9 results about "Visual query" patented technology

The application provides a kind of operation and maintenance scene visualization report generation method and system based on natural language question and answer, belongs to intelligent operation and maintenance technical field, method includes: receiving the natural language query input by user;Through the pre-training of large language model and the preset operation and maintenance terminology dictionary, the natural language query is parsed and entity is extracted, and the entity-field association table containing demand type is generated;According to demand type and semantic feature, the query type is judged to be data query or visual query;Based on the judgment result, generate structured query language sentence or visual instruction;Query is executed to the interface business database to obtain raw data;Raw data is processed and analyzed to obtain analysis result data;Call data feature adaptation algorithm to match chart type, generate and output visual report.The application realizes the full-link automation from natural language input to visual report output, reduces the operation and maintenance data interaction threshold, improves the operation and maintenance data processing efficiency and accuracy.

Systems and methods of obtaining data from a cellular network are disclosed. In some embodiments, visual query options are presented in a graphical user interface (GUI), wherein the visual query options define a query. Query option selections of the visual query options are received. A query template is generated in accordance with the query option selections. An audit of parameters is performed related with network elements of the cellular network in accordance with the query template. A report of the audit is transmitted to a user device. A visual representation of the report is presented in the GUI.

This invention belongs to the field of artificial intelligence and data visualization technology, and discloses a method for processing conversational visualization intelligent agents and the intelligent agent thereof. The method includes: performing structural expansion and semantic enhancement on the acquired raw data table to generate a unified data table summary containing field type mappings and data interpretations; combining the data table summary with dialogue history to perform relevance judgment, executability checks, and solution space uniqueness analysis on user questions, classifying the questions; based on the classification results, generating a Vega-Lite specification or generating a structured text interpretation or general response by constructing and filling in a visual query language sketch; executing and rendering the generated Vega-Lite specification, generating feedback based on execution errors or semantic deviations, and driving iterative correction. This invention improves the robustness and practicality of conversational visualization systems through the collaborative design of these four steps.

A system may include a content repository that stores multi-modal content comprising text, visual content, and / or audio content. The system may include a processor programmed to: access a prompt comprising a text query, a visual query comprising text that describes a visual to be found, and / or an audio query comprising text that describes audio to be found, execute a language model based on the prompt to identify content from the content repository, receive, from the language model, a request for a callback function that seeks additional information to satisfy the multi-modal query, execute the callback function to obtain the additional information and provide the additional information to the language model in response to the request for the callback function, re-execute the language model based on the multi-modal query and the additional information, obtain, from the language model, content responsive to the prompt based on the additional information.

This invention relates to the field of artificial intelligence technology, with applications in smart healthcare and finance. It discloses an image understanding method, apparatus, device, and medium. The method includes: acquiring an initial image and a question; decomposing the question into multiple fine-grained visual queries; constructing a relevance distribution of the initial image to the question using a visual-text alignment function; obtaining a visually relevant region map of the question; generating a perturbation image based on visual regions highly relevant to the question; generating an initial answer and a perturbation answer based on the initial image and question, and based on the perturbation image and question respectively; calculating the answer difference; obtaining visual utilization rate; when the visual utilization rate is lower than a preset threshold, calling at least two types of visual experts to process the image; superimposing the processing results onto the image to obtain an enhanced image; obtaining the enhanced visual utilization rate and the increase in visual utilization rate; selecting the visual expert with the largest increase in visual utilization rate as the optimal visual expert; and generating an answer based on the question and the enhanced image of the optimal visual expert.

An embodiment of the specification provides a processing method and device for email reading status, the method comprising: in the process of querying the email reading status of the sent email by the sender after sending the email, first, obtaining the access instruction of the email reading status of the sent email submitted by the sender, second, querying the reading status information of the sent email in the reading status record table based on the access instruction, wherein the tracking field contained in the signature picture link in the sent email is updated after the sent email is read, the reading status information is updated based on the tracking field, and finally, sending the queried reading status information to the sender to realize the visual query of the email reading status of the sent email by the sender.

This application discloses a method and electronic device for retrieval enhancement and knowledge base generation. The method includes: performing structured parsing and format reconstruction on an original knowledge document to convert it into a target document; rendering the target document into a target image; segmenting the target image into a sequence of image blocks, and visually encoding the image blocks using a visual encoding model to generate visual knowledge vectors based on visual encoding; constructing an index based on the visual knowledge vectors to form an external knowledge base for an artificial intelligence (AI) model, so that upon receiving query text, the query text is converted into an image using the same method as the target document and encoded into a visual query vector based on the visual encoding model; and the visual query vectors are used to retrieve matching visual knowledge vectors from the external knowledge base to enhance the AI ​​model's response content generation. This application embodiment can improve retrieval efficiency while ensuring semantic fidelity.

Provided are computer-implemented systems and methods for responding to visual queries using both a visual search engine and a vision language model (VLM). In particular, aspects of the present disclosure can improve the performance of a VLM at generating a response to a visual queries by supplementing the visual query with one or more annotations generated by or using the visual search engine.

The application discloses a low-code platform associated entity data access method and device. The method comprises the following steps: defining an entity association relationship based on entity association requirements and data model specifications; generating and binding a navigation column in combination with entity metadata; completing field selection and navigation configuration through the navigation column and query requirements to obtain a visual query configuration result; converting the visual query configuration result and the entity association relationship into a query definition; constructing a navigation path structure based on the query definition and executing the query by using a differentiated strategy to obtain a flat query result; and converting the flat result and the navigation column into a nested structure to generate structured data, and accessing associated entity data through the structured data. The application realizes low-code complex multi-table association query, guarantees type safety, avoids N+1 query problems, automatically outputs nested structured data, reduces the use threshold of non-technical users, and improves query efficiency and development convenience.