32545 results about "Learning models" patented technology

A system and method for an AI-controlled sensor network for threat mapping and characterization. The system deploys a network of honeypots and sensors across various geographic locations and network segments, collecting and aggregating data on network traffic and potential threats. An AI orchestrator analyzes this data using advanced machine learning models, generating dynamic honeypot profiles and a comprehensive threat landscape. The system can adapt in real-time to emerging threats, optimize resource allocation, and provide actionable intelligence. By correlating data across multiple points, the system offers enhanced threat detection capabilities and proactive cybersecurity measures, surpassing traditional security information and event management (SIEM) tools.

The invention relates to a dynamic data pipeline construction method based on artificial intelligence and multi-modal data processing, and belongs to the technical field of data fusion treatment and artificial intelligence. According to the method, a multi-modal deep learning model is adopted, semantic analysis is performed on business documents, user requirements and function specification instructions, and key indexes, data requirements and logic constraints are extracted; constructing a multi-modal knowledge graph, performing unified representation and feature extraction on structured and unstructured data resources, and performing embedded learning by using a graph neural network; based on a deep semantic matching model and a hybrid recommendation system, realizing accurate matching of business requirements and data resources; modeling generation of a data processing flow into a Markov decision process by adopting a reinforcement learning strategy, and automatically generating an optimized data processing workflow; the adaptability of the model to new business requirements is improved by utilizing a meta-learning algorithm; and dynamic scheduling and execution of tasks are realized through a workflow engine. According to the invention, the data processing efficiency can be improved.

The technology described herein relates to systems, methods, and computer storage media, among other things, for providing search query intent specificity. Embodiments may include identifying a search query performed using a search engine and generating a query vector for the search query by aggregating search result embeddings (e.g., item listing vectors) of search results from the search query. Further, in some embodiments, similarities (e.g., cosine similarities) between the query vector and the item listing vectors can be determined. As such, an intent specificity of the search query can be determined. Further, in some embodiments, the intent specificity can be used to train an intent specificity machine learning model for generating intent specificity scores for other search queries. Based on the intent specificity scores determined using the one or more trained intent specificity machine learning models, determinations can be made with respect to precision and recall, etc.

The invention relates to the technical field of equipment fault diagnosis, and discloses an equipment fault diagnosis and prediction method based on deep learning, and the method comprises the following steps: S1, collecting multi-modal data in real time through a plurality of sensors installed on equipment; s2, preprocessing the collected data; s3, constructing a hybrid deep learning model; s4, dynamic weighted fusion is performed on the features of different modal data by using an attention mechanism, and comprehensive feature representation is generated; s5, using the marked fault data and normal data to supervise and train the model; s6, inputting equipment operation data acquired in real time into the trained model, and judging the state of the equipment; and S7, generating a potential fault early warning signal based on a prediction result of the model. A piezoelectric vibration sensor and a thermal infrared imager are arranged on a motor bearing through vibration, temperature and sound sensors, vibration waveforms, thermal imaging slices and time-frequency diagrams are synchronously captured, and composite state characteristics such as mechanical wear and temperature anomaly of equipment are comprehensively reflected.

The invention provides a live broadcast room content identification and intelligent distribution method and system based on multi-modal fusion, and relates to the technical field of machine learning, and the method comprises the steps: collecting video, audio and bullet screen data of a live broadcast room, and carrying out the feature extraction; performing cross-modal interaction, feature alignment and feature reconstruction on the multi-modal features by using the multi-modal feature fusion model to generate cross-modal fusion features; and carrying out content identification based on a pre-trained content classification model to obtain a category label and a time sequence feature. Furthermore, a live broadcast room content portrait and an association network are constructed, and a live broadcast room real-time score is generated and a live broadcast room display weight and a push strategy are dynamically adjusted in combination with user historical watching behavior data and a deep reinforcement learning model. Through multi-modal data fusion and deep reinforcement learning, accurate identification and personalized recommendation of the live broadcast room content are realized, and the user experience and the platform operation efficiency are effectively improved.

