22892 results about "Data mining" patented technology

The invention discloses a method for analyzing a matching degree between a demand and an output result based on text semantics. The method comprises the following steps: step 1, labeling a data set; step 2, technical document preprocessing; 3, training and predicting a single-parameter model; 4, integrating prediction results of the multi-parameter model; the method has the beneficial effects thatthe method is simple; deep learning and the NLP technology are applied to the field of project association degree calculation of enterprise project management for the first time. Calculating an association matching degree between the two projects according to project requirements and result description; the associated project positioning difficulty is effectively reduced; meanwhile, the demand side can be helped to quickly and efficiently locate high-quality projects adapting to the demand of the demand side; time and resource investment for achievement screening and matching are greatly reduced, the association matching degree between projects is calculated by means of text data of existing project achievement technical documents and project declaration guidelines, and then large enterprises are assisted in screening high-quality projects with the high matching degree in the project bidding and tendering link.

The invention discloses a multi-user sharing-oriented multimedia network video recommendation method, which comprises the following steps: firstly, constructing multi-user characteristics by utilizing collected program information in a multi-user sharing environment, and constructing a leading user label according to the similarity of the program characteristics and the continuity of user watching behaviors, so that separation of multi-user mixed logs is realized; performing periodic multi-user identification prediction of future sessions; secondly, building a user interest mining model based on a time-varying LinUCB algorithm to learn interest changes of a user for each program theme, and enhancing the personalized ability and efficiency of a recommendation system from three aspects of parallel calculation, adaptive control of an exploration coefficient and incremental updating based on LSTM; and finally, establishing an article quality model based on a non-time-varying LinUCB algorithm to further ensure the program quality, and integrating the two algorithms into a final recommendation system model by adopting a cross weighting strategy to form a final program recommendation list. The novelty and accuracy of the recommendation result are ensured.

The invention discloses a multi-dimensional training method and device for a support vector machine, electronic equipment and a computer readable storage medium, and the method comprises the steps: carrying out the discretization of a training sample data set, and obtaining a discretized data set, the discretized data set comprises a plurality of different attributes, and each attribute corresponds to a plurality of feature vectors; calculating a classification contribution parameter of each attribute feature vector to obtain a plurality of classification contribution parameters; performing data mapping on the plurality of classification contribution degrees by using a kernel function to obtain a target function; and optimizing and training the objective function by using a gradient descent algorithm to obtain a support vector machine model. According to the method, different dimension data are mapped through the kernel function, the data gain weight can be determined, the linear separable effect of the mapped dimension data can be improved, and then the classification and analysis capability of the SVM can be improved.

Techniques for constraining the results of a generative language model to valid information using knowledge-grounded documentation. A generative language model may generate invalid results, including compound entities and incorrect entity relations. The techniques include, for a given user inquiry, determining a set of documented information, from a particular knowledge base, that corresponds to the user inquiry. The techniques further include determining a subgraph from a knowledge graph representing the knowledge base, as well as determining a trie data structure representation of the set of documented information. The user inquiry and subgraph are provided as input to a trained generative language model for generating a response to the user inquiry. The techniques include using the trie data structure to validate that the generated response corresponds to real information from the set of documented information.

This invention discloses an IGBT lifetime prediction method based on a GA-Elman-LSTM combined model. Specifically, the method involves: acquiring IGBT aging data; preprocessing the data; using a GA-optimized Elman neural network to import the training dataset into the GA-Elman model for training; constructing error correction training and validation sets using the trained GA-Elman preliminary prediction model; training the LSTM error correction prediction model; and building a GA-Elman-LSTM error correction combined model based on the GA-Elman preliminary prediction model and the LSTM error correction prediction model for final combined prediction. This invention conveniently and efficiently enables the model to converge to the global optimum, effectively improving the model's prediction accuracy; it also improves the model's prediction stability and reduces prediction risk.

The invention discloses a credit text analysis method and a credit object auditing method and device, and relates to the technical field of knowledge maps. A specific embodiment of the method comprises the steps of obtaining a target credit card text, wherein the target credit card text comprises at least one target element; a target semantic template corresponding to the target credit document text is obtained, the target semantic template comprises a statement template and an upper sharing hierarchy of the statement template, the statement template comprises at least one target slot position, and the target slot position corresponds to the target element; obtaining a full-link slot position of the target credit card text according to the statement template and the upper sharing hierarchy; and matching the target semantic template with the target credit text to obtain a target slot value corresponding to the full-link slot position. According to the implementation mode, the slot position and the slot value corresponding to the element in the credit card can be automatically obtained, and the related content in the credit card text can be analyzed.

