733 results about "Business rule" patented technology

The invention discloses a multi-modal visual arrangement recommendation method, belongs to the technical field of artificial intelligence and data visualization crossing, and realizes visual arrangement recommendation based on multi-modal input analysis, a dynamic mixed recommendation model and an intelligent optimization algorithm. Comprising the following steps: multi-modal intention analysis: realizing intelligent analysis of multi-modal input through combined use of a base model and a fine tuning model, realizing high-precision intention classification in combination with a pre-training language model and a domain adaptation fine tuning technology, and triggering dynamic prompt word recommendation; performing intelligent layout generation: performing global optimization of component space allocation by adopting a genetic algorithm, performing business rule adaptation by combining a constraint solver, and modeling an interaction relationship between components by utilizing a graph neural network; and dynamic mixed recommendation: constructing a three-level recommendation architecture including collaborative filtering, content matching and reinforcement learning. According to the method, a closed-loop recommendation process of user intention-intelligent recommendation-feedback optimization is realized, and the intelligent level of visual arrangement and the user experience are remarkably improved.

The invention relates to a large language model construction method and system fused with spatial semantic understanding, and the method comprises the steps: obtaining a multi-source heterogeneous corpus, and extracting an entity, an attribute and a business rule; extracting a semantic feature vector set based on the multi-source heterogeneous corpus, and constructing an entity relationship network and an enhanced knowledge graph; generating an enhanced training sample, and training the general large language model to obtain a primary large language model; generating a verification sample set and performing verification; identifying a specific weakness pattern, and generating a corresponding confrontation sample and a knowledge enhancement sample; training the primary large language model to obtain an optimized large language model; in conclusion, the enhanced knowledge graph fusing the spatial semantic features and the business rules is constructed, and the gradient training samples are generated based on the graph to perform multi-stage model training and optimization, so that the method has the effects of improving the internalized understanding ability of the model for the spatial semantics and the business rules and enhancing the reliability of multi-step spatial reasoning.

The invention relates to an enterprise multi-source data intelligent association analysis method based on artificial intelligence and a large model, and the method comprises the steps: introducing time sequence dynamic analysis, a business rule base and statistical correlation test, carrying out the multi-dimensional and automatic cross verification and consistency test of an association pair outputted by a semantic association engine, and carrying out the analysis of the association pair. Screening out a high-confidence correlation set conforming to the business logic, the time sequence evolution rule and the statistical significance; and packaging to form a reusable business insight analysis model based on the enterprise data knowledge graph, receiving a business query request by the model, automatically generating a deep analysis report for business process optimization and potential risk early warning through graph reasoning, path discovery or an abnormal sub-graph detection algorithm, and pushing a result to a decision support system.

The invention provides a Prompt prompt template automatic construction method for an intelligent reimbursement system, and belongs to the technical field of intelligent financial management. Comprising the steps of obtaining form structure information of a current reimbursement receipt, extracting field attributes, control configurations and business rules in a main table and a sub-table, and constructing a field semantic model; collecting context information in a user filling process, and generating a context semantic model; identifying a user intention in a current form filling stage, and determining a corresponding prompt task type; matching the target template structure from a preset template library, and generating a structured natural language Prompt prompt template; and transmitting the generated Prompt prompt template as input to a large language model agent, and driving the large language model agent to generate user-oriented intelligent prompt content. By constructing the field and context bilingual model, automatic and personalized generation of the Prompt prompt template is realized, and the accuracy, adaptability and user experience of large model prompt in an intelligent reimbursement system are remarkably improved.

The invention relates to the technical field of front ends, can be applied to the field of science and technology finance / digital medical treatment, and discloses an activity landing page intelligent building method and device, electronic equipment and a storage medium. The method comprises the following steps: analyzing activity demand data through a large language model, and matching the activity demand data with features of components in a component library according to an analysis result to obtain candidate components; determining a target component and component attributes according to the user operation instruction to generate an initial page structure; configuring a business association rule between the target components based on the rule engine, and converting the natural language description of the business rule into a form which can be recognized by the rule engine through a large language model; generating a preview page of the activity landing page according to the initial page structure and the business association rule, and updating the page structure and the business association rule according to the adjustment instruction; and compiling and packaging the updated page structure and the business association rule. According to the method, intelligent construction of the landing page from demand to deployment is realized, and the efficiency and normalization are improved.

