公开(公告)号：US20240365280A1

公开(公告)日：2024-10-31

申请号：US18630748

申请日：2024-04-09

申请人： Glowstik, Inc.

发明人： Adam Montoya Marez

IPC分类号： H04W64/00 ,  G06N3/043 ,  H04W4/021

CPC分类号： H04W64/003 ,  G06N3/043 ,  H04W4/021

摘要： A system and method for generating amorphous randomized and/or fuzzy logic dynamic display icons that visually communicate to its users and/or entities that a subject's geographic location is being obfuscated on a graphical mapping interface of the user's computing device by way of continuously generating a dynamic location display icon having amorphous and/or asymmetrical shape, size, position, color, opacity, outline, movement, and/or transformation rate, and displaying the generated dynamic display icon on the graphical mapping interface.

公开(公告)号：US20240305467A1

公开(公告)日：2024-09-12

申请号：US18119610

申请日：2023-03-09

申请人： MY JOB MATCHER, INC. D/B/A JOB.COM

发明人： Arran Stewart

IPC分类号： H04L9/32 ,  G06N3/043

CPC分类号： H04L9/3242 ,  G06N3/043

摘要： An apparatus and method for verifying ledger data, the apparatus comprising at least a processor and a memory communicatively connected to the at least a processor. The memory including a resource block data storage configured to store a plurality of entries, wherein the plurality of entries include a plurality of resource datums. The memory also including a set of instructions configuring the at least a processor to receive a communication associated with a first resource key securing at least a first resource and verify at least a first entry of the plurality of entries as a function of the first resource key, wherein verifying the first entry further comprises classifying the communication to a first flag identifying the first resource, retrieving the first resource as a function of the first flag, and validating the first resource as a function of the first resource key.

公开(公告)号：US12055464B2

公开(公告)日：2024-08-06

申请号：US18301059

申请日：2023-04-14

申请人： PIPESENSE, LLC

发明人： Dingding Chen ,  Michael David Nash ,  Kamy Tehranchi ,  Scott Bauer ,  Mitchell Stuart

IPC分类号： G01M3/28 ,  F17D5/02 ,  G06N3/043 ,  G06N3/0464

CPC分类号： G01M3/2815 ,  F17D5/02 ,  G06N3/043 ,  G06N3/0464

摘要： Provided herein are systems and methods to detect pipeline leaks. The systems and method can identify a pipeline pressure surge by applying a trained convolutional neural network (CNN) model for classifying pipeline pressure measurement images on each sensor site of a plurality of sensor sites, transfer pressure surge information obtained from at least a portion of the plurality of sensor sites to a cloud site, and determine whether the identified pressure surge is a pipeline leak at the cloud site using the pressure surge information. The plurality of sensor sites collect pipeline pressure measurement data. The pressure surge information corresponds to the identified pipeline pressure surge.

公开(公告)号：US11977388B2

公开(公告)日：2024-05-07

申请号：US16282210

申请日：2019-02-21

申请人： NVIDIA Corporation

发明人： Jon Hasselgren ,  Jacob Munkberg

IPC分类号： G06N3/088 ,  G05B13/02 ,  G05D1/00 ,  G06N3/02 ,  G06N3/04 ,  G06N3/043 ,  G06N3/045

CPC分类号： G05D1/0231 ,  G05B13/027 ,  G05D1/0088 ,  G06N3/02 ,  G06N3/04 ,  G06N3/043 ,  G06N3/045 ,  G06N3/088

摘要： The performance of a neural network is improved by applying quantization to data at various points in the network. In an embodiment, a neural network includes two paths. A quantization is applied to each path, such that when an output from each path is combined, further quantization is not required. In an embodiment, the neural network is an autoencoder that includes at least one skip connection. In an embodiment, the system determines a set of quantization parameters based on the characteristics of the data in the primary path and in the skip connection, such that both network paths produce output data in the same fixed point format. As a result, the data from both network paths can be combined without requiring an additional quantization.

