308 results about "Remote computing" patented technology

A system for managing a guardrail data structure is provided. The system includes one or more processors; and one or more transitory or non-transitory computer-readable media storing instructions that are executable to cause the one or more processors to perform operations, the operations comprising: receiving, from a first user of a plurality of users of a guardrail data structure, interaction data associated with chat data; processing, by a large language model (LLM), the interaction data to determine an update to at least one cluster membership of at least one content cluster of a plurality of content clusters; transmitting the update of the at least one cluster membership to a remote computing device; and instructing the remote computing device to update the guardrail data structure based on the update to the at least one cluster membership.

System and methods are disclosed relating to cloud edge based anomaly detection. In an example, an edge monitoring node can include a thermal camera to capture a thermal image of equipment that is under monitoring for an an anomaly event. The node further includes a machine learning (ML) model to process the thermal image of the equipment to detect the anomaly event. The node further includes a network interface to communicate the detected anomaly event over a network to a remote computing platform to determine one or more recommendations for proactive maintenance of the equipment.

A system, method and memory for managing remotely stored data is disclosed. The system is configured to provide an agent within a remote computing environment (RCE) with access to data stored on the RCE and custom applications. In some examples, the agent has access to one or more tools including aspects of a machine learning tool. Another agent on a local computing environment capable of communicating with the agent is provided. The other agent has access to configuration files. The other agent instructs the agent to execute custom applications based on the configuration files. The other agent receives metadata of the data in response to triggering the agent. The instructions cause the processor to populate, via the other agent, a catalogue of the plurality of data within the remote computing environment based on the received plurality of metadata, and provide the catalogue for display for reviewing the plurality of data.

The present disclosure describes a server computing device configured for receiving from a remote computing device via an application, in response to a low-risk access request to a data resource, a programming call with a URL comprising an second identification data item encrypted from a first identification data item acquired by the remote computing device, obtaining a first context information item associated with the programming call, inputting the first context information item into a machine learning model to generate a second context information item, extracting the second identification data item from the URL, generating a third identification data item by decrypting the second identification data item, retrieving a prestored property data item associated with the third identification data item, and transmitting requested data to the remote computing device if comparing the second context information with the prestored property data item produces a match.

A system includes a processing circuit configured to receive data from a sensor of a vehicle. The data includes one or more operational characteristics including at least one of a first operational characteristic indicating that a concrete mixture is being dispensed via a chute of the vehicle or a second operational characteristic indicating that the concrete mixture has stopped being dispensed via the chute. The processing circuit is also configured to receive a first time stamp responsive to receiving the first operational characteristic. The processing circuit is also configured to provide the first time stamp to at least one of a user device or a remote computing system.

Embodiments include a computing system, computing device, non-transitory computer readable medium, and computer-implemented method for automating artificial intelligence (AI) prompting and incident summarization. Embodiments provide for automatically generating AI prompts based at least in part on incident information, transmitting the AI prompts to a remote computing system on which a large language model (LLM) is implemented, and providing an AI summary of an incident.

A computing device may establish a call with a remote computing device, and may converse with the remote computing device in the call. The computing device may determine one or more candidate replies based at least in part on contextual information associated with the call. The computing device may receive an indication of a user input to select a candidate reply from the one or more candidate replies, and in response to receiving the indication of the user input to select a candidate reply, may send a reply in the conversation corresponding to the candidate reply.

A golf course geofence system includes a remote computing device. The remote computing device is configured to receive a request from a portable device to generate a geofence at a current location of the portable device on a golf course, acquire position data regarding a position of the portable device, and generate the geofence at the current location in response to the request and based on the position data. The request includes at least one of (a) a first request to generate a predefined shape geofence or (b) a second request to generate a freeform geofence.

Techniques for determining information about an event by one or more devices proximate to a roadway are described herein. For example, a device (e.g., a smart road stud) may include an integrated proximity sensor which can detect a static vehicle or a static object on the road. The device may process the sensor data to determine an event type and an event location for events. In some examples, the device can identify other devices in a vicinity of the event (e.g., other smart road studs located on the road), and select devices for performing actions, such as re-routing traffic. In some examples, the device can send sensor data and / or other types of data to other devices, such as an adjacent smart road stud, an edge device, and / or a remote computing device.

