1109 results about "Application programming interface" patented technology

The present technology relates to a computer-implemented method for enhancing enterprise-wide security when using Artificial intelligence (AI) models. Embodiments involve a JavaScript injection process facilitated by a proxy server. This process does not require manual installation as it is automatically injected during operation. The JavaScript injection process operates by intercepting unsecured AI input and output data provided by users interacting with AI websites. The input and output data are sent to a security Application Programming Interface (API) which applies an enterprise security policy to the data in real-time, thereby transforming unsecured input and output into secure data. Embodiments provide a robust framework for safe interaction with Artificial intelligence models while mitigating the risks associated with the transmission of sensitive information over the internet.

The invention provides a hydropower station fusion computing power intelligent server system, and relates to the technical field of carbon emission. The method comprises the following steps: an acquisition correction module acquires hydropower station generation power data and server cluster instantaneous energy consumption data in real time, establishes a time sequence corresponding relation between a power generation side and a power utilization side, and obtains clean energy supply quantity data; the prediction scheduling module generates a non-tampering carbon footprint tracking chain number, and predicts a clean energy supply fluctuation trend sequence by adopting a long short-term memory network algorithm; training a random forest model to predict a computing power demand trend sequence; optimizing task scheduling according to a carbon emission minimization target, and generating dynamic balance configuration data of a computing power task and clean energy supply; and the monitoring evaluation module outputs computing power task level carbon emission and a clean energy use ratio in real time through an application programming interface according to the dynamic balance configuration data, and generates quantitative evaluation data of a computing power service carbon neutralization target by adopting a data visualization technology monitoring technology.

Embodiments described herein related to systems and methods for generating comments in a page of a content collaboration platform. In some examples, the user interface of the platform is configured to display a page within a content region. The content region includes a comment control that, upon user selection, causes a prompt to be generated and submitted to a generative output engine via an Application Programming Interface (API). The prompt may include a portion of the content selected by a user and user-specific data, such as a user role. This data is used as context to the prompt alongside a predefined query. The output from the generative output engine may be used to generate a set of suggested comments, which are displayed in a comment interface within the content region of the content collaboration panel.

Apparatuses, systems, and techniques to facilitate graph code synchronization between application programming interfaces. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to cause graph code to wait on a semaphore used by another API.

An example operation may include one or more of receiving at least one natural language input via a software application, determining that the at least one natural language input matches an application programming interface (API) call from among a plurality of API calls configured for prescriptive tasks, identifying at least one parameter value of the API call from the at least one natural language input and transmitting the API call to a prescriptive artificial intelligence (AI) model, executing the prescriptive AI model on the at least one parameter value to generate a natural language response, and displaying the natural language response via a graphical user interface (GUI) of the software application.

The invention relates to the technical field of operation and maintenance, and provides an operation and maintenance operation processing method and device.The method comprises the steps that a natural language description text input by a user in a dialogue interface is obtained; performing intention recognition on the natural language description text to extract an operation and maintenance operation type and a parameter list related to the operation and maintenance operation type from the natural language description text; and calling an application programming interface corresponding to the operation and maintenance operation type based on the operation and maintenance operation type and the parameter list so as to execute the corresponding operation and maintenance operation. Compared with the prior art, a user does not need to input an instruction format, and the function of completing operation and maintenance operation processing through natural language description is achieved through intention recognition of the natural language description text.

Systems and methods for large language model (LLM)-agnostic data anonymization. Data anonymization includes data obfuscation (and data deobfuscation) to protect confidential information a user is going to send to an LLM service or application programming interface (API). Encryption can be used for data obfuscation and particularly, for securing data from unauthorized access. Likewise, decryption can be used for data de-obfuscation.

Configuration-driven conversational artificial intelligence (AI) for task completion is disclosed. An AI-first, conversational approach may be utilized that leverages the capabilities of large language models (LLMs) to streamline the process of mapping user queries to a series of actions (e.g., application programming interface (API) calls). Such implementations may provide the user with a more intuitive and efficient experience.

