309 results about "Multiple category" patented technology

A large language model (LLM) may be used to classify an input into one of a plurality of categories. However, given the machine-learning operation of the LLM, the output of the LLM does not represent a definitive statement, but is based on probability computations of the machine learning model. Therefore, the classification performed by the LLM might not be correct. Classification into the wrong category by the LLM results in downstream technical problems. In some implementations, when an LLM generates a response that classifies an input, one or more probability values associated with a token that forms the basis of the response may be used to determine a confidence value. The confidence value is indicative of confidence in the classification performed by the LLM. An action may be taken based on the confidence value.

The present disclosure relates to the technical field of smart power grids. Disclosed are a method and apparatus for constructing a high-order tensor network of a large-scale power grid, and a device and a medium. The method comprises: acquiring multiple category attribute sets of heterogeneous nodes in a large-scale power grid; performing feature extraction on the multiple category attribute sets, so as to obtain attribute features of the heterogeneous nodes; on the basis of the attribute features of the heterogeneous nodes, using a deep hash mapping model to establish multiple feature sub-spaces; using a breadth learning strategy to align the multiple feature sub-spaces to a unified feature dimension; on the basis of an attribute feature vector of the unified feature dimension, using a distance measurement method to calculate a distance measurement value between the heterogeneous nodes; on the basis of the attribute feature vector of the unified feature dimension and the weight of a heterogeneous node interaction relationship, determining a basis tensor for representing the heterogeneous node interaction relationship; and on the basis of the basis tensor and the weight of the heterogeneous node interaction relationship, using a tensor multiplication operation rule to construct a high-order tensor network of a large-scale power grid.

The invention discloses a short text clustering method and device based on a large language model, and belongs to the technical field of natural language processing, and the method comprises the steps: converting a short text into vector representation, and carrying out the preliminary clustering of the vector representation through a clustering algorithm, and obtaining an initial category; for all the corresponding short texts in each initial category, generating an abstract by using a large language model, generating a plurality of theme titles for summarizing the core meanings of the texts according to the abstract, respectively taking the theme titles as sub-titles, inputting the plurality of sub-titles into the large language model, and carrying out theme combination to obtain a parent title, constructing the parent title and the child titles into a hierarchical theme system; the large language model allocates to-be-classified short texts to one or more categories according to the hierarchical topic system to complete short text clustering; and performing quality evaluation and error pattern analysis on a short text clustering result to optimize a clustering algorithm and a large language model. According to the method, the theme of the short text can be automatically found and classified, and the classification accuracy and efficiency are improved.

Classifying results of a user's search query using a trained classification model. In response to the search query, an online system retrieves a set of candidate search results, each candidate search result associated with a respective item of a plurality of items. The online system accesses the classification model that is trained to compute a probability of classification of each item into each class of a plurality of classes, each class associated with a type of relevance to the search query. The online system applies the classification model to generate, for each item, a classification score associated with each class. The online system classifies, based on the classification score, each item into a corresponding type of relevance to the search query. The online system selects, based on the classification of each item, a list of items for displaying at a user interface of a device associated with the user.

To efficiently and effectively classify a large amount of document and text data by an LLM.SOLUTION: The present invention relates to a text classification system 1 which classifies documents accumulated in a document DB 14 by categories, and the text classification system has a classification processing part 12 which lets an LLM 2 proposes one or more categories based upon a classification policy specified by a user, a search processing part 13 which searches the document DB 14 for documents belonging to the respective proposed categories, and a UI processing part 11 which presents the respective categories and the numbers of documents belonging to the respective categories to the user.SELECTED DRAWING: Figure 1

Systems, apparatuses, and methods for analyzing log data are described. A processing unit ingests data blocks multiple data sources associated with a network-connected device. Each data block is associated with contextual meta tags identified from the ingested data. Further, one or more entities associated with the network-connected device are identified and for each entity a taxonomy is created. The taxonomy comprises a plurality of categories, each category comprising at least one contextual meta tag. Dashboards for presentation of processed log data are generated based at least in part on the taxonomy.

Systems, computer program products, and methods are described herein for implementing parametric optimization analysis for resource selection. The present invention is configured to determine a first set of requirements associated with a resource exchange agreement; identify one or more non-fungible tokens (NFTs) for one or more categories of past resource exchange agreements based on at least the first set of requirements; extract, from the one or more NFTs, one or more resource descriptors associated with one or more past resource exchange agreements in the one or more categories; predict, using a machine learning subsystem, an optimal resource valuation model for one or more resources that meet the first set of requirements using the one or more resource descriptors and the first set of requirements; and transmit control signals configured to cause a first end-point device to display the optimal resource valuation model.

