Abstract: Methods and apparatus for capturing, analyzing, and converting documents are provided. Provided in some embodiments is a method that includes obtaining a script document comprising script elements, assessing a layout of the script document, wherein the layout comprises a physical location of the script elements within the script document, determining an identification of one or more of the script elements based at least in part on the physical location of the one or more script elements within the script document, and assessing the one of the script elements using a finite state machine, wherein the finite state machine assesses one or more of the script elements preceding or following the one of the script elements to determine an accuracy of the identification of the one or more of the script elements determined.

Abstract: A method, apparatus and computer program product provides for a semantic analyzer to produce and rank semantic terms to reflect their relationship to the theme and topics of a document. The text and the document can have no relationship to any pre-selected keywords before the semantic analyzer performs text extraction. The semantic analyzer extracts text from a document and performs semantic analysis on the extracted text. The semantic analyzer provides a plurality of ranked semantic terms as a result of the semantic analysis and associates semantic terms with the document as semantic keywords. The semantic terms define content to be presented with the document where the content is an advertisement, a link to a remote information resource or a second document.

Abstract: A computer-implemented method includes receiving, in a computer system, a user query comprising at least a first term, parsing the user query to at least determine whether the user query assigns a field to the first term, the parsing resulting in a parsed query that conforms to a predefined format, performing a search in a metadata repository using the parsed query, the metadata repository embodied in a computer readable medium and including triplets generated based on multiple modes of metadata for video content, the search identifying a set of candidate scenes from the video content, ranking the set of candidate scenes according to a scoring metric into a ranked scene list, and generating an output from the computer system that includes at least part of the ranked scene list, the output generated in response to the user query.

Abstract: This specification describes technologies relating to multi core processing for parallel speech-to-text processing. In some implementations, a computer-implemented method is provided that includes the actions of receiving an audio file; analyzing the audio file to identify portions of the audio file as corresponding to one or more audio types; generating a time-ordered classification of the identified portions, the time-ordered classification indicating the one or more audio types and position within the audio file of each portion; generating a queue using the time-ordered classification, the queue including a plurality of jobs where each job includes one or more identifiers of a portion of the audio file classified as belonging to the one or more speech types; distributing the jobs in the queue to a plurality of processors; performing speech-to-text processing on each portion to generate a corresponding text file; and merging the corresponding text files to generate a transcription file.

Abstract: An extensible offer inventory database of offers in a domain is established. Further, an offer ontology is generated based on the extensible offer inventory database. The offer ontology provides an extensible vocabulary that correlates to categories in the offer inventory database. In addition, offers are automatically located. The offers are also semantically analyzed to generate semantic analysis data. Further, user data is obtained. In addition, an optimal offer match is automatically determined based upon the semantic analysis data and the user data.

Abstract: Methods and apparatus for capturing, analyzing, and converting documents are provided. Provided in some embodiments is a method that includes obtaining a script document comprising script elements, assessing a layout of the script document, wherein the layout comprises a physical location of the script elements within the script document, determining an identification of one or more of the script elements based at least in part on the physical location of the one or more script elements within the script document, and assessing the one of the script elements using a finite state machine, wherein the finite state machine assesses one or more of the script elements preceding or following the one of the script elements to determine an accuracy of the identification of the one or more of the script elements determined.

Abstract: A display manager allows for visualization and navigation of a semantic network via an interactive graphical user interface. The display manager accesses a semantic network model defining relationships amongst nodes in the semantic network. Given a portion of the semantic network, the display manager renders a radial perspective to represent the node relationships in the given portion of the semantic network. The radial perspective includes a centered node, around which other displayed nodes and relationships are substantially concentrically arranged. To navigate to and view a different portion of the semantic network, the display manager allows the user to select nodes currently displayed in the radial perspective. In response to selection of one or more nodes, the display manager updates the radial perspective to represent nodes and node relationships from a different portion of the semantic network with the user-selected node as a new center node.

Abstract: A lexical mapper enables efficient text mining via use of lexical identifiers such as numerical identifier values. For example, the lexical mapper receives a collection of text-based strings associated with a document. Based on values of the strings, the lexical mapper maps the text-based strings to a set of lexical identifiers. In lieu of using the character strings, text mining applications process the numerical identifier values to analyze the document.

Abstract: User data and a plurality of micro-segment definitions are received. Each micro-segment definition in the plurality of micro-segment definitions corresponds to one or more offers in an offer provider campaign. Further, a micro-segment parser parses each micro-segment definition from the plurality of micro-segment definitions into a plurality of parsed expression segments that indicate a plurality of micro-segment condition rules. In addition, a compiler compiles the plurality of parsed expression segments into an executable object that indicates a plurality of instructions to determine if the user data matches the plurality of micro-segment definitions. Each micro-segment definition is also serially processed, with a sequential evaluation engine, to apply the plurality of micro-segment condition rules to the user data to determine a match of a user belonging to a micro-segment. Further, the sequential evaluation engine assigns a score to indicate the strength of each match.

Abstract: A user policy definition of a user policy is received. The user policy definition indicates a user is receptive to receiving an offer and a user-controlled criterion for disclosure of user information. Further, persistent metadata that includes the user policy is generated. A compliance engine sends the user information with the persistent metadata to an offer provider. Further, offer data for a user is received from an offer provider. The offer data indicates a description of content in the offer and an offer criterion indicating a targeting characteristic. In addition, the compliance engine validates that the offer criterion complies with the user-controlled criterion. The offer is sent to the user upon determining that the offer criterion complies with the user-controlled criterion.

