Verdant Robotics, Inc.

Microsoft Technology Licensing, LLC

Shi Yan

Gabriel Thurston Sibley

Patrick Christopher Leger

Curtis Dale Garner

Lorenzo Ibarria

Andre Robert Daniel Michelin

Benjamin Rewis

Daniel Francis

Ahsan Latif Chudhary

Keren Baruch

Ted Tomlinson

Xiaochuan Zhou

Nikko David Bautista

Funing Xu

Various embodiments of apparatuses, methods, systems, and computer program products described herein are directed to a treatment system having a first group of in line emitters, a second group of in line emitters, and one or more payload sources, configured to identify a first group of one or more agricultural objects, select a first emitter from the first group of in line emitters to apply a first liquid-based treatment to the identified first group of one or more agricultural objects, identify a second group of one or more agricultural objects, select a second emitter from the second group of in line emitters to apply a second liquid-based treatment to the identified second group of one or more agricultural objects, simultaneously apply the first liquid-based treatment towards the first group of one or more agricultural objects and apply the second liquid-based treatment towards the second group of one or more agricultural objects.

Systems and methods for autonomously applying precision treatements to a group of plant objects

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include, at least, detecting an optical sight to align with an associated agricultural object, tracking the agricultural objects relative to the optical sight, predicting a parameter to track in association with agricultural object, and activating an emitter to apply an action based on the parameter.

Targeting agricultural objects via image pixel tracking

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include identifying an agricultural object being associated with a subset of agricultural objects, identifying an action corresponding to a subset of agricultural objects responsive, selecting an emitter with which to perform an action associated with the agricultural object, configuring an agricultural projectile delivery system to activate an emitter to propel an agricultural projectile to intercept an agricultural object.

Managing stages of growth of a crop with micro-precision via an agricultural treatment delivery system

Computer vision and autonomous identification of objects are used for the application of a treatment to an object. Robotics and mobility technologies navigate a delivery system which is configured to identify and apply an agricultural treatment to an identified agricultural object. The delivery system identifies a subset of payloads to provide one or more actions based on data representing a policy for one or more subsets of agricultural objects. The delivery system causes one or more cartridges to be charged based on the subset of payloads.

Payload selection to treat multiple plant objects having different attributes

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include, receiving data representing a policy specifying a type of action for an agricultural object, selecting an emitter with which to perform a type of action for the agricultural object as one of one or more classified subsets, and configuring the agricultural projectile delivery system to activate an emitter to propel an agricultural projectile to intercept the agricultural object.

Multiple emitters to treat agricultural objects from multiple payload sources

Precision treatment of agricultural objects on a moving platform

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include identifying an emitter of an agricultural projectile delivery system to calibrate a trajectory of an agricultural projectile to intercept a target, predicting a projectile impact site relative to the reference of alignment, determining a calibration parameter to align the projectile impact site and the target, and adjusting the trajectory based on the one or more calibration parameters.

Calibration of systems to deliver agricultural projectiles

Micro-precision application of multiple treatments to agricultural objects

Targeting agricultural objects to apply units of treatment autonomously

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include calculating an amount of light originating from a light source sensed at a location in which an agricultural object is interposed between the light source and an image capture device, causing emission of an obscurant, and directing the obscurant to a region interposed between the light source and the agricultural object.

Obscurant emission to assist image formation to automate agricultural management and treatment

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include receiving data representing an image, detecting a portion of the image associated with a unit of spatial translation relative to a reference, identifying a subset of pixels to be formed on the surface, and causing emission of a subset of pixel projectiles directed to impact a portion of a surface to form a replica of a portion of the image.

Pixel projectile delivery system to replicate an image on a surface using pixel projectiles

Systems and methods for computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include identifying a subset of payloads to provide one or more actions based on data representing a policy for one or more subsets of agricultural objects, causing one or more cartridges to be charged based on the subset of payloads, and, and implementing one or more cartridges at an agricultural projectile delivery system.

Cartridges to employ an agricultural payload via an agricultural treatment delivery system

In order to handle the security issues with regards to maintaining privacy of the submitted confidential data, in an example embodiment, no single service is permitted to access both confidential data and member identity data. This design ensures that an attacker would have to compromise more than two services to be able to associate a member with their corresponding compensation data. Thus, member privacy would be preserved if there were any single point of breach. In an example embodiment, an approach is taken where it is still possible for a member to delete his or her confidential data information.

Confidential data security

Apparatuses, computer readable medium, and methods are disclosed for dynamic sampling based on talent pool size. The method of dynamic sampling based on talent pool size may include determining, by at least one hardware processor, a talent pool based on a query from a user, where the talent pool comprises members and the members comprise attributes. The method further includes determining based on a size of the talent pool a sampling size number, where the attributes are represented by probability distribution functions. The method may further include determining an aggregate distribution of the attributes by combining the sampling size number of samples from each of the attributes and determining a range of a value of the aggregate distribution of the attributes. The method may further include causing the range of the value of the aggregate distribution of the attributes to be displayed to the user.

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San Jose, CA, United States of America

Shi Yan