54 results about "Request - action" patented technology

Disclosed are systems and methods to control a robotic device using natural language commands. A natural language command may be received by a system. The system may convert the command into low-level machine controls and logic for implementation by a robotic device to achieve a desired action. In some instances, an API module may include mapping data to associate high-level commands with low-level machine controls. A language model may process the natural language command (input), high-level commands, and / or other information, such as system state, sensor observation data, parameters, etc., to determine one or more commands to execute by a robotic device and possibly logic for execution by the robotic device. The robotic device may receive the low-level machine controls and logic to cause the robotic device to perform the requested actions.

This disclosure describes a framework for performing user-requested tasks automatically across an interactive interface using various types of machine learning models. Specifically, this disclosure outlines and describes a task execution system that utilizes a generative artificial intelligence (AI) action model and retrieval-augmented generation (RAG) to complete user-requested actions across an interactive interface. The task execution system solves many of the current limitations of LAMs by using a generative AI action model to determine a session plan, which includes a set of actions for accomplishing stages of the actionable task across the interactive interface, obtaining visual context information of each interactive interface segment, integrates RAG results to improve the accuracy of both the session plan and individual actions, and self-corrects when faced with unexpected obstacles.

The invention discloses a network security threat detection system and method based on deep learning, and particularly relates to the field of network security trapping analysis based on a honeynet and a honeypot, and the method comprises the steps: collecting trapping node log data in a network security behavior trapping process; extracting a behavior event set including access time, request action types, parameter structures and node identifiers, organizing the behavior event set according to a timestamp sequence, and constructing a basic behavior node chain; and performing access sequence consistency check on the behavior node chain. A path map structure based on access tension evolution is constructed in a honeynet environment, a deep learning model is fused to execute trend reasoning and behavior disturbance prediction on a high-tension path segment, and a redundant path induction feedback mechanism is combined to realize preposed recognition and active trapping intervention on an attack path. The problem that high-concealment attacks cannot be perceived and responded in advance in a behavior window period is solved.

A system for conserving computing and memory resources and enhancing security by performing application-level multifactor level authentication of a client requesting execution of an action by the system. A request to execute an action sent by the client and received by an action execution application of the system may include compiled client authentication information. The compiled client authentication information may comprise at least a client digital certificate and client login credentials that are unique to the action execution application. The action execution application can authenticate the client relative to the requested action by validating the received client digital certificate against a stored set of previously validated digital certificates, in addition to validating the received client login credentials against a stored set of previously validated login credentials. In response to authenticating the client, the action execution application may execute the requested action.

Techniques for masking data based on annotations are discussed herein. A system may receive a request to perform an action and leverage an LLM to assist in performing the requested action. When generating the input data to input to the LLM, the system can use a template to organize the input data. The template may include static data and / or slot(s) which can include a reference to data to input into such slots. The system may retrieve data to input to the slot based on the reference, retrieve annotations that define a classification of the data, and receive a policy that defines which types of data classifications are to be masked. Based on the data classification and the policy, the system can determine whether to mask the data. The system can generate the input data using the template, the data, and / or the mask(s) and input such data into the LLM.

Techniques for streaming generative machine learned model (or LLM) output to a virtual space are described herein. A system may receive a request to perform an action. The system may leverage LLMs to assist in performing aspects of the requested action. The system can generate input data to input into the LLM. When generating the input data, the system can identify sensitive data associated with the request. The system can modify the input data to mask and / or anonymize the sensitive data. The system can input the input data into an LLM trained to output a subset (e.g., less than all) of the response at a time. The system can add the output subset to a buffer and upon identifying the masked data in the buffer, the system can demask the sensitive data and output the sensitive data to the user profile in a streaming manner.

