304 results about "Feedback loop" patented technology

Systems and methods for adaptive edge computing using artificial intelligence (AI) include monitoring real-time accuracy of a neural network by using a feedback loop configured to detect changes in inference accuracy and dynamically adjusting the structure of the neural network by adding or removing hidden layers based on monitored error rates and predetermined computational constraints. A Kalman gain computation determines neural network weight adjustments based on monitored error rates. Weight matrices undergo incremental updates derived from these adjustments. Incremental weight adjustments remain stored in memory to enable low-bandwidth model updates. The neural network stores inference results and refined weights in an inference result database. Pre-trained models periodically receive incremental updates based on stored adjustments. Predictive holistic inference logic (PHIL) applied to stored inference results improves the accuracy of the inference results.

An AI-powered content generation system that creates consistent, coherent, and engaging multi-modal content by integrating multiple specialized AI components. The system analyzes user input, identifies key elements, and maintains continuity throughout the generation process. It incorporates a feedback loop to learn and adapt based on user preferences, enabling personalized content experiences. The modular architecture allows for seamless integration of AI components focusing on text, images, audio, and interactive elements. The system ensures consistency across modalities and over extended periods, while managing rights, licenses, and royalties using blockchain technology. This advanced platform revolutionizes content creation, consumption, and management in the digital age.

The invention discloses a new media-oriented interactive marketing method, which comprises the following steps of: 1, multi-source data acquisition: acquiring behavior data of a user in social media, a short video platform and an e-commerce scene in real time through an API (Application Program Interface), a burying point technology and cross-platform data crawling, comprising click tracks, staying duration, comment content, sharing behaviors and shopping cart operation; through the technical innovation of a dynamic content engine, a real-time feedback closed loop, edge calculation, reinforcement learning, a multi-modal material library, a GAN automatic synthesis technology, federated learning, a block chain and the like, the core benefits of improving the interactive conversion efficiency, data-driven real-time decision, reducing the content generation cost, balancing the safety and the effect, performing scene-based accurate touch and the like are realized; the user conversion rate, the click rate, the marketing response speed, the negative comment processing efficiency, the portrait accuracy and the target user coverage rate are remarkably improved.

An agentic artificial intelligence system processes insurance claims, medical claims, financial transactions, and sales leads by receiving and preprocessing claimant, patient, transaction, and prospect data to standardize formats, remove sensitive identifiers, and enrich records. It uses machine learning, deep learning, natural language processing, and computer vision to analyze both structured and unstructured data, identify errors, inconsistencies, or fraudulent patterns, verify eligibility and compliance, and assign relevant codes based on historical and contextual information. The system calculates expected payouts or reimbursements, assesses transaction feasibility, and generates risk scores while adapting its predictions to market conditions, contractual factors, or clinical guidelines. A multi-agent framework coordinates specialized agents for eligibility verification, coding, pricing, fraud detection, and sales outreach, supporting multi-channel communication, lead prioritization, and natural language generation of outreach messages and decision-making explanations. Continuous learning is achieved via retraining, feedback loops, federated learning, and blockchain-based recordkeeping, ensuring secure, transparent, and compliant operations across multiple domains.

The invention relates to the technical field of data processing, in particular to a data fusion intelligent equipment linkage management and control system based on a large model, and the system comprises a data collection module which outputs standardized data carrying privacy grading identifiers; the data processing module outputs an optimization algorithm identifier and a collaborative strategy identifier which are synchronously bound; the privacy algorithm fusion scheduling module generates atomization calculation graph data and strategy audit logs fusing privacy operation; the multiple optimization resource module executes calculation graph data and drives a parallel simulation environment rehearsal resource scheme, and the double-closed-loop feedback module is activated when an arbiter continuously detects that deviation exceeds the standard for three times; the safety instruction generation module fuses the output result and the audit log to generate a digital signature equipment control instruction; and the double-closed-loop feedback module updates knowledge base weight parameters through a data feedback loop, and controls the feedback loop to adjust a resource allocation threshold in a grading manner according to simulation confidence, so that dynamic balance between resource efficiency and privacy intensity is realized.

