61 results about "Business objectives" patented technology

The invention provides a user marking rule management method and system supporting a dynamic strategy, and relates to the technical field of user portrait analysis, and the method comprises the steps: carrying out the multi-modal feature fusion of a user behavior data flow and environment context data, obtaining a dynamic user portrait vector, carrying out the strategy intention analysis of a business target of a user, and obtaining a strategy parameter set; constructing a strategy evolution graph based on a semantic association network and a historical strategy, and performing dominant rule reasoning and implicit demand mining on the dynamic user portrait vector and the strategy parameter set through the strategy evolution graph to obtain a candidate strategy packet; performing simulation prediction on the candidates to obtain a deduction efficiency matrix, and performing confidence coefficient calibration on the deduction efficiency matrix to obtain a dynamic marking rule; according to the method and the device, closed-loop feedback of the user marking strategy can be realized based on iterative optimization and confidence guarantee of the strategy evolution graph, so that the dynamic adaptability of a user portrait is improved.

The invention belongs to the field of artificial intelligence, and discloses an electric power customer service dialogue model alignment method and system based on reinforcement learning and LoRA. Therefore, the model can comprehensively reflect the real quality requirements of the electric power customer service from multiple aspects such as accuracy, regularity, information extraction degree, emotion adaptation degree and business target orientation in the training process, a refined reward signal is provided for subsequent reinforcement learning, and the offset risk of strategy optimization is reduced from the source. According to the method, a LoRA structure is introduced in a supervision fine tuning stage, and a low-rank adapter is utilized to carry out lightweight directional training on a large model, so that the model can effectively master core knowledge such as electric charge query, fault repair and business handling, and meanwhile, the general expression capability of a basic model is reserved.

To provide an operating environment that allows business objectives to be easily achieved. [Solution] A business execution support system comprising: a request receiving unit that receives natural language text containing business execution details; an overview planning unit that analyzes the text and instructs a specified generation AI service to generate an overview plan containing execution targets and execution order of a specified business program; a detailed planning unit that acquires a group of data related to the business as integrated data, and instructs the generation AI service to concretize and update the overview plan according to the content of the integrated data and generate a detailed plan; and a data manipulation unit that instructs the generation AI service to generate command information for executing the execution targets of the business program according to the execution order included in the detailed plan, and executes the business program using the command information.

The present invention is directed to a software-based technology system for managing and executing high volumes of work on complex projects across multiple organizations involving multiple users that results in superior outcomes at significantly reduced time and cost. Litigation is the initial application of this invention; each case is handled as a project. As applied to litigation, the system of the invention empowers a novel method of end-to-end case assessment, planning, execution and management by integrating tools, processes and procedures allowing for more informed decision-making and deliberate execution of all case-related actions as well as enhanced coordination and communication among all constituents, resulting in efficient achievement of targeted outcomes. The system provides intuitive, menu-driven functionality for inputting, processing and utilizing vast amounts of information, including company and case-specific information, attorney discretionary evaluation and input, document templates and monitoring and reporting requirements. A threat algorithm cross-correlates via multivariate analysis critical data points with customized company-specific information and business logic to generate a numeric threat score as an output. The system also predicts the expected settlement range with a high degree of precision for a specific case as the output of one or a combination of multiple artificial intelligence (AI) / machine learning algorithms. The system then generates a unique case strategy (which includes an expected outcome, including projected outcome, Case Plan and estimated attorneys' fees and costs). System logic learns from thousands of examples of similar cases and directs highly effective and efficient execution of a specific Case Plan providing each attorney and staff member assigned a role in a case with a prioritized task list that is updated in real time as other users complete tasks and litigation priorities change. For each user, the system integrates assigned tasks across multiple cases and multiple client portfolios into a single, prioritized task list. The execution features become increasingly valuable given the volume and complexity of projects across multiple organizations and users, allowing users to focus on their area of expertise and avoid the wasted time and distraction of trying to determine what they should be working on or recording notes to communicate what they have been doing since the system automatically performs these functions and directs the various users' next steps while monitoring their progress against the plan. Integration of administrative functions, including automated time tracking, billing and reporting without the need to rely on users to update this information, further increases quality and efficiency of legal services. Key objectives of the system of the present invention include eliminating gaps between business objectives and litigation strategy and optimizing communication and coordination of case-related tasks and priorities in a fully automated fashion, which are all achieved by leveraging data and technology to enable a fundamentally different approach that becomes smarter and more precise via machine learning and predictive modeling.