A large language model (LLM) may be used to classify an input into one of a plurality of categories. However, given the machine-learning operation of the LLM, the output of the LLM does not represent a definitive statement, but is based on probability computations of the machine learning model. Therefore, the classification performed by the LLM might not be correct. Classification into the wrong category by the LLM results in downstream technical problems. In some implementations, when an LLM generates a response that classifies an input, one or more probability values associated with a token that forms the basis of the response may be used to determine a confidence value. The confidence value is indicative of confidence in the classification performed by the LLM. An action may be taken based on the confidence value.

Systems and methods for automatically populating documents about special entities are disclosed herein. An example method is performed by one or more processors of a computing system. The example method may include receiving user data, extracting a list of entities associated with the user and a list of events that occurred between the user and the entities, transforming metadata for the events associated with entities of interest into vectorized embeddings, selectively classifying, using a binary classifier model, ones of the entities as special entities and ones of the events as special events for a set of documents, assigning, using a multi-class classifier model, one of a plurality of categories to each special event associated with each special entity, each of the categories mapping to a corresponding section within the set of documents, and populating, for each special entity, the corresponding sections within the set of documents based on the categories.

A fraud call recognition method and system based on causal graph and agent collaboration comprises the steps of obtaining a fraud ASR text, mapping extracted multi-category to-be-mapped objects into semantic concept variable vectors, then learning a causal structure based on a data matrix formed by all semantic concept variable vectors, constructing a fraud causal graph, and recognizing a fraud call in the fraud ASR text. Calculating a causal effect of the reason variable on the result variable, and storing the causal effect as a relation weight in a causal graph; the method comprises the steps of obtaining a to-be-recognized ASR text, mapping multiple categories of to-be-mapped objects extracted from the to-be-recognized ASR text into semantic concept variable vectors, then retrieving matched causal chains in a causal graph to obtain a plurality of candidate causal chains, and then generating fraud judgment and risk degree evaluation values based on a large language model inference engine. And determining whether the to-be-identified call is a fraud call. The invention relates to the field of telecommunication anti-fraud, can deeply fuse a causal knowledge base and a large language model, and effectively improves the effectiveness, adaptability and reliability of communication anti-fraud identification.

Multimodal Data Loss Protection (DLP) includes receiving an input comprising data in any of a plurality of formats; processing the input to determine whether or not the data includes sensitive data; and responsive to the input including sensitive data, performing steps of: processing the input to classify the input into a category of a plurality of categories; and providing an indication of the category of the plurality of categories. Advantageously, the trained multimodal system can detect categories of data being accessed, transferred, etc., without the requirement of up-front dictionaries from corporate Information Technology (IT).

Methods, systems, and devices for classification. In one aspect, a method includes receiving an input and a request to classify the input into one of a plurality of categories, processing the input using a multi-modal model to generate (i) a description of the input and (ii) a category prediction, a description of the input and the category prediction are processed using a text encoder embedding neural network to generate (i) a text description feature embedding and (ii) a predictive feature embedding, a query feature embedding representing the input being generated from at least the description feature embedding and the predictive feature embedding, and classifying the input into one of a plurality of categories using the query embedding.

A database query processing method includes receiving a natural language request for information contained within a database, categorizing the natural language request in a particular category from a plurality of categories, and selecting a large language model and one or more database structures for which a SQL request is to be directed based in part on the particular category. The selected large language model is prompted to generate a SQL request directed to the selected set of one ore more database structures and the received SQL request is validated for the particular category. The validated particular SQL request is used to access data within the database and one or more visualizations is caused to be displayed based at least in part on a result of the validated particular SQL request.

The invention provides a data processing device for implementing a hybrid scheduling scheme, the hybrid scheduling scheme is used for scheduling data messages in a network, the data processing device comprises one or more processors, the one or more processors are used for processing the data messages, each data message belongs to one category, and the one or more processors are used for processing the data messages. Each category has a predetermined first transmission priority associated with each category; receiving a data message of data to be transmitted for one or more categories in the plurality of categories; and calculating a second indication of the transmission priority of each of the plurality of categories, and scheduling the plurality of categories of data messages for transmission according to the first transmission priority and the calculated second indication of the transmission priority.

A method for determining a category responsive to a user query is disclosed. The method includes receiving a training data set comprising a plurality of data pairs, each data pair including: (i) a query; and (ii) an associated one or more categories that are responsive to the query, wherein the one or more categories in the training data set defines a plurality of categories. The method includes training a machine learning algorithm, according to the training data set, to create a trained model, wherein training the machine learning algorithm includes: creating a first co-occurrence data structure defining co-occurrence of respective word representations of the queries with the plurality of categories, and creating a second co-occurrence data structure defining co-occurrence of respective categories in respective data pairs. The method also includes deploying the trained model to return one or more categories in response to a new query input.

