208 results about "System Generation" patented technology

Systems and methods are provided for facilitating management of interactions and training for AI (artificial intelligence) agents. Systems generate and display interfaces for training AI agents and for receiving user instructions. The systems parse user instructions to identify tasks to be performed by the AI agents. The systems cause the tasks to be split into subtasks to be performed by the AI agent. The systems also display a dialog frame that presents the user instructions along with AI agent responses that identify the subtasks. The systems also display a graph that visually identifies a processing flow of the subtasks and that dynamically updates the processing flow to reflect a status of progress for the AI agent performing the subtasks.

The invention provides a code agent construction system, a construction method thereof and a code generation method, and relates to the technical field of computers, in particular to the technical field of artificial intelligence such as large models. According to the specific implementation scheme, the code agent construction system comprises an intelligent cooperation engine, a layered template library, a semantic understanding and layered retrieval system, a T-RAG generation and optimization system and a closed loop verification system; wherein the layered template library comprises a project-level template, a function-level template and an API-level template; the semantic understanding and hierarchical retrieval system is configured as follows: analyzing user requirements through a natural language understanding module to generate an intention vector, and sequentially executing project-level, function-level and API-level three-level progressive template retrieval; and the T-RAG generation and optimization system is configured to inject the hierarchical retrieval result into the cue word of the large language model to generate deployable codes containing front-end and rear-end logics.

An assessment system can generate a software quality value based on testing results and analysis of a multitude of factors that impact a readiness evaluation. For example, the system generates a software quality score (e.g., an Applause Quality Score “AQS”) that enables development teams to understand the level of quality they are achieving for a given release and build-over-build. In various examples, the system generates a data-driven score to enable development teams or quality assurance teams to make decisions for when a build is ready for release. In further embodiments, the system can integrate user interfaces that present a software quality score in a user dashboard that is linked to version control systems. On review and acceptance of the score, a user can trigger the release of their new code or product.

In the implementation of techniques for deploying machine learning models with automated resource management, a system receives logic corresponding to a machine learning model and computing resource data corresponding to a plurality of computing resources available. Based on the logic and the computing resource data, the system generates the machine learning model and an allocation of one or more computing resources of the plurality of computing resources available for the machine learning model, in which the machine learning model conforms to the logic. Upon generation of the machine learning model and the allocation of the one or more computing resources, the system deploys the machine learning model and the allocation of the one or more computing resources of the plurality of computing resources available for the machine learning model.

A system generates an MLM comprising a plurality of layers. The system assigns a first encryption scheme for a first subset of layers in the plurality of layers. During a training phase of the MLM, the system determines whether a first input training vector comprises private data, in response to determining that the first input training vector does not comprise the private data, the system train the MLM such that, during backpropagation, an optimization algorithm is used to update any necessary weights in the plurality of layers; and in response to determining that the first input training vector comprises the private data, the system trains the MLM such that during the backpropagation, the optimization algorithm is used to update weights solely in the first subset of layers. The system executes the trained MLM on a user input vector to generate a user output value.

Techniques for generating proprietary software code using large language models (LLMs) are disclosed. An LLM is trained on billions of words, tokens, and code segments to generate non-proprietary software code. A system uses the LLM to generate proprietary software code by generating a set of LLM prompts and a proprietary code mapping. Based on receiving an instruction to generate a set of software code, a system generates a set of LLM prompts. The system prompts the LLM to generate a set of non-proprietary software code. The system further prompts the LLM to generate a set of pseudocode from the non-proprietary software code. The system further prompts the LLM to generate proprietary software code from the pseudocode and the proprietary code mapping.

A computing system can determine a subset of users that have been affected by an event occurring in a given area. Subsequent to the event, and for each respective user of the subset of users, the system generates interactive follow-up content specifically tailored for the respective user. The system can transmit updated content data to the computing device of the respective user, causing the computing device of the respective user to present the interactive follow-up content. Based on user interaction by the respective user with the interactive follow-up content, the system can receive contextual information provided by the respective user, the contextual information corresponding to damage or loss resulting from the event as indicated by the respective user.

A computer system generates and displays a first cell with a first visual characteristic. The computer system receives, via the first cell, a request associated with a task directed to a dataset. The computer system generates a set of system prompts and inputs the prompts into a data processing system to process the request. The data processing system includes one or more data processing models and is configured to operate in a single agent mode of operation and a multi-agent mode of operation. The computer system obtains, as output from the data processing system, a response to the request. The computer system generates, in real time, output data associated with the response and displays the output data in one or more second cells in the user interface, where each of the second cells has a second visual characteristic that is different from the first visual characteristic.

