49 results about "Query string" patented technology

Disclosed is a method of processing a query using a trained artificial neural network (ANN) implementing a large language model (LLM). A database of documents is maintained for use in processing queries, the database defining multiple repositories, each containing one or more documents. The documents in the database are processed to generate, for each repository, a vector store comprising vector embeddings encoding information obtained from documents of the repository. A user inputs a query string and a selection of one or more of the repositories to be used to process the query. A query embedding corresponding to the query string is generated and the vector stores corresponding to each selected repository are searched using the query embedding to identify one or more vectors that are relevant to the query. An LLM query is formulated to include the query string, a query context comprising information determined based on the identified relevant vectors and a predefined LLM prompt. The LLM is invoked with the LLM query as input whereby the LLM query is processed using the ANN to generate an LLM output. A query response is provided to the user based on query response data received from the LLM.

Provided are systems and methods to transform a specified source dataset into a fully defined data format that details every attribute value in all levels for each document or record in the source dataset. According to various embodiments, the systems and methods are configured to execute recursive analysis on a specified data source, build out a fully specified data format and associated meta-data, enable user adjustments to a fully schema, and trigger automatic generation of code to transform the source to the new destination format even in the presence of polymorphic data. The process to scan source data involves recursively evaluating every attribute value in all levels for each document or record in the source dataset to account for any polymorphic data. The scan determines each attribute's data type usage, datetime formats and can interrogate string data to identify objects within string data.

The invention discloses a construction method for replacing an iframe embedded page, which comprises the following steps of: replacing an original iframe component with a common component under a front-end framework, and mounting the common component on a DOM (Document Object Model) tree of a main page; obtaining an src link character string which is originally transmitted to the iframe component; analyzing the src link character string, extracting a query character string, and converting the query character string into a parameter object; transmitting the parameter object as an attribute to a common component; and acquiring data in the parameter object by monitoring the change of the attribute in the common component, and driving business logic execution by using the data. Under the condition that routing configuration is not changed, complex control logic is reduced, and the page rendering speed is greatly increased.

A multimodal content management system having a block-based data structure can include an artificial intelligence (AI)-based code unit generator that can generate code units executable against the block-based data structure to provide information requested by users. For example, the code units can be generated in response to natural language prompts received via a question and answer Q&A assistant engine. A neural network can be trained on block types, block dependencies, block content values, block content types, and / or block format. The neural network can receive a set of tokens generated based on a natural language prompt and generate one or more query strings to be included in a particular code unit. The tokens can be indicative of block properties, content, or other items in the block-based data structure. The code unit can be structured to execute more than one query against the block-based data structure such that a particular result set can include content items of different modalities.

A method of operating a health tracking system is disclosed herein. The health tracking system includes a processor and a database configured to store a plurality of data records, each of the plurality of data records comprising at least a descriptive string and nutritional data regarding a respective consumable item. The method includes receiving, with the processor, a query string. The method further includes searching, with the processor, based on the query string, the database to retrieve a list of data records in the plurality of data records from the database. The method also includes determining, with the processor, a ranked list of data records by ranking the list of data records using a machine learning-based ranking model, based on the descriptive string and the nutritional data of data records in the list of data records. The method then includes transmitting the ranked list of data records to an electronic device of a user of the health tracking system, the ranked list of data records being presented on the electronic device of the user.

Systems, computer program products, and methods are described herein for predictive generation of electronic query data. The present invention is configured to electronically receive a query string associated with an administrative body, wherein the query string corresponds to a response field, and wherein the response field is configured to receive a response string; retrieve, from a database associated with an entity, information associated with the administrative body; determine an administrative record associated with the administrative body, wherein the administrative record comprises one or more prior response strings; generate one or more customized autofill options for the response string based on at least the information associated with the administrative body and the administrative record associated with the administrative body; and transmit control signals configured to cause the endpoint device of the user to display, on a graphical user interface, the one or more customized autofill options to the user.

The invention relates to a method for verifying real-time communication on a communication link (21) of an industrial communication network (20), comprising: capturing communication on the communication link (21) at at least one location of the communication network (20) for a predetermined time using a network test access point (2) and providing an associated recording (10), feeding the recording (10) as input data to a trained large language model (4), wherein the language model (4) has, according to training, knowledge of at least one protocol structure of a protocol used on the communication link (21) and / or has access to such knowledge, and feeding at least one query string (11) to the trained large language model (4).wherein the query string (4) includes at least one command for checking a technical state of the communication link (21) and / or the industrial communication network (20) based on the recording (10), receiving and providing an output string (12) of the trained large language model (4) representing and / or describing the technical state, generating a verification signal (13) based on the output string (12), and outputting the generated verification signal (13). The invention further relates to a corresponding device (1).

