3226 results about "Application programming interface" patented technology

An emergency response and crisis management system comprises a computing system which performs steps comprising: aggregating real-time and historical data from multiple data sources; processing the data using artificial intelligence techniques; analyzing the processed data to detect and classify active crises to predict the likely development and trajectory of the ongoing crises, and assess potential short-term and long-term impacts across one or more sectors comprising one or more of public health, economy, infrastructure, environment, and social stability; generating multi-dimensional, interactive visualizations of the analysis results comprising one or more of geospatial maps, time series charts, and network diagrams; providing recommendations for crisis management and response tailored to different stakeholder roles; facilitating integration with external systems and applications through a comprehensive application programming interface (API) layer; and continuously adapting the system's models and algorithms based on one or more of real-world outcomes, expert input, and user feedback.

An example operation may include one or more of ingesting data from one or more websites via one or more application programming interfaces (APIs) and storing the data within a data store of a host platform, displaying one or more prompts on a user interface of a user profile page within a software application hosted by the host platform, receiving one or more responses to the one or more prompts, generating a travel itinerary based on execution of an artificial intelligence (AI) model on the data from the one or more websites, the one or more prompts, and the one or more responses, and displaying the travel itinerary via the user interface of the user profile page of the software application.

An example operation may include one or more of storing transaction content from transactions executed via one or more fiat payment accounts and one or more crypto accounts of a digital wallet of a user, and determining, via execution of a machine learning model on the stored transaction content, a recurring expense value of the user and a next point in time in which the recurring expense value, dividing the recurring expense value into a plurality of sub-values; generating a plurality of transactions which transfer the plurality of sub-values from a fiat payment account to a crypto account from among the one or more crypto accounts and store the plurality of transactions within a queue, initiating a plurality of time-to-live jobs for the plurality of transactions, respectively, wherein the plurality of time-to-live jobs comprise a plurality of different respective expiration times that are staggered such that the plurality of time-to live jobs expire in incremental intervals from a current time to the next point in time in which the recurring expense value is due and executing the plurality of transactions at the plurality of different expiration times to incrementally transfer the plurality of sub-values from the fiat account to a crypt account via an application programming interface (API).

An example operation may include one or more of ingesting profile data from one or more websites via one or more application programming interfaces (APIs) and storing the profile data within a data store, displaying one or more prompts on a user interface of a user profile page within a software application hosted by a host platform, receiving one or more responses to the one or more prompts, determining a goal for a user of the user profile page based on execution of an artificial intelligence (AI) model on the profile data, the one or more prompts, and the one or more responses to the one or more prompts, and displaying an identifier of the goal via the user interface of the user profile page of the software application.

An example operation may include one or more of ingesting data from one or more websites via one or more application programming interfaces (APIs) and storing the data within a data store of a host platform, generating one or more prompts and displaying the one or more prompts on a user interface of a user profile page within a software application hosted by the host platform, receiving one or more responses to the one or more prompts, generating a design of a structure based on execution of an artificial intelligence (AI) model on the data from the one or more websites, the one or more prompts, and the one or more responses, and displaying the design of the structure via the user interface of the user profile page of the software application.

An example operation may include one or more of receiving a loan application of a user, extracting a plurality of personal attributes about the user from the loan application, querying a machine learning model via an application programming interface (API) based on the plurality of attributes about the user to identify one or more rules for auto-adjudicating the loan application, determining whether or not to approve the loan application based on the one or more rules identified via the machine learning model, and transmitting notice of the determination to a device associated with the user.

The present disclosure includes computer-implemented methods of guardrails for securely using large language models (LLMs). The method comprises monitoring user data flow using an application programming interface (API) and receiving an administrative policy from an administration communication interface. The method involves dynamically applying a plurality of LLM input inspectors to LLM input data. The application of the plurality of LLM input inspectors is based on the administration policy. The dynamic application of the plurality of LLM input inspectors is in sequence for latency optimization. The plurality of LLM input inspectors serve as LLM input guardrails for a plurality of secure deployed large language models (LLMs). The plurality of LLM input inspectors are configured by the administrative policy and validate the LLM input data to validated LLM input data based on the administration policy. Additionally, the method comprises dynamically applying a plurality of LLM output inspectors to LLM output data.

A method for using a large language model to generate executable code for workflow execution in a manner that integrates user feedback and adjusts the workflow as needed while preserving data privacy is provided. The method includes: receiving first information that relates to a workflow context, second information that relates to at least one application programming interface (API), and third information that relates to a code generation request; using the received information to generate a lecture, and transmitting the lecture to a language model; receiving a user query that relates to performing a task, and transmitting the query to the language model; receiving a workflow that is automatically generated by the language model based on the lecture and the query; and executing the workflow in order to generate an output that is responsive to the query.