The invention discloses a sales demand analysis method and system based on AI and portraits. The method comprises the steps of collecting multi-source heterogeneous data such as basic information, transaction records and online behaviors of customers; according to the method, a self-developed large model of the Chinese human life research and development center is adopted, implicit requirements in a customer behavior sequence are deeply mined through an attention mechanism, a hierarchical clustering algorithm is used for generating a tree-shaped clustering graph based on a Ward connection method, and high-value features with variances larger than 0.8 and absolute values of correlation coefficients larger than 0.5 are screened out in combination with a rule engine; it is ensured that the extracted features have statistical significance and are directly associated with business targets, invalid feature redundancy is avoided, the efficiency and accuracy of subsequent portrait construction are improved, product recommendation priorities are determined through a decision tree algorithm, and a Q-learning framework is combined to optimize communication verbal skills and cross sales strategies, so that the sales strategies meet the current demands of customers, and the customer experience is improved. Potential requirements are guided, and the conversion rate and the customer satisfaction degree are both improved.

The invention discloses a method for matching a service scene and a fine tuning data set in a vertical field. The method comprises the following steps: determining service scene characteristics of the vertical field; selecting a fine tuning data set type according to business scene characteristics; matching a logical reasoning structure according to the fine tuning data set type; and configuring question-answer pair types according to the logical reasoning structure. According to the method, a four-layer technical matching framework of'business scene characteristics, data set types, logical reasoning structures and question and answer pair types' is constructed, so that data set construction is always performed around business requirements, the integrating degree of the data sets and business targets can be remarkably improved, the enterprise AI application threshold is effectively reduced, and the trial and error cost is controlled. Therefore, the method has important engineering practical significance and wide engineering application prospects.

The system may be configured to evaluate the state or condition of a stack of services. Some embodiments may include monitoring the source code, base image, and runtime environment of each of a plurality of microservices in the stack via a set of first, second, and third security tools configured to generate first, second, and third vulnerability scores, respectively. Each of the tools may be selectable and may include at least one scanner or sensor configured to output historical data. The microservices may then operate with respect to the same business objectives or set of rules. The method may further include iteratively predicting a security assessment of the stack of microservices via the model based on the first, second, and third scores, and determining, based on the predicted assessment, the amount of resources required by a security team to enhance the security assessment via resource implementation.

A method for generating a directive for maintaining an objective includes integrating a control center system with a plurality of healthcare provider data systems and obtaining information from the healthcare provider data system(s). The method may then use artificial intelligence to correlate the obtained information against an identified business objective of a donation center and generate, using a heuristic model, a directive to the donation center(s) to achieve the identified business objective. The method may then present the directive a user and / or user system such that the directive may carried out.

A business management system with integrated business intelligence and IT-supported optimization, consisting of: a computer device comprising at least one processor and a memory arrangement for storing executable instructions; a data acquisition and integration unit that is operationally connected to the at least one processor and is configured to receive heterogeneous enterprise data from a variety of source systems, including financial transaction systems, operational control systems, human resource information systems, supply chain systems, customer interaction systems, and IT infrastructure monitoring systems, wherein the data acquisition and integration unit is further configured to normalize syntactic formats, resolve temporal inconsistencies, and map source-specific attributes into a unified enterprise data representation stored in the storage system; a business intelligence processing unit that is operationally coupled with the data acquisition and integration unit and is configured to calculate business performance indicators through multidimensional aggregation, correlation analysis, and trend extraction over the unified business data representation, with the calculated business performance indicators being time-stamped and permanently stored; an optimization unit that is operationally coupled with the business intelligence processing unit and is configured to compare the calculated business performance indicators with stored business objectives and constraints and to determine optimized business control parameters that correspond to resource allocation, operational planning, cost efficiency or performance improvement; a control and orchestration unit that is operationally linked to the data acquisition and integration unit, the business intelligence processing unit, and the optimization unit, wherein the control and orchestration unit is configured to coordinate the execution sequence, forward the results of the business intelligence analysis to the optimization unit, and manage feedback by incorporating the effects of applied optimized business control parameters into subsequent data acquisition cycles; and a communication interface that is connected to at least one processor and configured to exchange data and control signals with external enterprise systems and user terminals.

