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公开(公告)号：US20170068892A1
公开(公告)日：2017-03-09
申请号：US15354122
申请日：2016-11-17
Applicant: SparkCognition, Inc.
Inventor： Syed Mohammad Amir Husain
Abstract: A system and method for generating a heuristic is provided. A heuristic is capable of identifying data patterns. The method includes: extracting a data set from multiple input sources; creating a set of unique elements used across the data set; organizing the data set into a geometric structure; grouping portions of the data in the geometric structure into a plurality sub geometric structures; determining base attributes for each sub geometric structure using the set of unique elements; identifying trends in the base attributes among the sub geometric structures; and outputting the heuristic as a combination of the base attributes and the trends.
Abstract translation: 提供了一种用于生成启发式的系统和方法。 启发式能够识别数据模式。 该方法包括：从多个输入源提取数据集; 创建一组在数据集中使用的唯一元素; 将数据集组织成几何结构; 将几何结构中的数据的部分分组成多个子几何结构; 使用所述一组唯一元素来确定每个子几何结构的基本属性; 识别子几何结构之间基本属性的趋势; 并输出启发式作为基本属性和趋势的组合。
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32.发明申请公开(公告)号：US20150262060A1
公开(公告)日：2015-09-17
申请号：US14644346
申请日：2015-03-11
Applicant: SparkCognition, Inc.
Inventor： Syed Mohammad Amir Husain , Martin Andreas Abel , Qasim Iqbal
CPC classification number: G06N3/08
Abstract: An aspect of the present invention is to provide a system and method for predicting the remaining useful time of mechanical components such as bearings. Another aspect of the present invention is to provide a system and method for predicting the remaining useful time of bearings based on available condition monitoring data. Another aspect of the present invention is to provide a system and method for automatically deciding which columns of input information are the most significant for predicting the remaining useful life of bearings. Another aspect of the present invention is to provide a system and method for performing an analysis of both test bearings and training bearings and determining which training bearings are most similar to a given test bearing. Another aspect of the present invention is to provide a system and method for training an artificial neural network.
Abstract translation: 本发明的一个方面是提供一种用于预测诸如轴承的机械部件的剩余有用时间的系统和方法。 本发明的另一方面是提供一种用于基于可用状态监视数据预测轴承的剩余有用时间的系统和方法。 本发明的另一方面是提供一种用于自动地确定输入信息列对于预测轴承剩余使用寿命最重要的系统和方法。 本发明的另一方面是提供一种用于对测试轴承和训练轴承进行分析并确定哪些训练轴承与给定的测试轴承最相似的系统和方法。 本发明的另一方面是提供一种用于训练人造神经网络的系统和方法。
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公开(公告)号：US20240125609A1
公开(公告)日：2024-04-18
申请号：US18487679
申请日：2023-10-16
Applicant: SparkCognition, Inc.
Inventor： Syed Mohammad Amir Husain
CPC classification number: G01C21/3602 , G01C21/3608 , G01C21/3647 , G10L15/26
Abstract: A method includes receiving, at one or more processors of a vehicle, user speech input, the user speech input including a navigation command and a description of a photograph. The method also includes transmitting, via a local network, query data based on the user speech input to a portable computing device associated with the vehicle to initiate an image search based on the user speech input. The method further includes receiving, at the one or more processors of the vehicle from the portable computing device via the local network, location data indicating a location associated with the photograph and setting, by the one or more processors of the vehicle, a navigation waypoint based on the location data and based on the navigation command.
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公开(公告)号：US20230118340A1
公开(公告)日：2023-04-20
申请号：US18068313
申请日：2022-12-19
Applicant: SparkCognition, Inc.
Inventor： Syed Mohammad Amir Husain , Milton Lopez
Abstract: A method includes receiving, at a server, first sensor data from a first vehicle. The method includes receiving, at the server, second sensor data from a second vehicle. The second sensor data includes condition data indicating a road condition. The method includes aggregating, at the server, a plurality of sensor readings to generate aggregated sensor data. The plurality of sensor readings include the first sensor data and the second sensor data. The method further includes transmitting a first message based on the aggregated sensor data to the first vehicle, wherein the first message causes the first vehicle to perform a first action, the first action comprising avoiding the road condition, displaying an indicator corresponding to the engine problem, displaying a booked route, or a combination thereof.
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35.发明申请公开(公告)号：US20220077879A1
公开(公告)日：2022-03-10
申请号：US17467975
申请日：2021-09-07
Applicant: SparkCognition, Inc.