In embodiments, systems and methods for improving machine-learning systems are disclosed. In embodiments, a system includes a data pool system that is configured to receive data from a plurality of different data sources and maintain a training data set that is used to train a specific machine-learning model based on the data from the plurality of different data sources. In embodiments, the system further includes a data scoring system that determines a data reliability score corresponding to the new data based on a set of intrinsic features of the new data and a data scoring model, wherein the data pool system selectively adds the new data to the training data set based on the reliability score of the new data. The system also includes a machine learning system that trains the specific machine-learning model based on the training data set.

The invention discloses an underground pipe network leakage detection method fusing big data analysis and machine learning. The underground pipe network leakage detection method comprises the step of deploying an acoustic sensor, a pressure sensor and a flow sensor at preset positions of an underground pipe network. Performing noise reduction and standardization processing on the acquired operation data, extracting frequency domain, energy and time sequence characteristics of acoustic signals to generate acoustic characteristic vectors, and calculating initial space coordinates of a leakage position based on time difference of arrival of the acoustic signals and sensor arrangement; and generating a pressure feature vector, and analyzing the spatial-temporal correlation of the flow data to generate a flow feature vector. And fusing acoustic, pressure and flow feature vectors to form a multi-dimensional feature vector, inputting the multi-dimensional feature vector and the initial space coordinates into a pre-trained cascade deep learning model, and outputting the leakage probability and the leakage position of underground pipe network leakage. And when the leakage probability exceeds a preset threshold value, triggering an alarm mechanism. According to the invention, efficient detection and accurate positioning of underground pipe network leakage are realized, and high real-time performance and positioning accuracy are achieved.

A data processing system comprising: inputting a tiled image of a vehicle including four different angle views of the vehicle combined into a single image to a first machine learning model (e.g. CNN), the model trained based on historical image data to predict a first likelihood of total loss vehicle; inputting a multi-fusion of images each into a second set of machine learning models; the multi-fusion of images including a set of separate and distinct images for each of the views input separately into the second set of machine learning models, and extracting features to predict a second likelihood of total loss vehicle; inputting tabular data relating to the vehicle into a third machine learning model to predict a third likelihood of total loss vehicle for the vehicle; and aggregating the first, second and third likelihood of total loss vehicle to determine the overall likelihood of total loss.

An example operation may include one or more of executing tests on a software application via a test environment of a test platform and logging results of the tests in a log file, identifying a new feature to be added to the software application based on execution of a machine learning model on logged results of the tests stored in the log file, generating source code for the new feature to be added to the software application based on execution of a generative artificial intelligence (GenAI) model on the new feature and a repository of source code, and displaying the generated source code via a user interface of the software application.

The invention discloses an Internet of Things multichannel earthquake early warning emergency linkage system, belongs to the technical field of earthquake early warning, and aims to solve the problems of insufficient real-time performance, reliability, scene coverage and safety in the prior art. The sensing layer collects data cooperatively through various sensors, eliminates environmental interference and ensures high-precision seismic wave detection, the edge calculation layer filters noise in real time by adopting an advanced algorithm, and confirms seismic events through multi-dimensional comparison, so that the local processing efficiency is remarkably improved, the cloud load is greatly reduced, a cloud platform fuses multi-source data, and the seismic detection efficiency is improved. A machine learning model is combined to dynamically optimize an early warning threshold value, the accuracy of earthquake type classification and intensity prediction is improved, full-link optimization from data acquisition to intelligent decision making is realized, a multi-channel distribution module is designed, instructions are transmitted according to priority levels, and urban and rural full-scene coverage is ensured through multiple communication technologies.

Systems and methods for assessing machine learning model fairness are provided. A graphical user interface (GUI) is rendered in a web browser. Input data from the web browser is obtained. A first input produced by a first GUI control is received to identify a prediction column in the input data. A second input produced by a second GUI control is received to identify a ground truth column. A third input produced by a third GUI control is received to identify a sensitive attribute column. A plurality of groups within a plurality of sensitive attribute values in the sensitive attribute column are automatically displayed. The system processes the input data based on the prediction column, the ground truth column and the sensitive attribute column to generate one or more algorithmic fairness analysis, which are used to display a visual report.