A computing system can automatically generate artificial intelligence (AI) prompts based on incident information, transmit the AI prompts to a remote computing system implementing a large language model (LLM) and receive an AI summary of an incident.

The present disclosure provides systems and methods for predictive analytics. For example, a system provided herein includes a data retriever implemented on a first unit configured to communicate over a data network with a second unit, a database coupled to the first unit and configured to store deal data, and a forecast toolkit implemented on a second unit configured to communicate over the data network with a remote computing device. The forecast toolkit may include a future deal manager configured to identify, within enhanced deal information, future open deals; a machine learning engine; a forecast manager configured to union the future open deals with an initial forecast to produce a second initial forecast, the initial forecast constructed by applying the machine learning engine to historical deal information; and a table builder configured to construct a predicted revenue output based on the second initial forecast.

A method for transmitting event data (21), in particular operator event data and / or status event data, from a field device (2) to a remote computing unit (3), comprising the steps of: generating the event data (21) by means of the field device (2) based on an event; providing an availability signal (22b) indicating the availability of the event data (21) for transmission (32) via a digital communication interface (15) to the remote computing unit (3); and providing the event data (21) for transmission via the digital communication interface (15) to the remote computing unit (3), wherein the event data (21) is provided separately from the availability signal (22b). The method comprises a computer program product, a field device (2) for executing the foregoing method, and a system (1) with the field device (2).

One or more unmanned or remotely controlled flying devices (a / k / a “drones”) are utilized as part of a parking management system to capture images and / or video for the purposes of recognizing vehicles located in parking (or no parking) locations. The image data is relayed to a remote computing system that can be is programmed to specifically identify a vehicle based upon its license plate and / or and unique characteristics of the vehicle (Handicap Placard, Parking Stickers, Make, Model, Color, Registration Stickers, etc.). This information is used to determine if the vehicle has the appropriate credentials to be parked at its location and to assess any parking charges and / or fines for violations. Images from satellites can be used to determine locations to image with the drones.

A clock device is in communication with a remote computing device that is configured to adjust the time of a countdown and define an adjusted time value. The remote device communicates the adjusted time to the clock device, which automatically displays the adjusted time on a screen. The adjusted time is further communicated to a cloud server via API. The adjusted time is accessible by authorized third parties for display or broadcast. Accordingly, a user may adjust and track a countdown and may define an official time that is automatically displayed to a variety of end-users in real-time. The clock device may be positioned adjacent a competition area and may include a rigid metal frame and a flexible LED screen extending around the perimeter of the frame. The clock device may include hardware and software for processing and outputting the adjusted time to the cloud server.

To provide a head-mounted display (HMD) for use in a virtual reality (VR) environment in which a user can view the real world without removing the head-mounted display.SOLUTION: The HMD104 includes a display 108 for providing virtual content and / or images to the user 100 and image capture devices, such as the first camera 110 and / or the second camera 112. The first camera 110 and / or the second camera 112 are disposed in or near the front of the MD104 to capture images of the surroundings 102 and pass through the images of the surroundings to the user for viewing on the display. The environment 102 includes a computer 150 that communicatively couples to the HMD, the controller 106, the tracking system 122, and / or the remote computing resources 142 via a network 124 and / or wired technologies.SELECTED DRAWING: Figure 1

A gas monitor includes a housing defining a first flow channel and a second flow channel with a gas sensing element fluidly coupled to each, and each channel including a flow path axis wherein the axes intersect at an angle of at least 60 degrees. Gas monitors can be stacked by engaging a support assembly of one with an engagement assembly of another such that a center of mass of two stacked gas monitors is positioned within a footprint of the support assembly of a lower one of the two stacked gas monitors. The gas monitor includes a battery assembly comprising a plurality of slots configured to each receive a battery pack, however, the gas monitor is configured to operate normally with power provided by a single battery pack. A controller can be configured to operate multiple gas monitors, along with a remote computing device, as a cooperative monitoring group.

A system for the monitoring of plunger lift systems within an oil and gas well, the system may include an acoustic sensor securable to at least a portion of an external surface of a wellhead portion, a transmitter communicatively coupled to the acoustic sensor, and one or more batteries configured to provide electrical power to the acoustic sensor and the transmitter. The monitoring system may measure acoustic vibrations from the internal mechanism of oil and gas equipment with the acoustic sensor and may transmit the acoustic vibration measurements with the transmitter. The transmitted acoustic vibration measurements may be received by a vehicle stereo and reproduced by the speakers of the stereo, or the acoustic vibration measurements may be received by local or remote computing devices. Acoustic vibration measurements may be used to enable the tuning of one or more performance factors for the oil and gas equipment.