The invention relates to the technical field of software engineering and automatic testing, in particular to an E2E scene test optimization method and system based on target man-machine cooperation. According to the scheme, an optimization target is manually set, an initial test case and a business process are input, and a test resource library is constructed; the system automatically analyzes a service path, generates a use case dependency graph and a key path, and determines a core scene through manual verification; a candidate optimization strategy is generated by the system, and a target strategy is determined through manual screening according to resources and priorities; the system carries out simplification, merging and scheduling optimization on the use cases, and deploys and executes the use cases after manual sampling verification of coverage; the system collects execution data in real time and generates an evaluation report, and whether a use case is supplemented or a strategy is adjusted is determined after manual analysis; and finally updating the resource library and the strategy model, establishing a dynamic feedback mechanism, and triggering continuous optimization during system iteration. According to the method, the E2E test efficiency and the software delivery quality are remarkably improved through man-machine cooperation and closed-loop optimization.

Techniques for predicting anomalies in forecasted time-series data are disclosed. A system. A system predicts whether a monitored computing system will experience anomalies by comparing forecasted values associated with components in the monitored computing system to threshold values. The system utilizes time-series machine learning models to forecast workloads of computing resources in the monitored computing system. The system trains and tests multiple different versions of a time-series model and selects the most accurate version to generate forecasts for a particular workload in the computing system. The system compares the forecasts to threshold values to predict anomalies. Based on detecting anomalies, the system generates recommendations for remediating predicted anomalies.

Systems and methods for detecting anomalies in generative outputs are disclosed herein. The system receives a user prompt indicating a request for data over a time period. The system inputs, into a model, the user prompt to cause the model to generate an output based on the user prompt. The system then generates the first tokens based on the output. To generate the second tokens, the system retrieves, based on the user prompt, sources relating to the data requested by the user prompt. The system then generates queries to request, from the sources, the data over the time period and generates the second tokens based on the retrieved data. The system then performs a comparison of the first tokens and the second tokens and accepts or rejects the output of the model based on the comparison.

Systems and methods are disclosed for optimizing a first machine learning (ML) model using a second ML model. In some examples, a system generates modifications to the first ML model. Each of the modifications is associated with a respective node of the first ML model. The system tracks a processing characteristic corresponding to modified variants of the first ML model (corresponding to the modifications) processing a test dataset to generate respective results. In some examples, the system trains the second ML model based on context (the modifications and the respective changes). The system identifies, using the second ML model and based on the context (e.g., the training), a modification to the first ML model that adjusts the processing characteristic of the first ML model in a predetermined direction. The system modifies the first ML model according to the modification to generate a modified first ML model.

The present technology, roughly described, provides for mocking an application program interface (API) using a large language model (LLM). The present system generates a prompt with API signature information and API desired behavior information. The prompt can include instructions, library functions, examples, executed programs, and a current function invocation, as well as other content. The prompt can be generated, and submitted to an LLM to mock an API and generate a response. The response can be audited and the LLM can be fine tuned to provide improved performance in subsequent calls.

Examples provide a system and method for dynamically improving and validating automation plans using generative artificial intelligence (AI). An automation manager analyzes automation plans during the design phase and the testing phase. The automation plan makes suggestions for improving the automation plan in real time using natural language prompts. The automation plan validates the automation plan against guardrails defining restrictions on the actions performed by the system during automation plan execution. The system generates explainability data describing the next planned actions and reasons why each planned action is selected for execution during the automation to increase user confidence in the automation. The system requests user approval prior to performing each action for more accurate and efficient workflow automation while reducing system resource usage consumed during execution of inefficient or ineffective automation plans.

A computer system detects occurrence of a first event of a first type, and in response: in accordance with a determination that the computer system is operating in a first mode in which notifications corresponding to events of the first type are permitted, the computer system generates a first notification corresponding to the first event; in accordance with a determination that the computer system is operating in a second mode in which notifications corresponding to events of the first type are suppressed and the first event includes first content that meets relevance criteria, the computer system generates the first notification; and in accordance with a determination that the computer system is operating in a second mode in which notifications corresponding to events of the first type are suppressed and the first event does not include content that meets the relevance criteria, the computer system forgoes generating the first notification.

