Neural Network Patents (Class 706/15)
  • Patent number: 11899101
    Abstract: A method, a computer program with instructions, and a device for predicting a course of a road based on radar data of a motor vehicle. The radar data to be processed is received and then accumulated in a measuring grid. Subsequently, clusters are formed for objects in the measuring grid. Cluster descriptions are generated for the clusters. The resulting clusters are processed to determine polynomials for describing the road edges. The polynomials are finally output for further use.
    Type: Grant
    Filed: September 21, 2020
    Date of Patent: February 13, 2024
    Assignee: ELEKTROBIT AUTOMOTIVE GMBH
    Inventors: Andreas Rottach, Mathias Trumpp, Stefan Frings, Dietmar Kling, Wilhelm Nagel
  • Patent number: 11900226
    Abstract: Some embodiments include a system operable to construct hierarchical training data sets for use with machine-learning for multiple controlled devices. Other embodiments of related systems and methods are also provided.
    Type: Grant
    Filed: February 7, 2022
    Date of Patent: February 13, 2024
    Assignee: SOURCE GLOBAL, PBC
    Inventors: Cody Alden Friesen, Paul Bryan Johnson, Heath Lorzel, Kamil Salloum, Jonathan Edward Goldberg, Grant Harrison Friesen, Jason Douglas Horwitz
  • Patent number: 11899787
    Abstract: To provide a robust information processing system against attacks by Adversarial Example. A neural network model 608, a latent space database 609 for storing position information in a latent space in which first output vectors, which are output vectors of a predetermined hidden layer included in the neural network model, are embedded concerning input data used for learning of the neural network model, and an inference control unit 606 for making an inference using the neural network model and the latent space database are provided. The inference control unit infers the input data based on the positional relationship between the second output vector, which is an output vector of the predetermined hidden layer concerning input data to be inferred, and the first output vectors in said latent space.
    Type: Grant
    Filed: March 16, 2020
    Date of Patent: February 13, 2024
    Assignee: HITACHI, LTD.
    Inventor: Tadayuki Matsumura
  • Patent number: 11893781
    Abstract: Certain aspects involve a machine-learning query system that uses a dual deep learning network to service queries and other requests. In one example, a machine-learning query system services a query received from a client computing system. A dual deep learning network included in the machine-learning query system matches an unstructured input data object, received from the client computing system, to an unstructured reference data object. The matching may include generating an input feature vector by an embedding subnetwork, based on the unstructured input data object. The matching may also include generating an output probability by a relationship subnetwork, based on the input feature vector and a relationship feature vector that is based on the unstructured reference data object. The machine-learning query system may transmit a responsive message to the client system.
    Type: Grant
    Filed: August 17, 2022
    Date of Patent: February 6, 2024
    Assignee: EQUIFAX INC.
    Inventors: Ying Xie, Linh Le
  • Patent number: 11887367
    Abstract: Disclosed herein are methods, systems, and computer-readable media for training a machine learning model to label unlabeled data and/or perform automated actions. In an embodiment, a method comprises receiving unlabeled digital video data, generating pseudo-labels for the unlabeled digital video data, the generating comprising receiving labeled digital video data, training an inverse dynamics model (IDM) using the labeled digital video data, and generating at least one pseudo-label for the unlabeled digital video data, wherein the at least one pseudo-label is based on a prediction, generated by the IDM, of one or more actions that mimic at least one timestep of the unlabeled digital video data. In some embodiments, the method further comprises adding the at least one pseudo-label to the unlabeled digital video data and further training the IDM or a machine learning model using the pseudo-labeled digital video data.
    Type: Grant
    Filed: April 19, 2023
    Date of Patent: January 30, 2024
    Assignee: OpenAI Opco, LLC
    Inventors: Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizanga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro Gonzalez, Jeffrey Clune
  • Patent number: 11886997
    Abstract: An off-policy reinforcement learning actor-critic neural network system configured to select actions from a continuous action space to be performed by an agent interacting with an environment to perform a task. An observation defines environment state data and reward data. The system has an actor neural network which learns a policy function mapping the state data to action data. A critic neural network learns an action-value (Q) function. A replay buffer stores tuples of the state data, the action data, the reward data and new state data. The replay buffer also includes demonstration transition data comprising a set of the tuples from a demonstration of the task within the environment. The neural network system is configured to train the actor neural network and the critic neural network off-policy using stored tuples from the replay buffer comprising tuples both from operation of the system and from the demonstration transition data.
