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1. 发明授权

US10783801B1 Simulation based training system for measurement of team cognitive load to automatically customize simulation content 有权
公开(公告)号：US10783801B1
公开(公告)日：2020-09-22
申请号：US15850998
申请日：2017-12-21
申请人： Aptima, Inc.
发明人： Jeffrey Beaubien , John J. Feeney , William N. DePriest , Scott M. Pappada
IPC分类号： A61B5/16 , G09B9/00 , G09B7/10 , A61B5/0476 , A61B5/0245 , G09B19/00
摘要： In one example embodiment of the invention, a simulation based training system is provided having a sensor that unobtrusively collects objective data for individuals and teams experiencing training content to determine the cognitive states of individuals and teams; time-synchronizes the various data streams; automatically determines granular and objective measures for individual cognitive load (CL) of individuals and teams; and automatically determines a cognitive load balance (CLB) and a relative cognitive load (RCL) measure in real or near-real time. Data is unobtrusively gathered through physiological or other activity sensors such as electroencephalogram (EEG) and electrocardiogram (ECG) sensors. Some embodiments are further configured to also include sociometric data in the determining cognitive load. Sociometric data may be obtained through the use of sociometric badges. Some embodiments further automatically customize the simulation content by automatically selecting content based on the CL of the individuals and teams.

2. 发明申请

US20180212834A1 SYSTEMS AND METHODS FOR NETWORK PATTERN MATCHING 审中-公开
公开(公告)号：US20180212834A1
公开(公告)日：2018-07-26
申请号：US15899607
申请日：2018-02-20
申请人： Aptima, Inc.
发明人： Georgiy Levchuk , E. Webb Stacy , Charlotte Shabarekh
IPC分类号： H04L12/24 , G06Q50/00
CPC分类号： H04L41/12 , G06Q50/01 , H04L41/14 , H04L41/142
摘要： Example embodiments of systems and methods for network pattern matching provide the ability to match hidden networks from noisy data sources using probabilistic matching analysis. The algorithms may map roles and patterns to observed entities. The outcome is a set of plausible network models. The pattern-matching methodology of these systems and methods may enable the solution of three challenges associated with social network analysis, namely network size and complexity, uncertain and incomplete data, and dynamic network structure.

3. 发明授权

US09594825B2 Systems and methods for analyzing entity profiles 有权
标题翻译：用于分析实体简档的系统和方法
公开(公告)号：US09594825B2
公开(公告)日：2017-03-14
申请号：US14809231
申请日：2015-07-26
申请人： Aptima, Inc.
发明人： Bruce Skarin , Andrew Duchon , Richard Alexis DeJordy , Paul David Allopenna
IPC分类号： G06F17/30 , G06Q10/10
CPC分类号： G06F17/30604 , G06F17/30312 , G06Q10/10
摘要： Embodiments of the subject invention comprise a computer based system and methods to collect and compare the attributes of a group of entities using data representing topic data of the entity and interaction data between entities. Embodiments of the invention comprise using minimally invasive means to automatically collect and model both an entity's attributes such as their knowledge/work/interest as well as model the social interactions of the entity together with a means to identify opportunities to influence changes in the entity attributes. Minimally invasive means to collect and model attributes include semantic analysis and topic modeling techniques. Means to model social interactions include social network analysis techniques that can incorporate location data of the entity. Embodiments of the invention further provide a sharable index of the attributes of the entities and the group of entities.
摘要翻译：本发明的实施例包括基于计算机的系统和使用表示实体的主题数据的数据的数据和实体之间的交互数据来收集和比较一组实体的属性的方法。本发明的实施例包括使用微创手段来自动地收集和模拟实体的属性，例如他们的知识/工作/兴趣，以及模拟实体的社交交互以及识别影响实体属性变化的机会的手段。收集和建模属性的微创手段包括语义分析和主题建模技术。模拟社会互动的手段包括可以结合实体的位置数据的社交网络分析技术。本发明的实施例还提供了实体和实体组的属性的可共享索引。

