16 results about "Request - action" patented technology

Systems and methods of selecting digital platforms for execution of voice-based commands are provided. The system receives an application that performs an action associated with a service via digital platforms. The system debugs the application to validate parameters of the action on at least two platforms of the digital platforms. The system receives data packets comprising an input audio signal detected by a sensor of a client device, and parses the input audio signal to identify the action and the service. The system selects a first platform from the digital platforms to perform the action. The system initiates, responsive to selection of the first platform, an interactive data exchange to populate parameters of an action data structure corresponding to the action. The system executes the action via the selected platform using the action data structure.

One wireless communication method includes: receiving a request action frame; and in response to the request action frame, generating and sending an acknowledgement (ACK) control frame that is configured to serve as a response action frame for the request action frame, wherein the response action frame is not solicited by the request action frame. Another wireless communication method includes: receiving a request action frame; and in response to the request action frame, generating and sending an acknowledgement (ACK) control frame and a time-constrained response action frame following the ACK control frame, wherein the time-constrained response action frame is solicited by the request action frame.

This relates generally to intelligent automated assistants and, more specifically, to provide intelligent device selections by the intelligent automated assistants for performing requested actions. An example method includes, at an electronic device receiving a user request from a user, identifying the user, a domain type of the user request, and one or more electronic devices available for handling the user request; retrieving one or more historical interactions involving at least one of the identified user, the domain type, and the one or more electronic devices, generating metadata based on the one or more historical interactions, location information of the one or more electronic devices, and context information associated with the one or more electronic devices; identifying a delivery device by interpreting the metadata using a preference model; and transmitting a response command to the delivery device for providing the result output.

A system and method for authentication of a requested action for account security are provided. The method may include receiving notification of the requested action associated with an account. The method may include transmitting an end-user prompt to a prompt endpoint associated with the account. The method may include receiving a response message including an end-user response which may indicate approval of the requested action from the prompt endpoint and which includes data points collected from the prompt endpoint. The method may include processing the collected data points to determine an updated authentication activity requirement, which may include a requirement to decline or further verify the requested action despite the end-user response message indicating approval of the action.

Methods and systems are described for utilizing machine learning models to generate a response. The system receives an operator input requesting actions to prompt a desired user interaction. The system selects user-specific machine learning models trained on interactions associated with each user of a plurality of users. Based on the operator input, the system determines a model subset and selects a candidate operator action to test. The system inputs the candidate action into each model of the subset to obtain a predicted user interaction. The system then assesses whether the candidate action causes the models to predict the desired interaction. If the candidate action is effective, the system executes the candidate action in relation to the user subset.

Systems and methods are provided for securely activating actions by work machines, independent of distance between the machines and users requesting the actions. An authentication network is established between a host server, authorized users, and work machines, wherein authorized user computing devices are assigned authentication keys. A user sends a first message to the host server comprising requested actions by an identified work machine and signed with an authentication key. The host verifies authenticity of the first message, optionally adds a host signature, and transmits the verified message to the work machine. The machine controller verifies authenticity of the first message and conditionally generates a second message comprising a confirmation challenge to the user. If the user responds satisfactorily to the challenge, the machine executes the requested actions, for example relating to startup / shutdown, access control, autonomous control of operations, adjustments to machine settings, and the like.

The electronic system is configured to process a natural language spoken (720) by a computer game player during game play using a customized large language model (740). Then, in response, a non-verbal output may be provided (750), where the non-verbal output may be visual, tactile, and / or auditory. As an example, non-verbal output may be responsive to a request from a user, may confirm that a natural language is being processed, and / or may indicate that a requested action has been registered for future execution.

An OTN network electric layer service dynamic route allocation method, apparatus and device, and a computer readable storage medium, the method comprising: training a main neural network according to constructed training set data to obtain target parameters of the trained main neural network, the training set data comprising multiple groups of training data, and the training set data comprising multiple groups of training data; the training data comprises a current global network state, a service request action, a return value and a next-moment global network state; updating parameters of a target neural network in an OTN network controller according to the obtained target parameters to generate a target model; outputting a target value of each path according to an obtained new service request based on the target model; and determining a target path according to the target value of each path, and allocating bandwidth resources to the target path, thereby solving the technical problem that the balance of service transmission delay, channel utilization rate and channel resources is difficult to optimize at the same time in the prior art.

Methods and systems for routing execution requests to autonomous artificial intelligence agents using performance-based selection are disclosed herein. An agent routing system may receive a prompt for completing an action using artificial intelligence agents from a plurality of agents, where each agent may be associated with computer-executable operations configured for autonomous execution on software applications. The system may determine a set of agents enabled to complete the requested action and may access action completion datasets for these agents. Each dataset may include action parameters for completed actions, including status parameters indicating successful completion and timestamps of completion. The system may select an artificial intelligence agent based on a frequency composite parameter generated from successful completion status parameters and a time composite parameter indicating completion timing. The system may identify parameters required by the selected agent to complete the action and may transmit these parameters to the artificial intelligence agent for execution.