An execution unit of the present invention provides a language model with a first query character string including the name of a function and instructing the model to respond according to the format of the function related to the name. The language model receives an input of a query character string and outputs a response character string to follow the query character string. The language model is adjusted so as to output, if a pre-defined function call condition is satisfied, a response character string in a predetermined format including arguments to be passed to the function. This response acquisition unit analyzes, according to the format, the first response character string output from the language model in response to the first query character string, and extracts values therefrom.

An electronic device and an application search method thereof are provided. The method includes the following steps. A plurality of semantic feature vectors of each of a plurality of application is recorded. The semantic feature vectors of each of the applications at least include a first semantic feature vector associated with one of application categories and a second semantic feature vector associated with one of expanded related words. A query string is obtained via the input device. A query semantic feature vector of the query string is generated by using a natural language model. A plurality of semantic similarities between the query semantic feature vector and the semantic feature vectors of each application are determined. A target application corresponding to the query string is determined from the applications according to the semantic similarities corresponding to the semantic feature vectors of each application.

Provided are systems and methods to transform a specified source dataset into a fully defined data format that details every attribute value in all levels for each document or record in the source dataset. According to various embodiments, the systems and methods are configured to execute recursive analysis on a specified data source, build out a fully specified data format and associated meta-data, enable user adjustments to a fully schema, and trigger automatic generation of code to transform the source to the new destination format even in the presence of polymorphic data. The process to scan source data involves recursively evaluating every attribute value in all levels for each document or record in the source dataset to account for any polymorphic data. The scan determines each attribute's data type usage, datetime formats and can interrogate string data to identify objects within string data.

The invention discloses a tag identification method, and relates to the technical field of RFID tag identification. The method comprises the following steps: acquiring a to-be-identified tag set; creating and initializing a query queue; the query queue comprises a query character string; selecting a first query character string in the query queue as a first query character string; popping the first query character string out of a query queue; according to the first query character string and a to-be-identified tag set, matching by using a first rule to obtain a first tag set; judging whether a first termination condition is met or not according to the first label set and the first query character string; if the first termination condition is met, the tags in the first tag set are identified tags; matching by utilizing a second rule according to the first query character string and a first label set to obtain a label conflict feature; generating one or more query character strings according to the first query character string and the label conflict feature, and pressing the generated query character strings into a query queue; and repeatedly executing until the query queue is empty, and taking all the identified tags as final identification tags. The method is high in label identification efficiency.

The application relates to big data, and provides a query method, a query device, equipment and a storage medium, which comprises the following steps: acquiring a query string, wherein the query string comprises a self-defined query expression; parsing the query expression, splitting the query expression according to a connection symbol to obtain corresponding basic query units comprising condition statements and field statements, acquiring corresponding configuration information, forming sub-query units by using the basic query units with the same configuration information according to the sequence of the basic query units, wherein the configuration information is used for determining a target query mode corresponding to the sub-query units, and obtaining corresponding sub-query results according to the target query mode; when the query result is a combined type, combining the sub-query results to obtain a target query result; when the query result is a quadratic type, a second query result corresponding to a second type of sub-query unit exists, a query database corresponding to the first type of sub-query unit is updated through the second query result, and the target query result is obtained by searching according to the updated query database.

In one aspect, semantic-based font searching may be performed in vector space using machine learning. Text and images may therefore be put into a same embedded vector space so that font images that are close, in the vector space, to a word from a font search query may be returned as search results. Thus, an end-user can enter a natural language search query string for the system to return fonts that semantically match the user's search string.

A system and method for augmented semantic search, including: a query execution service including functionality to receive a search request including a query string; a query classification service including functionality to execute a first large language model using a first prompt to generate a classification object representing classification of the query string in a structured classification format; a filter extraction service including functionality to execute a second large language model using a second prompt to generate a filter object including a set of filters inferred for the query string in the structured filter format; and a recaller service including functionality to: use the filter object to constrain search space of a vector store and execute a vector similarity operation on a query vector to generate a match set of embeddings; and provide a result set based on the match set of embeddings.

The invention provides an intelligent report generation method and system based on multi-source data query analysis, and the method comprises the following steps: extracting a user intention through semantic analysis based on a query character string which is received by a pre-training language model and is input by a user in a natural language form, and generating an intention dictionary; based on the cosine similarity between the intention embedding vector corresponding to the intention dictionary and each data source description vector, selecting a plurality of data sources of which the cosine similarity is greater than a first set threshold value from a database; on the basis of the multiple query statements, automatically obtaining corresponding original result data sets from all the data sources; and based on each original result data set, generating an analysis report with a convertible format through a pre-training language model and a time sequence analysis model. According to the intelligent report generation method and system based on multi-source data query analysis, the problems that generation of a data analysis report highly depends on manpower, the data source matching capacity is poor, efficiency is low, and intelligence is insufficient can be solved.