Access to secured items in a computing system is requested instead of being persistent. Access requests may be granted on a just-in-time basis. Anomalous access requests are detected using machine learning models based on historic patterns. Models utilizing conditional probability or collaborative filtering also facilitate the creation of human-understandable explanations of threat assessments. Individual machine learning models are based on historic data of users, peers, cohorts, services, or resources. Models may be weighted, and then aggregated in a subsystem to produce an access request risk score. Scoring principles and conditions utilized in the scoring subsystem may include probabilities, distribution entropies, and data item counts. A feedback loop allows incremental refinement of the subsystem. Anomalous requests that would be automatically approved under a policy may instead face human review, and low threat requests that would have been delayed by human review may instead be approved automatically.

The invention relates to the technical field of communication, in particular to a dynamic priority dual-mode communication fault-tolerant switching method and equipment, and aims to solve the problems of communication interruption and service quality reduction when a main link is degraded in a complex electromagnetic environment. According to the method, a dynamic priority evaluation model and a dual-mode protocol cooperation mechanism are constructed, throughput, bit error rate, delay jitter, task semantic priority and aging constraint data of a main link and a standby link are collected in real time, a priority weight and a communication mode decision matrix are generated through combined modeling, an atomization switching instruction is executed according to the priority weight and the communication mode decision matrix, and a communication mode is switched. And completing path reconstruction and resource redistribution. The system further introduces link health prediction, a resource reservation pool and an online learning feedback loop, and supports fault pre-judgment, key task preemption and model adaptive optimization. According to the method, the limitation of a static threshold is broken through, the task semantics and channel state joint scheduling is realized, and the communication reliability, the service continuity and the intelligent fault-tolerant capability of a distributed system in a high-risk scene are remarkably improved.

The invention belongs to but is not limited to the technical field of artificial intelligence, and particularly relates to a translation optimization system and method fusing an RAG term library and an anti-feedback loop. The technical defects of insufficient culture adaptation, poor term consistency, lack of an effective feedback mechanism and the like in the existing translation technology can be improved, and a more accurate and more natural translation result which better meets the requirements of target audiences can be provided in cross-culture and professional field translation. The innovation of the system lies in that a flexible reflection feedback mechanism is combined with an RAG term library, meanwhile, the adaptation of culture and context during translation is enhanced, and continuous optimization and improvement of translation quality are ensured.

Access to secured items in a computing system is requested instead of being persistent. Access requests may be granted on a just-in-time basis. Anomalous access requests are detected using machine learning models based on historic patterns. Models utilizing conditional probability or collaborative filtering also facilitate the creation of human-understandable explanations of threat assessments. Individual machine learning models are based on historic data of users, peers, cohorts, services, or resources. Models may be weighted, and then aggregated in a subsystem to produce an access request risk score. Scoring principles and conditions utilized in the scoring subsystem may include probabilities, distribution entropies, and data item counts. A feedback loop allows incremental refinement of the subsystem. Anomalous requests that would be automatically approved under a policy may instead face human review, and low threat requests that would have been delayed by human review may instead be approved automatically.

The invention discloses a structured data quality evaluation and optimization system using a large language model, which comprises a data access module used for formatting and preprocessing data, a data quality detection module used for establishing data quality evaluation indexes, performing statistical analysis and rule verification to detect the data and calculating the overall quality grade, and a big language model, the semantic analysis and SQL generation module is used for analyzing a database structure through a large language model, converting a natural language instruction into an executable SQL statement and is based on the large language model, and the anomaly detection and optimization suggestion module is used for positioning anomaly by utilizing a mixed detection algorithm and generating natural language interpretation and optimization suggestions in combination with the large language model. And the user feedback and dynamic adjustment module is used for absorbing adjustment suggestions through a feedback loop mechanism, and optimizing a prompt project and an SQL generation strategy. According to the method, semantic analysis is carried out on a database structure by adopting a large language model, and high-precision SQL query is automatically generated.