This invention belongs to the field of computer technology and discloses a financial information aggregation and recommendation method and system based on unsupervised cross-modal learning. It constructs a multi-channel data collection crawler engine to dynamically acquire heterogeneous data from cross-platform data sources; deploys a heterogeneous dual-engine architecture of semantic and numerical engines to perform semantic parsing and extract transaction features from the acquired heterogeneous data; constructs a cross-modal dynamic alignment matrix to generate a joint representation vector; acquires real-time search keywords and click behaviors to form short-term intent, analyzes historical cross-platform behavior trajectories to form long-term preferences, and constructs a two-layer intent recognition network based on short-term intent and long-term preferences; and outputs dynamic recommendation results integrating business objectives and user satisfaction indicators. This invention, based on unsupervised cross-modal learning technology, achieves cross-platform multi-channel financial information aggregation, deep user intent analysis, and personalized recommendation, thereby solving the problems existing in the prior art.

This invention provides a method and system for intelligent optimization and scheduling of process nodes for enterprise business, relating to the field of business processing technology. The method includes obtaining a description of business objectives and a sequence of executed process steps, retrieving historical process nodes based on deep vector embedding, generating business intent features using a bidirectional long short-term memory network, constructing a context state representation for structural matching, and finally constructing a dynamic heterogeneous graph structure to generate a weighted recommendation sequence. This invention achieves accurate recommendation and intelligent scheduling of enterprise processes, improving process execution efficiency and the achievement rate of business objectives.

This invention provides a control method and apparatus based on a lean manufacturing system, comprising: calculating the operational maturity value of a target lean manufacturing system to identify problems to be improved; constructing manufacturing strategic goals by comparing the target factory with business objectives based on pre-built quantitative indicators; streamlining the organizational structure based on pre-built lean processes, lean organizational structure, and lean departmental functions and responsibilities; constructing a multi-level key performance indicator system and a facilitating management meeting mechanism; and finally, constructing an implementation plan and controlling the target factory according to the implementation plan to achieve the optimization goals. This invention solves the problems of high deployment costs and poor application effects in existing technologies.

The invention discloses a network function opening method and device, equipment, a storage medium and a product, and is applied to an intention processor, and the method comprises the steps: receiving a network function opening request which is sent by a network function calling party and comprises a declarative service target, and carrying out the analysis of the network function opening request, and obtaining the information of determining an API calling strategy; an API calling request is sent to a CAPIF core function based on the API calling strategy information, an API calling response message returned by the CAPIF core function is received, and the API calling response message comprises an API calling execution result; and sending a network function calling notification message to the network function calling party based on the API calling execution result. The abstract intention of the user can be converted into a series of network actions and application program interface calling combinations, so that the user does not need to understand a complex network architecture, and the application program interface can be called directly based on a natural language to complete a service target.

The invention relates to a marketing decision system driven by multi-source data, and relates to the technical field of artificial intelligence, and the system comprises a state sensing module which is used for determining user state information representing the current state of a user according to the real-time interaction behavior of the user collected on terminal equipment; the intention acquisition module is used for acquiring marketing intention information representing a commercial target; the content generation module is used for dynamically generating marketing content by utilizing a generative model in response to the user state information and the marketing intention information; and the content presentation module is used for presenting the dynamically generated marketing content on an interface of the terminal equipment. According to the method and the device, the dynamic change of the user in a specific situation can be captured more accurately and more timely, so that the matching degree of the marketing content and the instant state of the user is remarkably improved, the accuracy and the real-time performance of a marketing decision are enhanced, and the flexibility and the intelligent level of the marketing decision are improved.

The application relates to an information recommendation method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: performing bottom layer feature extraction based on object attribute features to obtain object extraction features, performing bias prediction on each business target based on the object extraction features to obtain the bias degree of a to-be-recommended object to each business target; performing interaction prediction on each business target based on combined features, the object extraction features and to-be-recommended information features to obtain the interaction possibility of the to-be-recommended object to each business target; correcting the interaction possibility corresponding to each business target through the bias degree of the business target to obtain each target interaction possibility, fusing the target interaction possibilities to obtain a fusion recommendation degree corresponding to to-be-recommended information; and recommending the to-be-recommended information to a terminal corresponding to the to-be-recommended object based on the fusion recommendation degree. The method can improve the accuracy of information recommendation.

A non-transitory computer readable medium having computer instructions stored therein that when executed by a computer system cause the computer system to perform operations including receiving a first user input defining a dialog session comprising one or more steps of a natural language conversation flow, receiving a second user input defining a positive outcome of the dialog session, receiving, via a chat interface, a natural language input, initiating the dialog session in response to an association between the natural language input and the dialog session, receiving, via the chat interface, a subsequent natural language input, and updating a data entry associated with the custom metric in an outcome log in response the subsequent natural language input being indicative of the positive outcome of the dialog session.