公开(公告)号：US11971921B2

公开(公告)日：2024-04-30

申请号：US17428481

申请日：2019-03-01

申请人： GOOGLE LLC

发明人： Arthur Rodrigues ,  Mindy Brooks ,  Rafael Dantas De Castro ,  Sonia Franckel ,  Bruno Diniz de Paula

IPC分类号： G06F16/00 ,  G06F9/451 ,  G06F16/2457 ,  G06F16/335 ,  G06F16/438 ,  G06F16/44 ,  G06F16/901 ,  G06F16/9535 ,  G06F16/954 ,  G06N3/00 ,  G06N3/006 ,  G06N3/043 ,  G06N20/00

CPC分类号： G06F16/438 ,  G06F9/451 ,  G06F16/24578 ,  G06F16/337 ,  G06F16/447 ,  G06F16/9024 ,  G06F16/9535 ,  G06F16/954 ,  G06N3/006 ,  G06N3/043 ,  G06N20/00

摘要： Implementations disclose comprehensibility-based identification of educational content of multiple content types. A method includes determining respective comprehensibility ranking signals for content items corresponding to a user request, the comprehensibility ranking signals based on learning attribute scores generated for the content items from at least one machine learning model, determining a learning level of a user corresponding to the user request, ranking the content items based on a mapping between the learning level and the respective comprehensibility ranking signals of the content items, and providing a recommendation for the content items according to the ranking of the content items.

公开(公告)号：US11914674B2

公开(公告)日：2024-02-27

申请号：US17543485

申请日：2021-12-06

申请人： Z Advanced Computing, Inc.

发明人： Lotfi A. Zadeh ,  Saied Tadayon ,  Bijan Tadayon

IPC分类号： G06F16/43 ,  G06N3/043 ,  G06F18/21 ,  G06F16/953 ,  G06N3/006

CPC分类号： G06F18/2185 ,  G06F16/43 ,  G06F16/953 ,  G06N3/006 ,  G06N3/043

摘要： Specification covers new algorithms, methods, and systems for: Artificial Intelligence; the first application of General-AI (versus Specific, Vertical, or Narrow-AI) (as humans can do) (which also includes Explainable-AI or XAI); addition of reasoning, inference, and cognitive layers/engines to learning module/engine/layer; soft computing; Information Principle; Stratification; Incremental Enlargement Principle; deep-level/detailed recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, tilted or partial-face, OCR, relationship, position, pattern, and object); Big Data analytics; machine learning; crowd-sourcing; classification; clustering; SVM; similarity measures; Enhanced Boltzmann Machines; Enhanced Convolutional Neural Networks; optimization; search engine; ranking; semantic web; context analysis; question-answering system; soft, fuzzy, or un-sharp boundaries/impreciseness/ambiguities/fuzziness in class or set, e.g., for language analysis; Natural Language Processing (NLP); Computing-with-Words (CWW); parsing; machine translation; music, sound, speech, or speaker recognition; video search and analysis (e.g. “intelligent tracking”, with detailed recognition); image annotation; image or color correction; data reliability; Z-Number; Z-Web; Z-Factor; rules engine; playing games; control system; autonomous vehicles or drones; self-diagnosis and self-repair robots; system diagnosis; medical diagnosis/images; genetics; drug discovery; biomedicine; data mining; event prediction; financial forecasting (e.g., for stocks); economics; risk assessment; fraud detection (e.g., for cryptocurrency); e-mail management; database management; indexing and join operation; memory management; data compression; event-centric social network; social behavior; drone/satellite vision/navigation; smart city/home/appliances/IoT; and Image Ad and Referral Networks, for e-commerce, e.g., 3D shoe recognition, from any view angle.

