An e-commerce voice response determination system is provided and generally includes a server and a customer computing device. The server may determine, based on a user query, a plurality of product descriptions. The server may determine a similarity value between the user query and each of the product descriptions based on the similarity between each word of the user query and each word of the product descriptions. The server may determine the product description with the most similarity to the user query, and provide a response to the customer computing device based on that product description. In some embodiments, the server may determine whether the user query matches a predefined query. If the user query matches the predefined query, the server provides a response to the customer computing device based on a predefined response to the predefined query.

An improved search engine creates correlations linking terms from inputs provided by a user to selected target terms. The correlation search process receives pre-processed inputs from a user including a wide variety of input formats including keywords, phrases, sentences, concepts, compound queries, complex queries and orthogonal queries. The pre-processing also includes pre-processing of general digital information objects and static or dynamic generation of questions. After a correlation search of the information presented by the pre-processing, the search results are processed in novel ways to provide an improved relevance ranking of results.

A method is executed at a computer system to retrieve data from a database. Upon receiving a database query, a database engine of the computer system parses the query to form an operator tree including a plurality of join operators. For one of the plurality of clauses, the database engine adds to the operator tree a respective node that specifies a mark join operator, a single join operator, an inner join operator, or an outer join operator. Specifically, the database engine adds the mark join operator when the clause includes one of a predetermined set of predicate subqueries, and adds the single join operator when the clause includes a scalar subquery. The database engine performs one or more optimization passes on the operator tree to form an optimized execution plan, and executes the optimized execution plan to retrieve a result set from the database.

A computer-implemented method includes: receiving, by a computing device, an input file defining correct spellings of one or more transliterated words; generating, by the computing device, suffix outputs based on the one or more transliterated words; generating, by the computing device, a dictionary that maps the suffix outputs to the one or more transliterated words; recognizing, by the computing device, an alternatively spelled transliterated word included in a document as one of the one or more correctly spelled transliterated words using the dictionary; and outputting, by the computing device, information corresponding to the recognized transliterated word.

A computer-implemented method, medium, and system are disclosed. In one computer-implemented method, an invocation request sent by an initiator is received by a blockchain node in a blockchain network. The invocation request is associated with invocation of a smart contract in the blockchain network. The smart contract includes contract code, data, and pre-update metadata. A pre-update data structure described by the pre-update metadata is parsed by the blockchain node and by execution of the contract code. The pre-update data structure is associated with the data comprised in the smart contract. Following parsing of the pre-update data structure, the pre-update data structure is represented by the blockchain node using a computer programming language. The pre-update data structure specified by the computer programming language is sent by the blockchain node to the initiator.

The present disclosure provides a data pair generating method, apparatus, electronic device and storage medium, and the field of artificial intelligence such as natural language processing and deep learning. The method may include: generating M SQL query statements for a given database, where M is a positive integer greater than one; performing the following processing for each SQL query statement: dividing the SQL query statement into at least one SQL clause; obtaining a question description corresponding to each SQL clause; combining the question descriptions to obtain a question corresponding to the SQL query statement. The solution of the present disclosure may be applied to save manpower and time costs.

In some examples, workflow-based dynamic data model and application generation may include ascertaining, for an application that is to be generated, a plurality of fields that are declared. Based on the plurality of declared fields, a data model may be generated. The data model may include a plurality of application programming interface (API) keys associated with the plurality of declared fields. Based on the data model, a mapping file may be generated to map a plurality of APIs that are to be invoked relative to the API keys. Based on the data model and the mapping file, the application may be generated.

A voice-initiated item search is received along with a current geolocation of a device from which the voice-initiated item search was initiated. The search is translated to text, and the text search and the current geolocation are submitted as a search to locate an enterprise within a configured distance of the current geolocation and to locate a specific location for an item within that enterprise. The enterprise and the specific location for the item are provided back to an operator of the device as search results in voice and/or text. The geolocation represents a location of a consumer and the results provide enterprise specific item-level detail and item-location information for the item within an enterprise location for the consumer to quickly navigate to the item.

A non-transitory computer readable storage medium has instructions executed by a processor to receive from a network connection different sources of unstructured data. An entity is formed by combining one or more sources of the unstructured data, where the entity has relational data attributes. A representation for the entity is created, where the representation includes embeddings that are numeric vectors computed using machine learning embedding models, including trunk models, where a trunk model is a machine learning model trained on data in a self-supervised manner. An enrichment model is created to predict a property of the entity. A query is processed to produce a query result, where the query is applied to one or more of the entity, the embeddings, the machine learning embedding models, and the enrichment model.

Disclosed are systems and methods for generating search result data based on machine-encoded text generated by computer vision optical character recognition machine learning techniques performed on digital media. The disclosed systems and methods provide a novel framework for performing machine learning visual search or machine learning text extraction techniques on digital media in order to extract and analyze the data therein and further conduct search queries based on the extracted and analyzed data. The disclosed framework may leverage the aforementioned computer vision machine learning techniques in order to provide a user with relevant search results regarding objects and text detect in digital media captured on a user device.