The invention discloses an online training student management system and method, and the system comprises a data collection module, a data processing module and an authority verification module, and relates to the technical field of teaching and training management. According to the online training student management system and method, learning behavior details of students can be more completely captured by collecting text interaction data, operation track data and audio and video data, and the accuracy of collected data is improved by preprocessing behavior data, so that the learning efficiency is improved. Reliable data input is provided for generation of a structured intention report and calculation of course priorities, then the credibility of decision basis is improved, dynamic adjustment of a learning path is performed through a timing path dynamic adjustment mechanism, the learning efficiency and learning quality of students are improved, and in cooperation with calculation of a multi-dimensional evaluation report and a business value contribution value, the learning efficiency and learning quality of the students are improved. An enterprise can clearly judge the contribution of training to a business target, a data basis is provided for scientific distribution of training budget, and the situation of resource waste can be effectively reduced.

The present application relates to the technical field of artificial intelligence, and in particular to an automatic construction method of an end-to-end intelligent agent based on graph structure semantic fusion. The method comprises the following steps: receiving user input business requirement data; analyzing the business requirement data for business objectives and requirement constraints to generate a core workflow framework of the intelligent agent; performing end-to-end execution path analysis on the core workflow framework of the intelligent agent to obtain an end-to-end workflow; constructing a dynamic evolution semantic graph; constructing an end-to-end call chain execution strategy based on the end-to-end workflow, and performing intelligent agent instance encapsulation and intelligent agent instance reinforcement learning enhancement processing according to the dynamic evolution semantic graph, thereby automatically constructing an end-to-end intelligent agent. The present application combines graph structure knowledge with the generation capability of a large language model to provide an efficient, accurate and scalable intelligent agent automatic construction scheme for various complex business scenarios.

Described herein is a method for managing generating of developmental requirements for a software application for achieving a business objective comprising receiving user input comprising input user requirements for meeting the business objective, and processing the user input by a first language model using a first reference template for generating first artifacts therefrom, the first reference template comprising reference input, a plurality of reference requirements and reference descriptors and the first artifacts comprising output requirements and output descriptors substantially correlated with the reference requirements and reference descriptors. At least a portion of the first artifacts forms a portion of the functional requirements. The first artifacts are further processable by at least one additional language model using at least one additional reference template for generating additional artifacts.

The invention provides a business recommendation method and device, equipment, a medium and a program product, which can be applied to the field of artificial intelligence and the field of financial science and technology. The business recommendation method comprises the steps of obtaining business data, and performing preliminary screening processing on the business data to generate a business data set; generating a service target set according to the service demand, and generating N target schemes according to the service data set; wherein N is a positive integer; and based on the business target set, performing iterative optimization processing on the N target schemes through an improved fireworks algorithm to obtain a business recommendation scheme.

This invention discloses a multi-cluster data processing method and system based on a large-scale language model. The method collects multi-dimensional telemetry data from managed clusters; inputs the multi-dimensional telemetry data, business objectives, and dynamically generated prompts into a large-scale language model to obtain structured decisions; translates the structured decisions into configuration instructions for the target platform and executes the configuration instructions. This invention achieves global intelligent optimization and accurately proposes failover decisions, thereby improving data processing efficiency.

The invention discloses an AI native intelligent central operating system based on reinforcement learning, and relates to the technical field of reinforcement learning, and the AI native intelligent central operating system comprises the steps: determining a corresponding regulation action based on a preset business target and state representation; according to the invention, the plurality of sub-regulation items are determined based on the recognition of the regulation action, and the quantitative execution is carried out along each sub-regulation item, so that the change information of the regulated key performance indicator is predicted, and the accuracy of the change information of the regulated key performance indicator is improved. Determining a corresponding reward value according to the identification of the change information of the key performance indicator; determining to-be-optimized content according to reinforcement learning of the reward value; according to the method, each optimized sub-regulation item is determined according to the matching of the to-be-optimized content and each sub-regulation item, and the final operation content of the security industry AI native intelligent center is constructed in combination with the action constraint relationship of the security database, so that the accuracy of the final operation content of the security industry AI native intelligent center is improved.

This application provides a data fusion-based process development method, apparatus, device, and medium. It includes: acquiring user requirements and corresponding business data, and dividing the user requirements into business segments to obtain multiple business objectives; determining preset business rules corresponding to the user requirements, and standardizing the business data according to the business objectives and preset business rules to obtain corresponding standard data; determining the business inputs and outputs corresponding to each business objective, and merging the business inputs and outputs according to preset merging rules to obtain an initial business process; and inputting the standard data into the initial business process to generate the target business process. This improves the efficiency and quality of process development.

