23 results about "Category specific" patented technology

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 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.

An object of a system according to an embodiment is to generate video content in accordance with a specific category by effectively utilizing in-house information.SOLUTION: A system includes an information collection unit, an analysis unit, and a generation unit. The information collection unit collects necessary data from in-house information sources. The analysis unit analyzes the data collected by the information collection unit. The generation unit generates moving image content according to the specific category on the basis of the data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

A method, computer program product, and computing system for identifying a portion of a transcript associated with a particular category. A segment from the transcript is aligned with a segment from a model-generated summary of the transcript. A portion of the transcript omitted from the model-generated summary is identified based upon, at least in part, the identified portion of the transcript associated with the particular category and the aligned segments from the transcript and the model-generated summary.

A system is provided.SOLUTION: This system is provided with a means for analyzing the contents of an inquiry and classifying it into a designated category, a means for generating a reply sentence based on the analyzed result, a means for adding an attached file to the reply sentence and a means for transmitting a mail.SELECTED DRAWING: Figure 1

A system is provided.SOLUTION: A system comprising: means for analyzing article information in an electronic device using a generative artificial intelligence model to determine whether it is likely to contain spoiler information; means for hiding spoiler information from display to a user based on the determination; and means for enabling customization of the extent of spoiler information by filtering certain categories and content based on user settings.SELECTED DRAWING: Figure 1

Based on receiving data defining a new data item for a construction project corresponding to a particular category of data items, a computing system (1) automatically: (i) predicts that a change event for the construction project is needed by inputting the new data item into a first machine learning model trained to predict a need for a change event from data items corresponding to certain categories of data items, including the particular category of the new data item, (ii) determines initial recommended data for the predicted change event, and (iii) determines additional data for the predicted change event corresponding to a particular class of additional data by inputting the initial recommended data for the predicted change event into a second machine learning model trained to predict one or more classes of additional data for a change event, and (2) automatically create a data item representing the predicted change event.

A video processing apparatus includes a video analyzer that analyzes video data captured by a surveillance camera, detects an event belonging to a specific category, and outputs a detection result, a display controller that displays, together with a video of the video data, a category setting screen for setting a category of an event included in the video, and a learning data accumulator that accumulates, as learning data together with the video data, category information set in accordance with an operation by an operator to the category setting screen. The video analyzer performs learning processing by using the learning data accumulated in the learning data accumulator.

Methods and systems are provided for managing private data access for resolving a query. The system, e.g., accesses private data generated by a plurality of distinct applications associated with a user and inputs the data into a data classification model. The model classifies the private data into specific data categories based on data type. The classified data is then aggregated into a dataspace, which serves as a unified repository for private data corresponding to a specific category collected from the distinct applications. A unique access configuration is associated with the dataspace to define a set of authorized connection types permitted to access the repository. In response to a query requiring the private data, access to the dataspace is provided if the requesting entity matches the authorized connection types defined in the unique access configuration.

Systems and methods for using large language models (LLMs) to analyze category specific workflows for generating an optimized workflow that can be used in live conversations to provide a response are described. The methods include categorizing a plurality of projects into specific categories. The actions performed by agents for the plurality of projects are translated into workflows. The workflows are analyzed based on optimization factors and clustering options, such as including redundancies in workflow steps, using alternative solutions to a workflow step, determining whether any escalation performed is justified and if so, adopting escalation related steps. The workflows are consolidated and optimized into an optimized workflow that is tested and verified, and then used in a live conversation.

The present disclosure provides systems and methods for hierarchical multi-level model distillation that create specialized artificial intelligence models from large language models. A hierarchy for generating specialized generative models from a large language model is determined, including models associated with different categories and complexity levels for tasks with varying computational requirements. The specialized models are trained using the large language model based on specific categories to provide responses to category-related requests. Each model is pruned based on complexity levels to enable responses to requests of varying complexity. The models are quantized from common precision factors to corresponding precision factors associated with complexity levels. Deployment locations are determined for each pruned model based on complexity levels. Specialized models are transmitted to corresponding locations for deployment across diverse computational environments.

A video processing apparatus includes a video analyzer that analyzes video data captured by a surveillance camera, detects an event belonging to a specific category, and outputs a detection result, a display controller that displays, together with a video of the video data, a category setting screen for setting a category of an event included in the video, and a learning data accumulator that accumulates, as learning data together with the video data, category information set in accordance with an operation by an operator to the category setting screen. The video analyzer performs learning processing by using the learning data accumulated in the learning data accumulator.

This disclosure describes techniques for automated data extraction, validation, and routing based on unstructured text data. In some cases, the techniques described herein include receiving text data, segmenting the text data into multiple segments, assigning each segment to a category, generating a prompt for each segment based on the segment's category, extracting field values from each segment using the generated prompt, validating or rejecting the extracted field values based on category-specific validation rules, and routing the validated field values to category-specific target databases and / or reviewer platforms based on the validation results.

Information messages can be used to signal changes to graphics rendering information (GRI) with coded rendered graphics / video data in a bitstream. Syntax elements can be added to an information message to represent updated parameters in GRI categories such as rendering engine parameters, projection matrix, world to cam matrix, and / or depth parameters. Category presence indicators can be set to signal when information messages included syntax elements representing parameters in a particular GRI category. If a particular category presence indicator is set, a decoder can parse from the information message the syntax elements representing parameters in the associated GRI category. Alternatively, if a particular category presence indicator is not set, the information message will not include syntax elements representing parameters in the associated GRI category and the decoder can use previously-received and / or default parameter values when generating target graphics from the rendered graphics data.