Methods and systems for generating live digital documents are provided. The method includes receiving a static document file including human-readable data. Then, parsing the static document file into a plurality of subunits. Then, generating a sharable document splice of the static document file. The sharable document splice includes access to a subset of the plurality of subunits, and the access is provided through an Application Programming Interface (API) or Software Development Kit (SDK) endpoint for each subunit in the subset. Then, executing a digital thread script to generate the live digital document from the sharable document splice and an input digital model representation, using API or SDK endpoints in the sharable document splice. The live digital document is configured through the digital thread script to reflect changes in the input digital model representation. Finally, adding execution metadata for the execution of the digital thread script to the live digital document.

Disclosed herein are systems and methods for cross-domain training of sensing-system-model instances. In an embodiment, a system receives, via a first application programming interface (API), an input-dataset selection identifying an input dataset, which includes a plurality of dataframes that are in a first dataframe format and that have annotations corresponding to one or more sensing tasks performed with respect to the dataframes. The system executes a plurality of dataframe-transformation functions to convert the plurality of dataframes of the input dataset into a predetermined dataframe format. The system trains an instance of a first machine-learning model using the converted dataframes of the input dataset to perform at least a subset of the one or more sensing tasks. The system outputs, via the first API, one or more model-validation metrics pertaining to the training of the instance of the first machine-learning model.

The invention discloses a method and a system for quickly constructing a railway station structure engineering demand parameter matrix, which are characterized in that a time-history load function, a time-history load working condition and a ground acceleration of a railway station structure model are automatically created and set through a Python programming language on the basis of an application programming interface (API) opened by MIDAS Gen design software, and the time-history load function, the time-history load working condition and the ground acceleration of the railway station structure model are automatically established and set through a Python programming language. The method comprises the following steps of: firstly, carrying out time-history analysis on a plurality of key components, then carrying out elastic-plastic time-history analysis in batches, extracting a corner of a non-elastic hinge of the key components and interlayer acceleration of the floors, and finally, automatically assembling and forming an engineering demand parameter (EDP) matrix, so that the convenience and feasibility of shock resistance toughness evaluation of the railway station are remarkably improved.

The invention discloses a low-altitude airspace capacity dynamic evaluation method and system based on a time sequence diagram neural network, and belongs to the technical field of low-altitude traffic management, and the method comprises a data collection and fusion module which is used for accessing a multi-source heterogeneous data source, and carrying out the cleaning, standardization and fusion; the dynamic space-time diagram construction module is used for constructing a dynamic space-time diagram evolved along with time according to the processed multi-source heterogeneous data; a time sequence prediction engine module which is embedded with a time sequence diagram neural network model and is used for receiving the time sequence of the dynamic space-time diagram and outputting a predicted operation risk score; and the capacity evaluation and quantification module is used for calculating the dynamic operation capacity based on the predicted operation risk score, and providing services for an upper management platform through an application program interface. According to the method, real-time calculation and accurate prediction of a core decision variable, namely the airspace operation capacity, are realized, so that the most fundamental scientific basis is provided for refined and intelligent management of the airspace, and the safety and efficiency of urban low-altitude traffic are remarkably improved.

Described herein are techniques are provided for enabling a security orchestration, automation, and response (SOAR) service to automatically manage apps used to interface with an integrated security operations service and other related devices and services. Further described herein is a SOAR app generator service or application used to automate the creation of apps for a SOAR service based on application programming interfaces (API) specifications for related devices or services, as well as visual playbook editor interfaces for a SOAR service that enable the configuration of complex action input parameters including arrays and objects.

The present disclosure provides a northbound application programming interface (API) invoking method and apparatus. The method comprises: receiving an authorization request message that is sent by a common API framework (CAPIF) verification and authorization function and that requests acquisition of a specified authorization (101), the specified authorization being an authorization corresponding to a target resource of UE; on the basis of the authorization request message, determining whether to allow an API invoker to acquire the target resource (102); and sending an authorization response message to the CAPIF verification and authorization function (103), the authorization response message being used to indicate whether the UE agrees to provide the specified authorization for the API invoker.

