291 results about "Interpreter" patented technology

A system, method, and computer-program product includes identifying a plurality of code synthesis items for a target programming language, generating a code synthesis prompt based on a first sampling of the plurality of code synthesis items, synthesizing, via a large language model, a plurality of raw code segments using the code synthesis prompt, executing the plurality of raw code segments with a code interpreter associated with the target programming language, determining one or more valid code segments of the plurality of raw code segments that the code interpreter successfully executed, aggregating, via a second sampling, the one or more valid code segments into one or more validated code synthesis training samples, and training a code generation model using the one or more validated code synthesis training samples. User interfaces may be provided to allow target coding tasks to be specified via text or speech.

In various examples, an interactive agent platform that hosts development and / or deployment of interactive agent may include an interpreter that generates interaction modeling API events specifying commands to make agent (e.g., bot) expressions, poses, gestures, or other interactions or movements, which may be translated into corresponding agent animations. The interpreter may generate an interaction modeling API event using a standardized interaction categorization schema, which an action server implementing an animation service may use to identify a corresponding supported animation. The animation service may implement an action state machine and action stack for all events related to a particular interaction modality (e.g., bot gestures), connect with an animation graph that implements a state machine of animation states and transitions between animations, and instruct the animation graph to set a corresponding state variable based on a command to change the state of an agent movement represented by an interaction modeling API event.

In various examples, an interactive agent platform that hosts development and / or deployment of an interactive agent may use an interaction modeling language and corresponding interpreter that support the use of natural language descriptions and one or more LLMs to facilitate the development and deployment of more complex and nuanced human-machine interactions. For example, the interpreter may prompt an LLM to generate a natural language description of one or more instruction lines defining a flow, generate one or more instruction lines for a specified flow, determine whether an event matches a flow description of an active flow, determine whether an unmatched event matches the name and / or instruction(s) of an active flow, generate a flow in response to an unmatched event, and / or otherwise.

The invention provides a power grid field table data reasoning and question answering method based on a code enhancement framework, and the method comprises the steps: building the code enhancement framework, processing the reasoning and question answering tasks of power project WBS table data through a large language model, and firstly carrying out the structure self-interpretation of the power project WBS table data; the method comprises the steps that firstly, the understanding ability of a model for a complex WBS table hierarchical structure and a task decomposition relation is improved, then based on table structure description and natural language questions formed by work content description proposed by a user, executable codes are automatically generated, related task information and resource configuration data in WBS table data are extracted, and the task configuration information is extracted; executing the code through an external code interpreter to obtain a result, and finally combining the code execution result and the reasoning ability of the model to generate a natural language answer about the number of human resources required for completing the specific work content; according to the method, the understanding, reasoning and people number estimation capabilities of the large language model during processing of complex WBS table data in the power grid operation and maintenance process can be effectively improved.

The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating context engine outputs by utilizing an interpreter purpose-built to execute code generated by large language models. For example, the disclosed systems generate computer code executable for responding to a query by utilizing a large language model. In addition, the disclosed systems execute the model-generated computer code utilizing an interpreter integrated with the context engine that further includes swappable logic interchangeable across multiple executors. Moreover, the disclosed systems can further generate as part of executing the computer code utilizing the interpreter, a first context engine output by implementing the interpreter at a first executor. Additionally, the disclosed systems can further generate a second context engine output by implementing the interpreter at a second executor.

In various examples, an interactive agent platform that hosts development and / or deployment of an interactive agent may include an interpreter programmed to iterate though one or more flows until reaching an event matcher, a top level flow may specify instruction(s) to activate any number of flows comprising any number of event matchers, the interpreter may use any suitable data structure to keep track of active flows and corresponding event matchers (e.g., using a tree or other representation of nested flow relationships), and the interpreter may employ an event-driven state machine that listens for various events and triggers corresponding actions specified in matching flows (with event matchers that match an incoming interaction modeling API event). As such, the interpreter may execute a primary processing loop that processes incoming interaction modeling API events and generates outgoing interaction modeling API events that implement the interactive agent.