The invention relates to the technical field of industrial heat treatment equipment control, in particular to a heat treatment furnace safety control system based on a cloud platform. The multi-dimensional state sensing module is used for collecting real-time physical field data of an industrial controlled object; the signal orthogonal decomposition module is used for decoupling the original state data set into a low-frequency control signal flow and a high-frequency characteristic signal flow, and transmitting the high-frequency characteristic signal flow to a remote computing platform; the state reconstruction module is used for resolving a theoretical state value at the current moment and constructing a virtual sensor observation state; the dynamic envelope generation module is used for calculating a residual error between a theoretical state value and real-time physical field data and generating a dynamic security envelope threshold value and a confidence coefficient attenuation coefficient; and the self-adaptive safety judgment module is used for generating a final safety execution threshold value and executing safety interlocking control on the industrial controlled object. According to the method, the sensitivity of capturing the abnormal working condition in the heat treatment process is improved, and the problem that the static threshold value easily generates false alarm or missing alarm under the complex variable working condition is solved.

A vehicle can connect to multiple vehicles and can determine network metrics (e.g., available bandwidth, latency, signal strength, etc.) associated with the multiple vehicles and at least one cellular network. As the vehicle traverses an environment, the vehicle can collect sensor data of the environment and / or vehicle data (e.g., vehicle pose, diagnostic data, etc.). Based on the network parameters, the vehicle can optimize the use of the connection with the multiple vehicles and / or the cellular network to transmit the sensor data and / or the vehicle data to a remote computing device.

Systems and methods for increasing accuracy in an online chat interface are provided. Methods may be executed via a machine-learning (ML)-based chat monitoring engine. Methods may include intercepting a request utterance. Methods may include computing, via a trained ML model, an intent of the request utterance; generating a target response to the request utterance; intercepting a response utterance; and calculating a difference between the response utterance and the target response. In response to the difference being less than a threshold difference, methods may include releasing the response utterance to be transmitted as a response message. In response to the difference being more than a threshold difference, methods may include preventing the response utterance from being transmitted as a response message, generating a revised response utterance that is less than a threshold difference apart from the target response, and transmitting the revised response as a response message to a remote computing device.

In one aspect, a user's intent can be inferred based on voice analysis during a communications session, and prompts can be presented, or other actions taken, at least partly in response to the inferred intent. For example, a network microphone device (NMD) having one or more microphones can capture voice input and transmit the voice input to remote computing device(s) for a communication session (e.g., a videoconference). The NMD can analyze the voice input to detect one or more utterances. Based on the utterance(s), the NMD can cause a user prompt to be displayed via a display device communicatively coupled to the NMD. The particular prompt can depend at least in part on one or more context parameters associated with the communication session (e.g., a microphone state of one or more users, a screen share state of one or more users, or a recording status of the session, etc.).

A server comprises a communications module; a processor coupled with the communications module; and a memory coupled to the processor and storing processor-executable instructions which, when executed by the processor, configure the processor to provide, via the communications module and to a remote computing device, a signal causing the remote computing device to display an interface of a digital wallet management application, the interface allowing one or more digital wallets to be selected; receive, via the communications module and from the remote computing device, a signal indicating a selection of the one or more digital wallets; receive, via the communications module and from the remote computing device, a signal indicating a request to push account information to the selected one or more digital wallets; and send, via the communications module and to one or more servers associated with the one or more digital wallets, a signal configuring the one or more servers to add the account information to the one or more digital wallets.

Described herein is a system and a method for collaborative training of a machine learning model for pose estimation. The system includes a remote computing device in communication with a local computing device, wherein the remote computing device is configured to instruct the local computing device to receive a first image; and generate a first pose datum as a function of the first image using a local machine learning model. The remote computing device is configured to receive, from the local computing device, the first pose datum; generate a second pose datum as a function of the first pose datum and the first image using a remote machine learning model; and configure the local computing device to retrain the local machine learning model as a function of the second pose datum.