A system and method for machine learning classification of user sentiment is disclosed. The method includes storing including a plurality of category information. The plurality of category information includes a set of domain-specific category information. The method further includes extracting a plurality of aspects from textual data. The method further includes generating a sentiment by a machine learning model. The method further includes receiving the plurality of aspects and the set of domain-specific category information. The method further includes generating a sentiment based on the plurality of aspects and the set of domain-specific category information.

The invention provides a recommended verbal skill generation method and device and electronic equipment. The method comprises the steps of obtaining historical session data, classifying the historical session data to obtain a plurality of category identifiers, the historical session data being historical sessions between recommendation personnel and a user, and the category identifiers representing categories corresponding to contents of the historical session data; a large language model is obtained, a recommendation verbal skill corresponding to each category identifier is generated by adopting the large language model, the large language model is obtained through training of multiple groups of historical session data, and the recommendation verbal skill is the verbal skill used by a recommender to recommend products to the user; and performing feature classification on the user according to the historical session data to obtain a feature category, matching a recommended verbal skill corresponding to the feature category, and performing recommendation to the user by adopting the recommended verbal skill. Through the method and the device, the problem that the recommendation verbal skill cannot be adjusted according to the characteristics of the user when the product is recommended to the client in the prior art is solved.

Systems, apparatuses, and methods for analyzing log data are described. A processing unit ingests data blocks multiple data sources associated with a network-connected device. Each data block is associated with contextual meta tags identified from the ingested data. Further, one or more entities associated with the network-connected device are identified and for each entity a taxonomy is created. The taxonomy comprises a plurality of categories, each category comprising at least one contextual meta tag. Dashboards for presentation of processed log data are generated based at least in part on the taxonomy.

To provide an idea generation assisting method.SOLUTION: An idea generation assisting method is executed on a system comprising: a generative AI server to execute natural language processing using a large language model; and a terminal connected to the generative AI server through a network. The method comprises: a step of the terminal sending an instruction to generate elements of two or more categories for a prescribed commodity to the generative AI server; a step of the generation AI server generating elements of two or more categories for the prescribed commodity, and sending the elements of the two or more categories to the terminal; a step of the terminal sending an instruction to generate a concept of the commodity by combining the elements of the two or more categories to the generation AI server; and a step of the generation AI server generating a concept of the commodity by combining the elements of two or more categories and sending the concept of the commodity generated by combining of the two or more kinds of elements to the terminal.SELECTED DRAWING: Figure 3

A method can be implemented via execution of computing instructions configured to run at a processor and stored at a non-transitory computer-readable media. The method can include: determining one or more virtual categories associated with a search query by a user. The method can also include: classifying one or more search results for the search query into one or more category groups based on the one or more virtual categories. The method can further include: generating a respective user interface portion for each group of the one or more category groups. The method can additionally include: generating a user interface comprising the respective user interface portion for each group of the one or more category groups. The method can also include: transmitting, via a computer network, instructions to display the user interface on a user device for the user. Other embodiments are disclosed.

The disclosure presents systems and methods for hierarchical classification of input data across a plurality of categories. A machine learning model processes various data formats, starting with dimensional reduction using tokenization techniques, such as Bert-tiny tokenization, to create model-readable representations. The system predicts super-categories, sub-categories, and granular categories through selective activation of sub-layers tied to identified super-categories, optimizing computational efficiency. Label smoothing during training mitigates overconfidence in predictions, while softmax normalization refines inference outputs. Synthetic data generation using Large Language Models (LLMs) supplements training datasets, and an automated data labeling pipeline efficiently generates hierarchical labels. Modifications to the model, such as stop word removal and file size limitations, further reduce latency. Inference analyzes logits to predict hierarchical paths, providing detailed classifications with clear outputs. The method is adaptable for multimodal formats, ensuring scalable and accurate predictions across diverse data types while minimizing computational costs and improving reliability.

The invention provides an e-commerce product recommendation method and system based on big data analysis. The method comprises the following steps: determining to-be-analyzed user data of a target user, and mining and determining category data of various categories for the user data; based on the category data of the multiple categories, performing cross-category indexing from the level of the representation information of the multiple categories to obtain respective multi-category index portrait elements of the category data of the multiple categories; determining a plurality of to-be-determined portrait elements for the user data based on the respective multi-category index portrait elements of the category data of the plurality of categories; and performing portrait element reasoning based on the plurality of undetermined portrait elements and the category data of the plurality of categories to obtain user portrait elements for the user data, and further generating a target user portrait. And finally, realizing personalized product recommendation for the target user according to the user portrait. According to the method, the accuracy of the recommendation system is effectively improved by utilizing a big data analysis technology.

Inline Multimodal Data Loss Protection (DLP) includes training one or more machine learning models for classifying input data into categories of a plurality of categories; performing one or more modifications to the one or more machine learning models, wherein the one or more modifications reduce latency associated with the one or more machine learning models; receiving an input comprising data in any of a plurality of formats; processing the input to classify the input into a category of a plurality of categories; and providing an indication of the category of the plurality of categories. Advantageously, by performing the various modifications to the one or more models, the systems can accurately classify data inline with minimal latency.

