first machine learning model may receive a target image representing a scene in which a second depiction of the object-of-interest is visually represented. In various examples, the first machine learning model may generate annotated output image data that identifies a location of the second depiction of the object-of-interest within the target image. In some examples, an object detection model may be trained based at least in part on the annotated output image data.
    Type: Grant
    Filed: July 21, 2021
    Date of Patent: April 30, 2024
    Assignee: Amazon Technologies, Inc.
    Inventors: Ria Chakraborty, Madhur Popli, Rachit Lamba, Santosh Kumar Sahu, Rishi Kishore Verma
  • Patent number: 11972329
    Abstract: A system is provided for facilitating multi-label classification. During operation, the system maintains a set of training vectors. A respective vector represents an object and is associated with one or more labels that belong to a label set. After receiving an input vector, the system determines a similarity value between the input vector and one or more training vectors. The system further determines one or more labels associated with the input vector based on the similarity values between the input vector and the training vectors and their corresponding associated labels.
    Type: Grant
    Filed: December 31, 2018
    Date of Patent: April 30, 2024
    Assignee: Xerox Corporation
    Inventors: Hoda M. A. Eldardiry, Ryan A. Rossi
  • Patent number: 11972583
    Abstract: The present disclosure provides a fluorescence image registration method, the method includes: acquiring a fluorescence image of a biochip; selecting a preset local region of the fluorescence image; acquiring a position of a minimum value of a sum of brightness values of pixels in a first direction and a second direction, and obtaining pixel-level registration points; dividing the pixel-level registration points into non-defective pixels and defective pixels; if the fluorescence image meets the preset standard, correcting positions of the defective pixels according to positions of the non-defective pixels; acquiring a position of a center of gravity of image points of fluorescent molecules according to a center of gravity method; fitting straight lines in the first direction and the second direction respectively according to the position of the center of gravity; and acquiring boundary points of the fluorescence image and calculating the positions of the boundary points.
    Type: Grant
    Filed: January 31, 2019
    Date of Patent: April 30, 2024
    Assignee: BGI SHENZHEN
    Inventors: Jin-Jin Shen, Da-Wei Li, Yang-Bao Liu, Ge Feng, Mei Li, Yu-Xiang Li
  • Patent number: 11972870
    Abstract: Disclosed are systems and methods for predicting patient response to a treatment option. In one embodiment, the image slides from patient tissue samples are divided into patches and morphological patterns correlated with a disease outcome are labeled and given a patch-level score, based on whether the morphological patterns occur only in patients with good outcomes or patients with poor outcomes. A patient-level score can be generated based, at least partly, on the patch-level scores. Patch-level scores can identify regions of interest for targeted biomarker identification.
    Type: Grant
    Filed: October 24, 2022
    Date of Patent: April 30, 2024
    Assignee: PATHOMIQ INC.
    Inventors: Parag Jain, Rajat Roy, Bijay Shankar Jaiswal
  • Patent number: 11966204
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes repeatedly selecting, by a control system for the environment, control settings for the environment based on internal parameters of the control system, wherein: at least some of the control settings for the environment are selected based on a causal model, and the internal parameters include a first set of internal parameters that define a number of previously received performance metric values that are used to generate the causal model for a particular controllable element; obtaining, for each selected control setting, a performance metric value; determining that generating the causal model for the particular controllable element would result in higher system performance; and adjusting, based on the determining, the first set of internal parameters.
    Type: Grant
    Filed: September 11, 2019
    Date of Patent: April 23, 2024
    Assignee: 3M INNOVATIVE PROPERTIES COMPANY
    Inventors: Gilles J. Benoit, Brian E. Brooks, Peter O. Olson, Tyler W. Olson, Himanshu Nayar, Frederick J. Arsenault, Nicholas A. Johnson
  • Patent number: 11965667
    Abstract: This disclosure aims to provide a technique for improving the accuracy of prediction. A first trained model for inferring labels for measurement data is generated based on a first data set. The first data set includes: combined data that are a combination of first measurement data, which are related to a first air conditioning apparatus, and labels set for the first measurement data; and second measurement data related to the first air conditioning apparatus.
    Type: Grant
    Filed: September 2, 2021
    Date of Patent: April 23, 2024
    Assignee: DAIKIN INDUSTRIES, LTD.
