1597 results about "Unstructured data" patented technology

The method and system identifies and classifies and monetizes intangible asset (I.A.) unstructured digital data in a computer system by ownership characterizations and intellectual property (I.P.) classifications. Enterprise and non-enterprise data collections are searched based upon an expanded project specification (of descriptive functions, characteristics and objectives). Primary search terms from the project are expanded by content, contextual and taxonomic analysis to generate a plurality of search string parameters employed to search the data collections. The search engine returns data and documents, ownership characteristics are ascertained by comparison of the returns and I.P. classifications are made based upon the returns. This process also handles unstructured enterprise specific data not otherwise located in the data collections or unstructured data from other I.A. programs such as H.R. performance modules, supply chain modules or CRM modules. Unstructured data matching an organized data set from project and the search returns is integrated into the organized data set.

Systems and methods for configuring and evaluating policies that direct processing of one or more data streams are described. A configuration interface is described for allowing users to specify object oriented policies. These object oriented policies may allow any data structures to be applied with respect to a payload of a received packet stream, including any portions of HTTP traffic. A configuration interface may also allow the user to control the order in which policies and policy groups are executed, in addition to specifying actions to be taken if one or more policies are undefined. Systems and methods for processing the policies may allow efficient processing of object-oriented policies by applying potentially complex data structures to unstructured data streams. A device may also interpret and process a number of flow control commands and policy group invocation statements to determine an order of execution among a number of policies and policy groups. These policy configurations and processing may allow configuration and processing of complex network behaviors relating to load balancing, VPNs, SSL offloading, content switching, application security, acceleration, and caching.

Disclosed herein are systems and methods for syndication and management of structured and unstructured data to assist institutional healthcare delivery, healthcare providers' practices, healthcare providers' group practices, collaborative academic research and decision making in healthcare, including through the utilization of medical devices and healthcare pools.

Disclosed herein are systems and methods for syndication and management of structured and unstructured data to assist institutional healthcare delivery, healthcare providers' practices, healthcare providers' group practices, collaborative academic research and decision making in healthcare, including through the utilization of medical devices and healthcare pools.

Electronic data files are broadly characterized as “messages” and a social network is constructed by analyzing one or more messages exchanged between various entities. Additionally, messages from structured and/or unstructured data stores are correlated using one or more common/related data elements from two or more messages. Further, the social network and the concepts contained in the exchanged messages (i.e. semantic network) can be visualized using a series of multi-aspect viewing tools. Finally, in conjunction with the social network and the semantic network, a message network based on the chronological relationship of the messages (event network) can be constructed to analyze and visualize how the messages relate to each other in a time-based reference model. Once visualized, the relationship of the concepts contained in the messages as well as the relationship between the entities and the timing involved in the exchange of messages can be analyzed for desired information.

A system and methods are provided for interactive construction of data queries. One method comprises: generating a query based upon a plurality of user-identified data items, wherein the user-identified data items are data items representing desired results from a query, and wherein information related to the user-identified data items is included in a “given” clause of the query, assigning received input data to a hierarchical set of categories, presenting to a user a plurality of new query results, wherein the plurality of new query results are determined by scanning the received input data to find data elements in the same hierarchical categories as those in the “given” query clause and not in the same hierarchical categories as those of an “unlike” clause of the query, receiving from the user an indication as to whether each query result of the presented plurality of new query results is a desirable query result, adding query results indicated by the user as desirable to the “given” clause of the query, adding query results indicated by the user as undesirable to the “unlike” clause of the query, evaluating a metric indicative of the accuracy of the query, and responsive to a determination that the query achieves a predetermined threshold level of accuracy, storing the query.

Various embodiments of the present invention disclose a method for Business Intelligence (BI) metrics on unstructured data. Unstructured data is collected from numerous data sources that include unstructured data as ingested data. The ingested data is indexed and represents hyperlink and extracted data and metadata for each document. Thereafter, the ingested data is automatically classified into one or more relevance classes. Further, numerous analytics are performed on the classified data to generate business intelligence metrics that may be presented on an access device operated by a user.

A system and method of making unstructured data available to structured data analysis tools. The system includes middleware software that can be used in combination with structured data tools to perform analysis on both structured and unstructured data. Data can be read from a wide variety of unstructured sources. The data may then be transformed with commercial data transformation products that may, for example, extract individual pieces of data and determine relationships between the extracted data. The transformed data and relationships may then be passed through an extraction/transform/load (ETL) layer and placed in a structured schema. The structured schema may then be made available to commercial or proprietary structured data analysis tools.

Techniques for obtaining data from unstructured data for a structured data collection include receiving unstructured data that includes text; identifying an attribute associated with a structured data collection; obtaining at least one of historical data associated with the attribute or additional data associated with a user of the computing system; identifying one or more terms from the unstructured data as being associated with the attribute based on at least one of the historical data or the additional data; and storing the identified one or more terms in a data record of the unstructured data collection.

