Patents Examined by Sehwan Kim
  • Patent number: 12106212
    Abstract: The present disclosure is directed to methods and apparatus for validating and authenticating use of machine learning models. For example, various techniques are described herein to limit the vulnerability of machine learning models to attack and/or exploitation of the model for malicious use, and for detecting when such attack/exploitation has occurred. Additionally, various embodiments described herein promote the protection of sensitive and/or valuable data, for example by ensuring only licensed use is permissible. Moreover, techniques are described for version tracking, usage tracking, permission tracking, and evolution of machine learning models.
    Type: Grant
    Filed: January 14, 2020
    Date of Patent: October 1, 2024
    Assignee: Koninklijke Philips N.V.
    Inventors: Shawn Arie Peter Stapleton, Amir Mohammad Tahmasebi Maraghoosh
  • Patent number: 12106203
    Abstract: Systems and methods for analyzing the usage of a set of workloads in a hyper-converged infrastructure are disclosed. A neural network model is trained based upon historical usage data of the set of workloads. The neural network model can make usage predictions of future demands on the set of workloads to minimize over-allocation or under-allocation of resources to the workloads.
    Type: Grant
    Filed: July 12, 2018
    Date of Patent: October 1, 2024
    Assignee: VMware LLC
    Inventors: Alaa Shaabana, Gregory Jean-Baptiste, Anant Agarwal, Rahul Chandrasekaran, Pawan Saxena
  • Patent number: 12086713
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for evaluating candidate output sequences using language model neural networks. In particular, an auto-regressive language model neural network is used to generate a candidate output sequence. The same auto-regressive language model neural network is used to evaluate the candidate output sequence to determine rating scores for each of one or more criteria. The rating score(s) are then used to determine whether to provide the candidate output sequence.
    Type: Grant
    Filed: July 28, 2022
    Date of Patent: September 10, 2024
    Assignee: Google LLC
    Inventors: Daniel De Freitas Adiwardana, Noam M. Shazeer
  • Patent number: 12061960
    Abstract: A learning device is configured to perform learning of a decision tree. The learning device includes a gradient output unit and a branch score calculator. The gradient output unit is configured to output a cumulative sum of gradient information corresponding to each value of a feature amount of learning data. The branch score calculator is configured to calculate a branch score used for determining a branch condition for a node of the decision tree, from the cumulative sum without using a dividing circuit.
    Type: Grant
    Filed: July 31, 2019
    Date of Patent: August 13, 2024
    Assignee: RICOH COMPANY, LTD.
    Inventors: Takuya Tanaka, Ryosuke Kasahara
  • Patent number: 12008473
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting machine learning language models using search engine results. One of the methods includes obtaining question data representing a question; generating, from the question data, a search engine query for a search engine; obtaining a plurality of documents identified by the search engine in response to processing the search engine query; generating, from the plurality of documents, a plurality of conditioning inputs each representing at least a portion of one or more of the obtained documents; for each of a plurality of the generated conditioning inputs, processing a network input generated from (i) the question data and (ii) the conditioning input using a neural network to generate a network output representing a candidate answer to the question; and generating, from the network outputs representing respective candidate answers, answer data representing a final answer to the question.
    Type: Grant
    Filed: January 31, 2023
    Date of Patent: June 11, 2024
    Assignee: DeepMind Technologies Limited
    Inventors: Angeliki Lazaridou, Elena Gribovskaya, Nikolai Grigorev, Wojciech Jan Stokowiec
  • Patent number: 11954577
    Abstract: A computer-implemented method and system having computer-executable instructions stored in a memory for processing user behavior features by neural networks to identify user segments. The method includes receiving user datasets from a database along with respective user identifiers, retention labels, static user features and interactive user features associated with an online product during a time period. A first neural network processes the interactive user features to generate a time distributed concatenation representation. A second neural network is configured to generate a vector by embedding the time distributed concatenation representation and the static user features through an embedding layer. The second neural network is configured to process the vector through a plurality of layers. A cluster model is used to determine user segments based on values extracted from nodes of a second to last layer of the second neural network.
    Type: Grant
    Filed: September 13, 2019
    Date of Patent: April 9, 2024
    Assignee: Intuit Inc.
    Inventor: Runhua Zhao
  • Patent number: 11954613
    Abstract: A method, apparatus and computer program product for establishing a logical connection between an indirect utterance and a transaction is described. An indirect utterance is received from a user as an input to a conversational system. The indirect utterance is parsed to a first logical form. A first set of predicates and terms is mapped from the first logical form to a first subgraph in a knowledge graph. A second set of predicates and terms is mapped from a second logical form belonging to a transaction to a second subgraph of the knowledge graph. A best path in the knowledge graph between the first subgraph and the second subgraph is searched for while transforming the first logical form using the node and edge labels along the best path to generate an intermediate logical form. A system action is performed for a transaction if a graph structure of the intermediate logical form matches the graph structure of the logical form of the transaction above a threshold.
