517 results about "Graph recognition" patented technology

The invention discloses a large-model real-time voice text intention recognition method and system based on text prediction, and the method comprises the steps: obtaining the real-time voice data of a user, carrying out the real-time voice recognition processing through a streaming voice recognition interface, and obtaining a part of transcriptional text; inputting the partial transcription text into a mask language model for text prediction, and generating a plurality of high-credibility complete sentence candidates; based on the complete sentence candidates, the complete sentence candidates are input into a large language model in parallel for intention recognition, a corresponding intention result is obtained, and a mapping relation between the candidate sentences and the intention recognition result is established; and obtaining a sentence completely expressed by the user, calculating the similarity between the complete actual sentence and a plurality of high-credibility complete sentence candidates through a multi-level text similarity algorithm, selecting the candidate sentence with the highest similarity score, and directly obtaining a corresponding final intention recognition result based on the mapping relationship. The objective of the invention is to solve the technical problem of high response delay of an existing voice intention recognition system.

The invention relates to the field of computer vision, and particularly provides a table recognition reconstruction method and system, a terminal and a medium, and the method comprises the steps: firstly decomposing a large-size table image into a plurality of overlapped sub-images, and carrying out the table structure detection and OCR character recognition of each sub-image through parallel recognition; then, sub-graph recognition results are integrated through a coordinate mapping and confidence coefficient weighted fusion algorithm, and boundary errors are eliminated; then, automatically distinguishing common cells based on an area clustering algorithm, merging the cells and a header region, and reconstructing a complete table logic structure; further understanding header semantics through a natural language model and repairing identification errors; and finally, realizing intelligent splicing and standardized output of the cross-page table. According to the method, the memory limitation of the traditional OCR technology is broken through, an oversized table can be processed, the recognition accuracy of a complex structure is improved, and the digitization efficiency of professional documents such as financial statements and engineering drawings is improved.

The invention relates to the technical field of large model intention understanding, and discloses an APP dialogue type service reaching method and system based on large model intention understanding. The method comprises the steps of receiving a natural language text input by a user on an APP dialogue interface and performing large model semantic analysis to obtain service semantic data; inputting the business semantic data into an intention recognition model for business intention understanding to obtain business intention data; performing multi-agent cooperative process planning to obtain execution process data; carrying out interactive confirmation on the service parameters to obtain service execution parameters; transmitting the service execution parameters to corresponding service tool interfaces for calling execution to obtain a service tool calling result, and performing intelligent analysis and card rendering on the service tool calling result to obtain display card data. According to the method, the real business intention of the user is accurately understood, and the problems of execution efficiency and stability of a traditional single interface calling mode in a complex business scene are solved.

The invention relates to a power distribution network distributed energy bearing capacity assessment method and device. The method comprises the following steps: carrying out topology analysis on a network structure of a power distribution network to obtain an initial network topology model; obtaining a node dynamic feature data set based on the initial network topology model and the distributed energy access point data of the power distribution network; wherein the node dynamic characteristic data set comprises operation parameters of each node of the power distribution network in different load scenes; generating a parameter incidence matrix according to the node dynamic characteristic data set, and obtaining a bearing capacity reference model of the power distribution network according to the parameter incidence matrix and real-time data of the power distribution network in an operation state; wherein the parameter incidence matrix is used for quantifying the coupling degree between the operation parameters; and obtaining a risk distribution mapping graph according to the bearing capacity reference model, and identifying a potential overload area of the power distribution network based on the risk distribution mapping graph. According to the invention, power distribution network operation risk assessment can be accurately realized.

The invention discloses a behavior-driven twinborn prediction method, and relates to the technical field of intelligent information processing and prediction modeling, and the method comprises the following steps: building a unified event time baseline, collecting nanosecond clock offset information of each data source, building a cross time sequence suspicion chart, recognizing time synchronization abnormal nodes, and forming a credible time anchor point set; and based on the trusted time anchor point set, executing anti-fact playback, reconstructing a historical evolution process of behavior data, generating a time offset vector set, and constructing a corresponding time sequence offset spectrum. According to the method, through construction of a unified time baseline, trusted anchor points, anti-fact replay, causal topology and time reversal control, time sequence dislocation identification, calibration and false trajectory elimination of multi-source behavior data are realized, a dynamic self-healing closed-loop prediction mechanism is established, and the twin system prediction accuracy and stability are improved.