An information management system and method is provided for integrating structured and unstructured data using an ontological approach, and further comprises processes for creating, validating, augmenting, and combining ontologies for life science informatics and other disciplines.

A system and method of making unstructured data available to structured data analysis tools. The system includes middleware software that can be used in combination with structured data tools to perform analysis on both structured and unstructured data. Data can be read from a wide variety of unstructured sources. The data may then be transformed with commercial data transformation products that may, for example, extract individual pieces of data and determine relationships between the extracted data. The transformed data and relationships may then be passed through an extraction/transform/load (ETL) layer and placed in a structured schema. The structured schema may then be made available to commercial or proprietary structured data analysis tools.

Disclosed herein are systems and methods for syndication and management of structured and unstructured data to assist institutional healthcare delivery, healthcare providers' practices, healthcare providers' group practices, collaborative academic research and decision making in healthcare, including through the utilization of medical devices and healthcare pools.

Disclosed herein is a method and system for integrating an enterprise's structured and unstructured data to provide users and enterprise applications with efficient and intelligent access to that data. Queries can be directed toward both an enterprise's structured and unstructured data using standardized database query formats such as SQL commands. A coprocessor can be used to hardware-accelerate data processing tasks (such as full-text searching) on unstructured data as necessary to handle a query. Furthermore, traditional relational database techniques can be used to access structured data stored by a relational database to determine which portions of the enterprise's unstructured data should be delivered to the coprocessor for hardware-accelerated data processing.

A system and method of making unstructured data available to structured data analysis tools. The system includes middleware software that can be used in combination with structured data tools to perform analysis on both structured and unstructured data. Data can be read from a wide variety of unstructured sources. The data may then be transformed with commercial data transformation products that may, for example, extract individual pieces of data and determine relationships between the extracted data. The transformed data and relationships may then be passed through an extraction/transform/load (ETL) layer and placed in a structured schema. The structured schema may then be made available to commercial or proprietary structured data analysis tools.

Systems and methods for configuring and evaluating policies that direct processing of one or more data streams are described. A configuration interface is described for allowing users to specify object oriented policies. These object oriented policies may allow any data structures to be applied with respect to a payload of a received packet stream, including any portions of HTTP traffic. A configuration interface may also allow the user to control the order in which policies and policy groups are executed, in addition to specifying actions to be taken if one or more policies are undefined. Systems and methods for processing the policies may allow efficient processing of object-oriented policies by applying potentially complex data structures to unstructured data streams. A device may also interpret and process a number of flow control commands and policy group invocation statements to determine an order of execution among a number of policies and policy groups. These policy configurations and processing may allow configuration and processing of complex network behaviors relating to load balancing, VPNs, SSL offloading, content switching, application security, acceleration, and caching.

Chat categorization uses semi-supervised clustering to provide Voice of the Customer (VOC) analytics over unstructured data via an historical understanding of topic categories discussed to derive an automated methodology of topic categorization for new data; application of semi-supervised clustering (SSC) for VOC analytics; generation of seed data for SSC; and a voting algorithm for use in the absence of domain knowledge/manual tagged data. Customer service interactions are mined and quality of these interactions is measured by “Customer's Vote” which, in turn, is determined by the customer's experience during the interaction and the quality of customer issue resolution. Key features of the interaction that drive a positive experience and resolution are automatically learned via machine learning driven algorithms based on historical data. This, in turn, is used to coach/teach the system/service representative on future interactions.

A user interface for parsing unstructured data using pattern recognition. The patterns used in parsing data are formed from regular expressions. The parsed data may be displayed in a first format and unmatched strings in the unstructured text may be displayed in a second format. A format may comprise a desired color, font or any other user interface parameter. In addition, the data that parses according to a pattern may be placed in a column associated with the pattern in a tabular user interface, for example a spreadsheet like Excel™. Associating a pattern with a position to display successful matches in allows for breaking unstructured text into pieces associated with a particular field or column. Modification of the patterns allows for more and more of the unstructured text to match the patterns and when the data has been parsed to the desired level, the data may be imported into a database.

The invention discloses a system and method for constructing information-analysis-oriented knowledge maps. The system comprises a data acquisition module, a text extraction module, an entity recognition module, a semantic analysis module and an entity-relation extraction module, wherein the data acquisition module is used for carrying out cleaning and simple preprocessing on acquired data and outputting the data to the text extraction module; the text extraction module is used for carrying out data cleaning and preprocessing on structured and unstructured data and conveying clean data to the entity recognition module; the entity recognition module is used for segmenting words of a text, marking the word characteristics of the segmented words, then extracting terms and conveying extracted results to the semantic analysis module; the semantic analysis module is used for analyzing and extracting relation among bodies, generating a semantic metadata model by a body construction tool and outputting the semantic metadata model to the entity-relation extraction module; and the entity-relation extraction module is used for finally generating knowledge map language by extracting taxonomic relation and non-taxonomic relation. The system and method disclosed by the invention have the advantages that by combination of syntactic training and association rules, not only are external input and artificial intervention reduced, but also the entity relation can be continuously recognized.