    Type: Grant
    Filed: October 7, 2022
    Date of Patent: January 30, 2024
    Assignee: DeepMind Technologies Limited
    Inventors: Olivier Pietquin, Martin Riedmiller, Wang Fumin, Bilal Piot, Mel Vecerik, Todd Andrew Hester, Thomas Rothoerl, Thomas Lampe, Nicolas Manfred Otto Heess, Jonathan Karl Scholz
  • Patent number: 11887003
    Abstract: Systems and methods for improving a machine learning model are described. In an embodiment, a computing system stores a plurality of training examples comprising training inputs and training outputs. The computing system generates a machine learning model and training the machine learning model using the plurality of training examples. The computing system receives a particular input for the machine learning system and, using the particular input and the machine learning system, computes a particular output. For each training example of the plurality of training examples, the computing system adjusts a weight of the training example on the machine learning system and computes a relative numerical impact on the particular output for the training example, the relative numerical impact reflecting an importance of each training example on the particular output relative to an importance of the other training examples of the plurality of training examples on the particular output.
    Type: Grant
    Filed: May 4, 2018
    Date of Patent: January 30, 2024
    Inventors: Sunil Keshav Bopardikar, Nikhil Sunil Bopardikar, Rohan Bopardikar
  • Patent number: 11886977
    Abstract: There is provided a computing apparatus that includes: a retaining unit configured to retain an approximation table that approximately represents an activation function of a neural network, the approximation table mapping between a plurality of discrete input samples of the activation function and output samples respectively corresponding to the plurality of input samples; and a computing unit configured to convert an input value of activation function computation to an output value using the approximation table retained by the retaining unit when the activation function is selected for the activation function computation. The plurality of input samples of the approximation table are set such that input samples more distant from a reference point in the domain of the activation function have a larger neighboring sample interval.
    Type: Grant
    Filed: January 22, 2021
    Date of Patent: January 30, 2024
    Assignee: CANON KABUSHIKI KAISHA
    Inventor: Yoshihiro Mizuo
  • Patent number: 11885832
    Abstract: The invention relates to a signal analyzer, comprising a signal receiving unit configured to receive a signal, in particular a radio frequency (RF) signal, a digitizing unit configured to digitize the received signal, and a trigger detection unit configured to detect a trigger event in the digitized signal. The signal analyzer further comprises an acquisition unit configured to store a segment of the digitized signal in a memory of the signal analyzer if the trigger detection unit detects the trigger event in the digitized signal, and an anomaly search unit configured to analyze the stored segment of the digitized signal in order to detect signal anomalies, in particular glitches, in the stored segment of the digitized signal.
    Type: Grant
    Filed: October 12, 2020
    Date of Patent: January 30, 2024
    Assignee: Rohde & Schwarz GmbH & Co. KG
    Inventor: Thomas Guenther
  • Patent number: 11875551
    Abstract: In one embodiment, a method includes obtaining candidate data generated by a vehicle. The candidate data comprises a subset of sensor data identified based on a set of neural network models executing on the vehicle. The method also includes determining whether the candidate data can be associated with one or more categories of a set of categories for training data based on a set of categorization models. The method further includes associating the candidate data with the first category in response to determining that the candidate data can be associated with at a first category of the set of categories. The method further includes determining whether the candidate data can be associated with a second category. The set of categories lacks the second category. The method further includes including the second category in the set of categories in response to determining that the candidate data can be associated with the second category.
    Type: Grant
    Filed: June 9, 2020
    Date of Patent: January 16, 2024
    Assignees: NAVBIRSWAGEN AKTIENGESELLSCHAFT, PORSCHE AG, AUDI AG
    Inventors: Pratik Brahma, Nikhil George, Oleg Zabluda
  • Patent number: 11874736
    Abstract: A memory controller includes an interface and a processor. The interface communicates with memory cells organized in multiple Word Lines (WLs). The processor is configured to read a Code Word (CW) of an Error Correction Code (ECC) from a group of multiple memory cells belonging to a target WL, to calculate for a given memory cell (i) a first soft metric, depending on a first threshold voltage of a first neighbor memory cell in a first WL neighboring the target WL, and (ii) a second soft metric, depending on a second threshold voltage of a second neighbor memory cell in a second WL neighboring the target WL, to calculate a combined soft metric based on both the first and second soft metrics and assign the combined soft metric to the given memory cell, and to decode the CW based on the combined soft metric, to produce a decoded CW.
    Type: Grant
    Filed: August 11, 2021
    Date of Patent: January 16, 2024
    Assignee: APPLE INC.
    Inventors: Yonathan Tate, Nir Tishbi
  • Patent number: 11875261
    Abstract: A system and method is disclosed for automated cross-node communication in a distributed directed acyclic graph. The method can include identifying a directed acyclic graph (“DAG”) overlaying a plurality of nodes and identifying the nodes underlying the DAG. A subordinate DAG can be generated in an entry vertex of the DAG. The subordinate DAG can include a vertex for each of the nodes underlying the DAG. Data and metadata can be received at the entry vertex. The data can be delivered to a next vertex in the DAG, and the metadata can be communicated to nodes underlying the DAG via the subordinate DAG.