The invention provides a construction log generation method based on an intelligent agent and a large model. The method comprises the following steps: 1, constructing a multi-source heterogeneous data acquisition layer: automatically acquiring required original data from a built-in interface, an external application programming interface, Internet of Things equipment and a manual input channel through the intelligent agent; 2, establishing a data preprocessing and structuring layer: processing unstructured data by using an AI technology, extracting key information, and packaging the key information into a standardized data unit; 3, constructing a domain model of precise construction terms and log styles; 4, generating a log first draft by the large model; 5, man-machine collaboration auditing and optimizing, wherein a man-machine collaboration interface is provided for engineers to audit, correct and confirm the generated log first draft; and 6, based on manual feedback, establishing an optimization closed loop, and carrying out continuous iterative optimization on data acquisition, a preprocessing rule, a cue word template or a large model. According to the method, full-automatic acquisition is realized, various data sources are automatically docked through the intelligent agent, and manpower is thoroughly liberated.

An image processing system includes one or more processors operative to receive a graph application programming interface (API) call to add a complex node to a graph. The graph includes at least the complex node connected to other nodes by edges that are directed and acyclic. The one or more processors are further operative to process, by a graph compiler at compile time, the complex node by iteratively expanding the complex node into multiple nodes with each node corresponding to one operation in an image processing pipeline. The system further includes one or more target devices to execute executable code compiled from each node to perform operations of the image processing pipeline. The system further includes memory to store the graph compiler and the executable code.

Apparatuses, systems, and techniques to perform an application programming interface (API) to cause configuration information of one or more radio units to be stored. In at least one embodiment, configuration information is obtained based, at least in part, on one or more values received from at least one of said radio unit(s) and said configuration information is used to enable communication between said radio unit(s) and one or more distribution units.

A system for protecting sensitive data through vaulted and vaultless tokenization across a distributed computing environment is provided. The system includes a processor and memory containing multiple integrated modules to secure data transmission and storage. A tokenization application programming interface (API) receives sensitive data from the distributed computing environment through a network. An encryption module applies format-preserving cryptographic transformation to the sensitive data, creating encrypted data while preserving original data format and length characteristics. A tokenization engine receives the encrypted data and generates format-preserving tokens that include structural characteristics based on the sensitive data. A management module processes encrypted data and format-preserving tokens to generate cryptographic responses. An enforcement module applies authorization rules for access control based on user roles and data classification levels. The tokenization API provides the format-preserving tokens and the cryptographic responses back to the distributed computing environments according to established user roles and data classification.

Techniques for recognizing applications, screens, and UI elements and for recognizing user interactions and / or AI agent interactions with the applications, screens, and UI elements using agentic artificial intelligence (AI) are disclosed. An AI model may facilitate such recognition without other system inputs, such as system-level information (e.g., key presses, mouse clicks, locations, operating system operations, etc.) or application-level information (e.g., information from an application programming interface (API) from a software application executing on a computing system).

Systems and methods for using machine learning to predict tests for testing application programming interface updates. The system receives a prediction request to predict one or more tests for testing a candidate update to an application programming interface, wherein the prediction request comprises an indication of the application programming interface. The system may determine, using the indication of the application programming interface, a plurality of parameters associated with the application programming interface and obtain, based on the plurality of parameters using a machine learning model a prediction of a plurality of tests to be executed for the candidate update to the application programming interface. The system may determine, a first subset of the plurality of tests enabled to be executed without user input and cause the first subset of the plurality of tests to be executed without the user input.

An example device for communicating augmented reality (AR) media data includes: a memory configured to store AR media data; and a processing system implemented in circuitry and configured to: invoke a function of an application programming interface (API) to determine one or more supported tracking schemes of an augmented reality (AR) runtime; send data representative of the one or more supported tracking schemes to a receiving device with which an AR media communication session is to be performed; receive, from the receiving device, data representative of a selected one of the one or more supported tracking schemes; create a tracking session with the AR runtime using the selected one of the one or more supported tracking schemes; and send an animation stream representing tracking information conforming to the selected one of the one or more supported tracking schemes to the receiving device.

Apparatuses, systems, and techniques to perform multi-threaded memory allocation in parallel by one or more software programs being performed on a parallel processing unit (PPU), such as a graphics processing unit (GPU), or any other processing unit capable of supporting multi-threaded software execution. In at least one embodiment, one or more software programs expressed in part by code using an application programming interface for parallel computing, such as CUDA, perform allocation, search, and deallocation of memory efficiently and in parallel on a GPU.