The invention provides a large model reasoning and calculation enhancement system, method, device and medium, and belongs to the technical field of artificial intelligence, the system comprises a user interaction module for obtaining and processing a natural language question input by a user, and returning a calculation result to the user; the reasoning and calculating module is used for converting the processed problem into a standard Prolog code by using the adjusted large language model; the Prolog code integrity verification module is used for carrying out integrity verification on the converted Prolog code; and the Prolog interpreter module is used for analyzing and executing the Prolog code which passes the verification, and then providing a calculation result to the user interaction module. According to the method, the natural language understanding ability of the large language model is combined with the precise calculation ability of Prolog, so that the limitation of the large language model in the aspects of mathematical calculation and logical reasoning is effectively solved, and the accuracy and reliability of a reasoning result are improved.

In various examples, an interactive agent platform that hosts development and / or deployment of an interactive agent may provide an interpreter or compiler that interprets or executes code written in the interaction modeling language, and a designer may provide customized code written in the interaction modeling language for the interpreter to execute. The interaction modeling language may be used to define a flow of interactions that instruct the interpreter what actions or events to generate in response to a sequence of detected and / or executed human-machine interactions. The interaction categorization schema may classify interactions by standardized interaction modality and / or corresponding standardized action category. As such, a flow may be used to model an agent intent or inferred user intent, which a designer may use to build more complex interaction patterns with the interactive agent.

The invention provides an automatic spreadsheet operation and reasoning method and system based on a large language model, and the method comprises the steps: S1, uploading a spreadsheet file, and extracting related information; s2, the table state, the table description and the system prompt of the information provider module are provided for the information provider module; s3, generating an SQL query statement, executing the SQL query statement, and generating a sub-table corresponding to the spreadsheet; s4, extracting forward data from the sub-table, and providing the forward data to the planner module in combination with a table state, table description and a system prompt of the planner module; s5, generating a Python code through a planner module, and executing the Python code in an interpreter to obtain an execution result; s6, judging and skipping according to an execution result until the code is successfully executed; and S7, repeating the steps S1 to S6 until the task is completed. The method can effectively improve the capability and efficiency of automatically solving spreadsheet operation and reasoning tasks.

A computer-implemented method for real-time creative creation includes retrieving a creative definition object (CDO) that contains a set of instructions related to one or more creative assets, each associated with a set of layout and behavior instructions, retrieving the assets, rendering the assets according to the set of instructions and displaying the output. A computer-implemented system for real-time creative creation includes at least one memory and one processor communicatively coupled to the memory and a rendering module activated by a streaming application equipped with a video player. The rendering module is configured to retrieve a CDO that contains instructions related to one or more creative assets, each creative asset associated with a set of layout and behavior instructions, retrieve the creative assets, render them according to the set of instructions and display the rendered creative on a display.

A system for returning synthetic database query results. The system may include a memory unit for storing instructions, and a processor configured to execute the instructions to perform operations comprising: receiving a query input by a user at a user interface; determining, based on natural language processing, a type of the query input; determining, based on the received query input and a database language interpreter, an output data format; returning, based on a generation model and the output data format, a result of the query input; providing, to a plurality of training models and based on the determined query type, the query input and the result; and training the training models, based on the query input and the result.

The invention provides a graphical programming education system, which comprises a database used for storing coding information and drawing data; the scheduler is connected with the database and used for managing data flow between the database and the interpreter; the interpreter is connected with the scheduler and is used for analyzing the coding information and generating a task in an intermediate representation form which can be understood by the system; the judgment module is connected with the interpreter and is used for carrying out logic judgment on the task and providing a control signal for the operation module; the operation module is connected with the judgment module and is used for executing the task according to the control signal and transmitting the output to the output module; and the output module is used for displaying the result transmitted by the operation module. The system is used for realizing bidirectional mapping and real-time synchronization between the coded information and the graphical representation, and realizing dynamic visualization of the coded information logic based on the data flow.