    Type: Grant
    Filed: October 16, 2020
    Date of Patent: January 16, 2024
    Assignee: Ford Global Technologies, LLC
    Inventor: Bradley David Safnuk
  • Patent number: 11869171
    Abstract: Embodiments are generally directed to an adaptive deformable kernel prediction network for image de-noising. An embodiment of a method for de-noising an image by a convolutional neural network implemented on a compute engine, the image including a plurality of pixels, the method comprising: for each of the plurality of pixels of the image, generating a convolutional kernel having a plurality of kernel values for the pixel; generating a plurality of offsets for the pixel respectively corresponding to the plurality of kernel values, each of the plurality of offsets to indicate a deviation from a pixel position of the pixel; determining a plurality of deviated pixel positions based on the pixel position of the pixel and the plurality of offsets; and filtering the pixel with the convolutional kernel and pixel values of the plurality of deviated pixel positions to obtain a de-noised pixel.
    Type: Grant
    Filed: November 5, 2020
    Date of Patent: January 9, 2024
    Assignee: INTEL CORPORATION
    Inventors: Anbang Yao, Ming Lu, Yikai Wang, Xiaoming Chen, Junjie Huang, Tao Lv, Yuanke Luo, Yi Yang, Feng Chen, Zhiming Wang, Zhiqiao Zheng, Shandong Wang
  • Patent number: 11868900
    Abstract: One embodiment includes a method for generating representations of inputs with missing values. The method includes steps for receiving an input includes a set of one or more values for several features, wherein the set of values for at least one of the several features includes values for each of several points in time, and for identifying a missingness pattern of the input, wherein the missingness pattern for the at least one feature indicates whether the set of values is missing a value for each of the several points in time. The method further includes steps for determining a set of one or more transformation weights based on the missingness pattern, and transforming the input based on the determined transformation weights.
    Type: Grant
    Filed: June 9, 2023
    Date of Patent: January 9, 2024
    Assignee: Unlearn.AI, Inc.
    Inventors: Aaron Michael Smith, Charles Kenneth Fisher, Franklin D. Fuller
  • Patent number: 11861464
    Abstract: This disclosure involves generating graph data structures that model inter-feature dependencies for use with machine-learning models to predict end-user behavior. For example, a processing device receives an input dataset and a request to modify a first input feature of the input dataset. The processing device uses a graph data structure that models the inter-feature dependencies to modify the input dataset by propagating the modification of the first input feature to a second input feature dependent on the first input feature. The modification to the second input feature is a function of at least (a) the value of the first input feature and (b) a weight assigned to an edge linking the first input feature to the second input feature within the directed graph. The processing device then applies a trained machine-learning model to the modified input dataset to generate a prediction of an outcome.
    Type: Grant
    Filed: October 31, 2019
    Date of Patent: January 2, 2024
    Assignee: Adobe Inc.
    Inventors: Ritwik Sinha, Sunny Dhamnani
  • Patent number: 11863399
    Abstract: In order to enable stabilizing control of communication in a communication network, a system according to an aspect of the present disclosure includes: an obtaining means for obtaining work-related information related to human work in network operation; and a training means for training a machine learning based controller for controlling communication in a communication network, based on the work-related information.
    Type: Grant
    Filed: September 30, 2019
    Date of Patent: January 2, 2024
    Assignee: NEC CORPORATION
    Inventors: Anan Sawabe, Takanori Iwai, Kosei Kobayashi
  • Patent number: 11855970
    Abstract: A system and method are disclosed for providing a private multi-modal artificial intelligence platform. The method includes splitting a neural network into a first client-side network, a second client-side network and a server-side network and sending the first client-side network to a first client. The first client-side network processes first data from the first client, the first data having a first type. The method includes sending the second client-side network to a second client. The second client-side network processes second data from the second client, the second data having a second type. The first type and the second type have a common association. Forward and back propagation occurs between the client side networks and disparate data types on the different client side networks and the server-side network to train the neural network.
    Type: Grant
    Filed: September 7, 2022
    Date of Patent: December 26, 2023
    Assignee: TripleBlind, Inc.
    Inventors: Gharib Gharibi, Greg Storm, Ravi Patel, Riddhiman Das
  • Patent number: 11853911
    Abstract: A data structuring system that provides a user interface to enable data wrangling and modeling, and methods for making and using the same.
    Type: Grant
    Filed: January 10, 2020
    Date of Patent: December 26, 2023
    Assignee: PECAN AI LTD.