Methods, systems, and products for alert generation and augmentation using a large language model (‘LLM’), including: providing, to the LLM, information describing a monitored deployment; providing, to the LLM, information describing one or more alerts associated with the monitored deployment; and receiving, from the LLM, an enhanced alert, wherein the enhanced alert is based at least in part on at least one of the information describing the monitored deployment and the information describing one or more alerts associated with the monitored deployment.

Techniques for gathering historical interference data during the channel scanning phase of Auto Channel Selection (ACS) and using the historical interference data for channel selection. With these techniques, a Wi-Fi access point (AP) that implements ACS can more accurately select the optimal channel for a frequency band, resulting in an improved experience for Wi-Fi clients and other devices that communicate with the AP via that band.

Methods and apparatuses a joint operation of conditional handover and conditional PSCell addition or change in a wireless communication system. A method of a UE comprises: receiving information including association information, execution conditions, and configurations for candidate PCells and candidate PSCells; evaluating the execution conditions for the candidate PCells and associated candidate PSCells simultaneously; upon the execution conditions for (i) a candidate PCell among the candidate PCells and (ii) a candidate PSCell associated with the candidate PCell are satisfied, determining that the candidate PCell and the associated candidate PSCell as a target PCell and an associated target PSCell, respectively; applying the configurations for the target PCell and the associated target PSCell; performing a random access procedure for the target PCell; and performing a random access procedure for the associated target PSCell.

Certain aspects of the disclosure provide a method for generating context-augmented responses. In aspects, the method includes receiving a job application; extracting, from the job application, a question; generating, using an embedding model, a vector representation of the question; identifying, within a vector database, a most similar vector to the vector representation of the question; extracting, from a question-response database, an encoded context group; retrieving, from a job applicant database, professional context of the job applicant; generating, based on the context group and the professional context, a prompt chain for causing a language model to generate a response; processing, using the language model, the prompt chain to generate the response; calculating, using a risk scoring model, a risk score for the generated response; and in response to the risk score being below a predetermined threshold, populating a portion of the job application with the response.

Method, system, apparatus, and computer-readable media for reporting an automated medical opinion, including receiving, via a user interface, patient medical history, diagnostic tests, and current symptoms for a user; identifying, as automated output from a trained model, a diagnostic assessment including one or more of a medical diagnosis, a recommended medical treatment, or an recommended diagnostic medical test based on the patient medical history, diagnostic tests, and current symptoms received via the user interface; sending a report to the user based on the diagnostic assessment; receiving, via a feedback loop, feedback for the diagnostic assessment reported to the user; and updating the trained model based on the feedback.

A computing platform performs automated due diligence by acquiring project scope information and documents, standardizing all data across all document types into a uniform format optimized for analysis and subjecting the data to repeated querying by large language model agents to triangulate it against a body of human content expertise coded into the platform. The platform compares variables with a project proforma, verifies permits by parsing permit data and cross checking a permitting authority portal, evaluates design completeness by detecting standards of readiness status, and measures the bankability of project agreements compared to industry standards. The platform computes environmental, social, and governance metrics, assesses counterparty risk and community sentiment using searches of public sources, and validates a reported capital stack by analyzing documents and applying industry standards thereto. The platform calculates risk metrics, applies weights to produce two scores, and generates a report that includes remediation steps.

Methods and systems for artificial intelligence (AI) assisted automation of testing of a software functionality related to a user intent on a software platform is provided. In one embodiment, the method includes receiving user action data indicating the user intent related to a software platform and generating a human-readable test scenario. The test scenario, which includes a sequence of human-readable testing steps and an expected outcome, is generated using a scenario machine learning (ML) model. The method includes generating an interpretable test script based on the test scenario using a script ML model. The test script is interpreted to implement testing steps on the software platform, generating a test outcome. A human-readable test report is also generated to evaluate the test scenario on the software platform, based on the test outcome and the expected outcome.

Methods, apparatuses, system, devices, and computer program products for AI-driven data aggregation and analysis for entity scoring and assessment are disclosed. In a particular embodiment, a controller generates an entity profile for an entity by iteratively retrieving data related to an entity, the data including structured data and unstructured data, determining a structure for the unstructured data by applying the retrieved data to the AI model, the structure including a plurality of fields, and populating one or more fields of the entity profile with a respective value that is extracted from the retrieved data using the AI model. The controller stores the entity profile in a database comprising a plurality of entity profiles corresponding to different entities. The controller generates, using the AI model, a score for the entity in relation to an attribute based on an AI-driven analysis of the plurality of entity profiles.