Inventor： Syed Mohammad Amir Husain
Abstract: A method includes determining, based at least in part on parameters of a software defined radio (SDR), waveform data descriptive of an electromagnetic waveform. The method also includes generating feature data based on the waveform data. The method further includes providing the feature data as input to a first machine learning model to predict a future action of a device associated with at least a portion of the electromagnetic waveform and initiating a response action based on the predicted future action.
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Patent Agency Ranking
公开(公告)号：US10963790B2
公开(公告)日：2021-03-30
申请号：US15582496
申请日：2017-04-28
Applicant: SparkCognition, Inc.
Inventor： Sari Andoni , Keith D. Moore , Syed Mohammad Amir Husain
Abstract: A method includes receiving input that identifies one or more data sources and determining, based on the input, a machine learning problem type of a plurality of machine learning problem types supported by an automated model building (AMB) engine. The method also includes generating an input data set of the AMB engine based on application of one or more rules to the one or more data sources. The method further includes, based on the input data set and the machine learning problem type, initiating execution of the AMB engine to generate a neural network configured to model at least a portion of the input data set.
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37.发明申请公开(公告)号：US20190122119A1
公开(公告)日：2019-04-25
申请号：US15793890
申请日：2017-10-25
Applicant: SparkCognition, Inc.
Inventor： Syed Mohammad Amir Husain
CPC classification number: G06N3/086 , G06N3/0454
Abstract: A method includes determining, by a processor of a computing device, an expected performance or reliability of a first neural network of a first plurality of neural networks. The expected performance or reliability is determined based on a vector representing at least a portion of the first neural network, where the first neural network is generated based on an automated generative technique (e.g., a genetic algorithm) and where the first plurality of neural networks corresponds to a first epoch of the automated generative technique. The method also includes responsive to the expected performance or reliability of the first neural network failing to satisfy a threshold, adjusting a parameter of the automated generative technique. The method further includes, during a second epoch of the automated generative technique, generating a second plurality of neural networks based at least in part on the adjusted parameter.
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公开(公告)号：US20190082015A1
公开(公告)日：2019-03-14
申请号：US15704991
申请日：2017-09-14
Applicant: SparkCognition, Inc.
Inventor： Syed Mohammad Amir Husain , John Rutherford Allen
Abstract: A distributed sensor module system comprises a plurality of sensor modules configured to be aerially deployable from a deployment device, the deployment device including an unmanned aerial vehicle (UAV) or an aeronautically deployable unitized container, the plurality of sensor modules configured to communicate with each other. A first sensor module comprises a first sensor configured to obtain first sensor information from a first environment proximate to the first sensor, a processor coupled to the first sensor, the processor configured to process the first sensor information to obtain locally processed first sensor information, and a communication transceiver coupled to the processor, the communication transceiver configured to communicate the locally processed first sensor information to a second sensor module, the first sensor module and the second sensor module configured to be aerially deployable.
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公开(公告)号：US12028095B2
公开(公告)日：2024-07-02
申请号：US17467922
申请日：2021-09-07
Applicant: SparkCognition, Inc.
Inventor： Syed Mohammad Amir Husain
CPC classification number: H04B1/0003 , G06N20/00 , H04B1/713 , H04K3/25 , G06F2218/10
Abstract: A method includes determining, based at least in part on parameters of a software-defined radio (SDR), waveform data descriptive of an electromagnetic waveform. The method also includes generating feature data based on the waveform data and based on one or more symbols decoded from the electromagnetic waveform. The method further includes providing the feature data as input to a first machine-learning model and initiating a response action based on an output of the first machine-learning model.
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公开(公告)号：US20240095535A1
公开(公告)日：2024-03-21
申请号：US18520192
申请日：2023-11-27
Applicant: SparkCognition, Inc.
Inventor： Syed Mohammad Amir Husain
Abstract: A method includes selecting a subset of models from a plurality of models. The plurality of models is generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method includes performing at least one genetic operation of the genetic algorithm with respect to at least one model of the subset to generate a trainable model. The method includes determining a rate of improvement associated with prior backpropagation iterations. The method includes selecting, based on the rate of improvement, one of the trainable model or a prior trainable model as a selected trainable model. The method includes generating the trained model including training the selected trainable model. The method includes adding the trained model as input to a second epoch of the genetic algorithm that is subsequent to the first epoch.
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