15 results about "Multi dimensional search" patented technology

Systems and methods are described for a connected AI agent for managed multidimensional search based on an electronic message and management policies. A messaging application at a client device can send a new electronic message, such as an email, to a connector service. An attachment can be ingested and stored in a vector database. Then one or more artificial intelligence (“AI”) agents can be selected for responding to the body of the email, such as a query in the body. The responses can be formatted and sent to multiple parties, such as a sending user of the electronic message and a recipient that was copied or also sent the new electronic message. The AI agents can use different AI models, prompts, and vector databases depending on user permissions. This allows for building up vector databases with relevant content items and answering user questions based on those vector databases.

In a method and system for performing a multi-dimensional search of video content, a displayed three-dimensional interactive grid may include a plurality of cells populated with video content in the form of named chapters and subchapters associated with each chapter, forming headers of the cells in a two-dimensional multiple column by multiple row arrangement. The cells may include containers of video content associated with each subchapter, representing the third dimension of the grid, with each container including one or more nodes of video snippets. A multi-dimensional search of the video content may be enabled to select a node and / or container. Selection may initiate a hierarchical search to display at least one or more new containers related to the selected node or container that are accessible in the presently displayed three dimensional interactive grid or in one or more different grids related to the video content of the selected node.

The invention provides a historical character activation computing power resource scheduling method and system based on an improved artificial bee colony algorithm. The method comprises the following steps: constructing a task set and a resource set according to a task type needing to be executed in a historical figure activation process and a computing power resource capability available for scheduling; randomly initializing a nectar source set; wherein each nectar source in the nectar source set represents a task scheduling scheme; selecting a nectar source from the nectar source set; 4, for the current nectar source, updating the nectar source by adopting a multi-dimensional search strategy to obtain a new nectar source; the multi-dimensional search strategy comprises the following steps: selecting an excavable dimension from a current nectar source, and disturbing the selected dimension; the fitness of the new nectar source is calculated by adopting a pre-constructed resource scheduling cost function, if the fitness is improved, the new nectar source is adopted, and the disturbed dimension is marked as an excavable dimension; and otherwise, next iteration is carried out until a stop condition is met, and the current latest nectar source is output as the optimal task scheduling scheme.

Systems and methods are described for a connected AI agent for managed multidimensional search based on an electronic message and management policies. A messaging application at a client device can send a new electronic message, such as an email, to a connector service. An attachment can be ingested and stored in a vector database. Then one or more artificial intelligence (“AI”) agents can be selected for responding to the body of the email, such as a query in the body. The responses can be formatted and sent to multiple parties, such as a sending user of the electronic message and a recipient that was copied or also sent the new electronic message. The AI agents can use different AI models, prompts, and vector databases depending on user permissions. This allows for building up vector databases with relevant content items and answering user questions based on those vector databases.

The application discloses a radar detection and multi-dimensional parameter estimation method and system for high-maneuvering group targets, and the method comprises the following steps: constructing a radar echo signal model of the high-maneuvering group targets; obtaining a pulse compression echo signal of the high-maneuvering group targets by using a pulse compression technology; performing signal energy coherent accumulation through second-order phase compensation, slow-time dimension non-uniform fast Fourier transform and distance dimension inverse fast Fourier transform, so as to realize group target detection and distance-velocity-acceleration joint coarse estimation; constructing an over-complete dictionary according to the multi-dimensional parameter coarse estimation result, and performing sparse representation on the pulse compression signal of the high-maneuvering group targets; and adopting a sparse Bayesian learning method to perform sparse reconstruction on distance-velocity-acceleration estimation space of each target signal in the group, so as to obtain fine estimation results of multi-dimensional parameters of each target. The application can quickly detect the high-maneuvering group targets without multi-dimensional search, realizes coarse-to-fine estimation of multi-dimensional parameters of each target, and takes into account estimation accuracy and operation efficiency.

The invention discloses a radar detection and multi-dimensional parameter estimation method and system for a high-maneuverability group target. The method comprises the following steps: constructing a radar echo signal model of the high-maneuverability group target; utilizing a pulse compression technology to obtain a pulse pressure echo signal of the high-maneuverability group target; signal energy coherent accumulation is carried out through second-order phase compensation, slow time dimension non-uniform fast Fourier transform and distance dimension inverse fast Fourier transform, and group target detection and distance-speed-acceleration combined coarse estimation are achieved; constructing an over-complete dictionary according to a multi-dimensional parameter coarse estimation result, and performing sparse representation on the pulse pressure signal of the high-maneuverability group target; and performing sparse reconstruction on each target signal in the cluster in a distance-speed-acceleration estimation space by adopting a sparse Bayesian learning method to obtain a multi-dimensional parameter refined estimation result of each target. According to the method, the high-maneuverability group target can be rapidly detected without multi-dimensional search, coarse-to-fine estimation of multi-dimensional parameters of each target is realized, and both estimation precision and operation efficiency are considered.

