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公开(公告)号：US11111090B2
公开(公告)日：2021-09-07
申请号：US16296676
申请日：2019-03-08
Applicant: VERIZON CONNECT DEVELOPMENT LIMITED
Inventor： Marco Gualtieri , Leonardo Taccari , Andrea Benericetti , Tommaso Bianconcini , Alessio Frusciante , David Di Lorenzo
Abstract: A load planning platform generates a preliminary packing solution based on loading constraints for a three-dimensional container and parameters for a set of items to be loaded into the three-dimensional container, wherein the preliminary packing solution simulates placing unpacked items into the three-dimensional container according to one or more loading rules based on a sequence in which the set of items are to be unloaded from the three-dimensional container. The load planning platform generates a set of packing solutions by applying one or more available moves changing a simulated placement for one or more items in the set of items. The load planning platform selects a final packing solution from the set of packing solutions based on one or more optimization criteria and provides access to a three-dimensional rendering of the final packing solution and instructions for implementing the final packing solution.
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公开(公告)号：US11747152B2
公开(公告)日：2023-09-05
申请号：US16947502
申请日：2020-08-04
Applicant: Verizon Connect Development Limited
Inventor： Andrea Benericetti , David Leonardo Spain Contini , Luca Noce , Francesca Chiti , Andrea Giannoni , Francesco Sambo , S M Murtoza Habib
CPC classification number: G01C21/343 , B60W60/001 , G01C21/3461 , G01C21/3476 , G01C21/3605
Abstract: A trip planning system may receive, from a user device, trip data identifying a starting point and a destination point for a trip and feature data identifying weights for trip features. The trip planning system may determine a geographical region of interest based on the starting point and the destination point. The trip planning system may receive viewpoint data identifying viewpoints located within the geographical region and may calculate, based on the feature data, viewpoint relevance scores for the viewpoints. The trip planning system may determine, based on the viewpoint data and the trip data, paths for the trip and driving times for the paths. The trip planning system may calculate an optimized path, from the paths, based on the viewpoint relevance scores and the driving times associated with the paths. The trip planning system may perform one or more actions based on the optimized path.
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Patent Agency Ranking
公开(公告)号：US11756431B2
公开(公告)日：2023-09-12
申请号：US17659928
申请日：2022-04-20
Applicant: Verizon Connect Development Limited
Inventor： Leonardo Taccari , Luca Kubin , Tommaso Bianconcini , Andrea Benericetti , Leonardo Sarti , Tommaso Innocenti
IPC: G06V20/10 , G08G1/00 , G06N3/04 , B64C39/02 , G08G1/16 , G06V10/771 , G06V10/80 , G06V10/98 , G06V20/56 , B64U101/30
CPC classification number: G08G1/205 , B64C39/024 , G06N3/04 , G06V10/771 , G06V10/806 , G06V10/98 , G06V20/10 , G06V20/56 , G08G1/164 , B64U2101/30
Abstract: A device may receive sensor data and video data associated with a vehicle, and may process the sensor data, with a rule-based detector model, to determine whether a probability of a vehicle accident satisfies a first threshold. The device may preprocess acceleration data of the sensor data to generate calibrated acceleration data, and may process the calibrated acceleration data, with an anomaly detector model, to determine whether the calibrated acceleration data includes anomalies. The device may filter the sensor data to generate filtered sensor data, and may process the filtered sensor data and anomaly data, with a decision model, to determine whether the probability of the vehicle accident satisfies a second threshold. The device may process the filtered sensor data, the anomaly data, and the video data, with a machine learning model, to determine whether the vehicle accident has occurred, and may perform one or more actions.