    Inventors: Noam Brezis, Zohar Z. Bronfman
  • Patent number: 11849914
    Abstract: An endoscopic image processing method is provided. The method can include acquiring a current endoscopic image of a to-be-examined user, and predicting the current endoscopic image by using a deep convolutional network based on a training parameter. The training parameter can be determined according to at least one first endoscopic image and at least one second endoscopic image transformed from the at least one first endoscopic image, where the at least one endoscopic image corresponds to a human body part. The method can further include determining an organ category corresponding to the current endoscopic image. The method can make a prediction process more intelligent and more robust, thereby improving resource utilization of a processing apparatus.
    Type: Grant
    Filed: October 23, 2020
    Date of Patent: December 26, 2023
    Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
    Inventors: Xinghui Fu, Zhongqian Sun, Wei Yang
  • Patent number: 11854248
    Abstract: The present disclosure provides an image classification method, apparatus, and device, and a readable storage medium. The image classification method includes: processing an image to be processed, by using a first convolutional network, to obtain a first feature map; processing the first feature map, by using a residual network, to obtain a second feature map, wherein the residual network includes a depth separable convolutional layer; and processing the second feature map, by using a second convolutional network, to determine a category label of the image to be processed.
    Type: Grant
    Filed: December 29, 2020
    Date of Patent: December 26, 2023
    Assignee: BOE Technology Group Co., Ltd.
    Inventors: Yanhong Wu, Guannan Chen, Lijie Zhang
  • Patent number: 11847245
    Abstract: Systems as described herein may label data to preserve privacy. An annotation server may receive a document comprising a collection of text representing a plurality of confidential data from a first computing device. The annotation server may convert the document to a plurality of text embeddings. The annotation server may input the text embeddings into a machine learning model to generate a plurality of synthetic images, and receive a label for each of the plurality of synthetic images from a third-party labeler. Accordingly, the annotation server may send the confidential data and the corresponding labels to a second computing device.
    Type: Grant
    Filed: February 17, 2021
    Date of Patent: December 19, 2023
    Assignee: Capital One Services, LLC
    Inventors: Anh Truong, Austin Walters, Jeremy Goodsitt, Vincent Pham, Reza Farivar, Galen Rafferty
  • Patent number: 11847431
    Abstract: Embodiments for providing an enhanced codebase in a computing environment by a processor. One or more container specification files may be automatically generated for a codebase based on one or more extracted attribute names and values.
    Type: Grant
    Filed: March 3, 2022
    Date of Patent: December 19, 2023
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Gabriele Picco, Natalia Mulligan, Inge Lise Vejsbjerg, Thanh Lam Hoang
  • Patent number: 11847566
    Abstract: Computer systems and computer-implemented methods modify a machine learning network, such as a deep neural network, to introduce judgment to the network. A “combining” node is added to the network, to thereby generate a modified network, where activation of the combining node is based, at least in part, on output from a subject node of the network. The computer system then trains the modified network by, for each training data item in a set of training data, performing forward and back propagation computations through the modified network, where the backward propagation computation through the modified network comprises computing estimated partial derivatives of an error function of an objective for the network, except that the combining node selectively blocks back-propagation of estimated partial derivatives to the subject node, even though activation of the combining node is based on the activation of the subject node.
    Type: Grant
    Filed: June 13, 2023
    Date of Patent: December 19, 2023
    Assignee: D5AI LLC
    Inventor: James K. Baker
  • Patent number: 11842276
    Abstract: The display device includes a plurality of pixels, a data driver, and a timing controller. The plurality of pixels are connected to a plurality of scan lines and a plurality of data lines. The data driver supplies a data voltage to the plurality of data lines in a light-emitting mode and supplies a neural network input voltage to the plurality of data lines in an artificial neural network mode. The timing controller is in an artificial neural network mode, and supplies a weight value control signal for performing a deep learning operation by using at least one of the plurality of pixels to the data driver. The weight value control signal is generated based on a predetermined weight value.
    Type: Grant
    Filed: June 7, 2021
    Date of Patent: December 12, 2023
    Assignee: SAMSUNG DISPLAY CO., LTD.
    Inventors: Young Wook Yoo, Hyeon Min Kim, Jun Gyu Lee, Hyun Jun Lim, Byung Ki Chun
  • Patent number: 11842256
    Abstract: Embodiments for ensemble training in a distributed marketplace in a computing environment. One or more ensemble machine learning models may be provided from a plurality of machine learning models competing within the distributed marketplace that achieve a performance on ensemble training data equal to or greater than a selected performance threshold, wherein the distributed marketplace is a blockchain.
