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    REFERENCE IMAGE ENROLLMENT AND EVOLUTION FOR SECURITY SYSTEMS
    1.
    发明公开
    REFERENCE IMAGE ENROLLMENT AND EVOLUTION FOR SECURITY SYSTEMS 审中-公开

    公开(公告)号：US20240184868A1

    公开(公告)日：2024-06-06

    申请号：US18427370

    申请日：2024-01-30

    Applicant: INTELLIVISION TECHNOLOGIES CORP

    Inventor： Krishna Khadloya ,  Manuel Gonzalez

    IPC: G06F21/32 ,  G06F18/22 ,  G06N3/08 ,  G06V20/56 ,  G06V40/16

    CPC classification number: G06F21/32 ,  G06F18/22 ,  G06N3/08 ,  G06V20/56 ,  G06V40/172

    Abstract: Person or object authentication can be performed using artificial intelligence-enabled systems. Reference information, such as for use in comparisons or assessments for authentication, can be updated over time to accommodate changes in an individual's appearance, voice, or behavior. In an example, reference information can be updated automatically with test data, or reference information can be updated conditionally, based on instructions from a system administrator. Various types of media can be used for authentication, including image information, audio information, or biometric information. In an example, authentication can be performed wholly or partially at an edge device such as a security panel in an installed security system.

    MULTIPLE-FACTOR RECOGNITION AND VALIDATION FOR SECURITY SYSTEMS
    3.
    发明申请
    MULTIPLE-FACTOR RECOGNITION AND VALIDATION FOR SECURITY SYSTEMS 有权

    公开(公告)号：US20250014409A1

    公开(公告)日：2025-01-09

    申请号：US18735963

    申请日：2024-06-06

    Applicant: INTELLIVISION TECHNOLOGIES CORP

    Inventor： Krishna Khadloya ,  Manuel Gonzalez

    IPC: G07C9/37 ,  G06N3/063 ,  G06N3/08 ,  G06V40/16 ,  G06V40/20 ,  G08B21/22 ,  G08B27/00

    Abstract: A security system can conditionally grant or deny access to a protected area using an artificial intelligence system to analyze images. In an example, an access control method can include receiving candidate information about a face and gesture from a first individual and receiving other image information from or about a second individual. The candidate information can be analyzed using a neural network-based recognition processor that can provide a first recognition result indicating whether the first individual corresponds to a first enrollee of the security system, and can provide a second recognition result indicating whether the second individual corresponds to a second enrollee of the security system. The example method can include receiving a passcode, such as from the first individual. Access can be conditionally granted or denied based on the passcode and the recognition results.

    MULTIPLE-FACTOR RECOGNITION AND VALIDATION FOR SECURITY SYSTEMS
    4.
    发明公开
    MULTIPLE-FACTOR RECOGNITION AND VALIDATION FOR SECURITY SYSTEMS 审中-公开

    公开(公告)号：US20230351831A1

    公开(公告)日：2023-11-02

    申请号：US18197562

    申请日：2023-05-15

    Applicant: INTELLIVISION TECHNOLOGIES CORP

    Inventor： Krishna Khadloya ,  Manuel Gonzalez

    IPC: G07C9/37 ,  G06V40/16 ,  G06V40/20 ,  G06N3/063 ,  G06N3/08 ,  G08B21/22 ,  G08B27/00

    CPC classification number: G07C9/37 ,  G06V40/172 ,  G06V40/20 ,  G06N3/063 ,  G06N3/08 ,  G08B21/22 ,  G08B27/005

    Abstract: A security system can conditionally grant or deny access to a protected area using an artificial intelligence system to analyze images. In an example, an access control method can include receiving candidate information about a face and gesture from a first individual and receiving other image information from or about a second individual. The candidate information can be analyzed using a neural network-based recognition processor that can provide a first recognition result indicating whether the first individual corresponds to a first enrollee of the security system, and can provide a second recognition result indicating whether the second individual corresponds to a second enrollee of the security system. The example method can include receiving a passcode, such as from the first individual. Access can be conditionally granted or denied based on the passcode and the recognition results.

    AUDIO TYPE DETECTION
    5.
    发明申请
    AUDIO TYPE DETECTION 有权

    公开(公告)号：US20210210074A1

    公开(公告)日：2021-07-08

    申请号：US17203269

    申请日：2021-03-16

    Applicant: INTELLIVISION TECHNOLOGIES CORP.