A system and method for flexibly incorporating machine learning models into applications using a marketplace platform and distributed computational graph (DCG) architecture. The DCG enables dynamic selection, creation and incorporation of trained models with data sources and marketplaces for data, algorithms, simulation models, ontologies, knowledge corpora, and crowd or expert judgment. Multiple models can be used in series or parallel. An expert judgment marketplace allows human and artificial intelligence (AI) experts to score the accuracy of training data and model outputs. Consumers can select and rank AI agents or experts based on the helpfulness of their judgments. A symbolic knowledge corpora and retrieval augmented generation (RAG) marketplace enables selling access to proprietary datasets as RAGs and knowledge bases. The system includes knowledge corpora and RAG marketplaces with domain-specific components and user experience customization.

The invention discloses a multi-element sales planning agent system and method, and aims to improve the intelligence and precision of sales planning. The system comprises a collection module, an analysis module, an optimization module, a creation module and a generation module. The collection module is used for receiving multi-modal data such as marketing targets and extracting key marketing elements. The analysis module is used for generating a target user portrait and extracting marketing strategy analysis data. And the optimization module is used for calculating a medium putting weight by utilizing reinforcement learning and generating a medium strategy scheme. And the creation module generates a propagation theme and marketing content by adopting a generative artificial intelligence technology. And the generation module predicts a delivery effect by using a machine learning model and dynamically optimizes a medium strategy and a content scheme. Through multi-modal data fusion, intelligent analysis and optimization, closed-loop processing from data acquisition to marketing execution is realized, the marketing decision-making efficiency is improved, and brand promotion accuracy and market adaptability are enhanced.

A model engine is provided for managing transactions for one or more computing devices over a network. There is provided a machine learning model trained and tested on historical transaction data including labelled fraud data, to provide a target signal indicative of a likelihood of fraud within a given transaction, generating an ensemble of decision trees. The engine further includes extracting a set of rules by traversing each tree in the ensemble of decision trees from a root node to each leaf node of the tree, each path from the root node to a particular leaf node including splitting criterion providing a rule to form a set of rules. A proactive risk management system is also provided for applying the set of rules to a new transaction and when at least one rule is met, triggering predetermined actions on at least one computing device on the network.

The implementations herein disclose advanced systems and methods for integrating, analyzing, and reasoning over heterogeneous data at scale. In some implementations, the system comprises a synergistic data processing infrastructure featuring: a graph database core for unified data representation; specialized loaders for concurrent ingestion and processing of structured, unstructured, and time series data; a natural language reasoning engine leveraging large language models; and a multi-modal user interface.

A method includes obtaining an image, the image associated with a mask corresponding to a portion of the image, generating a plurality of images based on the image and the mask, each image of the plurality of images depicting a different color in the portion of the image corresponding to the mask, executing a machine learning model to generate an image performance score for each of the plurality of images, ranking the plurality of images according to the image performance scores for the plurality of images, and generating a record comprising one or more images of the plurality of images based on the rankings of the plurality of images.

A computing device for predicting an expected loss for a set of claim transactions is provided. The computing device predicts, at a first machine learning model, a claim frequency of the set of claim transactions over a given time period and trained using historical frequency data and based on a segment type defining a type of claim, each type of segment having peril types. The computing device also predicts, at a second machine learning model, claim severity of the set of claim transactions during the given time period, the second machine learning model trained using historical severity data and based on the segment type and the corresponding peril types. The computing device then determines the expected loss for the set of claim transactions over the given time period by applying a product of prediction of the first machine learning model and the second machine learning model.

An example operation may include one or more of storing transaction content from transactions executed via one or more fiat payment accounts and one or more crypto accounts of a digital wallet of a user, and determining, via execution of a machine learning model on the stored transaction content, a recurring expense value of the user and a next point in time in which the recurring expense value, dividing the recurring expense value into a plurality of sub-values; generating a plurality of transactions which transfer the plurality of sub-values from a fiat payment account to a crypto account from among the one or more crypto accounts and store the plurality of transactions within a queue, initiating a plurality of time-to-live jobs for the plurality of transactions, respectively, wherein the plurality of time-to-live jobs comprise a plurality of different respective expiration times that are staggered such that the plurality of time-to live jobs expire in incremental intervals from a current time to the next point in time in which the recurring expense value is due and executing the plurality of transactions at the plurality of different expiration times to incrementally transfer the plurality of sub-values from the fiat account to a crypt account via an application programming interface (API).

