Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for handling tokens. In one aspect, a token is intercepted by a data processor and a contact is made with a detokenization provider by the data processor. In another aspect, token service is aggregated. In another aspect, tokenization is extended to physical cards, to deter certain kinds of fraud.

Abstract: Systems and methods for handling tokens. In one aspect, a token is intercepted by a data processor and a contact is made with a detokenization provider by the data processor. In another aspect, token service is aggregated. In another aspect, tokenization is extended to physical cards, to deter certain kinds of fraud.

Abstract: Described herein are techniques for facilitating push provisioning of a user payment source into a user's digital wallet without the user having a physical card. The techniques allow an issuer to provide a button in an issuer's mobile application for the user to simply push the button to request that the payment source be imported into the user's digital pay wallet. In this way, the payment source information is “pushed” into the pay wallet. Using push provisioning, the user need not enter any physical card information. The described techniques generate a chain of trust that can be used to ensure that a user, through an issuer and using an encryption gateway, authorizes a token service provider to provision the payment source into the user's digital wallet.

Abstract: Systems and methods for handling tokens. In one aspect, a token is intercepted by a data processor and a contact is made with a detokenization provider by the data processor. In another aspect, token service is aggregated. In another aspect, tokenization is extended to physical cards, to deter certain kinds of fraud.

Abstract: Methods and system for determining reputation information are provided. The method includes analyzing transactional and non-transactional information for an alias associated with an entity and determining reputation information for that alias. The reputation information is transferrable across multiple domains. A user can request the reputation information about an entity prior to doing business with that entity. The reputation information is generated for merchants as well as users. The system can generate transaction risk score for every transaction to be conducted between two or more aliases. The system includes an alias identity and reputation validation server computer that receives inputs from various external systems and generated reputation information based on the inputs. Additionally, every individual user/merchant can manage his profile within the reputation database and enter some information manually.

Abstract: Methods and system for determining reputation information are provided. The method includes analyzing transactional and non-transactional information for an alias associated with an entity and determining reputation information for that alias. The reputation information is transferable across multiple domains. A user can request the reputation information about an entity prior to doing business with that entity. The reputation information is generated for merchants as well as users. The system can generate transaction risk score for every transaction to be conducted between two or more aliases. The system includes an alias identity and reputation validation server computer that receives inputs from various external systems and generated reputation information based on the inputs. Additionally, every individual user/merchant can manage his profile within the reputation database and enter some information manually.

Abstract: Methods and system for determining reputation information are provided. The method includes analyzing transactional and non-transactional information for an alias associated with an entity and determining reputation information for that alias. The reputation information is transferable across multiple domains. A user can request the reputation information about an entity prior to doing business with that entity. The reputation information is generated for merchants as well as users. The system can generate transaction risk score for every transaction to be conducted between two or more aliases. The system includes an alias identity and reputation validation server computer that receives inputs from various external systems and generated reputation information based on the inputs. Additionally, every individual user/merchant can manage his profile within the reputation database and enter some information manually.

Abstract: Systems and method for generating reputation information for an alias are provided. The alias is associated with an entity. Payment transaction information related to a first alias associated with a first entity is received. The payment transaction information includes payment transactions performed by the first entity. In addition, non-payment transaction information related to the first alias is also received. The non-payment transactions include transaction between the first alias and one or more objects. The payment transaction information and the non-payment transaction information is analyzed to associate the one or more objects with the first alias. Thereafter, reputation information for the first alias is generated based at least in part on the payment transaction information and the non-payment transaction information.

Abstract: Methods for creating an alias data structure are provided. A representative method includes receiving transaction information related to a first alias associated with an entity, receiving transaction information between a first object and the entity, and associating the first object with the first alias based at least in part on the transaction information related to the first alias and transaction information between the first object and the entity. The method further includes continually monitoring transaction information related to the first object and updating the association of the first object with the first alias in response to the transaction information.