    Inventor： Krishna Khadloya ,  Chandan Gope ,  Vaidhi Nathan

    IPC: G10L15/16 ,  G06N3/08 ,  G06N3/04 ,  G06F9/54 ,  G06N20/10 ,  G10L25/21 ,  G10L25/18 ,  G06N20/20

    Abstract: Artificial intelligence-based processing can be used to classify audio information received from an audio input unit. In an example, audio information can be received from a microphone configured to monitor an environment. A processor circuit can identify identifying one or more features of the audio information received from the microphone and use a first applied machine learning algorithm to analyze the one or more features and determine whether the audio information includes an indication of an abnormal event in the environment. In an example, the processor circuit can use a different second applied machine learning algorithm, such as a neural network-based deep learning algorithm, to analyze the same one or more features and classify the audio information as including an indication of a particular event type in the environment.

    METHOD OF TRACKING MOVEABLE OBJECTS BY COMBINING DATA OBTAINED FROM MULTIPLE SENSOR TYPES
    8.
    发明申请
    METHOD OF TRACKING MOVEABLE OBJECTS BY COMBINING DATA OBTAINED FROM MULTIPLE SENSOR TYPES 有权
    通过组合从多个传感器类型获得的数据跟踪移动物体的方法

    公开(公告)号：US20140379296A1

    公开(公告)日：2014-12-25

    申请号：US14311269

    申请日：2014-06-21

    Applicant: Intellivision Technologies Corp.

    Inventor： Vaidhi Nathan ,  Chandan Gope ,  Lev Afraimovich ,  Sergey Soprykin ,  Roman Kholodov Valerievich

    IPC: G01B21/00 ,  G06F17/18

    CPC classification number: G01B21/00 ,  G06F17/18 ,  G06K9/00771 ,  G06K9/6293 ,  G06K2017/0045 ,  G06Q10/08

    Abstract: Method of tracking moveable objects (typically tagged objects that are moved by actors e.g. people, vehicles) by combining and analyzing data obtained from multiple types of sensors, such as video cameras, RFID tag readers, GPS sensors, and WiFi transceivers. Objects may be tagged by RFID tags, NFC tags, bar codes, or even tagged by visual appearance. The system operates in near real-time, and compensates for errors in sensor readings and missing sensor data by modeling object and actor movement according to a plurality of possible paths, weighting data from some sensors higher than others according to estimates of sensor accuracy, and weighing the probability of certain paths according to various other rules and penalty cost parameters. The system can maintain a comprehensive database which can be queried as to which actors associate with which objects, and vice versa. Other data pertaining to object location and association can also be obtained.

    Abstract translation: 通过组合和分析从诸如摄像机，RFID标签读取器，GPS传感器和WiFi收发器的多种类型的传感器获得的数据来跟踪可移动对象（通常由演员例如人，车辆移动的标签对象）的方法。 对象可能被RFID标签，NFC标签，条形码标记，甚至可以通过视觉外观标记。 该系统近乎实时操作，通过根据多个可能的路径建模对象和演员移动来补偿传感器读数误差和传感器数据丢失，根据传感器精度的估计，来自某些传感器的数据加权比其他传感器更高;以及 根据各种其他规则和罚款成本参数权衡某些路径的概率。 该系统可以维护一个全面的数据库，可以查询与哪些对象相关联的哪个对象，反之亦然。 还可以获得与对象位置和关联有关的其他数据。

    Audio type detection
    9.
    发明授权
    Audio type detection 有权

    公开(公告)号：US12142261B2

    公开(公告)日：2024-11-12

    申请号：US17203269

    申请日：2021-03-16

    Applicant: INTELLIVISION TECHNOLOGIES CORP.

    Inventor： Krishna Khadloya ,  Chandan Gope ,  Vaidhi Nathan

    IPC: G10L15/16 ,  G06F9/54 ,  G06N3/045 ,  G06N3/08 ,  G06N20/10 ,  G06N20/20 ,  G10L25/18 ,  G10L25/21

    Abstract: Artificial intelligence-based processing can be used to classify audio information received from an audio input unit. In an example, audio information can be received from a microphone configured to monitor an environment. A processor circuit can identify identifying one or more features of the audio information received from the microphone and use a first applied machine learning algorithm to analyze the one or more features and determine whether the audio information includes an indication of an abnormal event in the environment. In an example, the processor circuit can use a different second applied machine learning algorithm, such as a neural network-based deep learning algorithm, to analyze the same one or more features and classify the audio information as including an indication of a particular event type in the environment.

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