    Type: Grant
    Filed: May 15, 2020
    Date of Patent: December 12, 2023
    Assignee: International Business Machines Corporation Armonk
    Inventors: Killian Levacher, Emanuele Ragnoli, Stefano Braghin, Gokhan Sagirlar
  • Patent number: 11836604
    Abstract: A method for programming an activation function is provided. The method includes generating segment data for segmenting the activation function; segmenting the activation function into a plurality of segments using the segment data; and approximating at least one segment of the plurality of segments as a programmable segment. An apparatus for performing the method may include a programmable activation function generator configured to generate segment data for segmenting an activation function; segment the activation function into a plurality of segments using the generated segment data; and approximate at least one segment of the plurality of segments as a programmable segment. By using segment data, various non-linear activation functions, particularly newly proposed or known activation functions with some modifications, can be programmed to be processable in hardware.
    Type: Grant
    Filed: May 23, 2022
    Date of Patent: December 5, 2023
    Assignee: DEEPX CO., LTD.
    Inventors: Lok Won Kim, Ho Seung Kim, Hyung Jin Chun
  • Patent number: 11836746
    Abstract: A diagnostic system for model governance is presented. The diagnostic system includes an auto-encoder to monitor model suitability for both supervised and unsupervised models. When applied to unsupervised models, the diagnostic system can provide a reliable indication on model degradation and recommendation on model rebuild. When applied to supervised models, the diagnostic system can determine the most appropriate model for the client based on a reconstruction error of a trained auto-encoder for each associated model. An auto-encoder can determine outliers among subpopulations of consumers, as well as support model go-live inspections.
    Type: Grant
    Filed: December 2, 2014
    Date of Patent: December 5, 2023
    Assignee: FAIR ISAAC CORPORATION
    Inventors: Jun Zhang, Scott Michael Zoldi
  • Patent number: 11829075
    Abstract: A processing apparatus includes a driver configured to drive a controlled object, and a controller configured to control the driver by generating a command value to the driver based on a control error. The controller includes a first compensator configured to generate a first command value based on the control error, a second compensator configured to generate a second command value based on the control error, and an adder configured to obtain the command value by adding the first command value and the second command value. The second compensator includes a neural network for which a parameter value is decided by learning, and input parameters input to the neural network include at least one of a driving condition of the driver and an environment condition in a periphery of the controlled object in addition to the control error.
    Type: Grant
    Filed: June 23, 2022
    Date of Patent: November 28, 2023
    Assignee: CANON KABUSHIKI KAISHA
    Inventor: Satoru Itoh
  • Patent number: 11829873
    Abstract: Disclosed herein is technology for performing predictive modeling to identify inputs for a manufacturing process. An example method may include receiving expected output data for a manufacturing process, wherein the expected output data defines an attribute of an output of the manufacturing process; accessing a plurality of machine learning models that model the manufacturing process; determining, using a first machine learning model, input data for the manufacturing process based on the expected output data for the manufacturing process, wherein the input data comprises a value for a first input and a value for a second input; combining the input data determined using the first machine learning model with input data determined using the second machine learning model to produce a set of inputs for the manufacturing process, wherein the set of inputs comprises candidate values for the first input and candidate values for the second input.
    Type: Grant
    Filed: May 21, 2020
    Date of Patent: November 28, 2023
    Assignee: Applied Materials, Inc.
    Inventors: Sidharth Bhatia, Dermot Cantwell, Serghei Malkov, Jie Feng
  • Patent number: 11822049
    Abstract: [Object] To provide a lightning threat information-providing apparatus, a lightning threat information-providing method, and a program that are capable of providing a user with accurate information regarding a lightning threat.
    Type: Grant
    Filed: August 9, 2018
    Date of Patent: November 21, 2023
    Assignee: Japan Aerospace Exploration Agency
    Inventors: Eiichi Yoshikawa, Tomoo Ushio
  • Patent number: 11823217
    Abstract: A segmentation system utilizes a supervised learning method and a clustering analysis to identify clusters, thereby segmenting a population into groups, where the clusters are associated with various conversion potentials that indicate the probability of an event. The segmentation system employs the supervised learning method to train a model on training data comprising historical conversion data and features associated with members of the group. A subset of features is selected from a ranked order that is determined using weights generated by the supervised learning. A clustering analysis is performed for a population with respect to the subset to generate clusters. A superior cluster is identified based on it having a conversion potential greater than a conversion potential of another cluster. In a marketing context, the system can be employed to identify a superior cluster of users that have a higher conversion potential in response to an advertisement campaign.
    Type: Grant
    Filed: November 1, 2019
    Date of Patent: November 21, 2023
    Assignee: Adobe Inc.
    Inventors: Rushil Mahajan, Kumar Mrityunjay Singh
  • Patent number: 11822887
    Abstract: Systems and methods for natural language processing are described. One or more embodiments of the disclosure provide an entity matching apparatus trained using machine learning techniques to determine whether a query name corresponds to a candidate name based on a similarity score. In some examples, the query name and the candidate name are encoded using a character encoder to produce a regularized input sequence and a regularized candidate sequence, respectively. The regularized input sequence and the regularized candidate sequence are formed from a regularized character set having fewer characters than a natural language character set.
