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公开(公告)号：US20200257943A1
公开(公告)日：2020-08-13
申请号：US16708166
申请日：2019-12-09
发明人： David J. Huber , Tsai-Ching Lu , Nigel D. Stepp , Aruna Jammalamadaka , Hyun J. Kim , Samuel D. Johnson
IPC分类号： G06K9/62 , G06N3/08 , G06N20/00 , G06F40/205 , G06F40/284 , G06F16/907
摘要： A method for generating human-machine hybrid predictions of answers to forecasting problems includes: parsing text of an individual forecasting problem to identify keywords; generating machine models based on the keywords; scraping data sources based on the keywords to collect scraped data relevant to the individual forecasting problem; providing the scraped data to the machine models; receiving machine predictions of answers to the individual forecasting problem from the machine models based on the scraped data; providing, by the computer system via a user interface, the scraped data to human participants; receiving, by the computer system via the user interface, human predictions of answers to the individual forecasting problem from the human participants; aggregating the machine predictions with the human predictions to generate aggregated predictions; and generating and outputting a hybrid prediction based on the aggregated predictions.
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2.发明申请公开(公告)号：US20190024493A1
公开(公告)日：2019-01-24
申请号：US16032826
申请日：2018-07-11
摘要： Described is a system for determining the current state of a drill using downhole sensors. The system includes a sensor suite mounted on a drill string proximate a drill bit and a computer mounted on the drill string proximate the sensor suite. The computer includes a trained classifier and is operable for performing operations of receiving online sensor data from the sensor suite; and classifying the drill bit as being in one of a plurality of pre-trained drill states based on the online sensor data. A drill bit controller can then be used to modify the operation of the drill bit based on the drill state classification.
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公开(公告)号：US11625562B2
公开(公告)日：2023-04-11
申请号：US16708166
申请日：2019-12-09
发明人： David J. Huber , Tsai-Ching Lu , Nigel D. Stepp , Aruna Jammalamadaka , Hyun J. Kim , Samuel D. Johnson
IPC分类号： G06F40/166 , G06K9/62 , G06F16/907 , G06N20/00 , G06F40/284 , G06F40/205 , G06N3/08 , G06F16/903 , G06F40/40 , G06Q30/0202 , G06Q30/0251 , G06Q50/00
摘要： A method for generating human-machine hybrid predictions of answers to forecasting problems includes: parsing text of an individual forecasting problem to identify keywords; generating machine models based on the keywords; scraping data sources based on the keywords to collect scraped data relevant to the individual forecasting problem; providing the scraped data to the machine models; receiving machine predictions of answers to the individual forecasting problem from the machine models based on the scraped data; providing, by the computer system via a user interface, the scraped data to human participants; receiving, by the computer system via the user interface, human predictions of answers to the individual forecasting problem from the human participants; aggregating the machine predictions with the human predictions to generate aggregated predictions; and generating and outputting a hybrid prediction based on the aggregated predictions.
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4.发明授权公开(公告)号：US11551156B2
公开(公告)日：2023-01-10
申请号：US16714068
申请日：2019-12-13
摘要： A method for computing a human-machine hybrid ensemble prediction includes: receiving an individual forecasting question (IFP); classifying the IFP into one of a plurality of canonical question topics; identifying machine models associated with the canonical question topic; for each of the machine models: receiving, from one of a plurality of human participants: a first task input including a selection of sets of training data; a second task input including selections of portions of the selected sets of training data; and a third task input including model parameters to configure the machine model; training the machine model in accordance with the first, second, and third task inputs; and computing a machine model forecast based on the trained machine model; computing an aggregated forecast from machine model forecasts computed by the machine models; and sending an alert in response to determining that the aggregated forecast satisfies a threshold condition.
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5.发明授权公开(公告)号：US11023904B1
公开(公告)日：2021-06-01
申请号：US15944267
申请日：2018-04-03
发明人： Samuel D. Johnson , Kang-Yu Ni
摘要： Described is a system for determining how opinions spread through a network. Opinion dynamics are applied to a network, each node having a corresponding opinion. Each node is described by an active state or an inactive state such that inactive nodes can update their opinions, and active nodes are fixed in their opinion at the time of activation. Inactive nodes can be influenced by both active nodes and inactive nodes. The opinion dynamics proceed in discrete time steps with an influence step for updating each inactive node's opinion, and a stochastic action step for determining whether an inactive node becomes activated. The system identifies how opinions spread through the network using the applied opinion dynamics, resulting in a set of opinion dynamics data. The opinion dynamics data is used to control information that a device or account is allowed to post to social media platform.