A remote support system for multiple geographically dispersed fueling sites comprises a remote computing resource in operative communication with the fueling sites, the remote computing resource having a machine learning supervisor and a plurality of machine learning workers, the workers each having an area of responsibility. The machine learning supervisor is operative to: (a) receive a query from a user regarding a condition at a respective one of the fueling sites; (b) process the query to identify a topic of the query; (c) based on the topic, select one or more of the workers to have responsibility for collecting information in response to the query; (d) receive the information in response to the query from the one or more workers; and (e) provide the response to the query to the user. Each of the machine learning workers is operative to: (a) when the topic falls at least in part in the worker's area of responsibility, receive a request based on the query to gather information responsive to the topic; (b) collect and curate the information responsive to the topic; and (c) provide the information responsive to the topic to the supervisor.

Methods, systems, and apparatus, including medium-encoded computer program products, for 3D model rendering, include: obtaining, by a computer having a display device and local memory, three-dimensional scene description data structure encoding location information in a three-dimensional model of an environment, wherein the three-dimensional model is stored on a remote computer system, and the location information comprises bounding volumes for objects in the three-dimensional model; downloading, by the computer and from the remote computer system, a portion of the objects to the local memory; and rendering, by the computer, the portion of the objects along with one or more visual indicators representing a portion of the three-dimensional model that is still to be downloaded, wherein the one or more visual indicators are placed in three-dimensional space in accordance with the bounding volumes in the three-dimensional scene description data structure to provide feedback to a user regarding a status of object downloading.

The invention relates to a method and a device for providing generation information of a digital access key for a motor vehicle (10), the method comprising: receiving a generation inquiry on a remote computing unit (20) in order to generate a digital access key for the motor vehicle (10); invoking a generation limit for the digital access key; and when the generation limit is reached: providing a release request to the management terminal (30); receiving, on the management terminal device (30), an administrator input from the management user; and if the administrator input comprises a release of the generation restriction: providing generation information for the digital access key by the remote arithmetic mechanism (20).

In some aspects, a computing system can train a risk assessment model, using a training process, for determining a risk indicator. The training process can include: accessing a set of features; determining, using a selector network, a set of selected features and an indicator vector; and training the risk assessment model using the set of selected features and the indicator vector. The computing system can determine the risk indicator for a target entity using the trained risk assessment model. The computing system can transmit, to a remote computing device, a responsive message including at least the risk indicator for use in controlling access of the target entity to one or more interactive computing environments.

The techniques may include capturing an image that includes a depiction of the media of exchange. In addition, the techniques may include communicating image information representing the image to a remote computing device that is configured to execute a machine learning model that receives as input the image information and outputs a quantity that corresponds to the media of exchange. The techniques may include receiving a quantity that corresponds to the media of exchange and a confidence score that includes a probability that the media of exchange in the image corresponds to the quantity. Moreover, the techniques may include comparing the confidence score with respect to one or more evaluation criteria. Also, the techniques may include in response to the comparing, presenting, via a user interface, an option for establishing a communication channel. Further, the techniques may include in response to receiving input from the user interface, establishing the communication channel.

An AI-assisted mobile tennis training system integrates a motorized ball delivery device, multi-camera vision, autonomous navigation, and artificial intelligence for adaptive, data-driven player development. The device includes a motorized chassis, ball hopper, programmable ball delivery mechanism with variable spin, speed, and trajectory control, and a navigation subsystem employing omnidirectional wheels and localization based on court line recognition. A multi-camera array captures real-time player and ball movement, while onboard and remote computing modules process the data to determine player position, shot type, and performance metrics. An AI model predicts optimal ball delivery parameters and adapts drills based on player progress. A mobile application provides remote control, drill customization, and performance analytics. The system supports autonomous repositioning, safety monitoring, and individualized training plans, enabling dynamic, responsive, and efficient tennis practice for skill acquisition and improvement in both amateur and professional players.

Techniques for providing a data confidence index are presented herein. In one embodiment, a method includes setting a default confidence index for a remote computing device, the confidence index indicating trustworthiness of data provided by the remote computing device, the remote computing device operating as part of a network of cooperating devices; applying a plurality of ordered rules for the remote computing device, respective rules comprising a rule pre-condition and a confidence index adjustment, respective rules considering one of a behavior of the remote computing device and a property of the remote computing device; and adjusting the confidence index for the remote computing device responsive to results of applying the plurality of ordered rules. A system and apparatus substantially perform steps of the disclosed method.