Patents Examined by Kamran Afshar
  • Patent number: 12265892
    Abstract: In some implementations, a device may identify an account associated with a user, and the account may be managed by an entity. The device may determine, using at least one machine learning model, a classification of the user that indicates a level of quality of a relationship between the user and the entity. The device may determine, based on the classification determined using the at least one machine learning model, one or more adjustments that are to be applied to one or more charges assessed to the account by the entity. The device may apply the one or more adjustments to the one or more charges.
    Type: Grant
    Filed: December 31, 2020
    Date of Patent: April 1, 2025
    Assignee: Capital One Services, LLC
    Inventors: Meghnath Sharma, Vivek Bharatam, Dinanath Nadkarni
  • Patent number: 12248871
    Abstract: The present invention relates to methods of sparsifying signals over time in multi-bit spiking neural networks, methods of training and converting these networks by interpolating between spiking and non-spiking regimes, and their efficient implementation in digital hardware. Four algorithms are provided that encode signals produced by nonlinear functions, spiking neuron models, supplied as input to the network, and any linear combination thereof, as multi-bit spikes that may be compressed and adaptively scaled in size, in order to balance metrics including the desired accuracy of the network and the available energy in hardware.
    Type: Grant
    Filed: October 30, 2020
    Date of Patent: March 11, 2025
    Assignee: APPLIED BRAIN RESEARCH INC.
    Inventor: Aaron Russell Voelker
  • Patent number: 12242952
    Abstract: According to one embodiment, in nth (n is a natural number) processing, a first node calculates a first gradient to update a first weight and a second node calculates a second gradient to update the first weight. In mth (m is a natural number) processing, a third node calculates a third gradient to update a third weight and a fourth node calculates a fourth gradient to update the third weight. If the calculation by the first and second nodes is faster than the calculation by the third and fourth nodes, in n+1th processing, a second weight updated from the first weight is further updated using the first and second gradients, and, in m+1th processing, a fourth weight updated from the third weight is further updated using the first to fourth gradients.
    Type: Grant
    Filed: September 12, 2018
    Date of Patent: March 4, 2025
    Assignee: Kabushiki Kaisha Toshiba
    Inventors: Takeshi Toda, Kosuke Haruki
  • Patent number: 12229665
    Abstract: Methods and systems are disclosed for securely searching for physical resources. Attributes of a plurality of shared physical resources are accessed. An encrypted communication is received and decrypted that provides attributes for a first user. A search is performed, using a first neural network, for physical resources corresponding to attributes of the user to identity a first set of physical resources using decrypted attributes of the user and attributes of the plurality of physical resources. Search match scores are generated for the first set of physical resources. A subset of physical resources that at least one other user has access to is identified. A second neural network identifies users associated with the subset of physical resources that have a temporal usage conflict likelihood with the user. Search match scores may be adjusted. The search results may be ranked using the adjusted search match scores, and the ranked search results may be displayed.
    Type: Grant
    Filed: January 29, 2021
    Date of Patent: February 18, 2025
    Assignee: Pacaso Inc.
    Inventors: Gregory Austin Allison, Douglas Anderson, Daivak Sunil Shah
  • Patent number: 12223734
    Abstract: Systems and methods for vehicle-to-vehicle communications are provided. An adverse system can obtain sensor data representative of an environment proximate to a targeted system. The adverse system can generate an intermediate representation of the environment and a representation deviation for the intermediate representation. The representation deviation can be designed to disrupt a machine-learned model associated with the target system. The adverse system can communicate the intermediate representation modified by the representation deviation to the target system. The target system can train the machine-learned model associated with the target system to detect the modified intermediate representation. Detected modified intermediate representations can be discarded before disrupting the machine-learned model.
    Type: Grant
    Filed: January 15, 2021
    Date of Patent: February 11, 2025
    Assignee: AURORA OPERATIONS, INC.