The invention provides a context-based intelligent voice semantic understanding analysis method and system, and relates to the technical field of voice interaction, and the method comprises the steps: obtaining an input voice signal, extracting an acoustic feature, and decoding the acoustic feature to obtain a candidate text; constructing context semantic representation to carry out disambiguation processing; establishing a semantic dependency graph and carrying out multi-level correlation analysis; spreading context constraint information to perform multi-hop reasoning; and finally generating intention recognition and slot filling results and updating the session state. The semantic comprehension accuracy and the intelligent degree of the voice interaction system are improved by introducing the context information and the multi-level semantic dependency relationship.

This disclosure describes systems, methods, and devices related to object detection in images. A device may input an image, representing an object, to a manual labeling learner system; identify, using the system, first coordinates of an upper left corner of a bounding box representing the object based on a heatmap indicative of a probability of the first coordinates representing the upper left corner; identify, using the system, second coordinates of a bottom right corner of the bounding box based on the first coordinates and a first distance regression map indicative of coordinate differences between the second coordinates and ground truth coordinates input to the machine learning model as training data; generate, using the system, adjustments to the first coordinates and the second coordinates based on a second regression map; and generate, using the system, the adjusted first and second coordinates, the bounding box.

The invention discloses a cross-cloud task arrangement optimization method based on multi-cloud resource scheduling, and the method comprises the steps: constructing a task topological weighted graph, and recognizing a parallelizable node set; constructing a task node preliminary scheduling feasibility table according to task topological weighted hierarchy information; constructing a granularity feature vector, and updating the task structure matrix; identifying granularity collaborative attenuation abnormal nodes, and performing cross-cloud task path repair and structure update; generating a cross-cloud scheduling time matrix based on each cloud platform resource supply capability index; predicting resource competition conflicts in combination with task execution index historical data, and generating a conflict prediction table; identifying granularity strategy deviation, and determining a repair trigger point; and generating a cross-cloud task granularity adaptive arrangement configuration file based on the repair trigger point information. According to the method, high reliability, high flexibility and high adaptability of task scheduling can be realized in a complex multi-cloud heterogeneous environment, and the stability, the resource utilization rate and the overall cooperation efficiency of cross-cloud task execution are remarkably improved.

The invention discloses an interaction control method and system for an intelligent agent and a front-end and back-end system, and the method achieves the automatic control of the intelligent agent on the front-end and back-end system through the integration of a large language model. The method comprises the following steps: an intelligent agent performs natural language intention recognition through a large language model, automatically selects a proper tool, extracts parameters required by the tool, converts the intention into a target task, generates a structured control instruction, maintains a complete session history and supports multiple rounds of dialogue interaction; the back end receives a user request, calls an intelligent agent to generate a structured instruction, transmits the structured instruction to the front end, and dynamically adjusts a subsequent instruction according to the received front end feedback; and the front end receives the instruction transmitted by the rear end, automatically identifies an instruction format through a regular expression, performs corresponding operation after parameter verification and feeds back a result. According to the method, unified control of the agent on the front-end UI and the back-end business logic is realized, complex multi-round dialogue and multi-step operation are supported, and the method has a wide application prospect.

The invention provides a system abnormity intelligent diagnosis and recovery method and system fusing time sequence logs, and relates to the field of distributed system operation and maintaining.The method comprises the steps that operation logs are collected from a plurality of service nodes of a distributed system, time sequence alignment is conducted, and calling relation and performance measurement data are extracted; time sequence change features are calculated in the sliding time window, dynamic feature vectors are generated through fusion coding, and a cross-service time sequence association graph is constructed; identifying an abnormal service node and matching the abnormal service node with historical fault data to obtain a historical propagation path and a feature vector; calculating a propagation convergence coefficient and a characteristic deviation degree based on a historical propagation path to obtain a causal intensity score; and identifying a fault influence degree according to the score, and adaptively adjusting a resource isolation and flow scheduling strategy. According to the invention, accurate diagnosis and efficient recovery of distributed system faults are realized.

