855results about "Model driven code" patented technology

Techniques for leveraging a large language model (LLM) in software development are described. A description of a software development task is received from a user. Data associated with the software system is obtained from a data source. An LLM is prompted to identify at least one aspect of the task which requires clarification from the user, at least partly by providing the obtained data to the LLM and asking the LLM to identify a question for the user which remains unanswered by the obtained data. The question is presented to the user. An answer to the question is received from the user. The LLM is prompted to respond to propose an implementation of the task at least partly based on the data associated with the software system and the answer received from the user. The proposed implementation is received from the LLM and caused to be displayed to the user.

The invention discloses a multi-agent task arrangement method and system, and relates to the technical field of artificial intelligence. In the method, firstly, a task demand is obtained, key information and intention of the task demand are extracted, and task semantics are obtained; secondly, based on the task semantics and the business standard process template, obtaining a business standard process template matched with the task demand; then, according to the matched business standard process template and task semantics, a task context is constructed, and a domain specific language DSL is generated through reasoning according to the task context; thirdly, the DSL is analyzed, a task dependency graph is constructed according to the DSL analysis result, and the task dependency graph is converted into BPMN process model data; and finally, sub-task scheduling and execution of the sub-agents are carried out according to the BPMN process model data. According to the method provided by the invention, the dependence on professional developers or process engineers can be reduced, the response and planning time of complex tasks can be shortened, the result predictability can be improved, and the reasoning cost can be obviously reduced.

Systems and methods disclosed herein automatically authorize, audit, and manage usage of protected digital content via agentic artificial intelligence (AI) models. A data access / usage request is received (e.g., from a graphical user interface) that is associated with digital assets licensed from third parties. The system uses a first AI agent set to identify the digital content and retrieve corresponding access policies from a distributed database. The system uses a second AI agent set (same as or different from the first AI agent set) to evaluate the request against the retrieved policy to generate a permission set and / or settlement instructions. The system uses a third AI agent set (same as or different from the first and / or second AI agent sets) to embed digital watermarks and / or cryptographic signatures into the accessed content, and to record an audit trail of access, authorization, and / or settlement events in a distributed ledger or database.

Systems and methods disclosed herein perform privacy-preserving evaluations of artificial intelligence (AI) agents. The system identifies an auditing AI agent from a set of auditing AI agents for assessing target AI agent sets. The system obtains a data structure that defines operative boundaries for a target AI agent set and generates a reference value by applying a first transformation operation set on the data structure. The system transmits the reference value to a multi-agent storage and receives, via the multi-agent storage, a verification artifact from the target AI agent set that indicates an observed value generated by applying a second transformation operation set on an artifact set generated by the target AI agent set. The system determines, via the auditing AI agent, a verification status and responsive to a particular artifact failing to satisfy one or more assessment metrics, generates an action set to modify the target AI agent set.

The invention relates to a low-code visualization optimization method and system for power credential development, and the method comprises the following steps: S1, obtaining data in a credential environment, recognizing the interface difference and performance bottleneck points between components through a compatibility test matrix, and building a standardized adaptive interface specification; s2, obtaining a standardized business object model based on the standardized adaptive interface specification; based on the business object model, defining attributes, relationships and constraint rules of each business entity, and forming a data dictionary and an interface specification special for the electric power field; s3, on the basis of the data dictionary and the interface specification special for the power field, constructing a DSL (field specific language) of power visualization; s4, a dragging type interactive design is adopted, and the visual operation is converted into a DSL configuration file; and S5, automatically converting the DSL configuration into a React or Vue component code through a code generation engine, and carrying out compatibility optimization on the kernel of the creative browser. According to the invention, the power credential development efficiency is effectively improved.

The invention discloses a code generation and restoration method and device based on multi-round feedback of a large language model, and the method comprises the following steps: 1, converting problem description into a structured tetrad (target, input, output and constraint) through a two-stage task demand deconstruction mechanism, and generating a pseudo-code frame according to the structured tetrad; 2, generating a plurality of candidate code schemes in parallel based on different implementation strategies; 3, selecting an optimal code implementation scheme through multi-dimensional evaluation; 4, generating an extended test case through boundary condition analysis and public test reasoning, and expanding a verification range; 5, classifying error types in combination with static analysis and dynamic execution information, and applying a special repair strategy; 6, providing detailed repair reasons and ideas by an interpretable repair mechanism; and 7, establishing a multi-round feedback repair verification iteration mechanism composed of testing, analysis, repair and verification. According to the method, the performance of an existing large language model in processing a complex programming task is effectively improved.

