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1.发明公开公开(公告)号：US20240355084A1
公开(公告)日：2024-10-24
申请号：US18751930
申请日：2024-06-24
Applicant: Lyft, Inc.
Inventor： Ritwik Subir Das , Joan Devassy , Nadha Nafeeza Gafoor , Aahel Iyer , Swarn Avinash Kumar , Angela Lam , Kia Nishimine , Wiebke Poerschke , John Michael Sparks , Hristo Stefanov Stefanov , Wei You
IPC: G06V10/24 , G06F18/214 , G06N20/00 , G06Q20/34 , G06V10/25
CPC classification number: G06V10/242 , G06F18/2148 , G06N20/00 , G06Q20/353 , G06Q20/3552 , G06V10/25
Abstract: This disclosure describes a card-scan system that can update a card-scan machine learning model to improve card-character predictions for character-bearing cards by using an active-learning technique that learns from card-scan representations indicating corrections by users to predicted card characters. In particular, the disclosed systems can use a client device to capture and analyze a set of card images of a character-bearing card to predict card characters using a card-scan machine learning model. The disclosed systems can further receive card-scan gradients representing one or more corrections to incorrectly predicted card characters. Based on the card-scan gradients, the disclosed systems can generate active-learning metrics and retrain or update the card-scan machine learning model based on such active-learning metrics. The disclosed systems can improve the accuracy with which card-character-detection systems predict card characters while preserving data security and verifying the presence of a physical character-bearing card.
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2.发明申请公开(公告)号：US20220108121A1
公开(公告)日：2022-04-07
申请号：US17063033
申请日：2020-10-05
Applicant: Lyft, Inc.
Inventor： Ritwik Subir Das , Joan Devassy , Nadha Nafeeza Gafoor , Aahel Iyer , Swarn Avinash Kumar , Angela Lam , Kia Nishimine , Wiebke Poerschke , John Michael Sparks , Hristo Stefanov Stefanov , Wei You
Abstract: This disclosure describes a card-scan system that can update a card-scan machine learning model to improve card-character predictions for payment cards, driver licenses, or other character-bearing cards by using an active-learning technique that learns from card-scan representations indicating corrections by users to predicted card characters. In particular, the disclosed systems can use a client device to capture and analyze a set of card images of a character-bearing card to predict card characters using a card-scan machine learning model. The disclosed systems can further receive card-scan gradients representing one or more corrections to incorrectly predicted card characters. Based on the card-scan gradients, the disclosed systems can generate active-learning metrics and retrain or update the card-scan machine learning model based on such active-learning metrics. By utilizing and updating the card-scan machine learning model, the disclosed systems can improve the accuracy with which card-character-detection systems predict card characters while preserving data security and verifying the presence of a physical character-bearing card.
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公开(公告)号：US12158351B2
公开(公告)日：2024-12-03
申请号：US16917672
申请日：2020-06-30
Applicant: Lyft, Inc.
Inventor： Ritwik Subir Das , Kris Richard Efland , Nadha Nafeeza Gafoor , Nastaran Ghadar , Meng Tao
Abstract: Examples disclosed herein may involve a computing system configured to (i) identify a stationary element in a real-world environment for which to infer information, (ii) detect a semantic relationship between the stationary element and one or more other stationary elements in the real-world environment, (iii) based on the detected semantic relationship, infer information about the stationary element, (iv) include the inferred information about the stationary element within a set of information that describes the stationary element.
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公开(公告)号：US20210404841A1
公开(公告)日：2021-12-30
申请号：US16917672
申请日：2020-06-30
Applicant: Lyft, Inc.
Inventor： Ritwik Subir Das , Kris Richard Efland , Nadha Nafeeza Gafoor , Nastaran Ghadar , Meng Tao
Abstract: Examples disclosed herein may involve a computing system configured to (i) identify a stationary element in a real-world environment for which to infer information, (ii) detect a semantic relationship between the stationary element and one or more other stationary elements in the real-world environment, (iii) based on the detected semantic relationship, infer information about the stationary element, (iv) include the inferred information about the stationary element within a set of information that describes the stationary element.
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公开(公告)号：US12018958B2
公开(公告)日：2024-06-25
申请号：US16917672
申请日：2020-06-30
Applicant: Lyft, Inc.
Inventor： Ritwik Subir Das , Kris Richard Efland , Nadha Nafeeza Gafoor , Nastaran Ghadar , Meng Tao
CPC classification number: G01C21/3602 , G01C21/3492 , G01C21/3833 , G01C21/3896 , G06F18/214 , G06V20/582 , G06V20/584 , G06V20/588
Abstract: Examples disclosed herein may involve a computing system configured to (i) identify a stationary element in a real-world environment for which to infer information, (ii) detect a semantic relationship between the stationary element and one or more other stationary elements in the real-world environment, (iii) based on the detected semantic relationship, infer information about the stationary element, (iv) include the inferred information about the stationary element within a set of information that describes the stationary element.
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Patent Agency Ranking
公开(公告)号：US20210406559A1
公开(公告)日：2021-12-30
申请号：US16916015
申请日：2020-06-29
Applicant: Lyft, Inc.