The invention discloses an intelligent artificial limb movement intention recognition method based on multi-modal information and a D-S evidence theory, and belongs to the technical field of artificial intelligence, mode recognition and intelligent artificial limb control. According to the method, firstly, electromyographic signals and kinematics signals of user limbs and three-dimensional point cloud information of the terrain where the user limbs are located are synchronously collected; filtering, segmenting and time domain / frequency domain feature extraction are carried out on the physiological signals, rotation correction and dimension reduction are carried out on the environment point cloud, and a binary image is generated; using a deep learning model to identify a preliminary motion mode from the physiological features and identify a terrain category from the environment image; and finally, inputting the two recognition results as evidence bodies into an improved D-S evidence theory model for decision-level fusion, and outputting a final comprehensive motion intention recognition result. According to the method, the human physiological signal and the external environment information are fused, so that the ambiguity and uncertainty of single information source identification are effectively solved, and the intention identification accuracy and adaptability of the intelligent artificial limb in different complex scenes are remarkably improved.

The invention relates to the crossing field of an artificial intelligence technology and an automotive electronic technology, and discloses a voice interaction question answering method and system based on a vehicle-mounted large model knowledge base, and the method comprises the following steps: receiving a user query text, and executing intention recognition and structured analysis; based on an analysis result, constructing and executing a mixed retrieval request including structured filtering, full-text keyword retrieval and semantic vector retrieval; fusing and reordering the plurality of retrieval result sets obtained respectively to obtain a high-quality final retrieval result set; and finally, constructing a context based on the final retrieval result set and a user query text, and calling a large language model to generate and output a final answer. By introducing a hybrid retrieval and fusion reordering mechanism, comprehensive context information is provided for a large language model, the accuracy and reliability of questions and answers are improved, and the ability of a system to understand and process complex fuzzy queries is enhanced.

The invention belongs to the technical field of artificial intelligence and data analysis, and discloses a data analysis method and system based on multi-layer intention recognition and multiple agents, which remarkably improves the accuracy and robustness of natural language understanding through an innovative multi-layer intention recognition technology and improves the accuracy and robustness of natural language understanding. A common user can perform complex data analysis without mastering professional skills; through a multi-agent cooperative working mechanism, specialized division of labor of different agents is realized, the problem of capability dispersion of a single agent is avoided, and the analysis efficiency and the result quality are remarkably improved; through a real-time streaming processing technology and intelligent visual matching, complete transparency of a code execution process and intelligent recommendation of chart types are realized, and the user experience and the professionality of an analysis result are greatly improved; through an end-to-end integration solution, full-link intelligent processing from data input to visual large screen generation is realized, and a complete data analysis platform is provided for enterprise decision support.

The invention discloses a clinical decision interaction method and system based on structured evidence reasoning, and relates to the technical field of computer technology and medical informatization. The method obtains a user input query; the method comprises the following steps: performing mixed entity identification from a query input by a user based on an entity identification model and a rule matching algorithm to obtain a candidate entity set, and performing conflict resolution and entity standardization processing on the candidate entity set to obtain a core entity; performing multi-granularity intention recognition based on confidence calculation between the classification systems of the core entity and the medical exclusive intention to obtain a target intention; performing screening query in a pre-constructed structured knowledge graph according to the core entity and the target intention to obtain a corresponding structured knowledge fragment; generating a cue word based on the structured knowledge fragment, user input query, instruction constraint and multiple verification; and inputting the cue word into the large language model, and outputting an answer. According to the invention, more accurate and more reliable dialogue service can be provided for the medical field.

The invention discloses an intelligent communication content adaptation system and method based on user intention recognition. The system integrates and processes texts, voices, emoticons and unstructured behavior data through a multi-modal input analysis module; the deep learning intention recognition engine adopts a triple attention mechanism, current input and weighted historical interaction features are fused, and multi-level intention classification including basic operation, semantic targets and emotion driving is output; the real-time emotion state analysis module fuses acoustics, semantics and physiological indexes to generate a dynamic emotion matrix; the content adaptation decision engine is based on the intention confidence and the emotional state; and the multi-channel output optimization module performs cooperative adjustment on the speech synthesis rhythm, the text abstract and the visual interface according to the speech synthesis rhythm and the text abstract. According to the method, the perception precision of the deep intention and emotion of the user in a complex scene is remarkably improved, the individuation and multi-channel adaptive optimization of the response content are realized, and the communication efficiency and the user experience are effectively improved.