The invention discloses a business system building method and system, and relates to the field of business process management, and the method comprises the steps: recognizing related entities such as roles, demands, strategic targets and the like through a bidirectional undifferentiated coding model, carrying out semantic coding and element classification through an attention polarity model, and generating a benefit related party graph in combination with a rule engine; calculating a demand weight by means of a graph neural network, embedding a semantic vector to generate a weighted strategic vector, constraining an AI model to extract flow nodes, and correlating to generate a flow element table; a final-stage flow chart is generated through state coding and a near-end strategy optimization learning network, a programming environment is determined based on requirements, a code segment is matched to generate a service system, full-process automation from strategic analysis to service system generation is achieved, the development efficiency of the service system is improved, and the technical threshold is reduced.

The invention provides an AI software development enhancement method and system, a storage medium and electronic equipment. The method comprises the following steps: acquiring a software development demand; generating a plurality of software subsystems based on the software development demand, and generating a user story based on each software subsystem; for each software subsystem, generating a corresponding Sprt to-do list; generating a UML (Unified Modeling Language) graph on the basis of the user story for each Sprt in the Sprt to-do list; for each software subsystem, generating a code of the Sprt based on the UML graph; the codes are rechecked, the rechecked codes are combined into subsystem codes, and the subsystem codes are combined into system codes. According to the AI software development enhancement method and system, the storage medium and the electronic equipment, AI software development enhancement is achieved based on man-machine cooperation, and the quality of codes generated in full-automatic AI software development is effectively improved.

The invention relates to an enterprise-level knowledge center system construction method and device based on a large language model, computer equipment, a storage medium and a computer program product. The method comprises the steps of preprocessing target enterprise data, and extracting structured knowledge information from the processed target enterprise data through a knowledge extraction mechanism combining few-sample learning and transfer learning; constructing a multi-modal knowledge graph according to the structured knowledge information; extracting features of multi-modal data in the target enterprise data, dynamically adjusting the weight of each modal through an attention mechanism, carrying out feature fusion, and fusing the fused knowledge into the multi-modal knowledge graph; and in response to a user request, generating reply content by combining a retrieval enhancement generation model, a large language model and the intelligent question and answer system of the multi-modal knowledge graph. By adopting the method, efficient and intelligent knowledge management requirements of enterprises can be met.

The present disclosure provides a system for generating gameplay content by a generative modeling system. The system can generate gameplay content via one or more machine-learning models trained using game and player data. The system can add content generated by the one or more machine-learning models to the game and player data and retrain the models using the generated content. The system can also localize generated content based on player locations and language preferences.

Disclosed herein are methods and systems for an industrial automation device interface system that leverages generative artificial intelligence (GAI). The system receives a user request associated with an industrial device. The system processes the request to identify an intent and user interface functionality based at least on an industrial device context. When no existing user interface functionality is identified in the programming, the system generates a prompt designed to elicit a response from a generative artificial intelligence (GAI) model. The prompt contains industrial device context and a request to generate user interface functionality in the form of executable code for performing an action corresponding to the intent. The system submits the prompt to the GAI model and receives user interface functionality in return. The system executes the user interface functionality or provides it to the user device for execution.

Techniques for generating a prompt for a language model to determine an action responsive to a user input, are described. In some embodiments, the system receives a user input, determines one or more application programming interfaces (APIs) configured to perform actions that are relevant to the user input and exemplars representing examples of using the APIs with respect to user inputs similar to the current user input. The system further determines device states of devices that are determined to be related to the user input and also determines other contextual information (e.g., weather information, time of day, geographic location, etc.). The system generates a prompt including the user input, the APIs, the exemplars, the device states, and the other contextual information. A language model processes the prompt to determine an action responsive to the user input and the system causes performance of the action.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate instructions for performing a next action of a task. For instance, in some cases, the disclosed systems receive, from a client device interacting with a software application, a query for performing a task via a user interface of the application. The disclosed systems generate a lookahead prompt having an execution example corresponding to the task, the execution example including an example task and an example action sequence for the example task. The disclosed systems also generate, from the lookahead prompt using a large language model, an estimated lookahead plan describing one or more actions for performing the task. The disclosed systems also use one or more large language models to generate, from the estimated lookahead plan, instructions to perform a next action for the task via user interaction with an interactive element of the user interface.