The invention relates to the technical field of information retrieval, and provides a multi-dimensional heterogeneous knowledge recall enhanced hybrid retrieval method and system.The method comprises the steps that texts recalled through keyword retrieval, sparse vector retrieval and dense vector retrieval are screened through a reciprocal sorting fusion algorithm, and a text type candidate knowledge list is obtained; based on user query, generating and checking a query statement through a large language model, and retrieving an entity-relationship-attribute triple from the knowledge graph library; based on user query, generating enhanced knowledge through a knowledge graph enhanced retrieval method fusing keyword retrieval, vector retrieval and community retrieval; and carrying out format alignment and duplicate removal on the text type candidate knowledge list, the triple result and the enhanced knowledge to form a multi-modal candidate pool, and carrying out reordering score calculation and ordering on each piece of recall knowledge in the multi-modal candidate pool through a reordering model and a business rule to obtain a final retrieval result. And the coverage blind area of single retrieval on heterogeneous knowledge is solved.

The invention provides an intelligent question and answer method and device based on large model multi-source information fusion and electronic equipment, relates to the technical field of computers, in particular to the fields of big data, intelligent search and the like, and can be used for application scenes such as intelligent question and answer. According to the specific implementation scheme, pre-retrieval is conducted from a data table structure information base and a data table field information base according to a user problem, and a pre-retrieval result is obtained; retrieving from a business knowledge base, a business rule base and a question number template base according to the user question to obtain a knowledge enhancement result; according to the pre-retrieval result and the knowledge enhancement result, utilizing a query instruction generation model to obtain a query statement; and querying from the data table set according to the query instruction to generate an answer corresponding to the query word. The scheme can improve the professionality of intelligent questioning and answering, and is suitable for application scenes of various industries.

The invention provides a business rule automatic processing method and system based on natural language understanding. The method comprises the following steps: extracting text content from an original document containing business rules and preprocessing the text content; utilizing a BERT-based metadata extraction model to extract structured metadata from the preprocessed text content; confirming and perfecting the structured metadata; on the basis of the confirmed metadata, a rule execution flow chart and / or a test case are / is automatically generated and used for verifying the correctness of rule logic; and according to the target format, converting the verified rule into a corresponding symbolic representation through a template engine to obtain a defined rule. According to the invention, the automatic conversion of the business rule from the natural language description to the symbolized executable form is realized, so that business personnel can directly use the natural language to define and maintain the business rule, the manual conversion cost is reduced, and the accuracy and efficiency of rule processing are improved.

The invention provides a transformer area line loss intelligent algorithm decision-making method based on a knowledge graph and a large model agent, semantic retrieval in the algorithm calling process is supported, dominant and implicit knowledge such as expert experience, technical standards and historical cases is coded in a unified mode, a mapping relation is established between algorithm nodes and process nodes in the knowledge graph, and the algorithm is optimized. An integrated reasoning path is formed, so that the intelligent agent can automatically combine and call an algorithm according to a task target, a large model is embedded into a transformer area line loss analysis process, and a hierarchical structure of'task analysis intelligent agent-algorithm decision intelligent agent-result analysis intelligent agent 'is formed; the task analysis agent is responsible for converting a problem input by a user into a structured task; the algorithm decision agent is responsible for algorithm selection and adaptive parameter adjustment; the result analysis agent is responsible for report generation and decision recommendation based on the knowledge base; according to the method, organized association can be carried out on scattered algorithm tools, expert experience and business rules, and a unified transformer area line loss agent tool integration framework is constructed.

The invention provides an enterprise intelligent finance and taxation system fusing supervised learning and a block chain, and solves the core technical problems of insufficient data credibility, transparent contradiction between privacy protection and supervision, AI model training data island and the like of a traditional finance and taxation system. According to the system, an innovative technical fusion scheme is adopted, bank API, OCR recognition, voice input and other multi-source financial data are integrated through a multi-modal data fusion unit, and high-quality data fusion is achieved through confidence evaluation and an intelligent conflict resolution algorithm; an intelligent classification unit based on BERT deep learning integrates a pre-training model with a business rule engine and a gradient boosting decision tree, and accurate and automatic classification of financial transactions is achieved. A complete technical solution is provided for enterprise finance and taxation digital transformation, intelligent, automatic and credible processing of finance and taxation businesses is achieved on the premise that data safety and privacy protection are guaranteed, and the method has wide market application prospects.