A method for automated control code generation and verification includes: receiving a natural language command, the natural language command being configured to instruct the large language model to output a code text that meets control requirements corresponding to the natural language command; performing matching retrieval on a vector database according to the natural language command to obtain a sample code snippet; obtaining API structured information corresponding to the sample code snippet from a knowledge graph database; generating an initial control code according to the sample code snippet and the API structured information; and performing a multi-level virtual operation verification on the initial control code in a software motion control system, and generating a target control code according to multi-level verification results confirmed multiple times by the user and the initial control code.

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 human-machine interface (HMI) development system leverages a generative AI model to assist in development of HMI projects in accordance with specified functional requirements, which can be provided to the development system as intuitive natural language spoken or written text. The system can formulate and implement HMI project edits during design time based on analysis of this natural language design input. After the HMI project is deployed as a runtime application, the system can also receive and process natural language requests to modify the runtime HMI in accordance with described modification criteria.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating webpage data. An example method includes accessing content data associated with a first webpage and determining context data corresponding to one or more users associated with the first webpage. Based on (i) the content data and (ii) the context data, prompt data for one or more trained machine learning (ML) models is generated. The prompt data is provided to the ML models to obtain output data corresponding to one or more variation content elements. Data representing one or more candidate webpages is generated by adapting the one or more variation content elements according to the content schema. An instruction is provided to a computing device to cause the display of a representation of at least one candidate webpage included in the one or more candidate webpages.

According to the complex nested SQL generation method based on KG-LLM two-way reasoning, a framework of'knowledge graph (KG) reasoning path constraint and large language model (LLM) semantic generation optimization 'is adopted, it is ensured that SQL statements conform to a database structure by means of deterministic constraint of KG, complex nested query is flexibly processed by means of semantic understanding capacity of LLM, and the method has the advantages of being simple in structure, high in efficiency and high in efficiency. The defect of flexibility of an existing method is overcome, and diversified query requirements and dynamically changing query logic can be flexibly coped with; besides, through an information interaction mechanism of KG reasoning and LLM reasoning, core constraint information of KG is injected into LLM to generate context, SQL fragments generated by LLM are verified and subjected to conflict resolution, it is ensured that generated SQL statements conform to database structure constraints and also conform to user intentions, and the user experience is improved. The problem that accuracy and reliability are limited when complex SQL statements are generated through an existing method is effectively solved.

A system and method for generating structured software application specifications through multi-phase conversational dialogue is disclosed. The system implements multiple specialized AI agents including a framework generation agent, an interactive coaching agent, specialized capsule agents for analyzing different concern categories, and a specification coaching agent. During Phase 1, the system conducts exploratory dialogue using a tailored question framework while detecting and storing user concerns in structured capsule entries. Concern injection into the dialogue is strategically timed based on algorithmic evaluation of cooldown periods and user sentiment analysis. During Phase 2, the system conducts comprehensive concern resolution dialogue and generates a final specification in structured JSON format with explicit traceability linking requirements to source conversations and concern resolutions. The system solves technical problems of preserving concern context across conversation phases, optimizing injection timing to avoid overwhelming users, and generating machine-readable specifications suitable for automated downstream processing.

Disclosed are various approaches for large language model (LLM) application risk mitigation. A large language model (LLM) application that interacts with a network LLM service can be identified. A portion of the LLM application can be provided as input to an LLM risk mitigation code generation function that outputs LLM-specific risk mitigation code. A runtime environment can be deployed to include a modified version of the LLM application that includes the LLM-specific risk mitigation code, a kernel-layer LLM risk mitigation program that can intercept LLM interaction system calls to apply the LLM-specific risk mitigation code, or any combination thereof.

This system provides a code conversion system that can promote labor savings in conversion, expand the range of data that can be converted, improve conversion accuracy, and reduce conversion costs. [Solution] The code conversion system 10 is configured with a group creation means 25 that divides the group of programs to be converted into groups with inputs and outputs, a subgroup creation means 26 that uses a large-scale language model (LLM) to create subgroups that will serve as code conversion processing units for each created group by instructing that programs to be converted to the same language be grouped together and programs called by other programs be kept separate, and the created subgroups be arranged in order from those with no dependencies, and a code conversion means 27 that uses the LLM to perform code conversion on each subgroup and in the received order.

