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公开(公告)号：US12131475B2
公开(公告)日：2024-10-29
申请号：US17191868
申请日：2021-03-04
发明人： Russell H. Amundson , Saurabh Bhargava , Rama Krishna Singh , Ravi Pande , Vishwakant Gupta , Gaurav Mantri , Abhinav Agrawal , Sapeksh Suman
IPC分类号： G06T7/00 , G06F18/2137 , G06F18/24 , G06T3/60 , G06T7/11 , G06T7/33 , G06T7/70 , G06T7/90 , G06V10/764 , G06V10/77 , G16H30/40 , G16H40/67
CPC分类号： G06T7/0014 , G06F18/21375 , G06F18/24 , G06T3/60 , G06T7/0012 , G06T7/11 , G06T7/337 , G06T7/70 , G06T7/90 , G06V10/764 , G06V10/7715 , G16H30/40 , G16H40/67 , G06T2207/10024 , G06T2207/20024 , G06T2207/20081 , G06T2207/20084 , G06T2207/30004 , G06T2207/30012 , G06T2207/30068 , G06T2207/30088 , G06T2207/30096
摘要： Systems and methods are configured for preprocessing of images for further content based analysis thereof. Such images are extracted from a source data file, by standardizing individual pages within a source data file as image data files, and identifying whether the image satisfies applicable size-based criteria, applicable color-based criteria, and applicable content-based criteria, among others, utilizing one or more machine-learning based models. Various systems and methods may identify particular features within the extracted images to facilitate further image-based analysis based on the identified features.
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公开(公告)号：US20240095457A1
公开(公告)日：2024-03-21
申请号：US18513406
申请日：2023-11-17
IPC分类号： G06F40/30 , G06F18/2137 , G06N3/04 , G06N20/20
CPC分类号： G06F40/30 , G06F18/21375 , G06N3/04 , G06N20/20
摘要： Described are methods and systems are for generating dynamic conversational queries. For example, as opposed to being a simply reactive system, the methods and systems herein provide means for actively determining a user's intent and generating a dynamic query based on the determined user intent. Moreover, these methods and systems generate these queries in a conversational environment.
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3.发明授权公开(公告)号：US11842530B2
公开(公告)日：2023-12-12
申请号：US17150995
申请日：2021-01-15
申请人： UATC, LLC
发明人： Sergio Casas , Cole Christian Gulino , Shun Da Suo , Katie Z. Luo , Renjie Liao , Raquel Urtasun
IPC分类号： G06V10/82 , B60W60/00 , G08G1/16 , G05D1/02 , G06V20/58 , G06F18/2137 , G06V30/19 , G06V30/24
CPC分类号： G06V10/82 , B60W60/0027 , G05D1/0212 , G06F18/2137 , G06V20/58 , G06V30/19173 , G08G1/166 , G06V30/2504
摘要： A computer-implemented method for determining scene-consistent motion forecasts from sensor data can include obtaining scene data including one or more actor features. The computer-implemented method can include providing the scene data to a latent prior model, the latent prior model configured to generate scene latent data in response to receipt of scene data, the scene latent data including one or more latent variables. The computer-implemented method can include obtaining the scene latent data from the latent prior model. The computer-implemented method can include sampling latent sample data from the scene latent data. The computer-implemented method can include providing the latent sample data to a decoder model, the decoder model configured to decode the latent sample data into a motion forecast including one or more predicted trajectories of the one or more actor features. The computer-implemented method can include receiving the motion forecast including one or more predicted trajectories of the one or more actor features from the decoder model.
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公开(公告)号：US11725924B2
公开(公告)日：2023-08-15
申请号：US17518417
申请日：2021-11-03
发明人： Shiwali Mohan , Matthew Klenk , Matthew Shreve , Aaron Ang , John Turner Maxwell, III , Kent Evans
IPC分类号： G05B15/02 , G01B7/00 , G01B7/02 , G06F40/10 , G06F40/40 , G06F18/2137 , B25J13/00 , B25J13/08 , B25J9/16
CPC分类号： G01B7/003 , B25J9/163 , B25J9/1664 , B25J13/003 , B25J13/089 , G01B7/023 , G05B15/02 , G06F18/21375 , G06F40/10 , G06F40/40
摘要： A method is provided. The method includes obtaining an enhanced state graph. The enhanced state graph represents a set of objects within an environment and a set of positions of the set of objects. The enhanced state graph includes a set of object nodes, a set of property nodes and a set of goal nodes to represent a set of objectives. The method also includes generating a set of instructions for a set of mechanical systems based on the enhanced state graph. The set of mechanical systems is configured to interact with one or more of the set of objects within the environment. The method further includes operating the set of mechanical systems to achieve the set of objectives based on the set of instructions.
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公开(公告)号：US20230166211A1
公开(公告)日：2023-06-01
申请号：US18058362
申请日：2022-11-23
IPC分类号： B01D53/14 , B01D53/50 , G06F18/2137
CPC分类号： B01D53/1412 , B01D53/50 , G06F18/21375 , B01D2258/0283
摘要： A state quantity prediction device includes: a first differential predictive value calculation unit configured to deal with a nonlinear component of a function whose variables are dynamic characteristics of the state quantity with respect to the input parameter and a difference value between a past predictive value of the state quantity and a predictive value of the physical model, input the input parameter and the past predictive value of the state quantity, and include a learned neutral network for outputting a first differential predictive value; a second differential predictive value calculation unit configured to deal with a linear component of the function, input the input parameter and the past predictive value, and output a second differential predictive value; and a state quantity predictive value calculation unit configured to calculate a predictive value of the state quantity.