The invention relates to a private domain traffic intelligent operation method and system based on data driving, and the method comprises the steps: loading the privacy preference of a user to form a reference, then carrying out the parallel calculation of the privacy cost and the expected business income of a data request according to a real-time interaction context, and carrying out the balance calibration based on a dynamic balance decision model, generating an optimized data request scheme; the scheme is executed to obtain user authorization data and give corresponding privacy compensation, and at the same time, an unauthorized data gap is subjected to intelligent synthesis filling to update a user portrait and privacy preference; and constructing a comprehensive loss function to continuously optimize the decision model according to an actual service effect and user trust feedback. According to the method, the optimal balance between privacy protection and a commercial target is automatically searched in each real-time interaction, and the sustainability of data driving is improved.

The application provides a user tagging rule management method and system supporting dynamic strategy, relates to the technical field of user portrait analysis, performs multi-modal feature fusion on user behavior data flow and environmental context data to obtain a dynamic user portrait vector, analyzes a strategy intention of a user's business target to obtain a strategy parameter set, constructs a strategy evolution graph based on a semantic association network and historical strategies, performs explicit rule reasoning and implicit demand mining on the dynamic user portrait vector and the strategy parameter set through the strategy evolution graph to obtain a candidate strategy package, performs simulation prediction on the candidate to obtain a deduction performance matrix, calibrates the confidence of the deduction performance matrix to obtain a dynamic tagging rule, and further updates the weight of nodes and associated edges in the strategy evolution graph, so that the application can realize closed-loop feedback of the user tagging strategy based on iterative optimization and confidence guarantee of the strategy evolution graph, thereby improving the dynamic adaptability of the user portrait.

This invention relates to a dialogue goal optimization device, method, and program for optimizing goal achievement in user interactions. Based on user utterances and actions, it estimates user states, including emotions, resistance, comprehension, and signs of detachment, as well as goal progress indicating the degree of achievement of primary and sub-goals, and maps these to a state space. After restricting the set of candidate response strategies based on constraints such as compliance and cost applied to the dialogue, it selects a response strategy that maximizes the objective function. Furthermore, it performs a self-correction process that dynamically updates the parameters of the response strategy in response to signs of failure detected during the dialogue. This makes it possible to suppress user detachment while adhering to practical constraints and continuously improve the business goal achievement rate.

Systems and methods for improving business processes. In some embodiments, the method includes receiving business process data; generating, based on the business process data, a process map representing a business process using a first machine learning model deployed at the cloud-based cluster; generating, based on the process map, at least one alternative process map using a second machine learning model; evaluating the generated process maps using at least one business objective; selecting an improved process map from the generated process maps based on the evaluation; presenting, at an interactive user interface, at least one of the generated process maps, the at least one generated process map including the improved process map; receiving, at the interactive user interface, a user selection indicating a preferred process map from the at least one generated process map; and implementing the preferred process map.

The invention discloses a target system for advanced planning and production scheduling (APS) and a unified construction method and an extension system thereof. By establishing a mapping relationship between business constraints and decision elements, standardizing a data structure and a management process of decision factors, and combining an operator target construction link with a template scene library, automatic construction and dynamic reconstruction of an optimization target are realized. In order to solve the problem that the business target cannot be satisfied, the invention provides a decision-making target switching mechanism and a constraint softening mechanism so as to generate an executable scheme which can be satisfied to the greatest extent. And meanwhile, weight management methods such as weight cutting, normalization, sensitive scanning and automatic optimization are provided, so that the numerical stability and interpretability of the target construction process are ensured. The system supports cloud, local and hybrid deployment, can improve solution robustness, business adaptability and decision efficiency, and promotes engineering landing and popularization of the APS in a manufacturing industry scene.

The application discloses a kind of intelligent service arrangement method and related device, it is related to telecommunication carrier network, value-added service, cloud computing field, method includes: by obtaining customer service demand information and carrying out semantic understanding and intention analysis to convert into service delivery target, get rid of the dependence on formatted input and artificial decomposition order, solve the problem of cross-domain cooperation difficulty;By generating service target arrangement strategy including action sequence and tool calling strategy based on service delivery target, the dynamic intelligent generation of business process strategy is realized, and the defect of rigidification of business process strategy is overcome;By monitoring and capturing abnormal information in real time during the operation of service and network delivery process and updating the service target arrangement strategy using the abnormal information, the process can automatically respond to exceptions and dynamically adjust the execution plan during execution, improving the fault tolerance and delivery success rate of the system.