The invention discloses an LLM (Logical Language Modelica) domain method for Modelica intelligent modeling optimization, and belongs to the field of computer-aided modeling. Structured information is converted and extracted by collecting field data of the new energy automobile; then, based on the RAG technology, intelligent blocking and vectorization processing is carried out on the structured information to form a structured knowledge base, and then a recall test is carried out by calculating a comprehensive weighted score to judge whether the structured knowledge base is available or not; then, the LLM is integrated into an AI-Agent framework, an AI-Agent is formed, and LLM parameters are configured; integrating the structured knowledge base which passes the test into the AI-Agent by utilizing an API (Application Program Interface) and an access protocol; and meanwhile, a specialized cue word template is designed and integrated into the AI-Agent, and a Modelica code generation template and related constraints are standardized. And finally, the user asks a question to the AI-Agent, the LLM works according to a specialized cue word instruction, an API interface is called to access the knowledge base, and Modelica codes which conform to language specifications and can be operated by the user are automatically output through the LLM. According to the invention, end-to-end conversion from unstructured input to high-fidelity model output is realized.

Various embodiments of the present disclosure include systems, methods, and / or apparatuses for executing an agentic-AI-based software modernization system. An example system may be configured to receive one or more portions of monolithic legacy code of a legacy software program product, the monolithic legacy code comprising a plurality of code segments, group the monolithic legacy code by functionally related code segments of the plurality of code segments to define a plurality of groups, generate an API for each identified group, generate modern microservice code for each identified group to define a modernized software program product, and validate the modernized software program product.

A method for generating an application program according to an embodiment disclosed herein includes inputting a natural language command through a developer input interface from a developer who develops the application program, inputting the input natural language command into a predetermined language model, acquiring predetermined output information generated by the language model in response to the input of the natural language command, and generating a source code or an execution code of the application program using the acquired output information.

The present disclosure of the various embodiments relates to using a large language model to assistant with the creation of secure code and / or the completion of threat modeling tasks in software development. In one example, a system comprises a computing device configure to identify a prompt that requests generating secure source code for source code with a security vulnerability. A security data source is queried for a security threat embedding. The security threat embedding is received from the security data source and an augmented prompt is generated. The augmented prompt is transmitted to the large language model. A secure source code is received from the large language model and imported into application source code in a software development environment.

A system enables agile model development to speed up innovation by data scientists. Model training and deployment are coordinated and standardized to reduce redundancy. Data is obtained for feature generation and reformatted and de-sensitized for storage. The features are stored in locations available to all models and training modules of a system so data does not need to be adjusted for new models. To generate a machine learning model, the system establishes a cohort for evaluation by the model. A model template and features for use by the model are identified. The selected template and features are used for experimentation and evaluation. Model training artifacts, such as model weights are subsequently recorded in a model store and the model scripts and settings can then be registered in a centralized database where it can be accessed for execution.

Methods, apparatuses, or computer program products that provide for monitoring performance of a server-based application associated with server interface functionality. In some examples, an interface definition data object that comprises a set of interface rules associated with managing communications between components of a multi-component system of an application framework is received, a server-based application associated with a particular type of programming format from a plurality of programming formats is generated based at least in part on the set of interface rules associated with the interface definition data object, utilization of the server-based application is initiated via the multi-component system of the application framework, and one or more actions associated with the server-based application is initiated based at least in part on whether the utilization of the server-based application satisfies defined performance criteria for the server-based application.

The invention discloses an operation automation generation method and device and a storage medium, and relates to the technical field of automatic operation, and discloses an operation automation generation method, which comprises the following steps: generating a skill model based on an operation behavior sequence of a user in a business system, the skill model comprising a plurality of atomic operations, the execution condition and the sequence dependency relationship of the atomic operation are determined; in response to the automatic execution request, obtaining a current interface of the target service system, and comparing the current interface with a historical interface associated with the corresponding atomic operation in the skill model; according to a comparison result, performing adaptive adjustment on the corresponding atomic operation in the skill model to obtain an adjusted skill model; and based on the adjusted skill model, executing automatic operation in the target business system. Through an adaptive adjustment mechanism, the automatic process can dynamically adapt to the interface change of the target business system, and the maintainability of cross-system operation automation is effectively improved.

Systems and methods obtain a translation request accompanying source code of source file(s) for conversion. Formatting of the source code of the source file(s) is verified and the source code is converted to target code that includes target file(s). The converting (i) maps programming language functions of the source code to corresponding programming language functions of the target code and (ii) utilizes, through customized prompt template(s), one or more large language models (LLMs) hosted by a cloud platform during the code conversion, where a source application that uses the source code includes TIBCO™ software. The target file(s) are distributed.

