公开(公告)号：US11934186B2

公开(公告)日：2024-03-19

申请号：US18370362

申请日：2023-09-19

申请人： STRONG FORCE TP PORTFOLIO 2022, LLC

发明人： Charles Howard Cella

IPC分类号： G07C5/08 ,  B60W40/08 ,  G01C21/34 ,  G01C21/36 ,  G05B13/02 ,  G05D1/00 ,  G06F40/40 ,  G06N3/04 ,  G06N3/045 ,  G06N3/08 ,  G06N3/086 ,  G06N20/00 ,  G06Q30/0208 ,  G06Q50/18 ,  G06V20/59 ,  G06V20/64 ,  G07C5/00 ,  G07C5/02 ,  G10L15/16 ,  G10L25/63 ,  G06N3/02 ,  G06Q30/02 ,  G06Q50/00

CPC分类号： G05D1/0022 ,  B60W40/08 ,  G01C21/3438 ,  G01C21/3461 ,  G01C21/3469 ,  G01C21/3617 ,  G05B13/027 ,  G05D1/0088 ,  G05D1/0212 ,  G05D1/0287 ,  G05D1/224 ,  G05D1/225 ,  G05D1/226 ,  G05D1/227 ,  G05D1/228 ,  G05D1/229 ,  G05D1/24 ,  G05D1/646 ,  G05D1/692 ,  G06F40/40 ,  G06N3/0418 ,  G06N3/045 ,  G06N3/08 ,  G06N3/086 ,  G06N20/00 ,  G06Q30/0208 ,  G06Q50/188 ,  G06Q50/40 ,  G06V20/59 ,  G06V20/64 ,  G07C5/006 ,  G07C5/008 ,  G07C5/02 ,  G07C5/08 ,  G07C5/0808 ,  G07C5/0816 ,  G10L15/16 ,  G10L25/63 ,  B60W2040/0881 ,  G06N3/02 ,  G06Q30/0281 ,  G06Q50/01

摘要： Vehicles and methods described herein include a vehicle that operates with a rider according to an operating parameter. The vehicle includes: a physiological monitoring sensor configured to measure a physiological parameter of the rider; an experience hybrid neural network trained on outcomes related to a rider in-vehicle experience associated with the physiological parameter to determine an emotional state of the rider; an augmented reality system configured to present augmented reality content to the rider of the vehicle based, at least in part, on the operating parameter; and an optimization hybrid neural network that identifies a variation in the operating parameter to change the emotional state of the rider and that generates a command to vary the operating parameter and the augmented reality content according to the variation.

公开(公告)号：US11868127B2

公开(公告)日：2024-01-09

申请号：US17976839

申请日：2022-10-30

申请人： STRONG FORCE TP PORTFOLIO 2022, LLC

发明人： Charles Howard Cella

IPC分类号： G05D1/00 ,  G06N20/00 ,  G06N3/086 ,  G07C5/00 ,  G07C5/08 ,  G01C21/34 ,  G01C21/36 ,  G06Q30/0208 ,  G10L15/16 ,  G10L25/63 ,  G06V20/59 ,  G06F40/40 ,  G06V20/64 ,  B60W40/08 ,  G05B13/02 ,  G05D1/02 ,  G06N3/04 ,  G06N3/08 ,  G06Q50/18 ,  G06Q50/30 ,  G07C5/02 ,  G06N3/045 ,  G06N3/02 ,  G06Q30/02 ,  G06Q50/00

CPC分类号： G05D1/0022 ,  B60W40/08 ,  G01C21/3438 ,  G01C21/3461 ,  G01C21/3469 ,  G01C21/3617 ,  G05B13/027 ,  G05D1/0088 ,  G05D1/0212 ,  G05D1/0287 ,  G06F40/40 ,  G06N3/045 ,  G06N3/0418 ,  G06N3/08 ,  G06N3/086 ,  G06N20/00 ,  G06Q30/0208 ,  G06Q50/188 ,  G06Q50/30 ,  G06V20/59 ,  G06V20/64 ,  G07C5/006 ,  G07C5/008 ,  G07C5/02 ,  G07C5/08 ,  G07C5/0808 ,  G07C5/0816 ,  G10L15/16 ,  G10L25/63 ,  B60W2040/0881 ,  G05D2201/0213 ,  G06N3/02 ,  G06Q30/0281 ,  G06Q50/01