    Inventor: Tomohiro Noda
  • Patent number: 11965765
    Abstract: The embodiments of the present disclosure provide a method for predicting gas transmission loss of smart gas, implemented by a smart gas equipment management platform of an Internet of Things (IoT) system for predicting gas transmission loss of smart gas, comprising: obtaining gas flow data, gas pressure data, and ambient temperature data of a plurality of time points respectively based on gas metering devices, pressure detection devices, and temperature monitoring devices at a plurality of positions of a gas pipeline network; predicting a gas metering error based on the ambient temperature data; determining whether gas loss is abnormal loss based on the gas flow data, the gas pressure data, and the gas metering error; and in response to a determination that the gas loss is the abnormal loss, sending a warning notice.
    Type: Grant
    Filed: March 15, 2023
    Date of Patent: April 23, 2024
    Assignee: CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD.
    Inventors: Zehua Shao, Yaqiang Quan, Xiaojun Wei, Guanghua Huang, Yuefei Wu
  • Patent number: 11966697
    Abstract: Systems and methods for automated, degradation-resistant, tuning of machine-learning (“ML”) models are provided. The systems and methods may identify an inoperative input utterance, retrieve a feature set associated with the inoperative input utterance, and generate an updated utterance-feature-intent (“UFI”) mapping based on the retrieved feature set. The systems and methods may retrain the ML model using the updated UFI mapping, and compare the accuracy of the system after the retraining and before the retraining. In a scenario where the accuracy of the system does not improve, the systems and methods may amplify the updated UFI mapping. In a scenario where the accuracy of the system does improve, the systems and methods may deploy the updated UFI mapping.
    Type: Grant
    Filed: June 11, 2019
    Date of Patent: April 23, 2024
    Assignee: Bank of America Corporation
    Inventors: Viju Kothuvatiparambil, Donatus Asumu
  • Patent number: 11966826
    Abstract: One or more default protected attribute values may be determined for a prediction model trained based on training data including a plurality of training observations. Each of the plurality of training observations may include a respective plurality of training data values corresponding with a plurality of features. Each of the plurality of training observations may also include a respective target value. Each of the plurality of training observations may include a respective protected attribute value corresponding with a protected attribute feature. A request to determine a designated predicted target value for a designated inference observation may be received after determining the one or more default protected attribute values. The predicted target value may be determined by applying the prediction model to an inference observation and a designated default protected attribute value of the one or more default protected attribute values.
    Type: Grant
    Filed: November 14, 2022
    Date of Patent: April 23, 2024
    Assignee: Epistamai LLC
    Inventor: Christopher Lam
  • Patent number: 11967418
    Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a healthcare analytics management system. A healthcare analytics development sub-system of the healthcare analytics management system develops an analytics pipeline of a set of analytics assets for a selected healthcare based on a set of business needs for a healthcare analytics client and a healthcare analytics model based on the set of analytics assets and the set of business needs. The healthcare analytics model links to the analytics pipeline. A model deployment module of a healthcare analytics operation sub-system of the healthcare analytics management system deploys the healthcare analytics model on a set of computing devices of the selected healthcare consumer.
    Type: Grant
    Filed: July 8, 2022
    Date of Patent: April 23, 2024
    Inventors: Francisco P Curbera, Shilpa N. Mahatma, Yajuan Wang, Rose M Williams, Gigi Y. C Yuen-Reed
  • Patent number: 11966836
    Abstract: According to one embodiment, a detection system includes an acquirer, a trainer, and a detector. The acquirer acquires first data, second data, and third data. The first data is based on an action of a first body part in a first work of a first worker having a first proficiency. The second data is based on an action of the first body part in the first work of a second worker having a second proficiency. The third data is based on an action of the first body part in the first work of a third worker. The trainer trains a recurrent neural network including a first output layer using the first data and the second data. The detector inputs the third data to the trained recurrent neural network and detects a response of the first neuron or the second neuron.
    Type: Grant
    Filed: August 13, 2018
    Date of Patent: April 23, 2024
    Assignee: KABUSHIKI KAISHA TOSHIBA
    Inventor: Yasuo Namioka
  • Patent number: 11966799
    Abstract: Example embodiments include systems and methods for transmitting, by one or more processors coupled to memory, a request associated with a first application programming interface endpoint to an application programming interface server. The systems and methods may include retrieving, by the application programming interface server, data from one or more databases responsive to the request. The systems and methods may include transmitting, by the application programming interface server, a response to the one or more processors, the response including the data associated with at least one of independent recommendations and rankings.
