In an approach for optimizing an intelligent virtual assistant by using phonetic comparison to find a response stored in a local database, a processor receives an audio input on a computing device. A processor transcribes the audio input to text. A processor compares the text to a set of user queries and commands in a local database of the computing device using a phonetic algorithm. A processor determines whether a user query or command of the set of user queries and commands meets a pre-defined threshold of similarity. Responsive to determining that the user query or command meets the pre-defined threshold of similarity, a processor identifies an intention of a set of intentions stored in the local database corresponding to the user query or command. A processor identifies a response of a set of responses in the local database corresponding to the intention. A processor outputs the response audibly.

Systems, apparatuses, and methods are provided for identifying a corresponding string stored in memory based on an incomplete input string. A system can analyze and produce phonetic and distance metrics for a plurality of strings stored in memory by comparing the plurality of strings to an incomplete input string. These similarity metrics can be used as the input to a machine learning model, which can quickly and accurately provide a classification. This classification can be used to identify a string stored in memory that corresponds to the incomplete input string.

A packet processing technique can include receiving a packet, and parsing the packet based on a protocol field to generate a parse result vector. The parse result vector is used to select between forwarding the packet to a virtual machine executing on a host processing integrated circuit, forwarding the packet to a physical media access controller, multicasting the packet to multiple virtual machines executing on the host processing integrated circuit, and sending the packet to a hypervisor.

The Search Extraction Matching, Draw Attention-Fit Modality, Application Morphing, and Informed Apply Apparatuses, Methods and Systems (“SEMATFM-AMIA”) transforms inputs including new job listing introduction inputs, via SEMATFM-AMIA components (e.g., the conductor component, the resume view controller component, the XY paths handler component, the title handler component, the resume librarian component, and the job listing librarian component), into outputs including relevant resume outputs and/or augmented new job listing record outputs. It is noted that the terms “component” and “object” may be used interchangeably hereinthroughout. In one embodiment, the SEMATFM-AMIA includes an apparatus, comprising: a memory, a component collection in the memory, and a processor disposed in communication with the memory, and configured to issue a plurality of processing instructions from the component collection stored in the memory. SEMATFM-AMIA may then receive, in connection with an application to a job, a resume adjustment request, where the request includes one or more raw terms of a resume, one or more normalized terms of the resume, one or more raw terms of a job listing corresponding to the job, and one or more normalized terms of the job listing. SEMATFM-AMIA may load said resume normalized terms and said job listing normalized terms into a joined normalized terms set, and add to a common normalized terms set normalized term members of the joined normalized terms set which meet a count criterion. SEMATFM-AMIA may visit each of one or more normalized term members of the common normalized terms set. After further receiving, adding, visiting, providing and otherwise processing data, SEMATFM-AMIA may receive, from the resume adjuster component, a request to formulate the adjusted resume record, wherein said record formulation request includes specification of the resume and substitution information, and formulate the adjusted resume record which substitutes each of user-selected resume raw terms with a corresponding user-selected job listing raw term, wherein the formulation includes accessing one or more stores.

A communications system implements instructions including maintaining a message object that includes an array of entries. Each entry of the array includes a field identifier, a data type, and a next entry pointer. The next entry pointers and a head pointer establish a linked list of entries. The instructions include, in response to a request to add a new entry to the message object, calculating an index based on a field identifier of the new entry and determining whether the entry at the calculated index within the array of entries is active. The instructions include, if the entry is inactive, writing a data type, field identifier, and data value of the new entry to the calculated index, and inserting the new entry into the linked list. The instructions include, if the entry is already active, selectively expanding the size of the array and repeating the calculating and determining.

A method, system, and computer program product is provided for tokenizing document citations. The method may include tokenizing each string of a plurality of strings into at least one citation token representing at least one citation parameter, resulting in a plurality of citation tokens, grouping the plurality of citation tokens into a plurality of token groups, each token group of the plurality of token groups including at least one citation token representing a separate citation in the at least one textual document, assigning metadata to each token group of the plurality of token groups, and generating a normalized citation for each token group of the plurality of token groups based on the metadata.

Systems and methods are disclosed herein for generating supplemental content for media content. One disclosed technique herein generates for display a page of an electronic book. A noun, and a word contextually related to the noun, are identified from the displayed page of the electronic book. Content structures are searched for a content structure that includes a matching object having an object name matching the noun. The content structure includes objects, where each object has attribute table entries. Upon finding an identified attribute table entry of the matching object that matches the related word, a new content structure is generated. The new content structure includes the matching object and the identified attribute table entry. A content segment is generated for output (e.g., for display on the electronic book) based on the new content structure.

Systems and methods are disclosed herein for generating supplemental content for media content. One disclosed technique herein generates for display a page of an electronic book. A noun, and a word contextually related to the noun, are identified from the displayed page of the electronic book. Content structures are searched for a content structure that includes a matching object having an object name matching the noun. The content structure includes objects, where each object has attribute table entries. Upon finding an identified attribute table entry of the matching object that matches the related word, a new content structure is generated. The new content structure includes the matching object and the identified attribute table entry. A content segment is generated for output (e.g., for display on the electronic book) based on the new content structure.

Provided is a system and method for hybrid windowing for string-matching of input patterns to a corpus. The method including: establishing a first window size and a hash function; performing hashing on input patterns having a size within a given range using dynamic-sized windows to determine a dynamic-windowed hash set, the given range established using the first window size; performing hashing on input patterns having a size outside the given range using fixed-sized windows to determine a fixed-windowed hash set; combining the dynamic-windowed hash set and the fixed-windowed hash set to determine a combined hash set; and outputting the combined hash set for use in the confidential string-matching.

A data string that includes potentially sensitive information, such as an account number for a payment card, may be evaluated to determine whether or not any portion of the data string encodes a known identifier of sensitive information, such as a known bank identification number (BIN). A fixed number of bytes of the data string may be analyzed using a trie algorithm, in which the value of a first byte is compared with the value of a corresponding first character of the known identifier. A second byte is then compared with a set of values of corresponding second characters, which accompany the first character of the known identifier. Then the value of a third byte of the data string is compared with a set of values of corresponding third values, which accompany the first and second characters of the known identifier. The use of a trie algorithm decreases the length of the search process by several orders of magnitude.