公开(公告)号：US11829886B2

公开(公告)日：2023-11-28

申请号：US15914222

申请日：2018-03-07

申请人： International Business Machines Corporation

发明人： Aaron K Baughman ,  Stephen C. Hammer ,  Micah Forster

IPC分类号： G06N3/10 ,  G06F17/18 ,  G06N3/084 ,  G06N3/047 ,  G06N3/043

CPC分类号： G06N3/10 ,  G06F17/18 ,  G06N3/043 ,  G06N3/047 ,  G06N3/084

摘要： Simulating uncertainty in an artificial neural network is provided. Aleatoric uncertainty is simulated to measure what the artificial neural network does not understand from sensor data received from an object operating in a real-world environment by adding random values to edge weights between nodes in the artificial neural network during backpropagation of output data of the artificial neural network and measuring impact on the output data by the added random values to the edge weights between the nodes. Epistemic uncertainty is simulated to measure what the artificial neural network does not know by dropping out a selected node from each respective layer of the artificial neural network during forward propagation of the sensor data and measuring impact of dropped out nodes on the output data of the artificial neural network. An action corresponding to the object is performed based on the impact of simulating the aleatoric and epistemic uncertainty.

公开(公告)号：US20230312132A1

公开(公告)日：2023-10-05

申请号：US17928251

申请日：2021-06-01

申请人： THALES

发明人： Aurélien THIRIET ,  Jaime DIAZ PINEDA ,  Daniel HAURET

IPC分类号： B64D47/00 ,  G06F40/35 ,  G06N3/086 ,  G06N3/042 ,  G06N3/043 ,  G05B13/02

CPC分类号： B64D47/00 ,  G06F40/35 ,  G06N3/086 ,  G06N3/042 ,  G06N3/043 ,  G05B13/0285

摘要： Systems and methods for improved human-machine dialog, include bidirectional translations notably through the translation of commands by the human into a form able to be manipulated by the machine, and conversely of results produced by the machine into a form intelligible to the human. Some developments describe notably the display of portions of intermediate reasoning followed by the machine (for example explanation of root causes).

公开(公告)号：US11755882B2

公开(公告)日：2023-09-12

申请号：US16557888

申请日：2019-08-30

申请人： LG ELECTRONICS INC.

发明人： Hak Joo Lee

IPC分类号： G05D1/00 ,  G06N3/00 ,  G06N3/043 ,  G05D1/02 ,  G06N3/08

CPC分类号： G06N3/043 ,  G05D1/0225 ,  G06N3/08

摘要： A method, an apparatus, and a system for recommending a location of a charging station of a robot are disclosed. The method includes obtaining movement path information from one or more robots in a space including a plurality of regions, determining density of each of the plurality of regions based on the obtained movement path information, and determining a recommended location of a charging station for charging the one or more robots from the plurality of regions based on the determined density. In a 5G environment connected for the Internet of things, the method for recommending a location of a charging station is implemented by executing an artificial intelligence algorithm or machine learning algorithm.

公开(公告)号：US20230184993A1

公开(公告)日：2023-06-15

申请号：US18106187

申请日：2023-02-06

申请人： BEYOND LIMITS, INC.

发明人： Shahram Farhadi Nia

IPC分类号： G01V99/00 ,  G06N3/08 ,  G06N3/04 ,  G06N3/088 ,  G06F30/20 ,  G06N3/043 ,  G06N3/045

CPC分类号： G01V99/005 ,  G06N3/08 ,  G06N3/04 ,  G06N3/088 ,  G06F30/20 ,  G06N3/043 ,  G06N3/045

摘要： Implementations described and claimed herein provide systems and methods for developing a reservoir. In one implementation, a static model of the reservoir is received. The static model has one or more clusters of rock types. A reservoir graph is generated from the static model. The reservoir graph represents each of the one or more clusters as a vertex. A graph connectivity of the reservoir graph is defined through a nodal connectivity of neighboring vertices. Pressure values are propagated across three-dimensional space of the reservoir graph using the connectivity. A dynamic model of the reservoir is generated using the pressure values and fluid saturation values.