The invention discloses a network line intelligent operation and maintenance monitoring management system and method. The method comprises the steps that an intelligent network monitoring and optimizing system regularly collects network equipment information through a protocol, and a monitoring agent collects server and machine room environment data; processing data in real time by adopting distributed storage and a stream processing framework; predicting a network fault by adopting threshold monitoring and a machine learning model, and identifying a bottleneck and scheduling resources based on flow analysis; in combination with deep packet inspection and an AI algorithm, network traffic is monitored in real time, security threats are identified, and IPS system protection is deployed; a repair operation is automatically triggered through a self-healing strategy, and backup resources are started; the AI engine analyzes historical and real-time data, generates network topology and link state visual content, and automatically generates reports, optimization and decision support; according to the intelligent operation and maintenance monitoring management system and method for the network line, a fault tree analysis and decision support system is introduced, and a plurality of fault reasons are intelligently analyzed and repair measures are automatically executed.

The invention discloses a fine-grained multi-modal prompt learning method based on a visual language pre-training model, and relates to the technical field of visual language pre-training models.The method comprises the steps that a multi-modal prompt learning vector used for learning visual features and text features is constructed, and a fine-grained multi-modal prompt learning model is constructed based on the fine-grained multi-modal prompt learning model; global differences, subtle distinguishing visual features and category difference text features among different visual categories are generated, and a multi-modal prompt learning vector is optimized based on a cosine similarity loss function. According to the invention, through combination of dual-granularity visual prompt and text prompt, the problems that in an existing prompt learning tuning method, cooperative reasoning cannot be carried out in a dual-mode prompt representation space, and fine distinguishing features are difficult to capture are effectively solved; and superior migration generalization ability and few-sample learning ability are verified on a plurality of main image identification reference data sets, and the performance is remarkably improved in downstream tasks.

A data processing system comprising: inputting a tiled image of a vehicle including four different angle views of the vehicle combined into a single image to a first machine learning model (e.g. CNN), the model trained based on historical image data to predict a first likelihood of total loss vehicle; inputting a multi-fusion of images each into a second set of machine learning models; the multi-fusion of images including a set of separate and distinct images for each of the views input separately into the second set of machine learning models, and extracting features to predict a second likelihood of total loss vehicle; inputting tabular data relating to the vehicle into a third machine learning model to predict a third likelihood of total loss vehicle for the vehicle; and aggregating the first, second and third likelihood of total loss vehicle to determine the overall likelihood of total loss.

An example operation may include one or more of establishing a network connection between a computing system and one or more external sources over a computer network, receiving a request from a user via a software application on a user device, collecting data about the user from the one or more external sources via the established network connection, executing a machine learning model on the collected data about the user to determine a goal of the user, and displaying an image of the goal via a user interface of the software application.

The invention discloses a building energy consumption dynamic optimization method and system based on BIM and reinforcement learning, and belongs to the technical field of building energy management and intelligent control, and the method comprises the steps: building a BIM containing building component physical attribute parameters, and generating a building digital twinborn body with dynamic thermal attribute evolution; extracting the spatial topological relation and the physical property parameters of the components, and constructing a multi-dimensional state space of a preset reinforcement learning model; embedding physical constraint conditions, and training the reinforcement learning model to generate a multi-objective optimization strategy of the energy equipment; and analyzing the multi-objective optimization strategy into an equipment control instruction set, and feeding back the equipment control instruction set to the building digital twin for real-time physical attribute simulation. According to the method, the physical accuracy of the BIM and the self-adaptive decision-making ability of reinforcement learning are combined, adversarial training under physical constraints is introduced, an energy consumption optimization strategy which conforms to actual operation limitation and dynamically adapts to environmental changes can be generated, and the energy utilization efficiency and the system response speed are remarkably improved.

