Machine Learning Patents (Class 706/12)
  • Patent number: 12218912
    Abstract: According to one or more embodiments of the disclosure, a networking device receives a policy for an endpoint in a network. The policy specifies one or more component tags and one or more activity tags that were assigned to the endpoint based on deep packet inspection of traffic associated with the endpoint. The networking device identifies a set of tags for a particular traffic flow in the network associated with the endpoint. The set of tags comprises one or more component tags or activity tags associated with the particular traffic flow. The networking device makes a determination that the particular traffic flow violates the policy based on the set of tags comprising a tag that is not in the policy. The networking device initiates, based on the determination that the particular traffic flow violates the policy, a corrective measure with respect to the particular traffic flow.
    Type: Grant
    Filed: April 21, 2020
    Date of Patent: February 4, 2025
    Assignee: Cisco Technology, Inc.
    Inventors: Robert Edgar Barton, Thomas Szigeti, Jerome Henry, Ruben Gerald Lobo, Laurent Jean Charles Hausermann, Maik Guenter Seewald, Daniel R. Behrens
  • Patent number: 12216635
    Abstract: An embodiment for improved linking of tabular columns to column types in an ontology unseen during training. The embodiment may for a target table, encode a target tabular query column, table headers, and target types independently to generate permutation invariant representations of tabular data associated with the target table. The embodiment may, for each of the target types, extract and further encode auxiliary information. The embodiment may process the encoded tabular data to obtain a first vector and a second vector. The embodiment may concatenate the first vector and the second vector to generate a final query vector. The embodiment may process the encoded target types through a third transformer to obtain a third vector. The embodiment may calculate a score to model interactions between the target tabular query column of the target table and the target types.
    Type: Grant
    Filed: June 6, 2023
    Date of Patent: February 4, 2025
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Sarthak Dash, Sugato Bagchi, Nandana Sampath Mihindukulasooriya, Alfio Massimiliano Gliozzo
  • Patent number: 12217065
    Abstract: An apparatus for determining system model comparison is disclosed. The apparatus includes a processor and a memory communicatively linked to the processor. The memory instructs the processor to receive a first plurality of system data, wherein the first plurality of system data represents a first state of a system, and a second plurality of system data representing a second of the system. The memory instructs the processor to generate a first and second model of the system using the system data. The memory instructs the processor to output a first model output using the first model of the system and the second plurality of system data. The memory instructs the processor to modify the second plurality of system data using perturbation function. The memory instructs the processor to output a second model output using the second model of the system and the modified second plurality of system data.
    Type: Grant
    Filed: January 17, 2024
    Date of Patent: February 4, 2025
    Assignee: The Strategic Coach Inc.
    Inventors: Barbara Sue Smith, Daniel J. Sullivan
  • Patent number: 12217828
    Abstract: A method, apparatus, and computer-readable medium for efficiently optimizing a phenotype with a combination of a generative and a predictive model, training a phenotype prediction model based on experiential genotype vectors, training a genotype generation model based on sample genotype vectors, generating new genotype vectors, applying the phenotype prediction model to the new genotype vectors to generate scores, determining result genotypes based on a ranking of the available genotypes according to the scores, and generating a result based on the result genotypes, the result indicating one or more genetic constructs for testing.
    Type: Grant
    Filed: February 6, 2023
    Date of Patent: February 4, 2025
    Assignee: TESELAGEN BIOTECHNOLOGY INC.
    Inventors: Eduardo Abeliuk, Andrés Igor Pérez Manríquez, Juan Andrés Ramírez Neilson, Diego Francisco Valenzuela Iturra
  • Patent number: 12216527
    Abstract: A computerized method is disclosed for automated handling of data ingestion anomalies. The method features operations of detecting a data ingestion anomaly and determining a cause for the data ingestion anomaly. The causal determination may be conducted by at least (i) determining features of an anomalous data ingestion volume, (ii) training a second data model, after a first data model being used to detect the data ingestion anomaly, with data sets consistent with the determined features, (iii) applying the second data model to predict whether a data ingestion sub-volume is anomalous, (iv) obtaining system state information during ingestion of the anomalous data ingestion sub-volume, and (v) determining the cause of the anomalous data ingestion volume based on the system state information.
    Type: Grant
    Filed: January 24, 2022
    Date of Patent: February 4, 2025
    Assignee: Splunk Inc.
    Inventors: Abraham Starosta, Francis Beckert, Chandrima Sarkar
  • Patent number: 12210943
    Abstract: Implementations disclosed herein relate to utilizing at least one existing manually engineered policy, for a robotic task, in training an RL policy model that can be used to at least selectively replace a portion of the engineered policy. The RL policy model can be trained for replacing a portion of a robotic task and can be trained based on data from episodes of attempting performance of the robotic task, including episodes in which the portion is performed based on the engineered policy and/or other portion(s) are performed based on the engineered policy. Once trained, the RL policy model can be used, at least selectively and in lieu of utilization of the engineered policy, to perform the portion of robotic task, while other portion(s) of the robotic task are performed utilizing the engineered policy and/or other similarly trained (but distinct) RL policy model(s).