    Inventors: Xuanyuan Tu, Raquel Urtasun, Tsun-Hsuan Wang, Sivabalan Manivasagam, Jingkang Wang, Mengye Ren
  • Patent number: 12210970
    Abstract: A server accesses a plurality of users' sessions with the web server. Each user session indicating a page flow of a corresponding user session for a plurality of web pages provided by the web server. The server generates a learning model using a neural network based on the plurality of users' sessions. The learning model is configured to predict a next user activity based on a current page flow of a current user session. The next user activity indicating one of continuing the current user session by visiting another web page provided by the web server and ending the current user session. The server dynamically adjusts a content of a web page based on the predicted next user activity.
    Type: Grant
    Filed: October 3, 2022
    Date of Patent: January 28, 2025
    Assignee: EBAY INC.
    Inventors: Keyu Nie, Yang Zhou, Zezhong Zhang, Tao Yuan, Qian Wang, Giorgio Ballardin, Liren Sun
  • Patent number: 12198058
    Abstract: Systems and methods for a tightly coupled end-to-end multi-sensor fusion with integrated compensation are described herein. For example, a system includes an inertial measurement unit that produces inertial measurements. Additionally, the system includes additional sensors that produce additional measurements. Further, the system includes one or more memory units. Moreover, the system includes one or more processors configured to receive the inertial measurements and the additional measurements. Additionally, the one or more processors are configured to compensate the inertial measurements with a compensation model stored on the one or more memory units. Also, the one or more processors are configured to fuse the inertial measurements with the additional measurements using a differential filter that applies filter coefficients stored on the one or more memory units.
    Type: Grant
    Filed: April 26, 2021
    Date of Patent: January 14, 2025
    Assignee: Honeywell International Inc.
    Inventors: Alberto Speranzon, Andrew Stewart, Shashank Shivkumar
  • Patent number: 12190236
    Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for predicting one or more properties of a material. One of the methods includes maintaining data specifying a set of known materials each having a respective known physical structure; receiving data specifying a new material; identifying a plurality of known materials in the set of known materials that are similar to the new material; determining a predicted embedding of the new material from at least respective embeddings corresponding to each of the similar known materials; and processing the predicted embedding of the new material using an experimental prediction neural network to predict one or more properties of the new material.
    Type: Grant
    Filed: April 26, 2021
    Date of Patent: January 7, 2025
    Assignee: DeepMind Technologies Limited
    Inventors: Annette Ada Nkechinyere Obika, Tian Xie, Victor Constant Bapst, Alexander Lloyd Gaunt, James Kirkpatrick
  • Patent number: 12182711
    Abstract: A method, computer program product and system for generating a neural network. Initial neural networks are prepared, each of which includes an input layer containing one or more input nodes, a middle layer containing one or more middle nodes, and an output layer containing one or more output nodes. A new neural network is generated that includes a new middle layer containing one or more middle nodes based on the middle nodes of the middle layers of the initial neural networks.
    Type: Grant
    Filed: June 13, 2019
    Date of Patent: December 31, 2024
    Assignee: International Business Machines Corporation
    Inventor: Takeshi Inagaki
  • Patent number: 12182701
    Abstract: The present invention discloses a memory and a training method for neural network based on memory. The training method includes: obtaining one or more transfer functions of a memory corresponding to one or more influence factors; determining a training plan according to an ideal case and the one or more influence factors; training the neural network according to the training plan and the one or more transfer functions to obtain a plurality of weights of the trained neural network; and programming the memory according to the weights.
    Type: Grant
    Filed: July 29, 2021
    Date of Patent: December 31, 2024
    Assignee: MACRONIX INTERNATIONAL CO., LTD.
    Inventors: Yu-Hsuan Lin, Po-Kai Hsu, Ming-Liang Wei
  • Patent number: 12175359
    Abstract: An apparatus for training and inferencing a neural network includes circuitry that is configured to generate a first weight having a first format including a first number of bits based at least in part on a second weight having a second format including a second number of bits and a residual having a third format including a third number of bits. The second number of bits and the third number of bits are each less than the first number of bits. The circuitry is further configured to update the second weight based at least in part on the first weight and to update the residual based at least in part on the updated second weight and the first weight. The circuitry is further configured to update the first weight based at least in part on the updated second weight and the updated residual.