Techniques for knowledge discovery based on indirect inference of association are presented. A data management component (DMC) can determine and extract, in a structured format, entities, relationships between entities, and concepts relating thereto in documents, based on analysis of information in the documents and / or keywords relating to concepts, to generate an association inference model. Using artificial intelligence techniques, DMC can embed the entities and relationships to a common representation to generate and train a scoring model that can be used to evaluate and score similarity strength between entities, including entities that do not have a known relationship, and can predict or infer relationships, including indirect relationships, between entities or between concepts. In that regard, DMC or user can evaluate concept-level scores to determine a level of relationship between concepts. DMC can feedback information from the scoring model or evaluation to update the association inference model.

This disclosure provides a method for assessing the health status of bearings. The method includes: receiving multimodal data and prompting information about the bearing; performing feature extraction on the multimodal data using a first set of agents to generate a feature set about the multimodal data; performing a health assessment on the bearing using a second set of agents based on the feature set and prompting information about the multimodal data to generate multiple health assessment results about the bearing; and combining the multiple health assessment results using a third set of agents to generate a health assessment report about the bearing. According to this bearing health status assessment method, users can input multimodal data to perform bearing health status assessments, thereby improving the flexibility of the bearing health status assessment method. Furthermore, the bearing health status assessment method of this disclosure, by leveraging the reasoning capabilities of a large language model, can better understand user questions, thereby providing better maintenance suggestions and other outputs.

Systems and methods are disclosed comprising techniques for signal conversion, such as monitoring digital communications signal data transmitted among a user set, converting signal data of a first discrete signal chunk into a first alphanumeric signal set, inputting the first alphanumeric signal set into a machine learning model to generate a first scoring distribution that maps to a first signal event category set, retrieving, for a second discrete signal chunk, a second alphanumeric signal set and a second scoring distribution that maps to a second signal event category set, determining a signal event divergence score via comparing the first and the second scoring distributions, and when the signal event divergence score fails to satisfy a tolerance threshold, transmitting, to at least one user of the user set, an interventive action that, when executed by the at least one user, causes a transformation of the digital communications signal data.

Technologies for providing self-healing of functions of a cellular network are described. One method includes obtaining first error codes associated with one or more operations of a first network function of a cellular network. The method further includes providing the first error codes as input to a first trained machine learning model. The method further includes obtaining, from the first trained machine learning model, first predictive output based on the first error codes. The method further includes performing a first corrective action in view of the predictive output.

The embodiment of the invention provides a health assessment method for train bearings. The health assessment method comprises the steps that a bearing temperature data set collected from a plurality of bearings of a train during the travel of the train and an environment temperature data set around the train during the travel of the train are obtained; performing feature extraction on the bearing temperature data set to generate a first temperature feature set; calculating a difference value between each piece of bearing temperature data in the bearing temperature data set and corresponding environment temperature data in the environment temperature data set to generate a first temperature difference value set, and executing feature extraction on the first temperature difference value set to generate a second temperature feature set; and based on at least one of the first temperature feature set and the second temperature feature set, generating an evaluation result of the health state of each bearing in the plurality of bearings.

This invention discloses a stepwise data assimilation method for a set subspace in a nonlinear inverse problem, relating to the field of data processing technology. The invention constructs an initial prior physical set based on the physical state variables to be inverted and optimized, and historical observation data. It extracts the static subspace basis anomaly matrix, initializes the latent variable set and particle weights, calculates fractional-step incremental reweighting, evaluates the current likelihood mismatch penalty using predicted data, updates and normalizes the particle weights in the logarithmic domain, obtains the effective sample number, performs system resampling operations in conjunction with a preset resampling tolerance coefficient, eliminates low-weight particles and replicates high-weight particles, synchronously updates the latent variable set and predicted data, executes a dynamic MCMC mutation loop to generate proposed latent state vectors and affinely maps them to a high-dimensional physical space, and updates the latent variable set by evaluating the annealing target energy within the latent variable subspace until all fractional steps are traversed. Finally, it outputs the latent variable set and maps it back to the physical space.

The disclosed method for generating virtual objects includes generating, based on object data, compressed object data, performing, based on the object data and scales, operations to train a first untrained machine learning model to generate a first trained machine learning model comprising a trained codebook and a trained decoder, wherein the first trained machine learning model is trained to generate a reconstruction of the compressed object data, generating, based on the compressed object data and the scales and using the first trained machine learning model, token maps data, performing, based on the token maps data and conditions, operations to train a second untrained machine learning model to generate a second trained machine learning model comprising a trained autoregressive model, wherein the second trained machine learning model is trained to generate predicted token maps, and generating, based on the scales, conditions, and using both trained models, a virtual object.