    Type: Grant
    Filed: February 1, 2018
    Date of Patent: April 9, 2024
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Mustafa Canim, Robert G Farrell, Achille B Fokoue-Nkoutche, John A Gunnels, Ryan A Musa, Vijay A Saraswat
  • Patent number: 11948058
    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize recurrent neural networks to determine the existence of one or more open intents in a text input, and then extract the one or more open intents from the text input. In particular, in one or more embodiments, the disclosed systems utilize a trained intent existence neural network to determine the existence of an actionable intent within a text input. In response to verifying the existence of an actionable intent, the disclosed systems can apply a trained intent extraction neural network to extract the actionable intent from the text input. Furthermore, in one or more embodiments, the disclosed systems can generate a digital response based on the intent identified from the text input.
    Type: Grant
    Filed: December 11, 2018
    Date of Patent: April 2, 2024
    Assignee: Adobe Inc.
    Inventors: Nedim Lipka, Nikhita Vedula
  • Patent number: 11928601
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for neural network compression. In one aspect, a method comprises receiving a neural network and identifying a particular set of multiple weights of the neural network. Multiple anchor points are determined based on current values of the particular set of weights of the neural network. The neural network is trained by, at each of multiple training iterations, performing operations comprising adjusting the values of the particular set of weights by backpropagating gradients of a loss function. The loss function comprises a first loss function term based on a prediction accuracy of the neural network and a second loss function term based on a similarity of the current values of the particular set of weights to the anchor points. After training, the values of the particular set of weights are quantized based on the anchor points.
    Type: Grant
    Filed: February 9, 2018
    Date of Patent: March 12, 2024
    Assignee: Google LLC
    Inventors: Yair Alon, Elad Eban
  • Patent number: 11900231
    Abstract: A method includes receiving a set of training data and selecting a first machine learning platform based on a first optimization function that metrics past machine learning platforms used for training on the set of training data. The method also includes selecting a first algorithm supported by the first machine learning platform based on a second optimization function that metrics past algorithms used for training on the set of training data. Further, the method includes determining one or more hyperparameters supported by the first algorithm based on a third optimization function that metrics past combinations of hyperparameters from the set of hyperparameters used for training on the set of training data. The method also includes training a machine learning model on the set of training data using the first machine learning platform, the first algorithm, and the one or more hyperparameters.
    Type: Grant
    Filed: December 31, 2019
    Date of Patent: February 13, 2024
    Assignee: PAYPAL, INC.
    Inventors: Lichao Liu, Xuyao Hao, Zhanghao Hu
  • Patent number: 11893499
    Abstract: Automated development and training of deep forest models for analyzing data by growing a random forest of decision trees using data, determining Out-of-bag (OOB) predictions for the forest, appending the OOB predictions to the data set, and growing an additional forest using the data set including the appended OOB predictions, and combining the output of the additional forest, then utilizing the model to classify data outside the training data set.
    Type: Grant
    Filed: March 12, 2019
    Date of Patent: February 6, 2024
    Assignee: International Business Machines Corporation
    Inventors: Jing Xu, Rui Wang, Xiao Ming Ma, Ji Hui Yang, Xue Ying Zhang, Jing James Xu, Si Er Han
  • Patent number: 11880741
    Abstract: Generate an automorphism of the problem graph, determine an embedding of the automorphism to the hardware graph and modify the embedding of the problem graph into the hardware graph to correspond to the embedding of the automorphism to the hardware graph. Determine an upper-bound on the required chain strength. Calibrate and record properties of the component of a quantum processor with a digital processor, query the digital processor for a range of properties. Generate a bit mask and change the sign of the bias of individual qubits according to the bit mask before submitting a problem to a quantum processor, apply the same bit mask to the bit result. Generate a second set of parameters of a quantum processor from a first set of parameters via a genetic algorithm.
    Type: Grant
    Filed: August 7, 2020
    Date of Patent: January 23, 2024
    Assignee: D-WAVE SYSTEMS INC.
    Inventors: Robert B. Israel, Trevor M. Lanting, Andrew D. King
  • Patent number: 11842251
    Abstract: A “Content Optimizer” applies a machine-learned relevancy model to predict levels of interest for segments of arbitrary content. Arbitrary content includes, but is not limited to, any combination of documents including text, charts, images, speech, etc. Various automated reports and suggestions for “reformatting” segments to modify the predicted levels of interest may then be presented. Similarly, the Content Optimizer applies a machine-learned comprehension model to predict what a human audience is likely to understand (e.g., a “comprehension prediction”) from the arbitrary content. Various automated reports and suggestions for “reformatting” segments to modify the comprehension prediction may then be presented.