The invention discloses a customer service information generation method and system based on multi-modal intention recognition, and the method comprises the steps: obtaining customer service session data, and carrying out the heterogeneous data classification operation of the session data; performing feature extraction operation on each piece of parting modal information; establishing a semantic bridging matrix among the modal features, performing hierarchical attention routing, and outputting an intention label; a knowledge graph is injected, the knowledge graph comprises a static knowledge base, a real-time service flow and a user historical portrait, and three-source dynamic knowledge is output; constructing a decision tree based on the intention label, the three-source dynamic knowledge and the space-time identifier; and executing a corresponding text generation operation, a visual mark generation operation or a voice synthesis operation based on a response action corresponding to the leaf node of the decision tree, and generating customer service information. According to the method and the system, the buyer intention recognition accuracy of the multi-modal session data can be greatly improved, and the response delay time of generating the customer service information is shortened.

The invention relates to the technical field of ownership intelligence, in particular to a ownership intelligence fuzzy language instruction recognition method based on scene risk guidance. In order to solve the problem that a fuzzy language instruction recognition method in the prior art easily causes understanding deviation and potential safety hazards, the invention provides an intelligent fuzzy language instruction recognition method based on scene risk guidance, which comprises the following steps of: 1) constructing a scene risk map according to a basic risk and a distance risk; 2) identifying an instruction type; 3) instruction analysis and risk assessment; and 4) instruction rewriting and object identification replacement. According to the method, a risk perception mechanism is introduced, the high-risk behavior execution probability caused by semantic deviation of the intelligent robot is effectively reduced, the understanding accuracy and interaction safety of the intelligent robot in a real scene are improved, and the method is suitable for a complex application environment with high requirements for task reliability and behavior controllability of the intelligent robot.

The invention discloses a production progress collaborative operation intelligent management system based on a large model, and relates to the technical field of artificial intelligence, and the system comprises a multi-source production data collection module, a cross-enterprise semantic adaptation module, a bidirectional collaboration module, a scene intention recognition and active collaboration module, and a large model decision engine module. And all the modules are linked based on the data stream and the decision stream. According to the invention, the large model decision engine module, the multi-source production data acquisition module, the cross-enterprise semantic adaptation module, the bidirectional collaboration module and the scene intention recognition and active collaboration module form bidirectional linkage of data flow and decision flow, so that intelligent closed-loop decision of production progress collaboration management is realized. According to the invention, the continuity of cross-module collaboration and the fitness of decision schemes can be improved, so that the problems of disjunction of decision support and business execution and insufficient practicability of scheduling schemes caused by multi-source data decentralized processing in a traditional production collaboration system can be solved.

The invention discloses an intention recognition and multi-path recall dynamic weighted fusion retrieval method and system, and the method comprises the steps: processing heterogeneous data such as PDF and pictures through a fruit industry unified document object model, and constructing a multi-type index through semantic adaptive slicing; after user query is preprocessed, intention categories and confidence coefficients are recognized through a fruit industry fine tuning model; performing keyword, vector and map retrieval in parallel, and dynamically distributing fusion weights based on intentions; performing authority, timeliness and consistency voting arbitration on conflict results; and after the answer is generated, the illusion is inhibited through entity and numerical value verification. According to the method, the problems of poor adaptation, model illusion, data islands and conflict arbitration-free of existing single retrieval are solved, the fruit industry retrieval accuracy and reliability are improved, the agricultural production risk is reduced, and the method is suitable for large-scale fruit industry knowledge service.

The invention relates to an industrial part surface defect detection method and system with robustness, and the method comprises the steps: inputting an industrial part input image into a parameter freezing static guide module, and extracting a multi-scale semantic feature map; blocking and randomly shielding the image to generate a partially missing image block sequence, and inputting the partially missing image block sequence into a dynamic reconstruction module for feature extraction and fusion to obtain fusion feature representation; performing multi-scale semantic reconstruction on the features to generate a reconstructed feature map corresponding to the multi-scale feature map of the static module; and calculating an abnormal scoring graph based on the multi-scale feature graph and the reconstructed feature graph, and identifying and positioning a defect area. According to the invention, through a dual-branch cooperation architecture of the static guidance module and the dynamic reconstruction module, and in combination with an image block random masking and multi-scale feature reconstruction mechanism, the adaptive capacity of the model to a shielding condition and a complex environment is enhanced, the identification sensitivity and positioning accuracy of a defect area are improved, and the identification accuracy of the defect area is improved. And good calculation efficiency is maintained while relatively high detection precision is ensured.