Aspects of the present disclosure relate to hierarchical content organization techniques, which may thus enable improved content indexing and / or retrieval, among other benefits. In examples, a content platform maintains a content hierarchy, where, for example, each category within the content hierarchy has an associated set of categories / subcategories. It will be appreciated that any number of hierarchical levels may be used. Accordingly, a post or other content item may be associated with one or more categories / subcategories (e.g., one or more nodes) of the content hierarchy, thereby facilitating subsequent retrieval of the content (e.g., responsive to a user query and / or to provide to a search platform). Thus, in contrast to discussion boards and other content management systems, the disclosed aspects ensure content is organized when created, thereby improving later retrieval and / or any of a variety of subsequent processing, among other benefits.

The invention discloses an overstocked material automatic inventory method based on multi-element collaborative cause tracing, relates to the technical field of material management, and solves the problems that a standardized multi-factor quantitative model is not established for evaluation of market demands during external docking, the matching logic of single-category and multi-category materials is not clear, and the market demands are not evaluated. According to the method, multi-dimensional identification is carried out by combining material attributes and dynamic states, misjudgment of a single warehouse age standard is avoided, the accuracy of accumulated material identification is improved, and the accuracy of the accumulated material identification is improved by quantitatively comparing the accumulated remaining amount with a preset value and combining accurate parameter matching of required materials and defining an internal reuse boundary, so that the accuracy of the accumulated material identification is improved. The internal consumption rate of reusable materials is improved, and a weighted quantization model of purchase quantity matching degree + date of delivery collaboration degree + cooperation stability is established for single-category materials; for multiple categories of materials, the combination matching logic of the same subject demand combination score is adopted, the external demand matching precision is improved, and the disposal period is shortened.

A method using a computing system is described that classifies each feedback text from a plurality of feedback texts into one or more categories. The method dynamically generates, using the plurality of feedback texts, a set of feedback text issues. The set of feedback text issues includes one or more issues associated with each feedback text from the plurality of feedback texts. The method dynamically generates, using the set of feedback text issues, one or more themes associated with the plurality of feedback texts. Each of the one or more themes is associated with a respective subset of feedback text issues from the set of feedback text issues. The method outputs a graphical user interface that includes one or more from the group consisting of at least one feedback text issue from the set of feedback text issues, and at least on theme from the one or more themes.

This application discloses a display method, apparatus, electronic device, and computer-readable medium. The method includes: when a client is displaying a live video page and a first live video is displayed on the live video page, after the client receives a first operation triggered on the live video page, displaying an object aggregation interface on the live video page, and displaying a first candidate object corresponding to first category description information on the object aggregation interface, the first candidate object being used to describe the first candidate live video; then, after the client receives a trigger operation on a first control on the object aggregation interface, displaying at least one second category description information on the object aggregation interface, so that the user can view live videos under multiple categories through the object aggregation interface, thereby better meeting the user's live video viewing needs and effectively improving the user's live video viewing experience.

The present disclosure generally relates to managing accessories. In some examples, a device displays a user interface having a plurality of category options and, in response to detecting selection of a category option, a device displays accessory controls and sub-category options.

Systems and methods for providing information responsive to a user search are provided. Such a method includes receiving a search query from a client device and generating a plurality of categories associated with the search query using a first artificial intelligence (AI) model. The method also includes selecting a plurality of sponsored content options that are responsive to the search query and, for each category, generating respective freeform descriptive text for the category using a second AI model. The method also includes causing the client device to present a user interface that includes (i) the respective freeform descriptive text for each category and (ii) links to third party information resources associated with the selected plurality of sponsored content options. An arrangement of the links within the user interface indicates which of the plurality of sponsored content options correspond to which of the plurality of categories.

The invention relates to the technical field of model lightweight, in particular to a construction full-process simulation method for construction of a green assembly type comprehensive pipe gallery, and the method comprises the steps: dividing all components in a pipe gallery model into a plurality of category clusters; acquiring a central component of each category cluster; acquiring a vertex important index of each vertex according to the density and size of the triangular surface at each vertex; according to the average level of the vertex important indexes of the two vertexes of each edge, the triangular face included angle of each edge and the edge degree, the folding delay degree of each edge is obtained, then the folding edges in the center components of all the category clusters are obtained, and then the folding edges of all the center components are folded; and obtaining the simplification rate of the center member of each category cluster according to the ratio of the number of the triangular facets of the center member of each category cluster after folding to the number of the triangular facets before folding, and then carrying out lightweight processing on all the members in each category cluster. According to the method, the model simplification effect is improved by adaptively calculating the simplification rate of the component in each category cluster.