摘要： A transportation system includes an artificial intelligence (AI) system for processing a sensory input from a wearable device in a self-driving vehicle to determine an emotional state of a rider and optimizing a vehicle operating parameter to improve the rider emotional state. The AI system includes a recurrent neural network to indicate a change in the emotional state of the rider by a recognition of patterns of emotional state indicative wearable sensor data from a set of wearable sensors worn by the rider. The patterns are indicative of a first degree of a favorable emotional state of the rider and/or a second degree of an unfavorable emotional state of the rider. The AI system further includes a radial basis function neural network to optimize, for achieving a target emotional state of the rider, the vehicle operating parameter in response to the indication of the change in the rider emotional state.

公开(公告)号：US11854401B2

公开(公告)日：2023-12-26

申请号：US18067176

申请日：2022-12-16

申请人： NVIDIA Corporation

发明人： Yue Wu ,  Pekka Janis ,  Xin Tong ,  Cheng-Chieh Yang ,  Minwoo Park ,  David Nister

IPC分类号： G08G1/16 ,  G06V10/82 ,  G06V20/58 ,  G06V20/10 ,  G06F18/214 ,  G05D1/00 ,  G05D1/02 ,  G06N3/04 ,  G06T7/20

CPC分类号： G08G1/166 ,  G05D1/0088 ,  G05D1/0289 ,  G06F18/214 ,  G06N3/0418 ,  G06T7/20 ,  G06V10/82 ,  G06V20/10 ,  G06V20/58 ,  G05D2201/0213

摘要： In various examples, a sequential deep neural network (DNN) may be trained using ground truth data generated by correlating (e.g., by cross-sensor fusion) sensor data with image data representative of a sequences of images. In deployment, the sequential DNN may leverage the sensor correlation to compute various predictions using image data alone. The predictions may include velocities, in world space, of objects in fields of view of an ego-vehicle, current and future locations of the objects in image space, and/or a time-to-collision (TTC) between the objects and the ego-vehicle. These predictions may be used as part of a perception system for understanding and reacting to a current physical environment of the ego-vehicle.

公开(公告)号：US20230385186A1

公开(公告)日：2023-11-30

申请号：US18321545

申请日：2023-05-22

申请人： General Electric Company

发明人： Hema K Achanta ,  Masoud Abbaszadeh ,  Weizhong Yan ,  Mustafa Tekin Dokucu

IPC分类号： G06F11/36 ,  H04L9/40 ,  H04L41/16 ,  G06N3/08 ,  G06F18/24 ,  G06F18/214 ,  H04L43/12 ,  G06V10/82 ,  G06F11/263 ,  H04W12/128

CPC分类号： G06F11/3684 ,  H04L63/1441 ,  H04L41/16 ,  G06N3/08 ,  G06F18/24 ,  G06F18/214 ,  H04L43/12 ,  G06V10/82 ,  G06F11/263 ,  H04W12/128 ,  G06N3/0418

摘要： According to some embodiments, a system, method and non-transitory computer-readable medium are provided to protect a cyber-physical system having a plurality of monitoring nodes comprising: a normal space data source storing, for each of the plurality of monitoring nodes, a series of normal monitoring node values over time that represent normal operation of the cyber-physical system; a situational awareness module including an abnormal data generation platform, wherein the abnormal data generation platform is operative to generate abnormal data to represent abnormal operation of the cyber-physical system using values in the normal space data source and a generative model; a memory for storing program instructions; and a situational awareness processor, coupled to the memory, and in communication with the situational awareness module and operative to execute the program instructions to: receive a data signal, wherein the received data signal is an aggregation of data signals received from one or more of the plurality of monitoring nodes, wherein the data signal includes at least one real-time stream of data source signal values that represent a current operation of the cyber-physical system; determine, via a trained classifier, whether the received data signal is a normal signal or an abnormal signal, wherein the trained classifier is trained with the generated abnormal data and normal data; localize an origin of an anomaly when it is determined the received data signal is the abnormal signal; receive the determination and localization at a resilient estimator module; execute the resilient estimator module to generate a state estimation for the cyber-physical system. Numerous other aspects are provided.