4. 发明申请

US20160007899A1 SYSTEMS AND METHODS TO DETERMINE USER STATE 审中-公开
标题翻译：确定用户状态的系统和方法
公开(公告)号：US20160007899A1
公开(公告)日：2016-01-14
申请号：US14772827
申请日：2014-03-13
申请人： Kevin DURKEE , APTIMA, INC.
发明人： Kevin Durkee , Scott Pappada , Andres Ortiz , William DePriest , John Feeney , Alexandra Geyer , Seamus Sullivan , Sterling Wiggins
IPC分类号： A61B5/16 , A61B5/0476 , A61B5/04 , A61B5/00
CPC分类号： A61B5/165 , A61B5/04012 , A61B5/0476 , A61B5/7203 , A61B5/7246 , A61B5/7267 , A61B5/7475 , G09B23/28 , G16H50/30
摘要： Computer based systems and methods for estimating a user state are disclosed. In some embodiments, the methods comprise inputting a first input at an intermittent interval and a second input at a frequent interval into a user state estimation model to estimate the user state. In some embodiments, the first inputs are enhanced by injecting a noise input to create a plurality of enhanced first inputs whereby the plurality of enhance first inputs correspond to the plurality of second inputs at the frequent interval. In some embodiments, the first input comprises a self-reported input and the second inputs comprise a physiological input, a performance input or a situational input. In some embodiments, a machine learning algorithm creates the state estimation model. In some embodiments, the state estimation model estimates a future user state. In some embodiments, a computer based system for estimating a user state is provided.
摘要翻译：公开了用于估计用户状态的基于计算机的系统和方法。在一些实施例中，所述方法包括以间歇间隔输入第一输入和频繁间隔的第二输入到用户状态估计模型中以估计用户状态。在一些实施例中，通过注入噪声输入以产生多个增强的第一输入来增强第一输入，由此多个增强的第一输入以频繁间隔对应于多个第二输入。在一些实施例中，第一输入包括自报告输入，第二输入包括生理输入，性能输入或情境输入。在一些实施例中，机器学习算法创建状态估计模型。在一些实施例中，状态估计模型估计未来的用户状态。在一些实施例中，提供了一种用于估计用户状态的基于计算机的系统。

5. 发明申请

US20190143215A1 SYSTEMS AND METHODS FOR SCENARIO GENERATION AND MONITORING 审中-公开
公开(公告)号：US20190143215A1
公开(公告)日：2019-05-16
申请号：US16198257
申请日：2018-11-21
申请人： Aptima, Inc.
发明人： E. Webb Stacy , Kevin Sullivan , Paul Picciano , Can Keskin
IPC分类号： A63F13/60 , G06F9/54 , G09B9/08
摘要： Processor based systems and methods of defining a scenario event comprising the steps of identifying an event having an event attribute and generalizing the event attribute to define a generalized event whereby the generalized event is the scenario event. In some embodiments, the steps further comprise identifying a first and second event, generalizing a first and second event attribute to define a first and second generalized event and connecting the first and second generalized event in a continuous envelope to create a scenario envelope. Processor based systems and methods of monitoring an activity comprising the steps of monitoring an activity having an activity attribute and comparing the activity attribute to an event envelope to determine a status of the activity relative to the event envelope.

6. 发明申请

US20190108459A1 SYSTEMS AND METHODS OF COGNITIVE PATTERNS KNOWLEDGE GENERATION 审中-公开
公开(公告)号：US20190108459A1
公开(公告)日：2019-04-11
申请号：US16196591
申请日：2018-11-20
申请人： Aptima, Inc.
发明人： E. Webb Stacy , Alexandra Geyer
IPC分类号： G06N99/00 , G06N5/02 , G06N5/04
摘要： A processor based system and method of generating new pattern knowledge of a sensory input is disclosed. The system comprises a memory to store abstract patterns as pattern knowledge, a sensor configured to receive a concrete pattern representing a sensory input, an adaption module configured to horizontally blended abstract patterns to create a horizontally blended abstract pattern as the new pattern knowledge if a degree of blending the abstract and concrete patterns exceeds a threshold. In some embodiments, the horizontally blended abstract pattern comprises a selective projection of parameters of two abstract patterns. In some embodiments, the systems and methods further comprise vertically blending the abstract pattern and the concrete pattern by selectively projecting parameters of an abstract pattern with the parameters of the concrete pattern.