    Type: Grant
    Filed: January 29, 2021
    Date of Patent: January 28, 2025
    Assignee: GOOGLE LLC
    Inventors: Adrian Li, Benjamin Holson, Alexander Herzog, Mrinal Kalakrishnan
  • Patent number: 12208522
    Abstract: A method for controlling a robot. The method includes providing demonstrations for performing each of a plurality of skills; training from the demonstrations, a robot trajectory model for each skill, each trajectory model is a hidden semi-Markov model having one or more initial states and one or more final states; training, from the demonstrations, a precondition model for each skill comprising, for each initial state, a probability distribution of robot configurations before executing the skill, and a final condition model for each skill comprising, for each final state, a probability distribution of robot configurations after executing the skill; receiving a description of a task, the task includes performing the skills of the plurality of skills in sequence and/or branches; generating a composed robot trajectory model; and controlling the robot according to the composed robot trajectory model to execute the task.
    Type: Grant
    Filed: May 6, 2021
    Date of Patent: January 28, 2025
    Assignee: ROBERT BOSCH GMBH
    Inventors: Meng Guo, Mathias Buerger
  • Patent number: 12210590
    Abstract: Embodiments of various systems, methods, and devices are disclosed for generating artificial intelligence or machine learning models for predicting denials of medical claims, predicting approvals of resubmitted medical claims, as well as automatic workflow clustering processes for automatically assigning medical claims to workflow queues using predictive segmentation and smart resource allocation.
    Type: Grant
    Filed: January 4, 2022
    Date of Patent: January 28, 2025
    Assignee: Experian Health, Inc.
    Inventors: Johnathan P. Menard, Robert J. Stucker, Elsie E. Henry, Ali Saffari, John R. Bush, Robert P. Hattori, Harry David Hickey
  • Patent number: 12210588
    Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. Machine learning is performed based on training data via a dual loop learning process that includes a first loop for data decoding learning and a second loop for label decoding learning. In the first loop, first parameters associated with decoding are updated to generate updated first parameters based on a first label, estimated via the decoding using the first parameters, and a second label, predicted via the label decoding using second parameters. In the second loop, the second parameters associated with the label decoding are updated to generate updated second parameters based on a third label, obtained via the decoding using the updated first parameters, and a ground truth label.
    Type: Grant
    Filed: September 2, 2020
    Date of Patent: January 28, 2025
    Assignee: YAHOO ASSETS LLC
    Inventor: Eliyar Asgarieh
  • Patent number: 12204426
    Abstract: There is provided a method of monitoring performance of a machine learning model externally to the machine learning model, comprising: monitoring data elements being fed into a machine learning model trained on a training dataset of historical training data elements, wherein the data elements are each associated with a respective time after the time associated with the training dataset, analyzing the data elements for identifying shift(s) between at least two subsets of the data elements, computing according to the shift(s), measurement(s) denoting an expected effect on output of the model, and detecting a misclassification event by the model when the measurement(s) exceeds a threshold of the model, wherein the monitoring, the analyzing, the computing, and the detecting are performed externally to the model, without accessing at least one of: data stored within the machine learning model, an implementation of the model, and data structures of the model.
    Type: Grant
    Filed: July 29, 2020
    Date of Patent: January 21, 2025
    Assignee: Data Science Consulting Group Ltd
    Inventors: Elan Sasson, Gideon Rosenthal
  • Patent number: 12205004
    Abstract: The present disclosure provides systems and methods for training probabilistic object motion prediction models using non-differentiable representations of prior knowledge. As one example, object motion prediction models can be used by autonomous vehicles to probabilistically predict the future location(s) of observed objects (e.g., other vehicles, bicyclists, pedestrians, etc.). For example, such models can output a probability distribution that provides a distribution of probabilities for the future location(s) of each object at one or more future times. Aspects of the present disclosure enable these models to be trained using non-differentiable prior knowledge about motion of objects within the autonomous vehicle's environment such as, for example, prior knowledge about lane or road geometry or topology and/or traffic information such as current traffic control states (e.g., traffic light status).
    Type: Grant
    Filed: October 26, 2023
    Date of Patent: January 21, 2025
    Assignee: AURORA OPERATIONS, INC.
    Inventors: Sergio Casas, Cole Christian Gulino, Shun Da Suo, Raquel Urtasun
  • Patent number: 12205030
    Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices for detecting objects are provided. For example, the disclosed technology can obtain a representation of sensor data associated with an environment surrounding a vehicle. Further, the sensor data can include sensor data points. A point classification and point property estimation can be determined for each of the sensor data points and a portion of the sensor data points can be clustered into an object instance based on the point classification and point property estimation for each of the sensor data points. A collection of point classifications and point property estimations can be determined for the portion of the sensor data points clustered into the object instance. Furthermore, object instance property estimations for the object instance can be determined based on the collection of point classifications and point property estimations for the portion of the sensor data points clustered into the object instance.
    Type: Grant
    Filed: October 30, 2023
    Date of Patent: January 21, 2025
    Assignee: AURORA OPERATIONS, INC.