An example method includes receiving a set of features representing attributes for a user and receiving data for multiple digital campaigns. The set of features can be processed using a propensity machine learning model to determine whether the first user is expected to perform an affirmative action in response to the digital campaign. A subset of contributing features that are indicative of a likelihood of the first user performing the affirmative action can be generated using a feature importance model for each digital campaign. For each digital campaign, a respective output indicating a likelihood that the first user will perform an affirmative action in response to the respective digital campaign can be obtained using a respective trained student-teacher neural network. These outputs can be compared to identify a particular digital campaign and one or more digital components associated with this campaign can be transmitted to the first user.

A computing device for predicting an expected loss for a set of claim transactions is provided. The computing device predicts, at a first machine learning model, a claim frequency of the set of claim transactions over a given time period and trained using historical frequency data and based on a segment type defining a type of claim, each type of segment having peril types. The computing device also predicts, at a second machine learning model, claim severity of the set of claim transactions during the given time period, the second machine learning model trained using historical severity data and based on the segment type and the corresponding peril types. The computing device then determines the expected loss for the set of claim transactions over the given time period by applying a product of prediction of the first machine learning model and the second machine learning model.

Herein disclosed is receiving predetermined content, receiving a request to transform the predetermined content into a derivative work, receiving a requested theme for the derivative work, using generative artificial intelligence to create the derivative work generated as a function of the predetermined content and the requested theme, determining if the generated derivative work is approved based on a machine learning model configured to determine a content approval score as a function of content owner preferences, in response to determining the generated derivative work is approved, applying a digital watermark to the approved derivative work, configuring an authorization server to govern use of the approved derivative work based on the digital watermark and providing user access to the authorized derivative work. The requested theme may be determined using a Large Language Model (LLM) and a chatbot interview. The generative artificial intelligence may comprise a diffusion model. The content may comprise music.

Described herein are a system, method, and device for configuring a generative machine learning model using a syntactic interface. A system may include a user interface, a memory, and a processor configured to, using a syntactic interface displayed using the user interface, receive a syntactic interface input from a user; identify an electronic medical record (EMR) by generating an EMR database query as a function of the syntactic interface input, querying an EMR database using the EMR database query, and receiving, from the EMR database, an EMR database response; generate a prompt as a function of the syntactic interface input; generate a first generative model output as a function of the prompt and the EMR using a trained generative machine learning model; and using a conversational interface displayed using the user interface, display the first generative model output to the user.

The invention discloses a vacuum dry pump bearing fault diagnosis method based on domain confrontation and attention transfer learning, and aims to solve the problem of performance reduction caused by data distribution difference of dry vacuum pump bearing faults in cross-domain fault diagnosis of a deep learning method. Domain adversarial training is carried out by introducing a gradient inversion layer, and feature weights are dynamically adjusted in combination with an attention mechanism. According to the method, the distribution difference between the bearing fault data source domain and the target domain can be effectively reduced, and the fault diagnosis precision of the model on the target domain is improved. The method specifically comprises the steps of constructing a deep transfer learning model combining a convolutional neural network, an attention mechanism and domain adversarial training, performing feature extraction and classification training on data of a source domain and a target domain, and optimizing the model by adopting cross entropy loss, domain adversarial loss and maximum mean difference loss. The method shows high diagnosis accuracy and good generalization ability under different working conditions, and is suitable for cross-domain fault detection and diagnosis.

An example operation may include one or more of receiving a loan application of a user, extracting a plurality of personal attributes about the user from the loan application, querying a machine learning model via an application programming interface (API) based on the plurality of attributes about the user to identify one or more rules for auto-adjudicating the loan application, determining whether or not to approve the loan application based on the one or more rules identified via the machine learning model, and transmitting notice of the determination to a device associated with the user.

The technology evaluates the compliance of an AI application with predefined vector constraints. The technology employs multiple specialized models trained to identify specific types of non-compliance with the vector constraints within AI-generated responses. One or more models evaluate the existence of certain patterns within responses generated by an AI model by analyzing the representation of the attributes within the responses. Additionally, one or more models can identify vector representations of alphanumeric characters in the AI model's response by assessing the alphanumeric character's proximate locations, frequency, and / or associations with other alphanumeric characters. Moreover, one or more models can determine indicators of vector alignment between the vector representations of the AI model's response and the vector representations of the predetermined characters by measuring differences in the direction or magnitude of the vector representations.