The invention provides a transformer substation operator dynamic authority management and control system based on AI vision, and belongs to the technical field of power system safety management. According to the system, identity living body verification is achieved through infrared imaging and visible light videos, a real-time data stream is formed by combining positioning data and environment sensing data, and analysis is conducted through a behavior intention recognition model. When the four conditions of the identity, the position, the action and the environment are all compliant, the system generates a dynamic authority token and authorizes an operator to execute operation within a short time limit; and if any condition is not compliant, the dynamic permission token immediately loses efficacy and triggers blocking feedback, and the permission is cancelled and the operation is stopped within microsecond-level time delay. According to the system, through a multi-parameter binding and parallel judgment mechanism, the dynamic and fine management and control of the operation authority are realized, the unauthorized operation is effectively avoided, and the safety and the real-time performance of the operation of the transformer substation are improved.

The invention discloses a response verbal skill generation method and device, electronic equipment and a storage medium, relates to the technical field of computers, can be applied to financial science and technology and medical health business scenarios, and comprises the steps of randomly covering a single character for an original text of a user to generate a difference text; the pre-training intention recognition model comprises a text coding module, a feature fusion module, a multi-task collaboration layer and a classification layer, wherein the feature fusion module comprises a bidirectional long-short-term memory network and fuses an auto-encoder and an attention mechanism. The model is coded to obtain a vector containing overall and local semantics, an enhanced local vector is generated through a bidirectional long-short-term memory network, a global fusion vector is obtained through joint modeling, a multi-task layer optimizes slot positions to extract key information, and a classification layer combines the key information and the global vector to determine intention; and finally, calling the knowledge graph based on the intention, and generating a response verbal skill matched with the demand. According to the method, the response verbal skill matched with the real demand of the user can be accurately generated.

The embodiment of the invention discloses a software license conflict identification method and device based on a graph, equipment and a medium, and the method comprises the steps: constructing a corresponding license knowledge graph for a to-be-identified software data packet; based on the license knowledge graph and a predefined high-risk conflict sub-graph, identifying a target sub-graph matched with the high-risk conflict sub-graph in structure; for each software license identifier in the target sub-graph, performing semantic recognition on detailed information of a software license of the software license identifier, and determining a first software license conflicting with the software license; and if the identifier of the first software license exists in the target sub-graph, determining that the software data packet has a software license conflict, thereby avoiding the conflict between the software data packet and the newly-developing software license when the newly-developing software license exists in the software data packet. According to the method, the problems that whether conflicting software licenses exist or not cannot be recognized in real time through a static detection tool depending on a traditional dependency rule base, and the false alarm rate is high are solved, and then the accuracy of software license conflict recognition is improved.

The invention discloses a man-machine cooperation method. The method comprises the following steps: acquiring user session information and user portrait data; based on the user session information, obtaining user intention recognition information through a preset intention recognition model; based on the user intention identification information, obtaining user request complexity through a preset semantic analysis model; based on the user intention identification information, obtaining an AI response capability score through a preset knowledge base; based on the user session information and the user portrait data, obtaining user request sensitivity through a preset text sentiment analysis model; and on the basis of the AI response capability score, the user request complexity and the user request sensitivity, a decision result about whether manual operation needs to be switched is obtained through joint hierarchical decision. The AI response capability, the user request complexity and the user request sensitivity are comprehensively evaluated, the limitation of single index decision making is overcome, and manual decision making is more scientific and accurate.

The invention relates to the technical field of three-dimensional modeling, in particular to a three-dimensional reconstruction method and device for a two-dimensional drawing, equipment and a medium. The method comprises the steps of firstly obtaining to-be-reconstructed two-dimensional drawings of at least two views in target object engineering views and collecting features to obtain feature points and geometric information, then constructing a target graph structure, updating nodes based on a graph neural network and clustering to obtain an overall view, converting the overall view into view feature vectors, and constructing a target image structure; a three-dimensional contour is calculated through an inference model containing a multi-view self-attention mechanism and an alignment model introducing projection consistency geometric constraints; and finally, three-dimensional space data is extracted based on a symbol distance function, and shape processing is performed to construct a three-dimensional grid model. Therefore, the two-dimensional drawing is automatically and accurately subjected to view recognition and three-dimensional alignment, and a three-dimensional grid model is efficiently completed.