Abstract: A campaign is received, at a micro-segmentation system, from an offer provider. The micro-segmentation system is a third-party system. The campaign indicates a set of target attributes and one or more offers corresponding to the set of target attributes. A set of user attributes pertaining to each of the plurality of users is received at the micro-segmentation system. The set of attributes is defined by an attribute knowledge structure. Permission is received at the micro-segmentation system from each of the plurality of users to receive an offer from the micro-segmentation system. Data associated with the micro-segment classification is provided from the micro-segmentation system without user information to the offer provider.

Abstract: User data and a plurality of micro-segment definitions such that each micro-segment definition in the plurality of micro-segment definitions corresponds to one or more offers in an offer provider campaign are received. Further, a dispatcher dispatches a first subset of the user data and a first subset of the plurality of the micro-segment definitions to a first node in a network. In addition, the dispatcher dispatches a second subset of the user data and a second subset of the plurality of the micro-segment definitions to a second node in the network. Parsing and compiling are performed at each node. Further, parallel processing is performed at a scalable evaluation engine at each node to apply micro-segment condition rules to user data to determine matches to micro-segments. Computation of micro-segments occurs in parallel and resulting micro-segment assignments are collected, filtered to remove duplicates, then ranked to produce a final set of micro-segments that can be used to find offers.

Abstract: A selection of one or more segment attributes from an offer provider campaign is received at a graphical user interface. The one or more segment attributes define one or more segments that correspond to one or more offers in the offer provider campaign. Further, an expression graph based on the one or more segment attributes is generated. In addition, a portable micro-segment object is generated based on the expression tree such that the portable micro-segment object lacks dependence on the offer provider campaign.

Abstract: Methods and apparatus for generating layout-preserved text output from portable document format (PDF) input are described. A layout-preserved text generation method may generate layout-preserved text output from PDF input that includes the text along with indentations, spaces, newlines, and paging and that thus preserves the global document layout view of the original PDF input document. The layout-preserved text generation method may transform the PDF (X, Y) document space into a text file grid space while preserving a similar global view of the text and layout from the PDF (X, Y) document space. This transformation may include determining a base size per grid that may produce accurate layout in the text output from the PDF input.

Abstract: A term analyzer receives an ordered collection of text-based terms. The ordered collection can contain terms from a document that have been filtered to remove “noise” such as stopwords. The term analyzer analyzes groupings of consecutive text-based terms in the ordered collection to identify occurrences of different combinations of text-based terms in the ordered collection. In addition, the term analyzer maintains frequency information representing the occurrences of the different combinations of text-based terms in the collection. The frequency information can then be used to determine relatively significant keywords and/or keyword phrases in the document. In an example configuration, the term analyzer creates a tree in which a first term in a given grouping of the groupings is defined as a parent node in the tree and a second term in the given grouping is defined as a child node of the parent node in the tree.

Abstract: A document analyzer receives a collection of text-based terms associated with a document. The document analyzer performs a statistical analysis on the text-based terms to identify a distribution of where the text-based terms appear in the document and relative frequency indicating how often the text-based terms appear in the document. The document analyzer utilizes the distribution and relative frequency information derived from the statistical analysis to rank multiple themes associated with the document. For example, a received listing of multiple themes may not be presented in any useful order, although it can be assumed that the themes in the listing are present in the document. Based on application of distribution and relative frequency information derived from the analysis, the document analyzer can identify which themes are most relevant to the document as a whole and/or which of themes correspond to different portions (e.g., pages or sections) of the document.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for converting a relational database into a triplestore.

Abstract: A method, apparatus and computer program product provides for a semantic analyzer to produce and rank semantic terms to reflect their relationship to the theme and topics of a document. The text and the document can have no relationship to any pre-selected keywords before the semantic analyzer performs text extraction. The semantic analyzer extracts text from a document and performs semantic analysis on the extracted text. The semantic analyzer provides a plurality of ranked semantic terms as a result of the semantic analysis and associates semantic terms with the document as semantic keywords. The semantic terms define content to be presented with the document where the content is an advertisement, a link to a remote information resource or a second document.

Abstract: A method, apparatus and computer program product provides for a semantic analyzer to produce and rank semantic terms to reflect their relationship to the theme and topics of a document. The text and the document can have no relationship to any pre-selected keywords before the semantic analyzer performs text extraction. The semantic analyzer extracts text from a document and performs semantic analysis on the extracted text. The semantic analyzer provides a plurality of ranked semantic terms as a result of the semantic analysis and associates semantic terms with the document as semantic keywords. The semantic terms define content to be presented with the document where the content is an advertisement, a link to a remote information resource or a second document.

Abstract: A document analyzer receives a collection of text-based terms associated with a document. The document analyzer performs a statistical analysis on the text-based terms to identify a distribution of where the text-based terms appear in the document and relative frequency indicating how often the text-based terms appear in the document. The document analyzer utilizes the distribution and relative frequency information derived from the statistical analysis to rank multiple themes associated with the document. For example, a received listing of multiple themes may not be presented in any useful order, although it can be assumed that the themes in the listing are present in the document. Based on application of distribution and relative frequency information derived from the analysis, the document analyzer can identify which themes are most relevant to the document as a whole and/or which of themes correspond to different portions (e.g., pages or sections) of the document.