Systems and methods improve security of vehicle-transfer processing using model-driven fraud flagging with cryptographic provenance. A transfer request message including a vehicle identifier is received. Historical event records for the vehicle identifier are retrieved from an append-only, hash-linked event log. A feature vector is generated from the historical event records and one or more external authoritative data sources. A trained fraud detection model is executed to produce a fraud score and, in some embodiments, a confidence value. When a fraud-flagging criterion is satisfied, a fraud-flag artifact is generated including a model identifier and version, a feature schema identifier, and a hash of at least a portion of the feature vector, and the artifact is digitally signed. Based on the signed artifact, the system selectively blocks, delays, or routes the transfer request to remediation, preventing commitment of a corresponding transfer record to the event log until a remediation condition is satisfied.

A system generates domain-expert agents for a particular domain. The system generates a hierarchy of topics for the domain by forming structured query inputs and requesting a machine learning-based language model to produce topics and associated sets of search terms. A knowledge store is built for the domain by identifying, for each topic, facts represented symbolically and / or in natural language. For at least one topic, programs comprising instructions for executing domain-specific procedures are generated and stored as symbolic and / or natural language programs. The knowledge store is evaluated to determine an expertise level for a domain-expert agent. If the knowledge store meets a threshold of knowledge, a deployment package is created comprising the knowledge store, and the domain-expert agent is deployed. The deployed agent utilizes the knowledge store together with a machine learning language model to address and solve problems specific to the domain.

One or more computing devices and / or methods for retrieval augmented multiple choice question and answer generation on search queries are provided. A pregeneration prompt may be generated based upon a user input query, one or more chunks of content, and / or instructions for a model. The pregeneration prompt is input into the model to generate an initial question. Another prompt is generated based upon the initial question, the one or more chunks of content, and / or the instructions. The prompt is input into the model to generate question and answer content in a multiple choice format that is provided through a user interface for user engagement.

The application provides a lightweight neural network architecture search method based on flatness, which is used for reducing the time cost and computing resources of searching the neural network architecture. From the perspective of predicting the network generalization, the application provides a method for comparing the networks by taking the flatness of the candidate network at the initial time as an evaluation index. The method comprises the following steps: determining an image processing task and determining a data set; determining a network search space; selecting a certain number of neural network architectures in the search space; verifying the effectiveness of the evaluation index; calculating the flatness of the neural network architecture, and sorting the selected architectures according to the flatness; selecting the architecture with the maximum flatness as the optimal architecture; and training the optimal architecture to complete the image classification task. The application can select an architecture with high accuracy in a small time consumption.

There is provided a method for merging data frames performed by a data frame merge device for a vehicle network system. The method comprises receiving a plurality of control data frames from a lower level of the vehicle network system; and merging the plurality of control data frames for a preset standby time in a traffic section in which the plurality of the control data frames are not transmitted to a higher level of the vehicle network system.

To provide an information processing device that can estimate the factors behind a phenomenon based on events described in text. [Solution] This information processing device comprises a question input unit into which questions inquiring about the factors of a phenomenon are input, an artificial intelligence interface unit that inputs a set of sentences representing the causal relationships of events and the questions input into the question input unit into a large-scale language model and obtains answers to the questions from the large-scale language model, and an output unit that outputs the answers obtained by the artificial intelligence interface unit.

Technology is disclosed for dynamically creating and / or validating new URLs based on a user's query and / or contextual data. This enables the discovery of relevant content that may not be pre-existing in the search engine's index. This process enhances the search engine's ability to deliver the most current and relevant results by generating potential content sources in real-time, validating them, and incorporating the discovered content into the index for immediate query execution. Various embodiments are additionally or alternatively directed to a process that involves iteratively discovering, crawling, and indexing new content by following links from seed URLs, which may be either pre-existing or generated. This process deepens the search engine's index by continuously exploring outlinks, fetching new content, and updating the index in real-time.

An information processing method, device, server, and V2X UE are provided, the information processing method includes: receiving first pre-configured information sent by a second server; obtaining communication mode decision information according to the first pre-configured information; sending the communication mode decision information to corresponding V2X UEs; wherein, the first pre-configured information includes at least one of the following information: service configuration information for at least two V2X UEs; communication mode decision information for the at least two V2X UEs; there being a correlation between the communication mode decision information and an V2X service.

We provide specimen analyzers, specimen analysis systems, specimen analysis methods, and programs that can contribute to highly accurate dementia diagnosis using dementia biomarkers. [Solution] The specimen analyzer 1 comprises a measuring device 2 that measures analytes related to dementia biomarkers contained in a specimen taken from a subject, a storage unit that stores reference values ​​for identifying outliers that may occur due to factors other than dementia, a control unit that determines whether the measured value is identified as an outlier based on the measured value of the analyte by the measuring device 2 and the reference value, and generates biomarker analysis results according to the result of the determination, and an output unit that outputs the biomarker analysis results.