A system and method for adaptive data processing combining compression and encryption. The system analyzes input data characteristics, compares probability distributions, and creates a transformation matrix to convert data into a dyadic distribution. It generates a main data stream of transformed data and a secondary stream of transformation information. The system dynamically selects and applies processing techniques, including transformation, encoding, compression, and encryption algorithms, based on analyzed characteristics and real-time performance metrics. It compresses the main data stream using Huffman coding and implements security measures to protect the output. A feedback loop monitors technique effectiveness, updates a knowledge base, and influences future selections. The system can operate in lossless, lossy, or modified lossless modes, adapting to different application requirements. This approach offers an efficient solution for scenarios where both data reduction and security are critical concerns.

The present invention discloses an artificial intelligence-based (AI-based) system and method for generating optimised operation planning and scheduling output. The AI-based system obtains at least one of: one or more data explanation videos, one or more process understanding videos, and unconstrained operational planning data, along with one or more prompts as an input. The AI-based system extracts one or more informative image frames and audio data, to train the one or more AI models and generate a planning standard operating procedure (SOP). The AI-based system processes the planning SOP, the constrained operational planning data, and the one or more prompts to generate the optimised operation planning and scheduling output based on an optimised function with a continuous feedback loop in response to at least one of: the one or more prompts, updated planning SOP, and real-time changes in the constrained operational planning data.

The invention relates to the technical field of audio and video emergency transmission, and discloses an audio and video low-delay return method and system in an extreme environment. The method comprises the following steps: acquiring original multi-modal data of audio and video acquisition equipment in a target area, and analyzing a data state of the original multi-modal data; and meanwhile, available network transmission links are monitored, and the quality is evaluated. Self-adaptive coding parameters are generated in combination with the link quality and the data state, dynamic coding is executed on original data, and a coding stream suitable for redundant transmission is formed. And carrying out cooperative distribution transmission based on the network link set and the coded stream to generate return data. According to the generation process, a device and energy management instruction is formed and issued to the acquisition and relay device. According to the method, the network state and the content characteristics are optimized in a dynamic coding link in a collaborative manner, and the audio and video quality, the real-time performance and the system energy efficiency which are transmitted back in an extreme environment are improved through a feedback closed loop for transmitting a result to equipment management.

The invention relates to the technical field of customer relationship management, and discloses an intelligent customer life cycle management SCRM system, which comprises a multi-source data acquisition and fusion engine; a dynamic customer portrait modeling engine; a customer life cycle state evaluation engine; a predictive intervention engine; an intelligent strategy decision engine; a task distribution and execution engine; and a feedback closed loop and learning engine. According to the intelligent customer life cycle management SCRM system and method, by setting a multi-modal decision engine, taking a customer state as input, learning an optimal strategy of maximizing a long term value (LTV) through a DQN / PPO algorithm, when a customer is in a high loss risk state, triggering an exclusive retention scheme for the customer, and generating a strategy according to a comprehensive conversion rate, cost and satisfaction target, so that the customer life cycle management is realized. Moreover, the personalized content template can be automatically generated, the optimal information channel is accurately sent to different clients, personalized matching is carried out for the clients, and the loss rate of the clients is reduced.

The invention provides an outbound routing method and system based on multi-dimensional index coupling and large model decision, and the method comprises the steps: carrying out the preprocessing of the historical data of each line provider, predicting the future performance index through an LSTM neural network, and optimizing the prediction result through a Kalman filter, and eliminating the noise. In the route decision-making link, the system calculates the Q value of each line provider based on the deep Q learning network, comprehensively considers the call completing rate, average cost, negative feedback rate and other factors of each line provider, and intelligently selects the optimal line provider in combination with the user portrait, scene recognition and real-time load state. The system also constructs a complete feedback closed loop, continuously optimizes the model by collecting call result data, and automatically adjusts decision parameters. The call completing rate and the service quality of the outbound service are improved, and the communication cost is reduced.