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公开(公告)号：US11820611B2
公开(公告)日：2023-11-21
申请号：US17446208
申请日：2021-08-27
Applicant: Verizon Connect Development Limited
Inventor： Marco Gualtieri , Leonardo Taccari , Andrea Benericetti , Tommaso Bianconcini , Alessio Frusciante , David Di Lorenzo
CPC classification number: B65G15/30 , G06T19/006 , G09B9/00 , B65G2814/0304
Abstract: A load planning platform may generate, according to one or more loading rules, a preliminary packing solution that simulates placing unpacked items, in a set of items, into a container. The load planning platform may generate a set of packing solutions by applying one or more available moves to the preliminary packing solution. The load planning platform may select a final packing solution from the set of packing solutions based on one or more optimization criteria associated with the container and the set of items. The load planning platform may provide access to a three-dimensional rendering of the final packing solution that differentiates each item in the set of items based on a sequence in which the set of items are to be unloaded from the container.
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公开(公告)号：US11651599B2
公开(公告)日：2023-05-16
申请号：US16947780
申请日：2020-08-17
Applicant: Verizon Connect Development Limited
Inventor： Douglas Coimbra De Andrade , Andrea Benericetti , Aurel Pjetri , Leonardo Taccari , Francesco Sambo , Alex Quintero Garcia , Luca Bravi
CPC classification number: G06V20/59 , B60W40/09 , B60W50/14 , G06N20/00 , G06T7/10 , G06V20/40 , G06V40/172 , B60W2540/229 , G06T2207/20081 , G06T2207/20132 , G06T2207/30201
Abstract: A device may process the video data, with a first machine learning model, to identify a driver of a vehicle and may process the video data associated with the driver, with a second machine learning model, to detect behavior data identifying a behavior of the driver. The device may process the behavior data, with a third machine learning model, to determine distraction data identifying whether the behavior is classified as a distracted behavior. The device may process the behavior data, with a fourth machine learning model, to determine policy compliance data identifying whether the behavior satisfies one or more policies. The device may calculate a distraction score based on the distraction data and the video data, and may calculate a policy compliance score based on the policy compliance data and vehicle data. The device may perform one or more actions based on the distraction score and the policy compliance score.
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公开(公告)号：US12136278B2
公开(公告)日：2024-11-05
申请号：US18302979
申请日：2023-04-19
Applicant: Verizon Connect Development Limited
Inventor： Douglas Coimbra De Andrade , Andrea Benericetti , Aurel Pjetri , Leonardo Taccari , Francesco Sambo , Alex Quintero Garcia , Luca Bravi
Abstract: A device may process the video data, with a first machine learning model, to identify a driver of a vehicle and may process the video data associated with the driver, with a second machine learning model, to detect behavior data identifying a behavior of the driver. The device may process the behavior data, with a third machine learning model, to determine distraction data identifying whether the behavior is classified as a distracted behavior. The device may process the behavior data, with a fourth machine learning model, to determine policy compliance data identifying whether the behavior satisfies one or more policies. The device may calculate a distraction score based on the distraction data and the video data, and may calculate a policy compliance score based on the policy compliance data and vehicle data. The device may perform one or more actions based on the distraction score and the policy compliance score.
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公开(公告)号：US11328505B2
公开(公告)日：2022-05-10
申请号：US16793829
申请日：2020-02-18
Applicant: Verizon Connect Development Limited
Inventor： Leonardo Taccari , Luca Kubin , Tommaso Bianconcini , Andrea Benericetti , Leonardo Sarti , Tommaso Innocenti
Abstract: A device may receive sensor data and video data associated with a vehicle, and may process the sensor data, with a rule-based detector model, to determine whether a probability of a vehicle accident satisfies a first threshold. The device may preprocess acceleration data of the sensor data to generate calibrated acceleration data, and may process the calibrated acceleration data, with an anomaly detector model, to determine whether the calibrated acceleration data includes anomalies. The device may filter the sensor data to generate filtered sensor data, and may process the filtered sensor data and anomaly data, with a decision model, to determine whether the probability of the vehicle accident satisfies a second threshold. The device may process the filtered sensor data, the anomaly data, and the video data, with a machine learning model, to determine whether the vehicle accident has occurred, and may perform one or more actions.
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