Abstract: A method and apparatus for coordinating payment of healthcare expenses is disclosed. The method comprises determining a co-payment amount based on a customer's insurance coverage, determining the availability of funds from a plurality of the consumer's prioritized accounts, and deducting at least a portion of the co-payment from the highest priority account having available funds.

Abstract: A method and apparatus for coordinating payment of healthcare expenses is disclosed. The method comprises determining a co-payment amount based on a customer's insurance coverage, determining the availability of funds from a plurality of the consumer's prioritized accounts, and deducting at least a portion of the co-payment from the highest priority account having available funds.

Abstract: An apparatus and method are disclosed for producing a semantic representation of information in a semantic space. The information is first represented in a table that stores values which indicate a relationship with predetermined categories. The categories correspond to dimensions in the semantic space. The significance of the information with respect to the predetermined categories is then determined. A trainable semantic vector (TSV) is constructed to provide a semantic representation of the information. The TSV has dimensions equal to the number of predetermined categories and represents the significance of the information relative to each of the predetermined categories. Various types of manipulation and analysis, such as searching, classification, and clustering, can subsequently be performed on a semantic level.

Abstract: An apparatus and method are disclosed for producing a semantic representation of information in a semantic space. The information is first represented in a table that stores values which indicate a relationship with predetermined categories. The categories correspond to dimensions in the semantic space. The significance of the information with respect to the predetermined categories is then determined. A trainable semantic vector (TSV) is constructed to provide a semantic representation of the information. The TSV has dimensions equal to the number of predetermined categories and represents the significance of the information relative to each of the predetermined categories. Various types of manipulation and analysis, such as searching, classification, and clustering, can subsequently be performed on a semantic level.

Abstract: An apparatus and method are disclosed for producing a semantic representation of information in a semantic space. The information is first represented in a table that stores values which indicate a relationship with predetermined categories. The categories correspond to dimensions in the semantic space. The significance of the information with respect to the predetermined categories is then determined. A trainable semantic vector (TSV) is constructed to provide a semantic representation of the information. The TSV has dimensions equal to the number of predetermined categories and represents the significance of the information relative to each of the predetermined categories. Various types of manipulation and analysis, such as searching, classification, and clustering, can subsequently be performed on a semantic level.

Abstract: An apparatus and method are disclosed for producing a semantic representation of information in a semantic space. The information is first represented in a table that stores values which indicate a relationship with predetermined categories. The categories correspond to dimensions in the semantic space. The significance of the information with respect to the predetermined categories is then determined. A trainable semantic vector (TSV) is constructed to provide a semantic representation of the information. The TSV has dimensions equal to the number of predetermined categories and represents the significance of the information relative to each of the predetermined categories. Various types of manipulation and analysis, such as searching, classification, and clustering, can subsequently be performed on a semantic level.

Abstract: An apparatus and method are disclosed for producing a semantic representation of information in a semantic space. The information is first represented in a table that stores values which indicate a relationship with predetermined categories. The categories correspond to dimensions in the semantic space. The significance of the information with respect to the predetermined categories is then determined. A trainable semantic vector (TSV) is constructed to provide a semantic representation of the information. The TSV has dimensions equal to the number of predetermined categories and represents the significance of the information relative to each of the predetermined categories. Various types of manipulation and analysis, such as searching, classification, and clustering, can subsequently be performed on a semantic level.

Abstract: The invention includes a method and a device for determining an emotive index for a message. In the method, a message is received and words in the message are identified for analysis. An emotive index corresponding to the identified words is then determined. The emotive index is preferably a function of a word count index corresponding to the number of identified words, an affinity index and an antagonism index. A device according to the present invention includes a computer readable storage medium having encoded thereon computer readable instructions capable of instructing a computer to carry out the method.

Abstract: An apparatus and method are disclosed for producing a semantic representation of information in a semantic space. The information is first represented in a table that stores values which indicate a relationship with predetermined categories. The categories correspond to dimensions in the semantic space. The significance of the information with respect to the predetermined categories is then determined. A trainable semantic vector (TSV) is constructed to provide a semantic representation of the information. The TSV has dimensions equal to the number of predetermined categories and represents the significance of the information relative to each of the predetermined categories. Various types of manipulation and analysis, such as searching, classification, and clustering, can subsequently be performed on a semantic level.