7. 发明申请

US20180225361A1 SYSTEMS AND METHODS FOR ANALYZING ENTITY PROFILES 审中-公开
公开(公告)号：US20180225361A1
公开(公告)日：2018-08-09
申请号：US15452781
申请日：2017-03-08
申请人： Aptima, Inc.
发明人： Bruce Skarin , Andrew Duchon , Richard Alexis DeJordy , Paul David Allopenna
IPC分类号： G06F17/30 , G06Q10/10
CPC分类号： G06F16/288 , G06F16/22 , G06Q10/10
摘要： Embodiments of the subject invention comprise a computer based system and methods to collect and compare the attributes of a group of entities using data representing topic data of the entity and interaction data between entities. Embodiments of the invention comprise using minimally invasive means to automatically collect and model both an entity's attributes such as their knowledge/work/interest as well as model the social interactions of the entity together with a means to identify opportunities to influence changes in the entity attributes. Minimally invasive means to collect and model attributes include semantic analysis and topic modeling techniques. Means to model social interactions include social network analysis techniques that can incorporate location data of the entity. Embodiments of the invention further provide a sharable index of the attributes of the entities and the group of entities.

8. 发明申请

US20140195475A1 PROBABILISTIC DECISION MAKING SYSTEM AND METHODS OF USE 审中-公开
标题翻译：概率决策制定系统及其使用方法
公开(公告)号：US20140195475A1
公开(公告)日：2014-07-10
申请号：US14143410
申请日：2013-12-30
申请人： WRIGHT STATE UNIVERSITY , Aptima, Inc.
发明人： Georgiy Levchuk , Jared Freeman , Wayne Shebilske
IPC分类号： G06N7/00
CPC分类号： G06N7/005 , G06Q50/20
摘要： Embodiments of this invention comprise modeling a subject's state and the influence of training treatments, or actions, on that state to create a training policy. Both state and effects of actions are modeled as probabilistic using Partially Observable Markov Decision Process (POMDP) techniques. Utilizing this model and the resulting training policy with subjects creates an effective decision aid for instructors to improve learning relative to a traditional scenario selection strategy.
摘要翻译：本发明的实施例包括对受试者的状态进行建模，以及训练治疗或动作对该状态的影响以创建训练策略。动态的状态和效果都使用部分可观测马尔可夫决策过程（POMDP）技术进行建模。利用这一模式和由此产生的培训策略为教师创造了有效的决策辅助，以提高相对于传统场景选择策略的学习。

9. 发明授权

US11532241B1 Simulation based training system for measurement of cognitive load to automatically customize simulation content 有权
公开(公告)号：US11532241B1
公开(公告)日：2022-12-20
申请号：US17024325
申请日：2020-09-17
申请人： Aptima, Inc.
发明人： Jeffrey Beaubien , John Feeney , William N. DePriest , Scott Pappada
IPC分类号： A61B5/16 , G09B9/00 , G09B7/10 , G09B19/00 , A61B5/0245 , A61B5/369
摘要： In one example embodiment of the invention, a simulation based training system is provided having a sensor that unobtrusively collects objective data for individuals and teams experiencing training content to determine the cognitive states of individuals and teams; time-synchronizes the various data streams; automatically determines granular and objective measures for individual cognitive load (CL) of individuals and teams; and automatically determines a cognitive load balance (CLB) and a relative cognitive load (RCL) measure in real or near-real time. Data is unobtrusively gathered through physiological or other activity sensors such as electroencephalogram (EEG) and electrocardiogram (ECG) sensors. Some embodiments are further configured to also include sociometric data in the determining cognitive load. Sociometric data may be obtained through the use of sociometric badges. Some embodiments further automatically customize the simulation content by automatically selecting content based on the CL of the individuals and teams.

10. 发明授权

US11464456B2 Systems and methods to support medical therapy decisions 有权
公开(公告)号：US11464456B2
公开(公告)日：2022-10-11
申请号：US15739460
申请日：2016-08-07
申请人： Aptima, Inc.
发明人： Scott M. Pappada , John J. Feeney , William N. DePriest
IPC分类号： A61B5/00 , G16H50/20 , G16H50/30 , G16H50/50 , G16H10/60 , A61B5/0205 , G16H20/40 , G16H80/00
摘要： Systems and methods for supporting medical therapy decisions are disclosed that utilize predictive models and electronic medical records (EMR) data to provide predictions of health conditions over varying time horizons. Embodiments also determine a 0-100 health risk index value that represents the “risk” for a patient to acquire a health condition based on a combination of real-time and predicted EMR data. The systems and methods receive EMR data and use the predictive models to predict one or more data values from the EMR data as diagnostic criteria. In some embodiments, the health condition trying to be avoided is Sepsis and the health risk index is a Sepsis Risk Index (SRI). In some embodiments, the predictive models are neural network models such as time delay neural networks.

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