The invention discloses a multi-category feature selection method for independent inspection of category specific conditions. The method comprises the following steps: step 1, data preprocessing and category division; 2, class specific condition independence testing is carried out; step 3, carrying out class specific Markov Blanket discovery, and carrying out class specific Markov Blanket discovery; step 4, carrying out Markov Blanket structure optimization, and carrying out Markov Blanket structure optimization; step 5, carrying out cross integration on the class specific features; 6, training and verifying the model; according to the method, a class-specific condition independence test method is adopted, a sample subset is independently constructed for each class, and the condition dependence intensity is calculated, so that statistical deviation caused by inter-class data distribution difference is effectively avoided. By accurately capturing a correlation structure only existing in a specific category, a condition independence test result is more fit with a real structure of data, and the resolution is remarkably improved.

A video processing apparatus includes a video analyzer that analyzes video data captured by a surveillance camera, detects an event belonging to a specific category, and outputs a detection result, a display controller that displays, together with a video of the video data, a category setting screen for setting a category of an event included in the video, and a learning data accumulator that accumulates, as learning data together with the video data, category information set in accordance with an operation by an operator to the category setting screen. The video analyzer performs learning processing by using the learning data accumulated in the learning data accumulator.

A multi-class classifier (MCC) is trained using annotated data. The annotated data comprises instances of sample data and associated label data. Creation of the annotated data and subsequent active learning by the MCC uses resources. A target-aware active learning system selects sample data for addition to an annotation queue based on factors such as current accuracy of a particular class determination and priority of that class. As each instance in the sample data in the annotation queue is annotated and used for subsequent training, accuracy of particular classes is improved until a specified accuracy for that class is attained. By being selective in the ordering of instances in the annotation queue, overall resource usage and corresponding costs associated with creating annotated data and training is reduced. Overall accuracy for all classes is improved using a smaller overall set of annotated data compared to naïve approaches.

To provide a gaming machine that can improve the enjoyment of the game. [Solution] A gaming machine comprising: a win / fail lottery means that performs a win / fail lottery triggered by the fulfillment of predetermined conditions; an effect execution means that performs a variation effect from the start of variation of decorative symbols for notifying the win / fail lottery result by the win / fail lottery means until it stops in a manner corresponding to the win / fail lottery result; and a customization function that allows the player to arbitrarily change the probability of occurrence of some effects that may occur during the variation effect, wherein the customization function is provided as a specific customization for which multiple types of target effects are subject to customization, and the customization image displayed when using the specific customization indicates that specific attribute effects belonging to a specific category are subject to customization, and effect α, which is one of the multiple types of target effects, is a non-specific attribute effect that does not belong to the specific category.

A video processing apparatus includes a video analyzer that analyzes video data captured by a surveillance camera, detects an event belonging to a specific category, and outputs a detection result, a display controller that displays, together with a video of the video data, a category setting screen for setting a category of an event included in the video, and a learning data accumulator that accumulates, as learning data together with the video data, category information set in accordance with an operation by an operator to the category setting screen. The video analyzer performs learning processing by using the learning data accumulated in the learning data accumulator.

Corresponding to individual ones of a plurality of categories of optimization tasks of a service, respective generative artificial intelligence models (GAIMs) are configured. A prompt which instructs a first GAIM to identify a candidate optimization task of a particular category is presented to the first GAIM. The candidate optimization task, identified by the first GAIM, is then initiated using another GAIM.

This invention discloses a privileged learning classification method based on category awareness and interpretability guidance, belonging to the field of artificial intelligence and machine learning. The method includes: first, dividing the training data into a privileged set containing complete information and a non-privileged set containing only source domain features; for a specific category, extracting feature importance through interpretability analysis and sparsifying it to generate a category-specific weight vector; then, calculating the weighted similarity between non-privileged samples and similar samples in the privileged set based on this weight vector, retrieving and transferring the privileged features of the best-matching sample as reconstructed features, and dynamically assigning confidence coefficients negatively correlated with similarity; finally, fusing real and reconstructed privileged features to construct a joint optimization objective function with an adaptive penalty mechanism for confidence coefficients for model training. This invention effectively improves the classification accuracy and robustness of the model in scenarios where some privileged information is missing.

This invention proposes a display screen design editing and management method and system, relating to the field of display screen design editing and management technology. The method involves displaying requirement information, obtaining process interface display information, and then obtaining the corresponding interface group for the current stage; obtaining interface difference information for the corresponding interface group, determining the display status of the stage interface display information, obtaining stage feedback information, triggering stage feedback instructions, and providing stage feedback; classifying the stage feedback information to obtain stage category feedback information, adjusting the stage interface display information according to the corresponding category to obtain category adjustment information; replacing the preset display requirement interface information with modified interfaces based on the obtained modification requirement information, obtaining interface modification positioning information, modifying the stage display parameters for specific categories in specific stages, and finally obtaining the modified final interface display information. This invention improves the accuracy and efficiency of display screen design editing.

The invention provides a personalized customization report generation method and device, equipment, a storage medium and a program product, and can be applied to the technical field of financial science and technology. The method comprises the steps of obtaining a report generation request of a target object; according to the report generation request, providing a plurality of selectable check item components corresponding to the target preset service scene, the check item components being used for obtaining specific category data of the target object from an associated data source based on the basic identification information; receiving a selection operation for at least one of the plurality of inspection item components, and generating and executing an inspection task sequence based on the selected inspection item components and the basic identification information, the arrangement of the inspection task sequence being determined according to a data dependency relationship among the inspection item components; after the authorization of the target object on the inspection result data is obtained, obtaining the inspection result data; and generating a personalized customization report including the target object risk information at least based on the inspection result data.