    Type: Grant
    Filed: March 12, 2021
    Date of Patent: November 21, 2023
    Assignee: ADOBE, INC.
    Inventors: Lidan Wang, Franck Dernoncourt
  • Patent number: 11823052
    Abstract: Certain aspects of the present disclosure are directed to methods and apparatus for configuring a multiply-accumulate (MAC) block in an artificial neural network. A method generally includes receiving, at a neural processing unit comprising one or more logic elements, at least one input associated with a use-case of the neural processing unit; obtaining a set of weights associated with the at least one input; selecting a precision for the set of weights; modifying the set of weights based on the selected precision; and generating an output based, at least in part, on the at least one input, the modified set of weights, and an activation function.
    Type: Grant
    Filed: October 11, 2019
    Date of Patent: November 21, 2023
    Assignee: QUALCOMM INCORPORATED
    Inventors: Giby Samson, Srivatsan Chellappa, Ramaprasath Vilangudipitchai, Seung Hyuk Kang
  • Patent number: 11816045
    Abstract: A computer-implemented method includes receiving, by a computing device, input activations and determining, by a controller of the computing device, whether each of the input activations has either a zero value or a non-zero value. The method further includes storing, in a memory bank of the computing device, at least one of the input activations. Storing the at least one input activation includes generating an index comprising one or more memory address locations that have input activation values that are non-zero values. The method still further includes providing, by the controller and from the memory bank, at least one input activation onto a data bus that is accessible by one or more units of a computational array. The activations are provided, at least in part, from a memory address location associated with the index.
    Type: Grant
    Filed: August 24, 2021
    Date of Patent: November 14, 2023
    Assignee: Google LLC
    Inventors: Dong Hyuk Woo, Ravi Narayanaswami
  • Patent number: 11818147
    Abstract: Systems, methods and computer program products for improving security of artificial intelligence systems. The system comprising processors for monitoring one or more transactions received by a machine learning decision model to determine a first score associated with a first transaction. The first transaction may be identified as likely adversarial, in response to the first score being lower than a certain score threshold and the first transaction having a low occurrence likelihood. A second score may be generated in association with the first transaction based on one or more adversarial latent features associated with the first transaction. At least one adversarial latent feature may be detected as being exploited by the first transaction, in response to determining that the second score falls above the certain score threshold. Accordingly, an abnormal volume of activations of adversarial latent features spanning across a plurality of transactions scored may be detected and blocked.
    Type: Grant
    Filed: November 23, 2020
    Date of Patent: November 14, 2023
    Assignee: Fair Isaac Corporation
    Inventors: Scott Michael Zoldi, Shafi Ur Rahman
  • Patent number: 11810174
    Abstract: Items within an index may be converted from classic geometry and embedded into a hyperbolic space. The hyperboloids within the hyperbolic space provide higher precision classifications of items within the index relative to their hierarchical structure. A received search query may also be converted to hyperbolic space and mapped as a query hyperboloid against an answer space that includes hyperboloids for items within the index. Distances or overlaps between the hyperboloids may be determined in order to generate a set of search results.
    Type: Grant
    Filed: March 31, 2021
    Date of Patent: November 7, 2023
    Assignee: Amazon Technologies, Inc.
    Inventors: Sumeet Katariya, Nikhil S. Rao, Chandan K. Reddy, Karthik Subbian, Nurendra Choudhary
  • Patent number: 11809521
    Abstract: A method for modularizing high dimensional neural networks into neural networks of lower input dimensions. The method is suited to generating full-DOF robot grasping actions based on images of parts to be picked. In one example, a first network encodes grasp positional dimensions and a second network encodes rotational dimensions. The first network is trained to predict a position at which a grasp quality is maximized for any value of the grasp rotations. The second network is trained to identify the maximum grasp quality while searching only at the position from the first network. Thus, the two networks collectively identify an optimal grasp, while each network's searching space is reduced. Many grasp positions and rotations can be evaluated in a search quantity of the sum of the evaluated positions and rotations, rather than the product. Dimensions may be separated in any suitable fashion, including three neural networks in some applications.
    Type: Grant
    Filed: June 8, 2021
    Date of Patent: November 7, 2023
    Assignee: FANUC CORPORATION
    Inventor: Yongxiang Fan
  • Patent number: 11803738
    Abstract: Hardware for implementing a Deep Neural Network (DNN) having a convolution layer, the hardware comprising a plurality of convolution engines each configured to perform convolution operations by applying filters to data windows, each filter comprising a set of weights for combination with respective data values of a data window; and one or more weight buffers accessible to each of the plurality of convolution engines over an interconnect, each weight buffer being configured to provide weights of one or more filters to any of the plurality of convolution engines; wherein each of the convolution engines comprises control logic configured to request weights of a filter from the weight buffers using an identifier of that filter.