公开(公告)号：US20230333553A1

公开(公告)日：2023-10-19

申请号：US18340255

申请日：2023-06-23

申请人： NIVIDIA Corporation

发明人： Minwoo Park ,  Xiaoin Lin ,  Hae-Jong Seo ,  David Nister ,  Neda Cvijetic

IPC分类号： G05D1/00 ,  G06N3/04 ,  G06V20/56 ,  G06F18/214 ,  G06F18/23 ,  G06F18/2411 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G06V10/44 ,  G06V10/48 ,  G06V10/94 ,  G06V10/75

CPC分类号： G05D1/0077 ,  G06N3/0418 ,  G05D1/0088 ,  G06V20/588 ,  G06F18/2155 ,  G06F18/23 ,  G06F18/2411 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G06V10/457 ,  G06V10/48 ,  G06V10/955 ,  G06V10/751 ,  G05D2201/0213

摘要： In various examples, systems and methods are disclosed that preserve rich spatial information from an input resolution of a machine learning model to regress on lines in an input image. The machine learning model may be trained to predict, in deployment, distances for each pixel of the input image at an input resolution to a line pixel determined to correspond to a line in the input image. The machine learning model may further be trained to predict angles and label classes of the line. An embedding algorithm may be used to train the machine learning model to predict clusters of line pixels that each correspond to a respective line in the input image. In deployment, the predictions of the machine learning model may be used as an aid for understanding the surrounding environment—e.g., for updating a world model—in a variety of autonomous machine applications.

公开(公告)号：US11782435B2

公开(公告)日：2023-10-10

申请号：US16803356

申请日：2020-02-27

申请人： STRONG FORCE TP PORTFOLIO 2022, LLC

发明人： Charles Howard Cella

IPC分类号： G05D1/00 ,  G05D1/02 ,  G06N20/00 ,  G06N3/08 ,  G07C5/08 ,  G07C5/00 ,  G06N3/086 ,  G01C21/34 ,  G01C21/36 ,  G06Q30/0208 ,  G10L15/16 ,  G10L25/63 ,  G06V20/59 ,  G06F40/40 ,  G06V20/64 ,  B60W40/08 ,  G05B13/02 ,  G06N3/04 ,  G06Q50/18 ,  G06Q50/30 ,  G07C5/02 ,  G06N3/045 ,  G06N3/02 ,  G06Q30/02 ,  G06Q50/00

CPC分类号： G05D1/0022 ,  B60W40/08 ,  G01C21/3438 ,  G01C21/3461 ,  G01C21/3469 ,  G01C21/3617 ,  G05B13/027 ,  G05D1/0088 ,  G05D1/0212 ,  G05D1/0287 ,  G06F40/40 ,  G06N3/045 ,  G06N3/0418 ,  G06N3/08 ,  G06N3/086 ,  G06N20/00 ,  G06Q30/0208 ,  G06Q50/188 ,  G06Q50/30 ,  G06V20/59 ,  G06V20/64 ,  G07C5/006 ,  G07C5/008 ,  G07C5/02 ,  G07C5/08 ,  G07C5/0808 ,  G07C5/0816 ,  G10L15/16 ,  G10L25/63 ,  B60W2040/0881 ,  G05D2201/0213 ,  G06N3/02 ,  G06Q30/0281 ,  G06Q50/01