    Type: Grant
    Filed: May 11, 2022
    Date of Patent: April 23, 2024
    Inventor: Renée Bunnell
  • Patent number: 11968221
    Abstract: A processor distributes, from a server, a trained supervised machine learning (ML) model and supervised and unsupervised feature information to a plurality of client devices; at each client device, trains the supervised ML model using local data to generate a local supervised ML model, constructs a local unsupervised ML model using the unsupervised feature information, and deploys the local supervised and unsupervised ML models; determining when a detection performance difference between the local supervised and unsupervised ML models reaches a threshold; identifies a proposed change to the supervised or unsupervised feature information; deploys the proposed change on one client device; responsive to determining the proposed change improves the detection performance of that client device, communicates the proposed change to a sampled set of client devices; and responsive to determining the proposed change improves the detection performance of a majority of the sampled set, communicates the proposed change to
    Type: Grant
    Filed: June 27, 2022
    Date of Patent: April 23, 2024
    Assignee: International Business Machines Corporation
    Inventors: Divyesh Jadav, Mu Qiao, Eric Kevin Butler
  • Patent number: 11960462
    Abstract: A method is described which includes receiving or obtaining a time series of data (S1). The method also includes storing the time series of data to a storage device without interrupting the reception of the time series of data (S2). The method also includes, for each of a plurality of base time periods, at the end of a most recently elapsed base time period (S5) and without interrupting the reception or storage of the time series of data, calculating (S6) one or more measurements based on the time series of data corresponding to the most recently elapsed base time period and updating a binary tree structure indexing the one or more measurements and the time series of data. Updating the binary tree structure includes generating a new binary tree leaf (1, 2, 4, 5) corresponding to the most recently elapsed base time period (S7).
    Type: Grant
    Filed: February 25, 2021
    Date of Patent: April 16, 2024
    Assignee: CRFS LIMITED
    Inventors: Stewart Hyde, Daniel Timson
  • Patent number: 11961115
    Abstract: In some examples, a computing device may receive data from a plurality of groups of data sources. The computing device may access a plurality of data synthetization machine learning models configured for generating synthetic data. Respective ones of the data synthetization machine learning models may correspond to respective ones of the groups of data sources. The computing device generates first synthetic data by inputting, to a first data synthetization machine learning model, first data received from a first data source group, and generates second synthetic data by inputting, to a second data synthetization machine learning model, second data received from a second data source group. The computing device determines an allocation of resources based at least in part on comparing the first data and the first synthetic data with the second data and the second synthetic data.
    Type: Grant
    Filed: May 18, 2023
    Date of Patent: April 16, 2024
    Assignee: DOORDASH, INC.
    Inventors: Robert Bryant Kaspar, Alok Gupta, Aman Dhesi
  • Patent number: 11960984
    Abstract: An active learning framework is provided that employs a plurality of machine learning components that operate over iterations of a training phase followed by an active learning phase. In each iteration of the training phase, the machine learning components are trained from a pool of labeled observations. In the active learning phase, the machine learning components are configured to generate metrics used to control sampling of unlabeled observations for labeling such that newly labeled observations are added to a pool of labeled observations for the next iteration of the training phase. The machine learning components can include an inspection (or primary) learning component that generates a predicted label and uncertainty score for an unlabeled observation, and at least one additional component that generates a quality metric related to the unlabeled observation or the predicted label. The uncertainty score and quality metric(s) can be combined for efficient sampling of observations for labeling.
    Type: Grant
    Filed: September 24, 2019
    Date of Patent: April 16, 2024
    Assignee: Schlumberger Technology Corporation
    Inventors: Nader Salman, Guillaume Le Moing, Sepand Ossia, Vahagn Hakopian
  • Patent number: 11960843
    Abstract: Techniques and systems are provided for training a machine learning model using different datasets to perform one or more tasks. The machine learning model can include a first sub-module configured to perform a first task and a second sub-module configured to perform a second task. The first sub-module can be selected for training using a first training dataset based on a format of the first training dataset. The first sub-module can then be trained using the first training dataset to perform the first task. The second sub-module can be selected for training using a second training dataset based on a format of the second training dataset. The second sub-module can then be trained using the second training dataset to perform the second task.