A wireless transmit / receive unit (WTRU) may receive a first message broadcast by a network. The first message may indicate one or more challenge values, an estimated required energy level for performing the requested action, and / or an indication that the WTRU should respond with an indication of an amount of energy available to the WTRU. The WTRU may determine whether to respond to the first message based on the amount of energy available to the WTRU. The WTRU may send a second message to the network based on the determination to respond to the first message. The second message may indicate a challenge response value, an amount of energy available to the WTRU, and / or a device-specific network challenge value.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for processing access requests on a service-to-service basis using a third-party identification token. In particular, the disclosed systems can identify that a user is authenticated for a first computer service based on detecting a third-party identification token. Further, the disclosed systems can generate, by the first computer service, an access request comprising a requested action and the third-party identification token to a second computer service. Additionally, the disclosed systems can determine, by the second computer service, whether the access request is authorized based on determining that an authorization policy defined at the second computer service authorizes the requested action by the first computer service and that the third-party identification token is valid. Moreover, the disclosed systems can provide, to the first computer service, a response to the access request in response to determining whether the access request is authorized.

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.

Methods and systems are described herein for authenticating user actions, while preventing social engineering attacks on the user. A request may be received for performing an action. The system may obtain, based on a plurality of parameters, a plurality of natural language representations of the action and prompt the user, on the user's mobile device, to describe what the user is attempting to do. The system may then use a machine learning model to determine whether the requested action matches the user's response. Based on determining that the natural language response matches a natural language representation, the system may generate a communication indicating that the action has been authenticated.

Methods, apparatuses, and computer readable media for multi-band discovery, where a station (STA) comprises processing circuitry configured to: decode, from a second STA, a quality of service (QoS) and connection reporting (QCR) request action frame, the QCR requestion action frame including a first quality of service (QoS) management element, the first QoS management element including a QCR request attribute and an analytics attribute, the QCR request attribute indicating that the first STA is to provide analytics indicated in the analytics attribute, and configured to: encode, for transmission to the second STA, a QCR response action frame, the QCR response action frame comprising a second QoS management element, the second QoS management element comprising a QCR response attribute, the QCR response attribute indicating whether the first STA will provide the analytics indicated in the analytics attribute.

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.

Disclosed are systems and methods to control a robotic device using natural language commands. A natural language command may be received by a system. The system may convert the command into low-level machine controls and logic for implementation by a robotic device to achieve a desired action. In some instances, an API module may include mapping data to associate high-level commands with low-level machine controls. A language model may process the natural language command (input), high-level commands, and / or other information, such as system state, sensor observation data, parameters, etc., to determine one or more commands to execute by a robotic device and possibly logic for execution by the robotic device. The robotic device may receive the low-level machine controls and logic to cause the robotic device to perform the requested actions.

Various embodiments of the present technology generally relate to systems and methods for providing an authentication engine for monitoring interactions associated with network functions. In an example, an authentication engine may determine a request from a client device that includes a requested action associated with a network function within a network. The client device may have authentication credentials for performing the requested action. The authentication engine may determine a deviant intent based on the requested action, such as determining the requested action being outside a range of normal activity and / or associated with an active network function. Based on the deviant intent, the authentication engine may generate a subsequent authorization requirement that is required to be completed for the client device to perform the requested action.

A system for processing a request to execute an action by a future date is disclosed. The system may access training data including historical action data, historical execution times, and historical utilization rate information for one or more execution objects, and can use the training data to train a machine-learning model. Upon receipt of a request to execute an action at a future date, action execution information provided in the request, the future date, and at least the current date, can be input to the trained machine-learning model, which may be configured to generate an output indicating a time-shifted target date on which the execution objects are available to execute the requested action that is no later than the future date. The system can then schedule the requested action for execution by the execution objects on the time-shifted target date.

A risk-aware access control system and related methods are provided. In accordance with one aspect of the present disclosure, there is a provided a method of risk-aware access control, comprising: detecting a request to perform an action with respect to two factors, the factors being of a factor type selecting people, devices, documents, and location, wherein the factors are of a different factor type; determining a coupling associated with the requested action based on the factors of the requested action; determining a risk level associated with the coupling; denying the requested action in response to a determination that the risk level does not match a security policy; and allowing the requested action in response to a determination that the risk level matches the security policy.

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.

Disclosed in the present application are a multi-modal request processing method and apparatus. The method comprises: receiving a multi-modal streaming media request; acquiring the type of a request action in the multi-modal streaming media request, and determining a target detection method corresponding to the type of the request action, wherein the target detection method is configured to determine whether the multi-modal streaming media request has been received; and using the target detection method to perform detection on the multi-modal streaming media request, and outputting a response result of the multi-modal streaming media request when the multi-modal streaming media request has been received.