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公开(公告)号：US11640446B2
公开(公告)日：2023-05-02
申请号：US17407181
申请日：2021-08-19
发明人： Mandis Beigi , Jacob Aptekar , Afrah Shafquat , Jason Mezey
IPC分类号： G06F21/62 , G06F16/27 , G06F18/214 , G06F18/2133 , G06F18/2135 , G06F18/21 , G06F18/2137
摘要： A method for generating a synthetic dataset from an original dataset includes encoding categorical features of the original dataset, embedding the encoded dataset in a low-dimensional space, selecting a seed record from the embedded dataset, identifying a plurality of nearest neighbor records to the seed record, generating a new record by randomly selecting features from the plurality of nearest neighbor records, and concatenating the new record into the synthetic dataset. For a synthetic dataset that contains N records, which may be the same as or different from the number of records in the original dataset, the selecting, identifying, generating, and concatenating operations operate a total of N times on the records in the embedded dataset.
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公开(公告)号：US11625932B2
公开(公告)日：2023-04-11
申请号：US17007790
申请日：2020-08-31
申请人： Adobe Inc.
IPC分类号： G06F17/00 , G06V30/244 , G06N3/04 , G06F40/109 , G06F3/0482 , G06N3/08 , G06V10/40 , G06V10/75 , G06F18/22 , G06F18/2137
摘要： Utilizing a visual-feature-classification model to generate font maps that efficiently and accurately organize fonts based on visual similarities. For example, extracting features from fonts of varying styles and utilize a self-organizing map (or other visual-feature-classification model) to map extracted font features to positions within font maps. Further, magnifying areas of font maps by mapping some fonts within a bounded area to positions within a higher-resolution font map. Additionally, navigating the font map to identify visually similar fonts (e.g., fonts within a threshold similarity).
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公开(公告)号：US20240126832A1
公开(公告)日：2024-04-18
申请号：US18482558
申请日：2023-10-06
申请人： Groq, Inc.
IPC分类号： G06F17/15 , G06F7/544 , G06F7/76 , G06F9/54 , G06F17/16 , G06F18/2137 , G06N3/04 , G06N3/08 , G06N7/00 , G06N20/10
CPC分类号： G06F17/153 , G06F7/5443 , G06F7/76 , G06F9/544 , G06F17/16 , G06F18/2137 , G06N3/04 , G06N3/08 , G06N7/00 , G06N20/10
摘要： A method comprises receiving a kernel used to convolve with an input tensor. For a first dimension of the kernel, a square block of values for each single dimensional vector of the kernel that includes all rotations of that single dimensional vector is generated. For each additional dimension of the kernel, group blocks of an immediately preceding dimension into sets of blocks, each set of blocks including blocks of the immediately preceding dimension that are aligned along a vector that is parallel to the axis of the dimension; and generate, for the additional dimension, one or more blocks of values, each block including all rotations of blocks within each of the sets of blocks of the immediately preceding dimension. The block of values corresponding to the last dimension in the additional dimensions of the kernel is output as the expanded kernel.
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9.发明公开公开(公告)号：US20240096083A1
公开(公告)日：2024-03-21
申请号：US18519976
申请日：2023-11-27
申请人： UATC, LLC
发明人： Sergio Casas , Cole Christian Gulino , Shun Da Suo , Katie Z. Luo , Renjie Liao , Raquel Urtasun
CPC分类号： G06V10/82 , B60W60/0027 , G05D1/0212 , G06F18/2137 , G06V20/58 , G06V30/19173 , G08G1/166 , G06V30/2504
摘要： A computer-implemented method for determining scene-consistent motion forecasts from sensor data can include obtaining scene data including one or more actor features. The computer-implemented method can include providing the scene data to a latent prior model, the latent prior model configured to generate scene latent data in response to receipt of scene data, the scene latent data including one or more latent variables. The computer-implemented method can include obtaining the scene latent data from the latent prior model. The computer-implemented method can include sampling latent sample data from the scene latent data. The computer-implemented method can include providing the latent sample data to a decoder model, the decoder model configured to decode the latent sample data into a motion forecast including one or more predicted trajectories of the one or more actor features. The computer-implemented method can include receiving the motion forecast including one or more predicted trajectories of the one or more actor features from the decoder model.
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公开(公告)号：US20240071037A1
公开(公告)日：2024-02-29
申请号：US18203185
申请日：2023-05-30
发明人： Ming-Jung SEOW , Gang XU , Tao YANG , Wesley Kenneth COBB
IPC分类号： G06V10/32 , G06F18/2137 , G06F18/23 , G06F18/28 , G06N7/01 , G06V10/762 , G06V30/19 , G06V30/262 , H01B1/02
CPC分类号： G06V10/32 , G06F18/2137 , G06F18/23 , G06F18/28 , G06N7/01 , G06V10/762 , G06V30/19127 , G06V30/1914 , G06V30/268 , H01B1/02
摘要： Techniques are disclosed for generating a sequence of symbols based on input data for a neuro-linguistic model. The model may be used by a behavior recognition system to analyze the input data. A mapper component of a neuro-linguistic module in the behavior recognition system receives one or more normalized vectors generated from the input data. The mapper component generates one or more clusters based on a statistical distribution of the normalized vectors. The mapper component evaluates statistics and identifies statistically relevant clusters. The mapper component assigns a distinct symbol to each of the identified clusters.
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