The invention relates to a method, a device and a system for locally deploying a cross-platform graph database program. The method mainly comprises the steps that a vcpkg package manager is deployed, and a vcpkg.json file is created under a directory where a program is located; creating CMakeLists.txt in a catalog where the program is located for defining how to construct the program and linking a third-party library installed through the vcpkg; an executable file of the database is generated by running the CMake command; recursively collecting all third-party libraries on which the program depends through an ldd command and an awk command, and copying all the third-party libraries on which the program depends and an ld interpreter of the program to a directory where the program is located; through the patchelf file, modifying an ld interpreter path of the program to be a relative path under a catalog where the program is located, and modifying a rpath of the program to be the relative path under the catalog where the program is located; and packaging and delivering the files in the catalog where the program is located to a user. According to the method, a developer can construct a database program deployed in a zero-dependence manner.

Provided is an architecture for facilitating creation and enforcement of policy templates. The architecture includes a policy template designer, a script generator, a policy enforcement engine, and a plurality of technology-specific interpreters. The policy template designer records, for creating a policy template, selection of a set of technologies. The script generator generates a policy template script indicative the set of technologies. The policy enforcement engine generates a set of provisioning scripts indicative of a set of resources to be provisioned by the set of technologies. The policy enforcement engine communicates the set of provisioning scripts to a set of technology-specific interpreters of the plurality of technology-specific interpreters. The set of technology-specific interpreters communicate with the set of technologies to provision the set of resources.

The present application provides a method, apparatus, computing device, and computer-readable storage medium for optimizing program execution. In this method, when the hot-updated code runs seamlessly with the main program code in the same runtime, in order to achieve two-way calls between the interpreter code and the AOT-compiled code, all potentially used functions are scanned in advance, and bridging functions for two-way calls are generated based on the signatures of the functions. When a call occurs, the function call can be implemented directly using the bridging functions without a third-party library, improving the execution efficiency of the program during hot updates.

The invention relates to the field of three-dimensional modeling and artificial intelligence control systems, in particular to a large language model driving type FMT automatic modeling method supporting an MCP protocol. According to the method, a Python interpreter is embedded into FMT modeling software, a core modeling operation function is registered as a Python callable script, a remote instruction format defined by an MCP (Model Control Protocol) protocol is matched, and a user describes a modeling intention through a natural language; then decomposing a natural language input by a user according to intentions by using a large language model, generating a complete FMT modeling program, and automatically manipulating the FMT to complete modeling operation; and finally, a full-chain closed loop of natural language-script instruction-software operation behavior-real-time feedback is realized, a large language model is supported to directly control an actual software modeling process, and the automatic modeling capability is greatly improved.

The invention belongs to the technical field of mathematical solving, and provides a mathematical application question solving method and system based on data synthesis, and the method comprises the steps: obtaining a to-be-solved mathematical application question; constructing a reasoning chain of the mathematical application questions based on a large language model; judging whether the constructed reasoning chain is converted into a program auxiliary chain or not according to the prompt judgment word; if conversion is needed, the large language model generates an inference chain and a program auxiliary chain, the program auxiliary chain is executed by using a code interpreter, and solving of the mathematical application question is completed; otherwise, solving the mathematical application problem directly according to the reasoning chain.

The invention relates to the technical field of computers, in particular to a method for optimizing the processing speed of a user mode program under an NUMA architecture. The method comprises the steps that in response to the optimization requirement of a user for the processing speed of an original file, first mark information representing the optimization requirement is added in the original file, a target file is generated, and the original file is a user mode program file; registering an interpreter corresponding to the target file; when a starting request corresponding to a target file is received and the starting request is scheduled to the non-uniform memory access node, calling an interpreter to process the target file to obtain a file copy; loading the file copy to a cache space of the non-uniform memory access node; according to the scheme, when the target file is executed, local access can be directly performed, so that delay and performance overhead caused by cross-node access are avoided, and the reading speed and the processing efficiency of the file copy are improved; and while the function complexity is reduced, the system stability is improved, and the system is easier to maintain and manage.