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.

Two party computer access control including a network interface for receiving, an initiate message including a first label, a first payload and a first internet protocol address and for receiving a control message including a second label, a second payload and a second internet protocol address, and a processor for executing the action in response to the first label corresponding to the action, the first internet protocol address corresponding to an expected internet protocol address of the first user system, the first payload corresponding to a first expected payload associated with the requested action, the second label corresponding to the action, the second internet protocol address corresponding to an expected internet protocol address of the second system, and the second payload corresponding to a first expected payload associated with the action.

A conversational interface receives a conversational request for an action which requires step-up authentication; responds to the user using conversational AI with a warning that an additional step is required; transmits the authorization intent and the spoken submission to a virtual private cloud (VPC), and in response to confirmation of the spoken submission, authenticates the user, thereby authorizing the requested action; and receives a confirmation of validation of the passphrase and that the action is being or will be performed.

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.

A computer-implemented method for providing location-based appointment operations is disclosed. The method includes receiving input indicating an instruction to perform an action related to appointments. Responsive to the input indicating the instruction to perform the action related to appointments, image recognition techniques may be used to determine that a computing device is proximate to a physical location where locations are schedulable. Then, responsive to determining that the computing device is proximate to the physical location, information related to scheduling appointments at the physical location is received via a network. The indication based on the received information is presented at the computing device. Input requesting an action in relation to an appointment at the physical location may be received at the computing device. An indication to initiate the requested action may be sent via the network. Related computing devices and computer-readable media are also disclosed.

Disclosed are a method and device for virtually simulating a quantum communication circuit. The simulation method according to the present invention comprises the steps of: displaying, on a display unit, a simulation screen including a first selection window, a second selection window, and a work window, wherein the first selection window is an area in which a plurality of element items associated one-to-one with a plurality of element blocks that can be used for modeling a quantum communication circuit are displayed, and the second selection window is an area in which a plurality of connection line items associated one-to-one with a plurality of block connection lines that can be used for modeling the quantum communication circuit are displayed; disposing an element block associated with an element item selected in the first selection window in the work window in response to a first circuit modeling request action; disposing a block connection line associated with a connection line item selected in the second selection window in the work window in response to a second circuit modeling request action; connecting each of the block connection lines disposed in the work window to two of the plurality of element blocks disposed in the work window, wherein at least one pair of element blocks among the plurality of element blocks disposed in the work window are interconnected by one of the block connection lines disposed in the work window to enable the bidirectional transfer of optical signals; and executing, in response to a simulation request action, a simulation of the quantum communication circuit virtually modeled in the work window by means of the plurality of element blocks and the plurality of block connection lines disposed in the work window.

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.

The present invention relates to a dual - frequency switching method, a dual - frequency access point and a dual - frequency station. The dual - frequency access point sends a band - switching request action management frame to notify the dual - frequency station of the wireless parameter information of the band to be switched. After receiving the band - switching response action management frame from the dual - frequency station, the dual - frequency access point switches the data and configuration to the new band, refreshes the wireless parameters of the dual - frequency station according to the negotiated wireless parameters of the new band, sends a band - switching confirmation action management frame to the dual - frequency station after transferring the data and configuration to the new band, and starts the data receiving function on the new band. After receiving the band - switching end action management frame from the dual - frequency station on the new band, the dual - frequency access point starts the data sending function on the new band. During the process of switching the band, there is no need for the device to re - associate. The switching process has fewer steps, is fast, and the service is not interrupted.

A wireless communication method includes: receiving a request action frame; and in response to the request action frame, generating and sending an acknowledgement 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. A wireless communication device (102) includes a network interface circuit (118) and a control circuit (116). In response to a request action frame received by the network interface circuit (118), the control circuit (116) generates an acknowledgement control frame that is configured to serve as a response action frame for the request action frame, and instruct the network interface circuit (118) to send the acknowledgement control frame, wherein the response action frame is not solicited by the request action frame.