286 results about "System Generation" patented technology

A computing system receives a prompt to be provided as input to a large language model. The computing system generates generating an input string to the large language model by appending a plurality of contexts to the prompt. The plurality of contexts defines rules for the large language model to follow when generating the prompt. The plurality of contexts includes a negative context. Based on the prompt and the plurality of contexts, the computing system generates an attention matrix representing relationships between the prompt and the plurality of contexts. The computing system provides the attention matrix to a trained neural network to determine a likelihood that the prompt is malicious. Responsive to determining that the prompt is likely a malicious prompt, the computing system initiates a remedial action.

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.

Techniques for summarizing a set of alert logs associated with a computer system using a generative machine learning model are described herein. In some cases, an example system receives a set of alert logs, such as logs associated with a detected security incident. The system generates a summarization prompt that includes the set of alert logs, instructions to summarize the logs, and one or more output constraints. The system then provides the summarization prompt to a generative machine learning model M times to determine M summarization outputs. The system determines N of the M summarization prompts that satisfy the output constraint(s) and are thus determined to be valid. The system then determines N scores for the N validated summarization outputs and determines an aggregated summarization output based on a subset of the N summarization outputs as determined based on the corresponding N output scores.

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.

A data processing system includes: a processor; and a memory comprising programming instructions for execution by the processor alone or in combination with other processors, to implement a service to generate a work as specified by a user. The service includes: a service-side component of a User Interface (UI) to receive user input about the work from the user, the user input including an initial prospective description of the work that the user intends to generate using the system and a set of parameters for the work; a prompt generator to generate prompts for a Large Language Model (LLM) based on the user input to generate both an outline for the work and a proposed version of the work, the prompt generator further to generate additional prompts to the LLM to update either the outline or the proposed version of the work based a user editing of the other of the outline or the proposed version of the work; and an Application Programming Interface to deliver prompts to the LLM and receive responses from the LLM for presentation in the UI.

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.

A computer-implemented method of generating electronic communication messages to one or more target computer systems that comprises determining user inputs, using a large language model (LLM) module to generate a proposed search for the one or more target computer systems, prompting the LLM to modify the search based on certain criteria or user input, generating and sending proposed messages to the proposed set of targets.

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.

Systems and methods for error mitigation in a software pipeline. In some aspects, the system receives a request for evaluating a pipeline at a recurrent interval of time. The request comprises a first version of software, a second version of software, evaluation tests to execute on the second version of the software at intermittent intervals, and the recurrent interval of time. The system, in response to determining the recurrent interval of time elapsed, executes the evaluation tests on the second version of the software. The system determines that the output of the evaluation tests does not match one or more target values. The system terminates deployment of the second version of the software. The system generates instructions to replace the second version of the software with the first version of the software and transmits a notification to a remote device.

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.

To provide facilitating a process for automatically deploying artificial intelligence (AI) models.SOLUTION: A system receives, for a first artificial intelligence (AI) model used by an entity, a first request to deploy a first AI model to make the first AI model available for use in a production environment to process input data and generate corresponding outputs. A first model deployment location for a first model is selected based on a model deployment engine. The system generates scripts to deploy the first AI model to the selected location, then monitors operations parameters associated with the deployment of the first AI model. Based on the values of the operations parameters, the system updates the model deployment engine. In response to a second request to deploy a second AI model, the system uses the updated model deployment engine to select a second model deployment location for a second model.SELECTED DRAWING: Figure 1

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.

Systems and methods are disclosed for emulating an environment created by the outputs of a plurality of devices. The system receives device control data for a device in a first venue. The control of the outputs of said devices according to the device control data creates an environment within the first venue. The system retrieves profile data for devices within a second venue. The system associates a device in the second venue with a device from the first venue, both devices having a similar output type. The system then generates control information adapted from the associated device of the first venue for the device in the second venue. The system controls the outputs of each device in the second plurality of devices according to the generated control information to emulate the environment within the first venue in the second venue.

A computing system stores representations of states of user interactions with an artificial intelligence assistant to enable the interactions to be resumed at any point. The system inputs a series of instructions to a large language model (LLM) to generate computer-readable code for performing tasks within a digital environment, where the code is executable by the system to perform the tasks. The system generates a transcript including the instructions in the series of instructions and corresponding computer-readable code generated by the LLM. The system stores a representation of each of a plurality of states of the transcript, wherein each state includes a portion of the transcript that corresponds to a task. A stored representation of a first state is accessed, and computer-readable code associated with the first state is executed, using a context of the environment corresponding to the first state, to reperform a corresponding task in the digital environment.

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.

Embodiments described herein relate to systems and methods for automation rule creation for collaboration platforms. A natural language user input may be input to a centralized automation rule service that creates prompts for a generative output service to automatically create an automation rule understandable to one or more collaboration platforms of a system. A trigger-selection prompt, component-selection prompt, and rule-selection prompt are generated by the system and provided to the generative output engine. An automation rule can then be identified from the generative response, verified, and used in the system for the one or more collaboration platforms. In some cases, the automation rule creation from natural language input may reduce the burden on a user to craft and manage automation rules in a collaboration platform.

The invention discloses an MES automatic task allocation method and system based on dynamic process matching and workload balancing, and the method comprises the steps: S1, a standard production task process set is manually created in an MES system, and the processes comprise at least one of sawing, drilling, milling, riveting, welding, boring, turning and grinding; s2, creating a process flow template library corresponding to the standard production task, wherein the process flow template library comprises a process sequence and a work type requirement required by part production; s3, generating a BOM list through the PLM system, wherein the BOM list at least comprises part names, specifications, models and raw material information; s4, importing the BOM list into an MES (Manufacturing Execution System); s5, key fields in the BOM list are matched with a process flow template library, and a process flow needed for producing the part is determined; s6, dynamically judging a matching result; s7, a task allocation stage; and S8, the task is pushed to an employee account through an MES system mobile terminal interface. The method has the advantages that a production line can be helped to better and more reasonably allocate resources, unfairness is avoided, new tasks can be helped to execute data statistics, management cost can be saved, and efficiency can be improved.

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.

The invention provides a computer system, a remote replication control method, and a remote replication control program product, which can easily and appropriately perform setting of asynchronous remote replication from a storage system of a replication source to a sub-storage system of a replication destination including a plurality of storage nodes and execution of asynchronous remote replication. A computer system (10) causes a storage system (100) to manage a plurality of first volumes belonging to a CTG, causes the storage system (101) to generate a plurality of second volumes in a distributed manner at a plurality of storage nodes (102), causes second log volumes to exist at the plurality of storage nodes, causes the storage system (100) to generate first log volumes corresponding to the respective second log volumes, and causes the storage system (100) to store the first log volumes. A write order assurance process for controlling a process for the plurality of first volumes is executed so that log data can be stored in the plurality of first log volumes in a state in which the write order can be ascertained.

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.

In some implementations, a system may receive policy data that defines one or more controls and a set of variable definitions associated with the one or more controls in a tabular format. The system may generate a two-dimensional dataframe in which each row corresponds to a policy rule, of the set of policy rules. The system may create a system-generated script that includes a sequence of commands based on the two-dimensional dataframe, wherein each command in the sequence of commands is generated from a row of the two-dimensional dataframe using string manipulations based on information in each column associated with the respective row and the set of variable definitions associated with the one or more controls. The system may execute the system-generated script to generate an output that indicates whether input data subject to the set of policy rules complies with the one or more controls.