    Type: Grant
    Filed: June 12, 2017
    Date of Patent: December 12, 2023
    Assignee: Microsoft Technology Licensing, LLC
    Inventor: Jacob M. Hofman
  • Patent number: 11822544
    Abstract: Aspects of the present disclosure provide techniques for FAQ retrieval. Embodiments include receiving, via a user interface of a computing application, a query related to a subject. Embodiments include generating a first multi-dimensional representation of the query. Embodiments include obtaining a plurality of question and answer pairs related to the subject and, for a given question and answer pair comprising a given question and a given answer, generating a second multi-dimensional representation of the given question and a third multi-dimensional representation of the given answer. Embodiments include providing input to a model based on the first multi-dimensional representation, the second multi-dimensional representation, and the third multi-dimensional representation and determining a match score for the query and the given question and answer pair based on an output of the model.
    Type: Grant
    Filed: July 30, 2019
    Date of Patent: November 21, 2023
    Assignee: INTUIT, INC.
    Inventors: Vitor R. Carvalho, Sparsh Gupta
  • Patent number: 11803746
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for neural programming. One of the methods includes processing a current neural network input using a core recurrent neural network to generate a neural network output; determining, from the neural network output, whether or not to end a currently invoked program and to return to a calling program from the set of programs; determining, from the neural network output, a next program to be called; determining, from the neural network output, contents of arguments to the next program to be called; receiving a representation of a current state of the environment; and generating a next neural network input from an embedding for the next program to be called and the representation of the current state of the environment.
    Type: Grant
    Filed: April 27, 2020
    Date of Patent: October 31, 2023
    Assignee: DeepMind Technologies Limited
    Inventors: Scott Ellison Reed, Joao Ferdinando Gomes de Freitas
  • Patent number: 11797826
    Abstract: A system is provided for classifying an instruction sequence with a machine learning model. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: processing an instruction sequence with a trained machine learning model configured to detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. Related methods and articles of manufacture, including computer program products, are also provided.
    Type: Grant
    Filed: December 18, 2020
    Date of Patent: October 24, 2023
    Assignee: Cylance Inc.
    Inventors: Xuan Zhao, Matthew Wolff, John Brock, Brian Wallace, Andy Wortman, Jian Luan, Mahdi Azarafrooz, Andrew Davis, Michael Wojnowicz, Derek Soeder, David Beveridge, Eric Petersen, Ming Jin, Ryan Permeh
  • Patent number: 11755959
    Abstract: The systems, methods, and computer program products for determining bins for a data model are provided. Variables in a training data set are binned into bins up to a configurable number of bins. Variables in the validation data set are also binned using the bins from the training data set. A first decision tree is generated using the bins and the binned variables from the training data set and is pruned. A second decision tree is generated using the structure of the first decision tree and the binned variables from the validation data set. The first and second decision tree are merged into a third decision tree. Leaf nodes of the third decision tree are sorted and merged until weights of evidence associated with the training data set and the validation data set are monotonic. The bins for the data model are determined from the merged leaf nodes.
    Type: Grant
    Filed: December 12, 2018
    Date of Patent: September 12, 2023
    Assignee: PayPal, Inc.
    Inventors: Fan Cai, Rongsheng Zhu, Yingying Yu
  • Patent number: 11681943
    Abstract: In some embodiments, user-selectable/connectable model representations may be provided via a user interface to facilitate artificial intelligence development. The model representations may comprises first and second machine learning model (ML) representations corresponding to first and second ML models, and non-ML model representations corresponding to non-ML models. Based on user input indicating selection of the first and second ML model representations and a non-ML model representation corresponding to a non-ML model, at least a portion of a software application may be generated such that the software application comprises (i) an instance of the first ML model, an instance of the second ML model, and an instance of the non-ML model and (ii) an input/output data path between the instance of the first ML model and at least one other instance, the at least one other instance comprising the instance of the second ML model or the instance of the non-ML model.
    Type: Grant
    Filed: September 26, 2017
    Date of Patent: June 20, 2023
    Assignee: CLARIFAI, INC.
    Inventors: Matthew Zeiler, Daniel Kantor, Marshall Jones, Christopher Fox
  • Patent number: 11620555
    Abstract: A method and system are herein disclosed. The method includes developing a joint latent variable model having a first variable, a second variable, and a joint latent variable representing common information between the first and second variables, generating a variational posterior of the joint latent variable model, training the variational posterior, and performing inference of the first variable from the second variable based on the variational posterior.
    Type: Grant
    Filed: April 3, 2019
    Date of Patent: April 4, 2023
    Inventors: Jongha Ryu, Yoo Jin Choi, Mostafa El-Khamy, Jungwon Lee
  • Patent number: 11610689
    Abstract: Provided are a method for adjusting a continuous variable, a method and an apparatus for analyzing a correlation using the same. A method for adjusting a continuous variable according to an exemplary embodiment of the present disclosure is a method for adjusting a continuous variable by an apparatus including: determining at least one confounder from analysis data; classifying the analysis data into a plurality of subgroups having the same combination of confounders; and generating a new continuous variable for each subgroup based on a representative value of a continuous variable distribution.
    Type: Grant
    Filed: February 7, 2019
    Date of Patent: March 21, 2023
    Assignee: AJOU UNIVERSITY INDUSTRY—ACADEMIC COOPERATION FOUNDATION
    Inventor: O Kyu Noh