The invention provides an MOFs synthesis automation simulation method and system based on a large language model, and belongs to the field of artificial intelligence and nanometer material synthesis. The method comprises the following steps: preparing MOFs samples under different synthesis conditions, carrying out structural characterization and performance testing, and generating and storing a synthesis condition-structure-performance data set; a user puts forward a sample synthesis requirement, an LLM generation general task is divided into data field analysis, data correlation analysis, feature engineering processing, machine learning model construction, machine learning model optimization, machine learning model prediction and analysis subtasks, and subtask planning is generated and fed back; after the user confirms, the LLM reads a related MOFs sample data set according to the disassembled subtasks; and according to the subtask planning, calling a code interpreter tool, performing programming and execution of the subtasks, integrating results of all the subtasks, generating a report and outputting an MOFs sample synthesis scheme. According to the invention, the research and development period of MOFs is shortened, and the cost is reduced.

Large Language Model (LLM) driven proactive scheduling may be provided. First, a proactive feedback module may be used that gathers user requests and device feedback. Next, an instructive interpreter module may be used that receives the user requests and the device feedback and produces instructive prompts based on the user requests and the device feedback. Then a user-reinforced scheduling optimization module may be used that receives responses to the instructive prompts and continuously enhances bandwidth scheduling based on the receives responses.

The invention relates to the technical field of computer data processing and mode recognition, and discloses an intelligent handwriting record analysis system supporting translation process traceability and capability assessment, which comprises the following steps: collecting multi-dimensional handwriting data generated when a user translates by using an intelligent pen; the Hidden Markov model decodes the handwriting into translation behavior primitive sequences such as smooth writing, hesitant pause and the like; automatically dividing semantic regions such as a main translation region and a draft region by a density clustering algorithm; performing space-time fusion attribution to generate a space-time attribution log for accurately recording behaviors, time and positions; and establishing a multi-dimensional capability index system containing translation efficiency, quality and strategies based on the log, and carrying out quantitative calculation and generating a visual user capability portrait. According to the method, the invisible translation cognition process is converted into traceable and quantifiable objective data, deep insight and comprehensive evaluation of the translation process are achieved, and a scientific and efficient analysis tool is provided for translation teaching and interpreter ability evaluation.

The invention provides a Python algorithm development debugging system and method based on a simulation platform, the system comprises a TonderPy simulation engine service, a platform interface dynamic library file TonderPy.pyd, a Python interpreter and a Python algorithm model, the TonderPy simulation engine service is used for establishing communication connection with the simulation platform, driving the Python algorithm model, and dynamically optimizing an interface calling time sequence based on a collaborative scheduling algorithm; the platform interface dynamic library file TonderPy.pyd packages an interactive interface of the simulation platform and is used for obtaining situation information of the simulation platform, transmitting the situation information to the Python algorithm model and sending a task instruction to the simulation platform; the Python interpreter is arranged in a TonderPy simulation engine service and is used for loading and executing a Python algorithm source file; the Python algorithm model generates a task instruction according to the situation information; according to the system, standardized calling of a simulation platform interface is realized, and the algorithm development complexity is reduced; the data interaction efficiency between the algorithm and the platform is improved; a real-time closed-loop verification mechanism is constructed, the debugging period is shortened, and the utilization rate of system resources is improved.

The invention discloses a distributed compiling tuning method and device based on a random forest model. The method comprises the following steps: searching in a current compilation option set based on a preset distributed meta-search strategy to generate a plurality of compilation option combinations; performing performance prediction on the compilation option combination based on a pre-trained random forest model to obtain performance data corresponding to the compilation option combination; establishing an analysis sample set based on the compilation option combination and the performance data; establishing an SHAP interpreter based on a random forest model, and analyzing the analysis sample set based on the SHAP interpreter to obtain a Shapley value of each compiling option and a Shapley interaction value of each compiling option pair; screening the current compiling option set based on the Shapley value of each compiling option and the Shapley interaction value of each compiling option pair, and updating the current compiling option set; and under the condition that a preset condition is met, determining the current option set as a target option set. According to the method, the search efficiency can be improved while the search space dimension is reduced.