Systems and methods are described for a connected AI agent for managed multidimensional search based on an electronic message and management policies. A messaging application at a client device can send a new electronic message, such as an email, to a connector service. An attachment can be ingested and stored in a vector database. Then one or more artificial intelligence (“AI”) agents can be selected for responding to the body of the email, such as a query in the body. The responses can be formatted and sent to multiple parties, such as a sending user of the electronic message and a recipient that was copied or also sent the new electronic message. The AI agents can use different AI models, prompts, and vector databases depending on user permissions. This allows for building up vector databases with relevant content items and answering user questions based on those vector databases.

Systems and methods are described for a managed multidimensional search based on an application query and management policies. The application can receive a pipeline endpoint. The query can be sent to the pipeline endpoint. The pipeline can vectorize the query for comparison against a vector database of an identified dataset. The closest vectors can be converted back to content chunks. The system can generate prompts related to the content chunks and send those prompts to an AI model. The AI model can then output a response that includes the most relevant content, citations, and hyperlinks. These can be displayed in the application.

An embodiment includes detecting a search request of a multi-dimensional search space by a system; responsive to detecting the search request, sampling representative parameters in the multi-dimensional search space based on a sampling threshold. The embodiment includes determining a parameter range of the representative parameters in the multi- dimensional search space. The embodiment also includes transforming a part of the multi-dimensional search space based on a statistical guarantee and the parameter range, where the part of the multi-dimensional search space is a refined space for a solution of the search request.

Systems and methods are described for a messaging agent for managed multidimensional search based on an electronic message and management policies. A messaging application at a client device can send a new electronic message, such as an email, to a connector service. An attachment can be ingested and stored in a vector database. Then one or more artificial intelligence (“AI”) agents can be selected for responding to the body of the email, such as a query in the body. The responses can be formatted and sent to multiple users, such as a sending user of the electronic message and a recipient user that was copied or also sent the new electronic message. The AI agents can use different AI models, prompts, and vector databases depending on user permissions. This allows for building up vector databases with relevant content items and answering user questions based on those vector databases.

Systems and methods are described for a messaging agent for managed multidimensional search based on an electronic message and management policies. A messaging application at a client device can send a new electronic message, such as an email, to a connector service. An attachment can be ingested and stored in a vector database. Then one or more artificial intelligence (“AI”) agents can be selected for responding to the body of the email, such as a query in the body. The responses can be formatted and sent to multiple users, such as a sending user of the electronic message and a recipient user that was copied or also sent the new electronic message. The AI agents can use different AI models, prompts, and vector databases depending on user permissions. This allows for building up vector databases with relevant content items and answering user questions based on those vector databases.

Techniques for detecting impactful performance anomalies in storage systems. The techniques include obtaining, for each performance metric of a storage system's workload, a training set of series diffs based on a threshold. Each diff represents a difference between an observed value from an observed set of time series values for the performance metric and a normalized value from a corresponding set of normalized time series values. The techniques include applying the training set of series diffs for each performance metric to an unsupervised anomaly detection algorithm and running the algorithm to identify potentially impactful anomalies in a multi-dimensional search space. The techniques include identifying impactful anomalies from among the potentially impactful anomalies that exceed an anomaly score. In this way, impactful anomalies having a causal effect on multiple performance metrics of the storage system's workload can be identified in a manner less complex and less costly than prior multivariate approaches.

An embodiment includes detecting a search request of a multi-dimensional search space by a system; responsive to detecting the search request, sampling representative parameters in the multi-dimensional search space based on a sampling threshold. The embodiment includes determining a parameter range of the representative parameters in the multi-dimensional search space. The embodiment also includes transforming a part of the multi-dimensional search space based on a statistical guarantee and the parameter range, where the part of the multi-dimensional search space is a refined space for a solution of the search request.

A method for operating a search engine may include determining a multi-dimensional search parameter space comprising a set of possible weight values for each of a plurality of search parameters and dividing the search parameter space into a grid of evenly-spaced values that is a subset of the set of possible values. The method may further include defining one or more initial populations of search parameter weight values, wherein each population of search parameter weight values comprises a plurality of initial individuals, wherein each initial individual comprises a respective one of the evenly-spaced values for each of the search parameters. The method may further include executing one or more genetic algorithms based on the one or more initial populations to select a final set of search parameter weight values, and returning results of a user search in the search engine according to the final set of search parameter weight values.