The invention provides a method and a system for constructing a common financial business system based on artificial intelligence, and relates to the field of financial system construction. Comprising the following steps: converting financial rule data into analog modulation signal feature vectors containing value intensity, risk association degree and aging attenuation rate through a three-dimensional feature conversion module, and executing market behavior capture, rule space positioning and strategy generation operations in combination with a multi-stage state machine; the supervision text is converted into a continuous control signal injection feature generation logic through a dynamic constraint feedback loop, and a closed-loop adjustment mechanism of value gain calibration, risk attenuation compensation and aging synchronous correction is formed; a physical signal cross validation module is adopted to perform dual coupling verification of main channel waveform characteristics and auxiliary channel time sequence characteristics on the strategy; the time sequence deviation of characteristic conversion, state transition and constraint feedback is synchronously coordinated through a reference clock, and it is ensured that the time sequence deviation of the operation rhythm of all modules of the system is lower than a preset tolerance.

The embodiment of the invention discloses a method for intelligently assisting in reviewing a water conservation scheme of a water and soil conservation production and construction project, and the method achieves the intelligent feature matching of a new scheme and similar cases through the construction of a tagged knowledge base integrating historical cases of multiple industries, so as to recognize the generality compliance. A double-engine review mechanism is adopted, a form review module automatically checks document structure integrity and format specifications, and a logic review module executes cross-chapter data consistency check and business logic compliance analysis and highlights and positions error positions in a document. And recording decision logic in real time to form a feedback closed loop. And dynamically optimizing case library matching weight and recognition rules based on high-frequency error statistics and expert correction records. The modes of positioning review key points to chapters and sections, describing specific requirements of each review key point in a language, adding a check formula among data to review content needing to be calculated, determining a check type to which each review key point belongs, and the like are adopted, and the model is trained more clearly, so that the model has relatively professional review ability.

The invention discloses an internet integral dynamic incentive mechanism design method based on reinforcement learning. The method comprises the following steps: S1, constructing a user behavior data set; s2, generating a situation label by adopting a natural language processing technology; s3, calculating a reward value by using a context-adaptive reward algorithm; s4, dynamically adjusting the reward strategy through the reward value and the user behavior feedback in combination with the task participation condition of the user and a feedback loop; s5, predicting a future behavior trend of the user by using the gating cycle unit model, and generating a behavior prediction result of the user; s6, dynamically optimizing the reward strategy in combination with a chaos search strategy and a wolf pack optimization algorithm to obtain an optimized reward strategy; and S7, evaluating effectiveness of rewards and user interest changes, obtaining a final global reward strategy based on an A3C algorithm, and realizing real-time dynamic adjustment of an internet integral incentive mechanism. According to the method, a situation self-adaptive reward algorithm, an optimization technology and the like are utilized, and the dynamic adjustment of an internet integral incentive strategy is realized.

A dynamic, infrastructure-aware incident response and automated problem resolution system (100) for cloud environments, comprising: (a) an infrastructure and topology mapping module configured to continuously scan, discover, and map virtualized and containerized cloud resources and their interdependencies in real time; (b) an incident detection and correlation module configured to ingest telemetry data from distributed monitoring sources and apply rule-based and machine learning models to detect, correlate and classify system incidents; (c) a context analysis and impact assessment module configured to analyse the scope, severity and business impact of incidents based on real-time infrastructure context and service dependency diagrams; (d) a policy-driven decision engine configured to dynamically select response actions based on predefined rules, compliance policies, service level agreements and historical incident data; e) an Automated Remediation Orchestrator configured to execute predefined or dynamic remediation playbooks through integrations with cloud orchestration and configuration tools; and (f) a feedback loop and learning module configured to collect post-incident data, evaluate the effectiveness of remedial actions, and continuously refine the detection and response logic using machine learning algorithms.