Artificial intelligence (AI), preferably a generative AI (Gen-AI) technology, powers an illustrative “dynamic API service” that interoperates with or within a data resiliency system that has well-defined API(s) for communicating with it. The illustrative dynamic API service is configured to recognize incoming information from an originator, which may arrive unstructured or in a format that is incompatible with the API(s). The illustrative dynamic API service interprets the incoming information, identifies pertinent components in the data resiliency system, issues API call(s) to the system components, and organizes response(s) to the originator. In some embodiments, the illustrative dynamic API service uses or incorporates Gen-AI technology to perform some of the disclosed features and functions. The Gen-AI training corpus preferably includes information extracted from the data resiliency system itself. The originator systems also may employ Gen-AI, and some embodiments function in an AI-to-AI configuration.

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.

Techniques for identifying malware based on system API function pointers are disclosed. In some embodiments, a system / process / computer program product for identifying malware based on system API function pointers includes monitoring changes in memory during execution of a malware sample in a computing environment; detecting a dynamic evasion behavior using an Application Programming Interface (API) vector comprising a plurality of system API function pointers identified in the memory during execution of the malware sample in the computing environment; and generating a signature based on the API vector for automatically detecting the malware during execution in the memory, wherein the malware sample was determined to be malicious.

A computer-implemented method for intelligent query response in an enterprise resource planning (ERP) system is disclosed. The method can receive, from a user interface of the ERP system, a natural language user query for processing data of a target object and invoke a function call through an application programming interface of the ERP system based on the user query. Invoking the function call includes extract denormalized data from one or more database tables, generating a prompt using a prompt template and the denormalized data, prompting a large language model using the prompt, and receiving a response generated by the large language model. The method can generate an output on the user interface based on the response generated by the large language model. Related systems and software for implementing the method are also disclosed.

A computer-implemented method can receive a natural language query input from a user interface, extract a target entity from the natural language query input, identify a target application programming interface (API) corresponding to the target entity, formulate an API query using the target API, and execute the API query to generate a query output on the user interface. Identifying the target API includes generating a vector representation of the target entity, searching an entity vector database containing vector representations of a plurality of APIs to return one or more candidate APIs whose vector representations match the vector representation of the target entity, and prompting a generative artificial intelligence model to select the target API from the one or more candidate APIs.

Disclosed is a cloud-native application programming interface (API) recommendation method fusing data augmentation and contrastive learning. Service information is included on the basis of a service information double-graph structure, and a mutual attention mechanism is designed to compute an importance degree of each layer of information. A data optimization method for sequence information based on functional similarity and a computation method for similarity between services based on two parts of information are provided; on this basis, data of a service invocation sequence is augmented with the idea of contrastive learning to form an augmented sequence pair; a computational contrastive loss function is combined with a pair-wise recommendation loss function to optimize an overall model, thereby improving the effect of a service recommendation model; and according to a feature embedding representation result of a service, pair-wise recommendation scores are computed to complete service recommendation.

Apparatuses, systems, and techniques to perform an application programming interface (API) to add one or more graph nodes to a software graph, wherein the API is to cause an event wait node to be added to a software graph based, at least in part, on a dependency type indicated by the API. In at least one embodiment, one or more nodes are added to a graph in accordance to one or more dependency types.

Various embodiments of the technology described herein cause an LLM to intelligently process data based on a user query and a schema determined for a data set. Certain embodiments programmatically leverage an LLM and utilize its output based on a user query. In this manner, data is processed without the LLM having to access an entire data set, and instead only utilizes information associated with the user query and the schema. The schema comprises a textual description, such as a string of alphanumeric characters, that describes the data, data types, and / or data structure of the data set. Embodiments of the technology described herein are performed by an LLM interface layer separate from a user device layer and an LLM layer. The LLM interface layer is positioned between an LLM abstraction layer and an application layer by which a user can interface with the LLM interface layer.

Apparatuses, systems, and techniques to perform a neural network. In at least one embodiment, an application programming interface schedules two or more graph nodes to be performed by two or more parallel processing pipelines based, at least in part, on an order of layers in the neural network.

The invention provides a method for realizing agent development MCP service through computing power of an intelligent computing center, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, the method comprises the following steps: S1, determining interface information of a target application programming interface API, the target API being an interface of a target platform; s2, generating a first coding task based on the interface information through an intelligent agent of the intelligent computing center; s3, generating a first MCP service component corresponding to the first coding task through the intelligent agent; and S4, registering the first MCP service component to the intelligent computing center. According to the invention, the development efficiency of the MCP service of the intelligent computing center can be greatly improved.