The invention discloses a multi-system collaborative data cross validation management system, and relates to the technical field of logistics data management, and the system comprises an initial credibility grading module which is used for obtaining a data stream and a preset static source reliability factor, and calculating an initial credibility score; the evidence weight dynamic quantification module is used for calculating a timeliness attenuation factor, matching a current service scene, distributing a service rule adjustment factor and determining a final evidence weight score of the data point; the gold record automatic judgment module is used for sequencing the data points; obtaining a highest score and a second high score; and the judgment confidence evaluation module is used for calculating a judgment confidence margin between the highest score and the second highest score, and automatically routing the conflict data set to a manual auditing queue or automatically confirming a golden record. Through quantitative evaluation, dynamic weighting and automatic judgment, the credible gold record is efficiently generated, and refined risk control and non-tampering decision traceability are realized.

The invention relates to a BI intelligent question answering system and method based on business rule retrieval and AI workflow, and belongs to the field of intelligent data processing. The system comprises a user interaction module, a knowledge base module and an AI workflow module. The method comprises the following steps: (1) business rule retrieval: taking a BI analysis problem written by a user interaction module as a target, retrieving business data table information from a knowledge base module, judging a business data table of the user problem in combination with a big language model, obtaining business indexes contained in the table according to the business table, retrieving BI business information related to the user problem, and performing business rule retrieval; and (2) AI workflow execution: taking a BI analysis problem as input, gradually scheduling workflow nodes of business rule retrieval, cue word construction, large language model interaction and database interaction, realizing an accurate Text-to-SQL function and a data visualization function, and further realizing a question-answer ability of converting the BI analysis problem input by a user into a BI analysis result.

The invention relates to the technical field of system monitoring, and discloses an information system full-link monitoring method based on high-frequency index acquisition optimization, which comprises the following steps: monitoring the running state of an information management system in real time, dynamically adjusting the sampling frequency by means of a customized service key identification component and a comprehensive load prediction model, and performing real-time monitoring on the sampling frequency. Transmitting the target data to the edge computing node; a lightweight monitoring agent is deployed at an edge node, and a wavelet signal decomposition algorithm is adopted to extract features and distinguish data types; constructing an information management business knowledge graph and an entity-relation-business rule base, associating abnormal features, and generating an abnormal root cause report in combination with a time sequence prediction model and a knowledge constraint large language model; based on report and information service priorities, monitoring resources are dynamically allocated in the edge-cloud collaborative architecture, and related model parameters, service association rules and constraint weights are optimized according to operation and maintenance feedback. According to the invention, targeted monitoring requirements in the business peak period and efficient utilization of system resources can be met at the same time.

The invention discloses an AI agent construction system based on a vector knowledge base and large model workflow, and belongs to the technical field of process management. The problem that the whole process of workflow scheduling and quality control cannot be managed smoothly due to the fact that an existing AI system cannot assemble heterogeneous and unstructured domain knowledge and business rules dispersed in internal documents, databases, APIs and expert experience through a standardized and low-code platform in a rapid and high-quality mode is solved. Comprising a project initialization module, a knowledge base configuration module, a model service module, a workflow arrangement module, a test verification module and a deployment monitoring module. According to the method, data, knowledge, reasoning and quality control are communicated through a unified vector knowledge base and a low-code drag workflow, a dynamic hypergraph is driven by using computable information potential energy / cognitive entropy, automatic topology operation cutting circulation and shortcut establishment are performed before deadlock outbreak, and predictable hard constraint management of knowledge fusion, process arrangement and cost process is realized.

The invention relates to the technical field of data security, in particular to a data desensitization method and system based on combination of an MCP protocol and rule driving. The method comprises the following steps: loading a service rule through an MCP Server and registering a desensitization tool as a standardized tool; user query is received through the MCP Client, and user intention is analyzed by the AI agent; the AI agent dynamically calls a standardized tool to form a desensitization task chain according to a user intention and a business rule; and executing the desensitization task chain to process the target data, wherein the calling process comprises dynamic strategy adjustment based on context information. The system comprises an MCP service layer, a desensitization tool library module and an intelligent decision and integration layer. Through MCP protocol standardization integration, service rule driving and AI agent decision making, dynamic weight distribution and probabilistic algorithm selection of a desensitization strategy are achieved, the flexibility, the intelligent level and the system integration efficiency of data desensitization are effectively improved, and meanwhile the safety and compliance guarantee capacity is enhanced.