In one embodiment, a computer-implemented method comprises using a script automation processor that is hosted using a virtual compute instance and a virtual storage instance associated with one or more second non-transitory computer-readable storage media storing one or more second sequences of instructions defining an API implementation of the API, a graph server, and one or more programming language runtime interpreters, receiving a user pipeline automation script comprising one or more first sequences of instructions specifying one or more API calls to an application programming interface (API), and a user application; using the script automation processor, creating and storing one or more programmatic containers in memory of the script automation processor, the containers corresponding to functions that user pipeline automation script and user application invoke; using the script automation processor, creating and storing a directed acyclic graph (DAG) in the memory of the script processor, the DAG comprising nodes and edges corresponding to dependencies of the containers; using the script automation processor, interpreting the user pipeline automation script using a particular programming language runtime interpreter among the one or more programming language runtime interpreters; using the script automation processor, running the user pipeline automation script to automatically build, test, or deploy the user application in a cloud computing service.

A system for standardizing units of measure, comprising: a database comprising standardized units, unit text variations, unit context information, and interpreter standards; a neural network configured to process at least one of: unit text input, unit context input, and interpreter input to generate suggested standardized unit mappings; a nearest unit search component configured to match neural network outputs to permitted standardized units from the database; and an output component configured to provide a standardized unit based on the matched neural network outputs.

Systems and methods are provided for providing a vulnerability applicability module, wherein a context detector, a context detection output interpreter module and / or an executable may be included in a catalog of known vulnerabilities. An IHS remote access controller may detect publication of a catalog of known vulnerabilities and execute the context detector, the context detection output interpreter module and / or the executable included in the catalog. This causes the remote controller to identify configurations for one or more of the plurality of hardware components of the IHS, determine whether any of the one or more of the hardware component configurations are identified as vulnerable in the catalog of known vulnerabilities, and generate comprehensive applicability determination module, which upon execution by the IHS, determines configuration vulnerabilities in the catalog of known vulnerabilities that are present on the IHS for remediation.

Embodiments of the present application provide a data processing method and data processing engine based on a trusted execution environment. The method comprises: in response to a data processing request sent by a data principal, determining ciphertext data carried in the data processing request and encrypted by means of a key; decrypting the ciphertext data on the basis of the key corresponding to the ciphertext data to obtain plaintext data corresponding to the ciphertext data; on the basis of the data processing request, determining a computing function written by means of an encrypted programing language for processing the plaintext data; and parsing the computing function by means of a ciphertext computation interpreter to generate a data processing execution plan, and performing data processing on the plaintext data on the basis of the data processing execution plan to obtain a data processing result.

The invention discloses a retrieval sample selection and multi-round error correction method for solving a mathematical problem of a large language model. The retrieval sample selection and multi-round error correction method comprises the following steps: S1, constructing a retrieval sample library; s2, a mathematical character problem is given, cue words and a plurality of fixed samples for successfully solving the problem are used as input, and the large language model is guided to generate an initial thinking chain and Python codes; s3, the generated initial Python code is submitted to a Python interpreter to be executed; s4, if the initial Python code is successfully executed, ending the process, and taking an execution result as a final answer; s5, if the execution of the initial Python code fails, selecting a retrieval sample from a pre-constructed retrieval sample library according to the error type and the problem semantic similarity returned by the Python interpreter; s6, taking the selected retrieval sample and the original mathematical character problem in the step S2 as new input, submitting the new input to the large language model again, and carrying out the next round of thinking chain and code generation; and S7, repeating the steps S2-S5 until the program runs normally or the preset maximum number of attempts is reached.

Example solutions for providing an artificial intelligence (AI) exception interpreter are disclosed. An exception interpreter receives an exception report from a software application, evaluates it (using AI), and generates a first exception handling response. This provides the benefit of learning over a large amount of exception handling data, which offers super performance over trial-and-error tactics by programmers who are either less experienced or unfamiliar with exceptions from third party resources. Some examples sanitize exception reports of sensitive information. Some examples track an exception event history, so that when the same exception is seen multiple times (meaning a prior response was not effective), a different exception handling response may be provided.

A system implements an interpreter for extensible markup language (XML) to validate XML produced by large language models (LLMs). The system uses an LLM to generate a first set of XML instructions to perform a first task in an environment communicatively coupled to the system. The system executes the first set of XML instructions and observes a result of the execution. Based on the observed result, the system either uses the LLM to generate a second set of XML instructions to perform a second task, or detects an error in the first set of XML instructions. When an error is detected, the system uses the LLM to modify the first set of XML instructions to correct the error.