Arrangements for optimizing software performance through generative artificial intelligence are provided. Execution data associated with a user interacting with an application programming interface through a user interface on a computing device may be received. Trace data based on the execution data may be generated. The trace data may indicate that a performance of an application is degraded. One or more performance prompts specifying program behavior may be generated. Application source code and the performance prompts may be sent to an artificial intelligence engine for performing key performance indicator driven code analysis. The one or more performance prompts may have been executed in the artificial intelligence engine for pretraining a large language model. Optimized source code may be received from the artificial intelligence engine. Performance testing may be executed on the optimized source code. An action may be performed to address the performance of the application.

The present technology relates to a computer-implemented method for enhancing enterprise-wide security when using Artificial intelligence (AI) models. Embodiments involve a JavaScript injection process facilitated by a proxy server. This process does not require manual installation as it is automatically injected during operation. The JavaScript injection process operates by intercepting unsecured AI input and output data provided by users interacting with AI websites. The input and output data are sent to a security Application Programming Interface (API) which applies an enterprise security policy to the data in real-time, thereby transforming unsecured input and output into secure data. Embodiments provide a robust framework for safe interaction with Artificial intelligence models while mitigating the risks associated with the transmission of sensitive information over the internet.

Methods and apparatuses for virtualizing building management systems. An apparatus embodiment comprises a processor that receives a temperature adjustment command from a building application via a first application programming interface (API). The command specifies a target temperature for a designated space. The processor validates the access rights of the user for the command, translates the command into one or more device-specific control parameters, and communicates these parameters to an on-premise building management system via a second API. This enables execution of the temperature adjustment to achieve the desired temperature value for the building space.

A method and a system of configuring custom operator defined rules for managing radio resources of user equipment in a wireless communication system. The system includes a plurality of User Equipment (UE), a plurality of wireless base stations configured to host Centralized Unit (CU) and Distributed Unit (DU) software, a rules engine, a first Application Programming Interface (API) wherein the rules engine is programmed with operator defined rules via the first API, and a second API coupled with the CU and DU software and configured to invoke the rules engine upon an occurrence of one or more defined events, wherein one or more of the operator defined rules is invoked via API calls to execute operator defined rules that match one or more inputs provided by at least one of the CU and DU hosted by at least one of the plurality of base stations.

An architecture is disclosed for submission and recordation of application programmer interfaces (APIs) using non-fungible tokens (NFTs) inserted into a blockchain. The API-NFT pairings are validated by nodes of the network. The system automatically searches for and generate NFTs for APIs and / or intermediate layers on a computer network based on metadata associated with the API and a hash of the API / intermediate layer. The API-NFT pair binding ensures that developers / consumers are ingesting APIs that are secure and do not include malicious rules. Furthermore, performance monitoring of the API is performed using artificial intelligence (AI) and machine learning (ML), including long short term memory (LSTM) neural networks.

Apparatuses, systems, and techniques to facilitate graph code synchronization between application programming interfaces. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to cause graph code to wait on a semaphore used by another API.

Systems and methods for identifying anomalous activities in a cloud computing environment are provided. According to one embodiment, a customer's infrastructure may be fortified by leveraging deep learning technology (e.g., an encoder-decoder machine-learning (ML) model) to predict events in the cloud environment. During a training phase, the ML model may be trained to make a prediction regarding a next event based on a predetermined or configurable length of a sequence of contextual events. For example, historical events (e.g., cloud application programming interface (API) events logged to a cloud activity trace) observed within the customer's cloud infrastructure over the course of a particular date range may be split into appropriate event / context pairs and fed to the ML model. Subsequently, during a run-time anomaly detection phase, the ML model may be used to predict a next event based on a sequence of immediately preceding events to facilitate identification of anomalous activity.

In some implementations, a device may identify a set of application programming interfaces (APIs) associated with a type of cloud service. The device may identify a set of APIs associated with a connectivity type. The device may identify a set of APIs associated with overlay components. The device may connect to one or more entities associated with a cloud service based on the set of APIs associated with the type of cloud service, the set of APIs associated with the connectivity type, and the set of APIs associated with the overlay components.