The invention discloses a dynamic document template generation system and method based on large model workflow, and relates to the field of artificial intelligence technology, natural language processing and document automatic generation. Comprising the steps of 1, performing demand analysis on document demand description input by a user by adopting a pre-training language model and extracting document types, structural elements and business rules, 2, constructing a template structure according to an analysis result, distinguishing fixed contents and variable fields, defining conditional logic and synchronously displaying a template effect, and 3, constructing a template structure according to the analysis result. The method comprises the steps of receiving a user adjustment instruction and carrying out template optimization, 3, automatically matching variable fields with business database data, establishing dynamic association and automatically filling field contents according to the association relationship, 4, carrying out compliance detection on generated template contents and formats, prompting potential problems and enabling a user to carry out correction according to prompts. 5, according to the template confirmed by the user, the template scheme is converted into a construction instruction, and a standard format document is output; and 6, operation behaviors of the user are recorded, optimization features are extracted, the template library is updated, logic is generated, and continuous evolution of the template is achieved.

The invention provides an AgenticRAG-based customer service multi-agent collaborative question-answering method and system in the technical field of artificial intelligence and natural language processing. The method comprises the following steps: S1, inputting a query statement into a Prompt template to obtain an initial cue word; s2, optimizing the initial cue word through a query and analysis Agent to obtain an enhanced cue word, performing query intention recognition and key entity extraction based on the enhanced cue word, and performing query complexity recognition based on the query intention and the key entity; s3, based on the query complexity, inputting the query intention and the key entity into a retrieval module to obtain retrieval data; s4, an answer generation module performs logical reasoning on the retrieval data, the enhanced cue words and the business rules based on the user portrait to generate reply answers; and S5, the answer evaluation module performs quality evaluation and safety evaluation on the reply answers and then outputs the reply answers. The method has the advantages that the adaptability of questions and answers, the semantic understanding depth and the logic continuity of multiple rounds of dialogues are greatly improved.

The invention relates to the technical field of streaming computing, and provides a real-time service computing system based on a dynamic rule engine, which comprises a rule management module and a streaming computing engine, the rule management module is used for providing visual configuration and release functions of service rules and performing versioning storage on the released service rules; the streaming computation engine comprises a rule synchronization unit and a plurality of parallel processing units; the rule synchronization unit is used for acquiring service rules from the rule management module and distributing the acquired service rules to each parallel processing unit based on a broadcast mechanism; and each parallel processing unit is used for receiving the business event flow and performing real-time rule matching on the business event flow according to the received business rule so as to output a decision result. According to the system, complete decoupling of business rule configuration and execution is realized, and the problem that rule updating in a traditional business system needs shutdown deployment is effectively solved.

The invention provides a data processing method based on request interception, which comprises the following steps of: intercepting a calling request for marking a service method through a request interception mechanism, preposing a decision point of service logic, and selecting whether the service method is executed or not according to a request context. According to the method and the device, the business rules are stored in the configuration center, external rule configuration is performed, the business rule expressions are pre-stored in the configuration center, dynamic acquisition is performed based on the rule identifiers, decoupling of the business rules and the codes is realized, the business rules can be configured independent of the codes, and flexible business processing can be realized only by modifying the expressions of the corresponding rules in the configuration center. The problems of code expansion and difficult maintenance caused by business rule change in a multi-channel environment are effectively solved, so that the flexibility and maintainability of a business data processing system are improved. The method can be applied to a business processing system in the financial field or the medical field, so that the flexibility of the business processing system in the financial field or the medical field is improved.

The invention provides an intelligent solitary policy processing method based on a neural network and machine learning, and relates to the field of data processing. According to the method, strategy byte codes and execution contexts are generated by extracting business rules, supervision calibers and field dependency relationships of the single insurance policy, discovery, repair and arbitration of field abnormity are achieved in combination with a dynamic data governance model, an account-falling confirmation receipt is output through a voting game and a compensation transaction, and a replayable trace and a rare mode index are generated. And a high-confidence label and an error correction prior increment are obtained in combination with triggering verification, and finally, full-link evidence is solidified through continuous distillation, chain type evidence storage and an auditable snapshot library, so that a traceable record of the single insurance policy is formed. By implementing the technical scheme provided by the invention, full-link data processing can be conveniently carried out on the single insurance policy, so that the processing efficiency is effectively improved.