The invention discloses a system (100) for generating conversational content using a generative artificial intelligence (AI), said system (100) comprising: a user (101), an administrator (102), an application programming interface (API) server (103), a generative artificial intelligence (AI) server (104), a plurality of databases (105), a generative artificial intelligence (AI) processor (106), an audio generate processor (107), a text-to-speech processor / service provider (108), a video generation service (109), a video generation processor (110), and a memory communicatively coupled to the processor, wherein the memory stores processors instructions, which, on execution, causes the processor to generate at least one of conversational script, audio, video, or combination thereof. The system (100) allows users to create and customize various aspects of conversational content, including characters / personas / speakers, groups (of personas / characters / speakers), tones, content types, topics, conversation formats, and tone.

A computer-implemented method can specify a source version and a target version of an application programming interface (API) and a target programming language; and retrieve a difference graph connecting from a source knowledge graph characterizing the source version of the API to a target knowledge graph characterizing the target version of the API. The difference graph includes one or more revision edges representing changes of the API between the source version and the target version. The method can install one or more function packages written in the target programming language and associated with the one or more revision edges; and run the one or more function packages to update the API from the source version to the target version.

A system, method, and computer-readable medium for performing a data center monitoring and management operation. The data center monitoring and management operation includes: generating a request for a client identifier or an access token for access to a target application programming interface (API); obtaining an access policy associated with the target API; determining a least privileged API access permission based upon the access policy associated with the target API; and, using the client identifier or access token to access the target API when the least privileged API access permission allows access to the target API.

Apparatuses, systems, and techniques to perform an application programming interface (API) to cause dependency type information of one or more user-indicated graph nodes of a software graph to be indicated. In at least one embodiment, one or more dependency types from a graph are indicated.

Embodiments of the present invention provide computer-implemented methods, computer program product, and computer systems. One or more processors analyze user prompts using one or more natural language understanding techniques. One or more processors then enrich the user prompts by integrating contextual data from user interaction history and adapt the enriched user prompts to align with characteristics of Large Language Models (LLMs) and Application Programming Interfaces (API) requirements of the LLMs.

A method for facilitating automated management of computing metrics is disclosed. The method includes aggregating, via an application programming interface, unstructured data from various computing components, the unstructured data including application log data; parsing the unstructured data to generate structured data sets, each of the structured data sets including textual data; extracting time series data from the structured data sets, the time series data relating to performance metrics of the computing components; generating, by using a model, a summary of the textual data in the structured data sets; correlating, by using the model, the summary of the textual data with the time series data; and determining, by using the model, a characteristic for each of the performance metrics based on a result of the correlating, the characteristic including a criticality characteristic.

Apparatuses, systems, and techniques to perform an API to indicate an allocation of wireless signal processing operations. In at least one embodiment, a distributed unit and radio unit perform one or more operations based, at least in part, on an indicated allocation. In at least one embodiment, a processor comprising one or more circuits performs an application programming interface (API) to indicate an allocation of wireless signal processing operations between one or more Open Radio Access Network (O-RAN) radio units (RUs) and one or more O-RAN distributed units (DUs).

Methods and systems for generating a live digital document 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. Hie 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.

Apparatuses, systems, and techniques to determine a matrix multiplication algorithm for a matrix multiplication operation. In at least one embodiment, a matrix multiplication operation is analyzed to determine an appropriate matrix multiplication algorithm to perform the matrix multiplication algorithm.

A system for controlling assignment and management of identities in ambient electromagnetic power harvesting (AEPH) chips. The system comprises a processor; a non-transitory memory; and an application stored in the non-transitory memory that, when executed by the processor, allocates a plurality of unique AEPH identities, wherein each unique identity comprises an identity of a product item and an instance identity; stores the plurality of unique AEPH identities in a first immutable record in a datastore, wherein the unique AEPH identities are associated with an initial state; provides the plurality of unique AEPH identities to an original equipment manufacturer of AEPH chips; provides an application programming interface (API); authorizes a request to update a state associated with a first unique AEPH identity in the datastore; and adds a second immutable record to the database that associates the first unique AEPH identity with an updated state of the first unique AEPH identity.

A user of an identity management system may configure a software agent with a set of parameters within one or more user interfaces of an agent management service. The set of parameters may include an identifier of an application programming interface (API) endpoint of a first service that the software agent is authorized to query and one or more permissions associated with queries to the API endpoint by the software agent. Further, the user may receive an authentication token for the software agent from the agent management service. The software agent may use the authentication token for accessing resources of the service via the API endpoint in accordance with the one or more permissions. Thus, the user may configure the first software agent with the authentication token to enable the software agent to perform the queries to the API endpoint of the first service.