Patent classifications
G06N5/02
Factor analysis device, factor analysis method, and storage medium on which program is stored
Provided is a factor analysis device capable of obtaining more useful knowledge relating to the degree of influence of pieces of data. A factor analysis device according to one embodiment of the present invention is provided with: a classification unit for classifying a type of data into a first group or a second group; and an influence degree calculation unit for calculating, as the degree of influence on target data, the degree of influence of the data of the type classified into the second group on the data of the first group type.
Automated honeypot creation within a network
Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.
Automated honeypot creation within a network
Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.
Device with built-in bill capture, analysis, and execution
Systems and methods for secure and efficient bill capture, analysis, and execution are provided. A method may include capturing, via a camera embedded in a smart card, an image of a bill. The bill may include a plurality of text fields. The method may include processing the text fields via a microprocessor embedded in the smart card. The method may include determining, based at least in part on the processing of the text fields, a balance amount and a payment recipient associated with the bill. The method may also include executing a payment for the balance amount from an account associated with a user of the smart card to an account associated with the payment recipient. The executing may be performed via a wireless communication element embedded in the smart card which may be configured to provide wireless communication between the smart card and a payment gateway.
Device with built-in bill capture, analysis, and execution
Systems and methods for secure and efficient bill capture, analysis, and execution are provided. A method may include capturing, via a camera embedded in a smart card, an image of a bill. The bill may include a plurality of text fields. The method may include processing the text fields via a microprocessor embedded in the smart card. The method may include determining, based at least in part on the processing of the text fields, a balance amount and a payment recipient associated with the bill. The method may also include executing a payment for the balance amount from an account associated with a user of the smart card to an account associated with the payment recipient. The executing may be performed via a wireless communication element embedded in the smart card which may be configured to provide wireless communication between the smart card and a payment gateway.
Artificial intelligence apparatus for cleaning in consideration of user's action and method for the same
An AI robot for cleaning in consideration of a user's action includes a camera to acquire a first image data for the user, a cleaning unit including a suction unit and a mopping unit, a driving unit configured to drive the AI robot, and a processor to determine the user's action using the first image data, determine a cleaning schedule in consideration of the user's action, and control the cleaning unit and the driving unit based on the determined cleaning schedule.
Artificial intelligence apparatus for cleaning in consideration of user's action and method for the same
An AI robot for cleaning in consideration of a user's action includes a camera to acquire a first image data for the user, a cleaning unit including a suction unit and a mopping unit, a driving unit configured to drive the AI robot, and a processor to determine the user's action using the first image data, determine a cleaning schedule in consideration of the user's action, and control the cleaning unit and the driving unit based on the determined cleaning schedule.
Efficient off-policy credit assignment
Systems and methods are provided for efficient off-policy credit assignment (ECA) in reinforcement learning. ECA allows principled credit assignment for off-policy samples, and therefore improves sample efficiency and asymptotic performance. One aspect of ECA is to formulate the optimization of expected return as approximate inference, where policy is approximating a learned prior distribution, which leads to a principled way of utilizing off-policy samples. Other features are also provided.
Efficient off-policy credit assignment
Systems and methods are provided for efficient off-policy credit assignment (ECA) in reinforcement learning. ECA allows principled credit assignment for off-policy samples, and therefore improves sample efficiency and asymptotic performance. One aspect of ECA is to formulate the optimization of expected return as approximate inference, where policy is approximating a learned prior distribution, which leads to a principled way of utilizing off-policy samples. Other features are also provided.
Automation system and method
A computer-implemented method, computer program product and computing system for receiving a complex task; processing the complex task to define a plurality of discrete tasks each having a discrete goal; executing the plurality of discrete tasks on a plurality of machine-accessible public computing platforms; determining if any of the plurality of discrete tasks failed to achieve its discrete goal; and if a specific discrete task failed to achieve its discrete goal, defining a substitute discrete task having a substitute discrete goal.