    Inventors: Eric Randall Kee, Carlos Vallespi-Gonzalez, Gregory P. Meyer, Ankit Laddha
  • Patent number: 12205002
    Abstract: There is provided a method and system for training an embedding model to perform relation predictions in a knowledge hypergraph to output a trained embedding model. A training dataset comprising tuples representing relations between entities in the knowledge hypergraph are received. The embedding model is trained to perform relation predictions for each given tuple from a subset of tuples in the training dataset by generating a respective entity vector for each entity and a respective relation matrix representing relations between the entities. The entity vectors and relation matrix are split into a plurality of windows, and interaction values between elements in each window are calculated. A relation score indicative of the relation in the given tuple being true is calculated. Parameters of the embedding model are updated based on the relation scores for the subset of tuples. The trained embedding model is then output.
    Type: Grant
    Filed: June 23, 2021
    Date of Patent: January 21, 2025
    Assignee: ServiceNow Canada Inc.
    Inventors: Perouz Taslakian, David Vazquez Bermudez, David Poole, Bahare Fatemi
  • Patent number: 12205025
    Abstract: The present application discloses a processor video memory optimization method and apparatus for deep learning training tasks, and relates to the technical field of artificial intelligence. In the method, by determining an optimal path for transferring a computing result, the computing result of a first computing unit is transferred to a second computing unit by using the optimal path. Thus, occupying the video memory is avoided, and meanwhile, a problem of low utilization rate of the computing unit of a GPU caused by video memory swaps is avoided, so that training speed of most tasks is hardly reduced.
    Type: Grant
    Filed: March 24, 2021
    Date of Patent: January 21, 2025
    Assignee: Beijing Baidu Netcom Science Technology Co., Ltd.
    Inventors: Haifeng Wang, Xiaoguang Hu, Dianhai Yu
  • Patent number: 12205277
    Abstract: Described herein are systems, methods, and instrumentalities associated with image segmentation such as tubular structure segmentation. An artificial neural network is trained to segment tubular structures of interest in a medical scan image based on annotated images of a different type of tubular structures that may have a different contrast and/or appearance from the tubular structures of interest. The training may be conducted in multiple stages during which a segmentation model learned from the annotated images during a first stage may be modified to fit the tubular structures of interest in a second stage. In examples, the tubular structures of interest may include coronary arteries, catheters, guide wires, etc., and the annotated images used for training the artificial neural network may include blood vessels such as retina blood vessels.
    Type: Grant
    Filed: December 29, 2021
    Date of Patent: January 21, 2025
    Assignee: Shanghai United Imaging Intelligence Co., Ltd.
    Inventors: Yikang Liu, Shanhui Sun, Terrence Chen, Zhang Chen, Xiao Chen
  • Patent number: 12205148
    Abstract: A method, system and product including obtaining offline user information at an end device, wherein the offline user information is obtained from offline sensors of the end device; based on the offline user information, generating a user profile indicating that a user of the end device matches at least one micro-segment, wherein the at least one micro-segment comprises at least one detailed population category; based on the at least one micro-segment, selecting a campaign from a set of one or more campaigns retained at a server, wherein the campaign comprises one or more rules for displaying at least one content item; monitoring the offline sensors of the end device to identify real time user activities; and upon identifying, based on the real time user activities, that a rule of the one or more rules for displaying a content item is complied with, displaying the content item in the end device.
    Type: Grant
    Filed: February 3, 2021
    Date of Patent: January 21, 2025
    Assignee: ANAGOG LTD.
    Inventors: Gil Levy, Tomer Radian
  • Patent number: 12204619
    Abstract: Embodiments of the present invention set forth a technique for predicting fraud based on multiple inputs including user behavior biometric data along with one or more other parameters associated with the user. The technique includes receiving cursor movement data generated via a client device. The technique further includes generating a image based on the cursor movement data. The technique further includes receiving client parameters generated via the client device. The technique further includes analyzing the image and the client parameters based on a model to generate a prediction result, where the model is generated based on second cursor movement data and a second set of client parameters associated with a first group of one or more users. The technique further includes determining, based on the prediction result, that a user of the client device is not a member of the first group.
    Type: Grant
    Filed: June 27, 2022
    Date of Patent: January 21, 2025
    Assignee: Cisco Technology, Inc.
    Inventor: Gleb Esman
  • Patent number: 12206758
    Abstract: A system for privacy-preserving distributed training of a global model on distributed datasets has a plurality of data providers being communicatively coupled. Each data provider has a local model and a local training dataset for training the local model using an iterative training algorithm. Further it has a portion of a cryptographic distributed secret key and a corresponding collective cryptographic public key of a multiparty fully homomorphic encryption scheme. All models are encrypted with the collective public key. Each data provider trains its local model using the respective local training dataset, and combines the local model with the current global model into a current local model. A data provider homomorphically combines current local models into a combined model, and updates the current global model based on the combined model. The updated global model is provided to at least a subset of the other data providers.