Abstract: An apparatus and method are disclosed for producing a semantic representation of information in a semantic space. The information is first represented in a table that stores values which indicate, a relationship with predetermined categories. The categories correspond to dimensions in the semantic space. The significance of the information with respect to the predetermined categories is then determined. A trainable semantic vector (TSV) is constructed to provide a semantic representation of the information. The TSV has dimensions equal to the number of predetermined categories and represents the significance of the information relative to each of the predetermined categories. Various types of manipulation and analysis, such as searching, classification, and clustering, can subsequently be performed on a semantic level.

Abstract: An apparatus and method are disclosed for producing a semantic representation of information in a semantic space. The information is first represented in a table that stores values which indicate a relationship with predetermined categories. The categories correspond to dimensions in the semantic space. The significance of the information with respect to the predetermined categories is then determined. A trainable semantic vector (TSV) is constructed to provide a semantic representation of the information. The TSV has dimensions equal to the number of predetermined categories and represents the significance of the information relative to each of the predetermined categories. Various types of manipulation and analysis, such as searching, classification, and clustering, can subsequently be performed on a semantic level.

Abstract: An apparatus and method are disclosed for producing a semantic representation of information in a semantic space. The information is first represented in a table that stores values which indicate a relationship with predetermined categories. The categories correspond to dimensions in the semantic space. The significance of the information with respect to the predetermined categories is then determined. A trainable semantic vector (TSV) is constructed to provide a semantic representation of the information. The TSV has dimensions equal to the number of predetermined categories and represents the significance of the information relative to each of the predetermined categories. Various types of manipulation and analysis, such as searching, classification, and clustering, can subsequently be performed on a semantic level.

Abstract: A method and system for efficiently analyzing databases. In one embodiment, the invention is used to analyze data represented in the form of attribute-value (a-v) pairs. A primary step in building the ontology is to identify parent, child and related a-v pairs of each given a-v pair in the database. A parent is an a-v pair that is always present whenever a given a-v pair is present. A child is an a-v pair that is never present unless the given a-v pair is present. Related pairs of a given a-v pair are those a-v pairs present some of the time when a given a-v pair is present. The system calculates relationships between a-v pairs to produce tables of a-v pairs presented according to the relationships. The user performs additional analysis by investigating the a-v pair relationships through a graphical user interface. Additional visualizations of the data are possible such as through Venn diagrams and animations. Plain-text data documents collected, for example, from the Internet can be analyzed.

Abstract: A method and apparatus for efficiently analyzing information on a computer is provided. The information contains information items where each information item has a plurality of attributes. The information is re-organized based on the attributes and displayed in graphical form on a computer display screen. By viewing the information in graphical form, a user can quickly analyze the information to determine trends or qualities. In addition, the graphical form provides a way for the user to quickly identify the information items most relevant to specific criteria.

Abstract: A method and system for efficiently analyzing databases. In one embodiment, the invention is used to analyze data represented in the form of attribute-value (a-v) pairs. A primary step in building the ontology is to identify parent, child and related a-v pairs of each given a-v pair in the database. A parent is an a-v pair that is always present whenever a given a-v pair is present. A child is an a-v pair that is never present unless the given a-v pair is present. Related pairs of a given a-v pair are those a-v pairs present some of the time when a given a-v pair is present. The system calculates relationships between a-v pairs to produce tables of a-v pairs presented according to the relationships. The user performs additional analysis by investigating the a-v pair relationships through a graphical user interface. Additional visualizations of the data are possible such as through Venn diagrams and animations. Plain-text data documents collected, for example, from the Internet can be analyzed.

Abstract: According to the present invention, a method for extracting information from a plurality of documents for display. Information is extracted from a set of documents according to a set of pre-defined categories. A visual representation of the extracted information is provided to a user, who can apply one or more filters to the information in order to produce for display a visual representation of information content in the filtered information.

Abstract: An interactive system for analyzing and displaying information contained in a plurality of documents employing both term-based analysis and conceptual-representation analysis. Particulars of the invention are especially effective for analyzing patent texts, such as patent claims, abstracts and other portions of a patent document.