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公开(公告)号：US12154043B2
公开(公告)日：2024-11-26
申请号：US17869708
申请日：2022-07-20
Applicant: CERNER INNOVATION, INC.
Inventor： Douglas S. McNair
Abstract: Technologies are provided for identifying individuals having a risk of non-adherence to or from a prescribed treatment program; for predicting and the risk, which may be determined as a forecast over a future time span; and evaluating it to further determine or invoke specific actions to mitigate the risk or otherwise improve likelihood of compliance. A singular spectrum analysis (SSA) is utilized to analyze temporal properties of a time series determined from measured or observational data to determine an emergent pattern. Based on this pattern, a risk of non-adherence, including relapse or absconding, over a future time interval by the individual may be determined and utilized to implement an intervening action.
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2.发明公开公开(公告)号：US20240214413A1
公开(公告)日：2024-06-27
申请号：US18086439
申请日：2022-12-21
Applicant: International Business Machines Corporation
CPC classification number: H04L63/145 , G06N7/08 , H04L41/16
Abstract: In one general embodiment, a computer-implemented method includes applying a plurality of known cyber-attack techniques and variations thereof against a simulated defender system using a simulated attacking system. Known cyber-attack defense techniques are applied to the defender system. Instances of the defender system are logged in association with various combinations of respective cyber-attack techniques, various cyber-attack defense techniques, simulated system configurations, and simulated system outcomes as training instances. A machine learning model is trained using the logged training instances. A production product configuration is input to the trained machine learning model. Information related to cyber-hardening of the production product is output from the trained machine learning model.
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公开(公告)号：US20240160970A1
公开(公告)日：2024-05-16
申请号：US18383247
申请日：2023-10-24
Applicant: NEC Corporation
Inventor： Kei TAKEMURA
IPC: G06N7/08
CPC classification number: G06N7/08
Abstract: The optimization system includes an acquisition unit, an amplification unit, a weight calculation unit, and an output unit. The acquisition unit acquires a loss caused as a result of decision-making by a plurality of experts in repetition of decision-making in which the plurality of experts are weighted and combined. The amplification unit amplifies each of the plurality of experts into a plurality of experts having different timings for initializing the information on the weight. The weight calculation unit calculates the weight of decision-making of each of the plurality of experts based on the weight of decision-making calculated using the loss for each of the experts amplified. The output unit outputs a weight of the decision-making of each of the plurality of experts.
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公开(公告)号：US20240160817A1
公开(公告)日：2024-05-16
申请号：US18503323
申请日：2023-11-07
Applicant: Dassault Systemes Simulia Corp.
Inventor： Pradeep Gopalakrishnan , Raoyang Zhang , Hudong Chen , Junye Wang , Avinash Jammalamadaka
CPC classification number: G06F30/28 , G06F7/485 , G06N7/08 , G06F2113/08
Abstract: Described are computer implemented techniques for simulating elements of a fluid flow. These techniques include storing in a memory state vectors for a plurality of voxels, the state vectors comprising a plurality of entries that correspond to particular momentum states of a plurality of possible momentum states at a voxel, storing in a memory a representation of at least one surface that is sized and oriented independently of the size and orientation of the voxels, perform interaction operations on the state vectors, the interaction operations modelling interactions between elements of different momentum states, perform surface interaction operations on the representation of the surface, the surface interaction operations modelling interactions between the surface and substantially all elements of voxels, and performing move operations on the state vectors to reflect movement of elements to new voxels.
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公开(公告)号：US11886780B2
公开(公告)日：2024-01-30
申请号：US17179458
申请日：2021-02-19
Applicant: FUJITSU LIMITED
Inventor： Takeshi Mishina , Yoshimasa Tani , Satoshi Matsuura
IPC: G06F30/20 , G06N7/08 , G06F119/06
CPC classification number: G06F30/20 , G06N7/08 , G06F2119/06
Abstract: An optimization device includes: a memory; and a processor and configured to: store a coefficient indicating magnitude of an interaction between bits in a bit string representing a state of an Ising model; output, when any bit in the bit string is inverted, a signal indicating inversion availability of an own bit according to calculation of energy change in the Ising model using the coefficient corresponding to the inverted bit and the own bit read from the memory as bit operations; output a signal indicating a bit to be inverted in the bit string selected on the basis of the signal indicating inversion availability output from bit operations of a first number of bits of the bit string, of the bit operations; and change the first number of bits and change a second number of bits of the coefficient for each bit operations of the first number of bits.