    Type: Grant
    Filed: May 2, 2019
    Date of Patent: April 16, 2024
    Assignee: Adobe Inc.
    Inventors: Zhe Lin, Trung Huu Bui, Scott Cohen, Mingyang Ling, Chenyun Wu
  • Patent number: 11961301
    Abstract: Disclosed herein are image-based object recognition method and system by and in which a learning server performs image-based object recognition based on the learning of environment variable data. The image-based object recognition method includes: receiving an image acquired through at least one camera, and segmenting the image on a per-frame basis; primarily learning labeling results for one or more objects in the image segmented on a per-frame basis; performing primary reasoning by performing object detection in the image through a model obtained as a result of the primary learning; performing data labeling based on the results of the primary reasoning, and performing secondary learning with weights allocated to respective boxes obtained by the primary reasoning and estimated as object regions; and finally detecting one or more objects in the image through a model generated as a result of the secondary learning.
    Type: Grant
    Filed: July 24, 2023
    Date of Patent: April 16, 2024
    Assignee: SMARTINSIDE AI INC.
    Inventors: Dai Quoc Tran, Yun Tae Jeon, Tae Heon Kim, Min Soo Park, Joo Ho Shin, Seung Hee Park
  • Patent number: 11960347
    Abstract: A computer-implemented method for testing failover may include: determining one or more cross-regional dependencies and traffic flow of an application in a first region of a cloud environment, wherein the one or more cross-regional dependencies include a dependency of the application in the first region of the cloud environment to one or more applications in at least one other region of the cloud environment; determining a risk score associated with performing failover of the application to a second region of the cloud environment at least based on the determined one or more cross-regional dependencies and traffic flow of the application; comparing the determined risk score with a predetermined risk score; in response to determining that the determined risk score is lower than the predetermined risk score, performing failover of the application to the second region of the cloud environment; isolating the second region of the cloud environment from the first region of the cloud environment for a predetermined
    Type: Grant
    Filed: December 19, 2022
    Date of Patent: April 16, 2024
    Assignee: Capital One Services, LLC
    Inventors: Ankit Kothari, Ann Hawkins, John Samos
  • Patent number: 11960971
    Abstract: A method of mitigating quantum readout errors by stochastic matrix inversion includes performing a plurality of quantum measurements on a plurality of qubits having predetermined plurality of states to obtain a plurality of measurement outputs; selecting a model for a matrix linking the predetermined plurality of states to the plurality of measurement outputs, the model having a plurality of model parameters, wherein a number of the plurality of model parameters grows less than exponentially with a number of the plurality of qubits; training the model parameters to minimize a loss function that compares predictions of the model with the matrix; computing an inverse of the model based on the trained model parameters; and providing the computed inverse of the model to a noise prone quantum readout of the plurality of qubits to obtain a substantially noise free quantum readout.
    Type: Grant
    Filed: November 18, 2022
    Date of Patent: April 16, 2024
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Sergey Bravyi, Jay M. Gambetta, David C. Mckay, Sarah E. Sheldon
  • Patent number: 11959807
    Abstract: A thermal imaging apparatus comprising: a thermal detector device (100) comprising an array of thermal sensing pixels (102) and signal processing circuitry (104) coupled to the detector device (100). The circuitry (104) supports a background identifier (110) and a pixel classifier (112), the background identifier (110) comprising a common intensity identifier (114) and an expected background intensity calculator (116). The background identifier (110) receives pixel measurement data captured by the detector device (100) in respect of pixels of the array (102) and the common intensity identifier (114) identifies a largest number of substantially the same pixel intensity values from the pixel measurement data. The expected background intensity calculator (116) uses the largest number of substantially the same pixel intensity values to generate a model of expected background intensity levels.
    Type: Grant
    Filed: October 28, 2021
    Date of Patent: April 16, 2024
    Assignee: Melexis Technologies NV
    Inventors: Jos Rennies, Wouter Reusen, Luc Buydens
  • Patent number: 11961017
    Abstract: A method may include receiving, from a client device, a reservation time and image data relating to a desired room; receiving, from a network storage device, facility data for a plurality of rooms; identifying, using an image recognition model, the desired room based on the image data and the facility data; determining an availability of the desired room based on the reservation time and the facility data; generating a first reservation option to reserve the desired room and/or a second reservation option to reserve an alternate room; transmitting, to the client device, the first reservation option and/or the second reservation option; receiving, from the client device, a user selection of the first reservation option and/or the second reservation option; and transmitting, to the network storage device, an instruction to reserve the desired room and/or the alternate room.