Inventor： Kris Richard Efland , Nadha Nafeeza Gafoor , Nastaran Ghadar , Amruta Kiran Kulkarni , Meng Tao , Ziyi Zhao
Abstract: Examples disclosed herein may involve a computing system configured to (i) maintain a map that is representative of a real-world environment, the map including a plurality of layers that are each encoded with a different type of map data, (ii) obtain sensor data indicative of a given area of the real-world environment, (iii) based on an evaluation of the obtained sensor data and map data corresponding to the given area, detect that a change has occurred in the given area, (iv) based on the collected sensor data, derive information about the detected change including at least a type of the change and a location of the change, (v) based on the derived information about the detected change, determine that one or more layers of the map is impacted by the detected change, and (vi) effect an update to the one or more layers of the map based on the derived information about the change.
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公开(公告)号：US12112535B2
公开(公告)日：2024-10-08
申请号：US16916015
申请日：2020-06-29
Applicant: Lyft, Inc.
Inventor： Kris Richard Efland , Nadha Nafeeza Gafoor , Nastaran Ghadar , Amruta Kiran Kulkarni , Meng Tao , Ziyi Zhao
CPC classification number: G06V20/10 , G01C21/3822 , G01C21/3841 , G01C21/3859 , G06F18/251 , G06V10/803 , G06V20/56
Abstract: Examples disclosed herein may involve a computing system configured to (i) maintain a map that is representative of a real-world environment, the map including a plurality of layers that are each encoded with a different type of map data, (ii) obtain sensor data indicative of a given area of the real-world environment, (iii) based on an evaluation of the obtained sensor data and map data corresponding to the given area, detect that a change has occurred in the given area, (iv) based on the collected sensor data, derive information about the detected change including at least a type of the change and a location of the change, (v) based on the derived information about the detected change, determine that one or more layers of the map is impacted by the detected change, and (vi) effect an update to the one or more layers of the map based on the derived information about the change.
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8.发明授权公开(公告)号：US12026926B2
公开(公告)日：2024-07-02
申请号：US17063033
申请日：2020-10-05
Applicant: Lyft, Inc.
Inventor： Ritwik Subir Das , Joan Devassy , Nadha Nafeeza Gafoor , Aahel Iyer , Swarn Avinash Kumar , Angela Lam , Kia Nishimine , Wiebke Poerschke , John Michael Sparks , Hristo Stefanov Stefanov , Wei You
IPC: G06N20/00 , G06F18/214 , G06Q20/34 , G06V10/24 , G06V10/25
CPC classification number: G06V10/242 , G06F18/2148 , G06N20/00 , G06Q20/353 , G06Q20/3552 , G06V10/25
Abstract: This disclosure describes a card-scan system that can update a card-scan machine learning model to improve card-character predictions for payment cards, driver licenses, or other character-bearing cards by using an active-learning technique that learns from card-scan representations indicating corrections by users to predicted card characters. In particular, the disclosed systems can use a client device to capture and analyze a set of card images of a character-bearing card to predict card characters using a card-scan machine learning model. The disclosed systems can further receive card-scan gradients representing one or more corrections to incorrectly predicted card characters. Based on the card-scan gradients, the disclosed systems can generate active-learning metrics and retrain or update the card-scan machine learning model based on such active-learning metrics. By utilizing and updating the card-scan machine learning model, the disclosed systems can improve the accuracy with which card-character-detection systems predict card characters while preserving data security and verifying the presence of a physical character-bearing card.
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公开(公告)号：US11682124B2
公开(公告)日：2023-06-20
申请号：US16916029
申请日：2020-06-29
Applicant: Lyft, Inc.
Inventor： Ritwik Subir Das , Kris Richard Efland , Nadha Nafeeza Gafoor , Nastaran Ghadar , Meng Tao
CPC classification number: G06T7/337 , G01C21/3833 , G06T3/0075 , G06T3/20 , G06T3/60 , G06T7/60 , G06T7/74 , G06T11/60
Abstract: Examples disclosed herein may involve a computing system that is operable to (i) identify a source map and a target map for transferring map data, where the source map and the target map have different respective coordinate frames and respective coverage areas that at least partially overlap, (ii) select a real-world element for which to transfer previously-created map data from the source map to the target map, (iii) select a source image associated with the source map in which the selected real-world element appears and has been labeled, (iv) select a target image associated with the target map in which the selected real-world element appears, (v) derive a geometric relationship between the source image and the target image, and (vi) use the derived geometric relationship between the source image and the target image to determine a position of the real-world element within the respective coordinate frame of the target map.
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公开(公告)号：US20210407114A1
公开(公告)日：2021-12-30
申请号：US16916029
申请日：2020-06-29
Applicant: Lyft, Inc.
Inventor： Ritwik Subir Das , Kris Richard Efland , Nadha Nafeeza Gafoor , Nastaran Ghadar , Meng Tao
Abstract: Examples disclosed herein may involve a computing system that is operable to (i) identify a source map and a target map for transferring map data, where the source map and the target map have different respective coordinate frames and respective coverage areas that at least partially overlap, (ii) select a real-world element for which to transfer previously-created map data from the source map to the target map, (iii) select a source image associated with the source map in which the selected real-world element appears and has been labeled, (iv) select a target image associated with the target map in which the selected real-world element appears, (v) derive a geometric relationship between the source image and the target image, and (vi) use the derived geometric relationship between the source image and the target image to determine a position of the real-world element within the respective coordinate frame of the target map.
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