    Type: Grant
    Filed: October 26, 2021
    Date of Patent: October 31, 2023
    Assignee: Imagination Technologies Limited
    Inventor: Christopher Martin
  • Patent number: 11794759
    Abstract: The present technology is effective to cause at least one processor to instruct an autonomous vehicle to navigate a specific course and to record diagnostic measurements while navigating the specific course, receive the diagnostic measurements from the autonomous vehicle, and analyze the diagnostic measurements from the autonomous vehicle in a context provided by a collection of diagnostic measurement data collected from a fleet of similar autonomous vehicles navigating the specific course.
    Type: Grant
    Filed: March 26, 2020
    Date of Patent: October 24, 2023
    Assignee: GM Cruise Holdings LLC
    Inventors: Erik Nielsen, Chase Kaufman
  • Patent number: 11798675
    Abstract: Methods, systems, apparatus, including computer programs encoded on computer storage media for generating psychiatric treatment recommendations. In one aspect, the method includes actions of receiving, by a server and from a first user device, one or more data structures that collectively include fields structuring data that represents (i) current patient symptoms and (ii) current patient diagnoses, generating, by the server, rendering data structure that includes fields structuring data that represents rendering data that, when rendered on a display device, presents a patient dashboard based on the data that is received from the mobile device, and providing, by the server, the rendering data structure to a second user device that is different than the first user device, wherein the second user device is configured to render the rendering data structured by the fields of the rendering data structure to output the patient dashboard on the display of the second user device.
    Type: Grant
    Filed: May 29, 2019
    Date of Patent: October 24, 2023
    Assignee: OTSUKA AMERICA PHARMACEUTICAL, INC.
    Inventors: Roland Larkin, Srikanth Gottipati, Reza Moghadam, Carolyn Tyler, Gregory Ho
  • Patent number: 11790212
    Abstract: Quantization-aware neural architecture search (“QNAS”) can be utilized to learn optimal hyperparameters for configuring an artificial neural network (“ANN”) that quantizes activation values and/or weights. The hyperparameters can include model topology parameters, quantization parameters, and hardware architecture parameters. Model topology parameters specify the structure and connectivity of an ANN. Quantization parameters can define a quantization configuration for an ANN such as, for example, a bit width for a mantissa for storing activation values or weights generated by the layers of an ANN. The activation values and weights can be represented using a quantized-precision floating-point format, such as a block floating-point format (“BFP”) having a mantissa that has fewer bits than a mantissa in a normal-precision floating-point representation and a shared exponent.
    Type: Grant
    Filed: March 18, 2019
    Date of Patent: October 17, 2023
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Kalin Ovtcharov, Eric S. Chung, Vahideh Akhlaghi, Ritchie Zhao
  • Patent number: 11790135
    Abstract: A computer-implemented method for providing a simulation model of an electric rotating machine is disclosed. The simulation model is defined by parameter values. Input data is obtained. The input data is collectable using the electric rotating machine when the electric rotating machine is not connected to an operating voltage and being characteristic of the electric rotating machine. The parameter values are determined from the input data using a trained function and the parameter values determined are provided.
    Type: Grant
    Filed: April 21, 2021
    Date of Patent: October 17, 2023
    Assignee: Siemens Aktiengesellschaft
    Inventor: Christian Deeg
  • Patent number: 11783037
    Abstract: Disclosed are a defense method and a model of deep learning model aiming at adversarial attacks in the technical field of image recognition, which makes full use of the internal relationship between the adversarial samples and the initial samples, and transforms the adversarial samples into common samples by constructing a filter layer in front of the input layer of the deep learning model; the parameters of the filter layer are trained by using the adversarial attack samples, so as to improve the ability of the model to resist adversarial attack; then the trained filter layer is combined with the learning model after the adversarial training, and a deep learning model with strong robustness and high classification accuracy is obtained, which ensures that the recognition ability of the initial sample is not reduced while resisting the adversarial attacks.
    Type: Grant
    Filed: May 15, 2023
    Date of Patent: October 10, 2023
    Assignee: Quanzhou Equipment Manufacturing Research Institute
    Inventors: Jielong Guo, Xian Wei, Xuan Tang, Hui Yu, Dongheng Shao, Jianfeng Zhang, Jie Li, Yanhui Huang
  • Patent number: 11775655
    Abstract: An artificial intelligence (AI) platform to support optimization of container builds and virtual machine mounts in a distributed computing environment. A provisioning file is subject to natural language processing (NLP) and a corresponding vector representation of the file is created and subject to evaluation by a set of artificial neural networks (ANN). A first ANN assesses the representation of the file with respect to compliance and operability, and the second ANN selectively assesses the representation of the file with respect to provisioning efficiency. The provisioning file is selectively process based on the provisioning efficiency, with the processing directed at provisioning a container build or mounting a VM.