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公开(公告)号：US20210019840A1
公开(公告)日：2021-01-21
申请号：US16825347
申请日：2020-03-20
摘要： Described is a system for guiding opinions of users of a social media network. The system determines a number of control agents to be inserted into a population of users of the social media network. The system also determines a rate at which an expressed opinion of each control agent should change over a period of time. A control strategy is output which includes a control schedule based on the number of control agents to be inserted into the population of users and the rate at which the expressed opinion of each control agent should change over the period of time. Finally, the control schedule is deployed in the social media network.
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公开(公告)号：US11494486B1
公开(公告)日：2022-11-08
申请号：US16684382
申请日：2019-11-14
发明人： Hyun (Tiffany) J. Kim , Rajan Bhattacharyya , Samuel D. Johnson , Soheil Kolouri , Christian Lebiere , Jiejun Xu
摘要： Described is a system for continuously predicting and adapting optimal strategies for attacker elicitation. The system includes a global bot controlling processor unit and one or more local bot controlling processor units. The global bot controlling processor unit includes a multi-layer network software unit for extracting attacker features from diverse, out-of-band (OOB) media sources. The global controlling processing unit further includes an adaptive behavioral game theory (GT) software unit for determining a best strategy for eliciting identifying information from an attacker. Each local bot controlling processor unit includes a cognitive model (CM) software unit for estimating a cognitive state of the attacker and predicting attacker behavior. A generative adversarial network (GAN) software unit predicts the attacker's strategies. The global bot controlling processor unit and the one or more local bot controlling processor units coordinate to predict the attacker's next action and use the prediction to disrupt an attack.
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公开(公告)号：US11263706B2
公开(公告)日：2022-03-01
申请号：US16825347
申请日：2020-03-20
IPC分类号： G06Q10/00 , G06Q50/00 , H04L51/043 , H04L51/52
摘要： Described is a system for guiding opinions of users of a social media network. The system determines a number of control agents to be inserted into a population of users of the social media network. The system also determines a rate at which an expressed opinion of each control agent should change over a period of time. A control strategy is output which includes a control schedule based on the number of control agents to be inserted into the population of users and the rate at which the expressed opinion of each control agent should change over the period of time. Finally, the control schedule is deployed in the social media network.
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9.发明申请公开(公告)号：US20200311615A1
公开(公告)日：2020-10-01
申请号：US16714068
申请日：2019-12-13
IPC分类号： G06N20/20 , G06N7/00 , G06F40/205 , G06K9/62
摘要： A method for computing a human-machine hybrid ensemble prediction includes: receiving an individual forecasting question (IFP); classifying the IFP into one of a plurality of canonical question topics; identifying machine models associated with the canonical question topic; for each of the machine models: receiving, from one of a plurality of human participants: a first task input including a selection of sets of training data; a second task input including selections of portions of the selected sets of training data; and a third task input including model parameters to configure the machine model; training the machine model in accordance with the first, second, and third task inputs; and computing a machine model forecast based on the trained machine model; computing an aggregated forecast from machine model forecasts computed by the machine models; and sending an alert in response to determining that the aggregated forecast satisfies a threshold condition.
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公开(公告)号：US10757061B1
公开(公告)日：2020-08-25
申请号：US15680069
申请日：2017-08-17
发明人： Jiejun Xu , Samuel D. Johnson , Kang-Yu Ni
摘要： Described is a system for automated event summarization. A multi-layer network representing a multimodal data set is generated, where nodes within a given layer represent information tokens in a given modality. A topically diverse set of nodes is ranked and selected from each layer to represent temporal event highlights. Temporal event highlights are linked into storylines. Using the storylines, the system monitors a progression of an event or opinions regarding a topic. A temporal summary of the progression of the event or the opinions regarding the topic is generated.
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