摘要： Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

公开(公告)号：US11763213B2

公开(公告)日：2023-09-19

申请号：US16692711

申请日：2019-11-22

申请人： Strong Force TX Portfolio 2018, LLC

发明人： Charles Howard Cella

IPC分类号： G06Q30/02 ,  G06N20/00 ,  G06N5/04 ,  G06Q50/06 ,  G06Q10/04 ,  G06Q30/0201 ,  G06F16/23 ,  G06Q30/0202 ,  G06F9/46 ,  G06F9/50 ,  G06N3/08 ,  G06Q10/0631 ,  G06Q10/067 ,  G06Q40/04 ,  G06Q50/18 ,  G06Q20/22 ,  G06Q20/38 ,  G06Q30/0241 ,  G06Q30/0273 ,  H02J3/00 ,  H02J3/28 ,  G06F21/10 ,  G06Q20/40 ,  G06F9/48 ,  G06Q20/36 ,  H04L47/78 ,  G06N3/02 ,  G06Q20/06 ,  G06F16/951 ,  G06F9/38 ,  H04L47/783 ,  G06Q30/0204 ,  G06Q40/10 ,  G06F16/24 ,  G06Q50/04 ,  H04L9/00 ,  H04L12/14 ,  H04L47/70 ,  G06F16/18 ,  H02J3/38 ,  G05B19/00 ,  G05B19/418 ,  G06F9/54 ,  G06Q30/06 ,  G06F18/214 ,  H02J3/14 ,  G06F16/27 ,  G06F16/182 ,  G06F30/27 ,  G06N3/04 ,  G06Q50/00 ,  H04L9/06 ,  G06F16/2457 ,  G06Q30/0251 ,  G06N3/044 ,  G06N3/047 ,  H04L67/12

CPC分类号： G06Q10/04 ,  G05B19/00 ,  G05B19/4188 ,  G05B19/41865 ,  G06F9/3836 ,  G06F9/3891 ,  G06F9/466 ,  G06F9/4806 ,  G06F9/4881 ,  G06F9/50 ,  G06F9/5005 ,  G06F9/5016 ,  G06F9/5027 ,  G06F9/5072 ,  G06F9/541 ,  G06F16/182 ,  G06F16/1865 ,  G06F16/23 ,  G06F16/2365 ,  G06F16/2379 ,  G06F16/24 ,  G06F16/27 ,  G06F16/951 ,  G06F18/2148 ,  G06F18/2155 ,  G06F21/105 ,  G06F30/27 ,  G06N3/02 ,  G06N3/04 ,  G06N3/08 ,  G06N5/04 ,  G06N20/00 ,  G06Q10/067 ,  G06Q10/0631 ,  G06Q10/06314 ,  G06Q10/06315 ,  G06Q20/06 ,  G06Q20/065 ,  G06Q20/0655 ,  G06Q20/29 ,  G06Q20/367 ,  G06Q20/389 ,  G06Q20/38215 ,  G06Q20/405 ,  G06Q20/4016 ,  G06Q30/0201 ,  G06Q30/0202 ,  G06Q30/0205 ,  G06Q30/0206 ,  G06Q30/0247 ,  G06Q30/0273 ,  G06Q30/06 ,  G06Q40/04 ,  G06Q40/10 ,  G06Q50/04 ,  G06Q50/06 ,  G06Q50/184 ,  H02J3/008 ,  H02J3/14 ,  H02J3/28 ,  H02J3/388 ,  H04L9/50 ,  H04L12/14 ,  H04L47/783 ,  H04L47/788 ,  H04L47/823 ,  G05B2219/36542 ,  G06F9/3838 ,  G06F16/2457 ,  G06N3/044 ,  G06N3/047 ,  G06N3/0418 ,  G06Q20/4015 ,  G06Q30/0254 ,  G06Q30/0276 ,  G06Q50/01 ,  G06Q2220/00 ,  G06Q2220/12 ,  G06Q2220/18 ,  H02J3/003 ,  H04L9/0643 ,  H04L67/12

摘要： Systems and methods for forward market renewable energy credit prediction from business entity behavior data are disclosed. An example transaction-enabling system may include a forward market circuit to access a forward energy credit market and a market forecasting circuit to automatically generate a forecast for a forward market price of an energy credit in the forward energy credit market. The example system may include wherein the forecast is based at least in part on a business entity behavior collected from at least one business entity behavioral data source, and wherein the energy credit comprises a renewable energy credit associated a renewable energy system. The example system may further include a smart contract circuit to perform at least one of selling the renewable energy credit or purchasing the renewable energy credit on the forward energy credit market in response to the forecasted forward market price.