    Type: Grant
    Filed: September 3, 2019
    Date of Patent: December 24, 2024
    Assignee: International Business Machines Corporation
    Inventors: Xiao Sun, Jungwook Choi, Naigang Wang, Chia-Yu Chen, Kailash Gopalakrishnan
  • Patent number: 12169763
    Abstract: Techniques are disclosed for providing a scalable multi-tenant serve pool for chatbot systems. A query serving system (QSS) receives a request to serve a query for a skillbot. The QSS includes: (i) a plurality of deployments in a serving pool, and (ii) a plurality of deployments in a free pool. The QSS determines whether a first deployment from the plurality of deployments in the serving pool can serve the query based on an identifier of the skillbot. In response to determining that the first deployment cannot serve the query, the QSS selects a second deployment from the plurality of deployments in the free pool to be assigned to the skillbot, and loads a machine-learning model associated with the skillbot into the second deployment, wherein the machine-learning model is trained to serve the query for the skillbot. The query is served using the machine-learning model loaded into the second deployment.
    Type: Grant
    Filed: April 13, 2021
    Date of Patent: December 17, 2024
    Assignee: Oracle International Corporation
    Inventors: Vishal Vishnoi, Suman Mallapura Somasundar, Xin Xu, Stevan Malesevic
  • Patent number: 12169793
    Abstract: A system and method for controlling a system, comprising estimating an optimal control policy for the system; receiving data representing sequential states and associated trajectories of the system, comprising off-policy states and associated off-policy trajectories; improving the estimate of the optimal control policy by performing at least one approximate value iteration, comprising: estimating a value of operation of the system dependent on the estimated optimal control policy; using a complex return of the received data, biased by the off-policy states, to determine a bound dependent on at least the off-policy trajectories, and using the bound to improve the estimate of the value of operation of the system according to the estimated optimal control policy; and updating the estimate of the optimimal control policy, dependent on the improved estimate of the value of operation of the system.
    Type: Grant
    Filed: November 16, 2020
    Date of Patent: December 17, 2024
    Assignee: The Research Foundation for The State University of New York
    Inventors: Robert Wright, Lei Yu, Steven Loscalzo
  • Patent number: 12165067
    Abstract: Systems and methods for anomaly detection in accordance with embodiments of the invention are illustrated. One embodiment includes a method for training a system for detecting anomalous samples. The method draws data samples from a data distribution of true samples and an anomaly distribution and draws a latent sample from a latent space. The method further includes steps for training a generator to generate data samples based on the drawn data samples and the latent sample, and training a cyclic discriminator to distinguish between true data samples and reconstructed samples. A reconstructed sample is generated by the generator based on an encoding of a data sample. The method identifies a set of one or more true pairs, a set of one or more anomalous pairs, and a set of one or more generated pairs. The method trains a joint discriminator to distinguish true pairs from anomalous and generated pairs.
    Type: Grant
    Filed: June 25, 2020
    Date of Patent: December 10, 2024
    Assignees: The Board of Trustees of the Leland Stanford Junior University, Ford Global Technologies, LLC
    Inventors: Ziyi Yang, Eric Felix Darve, Iman Soltani Bozchalooi
  • Patent number: 12147892
    Abstract: Provided is an electronic apparatus. The electronic apparatus includes a memory and a processor. The processor is configured to apply a low rank approximation using a matrix decomposition for a first square matrix among a plurality of square matrices based on parameter values of a deep learning model, and obtain a first approximated matrix and a second approximated matrix for the first square matrix, obtain second approximated matrices for each of a plurality of remaining square matrices other than the first square matrix among the plurality of square matrices, based on the first approximated matrix for the first square matrix, and store the first approximated matrix the first square matrix and the second approximated matrices for each of the plurality of square matrices in the memory.
    Type: Grant
    Filed: April 8, 2020
    Date of Patent: November 19, 2024
    Assignee: SAMSUNG ELECTRONICS CO., LTD.