    Type: Grant
    Filed: May 8, 2020
    Date of Patent: January 21, 2025
    Assignee: Ecole Polytechnique Federale De Lausanne (EPFL)
    Inventors: David Froelicher, Juan Ramon Troncoso-Pastoriza, Apostolos Pyrgelis, Sinem Sav, Joao Gomes De Sa E Sousa, Jean-Pierre Hubaux, Jean-Philippe Bossuat
  • Patent number: 12205005
    Abstract: Systems and methods for low bias negative sampling of classes according to the sampled softmax method are described herein. The systems and methods can include training a machine-learned model for classifying inputs into one or more classes of a plurality of classes, each of the plurality of classes having an associated class embedding in a plurality of class embeddings. The systems and methods can include selecting, by the one or more computing devices, one or more negative classes from the plurality of classes based at least in part on a probability distribution approximating a softmax distribution, wherein the probability distribution is determined based at least in part on a Random Fourier Features map.
    Type: Grant
    Filed: July 17, 2020
    Date of Patent: January 21, 2025
    Assignee: GOOGLE LLC
    Inventors: Xinnan Yu, Ankit Singh Rawat, Jiecao Chen, Ananda Theertha Suresh, Sanjiv Kumar
  • Patent number: 12205053
    Abstract: Disclosed are techniques for using machine learning models to more reliably predict likelihoods of application failure. A model is trained to identify and display events that may cause high severity application failures. Logistic regression may be used to fit the model such that application features are mapped to a high severity event flag. Significant features of applications that relate to the high severity flag may be selected using stepwise regression. The identified applications may be displayed on a graphical user interface for review and reprioritization. Information may be ranked and displayed according to multiple different ranking criteria, such as one ranking generated by a first model, and another determined by one or more users. The multiple ranking criteria may be used to inform steps taken, and/or to retrain or tune the parameters of the model for subsequent predictions or classifications.
    Type: Grant
    Filed: December 11, 2020
    Date of Patent: January 21, 2025
    Assignee: Wells Fargo Bank, N.A.
    Inventors: Michael Goodwin, Stacy R. Henryson, Brian Karp, Manoranjan Kumar, Monte Nash
  • Patent number: 12205690
    Abstract: A suite of fluidless predictive machine learning models includes a fluidless mortality module, smoking propensity model, and prescription fills model. The fluidless machine learning models are trained against a corpus of historical underwriting applications of a sponsoring enterprise, including clinical data of historical applicants. A data appended procedure supplements historical applications data with public records and credit risks. Various features of this data are engineered for improved predictive characteristics. Fluidless models are trained by application of a random forest ensemble including survival, regression and classification models. The trained models produce high-resolution, individual mortality scores. A fluidless underwriting protocol runs these predictive models to assess mortality risk and other risk attributes of a fluidless application that excludes clinical data to determine whether to present an accelerated underwriting offer.
    Type: Grant
    Filed: March 9, 2023
    Date of Patent: January 21, 2025
    Assignee: Massachusetts Mutual Life Insurance Company
    Inventors: Marc Maier, Shanshan Li, Hayley Carlotto
  • Patent number: 12205351
    Abstract: A system described herein may train an explanation model based on a set of images and a set of explanation labels. The system may receive input data, and may provide the input data to the explanation model and a second model. The second model may provide a set of output labels, which may include performing unknown or “black box” processing on the input data. The explanation model may generate one or more images based on the input data, compare the images to the set of images based on which the explanation model was trained, and accordingly identify one or more explanation labels with bounding boxes associated with the generated one or more images. The system may output, in response to the input data, the set of output labels provided by the second model as well as the identified explanation labels.
    Type: Grant
    Filed: June 1, 2022
    Date of Patent: January 21, 2025
    Assignee: Verizon Patent and Licensing Inc.
    Inventors: Said Soulhi, Bryan Christopher Larish
  • Patent number: 12202517
    Abstract: A system and method for connected vehicle risk detection are presented. The includes computing risk scores for a plurality of behaviors detected with respect to a connected vehicle, wherein each of the detected plurality of behaviors is associated with the connected vehicle based on a contextual vehicle state of the connected vehicle; aggregating the computed risk scores to determine an aggregated risk score; determining a risk level for the connected vehicle based on the aggregated risk score; and causing execution of at least one mitigation action based on the determined risk level.
    Type: Grant
    Filed: October 1, 2020
    Date of Patent: January 21, 2025
    Assignee: Upstream Security, Ltd.
    Inventors: Yoav Levy, Yonatan Appel, Dor Attias, Dan Sahar
  • Patent number: 12205164
    Abstract: Systems and methods of generating rating indicators for a portfolio of financial assets are described. A machine learning model is trained using a training data set that includes one or more qualitative features and one or more first quantitative features to generate an output predictor of the performance of the portfolio. The qualitative features are converted into quantitative features before being used as input to the machine learning model. Input features are generated for a new portfolio of financial assets whose rating indicator is to be generated, and fed into the trained machine learning model to generate a new output predictor for the new portfolio of financial assets. The rating indicator for the new portfolio of financial assets is determined based at least on the generated new output predictor.