The invention relates to the technical field of data query, can be applied to business scenes such as financial science and technology and medical health, and discloses a data query statement dynamic generation method and device, equipment and a medium, and the method comprises the steps: receiving query request information, matching business strategies, completing conflict resolution, and generating a target business strategy set; obtaining dynamic context information formed by a database structure, an index, statistical information and a system load; and generating an initial data query statement based on the target business strategy set, the dynamic context information and the query request information, and obtaining an executable data query statement through grammar verification, logic optimization and execution cost analysis. According to the method, service rule analysis, conflict resolution and environment perception are combined, so that the query statement generation process can be adaptively adjusted along with the change of the service rules and the change of the running state of the database, and the accuracy and execution efficiency of query statement generation are improved while the correctness of service logic is kept.

The invention relates to the technical field of adaptive testing, and particularly provides a WebUI adaptive testing system and method based on multi-agent collaboration and multi-modal perception, and the system comprises a multi-modal perception subsystem, an agent collaboration testing subsystem and an assertion and verification subsystem. The method comprises the following steps: adjusting an element positioning strategy for a multi-dimensional interface feature set through a continuous interaction feedback mechanism, and outputting an element descriptor set; deploying a multi-agent collaborative decision-making unit comprising an interface understanding agent, a test logic agent and a data agent; outputting to form a semantic test script through a collaborative fusion mechanism; comprising the following steps: carrying out pixel-level difference calculation, and identifying a visual inconsistent region through multi-scale image comparison; and performing service-level logic difference, analyzing the consistency of the interface element state and the service rule, and outputting a hierarchical verification report. According to the invention, high coverage rate, low maintenance cost and reliable defect positioning capability of the test can be maintained in an environment with frequent evolution of pages.

The invention relates to an electric power marketing business abnormity real-time detection method and system based on stream-oriented computation, and belongs to the technical field of electric power system optimizing.The method comprises the steps that data snapshots are extracted from an electric power marketing business system, difference comparison is conducted on the data snapshots and historical snapshots of an intermediate library, and standardized increment events are generated and stored; capturing an incremental event in real time through a data change capturing tool and pushing the incremental event to a message queue; a streaming computation engine consumes the event stream, sequentially performs data cleaning, association with a static dimension table and sliding window statistical feature calculation, and constructs a feature vector; and performing parallel analysis and weighted fusion on the feature vectors based on a business rule base and an online machine learning model to generate a comprehensive risk score, and outputting an abnormal event when the score exceeds a threshold value. According to the method, the problems of exception identification lagging and complex work order process in a traditional batch processing mode are solved, the crossing of the business risk from hour-level detection to minute-level real-time perception is realized, and the timeliness and accuracy of power marketing risk management and control are improved.

The invention discloses a power transmission line path planning method and system based on a natural language and a knowledge graph, and the method comprises the steps: analyzing a natural language path planning target through a large language model, obtaining an intention classification result and a named entity, mapping the intention classification result and the named entity to a business rule knowledge graph for reasoning, and generating a task step sequence; receiving confirmation or modification of a user on each task step to obtain each target task step; searching a combination corresponding to each target task step from the GIS operation knowledge graph by utilizing a large language model, and disassembling the combination into a GIS operator call chain; and calling a corresponding GIS tool through an MCP protocol, executing spatial analysis operation in the GIS operator calling chain corresponding to each target task step according to the sequence of all the target task steps, and generating a path planning result of the power transmission line. According to the invention, full automation, process interpretability and man-machine interaction cooperation of power transmission line path planning are realized, and the power transmission line path planning efficiency is improved.