The systems and methods disclosed herein enable the dynamic selection of one or more AI models to generate an output in response to an input. The system receives, from a computing device, an output generation request including an input for the generation of an output using one or more models from a plurality of models. The system generates expected values for a set of output attributes of the output generation request. For each particular model in the plurality of models, the system determines the capabilities of the particular model, and dynamically select a subset of models from the plurality of models. The system dynamically selects a subset of available system resources to process the input included in the output generation request. The system generates the output by processing the input included in the output generation request using the selected subset of available system resources.

The invention relates to a method for docking an external system in a supply chain system, and the method comprises the steps: designing a unified external system interaction framework based on message driving: defining a BaseHandler base class, and integrating getParams parameter analysis, sendRequest request sending and handleResponse response processing logic; external system configuration is stored through the relational database; the business system generates a standardized message and pushes the standardized message to a corresponding MQ theme; and monitoring Topic by the unified consumption service, querying configuration of an enabled state according to the actionCode in the message, dynamically instantiating and specifying a processor subclass, and sequentially executing parameter analysis, external request calling and response processing flows, so as to realize decoupling and dynamic expansion capabilities of business logic and external interaction. According to the method, through unified management configuration and logic, the code repetition rate is reduced, and the maintenance cost is reduced; dynamic configuration supports a quick start-stop docking function and adapts to business change requirements; the standardized process enables the code structure to be clear, and team cooperation and subsequent expansion are facilitated.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying labels for a dataset without revealing the dataset to any individual computing system. Methods can include receiving, by a first computing system of a multi-party computation (MPC) system, a query that includes a first and second share of a given user profile. The second share is encrypted with a key that prevents the first computing system from accessing the second share. The second share is transmitted to a second computing system of the MPC system. The first and the second computing system generates a machine learning model and identifies a respective first and a second label. The first computing system receives the second label as a response from the second computing system. The first computing system responds to the query with a response that includes the first and the second label.

Techniques are disclosed for detecting a drift experienced by computing system(s). The system generates multiple snapshots as part of a drift detection process. Each snapshot contains state information of a computing system. Based on the snapshots, the system generates metrics sets according to a general specification. The general specification defines metrics generally suitable for detecting drift in the computing system(s). Based on the circumstances of the drift detection process, the system generates a custom specification. The system optionally employs trained machine learning model(s) for custom specification generation. The custom specification defines modifications to the metric sets designed to make the metric sets more suitable for the circumstances of the drift detection process. The system modifies the metric sets according to the custom specification. Subsequently, the system generates flattened vectors based on the modified metric sets, and the system performs a cluster analysis on the flattened vectors to detect any drift.

A method, computer program product, and computing system for generating, via a video recording subsystem of an ACI calibration platform, a three-dimensional model of at least a portion of a three-dimensional space containing an ACI system; and generating, via an audio generation subsystem of the ACI calibration platform, one or more audio calibration signals for receipt by an audio recording system included within the ACI system.

The invention relates to an instrumentation kit and a system capable of implementing a method for detecting a material leak from an industrial device, comprising the steps of measuring a vibration of a peripheral element arranged to receive a portion of material falling from the industrial device; and, in response to this measurement, and by means of an electronic computing system, generating a calculated signal representative of the fall of the falling portion of material at a drop point located on the peripheral element. Figure to be published with the abstract: Fig. 1

A code processing system (100) is provided with an operating system (30) and a standby system (40). The operating system (30) and the standby system (40) are each configured so as to generate a multi-bit user code (12) when a code request from a user terminal (11) is received. The user code (12) includes a system code unique to a system (30, 40) that generates the user code (12) in a prescribed bit. The user code (12) generated by the working class system (30) is a working class code, and the user code (12) generated by the standby class system (40) is a standby class code. The code processing system (100) is configured so as to transmit, to a user terminal (11), either a working-class code or a standby-class code on the basis of error information in the working-class system (30).

The invention relates to an interface testing method and device, equipment, a storage medium and a program product. The method comprises the steps of responding to an interface test event for a target interface, calling a work order system to generate a target test work order corresponding to the target interface according to original configuration parameters in the interface test event, and determining an interface test result of the target interface according to target feedback information when the target test work order flows in the work order system, and in response to a test ending request, according to the work order identification information of the target test work order, performing handling processing on the target test work order, the test ending request being initiated based on the target feedback information and / or the interface test result. By adopting the method, the reliability of interface testing can be improved.