    Type: Grant
    Filed: June 4, 2024
    Date of Patent: January 21, 2025
    Assignee: 2GENPEN LLC
    Inventors: Bailu Pan, Laurence H. Wadler
  • Patent number: 12197767
    Abstract: Disclosed is an operation method of a storage device supporting a multi-stream, which includes receiving an input/output request from an external host, generating a plurality of stream identifier candidates by performing machine learning on the input/output request based on a plurality of machine learning models that are based on different machine learning algorithms, generating a model ratio based on a characteristic of the input/output request, applying the model ratio to the plurality of stream identifier candidates to allocate a final stream identifier for the input/output request, and storing write data corresponding to the input/output request in a nonvolatile memory device of the storage device based on the final stream identifier.
    Type: Grant
    Filed: March 10, 2022
    Date of Patent: January 14, 2025
    Assignee: SAMSUNG ELECTRONICS CO., LTD.
    Inventors: Seungjun Yang, Kangho Roh
  • Patent number: 12198046
    Abstract: A visualization tool for machine learning models obtains metadata from a first training node at which a multi-layer machine learning model is being trained. The metadata includes a parameter of an internal layer of the model. The tool determines a plurality of metrics from the metadata, including respective loss function values corresponding to several training iterations of the model. The tool indicates the loss function values and the internal layer parameter values via a graphical interface.
    Type: Grant
    Filed: October 16, 2020
    Date of Patent: January 14, 2025
    Assignee: Amazon Technologies, Inc.
    Inventors: Wei Xia, Weixin Wu, Meng Wang, Ranju Das
  • Patent number: 12197929
    Abstract: Systems and methods of generating an interface including elements related to a next best state prediction are disclosed. A request for an interface including a user identifier is received. A next state prediction engine receives a sequence unit set including at least one sequence unit associated with the user identifier and a set of features associated with the at least one sequence unit and generates at least one next state prediction using a trained sequential prediction model. The trained sequential prediction model is configured to receive the sequence unit set and the set of features for the at least one sequence unit and output at least one predicted next state for the sequence unit set. An interface generation engine generates an interface including at least one element related to the at least one predicted next state and transmits the interface to a user device associated with the user identifier.
    Type: Grant
    Filed: December 29, 2022
    Date of Patent: January 14, 2025
    Assignee: Walmart Apollo, LLC
    Inventors: Ali Arsalan Yaqoob, Yue Xu, Hyun Duk Cho, Sushant Kumar, Kannan Achan
  • Patent number: 12197911
    Abstract: A method may include: retrieving a plurality of code snippets from code repositories; generating a syntax representation, a property representation for each of the code snippets; receiving a query comprising a query code snippet, natural language keywords, and/or a string pattern; performing string-based matching and parser/syntax tree matching on the query and the tree representations, syntax matching on the query and the syntax representations, and property matching on the query and the property representations, wherein each of the matchings results in a score; combining the scores of the string-based matching, the parser/syntax tree matching, the syntax matching, and/or the property matching; identifying a plurality of code snippets of interest based on the combined scores; classifying the code snippets of interest using a machine learning classifier; outputting a list of the code snippets of interest with their classifications; and training the machine learning classifier based on user feedback.
    Type: Grant
    Filed: March 4, 2022
    Date of Patent: January 14, 2025
    Assignee: JPMORGAN CHASE BANK, N.A.
    Inventors: Fanny Silavong, Sean Moran, Georgios Papadopoulos, Solomon Olaniyi Adebayo, William Covell, Rob Otter
  • Patent number: 12198014
    Abstract: There is provided a providing device including a processing unit that enables acquisition of one or both of control information for causing artificial intelligence to function in a device and information for specifying the control information from a distributed network.
    Type: Grant
    Filed: April 6, 2018
    Date of Patent: January 14, 2025
    Assignee: Sony Corporation
    Inventor: Hiroaki Kitano
  • Patent number: 12197852
    Abstract: A natural language description of a desired function to be achieved using an automated communication flow is received. A prompt template specifically for a communication channel is selected based on an analysis of the natural language description of the desired function to be achieved. A prompt for a large language model is automatically generated based on the natural language description, including by inserting at least a portion of the selected prompt template in the automatically generated prompt. The automatically generated prompt is provided to the pre-trained large language model. Based on an output of the large language model, an automated communication flow to be implemented for the communication channel is automatically generated.
    Type: Grant
    Filed: March 21, 2024
    Date of Patent: January 14, 2025
    Assignee: ManyChat, Inc.
    Inventors: Dmitrii Kushnikov, Ilia Kolesnikov, Mikael Yan, Nikolai Golov
  • Patent number: 12197400
    Abstract: A data processing service receives a request from a first collaborator to create a clean room for data sharing collaboration with at least a second collaborator. In response, the data processing service creates an execution environment separate from the data environment of the first collaborator and the second collaborator. The first and second collaborators can then add content into the clean room in the form of data tables and executable notebooks. Approval from each collaborator is required before a notebook can be executed using any data table shared into the clean room. Upon receiving notebook approval from each collaborator, the data processing service creates a notebook job to execute the notebook on one or more cluster computing resources of the data processing service to generate an output.
    Type: Grant
    Filed: September 25, 2023
    Date of Patent: January 14, 2025
    Assignee: Databricks, Inc.