An information handling system for an on-the-box artificial intelligence productivity tool may comprise a memory storing descriptions of capabilities associated with software applications in a decision tree with each capability and a generated capability intent value stored as a capability node grouped under one of several branches in parent-child relationships, and a hardware processor executing machine readable code instructions to generate a query input intent value from a user query input requesting action by one of the software applications, comparing the capability intent values of each of the capability nodes in the decision tree, as weighted by a lexical comparison between the user query input and each of the descriptions of capabilities to conduct a breadth-first search to identify a best match childless capability node having a highest lexical weighted cosine semantic similarity search score, and executing the associated best match capability from the best match childless capability node.

An information handling system managed by an enterprise management system and operating an on-the-box artificial intelligence productivity tool may comprise a hardware processor executing machine readable code instructions to generate vectorized capability intent values from natural language descriptions of capabilities including a capability-with-reboot requiring reboot to a basic input / output system (BIOS) and a vectorized query input intent value for a user query input requesting action from information handling system, compare the query input intent value to the capability intent values to identify a best match responsive capability-with-reboot for the received user query input having a capability intent value that generates a highest semantic similarity search score and requiring authorization for execution of pre-boot machine readable code instructions for the best match capability-with-reboot received from the enterprise management system, store it in a pre-boot memory partition, reboot, and execute the best match responsive capability-with-reboot upon boot to BIOS.

A method for controlling a remote control device includes capturing with a biometric device biometric data associated with a user, determining with processor whether the user is authorized to interact with the smart device, in response to the biometric data, determining with the processor user data, in response to the user being authorized to interact with the smart device, receiving with a short-range transceiver an authentication request from a reader device associated with a remote control device, outputting with the short-range transceiver a session token in response to the authentication request and to the user data, thereafter determining with a physical sensor physical perturbations in response to physical actions of the user, determining with the processor a requested action for the remote control device, in response to the user data and the physical perturbations, and outputting with the short-range transceiver the requested action to the reader device.

This invention provides an electronic device and a method for waking the electronic device from sleep mode. When the electronic device is in sleep mode, if it receives an intermittent low-power threshold detection enable signal, it enters a low-power threshold detection mode. When the electronic device is in sleep mode, if it receives a sampling request action signal or acceleration data exceeding a first threshold range, it enters a continuous operating mode. If it does not receive a sampling request action signal within a predetermined time period or the acceleration data does not exceed a second threshold range within a predetermined time period, it enters sleep mode. When the electronic device is in the low-power threshold detection mode, the low-power threshold detection circuit operates to detect the acceleration of the electronic device and determine whether the detected acceleration exceeds a third predetermined threshold range. If not, the low-power threshold detection mode continues; if so, the continuous operating mode is entered. Compared with the prior art, this invention can accurately and timely wake the electronic device from sleep mode, thereby reducing power consumption.

A method for a wireless device includes determining to transition to a low power mode in a network and sending a request message to the network requesting a change to operate in the low power mode. The wireless device receives a response message from the network indicating that the wireless device can transition to the low power mode and the wireless device transitions to the low power mode based on the received response message. The device can also receive a request action signal from the network indicating that the wireless device should transition from the low power mode to perform an action. The device sends a request response signal indicating whether the wireless device will perform the action of the request action signal and transitions from the low power mode to perform the action. Messages to and from the device in the low power mode can use backscatter transmissions. Additional embodiments are disclosed.

A computing system facilitates a privileged action approval system that receives approval for a user entity to perform a privileged action with respect to a protected computing resource or platform. One or more approval entities may be assigned by an administrative entity to approve execution of the privileged action, wherein the administrative entity is not assigned an ability to approve execution of the privileged action. The approval system may be configured with information indicative of the one or more approval entities and upon obtaining approval by the approval entities may direct, to the user entity, approval information, for example a token, that may comprise a request identifier. The approval system may determine whether a privileged action matches information associated with the request identifier and may facilitate execution of the requested action based on the requested privileged action being determined to match information associated with the request identifier.

Systems and methods of selecting digital platforms for execution of voice-based commands are provided. The system receives an application that performs an action associated with a service via digital platforms. The system debugs the application to validate parameters of the action on at least two platforms of the digital platforms. The system receives data packets comprising an input audio signal detected by a sensor of a client device, and parses the input audio signal to identify the action and the service. The system selects a first platform from the digital platforms to perform the action. The system initiates, responsive to selection of the first platform, an interactive data exchange to populate parameters of an action data structure corresponding to the action. The system executes the action via the selected platform using the action data structure.