Systems and methods for parsing a software component search query to enable multi entity searches are provided. An exemplary method includes filtering a search query for unwanted characters, correcting a spelling of the search query, applying a first machine learning model to remove filter terms from the search query, parsing the query to extract entities, categorizing the entities as natural language, programming language, or keyword text, applying a second machine learning model to enhance search characteristics of the entities characterized as natural language, applying a third machine learning model to enhance search characteristics of the entities characterized as programming language, applying a fourth machine learning model to enhance search characteristics of the entities categorized as keyword text, consolidating the enhanced entities into an enhanced search query, applying a fifth machine learning model to determine intended sources for the search query, and searching the intended sources for results.

When analyzing operation of an online system, such as an online concierge system, that receive queries from users and return results in response to those queries, queries received by the online system may include different numbers of terms. The variations in numbers of terms included in different queries makes it difficult to efficiently group queries for analysis, resulting in inefficient analysis of individual queries when analyzing different queries based on their structure. To more efficiently analyze different queries, the online system groups queries based on a number of terms in the query. In some embodiments, queries are grouped based on a logarithmic scale of the number of terms in the query, while in other embodiments queries are grouped based on a square root of the number of terms in the query.

A method and system are provided for searching a search query having a non-character-based input. The method comprises receiving the search query comprising a first part and a second part. The first part comprises a non-character-based input. The method further comprises identifying a first plurality of keywords associated with the non-character-based input and receiving a selection of at least one of the first plurality of keywords. The method further comprises generating a modified search query comprising the at least one selected keyword and the second part. The method further comprises retrieving search results based on the modified search query and generating for presentation the search results.

A computer-implemented method is provided that includes receiving a search query and, responsive to the search query, providing one or more textual comments relevant to the search query. This includes tokenizing the search query and calculating a set of query term frequency metrics. A set of records relevant to the search query is then selected, from a persistent storage, based on determined similarities between the query term frequency metrics and frequency metrics determined for the records in the persistent storage. Textual comments within the selected records are associated with usefulness metrics. The textual comments relevant to the search query are selected by selecting those textual comments within the selected records that are associated with usefulness metrics that are within a pre-determined range, e.g., an inter-quartile range for a population of usefulness metrics.

An information processing apparatus includes: a processor configured to: acquire a message for another person that a requester has input when requesting the other person to register a document in a specific storage location of a storage device; if the other person registers the document, which the requester has requested to register, in the storage location, associate at least one element of the message with the registered document; and upon receiving a search for a document with a search term from the requester, search for and output a document associated with an element, including the search term, of the at least one element of the message.

The present disclosure generally relates to the connection, operation, automation, and autonomation of disparate computing systems, networks, and databases. The present disclosure further relates to a method for operating a plurality of legacy systems, comprising: receiving a user input string from a user; parsing the user input string into a plurality of terms; generating, for each of the plurality of legacy systems, based on the user input string, one or more legacy input strings, each of the one or more legacy input strings being executable by each of the plurality of legacy systems and comprising a syntactical arrangement of one or more of the plurality of terms; and instructing the plurality of legacy systems to execute the one or more legacy input strings.

System and methods are disclosed for redacting analyzing unstructured data in a request for data associated with a data subject to determine whether the unstructured data is relevant to the request. The relevancy of pieces of the unstructured data may be determined by determining a categorization for each such piece of unstructured data and comparing them to known personal data associated with the data subject having the same categorization. Pieces of the unstructured data that do not match known personal data having the same categorization are redacted from the request before the request is processed.

An electronic device according to various embodiments, may include a camera, a communication circuit, a display and a processor, and the processor may be configured to acquire an image of an external object, transmit the image to an external electronic device, so that the external electronic device transmits product information related to the external object included in the image to the electronic device, using the communication circuit, determine one or more keywords related to the product information transmitted from the external electronic device, identify preference category information and preference brand information of a user, based on a usage record related to a product of the user related to the electronic device, determine weights for the one or more keywords, using the preference category information and the preference brand information, and provide at least some keywords having weights which satisfy a specified condition among the one or more keywords, on the display together with the product information.

At least some embodiments are directed to a system to compute uniform structured summarization of customer chats. In at least some embodiments, the system may operate a processor and receive a corpus of chats between customers and customer service representatives of an enterprise. Grouping the corpus of chats into subgroup task types and then extracting chat keywords and chat related words for each subgroup task type. Generating an expandable template data structures for each subgroup task type. Processing at least one chat to extract chat utterances and chat snippets ranking the chat utterances and chat snippets. Populating the expandable template data structure based on rankings to generate a chat summary data structure.

A method for automatically splitting row-based table content into columns is provided, including: receiving first table content sent by a client, the first table content including one or more rows of text data to be split into columns; performing information extraction on the one or more rows of text data in the first table content to obtain an information tag in the one or more rows of text data; performing column splitting on the one or more rows of text data according to the information tag to obtain second table content, the second table content comprising one or more columns of text data after the column splitting; and transmitting the second table content to the client.