    Type: Grant
    Filed: May 11, 2021
    Date of Patent: October 3, 2023
    Assignee: International Business Machines Corporation
    Inventors: Abhishek Malvankar, John M. Ganci, Jr., Carlos A. Fonseca, Charles E. Beller
  • Patent number: 11775801
    Abstract: A neural processor. In some embodiments, the processor includes a first tile, a second tile, a memory, and a bus. The bus may be connected to the memory, the first tile, and the second tile. The first tile may include: a first weight register, a second weight register, an activations buffer, a first multiplier, and a second multiplier. The activations buffer may be configured to include: a first queue connected to the first multiplier and a second queue connected to the second multiplier. The first queue may include a first register and a second register adjacent to the first register, the first register being an output register of the first queue. The first tile may be configured: in a first state: to multiply, in the first multiplier, a first weight by an activation from the output register of the first queue, and in a second state: to multiply, in the first multiplier, the first weight by an activation from the second register of the first queue.
    Type: Grant
    Filed: August 27, 2019
    Date of Patent: October 3, 2023
    Assignee: Samsung Electronics Co., Ltd.
    Inventors: Ilia Ovsiannikov, Ali Shafiee Ardestani, Joseph H. Hassoun, Lei Wang, Sehwan Lee, JoonHo Song, Jun-Woo Jang, Yibing Michelle Wang, Yuecheng Li
  • Patent number: 11776292
    Abstract: An object identification method includes: generating a tracking sample and an adversarial sample; training a teacher model according to the tracking sample; and initializing a student model according to the teacher model. The student model adjusts a plurality of parameters according to the teacher model and the adversarial sample, in response to the vector difference between the output result of the student model and the output result of the teacher model being lower than the learning threshold, the student model is deemed to have completed training, and the student model is extracted as an object identification model.
    Type: Grant
    Filed: March 15, 2021
    Date of Patent: October 3, 2023
    Assignee: WISTRON CORP
    Inventor: Kuo-Lun Huang
  • Patent number: 11769036
    Abstract: An apparatus for optimizing a computational network is configure to receive an input at a first processing component. The first processing component may include at least a first programmable processing component and a second programmable processing component. The first programmable processing component is configured to compute a first nonlinear function and the second programmable processing component is configured to compute a second nonlinear function which is different than the second nonlinear function. The computational network which may be a recurrent neural network such as a long short-term memory may be operated to generate an inference based at least in part on outputs of the first programmable processing component and the second programmable processing component.
    Type: Grant
    Filed: April 18, 2018
    Date of Patent: September 26, 2023
    Assignee: QUALCOMM Incorporated
    Inventors: Rosario Cammarota, Michael Goldfarb, Manu Rastogi, Sarang Ozarde
  • Patent number: 11769059
    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: December 21, 2022
    Date of Patent: September 26, 2023
    Assignee: Movidius Limited
    Inventor: David Moloney
  • Patent number: 11769576
    Abstract: Embodiments of a method and system for improving care determination for care providers in relation to a condition of a user associated with a mobile device can include: collecting a log of use dataset associated with user digital communication behavior at the mobile device; collecting a mobility supplementary dataset corresponding to a mobility-related sensor of the mobile device; determining a medical status analysis for a condition of the user based on at least one of the log of use dataset and the mobility supplementary dataset, the medical status analysis including at least one of a diagnosis and a therapeutic intervention associated with the condition; and promoting the at least one of the diagnosis and the therapeutic intervention to a care provider.
    Type: Grant
    Filed: March 24, 2020
    Date of Patent: September 26, 2023
    Assignee: OrangeDot, Inc.
    Inventors: Sai Moturu, Anmol Madan
  • Patent number: 11763133
    Abstract: Systems and methods relating to machine learning. An edge device runs a new data point on a first neural network and determines activations on the layers within that neural network. The first neural network is a fully trained network based on a second neural network on a server. The activation data for the various layers in the first neural network are, starting with the output layer, sequentially transmitted to the server. The server continuously receives this activation data and continuously compares it with previously encountered activation data for the second neural network. If the received activation data is within an expected range, then the edge device is instructed to stop sending activation data. Otherwise, the server continues to receive the activation data for the other layers until the new data point is received by the server or the activation data is within the expected range of previously encountered activation data.
    Type: Grant
    Filed: August 30, 2019
    Date of Patent: September 19, 2023
    Assignee: SERVICENOW CANADA INC.
    Inventor: Philippe Beaudoin