131 results about "System Generation" patented technology

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.

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.

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.

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.

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.

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

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.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying labels for a data set without disclosing the data set to any individual computing system. A method can include receiving, by a first computing system in a multi-party computation (MPC) system, a query including a first share and a 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 in the MPC system. The first computing system and the second computing system generate a machine learning model and identify respective first and second labels. The first computing system receives the second label from the second computing system as a response. The first computing system responds to the query with a response including the first label and the second label.

A machine has a multitude of different control systems. Each control system generates a request or control signal to control a controllable subsystem. A control supervisor selects which of the multitude of control systems should control the controllable subsystem and provides feedback to the multitude of control systems indicating which system has been selected and a current control signal value. A time-based transition constraint processing system generates a time-based transition constraint, which is used to control the transition from the current control signal value generated by the first control system of the multitude to the selected control signal value generated by the selected control system. The selected control system then controls the controllable subsystem based on the time-based transition constraint.

Techniques are disclosed that relate to skip lists. A computer system maintains a skip list having towers of varying depths and entries storing pointers to other towers. A first tower includes an entry at a particular depth storing a pointer to access an entry of a second tower. The pointer includes first similarity information indicating an amount of similarity between a key of the first tower and a key of the second tower. The computer system performs a traversal of the skip list for a search key. The computer system generates second similarity information indicating an amount of similarity between the first tower's key and the search key. Based on a comparison involving the first and second similarity information and without accessing the second tower to obtain information about its key, the computer system determines whether to traverse to the second tower using the pointer or descend the first tower.

Techniques are provided for workflow generation, including: (a) receiving, from a user, an instruction to create a workflow to perform a task; (b) generating one or more prompts based on the instruction and a predefined workflow schema; (c) querying a large language model (LLM) with the one or more prompts; (d) in response to querying the LLM, receiving a workflow in a format consistent with the predefined workflow schema from the LLM; and (e) displaying a visualization of the received workflow to the user.

A computer system retrieves provisioning data and observability data (metrics, logs, events, alerts, inventory) for a plurality of components from remote servers. The computer system extracts component identifiers from the provisioning data and the log data. The computer system identifies relationships between components from the provisioning data and the log data, such as environmental variable relationships, network relationships, session relationships, access relationships, and network connection relationships. The computer system generates a topology in which nodes represent components and edges represent relationships. The topology is updated to show changes to components and relationships. A user may interact with a visual representation of the topology to change data displayed or invoke changes to the components and relationships represented by the topology.

A system is provided.SOLUTION: A system includes generation means, acquisition means, change detection means, summarization means, notification means, evaluation transmission means, aggregation means, and report generation means.SELECTED DRAWING: Figure 1

We provide the system. [Solution] A means by which the user enters an inquiry into the terminal, The means by which the terminal sends a query to the server, A server receives an inquiry and generates an answer using a generative artificial intelligence system. A means by which the server sends the generated response to the terminal, The means by which the device displays the answer to the user, A system that includes this.

Systems and methods for generating rule sets for machine learning models are described herein. In some aspects, the system receives a first rule set to regulate training of a language processing model. The system trains the language processing model to produce an output text sequence. The system generates a first performance metric for the language processing model as a result of the training. Using a genetic algorithm, the system generates a second rule set based on the first rule set. Using a reinforcement learning algorithm and the second rule set, the system updates parameters of the language processing model. The system iteratively generates a second performance metric for the updated language processing model, uses the reinforcement learning algorithm to generate an updated genetic algorithm, and uses the updated genetic algorithm to further modify the second rule set. The system produces a final language processing model based on the iterative repetition.

A system for positioning the working attachment of a road construction machine, wherein first and second zones are located in front of and behind the working attachment, respectively. A positioning system includes a controller for changing the position or orientation of the working attachment. A computer system receives and processes topographic data collected by sensors for points of interest in the first zone to generate a first-level equation for a first zone containing at least three of the points of interest. By comparing the first-level equation with an initial position, the computer system determines a second-level position where the working attachment can change the road surface from a first-level state to a second-level state. The computer system generates instructions for changing the position or orientation of the working attachment from the initial-level position to the second-level position, and the controller executes these instructions.

The invention relates to an application flexible assembly system based on a service component, and belongs to the technical field of computers. The application flexible assembly system comprises a basic platform, a service assembly, a service bus and an application framework. The invention provides a one-stop application assembly capability taking a business service component as a core. Each business service component is developed and packaged on the business level based on a unified data standard, a communication protocol and a style and style to serve as a minimum business and operation unit of application assembly. And then, an application system developer can select and assemble the business service components through the application framework to form an application system meeting various business scenes. According to the architecture and the technology, the difficulty that business codes need to be developed from zero every time in a traditional development mode is solved, a batch of atomic-scale business service components are provided on the business level, the ability of business functions to take urgent use is formed, and the generation efficiency of an application system is improved.

