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    Image realism predictor
    4.
    发明授权
    Image realism predictor 有权

    公开(公告)号：US11068746B2

    公开(公告)日：2021-07-20

    申请号：US16235697

    申请日：2018-12-28

    Applicant: Palo Alto Research Center Incorporated

    Inventor： Raja Bala ,  Matthew Shreve ,  Jeyasri Subramanian ,  Pei Li

    IPC: G06K9/62 ,  G06K9/00 ,  G06T7/00

    Abstract: A method for predicting the realism of an object within an image includes generating a training image set for a predetermined object type. The training image set comprises one or more training images at least partially generated using a computer. A pixel level training spatial realism map is generated for each training image of the one or more training images. Each training spatial realism map configured to represent a perceptual realism of the corresponding training image. A predictor is trained using the training image set and the corresponding training spatial realism maps. An image of the predetermined object is received. A spatial realism map of the received image is produced using the trained predictor.

    Apparatus and method for identifying an articulatable part of a physical object using multiple 3D point clouds
    5.
    发明授权
    Apparatus and method for identifying an articulatable part of a physical object using multiple 3D point clouds 有权

    公开(公告)号：US10896317B2

    公开(公告)日：2021-01-19

    申请号：US16235434

    申请日：2018-12-28

    Applicant: Palo Alto Research Center Incorporated

    Inventor： Matthew Shreve ,  Sreenivas Venkobarao

    IPC: G06K9/00 ,  G06K9/62 ,  G06T17/00 ,  G06T19/20

    Abstract: An apparatus comprises an input interface configured to receive a first 3D point cloud associated with a physical object prior to articulation of an articulatable part, and a second 3D point cloud after articulation of the articulatable part. A processor is operably coupled to the input interface, an output interface, and memory. Program code, when executed by the processor, causes the processor to align the first and second point clouds, find nearest neighbors of points in the first point cloud to points in the second point cloud, eliminate the nearest neighbors of points in the second point cloud such that remaining points in the second point cloud comprise points associated with the articulatable part and points associated with noise, generate an output comprising at least the remaining points of the second point cloud associated with the articulatable part without the noise points, and communicate the output to the output interface.

    SYSTEM FOR INTERACTING WITH MACHINES USING NATURAL LANGUAGE INPUT
    9.
    发明申请
    SYSTEM FOR INTERACTING WITH MACHINES USING NATURAL LANGUAGE INPUT 有权

    公开(公告)号：US20230048827A1

    公开(公告)日：2023-02-16

    申请号：US17518429

    申请日：2021-11-03

    Applicant: PALO ALTO RESEARCH CENTER INCORPORATED

    Inventor： Shiwali Mohan ,  Matthew Klenk ,  Matthew Shreve ,  Aaron Ang ,  John Turner Maxwell, III ,  Kent Evans

    IPC: G01B7/00 ,  G06F40/10 ,  G01B7/02

    Abstract: A method is provided. The method includes obtaining sensor data indicative of a set of objects detected within an environment. The method also includes generating a state graph based on the sensor data. The state graph includes a set of object nodes and a set of property nodes. The method further includes obtaining user input data generated based on a natural language input. The method further includes updating the state graph based on the user input data to generate an enhanced state graph. The enhanced state graph includes additional nodes generated based on the user input data. The method further includes generating a set of instructions for a set of mechanical systems based on the enhanced state graph. The method further includes operating the set of mechanical systems to achieve a set of objectives based on the set of instructions.

    USING MULTIPLE TRAINED MODELS TO REDUCE DATA LABELING EFFORTS
    10.
    发明申请
    USING MULTIPLE TRAINED MODELS TO REDUCE DATA LABELING EFFORTS 有权

    公开(公告)号：US20220318229A1

    公开(公告)日：2022-10-06

    申请号：US17221661

    申请日：2021-04-02

    Applicant: PALO ALTO RESEARCH CENTER INCORPORATED

    Inventor： Matthew Shreve ,  Francisco E. Torres ,  Raja Bala ,  Robert R. Price ,  Pei Li

    IPC: G06F16/23 ,  G06N20/00

    Abstract: A method of labeling a dataset of input samples for a machine learning task includes selecting a plurality of pre-trained machine learning models that are related to a machine learning task. The method further includes processing a plurality of input data samples through each of the pre-trained models to generate a set of embeddings. The method further includes generating a plurality of clusterings from the set of embeddings. The method further includes analyzing, by a processing device, the plurality of clusterings to extract superclusters. The method further includes assigning pseudo-labels to the input samples based on analysis.

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