    Inventors: William Chau, Abhijit Chakankar, Stephen Michael Mahoney, Daniel Seth Morris, Itai Shlomo Weiss
  • Patent number: 12198019
    Abstract: An apparatus for training a reinforcement learning model according to an embodiment includes a starting point determinator configured to determine starting points from an input value of a combinatorial optimization problem, a multi-explorer configured to generate exploration trajectories by performing exploration from each of the starting points using a reinforcement learning model, a trajectory evaluator configured to calculate an evaluation value of each of the exploration trajectories using an evaluation function of the combinatorial optimization problem, a baseline calculator configured to calculate a baseline for the input value from the evaluation value of each exploration trajectory, an advantage calculator configured to calculate an advantage of each of the exploration trajectories using the evaluation value of each exploration trajectory and the baseline, and a parameter updater configured to update parameters of the reinforcement learning model by using the exploration trajectories and the advantage
    Type: Grant
    Filed: October 22, 2020
    Date of Patent: January 14, 2025
    Assignee: SAMSUNG SDS CO., LTD.
    Inventors: Yeong Dae Kwon, Jin Ho Choo, Il Joo Yoon, Byoung Jip Kim
  • Patent number: 12197734
    Abstract: A conflict-free parallel radix sorting algorithm, and devices and systems implementing this algorithm, schedules memory copies of data elements of a large dataset so that there is always a single copy to each target memory each cycle of operation for the system implementing the algorithm. The conflict-free parallel radix sorting algorithm eliminates memory copying conflicts in copying data elements from different source memories to the same target memory and in this way maintains maximum throughput for the copying of data elements from source memories to target memories, reducing the time required to sort the data elements of the large dataset.
    Type: Grant
    Filed: January 13, 2023
    Date of Patent: January 14, 2025
    Assignee: Achronix Semiconductor Corporation
    Inventors: Marcel Van der Goot, Raymond Nijssen, Christopher C. LaFrieda
  • Patent number: 12194631
    Abstract: A method for controlling a physical system. The method includes training a neural network to output, for a plurality of tasks, a result of the task carried out, in each case in response to the input of a control configuration of the physical system and the input of a value of a task input parameter; ascertaining a control configuration for a further task with the aid of Bayesian optimization, the neural network, parameterized by the task input parameter, being used as a model for the relationship between control configuration and result; and controlling the physical system according to the control configuration to carry out the further task.
    Type: Grant
    Filed: August 27, 2021
    Date of Patent: January 14, 2025
    Assignee: ROBERT BOSCH GMBH
    Inventors: Felix Berkenkamp, Jonathan Spitz, Kathrin Skubch, Lukas Grossberger, Stefan Falkner, Anna Eivazi
  • Patent number: 12200014
    Abstract: A lifelong learning intrusion detection system and methods are provided. The system may capture network data directed to a host node. The host node may include a honeypot. The honeypot may emulate operation of a physical or virtual device to attract malicious activity. The system may classify, based on a supervised machine learning model, the network data as being not malicious or not malicious. The system may classify, based on an unsupervised machine learning model, the network data as being anomalous or not anomalous. The system may alter operation of the honeypot to induce malicious activity. The system may determine, after operation of the honeypot is altered, the honeypot is accessed. The system may retrain the supervised machine learning model and/or unsupervised machine learning model based the network data.
    Type: Grant
    Filed: November 25, 2020
    Date of Patent: January 14, 2025
    Assignee: Purdue Research Foundation
    Inventors: Aly El Gamal, Ali A. Elghariani, Arif Ghafoor
  • Patent number: 12198021
    Abstract: Disclosed herein is a computer-implemented tool that facilitates data analysis by use of machine learning (ML) techniques. The tool cooperates with a data intake and query system and provides a graphical user interface (GUI) that enables a user to train and apply a variety of different ML models on user-selected datasets of stored machine data. The tool can provide active guidance to the user, to help the user choose data analysis paths that are likely to produce useful results and to avoid data analysis paths that are less likely to produce useful results.
    Type: Grant
    Filed: March 3, 2021
    Date of Patent: January 14, 2025
    Assignee: Cisco Technology, Inc
    Inventors: Manish Sainani, Sergey Slepian, Iman Makaremi, Adam Jamison Oliner, Jacob Leverich, Di Lu
  • Patent number: 12190265
    Abstract: Described in detail herein is a forecasting system. In one embodiment, the system can generate forecast data for the amount of labor and physical objects needed at various facilities.
    Type: Grant
    Filed: September 29, 2023
    Date of Patent: January 7, 2025
    Assignee: WALMART APOLLO, LLC
    Inventors: Timothy Ryan Hodges, Christopher Wade Spencer
  • Patent number: 12192120
    Abstract: The present disclosure describes a patent management system and method for remediating insufficiency of input data for a machine learning system. A plurality of data vectors using data from a plurality of data sources are extracted. A user input with respect to an input data context is received. An input vector based on the user input is generated and a set of matching data vectors are determined from the plurality of data vectors based on the input vector. Data vectors in the set of matching data vectors are determined to be thick data or thin data based on a comparison of a number of matching data vectors with a first pre-determined threshold, and/or a variance with a second pre-determined threshold. Further, the set of matching data vectors are expanded by modifying the input vector when the input data is determined to be insufficient based on a selection of a recommendation.