The invention discloses a multi-source heterogeneous data integration analysis treatment method, system and device and a medium, and the method comprises the steps: obtaining multi-source heterogeneous original data, carrying out the semantic mapping through a power grid business ontology library, and carrying out the coordinate dynamic normalization through a self-adaptive conversion matrix, and obtaining standardized spatial data; carrying out spatial query, dynamic buffer area calculation and protection area grading fusion through a spatial superposition optimization algorithm, and carrying out topological relation identification and regularization processing on the power line and an external linear ground feature to obtain a spatial conflict analysis result; through a three-level conflict resolution mechanism, recognition and cooperative resolution operation of geometry, attribute and temporal conflicts are carried out, and a resolved compliance space data set is obtained; and performing governance flow driving and rule adaptation through a dynamic business rule engine to obtain a spatial data governance report. According to the method, efficient integration and intelligent treatment of spatial data in the power transmission line project are realized, the data processing timeliness is remarkably improved, and spatial conflicts are eliminated.

The invention relates to the technical field of intelligent data correction, and discloses an intelligent accounting data correction method and system based on data difference identification, and the method comprises the steps: S, synchronously executing data standardization and data portrait construction based on accounting data and a preset business rule, business rule injection type feature extraction is carried out on the standardized accounting data to obtain a difference composite feature vector, multi-level confidence classification is carried out based on the vector, difference type labels are generated, a cost-risk-efficiency optimization correction decision is carried out, an optimal correction strategy instruction and a traceability record are obtained, and the traceability record is obtained. Performing directional correction process management and control on the data, then searching similar cases, performing case auxiliary verification on the corrected accounting data, generating a correction verification result, and finally outputting a correction traceability report; according to the invention, the accuracy of intelligent accounting data correction based on data difference identification can be improved.

The invention discloses an intelligent bidding method and device based on an Apple advertisement marketing interface, and relates to the technical field of advertisement putting, and the method comprises the steps: constructing a double-branch machine learning model comprising a long and short-term memory time sequence branch and a gradient boosting tree feature branch; training the model by using historical delivery data to obtain a target prediction model; collecting real-time delivery data through an Apple advertisement marketing interface, and generating a plurality of candidate bidding strategies according to a preset business rule; inputting the real-time data and the candidate strategy into a target prediction model to obtain a conversion probability prediction value and a cost distribution prediction value of the candidate bidding strategy; based on the predicted value, calculating a strategy score value through a score function, and screening out a high-score strategy combination; and integrating keywords, audiences, time periods and region adjustment parameters of the strategy combination, and generating an optimal bidding strategy package. According to the application, the Apple Ads advertisement bidding strategy can be efficiently and automatically optimized so as to improve the putting efficiency and the cost control.

Methods, systems and apparatuses, including computer programs encoded on computer storage media, are provided for automatically creating a virtual administrative system based on one or more legacy administrative systems. The data definitions, product rules, business functions / processes, business rules, and calculation modules of the legacy system and the integrated system are analyzed by an analysis system to determine elements in the legacy system and not in the integrated system. Elements of the legacy system not present in the integrated system are generated in the integrated system.

The invention discloses a test case automatic generation method and device, and relates to the technical field of software testing, and the method comprises the steps: S1, constructing a system behavior graph and a business knowledge graph; s2, integrating the system behavior graph and the business knowledge graph into a multi-layer behavior graph; s3, acquiring test demand data; s4, performing retrieval in the multi-layer behavior map according to the demand data to obtain a retrieval result; s5, fusing the demand data and the retrieval result, and generating an enhancement prompt; s6, generating a test case according to the enhancement prompt; a system behavior graph and a business knowledge graph are independently constructed, and a double-graph technical architecture of cooperative work is adopted, so that double guarantee of technical correctness and business rationality is realized; complete contexts can be obtained from two dimensions of technology implementation and business rules simultaneously through a double-atlas collaborative retrieval technology; high-level business requirements, UI design and specific implementation logic of a system bottom layer are effectively connected, and real efficient and full-automatic test case generation is achieved.

The invention provides a business rule cue word generation method and device, electronic equipment, a medium and a program product, relates to application of a large model in a financial science and technology scene, and can be applied to the technical field of artificial intelligence and the technical field of big data. The method comprises the steps of obtaining business demand information submitted based on a target interactive interface, and converting the business demand information into structured demand data; generating an initial cue word based on the structured demand data; inputting the initial cue word and the test data into the large model to obtain a first output result; comparing the first output result with a corresponding expected label, identifying a difference sample, and generating a cue word variant based on the difference sample; and utilizing the cue word variant to drive the large model to carry out iterative verification on the test data until the verification performance meets a preset performance condition, and outputting the service rule cue word.