The invention provides an information retrieval method, device and equipment based on large model questions and answers and a medium, and the method comprises the following steps: firstly, receiving a natural language question input by a user through a terminal, and forwarding the natural language question to a semantic analysis engine after authentication of an API (Application Program Interface) gateway; then oral vocabularies are removed through a normalization module, abbreviation is completed, a retrieval category is determined in combination with an intention recognition module, key information is extracted through a slot extraction module, and meanwhile permission-semantic coupling retrieval is completed in combination with a company organization structure; then adopting a multi-path recall mode of vector semantic retrieval and keyword supplementary retrieval to obtain a candidate result, and performing correlation rearrangement and permission filtering to obtain a target retrieval result; and finally, performing fragment positioning on different types of contents, and displaying a result with specific position information at a front end in a preset form. According to the method, the retrieval accuracy and efficiency can be improved, cross-modal unified retrieval is realized, authority security is guaranteed, and efficient and accurate information retrieval requirements in large enterprises are met.

The invention relates to the technical field of model training learning, in particular to a training method of an intention recognition model for understanding user requirements in an intext software scene, which comprises the following steps of: traversing paths to determine levels and establish variable-dimension projection mapping, generating semantic orthogonal conflict samples, obtaining variable-dimension features by utilizing matrix transformation, and obtaining an intention recognition model for understanding user requirements in an intext software scene. And quantizing the brother node overlapping degree to apply orthogonal constraint, updating the weight by minimizing the positive sample distance and maximizing the conflict sample distance, and constructing an intext software intention recognition optimization model. According to the method, a mapping mechanism of a hierarchical position and a variable-dimension projection matrix is established through software architecture topology, a semantic orthogonal logic conflict sample is constructed, brother node feature branch orthogonal constraint is introduced, and feature representation of parallel architecture branches is forcibly separated in a vector space; the logic conflict sample spacing is maximized, semantic overlapping of similar function nodes is eliminated, and a decision boundary with high distinction degree is constructed to accurately analyze a complex nested instruction.

The invention relates to a mining and protecting method and system for a multi-dimensional high-value target of a big data platform, and the method comprises the steps: collecting multi-source heterogeneous data from a plurality of platform data sources, carrying out the information extraction, and forming a fusion triple comprising entity type feature data, attribute type feature data and relation type feature data, constructing a dynamically updated platform structure map based on the fusion triad; performing asset function value evaluation, asset topology importance evaluation and attacker accessibility evaluation on each type of nodes in the platform structure map to obtain a corresponding comprehensive risk score, and identifying a high-value target set; and in response to a security alarm event, associating alarm information with the platform structure map to position an alarm node, thereby extracting a serialized context sub-graph from the platform structure map in combination with the high-value target set, performing attack intention recognition, and outputting an attack intention report. According to the method, a security protection system with perspectiveness and semantic understanding capability can be provided for a big data platform.

The invention discloses an intention recognition model reinforcement learning training method and system controlled by an operating system, and the method comprises the steps: constructing intention and slot labels for natural language commands controlled by the operating system, and constructing a training data set according to the natural language commands and the corresponding intention and slot labels; a LoRA technology is adopted to supervise and finely adjust the pre-trained intention recognition model based on the training data set, and a cold start intention recognition model is obtained; and carrying out reinforcement learning training on the cold-start intention recognition model by adopting a reinforcement learning method introducing group relative strategy optimization, so as to obtain the intention recognition model which can be finally used for end-side deployment. The method aims at solving the problems that in the prior art, coverage of an intention and slot label system is insufficient, data quality requirements are high but acquisition is difficult, model customization cost is too high, and user feedback driving optimization is lacked, and the speed and precision of operating system intention recognition on end side equipment are improved.

A computing system for determining a user intent from a speech input to effect a user intended action is provided. The computer system comprises a set of processing nodes and a controller module. Each processing node is capable of understanding only a subset of words directly relevant to a particular context. The processing nodes of the set are arranged to receive a same speech input, and each processing node attempts to interpret the input, based on its subset of words, to extract therefrom an output indicative of user intent. Each node is unable to interpret any portion of the input containing a word outside of its subset. The controller module receives the outputs from the set of processing nodes and determine a most likely user intent based on the outputs.