    Type: Grant
    Filed: December 19, 2023
    Date of Patent: January 7, 2025
    Assignee: Triangle IP, Inc.
    Inventor: Thomas D. Franklin
  • Patent number: 12189551
    Abstract: The present disclosure relates to a computing system. The computing system may include a memory system including a plurality of memory devices configured to store raw data and a near data processor (NDP) configured to receive the raw data by a first bandwidth from the plurality of memory devices and generate intermediate data by performing a first operation on the raw data, and a host device coupled to the memory system by a second bandwidth and determining a resource to perform a second operation on the intermediate data based on a bandwidth ratio and a data size ratio.
    Type: Grant
    Filed: November 30, 2022
    Date of Patent: January 7, 2025
    Assignee: SK hynix Inc.
    Inventor: Joon Seop Sim
  • Patent number: 12189717
    Abstract: Automatic partitioning of a machine learning model may be performed for training across multiple processing devices. A training job for a machine learning model may specify a number of partitions for a machine learning model. An optimization parameter may be determined for the machine learning model. Different partitions of the machine learning model to be trained across multiple processing devices may be determined based on the specified number of partitions and the optimization parameter. A schedule for executing the training job may be generated according to the respective partitions of the machine learning model. The training job may be executed according to the schedule.
    Type: Grant
    Filed: November 27, 2020
    Date of Patent: January 7, 2025
    Assignee: Amazon Technologies, Inc.
    Inventors: Can Karakus, Rahul Raghavendra Huilgol, Anirudh Subramanian, Fei Wu, Christopher Cade Daniel, Akhil Mehra, Ajay Paidi, Yutong Zhang, Indu Thangakrishnan, Luis Alves Pereira Quintela
  • Patent number: 12192220
    Abstract: Techniques for anomaly and causality detection are described. An example includes receiving time series data; performing anomaly detection on the received time series data to detect at least one anomaly using an anomaly detection model; detecting a causal relationship between measures, wherein a set of measures are related when a first of the set of measures has a causal influence on a second of the set of measures, wherein a single time series is a metric and a measure is a numerical or categorical quantity a metric describes; and outputting a result of the anomaly and causality relationship detections.
    Type: Grant
    Filed: June 28, 2022
    Date of Patent: January 7, 2025
    Assignee: Amazon Technologies, Inc.
    Inventors: Syed Ahsan Ishtiaque, Ketan Vijayvargiya, Mohammed Talal Yassar Azam, Jill Blue Lin, Mohammed Saad Ather, Ankur Mehrotra, Peter Goetz, Lenon Alexander Minorics, Patrick Bloebaum, Dominik Janzing, David Kernert, Sadanand Murthy Sachidananda, Shashank Srivastava, Laurent Callot, Ali Caner Turkmen
  • Patent number: 12192371
    Abstract: Data verification in federate learning is faster and simpler. As artificial intelligence grows in usage, data verification is needed to prove custody and/or control. Electronic data representing an original version of training data may be hashed to generate one or more digital signatures. The digital signatures may then be incorporated into one or more blockchains for historical documentation. Any auditor may then quickly verify and/or reproduce the training data using the digital signatures. For example, a current version of the training data may be hashed and compared to the digital signatures generated from the current version of the training data. If the digital signatures match, then the training data has not changed since its creation. However, if the digital signatures do not match, then the training data has changed since its creation. The auditor may thus flag the training data for additional investigation and scrutiny.
    Type: Grant
    Filed: May 18, 2021
    Date of Patent: January 7, 2025
    Assignee: Inveniam Capital Partners, Inc.
    Inventors: Paul Snow, Brian Deery, Mahesh Paolini-Subramanya, Jason Nadeau
  • Patent number: 12190247
    Abstract: Systems and methods for distributed training of deep learning models are disclosed. An example local device to train deep learning models includes a reference generator to label input data received at the local device to generate training data, a trainer to train a local deep learning model and to transmit the local deep learning model to a server that is to receive a plurality of local deep learning models from a plurality of local devices, the server to determine a set of weights for a global deep learning model, and an updater to update the local deep learning model based on the set of weights received from the server.
    Type: Grant
    Filed: August 14, 2023
    Date of Patent: January 7, 2025
    Assignee: Intel Corporation
    Inventor: David Moloney
  • Patent number: 12190501
    Abstract: Implementations are described herein for training and applying machine learning models to digital images capturing plants, and to other data indicative of attributes of individual plants captured in the digital images, to recognize individual plants in distinction from other individual plants. In various implementations, a digital image that captures a first plant of a plurality of plants may be applied, along with additional data indicative of an additional attribute of the first plant observed when the digital image was taken, as input across a machine learning model to generate output. Based on the output, an association may be stored in memory, e.g., of a database, between the digital image that captures the first plant and one or more previously-captured digital images of the first plant.