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Patent Agency Ranking
公开(公告)号：US20240005185A1
公开(公告)日：2024-01-04
申请号：US18057084
申请日：2022-11-18
Applicant: International Business Machines Corporation
Inventor： Sergey Bravyi , Jay M. Gambetta , David C. Mckay , Sarah E. Sheldon
Abstract: A method of mitigating quantum readout errors by stochastic matrix inversion includes performing a plurality of quantum measurements on a plurality of qubits having predetermined plurality of states to obtain a plurality of measurement outputs; selecting a model for a matrix linking the predetermined plurality of states to the plurality of measurement outputs, the model having a plurality of model parameters, wherein a number of the plurality of model parameters grows less than exponentially with a number of the plurality of qubits; training the model parameters to minimize a loss function that compares predictions of the model with the matrix; computing an inverse of the model based on the trained model parameters; and providing the computed inverse of the model to a noise prone quantum readout of the plurality of qubits to obtain a substantially noise free quantum readout.
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公开(公告)号：US11853658B2
公开(公告)日：2023-12-26
申请号：US17125101
申请日：2020-12-17
Applicant: NEC Corporation
Inventor： Yoshihiro Nambu
IPC: G06F30/20 , G06F17/16 , G06N7/08 , G06F111/10
CPC classification number: G06F30/20 , G06F17/16 , G06N7/08 , G06F2111/10
Abstract: The present disclosure provides an information processing apparatus capable of improving the accuracy of solving a problem when an annealing algorithm is executed. An information processing apparatus 1 includes a calculation unit 2 and an arithmetic unit 4. The calculation unit 2 calculates at least one eigenvector of a coupling coefficient matrix including a coupling coefficient indicating a strength of interaction between each of a plurality of binary variables that indicate states of a plurality of respective spins in an Ising model, the coupling coefficient matrix being given in advance in accordance with a problem to be solved by an annealing algorithm. The arithmetic unit 4 executes the annealing algorithm from an initial state set based on the at least one eigenvector.
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公开(公告)号：US11842306B2
公开(公告)日：2023-12-12
申请号：US17351098
申请日：2021-06-17
Applicant: Shandong University
Inventor： Chenghui Zhang , Che Liu , Bo Sun
CPC classification number: G06Q10/06315 , G06N7/08 , G06N20/00 , G06Q10/04 , G06Q50/06
Abstract: The present disclosure provides a method and system for predicting building energy consumption based on a Holt-Winters and an extreme learning machine, the method including: constructing a building simulation model based on actual operation parameters of a building to obtain an original energy consumption data set of the building; decomposing the original energy consumption data set to obtain a linear energy consumption data set and a nonlinear energy consumption data set; performing prediction on the linear energy consumption data set by using a trained Holt-Winters model to obtain a linear energy consumption prediction result; and inputting the nonlinear energy consumption data set, the original energy consumption data set, and the linear energy consumption prediction result into a trained extreme learning machine model to output a building energy consumption prediction value of the building simulation model.
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公开(公告)号：US11816595B2
公开(公告)日：2023-11-14
申请号：US17249293
申请日：2021-02-25
Applicant: KABUSHIKI KAISHA TOSHIBA
Inventor： Ryo Hidaka , Kosuke Tatsumura , Masaya Yamasaki , Yohei Hamakawa , Hayato Goto
IPC: G06N7/08 , H03K19/177
CPC classification number: G06N7/08 , H03K19/177
Abstract: According to an embodiment, an information processing system solves a combinatorial optimization problem. The information processing system includes an Ising machine and a host unit. The Ising machine is hardware configured to perform a search process for searching for the ground state of an Ising model that represents the combinatorial optimization problem. The host unit is hardware connected to the Ising machine via an interface and configured to control the Ising machine. In the search process, for each of a plurality of Ising spins, the Ising machine alternately repeats an auxiliary variable update process for updating an auxiliary variable by a main variable and a main variable update process for updating the main variable by the auxiliary variable multiple times. Prior to the search process, the host unit transmits, to the Ising machine, an initial value of the auxiliary variable corresponding to each of the plurality of Ising spins.
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公开(公告)号：US11714936B2
公开(公告)日：2023-08-01
申请号：US17152831
申请日：2021-01-20
Applicant: FUJITSU LIMITED
Inventor： Daisuke Kushibe , Hirotaka Tamura
Abstract: An optimization method executed by a computer upon attempting to solve a ground state of an Ising model by simulating a state change of the Ising model when a magnetic field applied to the Ising model is reduced, the Ising model representing a problem to be solved, the method including: executing a first process, the first process being a real time propagation in which an intensity of the magnetic field is reduced with progress of time in simulation; and in response to the progress of time in the real time propagation of the first process, executing a second process, the second process including reducing energy of the Ising model based on an imaginary time propagation method.
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