Methods, system, and non-transitory processor-readable storage medium for a text-based vector retrieval system are provided herein. An example method includes generating, by a text-based vector retrieval system, a set of topics Tx for a document set DX, generating for each document DN a set of topics TD, comparing the set of topics TD against the set of topics TX to generate a filtered set of topics TDFinal, comparing the topics TDFinal against final topics of all other documents to generate a set DNMatching, encoding each document from the document set into a vector database with corresponding DNMatching and TDFinal as metadata, grouping vectors into N search clusters, retrieving N topics from a query string to generate TQuery topics, comparing the TQuery topics against topics in each cluster to identify matching clusters having R matching topics, and calculating similarity scores for documents in the matching clusters.

An electronic device and an application search method thereof are provided. The method includes the following steps. A plurality of semantic feature vectors of each of a plurality of application is recorded. The semantic feature vectors of each of the applications at least include a first semantic feature vector associated with one of application categories and a second semantic feature vector associated with one of expanded related words. A query string is obtained via the input device. A query semantic feature vector of the query string is generated by using a natural language model. A plurality of semantic similarities between the query semantic feature vector and the semantic feature vectors of each application are determined. A target application corresponding to the query string is determined from the applications according to the semantic similarities corresponding to the semantic feature vectors of each application.

Embodiments provide systems, methods, and computer storage media for determining string similarity and pattern matching in strings that arrive in a stream. A stream representing string of characters is received and used to compute mapping values that are compared to a mapping value of a query string to identify a match between strings in the stream of characters and the query string. The stream of characters is searched in a single sequential pass to detect a match or the longest matching substring with a query string. An identified match or absence of a match is provided.

A system and method for augmented semantic search, including: a vector store including a set of embeddings, each representing a structured data representation of a media perspective of a media item; a query execution service including functionality to receive a search request including a query string from a client application; a query classification service including functionality to execute a first machine learning model to generate a classification object in a structured classification format; a filter extraction service including functionality to execute a second machine learning model to generate a filter object including a set of filters in a structured filter format; a recaller service including functionality to: execute an encoder model on the input query and execute a vector similarity operation on the query embedding to generate a result set; and a re-ranking service including functionality to: execute a large language model to re-rank the result set.

The invention provides a secure data writing method for an NAND flash memory, and the method comprises the steps: receiving a writing request of to-be-stored data, and analyzing the writing request to obtain the security level of the to-be-stored data and the data length of the to-be-stored data; splicing the data length of the to-be-stored data and the security level of the to-be-stored data according to a preset splicing sequence to obtain a query character string of the to-be-stored data; the query character string is processed according to a preset Bloom filter, whether an available secure storage space of the to-be-stored data exists in the NAND flash memory or not is determined, and the Bloom filter is constructed according to the available secure storage space; if the available secure storage space of the to-be-stored data exists in the NAND flash memory, distributing a target storage space for the to-be-stored data in the available secure storage space; and writing the to-be-stored data into the target storage space. The method solves the problems that an existing data writing method cannot reasonably manage and utilize an NAND flash memory, and cannot guarantee the safety of data.

A system and method for iteratively populating, querying, and ranking governmental data across heterogeneous databases may include iteratively populating at least one database with governmental data elements by obtaining source data from governmental data sources at predefined times, transforming the source data using at least one database schema, and populating the database, which may be a vector, relational, or graph database. A user query is received, comprising a query string, and processed using a routing agent to determine the data type or semantic scope and select at least one agent from a plurality of agents, including structured data agents, unstructured data agents, graph data agents, semantic search agents, validation agents, bias mitigation agents, or fallback agents. The query is modified using metadata, executed to retrieve responses, and input into a relevance machine learning model to determine relevance scores and rank responses. Ranked query responses are outputted to the user.

Disclosed herein is a method (400) for performing a fuzzy search on database records. The method includes receiving (402) a query string from a user device (102). The method further includes extracting (404), based on the query string, one or more fuzzy search result strings from a plurality of records present in a cache memory. Further, the method includes determining (406) a similarity score between each of the one or more fuzzy search result strings and the query string. Furthermore, the method includes filtering (408) one or more result strings from the one or more fuzzy search result strings having the similarity score greater than a specific value. Thereafter, the method includes displaying (410), on a display interface of the user device, the one or more result strings.

A system for performing private information retrieval may include a data requesting computer that is configured to receive a query string, homomorphically encrypt the query string to generate an encrypted query, transmit the encrypted query to a data repository computer, and receive resultant encrypted datasets. The data repository computer may be configured to access a database comprising a plurality of datasets, receive the encrypted query, and perform a search of the plurality of datasets to identify the resultant encrypted datasets that include data that matches the encrypted query for provision to the data requesting computer. The search may be performed such that encryption of the encrypted query is maintained while performing computational operations of the search.

