Ungria López, Javier

Frameworks and agentic loops for auto-generating tools and/or building artificial intelligence (AI) agents using tools are disclosed. The agentic loops may be represented in Business Process Model and Notation (BPMN) or any other desired workflow language. The tools can be implemented as static, invocation-specific, or dynamic caller-side or server-side tools. The tools may not be defined at all, may only be abstractly defined, or may be entirely defined within an Al agent definition. The implementation of these tools can be injected as part of the agentic process. The Al agent that implements these tools may also build other Al agents that can leverage the caller-side and server-side tools.

Systems and methods for evaluating computer security issues of a computer system are provided. An alert for a computer security issue of a computer system is received. An initial AI (artificial intelligence) agent determines 1) a strategy for evaluating the computer security issue and 2) one or more additional AI agents from a pool of AI agents for evaluating the computer security issue. The computer security issue is evaluated according to the strategy using the one or more additional AI agents. Results of the evaluation of the computer security issue are output.

Systems and methods for automatically requesting user input for performing a task by an AI (artificial intelligence) agent are provided. The AI agent is executed on a computing system for performing a task. It is determined, by the AI agent, whether to request user input during the performance of the task. In response to determining to request the user input, a particular user to request the user input from is identified by the AI agent. A request for the user input from the particular user is generated by the AI agent based on the task. The request for the user input is sent to the particular user. The performance of the task is continued by the AI agent based on the user input received from the particular user.

Systems and methods for automatically modifying a process by an Al (artificial intelligence) agent for adjusting user input of the process are provided. Process data associated with a process is received. The process is automatically modified based on the process data by the Al agent to adjust user input of the process. The modified process is output.

The present invention relates to a recycling system for recycling a volume made up of waste comprising plastics, particularly waste from packaging comprising plastics, and more particularly cardboard food packaging comprising plastics. The present invention also relates to a recycling method for recycling a volume made up of waste comprising plastic by means of said recycling system. The present invention thereby relates to the recycling process for recycling waste comprising plastic and other elements, such as metals, for example aluminium, for subsequent use. In particular, said recycling process for recycling waste is suitable for waste of the cardboard packaging waste for foodstuffs, comprising plastics as well as the materials indicated.

There is disclosed a portable emergency beacon light (1) for emergency vehicles, comprising: a lower casing (2), which has a cylindrical shape with a diameter of less than 8 cm; an upper lens (3); a control plate (4); a light emitter (5), which has a hexagonal prism shape. The light emitter (5) is fixed perpendicular to the control plate. The light emitter comprises, on each of the faces of the hexagonal prism: a set of LED lights (6) and an optic (7) located over each set of LED lights (6). The beacon light further comprises a microcontroller (8) configured to send control signals, and a set of drivers (9), which is connected with the microcontroller and with the set of LED lights (6). The set of drivers is configured to activate/deactivate each LED according to the control signals received from the microcontroller (8).

Automation generation, execution validation, and healing by artificial intelligence (AI) agents is disclosed. In order to generate an automation, a user enters a description of the desired task to accomplish into a prompt or some other description and the Al agent attempts to perform the task by employing a screen understanding and document object model (DOM) extraction engine to extract screen content and/or using an object repository. A user may validate the steps that the Al agent will take when it executes before deploying the automation. Al agents may also be used to heal existing robotic process automations (RPA), application programming interface (API) automations, agentic orchestration processes (AOPs), etc. by searching the object repository for solutions for an RPA workflow and then also applying these solutions to other RPA workflows and then also applying the solutions to similar activities of other RPA workflows.

Present invention refers to a triboelectric nanogenerator and manufacturing method thereof, comprising: an electrode having a composite material layer attached to the electrode; and a counter-electrode having a tribo-negative material layer attached to the counter-electrode, configured to contact the composite material layer by an external force; wherein the composite material layer comprises a tridimensional nanostructure of a first tribo-positive isolating material having a plurality of interconnected pores filled with a second tribo-positive isolating polymeric material.

There is disclosed an emergency directional spotlight (1) for emergency vehicles, comprising: a base plate (2), a microcontroller (3), a driver (4), and a light guide comprising: two horizontally arranged sets of LED lights (6, 6') and two flat refractors (5, 5'). The spotlight further comprises: a horizontal intermediate set of LED lights (7, 8, 9), a vertical intermediate set of LED lights (10, 11), an intermediate set of semicylindrical collimators (17), and an intermediate set of cylindrical collimators (18, 19). The sets of LED lights of the light guide, the horizontal intermediate set of LED lights, and the vertical intermediate set of LED lights are configured to generate the emission of predetermined light sequences according to the regulations in force. The double light guide and the LEDs between the light guide lead to a device having a small size.

The present invention discloses a system (1) and a method for an automatic classification of recyclable containers. The system comprises: a receptacle (2) for holding a recyclable container (3); a computing unit (4); a loudspeaker (5) for generating ultrasonic (50) and/or audible sound waves (51); microphones (7) for capturing the sound waves. The computing unit is configured to generate signal: an exponential sine sweep (ESS) in the audible range, an ESS in the ultrasonic range, or an audible ESS and modulate it to the ultrasonic range; send the signal to the loudspeaker; measure the sound waves and compute the acoustic impulse responses (52); postprocessing the acoustic impulse responses; classify the type of recyclable container using Machine Learning, trained using a training dataset. The training dataset includes acoustic impulse responses of recyclable containers to ultrasonic and/or audible sound waves, and types of recyclable containers issued from a classification test.