One embodiment of a method for modifying program code includes processing, using a first trained language model, program code to identify one or more modifications to the program code; processing, using a second trained language model, the program code and the one or more modifications to generate a plan for applying the one or more modifications; and processing, using a third trained language model, the program code and the plan to generate a modified program code.

Systems and methods disclosed herein perform privacy-preserving evaluations of artificial intelligence (AI) agents. The system identifies an auditing AI agent from a set of auditing AI agents for assessing target AI agent sets. The system obtains a data structure that defines operative boundaries for a target AI agent set and generates a reference value by applying a first transformation operation set on the data structure. The system transmits the reference value to a multi-agent storage and receives, via the multi-agent storage, a verification artifact from the target AI agent set that indicates an observed value generated by applying a second transformation operation set on an artifact set generated by the target AI agent set. The system determines, via the auditing AI agent, a verification status and responsive to a particular artifact failing to satisfy one or more assessment metrics, generates an action set to modify the target AI agent set.

Solutions for more efficient and effective predictive code recommendation are disclosed. In one example, a method includes identifying a graph-based code recommendation machine learning model, wherein each inferred edge weight value of the graph-based code recommendation machine learning model is updated based at least in part on each compressed forward-adjusted temporal distance measure for an observed co-occurrence of any observed co-occurrences of a predictive code pair for the inferred edge weight value within one or more temporally-proximate occurrence subsets determined based at least in part on a plurality of training predictive code occurrences; processing the input predictive code using the graph-based code recommendation machine learning model to generate one or more related codes of the plurality of predictive codes for the input predictive code; and performing one or more prediction-based actions based at least in part on the one or more related codes.

The invention relates to the technical field of computer data processing, in particular to an intelligent model system for automatically generating a standard protocol interface, which comprises the following steps: acquiring demand information input by a user through a demand acquisition module and converting the demand information into standardized demand parameters; the protocol knowledge base module outputs a compliance rule set according to the target protocol type, and the interface generation module automatically generates an interface source code, a configuration file and an interface document according to the standardized demand parameters and the compliance rule set. The verification module verifies and optimizes the generated interface, and the self-learning module updates system analysis logic and rule content according to a verification result. The system adopts a natural language processing and structured analysis double-path fusion technology to realize accurate analysis of requirements, realizes intelligent matching of protocol rules through a protocol knowledge graph, completes automatic generation of codes based on a symbol reasoning and neural code generation technology, forms a complete closed loop of requirement analysis, protocol adaptation, code generation and verification optimization, and realizes automatic analysis of the requirements. And the interface development efficiency and quality are effectively improved.

The invention provides an intelligent script generation method based on intelligent IC card making, which comprises the following steps: calling different interfaces in a card making production system through an artificial intelligence body, and sequentially realizing full-process automatic control of parameter configuration, script generation, component program generation, automatic card making and card testing in a modular manner; constructing an intelligent parameter configuration system by adopting a dual-core driving architecture comprising a knowledge graph business rule base and a reinforcement learning optimization model; an incremental learning mechanism is utilized to automatically collect card making results and verification data after each card making period is ended, and a dynamic rule knowledge base is generated; and on the basis of the multi-modal large language model, generating a card making script meeting the current requirement through a field adaptation pre-training and parameter fine tuning technology. Through deep integration of artificial intelligence and a digital twinning technology, the problems of low efficiency, high cost, poor flexibility and the like of a traditional card making mode are effectively solved, and the method has remarkable economic benefits and social values.

The invention provides a model-driven double-engine micro-front-end framework compatible with multiple instruction sets, which is characterized in that the model-driven double-engine micro-front-end framework compatible with multiple instruction sets is introduced, and an infrastructure layer is utilized to support a multi-instruction-set environment; the capability layer integrates a component engine, a rendering engine and a code generation assistant to realize componentized development and automatic generation of model driving codes, the service arrangement layer coordinates data flow, the front-end application layer processes interactive presentation, and the instruction set adaptation layer is combined to realize cross-instruction-set dynamic compatibility, so that the development cost and the technical threshold are remarkably reduced, and the development efficiency is improved. And meanwhile, the page response speed and the user experience are improved through high-performance rendering optimization, and comprehensive support is provided for collaborative development of software and hardware ecology.