Files can be located using a durable and universal file identifier. A content URI includes a file protocol URI specifying a path to a file. The file protocol URI includes a query string specifying properties of the file that can be utilized to locate the file, such as an object ID property specifying a GUID for the file and a volume ID property specifying a GUID for a storage volume storing the file. The content URI can be utilized to locate the file using the file protocol URI and its associated query string even if the file has been moved, renamed, or is accessed on a different computing device. Operations can then be performed using the file, such as resuming a previously performed activity that used the file.

Computer-implemented techniques for representing, storing, retrieving, and reasoning over knowledge are disclosed herein. One aspect is an axis-lattice query language paradigm that turns every query string into a rectangular Matrix→Vector→Limit→Data tensor whose shape is dictated by token position. The invention improves the operations of a computing device by increasing the amount of significant information manipulated per unit of time by a processor.

The embodiment of the invention discloses a fuzzy query method, system and device, a medium and a product, and the method comprises the steps: carrying out the encryption mapping of characters of plaintext data based on a dynamic disturbance factor, and generating a coding sequence according to the encryption mapping result; constructing a reverse index based on the coding sequence; performing encryption mapping on characters of a query character string based on the dynamic perturbation factor to generate query segmented words; searching the query segmented word according to the inverted index to obtain a candidate result set containing position information corresponding to the query segmented word; and performing sequential semantic verification on the candidate result set according to the position information to obtain a fuzzy query result. According to the method and the device, high-efficiency encrypted field fuzzy retrieval is realized while the security of the stored sensitive field data is ensured.

Provided are systems and methods to transform a specified source dataset into a fully defined data format that details every attribute value in all levels for each document or record in the source dataset. According to various embodiments, the systems and methods are configured to execute recursive analysis on a specified data source, build out a fully specified data format and associated meta-data, enable user adjustments to a fully schema, and trigger automatic generation of code to transform the source to the new destination format even in the presence of polymorphic data. The process to scan source data involves recursively evaluating every attribute value in all levels for each document or record in the source dataset to account for any polymorphic data. The scan determines each attribute's data type usage, datetime formats and can interrogate string data to identify objects within string data.

This invention provides a general-purpose robotic wounded soldier for combat trauma training, belonging to the technical field of combat trauma training models. It includes: a robotic wounded soldier model; a human-computer interaction system for presetting initial combat trauma states; a wound simulation control module that generates query strings containing location, type, severity, and duration based on the preset states, calls a wound simulation knowledge graph to match corresponding wound state nodes, thereby obtaining descriptions of skin appearance, skeletal movements, language behavior, and physiological data, and uses this to drive the model to perform real-time morphological and appearance simulation, real-time voice output, and physiological data numerical output; and a training interaction module that collects real-time data on treatment actions and on-site environmental conditions, inputs this data into a state transition decision tree model integrating the influence of environmental and treatment conditions, and generates new wound state nodes that conform to the laws of medical evolution. This invention can accurately and in real-time simulate the physiological reactions and wound changes of wounded soldiers based on dynamic battlefield environments and treatment operations.

The application provides a legal regulation retrieval processing method and device of a large language model, which comprises receiving a legal query, identifying the intention of the legal query by the large language model, routing the legal query to an intention processing branch, performing characteristic fission on the legal query when it is determined to be a legal regulation retrieval branch, generating a plurality of retrieval element objects, encapsulating and sending the retrieval element objects to a legal regulation vector database, sorting the legal regulation vector database according to a similarity score to obtain an initial legal regulation metadata set, splicing the metadata set and a query string, inputting the spliced result into the large language model, performing secondary cleaning and filtering on the spliced result according to a preset metadata verification rule set, outputting a correlation determination identifier, removing irrelevant legal regulations, constructing a precise legal regulation data set, inputting the data set into the large language model, and rendering and outputting a structured legal analysis data object to a client according to a preset entity law and procedural law analysis priority order and a legal regulation reference regular expression. Through the processing scheme, the absolute purity and anti-illusion traceability of the output data are effectively ensured.

The invention belongs to the technical field of artificial intelligence security, and discloses a generation method of a jailbreak attack sample of a multi-mode large language model, and a security assessment method and device. Identifying a sensitive word set from the query character string of the malicious query image pair; rewriting each sensitive word to obtain an alternative word, and replacing the corresponding sensitive word in the query character string with each alternative word to obtain a rewritten instruction; extracting a keyword most relevant to the image from the query character string, rewriting the keyword to obtain an alternative word, rendering the alternative word into a typesetting image, and adding the typesetting image below the image to obtain an image; embedding the rewritten instruction and image into a scene template to obtain a jail break attack sample; and inputting the jail break attack sample into the to-be-evaluated multi-modal large language model, and evaluating the security alignment capability of the multi-modal large language model according to the corresponding generation content.