    Inventors: Sejung Kwon, Baeseong Park, Dongsoo Lee
  • Patent number: 12147906
    Abstract: Methods, systems, and computer program products for localization-based test generation for individual fairness testing of AI models are provided herein. A computer-implemented method includes obtaining at least one artificial intelligence model and training data related to the at least one artificial intelligence model; identifying one or more boundary regions associated with the at least one artificial intelligence model based at least in part on results of processing at least a portion of the training data using the at least one artificial model; generating, in accordance with at least one of the one or more identified boundary regions, one or more synthetic data points for inclusion with the training data; and executing one or more fairness tests on the at least one artificial intelligence model using at least a portion of the one or more generated synthetic data points and at least a portion of the training data.
    Type: Grant
    Filed: April 26, 2021
    Date of Patent: November 19, 2024
    Assignee: International Business Machines Corporation
    Inventors: Diptikalyan Saha, Aniya Aggarwal, Sandeep Hans
  • Patent number: 12141238
    Abstract: Discussed herein are devices, systems, and methods for classification using a clustering autoencoder. A method can include obtaining content to be classified by the DNN classifier, and operating the DNN classifier to determine a classification of the received content, the DNN classifier including a clustering classification layer that clusters based on a latent feature vector representation of the content, the classification corresponding to one or more clusters that are closest to the latent feature vector providing the classification and a corresponding confidence.
    Type: Grant
    Filed: October 27, 2020
    Date of Patent: November 12, 2024
    Assignee: Raytheon Company
    Inventors: Philip A. Sallee, James Mullen
  • Patent number: 12136039
    Abstract: Some embodiments provide a method for training multiple parameters of a machine-trained (MT) network subject to a sparsity constraint that requires a threshold portion of the parameters to be equal to zero. A first set of the parameters subject to the sparsity constraint are grouped into groups of parameters. For each parameter of a second set of the parameters subject to the sparsity constraint, the method determines an accuracy penalty associated with the parameter being set to zero. For each group of parameters in the first set of parameters, the method determines a minimum accuracy penalty for each possible number of parameters in the group being set to zero. The method uses the determined accuracy penalties to set to the value zero at least the threshold portion of the plurality of parameters.
    Type: Grant
    Filed: July 7, 2020
    Date of Patent: November 5, 2024
    Assignee: PERCEIVE CORPORATION
    Inventors: Eric A. Sather, Steven L. Teig
  • Patent number: 12112264
    Abstract: A device which comprises an array of resistive processing unit (RPU) cells, first control lines extending in a first direction across the array of RPU cells, and second control lines extending in a second direction across the array of RPU cells. Peripheral circuitry comprising readout circuitry is coupled to the first and second control lines. A control system generates control signals to control the peripheral circuitry to perform a first operation and a second operation on the array of RPU cells. The control signals include a first configuration control signal to configure the readout circuitry to have a first hardware configuration when the first operation is performed on the array of RPU cells, and a second configuration control signal to configure the readout circuitry to have a second hardware configuration, which is different from the first hardware configuration, when the second operation is performed on the array of RPU cells.
    Type: Grant
    Filed: December 15, 2020
    Date of Patent: October 8, 2024
    Assignee: International Business Machines Corporation
    Inventors: Malte Johannes Rasch, Tayfun Gokmen, Seyoung Kim
  • Patent number: 12106491
    Abstract: Embodiments of this application disclose a target tracking method performed at an electronic device. The electronic device obtains a first video stream and detects candidate regions within a current video frame in the first video stream. The electronic device then extracts, from the candidate regions, a deep feature corresponding to each candidate region and calculates a feature similarity for each candidate region and a deep feature of a target detected in a previous video frame. Finally, the electronic device determines, based on the feature similarity corresponding to the candidate region, that the target is detected in the current video frame. Target detection is performed in a range of video frames by using a target detection model, and target tracking is performed based on the deep feature, so that occurrence of cases such as a target tracking drift or loss can be effectively prevented, to ensure the accuracy of target tracking.
    Type: Grant
    Filed: October 6, 2020
    Date of Patent: October 1, 2024
    Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
    Inventors: Hao Zhang, Zhiwei Niu