The invention relates to the technical field of information processing, and discloses a method for dynamically regulating and controlling production parameters of a plastic film by utilizing program process scheduling. The method comprises the following steps: converging a real-time parameter sequence to a digital twinborn body to establish virtual mapping, and constructing a multi-dimensional data model; analyzing a material state evolution trend in the digital twinborn body, synchronizing with a physical production line state, fusing historical data to predict a potential influence range, and generating a regulation and control demand report; generating an adjustment scheme draft, performing priority ranking on simulation tasks in the adjustment scheme draft by adopting a program process scheduling algorithm, dynamically allocating computing resources, executing the simulation tasks in parallel, and determining an optimization parameter set in combination with a gradient descent algorithm; and iteratively correcting the parameters in the digital twinborn body through feedback circulation to generate a final coordination control instruction, inputting the final coordination control instruction into the digital twinborn body, confirming synchronism and outputting a report. According to the invention, the precision of dynamic regulation and control of the production parameters of the plastic film is improved.

The invention discloses a self-adaptive pushing method and system based on user behavior dynamic analysis, relates to the technical field of intelligent information pushing and user behavior modeling, and is used for solving the problems that user interest description is not accurate, multi-modal data is difficult to fuse, and the pushing matching degree is low. Collecting behavior data of a user on multiple platforms, and cleaning the data; then, multi-modal interest features of the user are extracted, and interest state vectors are constructed; time sequence modeling is carried out on user interest evolution, a stability scoring mechanism is constructed by introducing cold start density and interest drift frequency, and dynamic compensation and classification adjustment are carried out on different stability behavior sequences. The behavior stage of the user is identified by combining the network topology change of the user behavior, a staged pushing strategy is formulated, and the fine matching and opportunity control of the pushed content are realized. And finally, a feedback closed loop is constructed, the model is continuously optimized, and the accuracy and adaptability of pushing are improved.

The invention discloses a medical visual question and answer model construction method based on a feedback loop. The method comprises the following steps: step 1, generating initial candidates; step 2, performing multi-dimensional candidate evaluation; 3, iterative optimization of feedback driving is carried out; step 4; and 5, efficient fine adjustment of model parameters: performing fine adjustment on a pre-trained multi-modal large language model by using the enhanced training data set constructed in the step 4 to obtain a final medical visual question and answer model. According to the method, a feedback loop mechanism is introduced, and cooperative and iterative self-correction and optimization are carried out on generated text interpretation (Ratio) and a visual localization region of interest (RoI), so that the fact reliability, the logic consistency and the visual strong relevance of a final output result are systematically ensured.

A method and system for enhancing information retrieval and understanding in language models when processing heterogeneous documents containing text, tables, figures, formulas, and images is described. The method involves traversing a document to identify specific types of content and using a language model to generate text descriptions of the identified content, including detailed interpretations. These descriptions are stored in a storage means, indexed by content type. During retrieval, the language model references these descriptions to enhance its understanding and analysis of the document, improving accuracy and contextual relevance. The system comprises a document traversing module, a language model, a storage means, and an information retrieval module, with a feedback loop for continuous improvement based on user feedback. This invention addresses limitations in current retrieval-augmented generation applications by improving the interpretation of complex technical content, thereby enhancing the performance of language models.

A computer-implemented system and method for dynamic, explainable, and adaptive prioritization of cybersecurity controls is disclosed. The system ingests unstructured threat reports and employs a natural language processing (NLP) module to automatically extract adversary tactics, techniques, and procedures (TTPs). A scoring module applies a mathematical time-decay function to the extracted intelligence. A novel hybrid prioritization engine provides explainability-by-design by computationally integrating these objective, data-driven scores with organization-specific context within a transparent multi-criteria decision analysis (MCDA) model. Critically, the system establishes a self-optimizing closed feedback loop; it receives real-world control effectiveness metrics from the operational environment and uses this data as new ground-truth labels to continuously and automatically retrain internal machine learning models. This adaptive mechanism improves the computer's own predictive accuracy and resource allocation efficiency over time, representing a tangible technical improvement.