    Type: Grant
    Filed: September 22, 2023
    Date of Patent: January 7, 2025
    Assignee: DEERE &COMPANY
    Inventors: Jie Yang, Zhiqiang Yuan, Hongxu Ma, Cheng-en Guo, Elliott Grant, Yueqi Li
  • Patent number: 12190080
    Abstract: A user experience theme description is obtained, along with a new user experience feature image set. The theme description and new user experience feature image set are input into a generative adversarial network (GAN). The GAN is trained to output multiple user experience designs based on the new feature image set. The multiple designs are displayed on an electronic display device that includes an eye gaze tracking system. User interface elements and their corresponding positions within a user interface are identified based on eye gaze of a user towards the electronic display device. The position and type of user interface elements are compared between a desired user interface design and a generated user interface design. Errors between the desired user interface design and the generated user interface design are input as feedback into the GAN to further enhance the results.
    Type: Grant
    Filed: August 10, 2022
    Date of Patent: January 7, 2025
    Assignee: Kyndryl, Inc.
    Inventors: Mouleswara Reddy Chintakunta, Omar Odibat, Pritpal S. Arora
  • Patent number: 12190215
    Abstract: Automatically selecting data for machine learning datasets is provided. The method comprises receiving an input dataset and user-specified data quality metrics. The input dataset is matched to a subset of candidate datasets in a repository according to schema characteristics. A second subset of candidate datasets having a distance from the input dataset above a specified threshold is selected from the first subset of candidate datasets. The second subset of candidate datasets are merged into a merged dataset. Top ranked samples above a specified second threshold are identified from the merged dataset based on the user-specified data quality metrics. The input dataset, augmented with the top ranked samples, is returned to the user.
    Type: Grant
    Filed: October 25, 2023
    Date of Patent: January 7, 2025
    Assignee: International Business Machines Corporation
    Inventors: Nitin Gupta, Shashank Mujumdar, Ruhi Sharma Mittal, Hima Patel
  • Patent number: 12190235
    Abstract: Embodiments of the present disclosure include a system for optimizing an artificial neural network by configuring a model, based on a plurality of training parameters, to execute a training process, monitoring a plurality of statistics produced upon execution of the training process, and adjusting one or more of the training parameters, based on one or more of the statistics, to maintain at least one of the statistics within a predetermined range. In some embodiments, artificial intelligence (AI) processors may execute a training process on a model, the training process having an associated set of training parameters. Execution of the training process may produce a plurality of statistics. Control processor(s) coupled to the AI processor(s) may receive the statistics, and in accordance therewith, adjust one or more of the training parameters to maintain at least one of the statistics within a predetermined range during execution of the training process.
    Type: Grant
    Filed: January 29, 2021
    Date of Patent: January 7, 2025
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Maximilian Golub, Ritchie Zhao, Eric Chung, Douglas Burger, Bita Darvish Rouhani, Ge Yang, Nicolo Fusi
  • Patent number: 12184527
    Abstract: According to implementations of the subject matter described herein, there is provided a solution of providing a health index of a service. In this solution, a plurality of incident information sets associated with a plurality of services are obtained. The plurality of services are provisioned in a computing environment. An incident information set indicates at least one incident reported during operation of a service. Respective health indices are determined for the plurality of services based on respective ones of the plurality of incident information sets and a health classification policy. The respective health indices indicate respective health statuses of the plurality of services and being determined from a same health index range. Through unified use of incident information, the determined health indices can indicate universal and consistent health statuses for different services.
    Type: Grant
    Filed: June 30, 2020
    Date of Patent: December 31, 2024
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Yu Kang, Rulei Yu, Bo Qiao, Pu Zhao, Qingwei Lin, Jian Sun, Li Yang, Xiaofeng Gao, Pochian Lee, Dongmei Zhang, Zhangwei Xu, Liqun Li, Xu Zhang
  • Patent number: 12182733
    Abstract: Provided is a label inference system including a data generator configured to generate a training set and a test set, each including a plurality of images labeled with experts' annotations, a data trainer configured to perform training for a base model based on the generated training set and test set, a determiner configured to identify whether an evaluation metric f1 of the training model satisfies a base evaluation metric f1base, and a data inference unit configured to perform inference using the training set, the test set, and an unlabeled data set with the training model satisfying the base evaluation metric f1base.
    Type: Grant
    Filed: August 5, 2021
    Date of Patent: December 31, 2024
    Assignee: Vinbrain Joint Stock Company
    Inventors: Chanh DT. Nguyen, Hoang N. Nguyen, Thanh M. Huynh, Steven QH. Truong
  • Patent number: 12185209
    Abstract: A system may provide for the design and/or modification of network slices associated with a wireless network. The wireless network may include different slices that are associated different sets of service parameters. Slices may include radio access networks (“RANs”), core networks, or other types of networks, which may include respective sets of network functions (“NFs”), which may perform specific functions with respect to a given RAN and/or core network. Different slices, RANs, core networks, and/or NFs may be associated with particular policies and/or tags which may be specified by one or more users associated with a first access level. One or more users associated with a second access level may configure portions of the wireless network, and the policies and/or tags associated with particular slices, RANs, core networks, or NFs may be automatically implemented by an orchestration system that configures the wireless network based on the provided configuration information.
    Type: Grant
    Filed: November 28, 2023
    Date of Patent: December 31, 2024
    Assignee: Verizon Patent and Licensing Inc.
    Inventors: Sabareeswar P. Balakrishnan, Viswanath Kumar Skand Priya