    Type: Grant
    Filed: November 12, 2020
    Date of Patent: April 16, 2024
    Assignee: Capital One Services, LLC
    Inventors: Sneha Anand Yeluguri, Christopher Lanoue
  • Patent number: 11960904
    Abstract: A device may receive historic temporal data identifying events associated with a system, and may perform block bootstrapping of the hierarchical time series data, based on a hyperparameters, to generate blocks of data points of the historic time series data. The device may process the blocks of data points, with a plurality of different machine learning models, to calculate predictions, and may apply weights to the predictions to generate weighted predictions. The device may aggregate the weighted predictions to generate aggregated predictions, and may apply final weights to the aggregated predictions to generate weighted aggregated predictions. The device may aggregate the weighted aggregated predictions to generate a final prediction, and may perform one or more actions based on the final prediction.
    Type: Grant
    Filed: January 4, 2022
    Date of Patent: April 16, 2024
    Assignee: Accenture Global Solutions Limited
    Inventors: Femida Eranpurwala, Satyan Kumar, Rahul Maheshwari, Balaji Poonkundran
  • Patent number: 11960456
    Abstract: A graph-based clinical concept mapping algorithm maps ICD-9 (International Classification of Disease, Revision 9) and ICD-10 (International Classification of Disease, Revision 10) codes to unified Systematized Nomenclature of Medicine (SNOMED) clinical concepts to normalize longitudinal healthcare data to thereby improve tracking and the use of such data for research and commercial purposes. The graph-based clinical concept mapping algorithm advantageously combines a novel graph-based search algorithm and natural language processing to map orphan ICD codes (those without equivalents across codebases) by finding optimally relevant shared SNOMED concepts. The graph-based clinical concept mapping algorithm is further advantageously utilized to group ICD-9/10 codes into higher order, more prevalent SNOMED concepts to support clinical interpretation.
    Type: Grant
    Filed: February 25, 2020
    Date of Patent: April 16, 2024
    Assignee: IQVIA Inc.
    Inventors: Shaun Gupta, Frederik B. C. Dieleman, Daniel Homola, Adam Webber, Orla M. Doyle, Nadejda Leavitt, John Rigg, Patrick Long, Rabe'e Cheheltani
  • Patent number: 11960484
    Abstract: Identifying table joins includes obtaining respective casting similarities between pairs of columns of a first table and a second table. Each pair of columns includes a first column of the first table and a second column of the second table. Ones of the pairs of columns not satisfying a casting similarity condition are discarded to obtain first join candidates. Respective string similarities for the first join candidates are obtained. Ones of the first join candidates not satisfying a string similarity condition are discarded to obtain second join candidates. Final join candidates are obtained using the respective casting similarities and the respective string similarities of the second join candidates. A selected join candidate of the final join candidates is received from a user.
    Type: Grant
    Filed: October 13, 2021
    Date of Patent: April 16, 2024
    Assignee: ThoughtSpot, Inc.
    Inventors: Kireet Agrawal, Juliette May Hu, Aditya Singh Chand
  • Patent number: 11960291
    Abstract: A computer-implemented method for determining a motion trajectory for a mobile robot based on an occupancy prior indicating probabilities of presence of dynamic objects and/or individuals in a map of an environment. Occupancy priors are determined by a reward function defined by reward function parameters. The determining of the reward function parameters includes: providing semantic maps; providing training trajectories for each of semantic maps; computing a gradient as a difference between an expected mean feature count and an empirical mean feature count depending on each of the semantic maps and on each of the training trajectories, the empirical mean feature count is the average number of features accumulated over the provided training trajectories of the semantic maps, wherein the expected mean feature count is the average number of features accumulated by trajectories generated depending on the current reward function parameters; and updating the reward function parameters depending on the gradient.
    Type: Grant
    Filed: June 11, 2021
    Date of Patent: April 16, 2024
    Assignee: ROBERT BOSCH GMBH
    Inventors: Andrey Rudenko, Johannes Maximilian Doellinger, Kai Oliver Arras, Luigi Palmieri