The present disclosure is directed to systems and methods for data synchronization by an edge computing device. The method includes (1) receiving, by a feedback loop interface, a request for an update of a data set via a data-acquisition-and-computing (DAC) engine in an edge computing device, (2) transmitting, by the feedback loop interface, the request for the update of the data set to an actor node in a network; (3) receiving, by the feedback loop interface, an indication regarding the update of the data set from the actor node; and (4) transmitting, by the feedback loop interface, the indication regarding the update of the data set via the DAC engine in the recurring cycle. The DAC engine includes a synchronizer to control sequences in a recurring cycle of the network. The synchronizer communicates with a configuration manager or receives configuration information from network nodes for managing the data set.

According to the financial risk control risk assessment method based on the dynamic list library, the problems that a traditional method is poor in real-time performance, low in precision and insufficient in adaptability are solved through dynamic weight self-calibration list library construction, multi-model fusion of a self-adaptive scene, real-time assessment of dual-channel cross validation and a closed-loop self-adaptive response mechanism. The core innovation of the method lies in that a list library and model evaluation are deeply coupled, risk attenuation factors, dynamic weight adjustment and feedback closed loops are introduced, the fraud recognition rate is improved, the misjudgment rate is reduced, and the method can be widely applied to financial scenes such as banks and payment platforms.

Described are various embodiments of a vehicle guiding system and method. In some embodiments, the system comprises an AI-augmented vehicle-specific constraints database combining multiple data sources and used for generating optimized routes. The system may further include an intermediate layer of links or interface configured to connect with known and new turn-by-turn driver tools / applications and allow the AI-augmented constraints database to be used. The system may be coupled with a plurality of telematics systems monitoring the vehicle to provide an independent feedback loop which allows the system to react in real-time, and establish performance metrics and measurements for invoicing and reporting purposes. In some embodiments, the system may be coupled to one or more traffic management subsystems or devices.

The embodiment of the invention provides a big language model reasoning optimization system, method and equipment and a storage medium. The system comprises an asynchronous KV cache pre-fetching module, a heterogeneous Specific decoding module, a dynamic model re-fragmentation scheduling module, a Memory-Aware dynamic batch module and a scheduling control engine, and can organically integrate the asynchronous KV cache pre-fetching module, the heterogeneous Specific decoding module, the dynamic model re-fragmentation scheduling module and the Memory-Aware dynamic batch module, so that the dynamic model re-fragmentation scheduling module and the Memory-Aware dynamic batch module are integrated, and the dynamic model re-fragmentation scheduling module and the Memory-Aware dynamic batch module are integrated. And a set of closed-loop adaptive optimization system is formed under the unified coordination of a scheduling control engine, so that module-level linkage, adaptive coordination and performance feedback closed loop in the reasoning process are realized, and finally, the throughput, the response speed and the resource utilization rate in the reasoning stage are remarkably improved on the premise of not influencing the accuracy of a large language model.

The invention discloses an intelligent main plan scheduling system oriented to the chemical industry, and the system comprises a data center layer which is used for aggregating order, equipment, materials, inventory and process constraint data; the MPS optimizer operates the tabu search module, the step-counting hill-climbing module and the flood algorithm module in sequence or in parallel in the same optimization period, and automatically switches algorithms under the following conditions: when the continuous iteration improvement rate is lower than a set threshold value, the tabu search is switched to step-counting hill-climbing, and the step-counting hill-climbing is switched to step-counting hill-climbing; when the local search step number is not improved, starting the flood algorithm to re-initialize the candidate solution; and the process scheduler is used for splitting the coarse-grained plan output by the MPS optimizer into minute-level process schedules, and transmitting the execution deviation back to the data center layer in real time through a bidirectional feedback loop so as to trigger incremental rearrangement. According to the intelligent main plan scheduling system for the chemical industry, through a hybrid algorithm dynamic switching mode, the thousand-level order rearrangement time is greatly shortened, and the plan response speed is remarkably improved.