The present application relates to material conveying technical field, especially to a kind of abnormal control method for solving material conveying.Its technical scheme includes: S1, system generates material conveying task, and sets each scene setting trigger condition in system, until after exception sends, system will automatically find solution condition according to exception, solve current exception;S2, through server issue handling task, and whether the success of handling task is judged to judge whether server is abnormal;S3, when task is issued successfully, and whether the feedback of system is judged to judge whether material taking is successful;S4, when material taking is successful, product is readed code, and simultaneously reported to system;S5, when reading code is consistent, whether the storage position is full is judged;S6, when the storage position is not full, send goods and convey, and detect whether there is material in discharging storage position;S7, when waste storage position has no material, discharging is completed, and handling task is completed.The present application not only covers software exception, but also solves the problem of hardware exception.

A cloud computing service (e.g., a "Puffin service") is described. The service may maintain backward and forward compatibility between skills and capabilities. The skills may be configured to enable improved tracking of processing that builds the data center. Sometimes, an orchestrator may simultaneously use skills and capabilities to drive build operations. In order to enable both configurations to be utilized, the Puffin service maintains associations between skills and capabilities. These associations enable a skill to be published upon identification of published capabilities, and corresponding capabilities to be published for the published skill, which in turn allows an orchestrator to drive build operations based on any suitable combination of capabilities and / or skills. In this way, previously published capabilities may be identified and represented using system generated skills ("shadow skills"), further enabling compatibility between constructs while avoiding cumbersome data entry.

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.

An example computing system receives an indication of an input detected at a location of an input device that corresponds to a graphical component from a first plurality of graphical components. The computing system retrieves information associated with at least a portion of content included in a current graphical user interface, and determines, based on one or more of the information associated with at least the portion of the content and the indication of the input, at least one prompt. The computing system determines, by applying the machine learning model to the at least one prompt and at least the portion of the content, one or more suggested outputs. The computing system generates instructions for generating a second plurality of graphical components, in which the second plurality of graphical components is associated with the one or more suggested outputs.

The invention discloses a high-real-time virtual PLC implementation method and system for wind power control, and relates to the technical field of industrial control, and the method comprises the steps: deploying a real-time patch host machine operation system, generating a Hypervisor layer, and creating a real-time virtual machine based on a wind power plant private cloud server; by modifying system guide parameters, exclusive binding of a specific CPU on a physical server and a virtual CPU of a virtual PLC instance is isolated, a large-page memory is distributed for the virtual PLC instance, and memory locking operation is executed; configuring an SR-IOV network card, transmitting the SR-IOV network card to a virtual PLC instance in a PCIe unvarnished transmission mode, deploying a user mode real-time protocol stack, and realizing extremely low delay and high-precision time synchronization of network I / O; according to the method, an IEC 61131-3 standard control logic program is followed, a local machine code is compiled by an LLVM compiler, a dynamic shared library is generated for a virtual PLC real-time task to be directly loaded and run, a double-cycle task scheduling architecture is set to monitor task execution jitter online, and real-time performance self-diagnosis and operation and maintenance analysis are supported. The method has the beneficial effect that the downtime is shortened.

According to one embodiment, a generation system generates a production plan. The production plan is for producing products of product types by processes using production lines. The system includes an optimization calculation part, an input plan generator, and a verification part. The optimization calculation part generates a processing plan by an optimization calculation using first input data. The first input data includes a production plan amount of each of the product types and a processing capacity of each equipment. The input plan generator generates an input plan by using second input data. The second input data includes an input amount of workpieces to each of the production lines for each of the product types. The second input data further includes a processing route in the plurality of processes for each of the product types. The verification part determines an appropriateness of the processing plan and the input plan.

The invention discloses a digital twinning system generation method, computer equipment and a storage medium, and belongs to the technical field of digital twinning and information physical systems. The method comprises the following steps: S1, carrying out general algorithm operator packaging; s2, operator modeling combination is carried out; and S3, performing intelligent agent enabling on the model. According to the invention, high modularization and reusability are realized, and the development cost and repeated labor are greatly reduced; the expandability and maintainability of the digital twin system are obviously improved, and the system is endowed with the ability of sustainable evolution; a construction path and a cooperation framework of the intelligent system are clarified, and the team cooperation efficiency is remarkably improved; intelligent layering from calculation to autonomous decision making is achieved, and cognitive intelligence is introduced to solve complex problems.

Optimizing route modification using a quantum generated route repository is provided herein. In particular, a classical computing system determines an initial route optimization request comprising at least one initial constraint. The at least one initial constraint includes a starting location and an ending location for a desired route. The classical computing system determines a plurality of initial optimized routes from a plurality of routes based on the at least one initial constraint. The plurality of routes are generated by a quantum computing system. The classical computing system determines a modified route optimization request. The modified route optimization request includes at least one modified constraint. The classical computing system determines a plurality of modified optimized routes from the plurality of routes based on the at least one modified constraint. The plurality of routes are previously generated by the quantum computing system.