243 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.

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, 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.

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.

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.

Fine-tuning AI models is described. According to some aspects, one of a number of pre-trained AI models is selected based on the explicit input and the implicit input. In addition, one of a number of fine-tuning methods is selected. Also, a set of one or more of a plurality of categories is selected, where a categorized data set associated with an organization was classified into the categories using a classifier, and where the selected set of categories identify a selected subset of the categorized data set. A version of the selected subset is used to fine-tune the selected AI model using the selected fine-tuning method.

Techniques for global search of a security related data store using natural language processing are disclosed. In some embodiments, a system, a process, and / or a computer program product for global search of a security related data store using natural language processing includes receiving a query for a security related data store; translating the query using a natural language processing (NLP) classifier to extract one or more categories; performing another query of one or more tables in the security related data store based on the one or more categories; and returning results for the query based on the another query of the one or more tables in the security related data store based on the one or more categories.

For at least a selected class attribute of the multiple class attributes, one or more bias metrics are determined that estimate a degree to which a particular workflow (having a set of processing stages) is biased in association with the class attribute. Each user of a set of users is associated with a set of user data to be processed by the particular workflow. At least one of the set of processing stages includes executing a machine-learning model. It can be detected that a bias-mitigation option corresponding to a specific class attribute has been selected. For each of at least two of the set of processing stages: a de-biasing technique is selected; and the processing stage is modified by applying the de-biasing technique. A modified version of the particular workflow (which includes the modified processing stages) is applied to each of a set of input data sets.

Systems or methods for the selection of large language models (LLMs). A system receives a request from a service that hosts an application. The request is configured to be processed by an LLM to generate a response. The system applies a classification model to the request to determine the class of the request. The classification model is a language model trained to receive text and classify the text into a plurality of classes. The system selects an LLM from a plurality of candidate LLMs based in part on the determined class of the request and recommends the selected LLM to the application.

A program is provided for causing a computer providing a virtual space (VS) to function as: a category presenting unit (41) that presents, on a screen, one or more categories related to one or more items used by an object (O) in the virtual space (VS) in a manner whereby a category can be specified; a category specifying unit (42) that specifies a particular category from among the categories, based on a user input; an item presenting unit (43) that presents, on the screen, one or more items associated with the specified particular category in a manner whereby an item can be specified; an item specifying unit (44) that specifies a particular item from among the one or more items, based on a user input; and a variation presenting unit (45) that presents one or more variation items for the specified particular item on the screen in association with the particular item in a manner whereby a variation item can be specified. This program provides an information processing technique that enables variation items for a particular item to be specified in an efficient manner.

To provide an information processing program and an information processing device capable of improving efficiency of processing related to an inquiry.SOLUTION: An information processing program causes a computer to execute the steps of: receiving, from a user, an inquiry about at least one category among a plurality of categories associated with an ID of the user; specifying an answering entity in response to the inquiry based on content of the inquiry and information associated with the plurality of categories; and responding in accordance with providing means of an answer in response to the inquiry.SELECTED DRAWING: Figure 2

A student performance prediction method, apparatus, electronic device, and computer-readable storage medium, wherein the student performance prediction method includes: obtaining learning behavior data corresponding to different target behaviors among multiple behavior categories of a student within a preset time period; and aggregating the learning behavior data corresponding to the different target behaviors in a preset time unit, and performing feature fusion on the aggregated data separately for each behavior category to obtain a category feature set, determining the multiple category feature sets organized in chronological order as a category feature time series set, inputting the category feature time series set into a pre-trained feature reconstruction network to obtain a reconstructed time series set, and inputting the reconstructed time series set into a student performance prediction model to obtain a performance prediction result for the student. The above student performance prediction method can objectively and efficiently predict the student performance.

The present disclosure relates to systems, software, and computer-implemented methods for automatically identifying handwritten tips. An example method includes obtaining an image of a receipt, where the image includes a reference region in a pre-set format, a plurality of category identifiers, and at least one set of handwritten characters. The reference region and the plurality of category identifiers can be printed. The plurality of category identifiers can be located at pre-set positions relative to the reference region. The method further includes obtaining optical character recognition (OCR) information of the image and identifying the reference region from the OCR information based on the pre-set format of the reference region. The method further includes determining a plurality of amounts based on the OCR information and the reference region, where at least one of the plurality of amounts is associated with a tip category.

The invention discloses a transformer substation multi-source heterogeneous data processing method and system based on space layer superposition, and relates to the technical field of electric power data processing, and the method comprises the steps: classifying the multi-source heterogeneous data of a transformer substation based on a data structure type, and obtaining a plurality of category data sets; pre-processing the plurality of category data sets to obtain a plurality of pre-processed category data sets, and storing the pre-processed category data sets in different domains; establishing a unified model based on equipment common attributes of the transformer substation, and embedding the plurality of preprocessed category data sets into a bottom geographic distribution map based on the unified model; the bottom geographic distribution map comprises a plurality of space map layers, and each space map layer corresponds to one data type; and performing dynamic coupling on the plurality of space layers, and constructing a three-dimensional fusion model with a space-time correlation characteristic. According to the method, the technical problems of long time consumption and weak spatial dimension analysis capability of spatial correlation analysis of multi-source heterogeneous data in the prior art are solved.