公开(公告)号：US11721095B2

公开(公告)日：2023-08-08

申请号：US17992815

申请日：2022-11-22

申请人： RAYTHEON BBN TECHNOLOGIES CORP.

发明人： Kin Chung Fong ,  Man-Hung Siu ,  Zhuolin Jiang

IPC分类号： G06V10/82 ,  G06N3/04 ,  C01B32/19 ,  G06F18/24 ,  G06V20/69

CPC分类号： G06V10/82 ,  C01B32/19 ,  G06F18/24 ,  G06N3/0418 ,  G06V20/698

摘要： A method for classifying images of oligolayer exfoliation attempts. In some embodiments, the method includes forming a micrograph of a surface, and classifying the micrograph into one of a plurality of categories. The categories may include a first category, consisting of micrographs including at least one oligolayer flake, and a second category, consisting of micrographs including no oligolayer flakes, the classifying comprising classifying the micrograph with a neural network.

公开(公告)号：US20230176567A1

公开(公告)日：2023-06-08

申请号：US17977972

申请日：2022-10-31

申请人： STRONG FORCE TP PORTFOLIO 2022, LLC

发明人： Charles Howard Cella

IPC分类号： G05D1/00 ,  G06N20/00 ,  G06N3/086 ,  G07C5/00 ,  G07C5/08 ,  G01C21/34 ,  G01C21/36 ,  G06Q30/0208 ,  G10L15/16 ,  G10L25/63 ,  G06V20/59 ,  G06F40/40 ,  G06V20/64 ,  B60W40/08 ,  G05B13/02 ,  G05D1/02 ,  G06N3/04 ,  G06N3/08 ,  G06Q50/18 ,  G06Q50/30 ,  G07C5/02 ,  G06N3/045

CPC分类号： G05D1/0022 ,  B60W40/08 ,  G01C21/3438 ,  G01C21/3461 ,  G01C21/3469 ,  G01C21/3617 ,  G05B13/027 ,  G05D1/0088 ,  G05D1/0212 ,  G05D1/0287 ,  G06F40/40 ,  G06N3/08 ,  G06N3/045 ,  G06N3/086 ,  G06N3/0418 ,  G06N20/00 ,  G06Q30/0208 ,  G06Q50/30 ,  G06Q50/188 ,  G06V20/59 ,  G06V20/64 ,  G07C5/02 ,  G07C5/006 ,  G07C5/008 ,  G07C5/08 ,  G07C5/0808 ,  G07C5/0816 ,  G10L15/16 ,  G10L25/63 ,  B60W2040/0881 ,  G05D2201/0213 ,  G06N3/02

摘要： A system for transportation includes a vehicle occupied by a rider, and an artificial intelligence system for processing a voice of the rider to classify an emotional state of the rider and optimizing at least one operating parameter of the vehicle to improve the emotional state of the rider.

公开(公告)号：US09904897B2

公开(公告)日：2018-02-27

申请号：US14672309

申请日：2015-03-30

申请人： International Business Machines Corporation

发明人： Gokul B. Alex ,  Vijay Kumar Ananthapur Bache ,  Felice Mansi ,  Rengia R. Vasudevan

IPC分类号： G06Q10/00 ,  G06Q10/06 ,  G06N3/04 ,  G06Q50/00

CPC分类号： G06Q10/0637 ,  G06N3/0418 ,  G06Q50/01

摘要： Generating a social business insight is provided. An analysis parameter is selected. The analysis parameter represents a characteristic of a plurality of entities. A first data stream is retrieved. The first data stream is associated with a first entity of the plurality of entities and includes data from at least a CRM platform, a media platform, or a company asset database. A second data stream is retrieved. The second data stream is associated with a second entity of the plurality if entities and includes data from the media platform. A fractal analysis is performed based on the first data stream, the second data stream, and the analysis parameter. A fractal map is generated, where the fractal map depicts a relationship between the first data stream and the second data stream.